{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a348c69a-fed3-4912-ba4a-724a680f1585",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "import missingno as msno\n",
    "import sklearn.metrics \n",
    "import statsmodels.api as sm\n",
    "import statistics as stats\n",
    "from minisom import MiniSom\n",
    "from sklearn.cluster import KMeans\n",
    "from fcmeans import FCM\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.mixture import GaussianMixture, BayesianGaussianMixture\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import plotly.express as px\n",
    "from datetime import datetime, date\n",
    "from kneed import KneeLocator\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd1c2f2f-b1ca-4281-b2ca-e3bce8f58535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reach that point here ...\n",
      "And here...\n",
      "Here\n",
      "(954829, 16)\n"
     ]
    }
   ],
   "source": [
    "import snowflake.connector\n",
    "from cryptography.hazmat.backends import default_backend\n",
    "from cryptography.hazmat.primitives.asymmetric import rsa\n",
    "from cryptography.hazmat.primitives.asymmetric import dsa\n",
    "from cryptography.hazmat.primitives import serialization\n",
    "\n",
    "from password import PRIVATE_KEY_PASSPHRASE\n",
    "\n",
    "\n",
    "with open(\"/Users/impr001/Keys_snowflake/rsa_key.p8\", \"rb\") as key: # THIS MAY NOT APPLY TO OTHERS\n",
    "    p_key= serialization.load_pem_private_key(\n",
    "        key.read(),\n",
    "        password=PRIVATE_KEY_PASSPHRASE.encode(),\n",
    "        backend=default_backend()\n",
    "    )\n",
    "\n",
    "pkb = p_key.private_bytes(\n",
    "    encoding=serialization.Encoding.DER,\n",
    "    format=serialization.PrivateFormat.PKCS8,\n",
    "    encryption_algorithm=serialization.NoEncryption())\n",
    "\n",
    "print(\"We reach that point here ...\")\n",
    "ctx = snowflake.connector.connect(\n",
    "    user='eimpara@theorchard.com', \n",
    "    account='orchard',\n",
    "    private_key=pkb,\n",
    "    role= 'PROD_DATALYTICS_ROLE',\n",
    "    warehouse = \"DEV_ANALYTICS_ORCHARD\"\n",
    "    )\n",
    "print(\"And here...\")\n",
    "\n",
    "try:\n",
    "    cs = ctx.cursor()\n",
    "    print(\"Here\")\n",
    "    sql = \"\"\"\n",
    "select ACTIVITY_MONTH,\n",
    "USER_COUNTRY_CODE,\n",
    "USER_ID,\n",
    "COLLECTION_STREAMS, \n",
    "RADIO_STREAMS, \n",
    "ARTIST_STREAMS,\n",
    "SEARCH_STREAMS,  \n",
    "ALBUM_STREAMS,\n",
    "THIS_IS_STREAMS, \n",
    "EDITORIAL_PLAYLIST_STREAMS, \n",
    "THIRD_PARTY_PLAYLIST_STREAMS, \n",
    "USER_PLAYLIST_STREAMS, \n",
    "USER_COMPLETION_RATE,\n",
    "FRONTLINE_STREAMS, \n",
    "MIDLINE_STREAMS, \n",
    "CATALOG_STREAMS \n",
    "from dev_engineering.eimpara.test_listener_segmentation_data\n",
    "where ARTIST_NAME = 'Jorja Smith' and \n",
    "activity_month = '2023-03-01'\n",
    "\n",
    "    \"\"\"\n",
    "    cs.execute(sql)\n",
    "    original = cs.fetch_pandas_all()\n",
    "    \n",
    "    print(original.shape)\n",
    "\n",
    "finally:\n",
    "    cs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9747e9ee-1812-4815-926b-af236cad6abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:0f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb32a3bb-5c1f-48b5-a64b-e87845cffc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = original.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "647119e7-27ac-49ad-be70-1a63b3e6c351",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 954829 entries, 0 to 954828\n",
      "Data columns (total 16 columns):\n",
      " #   Column                        Non-Null Count   Dtype  \n",
      "---  ------                        --------------   -----  \n",
      " 0   ACTIVITY_MONTH                954829 non-null  object \n",
      " 1   USER_COUNTRY_CODE             954829 non-null  object \n",
      " 2   USER_ID                       954829 non-null  object \n",
      " 3   COLLECTION_STREAMS            954829 non-null  int32  \n",
      " 4   RADIO_STREAMS                 954829 non-null  int16  \n",
      " 5   ARTIST_STREAMS                954829 non-null  int16  \n",
      " 6   SEARCH_STREAMS                954829 non-null  int16  \n",
      " 7   ALBUM_STREAMS                 954829 non-null  int32  \n",
      " 8   THIS_IS_STREAMS               954829 non-null  int16  \n",
      " 9   EDITORIAL_PLAYLIST_STREAMS    954829 non-null  int16  \n",
      " 10  THIRD_PARTY_PLAYLIST_STREAMS  954829 non-null  int32  \n",
      " 11  USER_PLAYLIST_STREAMS         954829 non-null  int32  \n",
      " 12  USER_COMPLETION_RATE          954829 non-null  float64\n",
      " 13  FRONTLINE_STREAMS             954829 non-null  int32  \n",
      " 14  MIDLINE_STREAMS               954829 non-null  int32  \n",
      " 15  CATALOG_STREAMS               954829 non-null  int32  \n",
      "dtypes: float64(1), int16(5), int32(7), object(3)\n",
      "memory usage: 63.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fcfbdaa1-bd68-4ccf-9477-bb493df78e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_MONTH</th>\n",
       "      <th>USER_COUNTRY_CODE</th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>GB</td>\n",
       "      <td>79083bf95693c23fc6f55b7c92cf600a44e2fe503ba579...</td>\n",
       "      <td>37</td>\n",
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       "      <td>0.842105</td>\n",
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       "      <td>0</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-03-01</td>\n",
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       "      <td>e3eec35cdff6cb8fab3516ddb5b5229f4c5c14a8f3fe2f...</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>GB</td>\n",
       "      <td>95ad54e2451922174d0c3cc37612b7ae619aae4bc5c658...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>GB</td>\n",
       "      <td>7d4bee1ba9d5674644cc94a994676a25cb64cb88915afd...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>GB</td>\n",
       "      <td>7a438736eb1ef5d7d332d4f4a6eed586dab1140d30898f...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ACTIVITY_MONTH USER_COUNTRY_CODE  \\\n",
       "0     2023-03-01                GB   \n",
       "1     2023-03-01                GB   \n",
       "2     2023-03-01                GB   \n",
       "3     2023-03-01                GB   \n",
       "4     2023-03-01                GB   \n",
       "\n",
       "                                             USER_ID  COLLECTION_STREAMS  \\\n",
       "0  79083bf95693c23fc6f55b7c92cf600a44e2fe503ba579...                  37   \n",
       "1  e3eec35cdff6cb8fab3516ddb5b5229f4c5c14a8f3fe2f...                   1   \n",
       "2  95ad54e2451922174d0c3cc37612b7ae619aae4bc5c658...                   5   \n",
       "3  7d4bee1ba9d5674644cc94a994676a25cb64cb88915afd...                   0   \n",
       "4  7a438736eb1ef5d7d332d4f4a6eed586dab1140d30898f...                   4   \n",
       "\n",
       "   RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  ALBUM_STREAMS  \\\n",
       "0              0               0               0              0   \n",
       "1              0               0               0              0   \n",
       "2              2               0              17              0   \n",
       "3              1               0               0              0   \n",
       "4              0               0               0              0   \n",
       "\n",
       "   THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  THIRD_PARTY_PLAYLIST_STREAMS  \\\n",
       "0                0                           0                             0   \n",
       "1                0                           0                             0   \n",
       "2                0                           0                             0   \n",
       "3                0                           0                             0   \n",
       "4                0                           0                             0   \n",
       "\n",
       "   USER_PLAYLIST_STREAMS  USER_COMPLETION_RATE  FRONTLINE_STREAMS  \\\n",
       "0                      0              0.842105                  0   \n",
       "1                      0              1.000000                  0   \n",
       "2                      0              0.500000                  0   \n",
       "3                      0              1.000000                  0   \n",
       "4                      0              0.666667                  0   \n",
       "\n",
       "   MIDLINE_STREAMS  CATALOG_STREAMS  \n",
       "0                0               37  \n",
       "1                0                1  \n",
       "2                0               30  \n",
       "3                0                1  \n",
       "4                3                1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87b54892-aa90-4003-bf15-fef926e17e45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f35d7ff9-35be-412d-ae23-ac364722d0a2",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "631f0789-d1e8-4f99-9337-eb325cbe1dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "scaled = scaler.fit_transform(df[numeric_cols])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3cac9fd5-65fd-4435-8f2d-657c44e4be87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.370000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>4.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.070000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>2.370000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>1.720000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>11.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>754.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>3665.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       954829.000000  954829.000000   954829.000000   954829.000000   \n",
       "mean             2.370000       0.440000        0.150000        0.140000   \n",
       "std              6.070000       2.660000        2.070000        1.590000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              2.000000       0.000000        0.000000        0.000000   \n",
       "max            754.000000     534.000000      347.000000      515.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  954829.000000    954829.000000               954829.000000   \n",
       "mean        0.210000         0.150000                    0.110000   \n",
       "std         3.740000         2.370000                    0.840000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max      1287.000000       523.000000                  372.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 954829.000000          954829.000000   \n",
       "mean                       0.040000               0.220000   \n",
       "std                        0.640000               1.720000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                      223.000000             492.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         954829.000000      954829.000000    954829.000000   \n",
       "mean               0.680000           0.000000         0.040000   \n",
       "std                0.390000           0.000000         0.680000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.500000           0.000000         0.000000   \n",
       "50%                0.860000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000       241.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    954829.000000  \n",
       "mean          4.560000  \n",
       "std          11.010000  \n",
       "min           0.000000  \n",
       "25%           1.000000  \n",
       "50%           2.000000  \n",
       "75%           4.000000  \n",
       "max        3665.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c7ab431-4693-4178-a7ca-4f45590db47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.kdeplot(df['CATALOG_STREAMS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3ac065d-c30e-4ddc-8e9b-93f10980bde9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['tot'] = df['COLLECTION_STREAMS']+  df['RADIO_STREAMS']+ \t df['ARTIST_STREAMS']+ \tdf['SEARCH_STREAMS']+ \tdf['ALBUM_STREAMS']+ \tdf['THIS_IS_STREAMS']+ \tdf['EDITORIAL_PLAYLIST_STREAMS']+ \tdf['THIRD_PARTY_PLAYLIST_STREAMS']+ \tdf['USER_PLAYLIST_STREAMS']+ \tdf['USER_COMPLETION_RATE']+ \tdf['FRONTLINE_STREAMS']+ \tdf['MIDLINE_STREAMS']+ df['CATALOG_STREAMS']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de9f3d3-9138-4d6b-b87f-3f9c8d6a69dc",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "29aa33ec-d7ea-438f-a6b6-b15ce6972798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.81558144e-01, 9.00961236e-02, 8.78414083e-02, 8.47654412e-02,\n",
       "       8.43163854e-02, 8.20918035e-02, 8.13307897e-02, 8.02342038e-02,\n",
       "       7.81540994e-02, 7.38865947e-02, 6.72713402e-02, 8.45366668e-03,\n",
       "       9.50533359e-18])"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(scaled)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "d16e8e68-4456-44e0-846f-341cdfaa9dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18155814, 0.27165427, 0.35949568, 0.44426112, 0.5285775 ,\n",
       "       0.61066931, 0.6920001 , 0.7722343 , 0.8503884 , 0.92427499,\n",
       "       0.99154633, 1.        , 1.        ])"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "f86b3475-c272-4ee8-9a3c-8162ffa26a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Explained variance')"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(pca.explained_variance_ratio_) + 1), pca.explained_variance_ratio_.cumsum())\n",
    "plt.xlabel('N. of components')\n",
    "plt.ylabel('Explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "24fe3000-0bb8-47a0-888d-2c1542814b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=9).fit(scaled)\n",
    "pca_scores = pca.transform(scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90492e2-344b-41d6-90f1-72127cdc888c",
   "metadata": {},
   "source": [
    "## k-means with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "be8dba39-e44a-484d-ab48-3145ae3131c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cspca = []\n",
    "for i in range(1,20):\n",
    "    kmeanspca = KMeans(n_clusters = i, init = 'k-means++', max_iter = 1000, n_init = 10, random_state=2024)\n",
    "    kmeanspca.fit(pca_scores)\n",
    "    cspca.append(kmeanspca.inertia_)\n",
    "plt.plot(range(1, 20), cspca)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "eaf10cbd-6299-47d6-a6aa-edb292bbbdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kneepca = KneeLocator(range(1,20), cspca, curve='convex', direction='decreasing')\n",
    "kneepca.elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "99f8f541-1125-4fe1-8cb1-dc362ab44793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    608406\n",
       "3    316072\n",
       "0     27458\n",
       "4       970\n",
       "1       968\n",
       "5       788\n",
       "6       167\n",
       "dtype: int64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model_pca = KMeans(n_clusters=kneepca.elbow, init = 'k-means++',  n_init = 10, random_state=2024)\n",
    "kmeans_model_pca.fit(pca_scores)\n",
    "clusters_pca = kmeans_model_pca.predict(pca_scores)\n",
    "pd.Series(clusters_pca).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "d2709e5b-e687-4878-a5ae-81232d7a51fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "centroid_pca = kmeans_model_pca.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "6fb7129e-04bb-4a43-93b1-122c3df67a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['k_means_Clusters_PCA'] = kmeans_model_pca.labels_\n",
    "\n",
    "#df[\"Clusters\"] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "b3ef49da-2b98-4f7b-a53b-ccc968cbb9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    664621\n",
       "2    235599\n",
       "3     43643\n",
       "6      4309\n",
       "8      2560\n",
       "1      1695\n",
       "5      1326\n",
       "7       972\n",
       "4       104\n",
       "Name: k_means_Clusters_PCA, dtype: int64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['k_means_Clusters_PCA'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "d0d95d0c-8fbc-420c-9485-53ddfc7f637b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "      <td>954829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.369863</td>\n",
       "      <td>0.441929</td>\n",
       "      <td>0.148286</td>\n",
       "      <td>0.139488</td>\n",
       "      <td>0.207655</td>\n",
       "      <td>0.148818</td>\n",
       "      <td>0.108175</td>\n",
       "      <td>0.040703</td>\n",
       "      <td>0.217760</td>\n",
       "      <td>0.677461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042800</td>\n",
       "      <td>4.555950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.068478</td>\n",
       "      <td>2.663249</td>\n",
       "      <td>2.074443</td>\n",
       "      <td>1.588793</td>\n",
       "      <td>3.736344</td>\n",
       "      <td>2.370236</td>\n",
       "      <td>0.841998</td>\n",
       "      <td>0.635832</td>\n",
       "      <td>1.715326</td>\n",
       "      <td>0.386130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.678806</td>\n",
       "      <td>11.005598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>754.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>3665.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       954829.000000  954829.000000   954829.000000   954829.000000   \n",
       "mean             2.369863       0.441929        0.148286        0.139488   \n",
       "std              6.068478       2.663249        2.074443        1.588793   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              2.000000       0.000000        0.000000        0.000000   \n",
       "max            754.000000     534.000000      347.000000      515.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  954829.000000    954829.000000               954829.000000   \n",
       "mean        0.207655         0.148818                    0.108175   \n",
       "std         3.736344         2.370236                    0.841998   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max      1287.000000       523.000000                  372.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 954829.000000          954829.000000   \n",
       "mean                       0.040703               0.217760   \n",
       "std                        0.635832               1.715326   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                      223.000000             492.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         954829.000000      954829.000000    954829.000000   \n",
       "mean               0.677461           0.000000         0.042800   \n",
       "std                0.386130           0.000000         0.678806   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.500000           0.000000         0.000000   \n",
       "50%                0.857143           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000       241.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    954829.000000  \n",
       "mean          4.555950  \n",
       "std          11.005598  \n",
       "min           0.000000  \n",
       "25%           1.000000  \n",
       "50%           2.000000  \n",
       "75%           4.000000  \n",
       "max        3665.000000  "
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a568674b-ae26-4f57-b1ee-1c2fb552461f",
   "metadata": {},
   "source": [
    "## Cluster descriptive analysis K-means with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c314824a-f26d-4f92-9b60-2016d8ee31ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3c3124db-6438-4cd9-9e2e-273c6ba9959a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reach that point here ...\n",
      "And here...\n",
      "Here\n",
      "(969986, 23)\n"
     ]
    }
   ],
   "source": [
    "with open(\"/Users/impr001/Keys_snowflake/rsa_key.p8\", \"rb\") as key: # THIS MAY NOT APPLY TO OTHERS\n",
    "    p_key= serialization.load_pem_private_key(\n",
    "        key.read(),\n",
    "        password=PRIVATE_KEY_PASSPHRASE.encode(),\n",
    "        backend=default_backend()\n",
    "    )\n",
    "\n",
    "pkb = p_key.private_bytes(\n",
    "    encoding=serialization.Encoding.DER,\n",
    "    format=serialization.PrivateFormat.PKCS8,\n",
    "    encryption_algorithm=serialization.NoEncryption())\n",
    "\n",
    "print(\"We reach that point here ...\")\n",
    "ctx = snowflake.connector.connect(\n",
    "    user='eimpara@theorchard.com', \n",
    "    account='orchard',\n",
    "    private_key=pkb,\n",
    "    role= 'PROD_DATALYTICS_ROLE',\n",
    "    warehouse = \"DEV_ANALYTICS_ORCHARD\"\n",
    "    )\n",
    "print(\"And here...\")\n",
    "\n",
    "try:\n",
    "    cs = ctx.cursor()\n",
    "    print(\"Here\")\n",
    "    sql = \"\"\"\n",
    "with main as (select * from dev_engineering.eimpara.test_listener_segmentation_data\n",
    "where ARTIST_NAME = 'Jorja Smith' and \n",
    "activity_month = '2023-03-01'),\n",
    "\n",
    "spotify_users as (select month(ACTIVITY_DATE) as month, \n",
    "       user_id as id,\n",
    "       user_gender,\n",
    "       user_age_group,\n",
    "       user_country_code as country\n",
    "from INTELLIGENCE.DBT_PROD.AGG_SPOTIFY_MODEL\n",
    "where user_country_code = 'GB'\n",
    "group by month(ACTIVITY_DATE), user_id, user_gender,user_age_group, user_country_code\n",
    "),\n",
    "\n",
    "final as (\n",
    "select * from main\n",
    "left join spotify_users on spotify_users.id = main.USER_ID \n",
    ")\n",
    "\n",
    "select * from final\n",
    "\n",
    "    \"\"\"\n",
    "    cs.execute(sql)\n",
    "    users = cs.fetch_pandas_all()\n",
    "    \n",
    "    print(users.shape)\n",
    "\n",
    "finally:\n",
    "    cs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4718ce22-e9bc-4ed0-a71c-c34733c39064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 969986 entries, 0 to 969985\n",
      "Data columns (total 23 columns):\n",
      " #   Column                        Non-Null Count   Dtype  \n",
      "---  ------                        --------------   -----  \n",
      " 0   ACTIVITY_MONTH                969986 non-null  object \n",
      " 1   ARTIST_NAME                   969986 non-null  object \n",
      " 2   USER_COUNTRY_CODE             969986 non-null  object \n",
      " 3   USER_ID                       969986 non-null  object \n",
      " 4   TOTAL_STREAMS                 969986 non-null  int32  \n",
      " 5   COLLECTION_STREAMS            969986 non-null  int32  \n",
      " 6   RADIO_STREAMS                 969986 non-null  int16  \n",
      " 7   ARTIST_STREAMS                969986 non-null  int16  \n",
      " 8   SEARCH_STREAMS                969986 non-null  int16  \n",
      " 9   ALBUM_STREAMS                 969986 non-null  int32  \n",
      " 10  THIS_IS_STREAMS               969986 non-null  int16  \n",
      " 11  EDITORIAL_PLAYLIST_STREAMS    969986 non-null  int16  \n",
      " 12  THIRD_PARTY_PLAYLIST_STREAMS  969986 non-null  int32  \n",
      " 13  USER_PLAYLIST_STREAMS         969986 non-null  int32  \n",
      " 14  USER_COMPLETION_RATE          969986 non-null  float64\n",
      " 15  FRONTLINE_STREAMS             969986 non-null  int32  \n",
      " 16  MIDLINE_STREAMS               969986 non-null  int32  \n",
      " 17  CATALOG_STREAMS               969986 non-null  int32  \n",
      " 18  MONTH                         969986 non-null  int8   \n",
      " 19  ID                            969986 non-null  object \n",
      " 20  USER_GENDER                   969986 non-null  object \n",
      " 21  USER_AGE_GROUP                969986 non-null  object \n",
      " 22  COUNTRY                       969986 non-null  object \n",
      "dtypes: float64(1), int16(5), int32(8), int8(1), object(8)\n",
      "memory usage: 106.4+ MB\n"
     ]
    }
   ],
   "source": [
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "79cc09ae-13d9-4e8b-ac42-44d9f3355dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(969986, 23)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = users.drop_duplicates()\n",
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b602d33f-5c3a-4629-976e-fb99ad9f0652",
   "metadata": {},
   "outputs": [],
   "source": [
    "dflist =df['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3517e8f4-483e-4d5c-8f86-0ba25218b641",
   "metadata": {},
   "outputs": [],
   "source": [
    "subusers = users[users['USER_ID'].isin(dflist)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a9f43893-cef1-414a-8129-a43caec39c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    604379\n",
       "male      346577\n",
       "           19030\n",
       "Name: USER_GENDER, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subusers['USER_GENDER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ffaa516e-2fe3-4b50-b66c-3cdb109830fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6230801269296671"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "604379/sum(subusers['USER_GENDER'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7d064098-f4f3-4516-8b88-032fd68442bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      310782\n",
       "18-22      264343\n",
       "28-34      198320\n",
       "35-44       84081\n",
       "45-59       61311\n",
       "0-17        38043\n",
       "60-150      10919\n",
       "unknown      1115\n",
       "Unknown      1072\n",
       "Name: USER_AGE_GROUP, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['USER_AGE_GROUP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cb25514b-db64-47d6-8341-1acfa978d8ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "954829\n",
      "954829\n",
      "['Jorja Smith']\n",
      "['GB']\n",
      "[datetime.date(2023, 3, 1)]\n",
      "[3]\n",
      "['male' '' 'female']\n",
      "['35-44' '28-34' '23-27' '18-22' '45-59' '0-17' '60-150' 'Unknown'\n",
      " 'unknown']\n"
     ]
    }
   ],
   "source": [
    "print(users['USER_ID'].nunique())\n",
    "print(users['ID'].nunique())\n",
    "print(users['ARTIST_NAME'].unique())\n",
    "print(users['USER_COUNTRY_CODE'].unique())\n",
    "print(users['ACTIVITY_MONTH'].unique())\n",
    "print(users['MONTH'].unique())\n",
    "print(users['USER_GENDER'].unique())\n",
    "print(users['USER_AGE_GROUP'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2b8d60a0-3e88-4daa-b6eb-51ffa86e70ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      310782\n",
       "18-22      264343\n",
       "28-34      198320\n",
       "35-44       84081\n",
       "45-59       61311\n",
       "0-17        38043\n",
       "60-150      10919\n",
       "unknown      1115\n",
       "Unknown      1072\n",
       "Name: USER_AGE_GROUP, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['USER_AGE_GROUP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "779d1614-1fa2-4dc1-b80c-6ce6f5c08b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    604379\n",
       "male      346577\n",
       "           19030\n",
       "Name: USER_GENDER, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['USER_GENDER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "50a1ed13-3124-421e-b5ba-d091095f0eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted = users.sort_values(by=['USER_ID', 'USER_AGE_GROUP'], ascending=[True, True])\n",
    "users_cleaned = df_sorted.drop_duplicates(subset='USER_ID', keep='last').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7c18dc9e-9651-4d06-a07a-b027595ab5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      307326\n",
       "18-22      256781\n",
       "28-34      197335\n",
       "35-44       83729\n",
       "45-59       61172\n",
       "0-17        36432\n",
       "60-150      10919\n",
       "unknown      1115\n",
       "Unknown        20\n",
       "Name: USER_AGE_GROUP, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_cleaned['USER_AGE_GROUP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4bfc4c2f-51f9-4600-be15-dd8cd0acc51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "users_cleaned['USER_AGE_GROUP_cleaned'] = np.where(users_cleaned['USER_AGE_GROUP']=='Unknown', 'unknown', users_cleaned['USER_AGE_GROUP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5a00fbfa-4a99-464e-afe0-dd0abdb90092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      307326\n",
       "18-22      256781\n",
       "28-34      197335\n",
       "35-44       83729\n",
       "45-59       61172\n",
       "0-17        36432\n",
       "60-150      10919\n",
       "unknown      1135\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_cleaned['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c99903d2-317d-4ea8-9984-c1dcfc78a6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp = df[df['k_means_Clusters_PCA']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cdf6362b-8696-417e-b761-f3781a44a32a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_MONTH</th>\n",
       "      <th>USER_COUNTRY_CODE</th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
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       "<p>454942 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "       ACTIVITY_MONTH USER_COUNTRY_CODE  \\\n",
       "1          2023-03-01                GB   \n",
       "3          2023-03-01                GB   \n",
       "10         2023-03-01                GB   \n",
       "11         2023-03-01                GB   \n",
       "15         2023-03-01                GB   \n",
       "...               ...               ...   \n",
       "954820     2023-03-01                GB   \n",
       "954822     2023-03-01                GB   \n",
       "954824     2023-03-01                GB   \n",
       "954825     2023-03-01                GB   \n",
       "954828     2023-03-01                GB   \n",
       "\n",
       "                                                  USER_ID  COLLECTION_STREAMS  \\\n",
       "1       e3eec35cdff6cb8fab3516ddb5b5229f4c5c14a8f3fe2f...                   1   \n",
       "3       7d4bee1ba9d5674644cc94a994676a25cb64cb88915afd...                   0   \n",
       "10      a645551a8e8232e54409de3f0a0e19318e3ec0ac97dadc...                   3   \n",
       "11      f83fe6648d92af0a4302418d3340fbf3bb795fc8c55289...                   5   \n",
       "15      6338596b4d0b6783c8b9843746e2b46e3e8e094b25fde7...                   2   \n",
       "...                                                   ...                 ...   \n",
       "954820  f303abaee3a7286031045bd2baf3e9ae5e49f2cbd8c4bf...                   2   \n",
       "954822  79c5c622259d89a327dd6b19b1fc452ac69f74282c9196...                   1   \n",
       "954824  fffd50166f39b3ec0b75d20e497c768aa882e858553e02...                   1   \n",
       "954825  ec6955d632e5a4277bb82a85f3adc68123917be10cde17...                   1   \n",
       "954828  9bf416dad660b65e587dd015a0b9c5d22dedca1edc29cb...                   1   \n",
       "\n",
       "        RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  ALBUM_STREAMS  \\\n",
       "1                   0               0               0              0   \n",
       "3                   1               0               0              0   \n",
       "10                  0               0               0              0   \n",
       "11                  0               0               0              0   \n",
       "15                  0               0               0              0   \n",
       "...               ...             ...             ...            ...   \n",
       "954820              0               0               0              0   \n",
       "954822              1               0               0              0   \n",
       "954824              0               0               0              0   \n",
       "954825              0               0               0              0   \n",
       "954828              0               0               0              0   \n",
       "\n",
       "        THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "1                     0                           0   \n",
       "3                     0                           0   \n",
       "10                    0                           0   \n",
       "11                    0                           0   \n",
       "15                    0                           0   \n",
       "...                 ...                         ...   \n",
       "954820                0                           0   \n",
       "954822                0                           0   \n",
       "954824                0                           0   \n",
       "954825                0                           0   \n",
       "954828                0                           0   \n",
       "\n",
       "        THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "1                                  0                      0   \n",
       "3                                  0                      0   \n",
       "10                                 0                      0   \n",
       "11                                 0                      0   \n",
       "15                                 0                      0   \n",
       "...                              ...                    ...   \n",
       "954820                             0                      0   \n",
       "954822                             0                      0   \n",
       "954824                             0                      0   \n",
       "954825                             0                      0   \n",
       "954828                             0                      0   \n",
       "\n",
       "        USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "1                   1.000000                  0                0   \n",
       "3                   1.000000                  0                0   \n",
       "10                  1.000000                  0                0   \n",
       "11                  1.000000                  0                0   \n",
       "15                  1.000000                  0                2   \n",
       "...                      ...                ...              ...   \n",
       "954820              1.000000                  0                0   \n",
       "954822              1.000000                  0                0   \n",
       "954824              1.000000                  0                0   \n",
       "954825              1.000000                  0                0   \n",
       "954828              1.000000                  0                0   \n",
       "\n",
       "        CATALOG_STREAMS  k_means_Clusters_PCA  \n",
       "1                     1                     0  \n",
       "3                     1                     0  \n",
       "10                    3                     0  \n",
       "11                    5                     0  \n",
       "15                    0                     0  \n",
       "...                 ...                   ...  \n",
       "954820                2                     0  \n",
       "954822                2                     0  \n",
       "954824                1                     0  \n",
       "954825                1                     0  \n",
       "954828                1                     0  \n",
       "\n",
       "[454942 rows x 17 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8c15e93f-4523-4c97-8fa4-a968761f4dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean</th>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
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       "      <th>std</th>\n",
       "      <td>1.270000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.030000</td>\n",
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       "      <td>0.130000</td>\n",
       "      <td>1.850000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       454942.000000  454942.000000   454942.000000   454942.000000   \n",
       "mean             0.940000       0.260000        0.040000        0.020000   \n",
       "std              1.270000       0.840000        0.370000        0.170000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              1.000000       0.000000        0.000000        0.000000   \n",
       "max             14.000000      19.000000       12.000000        5.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  454942.000000    454942.000000               454942.000000   \n",
       "mean        0.010000         0.020000                    0.100000   \n",
       "std         0.190000         0.210000                    0.420000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        11.000000        11.000000                    5.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 454942.000000          454942.000000   \n",
       "mean                       0.050000               0.130000   \n",
       "std                        0.410000               0.540000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                       15.000000              14.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         454942.000000      454942.000000    454942.000000   \n",
       "mean               0.990000           0.000000         0.010000   \n",
       "std                0.030000           0.000000         0.130000   \n",
       "min                0.250000           0.000000         0.000000   \n",
       "25%                1.000000           0.000000         0.000000   \n",
       "50%                1.000000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         6.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    454942.000000  \n",
       "mean          2.010000  \n",
       "std           1.850000  \n",
       "min           0.000000  \n",
       "25%           1.000000  \n",
       "50%           1.000000  \n",
       "75%           2.000000  \n",
       "max          45.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2de4e63e-8c39-4e3b-9d54-66e84d29bbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist =kmp['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dce786b4-dedc-424b-bd37-adeba4d66478",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp = users_cleaned[users_cleaned['USER_ID'].isin(kmplist)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c2b3d723-dfb7-4dc9-8cac-15088cc1239b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f3191483-feaf-4cf8-b7d7-a4f2cfb2d871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c50a3537-b2df-45f0-a85d-cc31d1e08f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      145312\n",
       "18-22      106665\n",
       "28-34      103429\n",
       "35-44       46971\n",
       "45-59       31728\n",
       "0-17        14322\n",
       "60-150       5915\n",
       "unknown       600\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "43f3b4f7-a9ea-42d1-b48c-e72f04c8c28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "21cb0aba-e40a-4b16-ba1f-991d76f05da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ca704de2-1b14-480a-bb00-7292c84d5bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp1 = df[df['k_means_Clusters_PCA']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6f98db82-80fb-43b7-b0ba-1cfd698a61b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.540000</td>\n",
       "      <td>14.230000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>39.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.140000</td>\n",
       "      <td>19.560000</td>\n",
       "      <td>14.040000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>23.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>185.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count         3389.000000    3389.000000     3389.000000     3389.000000   \n",
       "mean             3.540000      14.230000       15.500000        0.500000   \n",
       "std              8.140000      19.560000       14.040000        1.810000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       3.000000       15.000000        0.000000   \n",
       "75%              3.000000      25.000000       21.000000        0.000000   \n",
       "max             79.000000     133.000000       73.000000       24.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count    3389.000000      3389.000000                 3389.000000   \n",
       "mean        1.710000         0.620000                    0.200000   \n",
       "std         6.130000         2.880000                    0.830000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        60.000000        43.000000                   16.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                   3389.000000            3389.000000   \n",
       "mean                       0.030000               0.340000   \n",
       "std                        0.410000               1.450000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                       15.000000              17.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count           3389.000000        3389.000000      3389.000000   \n",
       "mean               0.800000           0.000000         0.420000   \n",
       "std                0.180000           0.000000         1.340000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.730000           0.000000         0.000000   \n",
       "50%                0.850000           0.000000         0.000000   \n",
       "75%                0.930000           0.000000         0.000000   \n",
       "max                1.000000           0.000000        11.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count      3389.000000  \n",
       "mean         39.610000  \n",
       "std          23.700000  \n",
       "min           5.000000  \n",
       "25%          23.000000  \n",
       "50%          33.000000  \n",
       "75%          50.000000  \n",
       "max         185.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp1[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e429c7cf-9a19-4444-b18f-c6d8e33452e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist1 =kmp1['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6007a42a-494d-4e2a-8ae3-8a51bd6eec4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp1 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist1)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "021a0242-457f-4b42-a759-fb0e0bae3280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp1, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b929138b-c423-4b94-8faf-472ad1500dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp1, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "60546722-c87a-4e5e-b1da-a1e8a7b32e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      1105\n",
       "18-22       825\n",
       "28-34       769\n",
       "35-44       335\n",
       "45-59       220\n",
       "0-17         92\n",
       "60-150       40\n",
       "unknown       3\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp1['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eae825b7-17d5-4182-b81a-6d1e93adcafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5243935b-a818-4777-b721-0fe40cc280e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e0d62d69-463f-48ff-84f8-af2f0ab661b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp2 = df[df['k_means_Clusters_PCA']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "915f6af7-2119-47a5-bede-f0e2800d3db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "      <td>200111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.230000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       200111.000000  200111.000000   200111.000000   200111.000000   \n",
       "mean             0.890000       0.130000        0.060000        0.110000   \n",
       "std              1.230000       0.430000        0.390000        0.570000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              1.000000       0.000000        0.000000        0.000000   \n",
       "max             14.000000      10.000000       13.000000        9.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  200111.000000    200111.000000               200111.000000   \n",
       "mean        0.040000         0.030000                    0.060000   \n",
       "std         0.400000         0.270000                    0.290000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        24.000000        13.000000                    5.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 200111.000000          200111.000000   \n",
       "mean                       0.030000               0.100000   \n",
       "std                        0.220000               0.450000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                       10.000000              15.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         200111.000000      200111.000000    200111.000000   \n",
       "mean               0.030000           0.000000         0.020000   \n",
       "std                0.080000           0.000000         0.180000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.000000           0.000000         0.000000   \n",
       "50%                0.000000           0.000000         0.000000   \n",
       "75%                0.000000           0.000000         0.000000   \n",
       "max                0.380000           0.000000         8.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    200111.000000  \n",
       "mean          1.780000  \n",
       "std           1.600000  \n",
       "min           0.000000  \n",
       "25%           1.000000  \n",
       "50%           1.000000  \n",
       "75%           2.000000  \n",
       "max          35.000000  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp2[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "47adf7c0-33f1-4446-9a97-1db58d07810b",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist2 =kmp2['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "39f2fff4-8b1d-4c67-a11f-0d64b38f1ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp2 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist2)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "06f201de-682f-4f34-b572-dccf8213493a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp2, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7937dfce-c12a-4fe5-8dd3-ca64acaaaa64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp2, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a908693e-a8ef-4877-bde6-a55a65eeb905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      66409\n",
       "18-22      54705\n",
       "28-34      41433\n",
       "35-44      15926\n",
       "45-59      11612\n",
       "0-17        7703\n",
       "60-150      2080\n",
       "unknown      243\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp2['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "380427cb-2ba5-472f-96fa-087f5df7103b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5526aa18-55bf-47b2-b94b-3f06a04e5114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AcE3lOcyYmQ4ePKiEhAS98MILWrdunYKDg1WvXj1VrFjxatQIAACQq3yFmapVq2rTpk2qVq2aqlWrdjXqAgAA8Eie18z4+vqqWrVqOnLkyNWoBwAAIE/ytQB4zJgxGjRokDZu3FjQ9QAAAORJvhYA9+jRQ6dOnVL9+vUVEBCg4OBgt+NHjx4tkOIAAAAuJ19h5rXXXivgMgAAAPInX2GmZ8+eBV0HAABAvuRrzYwk7dy5U0OHDlW3bt10+PBhSdJXX32lTZs2FVhxAAAAl5OvMLNs2TLVq1dPq1at0ieffKKTJ09KOnc5g+HDhxdogQAAAJeSrzDz7LPP6oUXXtCiRYsUEBDg2t+8eXMlJSUVWHEAAACXk68ws2HDBt17773Z9oeHh+u333674qIAAAA8la8wU7JkSaWkpGTbv3btWpUvX/6KiwIAAPBUvsJM165dNWTIEB08eFA+Pj7KysrSihUr9Mwzz6hHjx4FXSMAAECu8hVmXnrpJdWsWVPR0dE6efKkateurdtuu01NmjTR0KFDC7pGAACAXOXre2YCAgI0ZcoUDRs2TBs2bFBaWpquv/56Va1ataDrAwAAuKR8hRlJevfddzV+/Hht375dklStWjX1799f//d//1dgxQEAAFxOvt5mGjZsmJ566im1a9dOs2bN0qxZs9SuXTsNGDBAw4YN87if5cuXq127dipXrpx8fHw0Z84ct+MPPfSQfHx83LY2bdrkp2QAAPAnla+ZmUmTJmnKlCnq1q2ba98999yj2NhYPfHEExo1apRH/aSlpal+/fp65JFH1LFjxxzbtGnTRlOnTnXdDgwMzE/JAADgTypfYSYjI0M33nhjtv0NGzbU2bNnPe6nbdu2atu27SXbBAYGKjIyMs81AgCAv4Z8vc304IMPatKkSdn2v/3223rggQeuuKgLLV26VOHh4apRo4b69OmjI0eOXLJ9enq6UlNT3TYAAPDndUULgBcuXKibb75ZkrRq1Srt3btXPXr00MCBA13tXn311XwX16ZNG3Xs2FExMTHauXOn/vnPf6pt27ZauXKlihQpkuM5o0eP1siRI/N9nwAAwFnyFWY2btyoG264QdK5q2dLUpkyZVSmTBlt3LjR1c7Hx+eKiuvatavr3/Xq1VNsbKyqVKmipUuXqkWLFjmeEx8f7xamUlNTFR0dfUV1AACAwitfYWbJkiUFXYdHKleurDJlymjHjh25hpnAwEAWCQMA8BeSrzUz3rJv3z4dOXJEZcuW9XYpAACgkMj3mpmCcPLkSe3YscN1e/fu3Vq3bp3CwsIUFhamkSNHqlOnToqMjNTOnTs1ePBgVa1aVa1bt/Zi1QAAoDDxaphZvXq17rjjDtft82tdevbsqUmTJmn9+vV6//33dezYMZUrV06tWrXSv/71L95GAgAALl4NM82aNZOZ5Xp8wYIF17AaAADgRI5aMwMAAHAxwgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0r4aZ5cuXq127dipXrpx8fHw0Z84ct+NmpmHDhqls2bIKDg5Wy5YttX37du8UCwAACiWvhpm0tDTVr19fEydOzPH42LFj9frrr+utt97SqlWrVKxYMbVu3VqnT5++xpUCAIDCys+bd962bVu1bds2x2Nmptdee01Dhw5V+/btJUnTpk1TRESE5syZo65du17LUgEAQCFVaNfM7N69WwcPHlTLli1d+0JDQ9W4cWOtXLnSi5UBAIDCxKszM5dy8OBBSVJERITb/oiICNexnKSnpys9Pd11OzU19eoUCAAACoVCOzOTX6NHj1ZoaKhri46O9nZJAADgKiq0YSYyMlKSdOjQIbf9hw4dch3LSXx8vI4fP+7afvnll6taJwAA8K5CG2ZiYmIUGRmpxMRE177U1FStWrVKcXFxuZ4XGBiokJAQtw0AAPx5eXXNzMmTJ7Vjxw7X7d27d2vdunUKCwtThQoV1L9/f73wwguqVq2aYmJi9Pzzz6tcuXLq0KGD94oGAACFilfDzOrVq3XHHXe4bg8cOFCS1LNnTyUkJGjw4MFKS0tT7969dezYMd16662aP3++goKCvFUyAAAoZLwaZpo1ayYzy/W4j4+PRo0apVGjRl3DqgAAgJMU2jUzAAAAniDMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAARyPMAAAAR/PzdgEAcDU0HDTN2yUUiDUv9/B2CUChx8wMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwND9vFwAAKDgNB03zdgkFYs3LPbxdAhyEmRkAAOBohBkAAOBohBkAAOBohTrMjBgxQj4+Pm5bzZo1vV0WAAAoRAr9AuA6depo8eLFrtt+foW+ZAAAcA0V+mTg5+enyMhIb5cBAAAKqUL9NpMkbd++XeXKlVPlypX1wAMPaO/evd4uCQAAFCKFemamcePGSkhIUI0aNZSSkqKRI0fqtttu08aNG1WiRIkcz0lPT1d6errrdmpq6rUqFwAAeEGhDjNt27Z1/Ts2NlaNGzdWxYoV9fHHH6tXr145njN69GiNHDnyWpUIAAC8rNC/zXShkiVLqnr16tqxY0eubeLj43X8+HHX9ssvv1zDCgEAwLXmqDBz8uRJ7dy5U2XLls21TWBgoEJCQtw2AADw51Wow8wzzzyjZcuWac+ePfruu+907733qkiRIurWrZu3SwMAAIVEoV4zs2/fPnXr1k1HjhzRddddp1tvvVVJSUm67rrrvF0aAAAoJAp1mJk5c6a3SwAAAIVcoX6bCQAA4HIIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEK9Uezr7aGg6Z5u4QCseblHt4uAQAAr2FmBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOBphBgAAOJqftwsAAOBKNRw0zdslFIg1L/fwdgmOxMwMAABwNMIMAABwNMIMAABwNMIMAABwNMIMAABwNEeEmYkTJ6pSpUoKCgpS48aN9f3333u7JAAAUEgU+jDz0UcfaeDAgRo+fLh+/PFH1a9fX61bt9bhw4e9XRoAACgECn2YefXVV/Xoo4/q4YcfVu3atfXWW2+paNGieu+997xdGgAAKAQK9ZfmnTlzRmvWrFF8fLxrn6+vr1q2bKmVK1d6sTIAALyPLws8p1CHmd9++02ZmZmKiIhw2x8REaGtW7fmeE56errS09Ndt48fPy5JSk1NzdY2M/2PAqzWe3Ia26Uwbmdj3J5h3M7GuD3zZx73+X1mdvkOrBDbv3+/SbLvvvvObf+gQYOsUaNGOZ4zfPhwk8TGxsbGxsb2J9h++eWXy+aFQj0zU6ZMGRUpUkSHDh1y23/o0CFFRkbmeE58fLwGDhzoup2VlaWjR4+qdOnS8vHxuar1Xiw1NVXR0dH65ZdfFBISck3v25sYN+P+K2DcjPuvwJvjNjOdOHFC5cqVu2zbQh1mAgIC1LBhQyUmJqpDhw6SzoWTxMRE9evXL8dzAgMDFRgY6LavZMmSV7nSSwsJCflL/fCfx7j/Whj3Xwvj/mvx1rhDQ0M9aleow4wkDRw4UD179tSNN96oRo0a6bXXXlNaWpoefvhhb5cGAAAKgUIfZrp06aJff/1Vw4YN08GDB9WgQQPNnz8/26JgAADw11Tow4wk9evXL9e3lQqzwMBADR8+PNvbXn92jJtx/xUwbsb9V+CUcfuYefKZJwAAgMKp0H8DMAAAwKUQZgAAgKMRZgAA+AtYunSpfHx8dOzYMW+XUuAIM3kwceJEVapUSUFBQWrcuLG+//77S7Z/8cUX1aRJExUtWjTH77pJSEiQj49Pjps3rgq+fPlytWvXTuXKlZOPj4/mzJnjdvzkyZPq16+foqKiFBwc7Lrw56Xs2bNHvXr1UkxMjIKDg1WlShUNHz5cZ86ccbVZunSp2rdvr7Jly6pYsWJq0KCBpk+ffjWGmKPRo0frpptuUokSJRQeHq4OHTooOTnZrc1jjz2mKlWqKDg4WNddd53at2+f6yU1zvNkXM2aNcvx+b/77rsLfJwX82TcBw8e1IMPPqjIyEgVK1ZMN9xwg2bPnn3Jfo8cOaI2bdqoXLlyCgwMVHR0tPr165fr17SvWLFCfn5+atCgQUEN7ZImTZqk2NhY1/dmxMXF6auvvnIdz+k5efzxxz3uf8eOHSpRosQlv99q5syZ8vHxcX1/ljeMGTNGPj4+6t+/v2tffsa+Z8+eHH+Gk5KSXG0yMjI0atQoValSRUFBQapfv77mz59/Vca1f/9+de/eXaVLl1ZwcLDq1aun1atXu46bmYYNG6ayZcsqODhYLVu21Pbt2y/b75NPPqmGDRsqMDAwx59VTx4HSZo1a5Zq1qypoKAg1atXT19++eUVjxmEGY999NFHGjhwoIYPH64ff/xR9evXV+vWrS8ZOs6cOaP77rtPffr0yfF4ly5dlJKS4ra1bt1aTZs2VXh4+NUaSq7S0tJUv359TZw4McfjAwcO1Pz58/XBBx9oy5Yt6t+/v/r166e5c+fm2ufWrVuVlZWlyZMna9OmTRo/frzeeust/fOf/3S1+e677xQbG6vZs2dr/fr1evjhh9WjRw/NmzevwMeYk2XLlqlv375KSkrSokWLlJGRoVatWiktLc3VpmHDhpo6daq2bNmiBQsWyMzUqlUrZWZm5tqvJ+P65JNP3J7/jRs3qkiRIrrvvvuu6pglz8bdo0cPJScna+7cudqwYYM6duyozp07a+3atbn26+vrq/bt22vu3Lnatm2bEhIStHjx4hxfFI8dO6YePXqoRYsWV2WMOYmKitKYMWO0Zs0arV69Ws2bN1f79u21adMmV5tHH33U7XkZO3asR31nZGSoW7duuu2223Jts2fPHj3zzDOXbHO1/fDDD5o8ebJiY2OzHcvv2BcvXux2XsOGDV3Hhg4dqsmTJ+uNN97Q5s2b9fjjj+vee++95M9Rfvz++++65ZZb5O/vr6+++kqbN2/WuHHjVKpUKVebsWPH6vXXX9dbb72lVatWqVixYmrdurVOnz592f4feeQRdenS5ZJtLvU4fPfdd+rWrZt69eqltWvXqkOHDurQoYM2btyY/0HjnCu9ftJfRaNGjaxv376u25mZmVauXDkbPXr0Zc+dOnWqhYaGXrbd4cOHzd/f36ZNm3YlpRYISfbpp5+67atTp46NGjXKbd8NN9xgzz33XJ76Hjt2rMXExFyyzV133WUPP/xwnvotKIcPHzZJtmzZslzb/PTTTybJduzYkae+Lzeu8ePHW4kSJezkyZN56rcg5DTuYsWKZft5DAsLsylTpuSp7wkTJlhUVFS2/V26dLGhQ4fa8OHDrX79+vmquyCUKlXK3nnnHTMza9q0qT311FP56mfw4MHWvXv3XP/Pnz171po0aWLvvPOO9ezZ09q3b5//ovPpxIkTVq1aNVu0aFG2seZn7Lt37zZJtnbt2lzblC1b1v7zn/+47evYsaM98MADebqvyxkyZIjdeuutuR7PysqyyMhIe/nll137jh07ZoGBgfbhhx96dB+5/ax68jh07tzZ7r77brd9jRs3tscee8yj+65YsaKNHz/ebV/9+vVt+PDhZnbu9/aUKVOsQ4cOFhwcbFWrVrXPPvvM1XbJkiUmyX7//XczM0tLS7M2bdpYkyZN7Pfff3eNYfbs2dasWTMLDg622NjYbNdH/N///me1a9e2gIAAq1ixor3yyiuuY2+88YbVqVPHdfvTTz81STZp0iTXvhYtWrheN84/ntOmTbOKFStaSEiIdenSxVJTUz16TM5jZsYDZ86c0Zo1a9SyZUvXPl9fX7Vs2VIrV64ssPuZNm2aihYtqr///e8F1mdBatKkiebOnav9+/fLzLRkyRJt27ZNrVq1ylM/x48fV1hY2BW3uVrOX2k9t/tPS0vT1KlTFRMTo+jo6Dz3falxvfvuu+ratauKFSuWp34LQk7jbtKkiT766CMdPXpUWVlZmjlzpk6fPq1mzZp53O+BAwf0ySefqGnTpm77p06dql27dmn48OEFUn9+ZGZmaubMmUpLS1NcXJxr//Tp01WmTBnVrVtX8fHxOnXq1GX7+vrrrzVr1qxcZzYladSoUQoPD1evXr0KpP786Nu3r+6++26332cXys/YJemee+5ReHi4br311myztenp6QoKCnLbFxwcrG+//TZ/g8jF3LlzdeONN+q+++5TeHi4rr/+ek2ZMsV1fPfu3Tp48KDb2ENDQ9W4ceMC+11+qcdh5cqV2R731q1bF+jryMiRI9W5c2etX79ed911lx544AEdPXo0W7tjx47pzjvvVFZWlhYtWuT2tuhzzz2nZ555RuvWrVP16tXVrVs3nT17VpK0Zs0ade7cWV27dtWGDRs0YsQIPf/880pISJAkNW3aVJs3b9avv/4q6dwMcJkyZbR06VJJ52YvV65c6fY7ZOfOnZozZ47mzZunefPmadmyZRozZkzeBp6n6PMXlZ+rd1/I05mZWrVqWZ8+ffJbZoFSDjMzp0+fth49epgk8/Pzs4CAAHv//ffz1O/27dstJCTE3n777VzbfPTRRxYQEGAbN27MT+lXJDMz0+6++2675ZZbsh2bOHGiFStWzCRZjRo18jwrc7lxrVq1yiTZqlWr8lX7lcht3L///ru1atXK9ZyHhITYggULPOqza9euFhwcbJKsXbt29scff7iObdu2zcLDwy05OdnMcv9r92pZv369FStWzIoUKWKhoaH2xRdfuI5NnjzZ5s+fb+vXr7cPPvjAypcvb/fee+8l+/vtt98sOjraNauV0//5b775xsqXL2+//vqrmZlXZmY+/PBDq1u3ruu5uHgmJj9j//XXX23cuHGWlJRk33//vQ0ZMsR8fHzcZgS6detmtWvXtm3btllmZqYtXLjQgoODLSAgoEDHFxgYaIGBgRYfH28//vijTZ482YKCgiwhIcHMzFasWGGS7MCBA27n3Xfffda5c2eP7iO3n1VPHgd/f3+bMWOG23kTJ0608PBwj+7bk5mZoUOHuo6dPHnSJNlXX31lZv9vZmbLli0WGxtrnTp1svT0dFf78zMz52cpzcw2bdrkOsfM7P7777c777zTrYZBgwZZ7dq1zezc7Ffp0qVt1qxZZmbWoEEDGz16tEVGRpqZ2bfffmv+/v6WlpZmZucez6JFi7rNxAwaNMgaN27s0WNyHmHGA5cLM4899pgVK1bMtV3MkzDz3XffmSRbvXp1QZaebzmFmZdfftmqV69uc+fOtZ9++sneeOMNK168uC1atMjM7LKPw759+6xKlSrWq1evXO/366+/tqJFi+Y5JBWUxx9/3CpWrJjjJeePHTtm27Zts2XLllm7du3shhtucL0o1K5d2zXuNm3aZDvXk3H17t3b6tWrV3CDyYPcxt2vXz9r1KiRLV682NatW2cjRoyw0NBQW79+vZmZtWnTxjXu87/MzktJSbEtW7bYZ599ZrVr13YF9bNnz9qNN97oNu18rcNMenq6bd++3VavXm3PPvuslSlTxjZt2pRj28TERLe3FHN6ru+9914bMmSI65yL/8+npqZapUqV7Msvv3Ttu9ZhZu/evRYeHm4//fSTa9/l3lbyZOw5efDBB93e7jl8+LC1b9/efH19rUiRIla9enX7xz/+YUFBQVc+sAv4+/tbXFyc274nnnjCbr75ZjPzLMxc6mfaLG8/qxc/DtcizHz88cdux0NCQly/d86HmaioKOvYsaOdPXvWre35MPP999+79h09etTt7efrr7/eRowY4XbenDlzzN/f39Xfvffea3379rXff//dAgIC7Pjx41aqVCnbsmWLvfjii9akSRPXucOHD8/2OL/66quXXYpwMcKMB9LT061IkSLZXtx79Ohh99xzjx06dMi2b9/u2i7mSZh55JFHrEGDBgVY9ZW5OMycOnXK/P39bd68eW7tevXqZa1btzYzu+TjsH//fqtWrZo9+OCDlpmZmeN9Ll261IoVK2aTJ08u2MF4qG/fvhYVFWW7du26bNv09HQrWrSo6xfTnj17XOPet2+fW1tPxnXy5EkLCQmx11577coGkQ+5jXvHjh0mKdtMUosWLVzv8e/bt8817j179uR6H998843rReT33383SVakSBHX5uPj49qXmJhY8IO8jBYtWljv3r1zPHb+r9v58+ebWc7PdWhoqNt4fH19XeN59913be3atTmO2cfHx4oUKZLnWb78OL924cIaJLlquPiFzdOx5+Q///mP6y/xC/3xxx+2b98+y8rKssGDB+cYFq5EhQoVsv2x9Oabb1q5cuXMzGznzp05rmu5/fbb7cknnzSzy/9M5yXMXPw4REdHZwsjw4YNs9jYWI/6i4mJsVdffdVtX+3atd3CzMWvU6GhoTZ16lQz+39h5rHHHrMyZcq4/ig5L6d1P+f/vy5ZssTMPAszEyZMsDp16tjcuXNdMyzt27e3SZMmWatWrSw+Pt51bk6P5/jx461ixYoePSbnOeLaTN4WEBCghg0bKjEx0fVRyqysLCUmJqpfv34KDw+/ok8fnTx5Uh9//LFGjx5dQBUXvIyMDGVkZMjX132ZVZEiRZSVlSVJuT4O+/fv1x133OH6RNDFfUjnPsb8t7/9Tf/+97/Vu3fvqzOIXJiZnnjiCX366adaunSpYmJiPDrHzJSeni5JqlixYo7tPB3XrFmzlJ6eru7du+dvEPlwuXGfXytxqee8fPnyHt3X+fbp6emKiIjQhg0b3I6/+eab+vrrr/W///3Po8e/oGVlZbmey4utW7dOklS2bFlJOT/XK1eudPtk22effaZ///vf+u6771S+fHkFBwdnG/PQoUN14sQJTZgwIc9rr/KjRYsW2Wp4+OGHVbNmTQ0ZMkRFihTJdo4nY8/JunXrXOdcKCgoSOXLl1dGRoZmz56tzp0753EUl3bLLbdk+3qBbdu2ueqOiYlRZGSkEhMTXR+vTk1N1apVq1yfOvX0Z9oTFz8OcXFxSkxMdPs4/KJFi9zWa13Kddddp5SUFNft1NRU7d69O891jRkzRsWLF1eLFi20dOlS1a5d2+Nza9WqpRUrVrjtW7FihapXr+76GWratKn69++vWbNmudbGNGvWTIsXL9aKFSv09NNP57nmy8pT9PkLmzlzpgUGBlpCQoJt3rzZevfubSVLlrSDBw/mes7PP/9sa9eutZEjR1rx4sVt7dq1tnbtWjtx4oRbu3feeceCgoJcK8y95cSJE64aJdmrr75qa9eutZ9//tnMzk1J16lTx5YsWWK7du2yqVOnWlBQkL355pu59rlv3z6rWrWqtWjRwvbt22cpKSmu7bzzb8HEx8e7HT9y5MhVH7OZWZ8+fSw0NNSWLl3qdv+nTp0ys3N/zb300ku2evVq+/nnn23FihXWrl07CwsLs0OHDuXab17Gdeutt1qXLl2u2hhzcrlxnzlzxqpWrWq33XabrVq1ynbs2GGvvPKK+fj4uK0xudgXX3xh7733nm3YsMF2795t8+bNs1q1auW4Dum8a/k207PPPmvLli2z3bt32/r16+3ZZ581Hx8fW7hwoe3YscNGjRplq1evtt27d9tnn31mlStXtttvvz1P9+HJbKy3Ps10oQvfZsrv2BMSEmzGjBm2ZcsW19sIvr6+9t5777naJCUl2ezZs23nzp22fPlya968ucXExBT477zvv//e/Pz87MUXX7Tt27fb9OnTrWjRovbBBx+42owZM8ZKlixpn332ma1fv97at29vMTExbmu6crJ9+3Zbu3atPfbYY1a9enXX78rza048eRxWrFhhfn5+9sorr9iWLVts+PDh5u/vbxs2bPBofM8++6xFRkba8uXLbf369dahQwcrXrx4nmdmzj/u/fv3t4iICNd6GE9mZtasWWO+vr42atQoS05OtoSEBAsODnbdh9m5dTNhYWFWpEgR13qdtWvXWpEiRczPz8/t05oFNTNDmMmDN954wypUqGABAQHWqFEjS0pKumT7nj17mqRs2/kfivPi4uLs/vvvv4qVe+b8D/rFW8+ePc3s3BqIhx56yMqVK2dBQUFWo0YNGzdunGVlZeXa59SpU3Ps88Icndvj1LRp06s84nNyq+/8f879+/db27ZtLTw83Pz9/S0qKsruv/9+27p16yX79XRcW7duNUm2cOHCqzTCnF1u3GbnFup27NjRwsPDrWjRohYbG3vZrw74+uuvLS4uzkJDQy0oKMiqVatmQ4YMueQL17UMM4888ohVrFjRAgIC7LrrrrMWLVq4Hvu9e/fa7bffbmFhYRYYGGhVq1a1QYMG2fHjx/N0H04MM/kde0JCgtWqVcuKFi1qISEh1qhRI9fiz/OWLl1qtWrVssDAQCtdurQ9+OCDtn///qsyps8//9zq1q1rgYGBVrNmzWwfNsjKyrLnn3/eIiIiLDAw0Fq0aOFaiH4pTZs2zfH/y+7du83Ms8fBzOzjjz+26tWrW0BAgNWpU+eSfxhc7Pjx49alSxcLCQmx6OhoS0hIyLZmJi9hxuzcmqKyZctacnKyR2HG7P99NNvf398qVKjg9lH389q3b29+fn6uP94zMzOtVKlSrvVL5xVUmOGq2QAAwNH4nhkAAOBohBkAAOBohBkAAOBohBkAAOBohBkAAOBohBkAAOBohBkAAOBohBkAAOBohBkA8KKEhASVLFnS22V4ZM+ePfLx8XFdswkoLAgzQAFo1qyZ28XjzrvwherUqVOKj49XlSpVFBQUpOuuu05NmzbVZ5995taPj49Ptu3xxx93tblwf0hIiG666Sa3Pjz1xx9/KCwsTGXKlMn1IouzZ89W8+bNVapUKQUHB6tGjRp65JFHtHbtWrcx5lRzUFCQx7UcPHhQTz31lKpWraqgoCBFRETolltu0aRJk1wXvJSkSpUqufovWrSo6tWrp3feeSdbf5mZmRo/frzq1aunoKAglSpVSm3bts12gbwRI0a4Ljh4oYtftJcuXeo2toiICHXq1Em7du3yeIwArh7CDHCNPP744/rkk0/0xhtvaOvWrZo/f77+/ve/68iRI27tHn30UaWkpLhtY8eOdWszdepUpaSkaPXq1brlllv097//PdsVkS9n9uzZqlOnjmrWrKk5c+ZkOz5kyBB16dJFDRo00Ny5c5WcnKwZM2aocuXKio+Pd2sbEhKSreaff/7Zozp27dql66+/XgsXLtRLL72ktWvXauXKlRo8eLDmzZunxYsXu7UfNWqUUlJStHHjRnXv3l2PPvqovvrqK9dxM1PXrl01atQoPfXUU9qyZYuWLl2q6OhoNWvWLMexeio5OVkHDhzQrFmztGnTJrVr187tatkAvCRPV3ICkKMLL9h3oQsvOBgaGmoJCQn56udCuuhicqmpqSbJJkyYkKeamzVrZm+99ZZNmjTJ7rzzTrdjK1euvGSfF15c1JOLKl5K69atLSoqyu1KurndV8WKFW38+PFux8PCwmzAgAGu2zNnzjRJNnfu3Gx9dezY0UqXLu26r9wucHnxBfdyukDf9OnTTdJlLzhqdu5ifb1797bw8HALDAy0OnXq2Oeff25mOT9+c+bMseuvv94CAwMtJibGRowYYRkZGa7j48aNs7p161rRokUtKirK+vTp47qg34V9zp8/32rWrGnFihWz1q1b24EDB9zuZ8qUKVazZk0LDAy0GjVq2MSJE92Or1q1yho0aGCBgYHWsGFD++STT7JdiBAoDJiZAa6RyMhIffnllzpx4kSB9Xn27Fm9++67kqSAgACPz9u5c6dWrlypzp07q3Pnzvrmm2/cZlI+/PBDFS9eXP/4xz9yPN/Hx+fKCv//HTlyRAsXLlTfvn1VrFixPN1XVlaWZs+erd9//91t7DNmzFD16tXVrl27bOc8/fTTOnLkiBYtWnTFtQcHB0uSzpw5c8l2WVlZrre4PvjgA23evFljxoxRkSJFcmz/zTffqEePHnrqqae0efNmTZ48WQkJCXrxxRddbXx9ffX6669r06ZNev/99/X1119r8ODBbv2cOnVKr7zyiv773/9q+fLl2rt3r5555hnX8enTp2vYsGF68cUXtWXLFr300kt6/vnn9f7770uSTp48qb/97W+qXbu21qxZoxEjRridDxQq3k5TwJ+BJzMzy5Yts6ioKPP397cbb7zR+vfvb99++222fvz9/a1YsWJu2wcffOBqI8mCgoKsWLFi5uvra5KsUqVKduTIEY/r/ec//2kdOnRw3W7fvr0NHz7cdbtNmzYWGxvrds64cePcajp27JhrjJKy1dymTZvL1pGUlGSS7JNPPnHbX7p0aVc/gwcPdu2vWLGiBQQEWLFixczPz88kWVhYmG3fvt3VpmbNmta+ffsc7+/o0aMmyf7973+bWf5nZg4cOGBNmjSx8uXLW3p6+iXHuGDBAvP19bXk5OQcj188M9OiRQt76aWX3Nr897//tbJly+Z6H7NmzbLSpUu79SnJduzY4do3ceJEi4iIcN2uUqWKzZgxw62ff/3rXxYXF2dmZpMnT7bSpUvbH3/84To+adIkZmZQKPl5K0QBfzW33367du3apaSkJH333XdKTEzUhAkTNHLkSD3//POudg888ICee+45t3MjIiLcbo8fP14tW7bUrl27NGDAAL3++usKCwvzqI7MzEy9//77mjBhgmtf9+7d9cwzz2jYsGHy9c15wvaRRx7RPffco1WrVql79+4yM9exEiVK6Mcff3Rrf37mIj++//57ZWVl6YEHHsi2OHnQoEF66KGHlJKSokGDBukf//iHqlat6tbmwtoKUlRUlMxMp06dUv369TV79uzLzoitW7dOUVFRql69ukf38dNPP2nFihVuMzGZmZk6ffq0Tp06paJFi2rx4sUaPXq0tm7dqtTUVJ09e9btuCQVLVpUVapUcfVRtmxZHT58WJKUlpamnTt3qlevXnr00Uddbc6ePavQ0FBJ0pYtWxQbG+u2kDsuLs6jMQDXGmEGKAAhISE6fvx4tv3Hjh1zvThIkr+/v2677TbddtttGjJkiF544QWNGjVKQ4YMcb0ohoaGZntxvlhkZKSqVq2qqlWraurUqbrrrru0efNmhYeHX7bWBQsWaP/+/erSpYvb/szMTCUmJurOO+9UtWrV9O233yojI0P+/v6SpJIlS6pkyZLat29ftj59fX0vW3NOqlatKh8fHyUnJ7vtr1y5sqScA1GZMmVcY581a5bq1aunG2+8UbVr15YkVa9eXVu2bMnx/s7vPx8sLvW8SXJ77qRzbwGFhIQoPDxcJUqU8GiMeQ11J0+e1MiRI9WxY8dsx4KCgrRnzx797W9/U58+ffTiiy8qLCxM3377rXr16qUzZ864wsz55+08Hx8fV8g7efKkJGnKlClq3LixW7vc3v4CCjPWzAAFoEaNGtlmJiTpxx9/vORf5LVr13b9VZ1fjRo1UsOGDd3+kr+Ud999V127dtW6devctq5du7rW33Tr1k0nT57Um2++me+6PFG6dGndeeed+s9//qO0tLQ8nx8dHa0uXbq4fbqqa9eu2r59uz7//PNs7ceNG+e6T+nc87Zv3z4dOnTIrd2PP/6ooKAgVahQwW1/TEyMqlSp4nGQkaTY2Fjt27dP27Zt86j9DTfcoOTkZFdgu3Dz9fXVmjVrlJWVpXHjxunmm29W9erVdeDAAY/rkc7N9JUrV067du3Kdh8xMTGSpFq1amn9+vVuP5tJSUl5uh/gmvHuu1zAn8POnTstKCjInnjiCfvpp59s69atNm7cOPPz87OvvvrKzM6th3nrrbds9erVtnv3bvviiy+sRo0a1rx5c1c/TZs2tUcffdRSUlLctqNHj7ra6KJPM5mZffnllxYYGGj79u27ZJ2HDx82f39/V0059XF+7c3TTz9tRYoUsQEDBtg333xje/bssZUrV1r37t3Nx8fHjh8/bmbn1meEhIRkqzklJcUyMzMv+9jt2LHDIiIirGbNmjZz5kzbvHmzbd261f773/9aRESEDRw40NU2p08zbdq0yXx8fOyHH34ws3Offrr33nutVKlS9s4779ju3bvtp59+st69e5ufn5/bY5eRkWF16tSxO+64w1asWGE7d+60WbNmWdmyZW3IkCGudjl9mikvmjVrZnXr1rWFCxfarl277Msvv3Q9BxevmZk/f775+fnZiBEjbOPGjbZ582b78MMP7bnnnjMzs3Xr1pkke+2112znzp02bdo0K1++vFt9OX1C6tNPP7ULf+VPmTLFgoODbcKECZacnGzr16+39957z8aNG2dmZidOnLAyZcpY9+7dbdOmTfbFF19Y1apVWTODQokwAxSQ77//3u6880677rrrLDQ01Bo3buz2wvnSSy9ZXFychYWFWVBQkFWuXNmefPJJ++2331xtmjZtapKyba1bt3a1ySnMZGVlWc2aNa1Pnz6XrPGVV16xkiVL2pkzZ7IdS09Pt5IlS7p9HPujjz6yZs2aWWhoqPn7+1tUVJTdf//9lpSU5GpzfrFpTltKSopHj92BAwesX79+FhMTY/7+/la8eHFr1KiRvfzyy5aWluZql1OYMTv38e62bdu6bmdkZNjLL79sderUsYCAAAsJCbHWrVtnW3BtZrZ//37r2bOnVahQwYKDg6127do2ZswYt8foSsPMkSNH7OGHH7bSpUtbUFCQ1a1b1+bNm2dmOQeP+fPnW5MmTSw4ONhCQkKsUaNG9vbbb7uOv/rqq1a2bFkLDg621q1b27Rp0/IcZszOfby8QYMGFhAQYKVKlbLbb7/dbTH2ypUrrX79+hYQEGANGjSw2bNnE2ZQKPmYXaWVcgAAANcAa2YAAICjEWaAP5k6deqoePHiOW7Tp0+/prXs3bs311qKFy+uvXv3XtN6robp06fnOr46dep4uzzgL4G3mYA/mZ9//lkZGRk5HouIiMjTJ3Gu1NmzZ7Vnz55cj1eqVEl+fs7+hogTJ05k+zTUef7+/qpYseI1rgj46yHMAAAAR+NtJgAA4GiEGQAA4GiEGQAA4GiEGQAA4GiEGQAA4GiEGQAA4GiEGQAA4GiEGQAA4Gj/H+TW8eX8yIE4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "91fb66e4-c523-4ed3-85cc-f6245c2f2edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp3 = df[df['k_means_Clusters_PCA']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9e1867ef-f526-4089-ab36-816d10d73976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.470000</td>\n",
       "      <td>8.490000</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>41.290000</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>139.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>79.650000</td>\n",
       "      <td>17.980000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>51.200000</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>72.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>754.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>314.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>772.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count         1014.000000    1014.000000     1014.000000     1014.000000   \n",
       "mean            75.470000       8.490000        1.550000        1.290000   \n",
       "std             79.650000      17.980000        5.190000        4.500000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              6.000000       0.000000        0.000000        0.000000   \n",
       "50%             73.000000       1.000000        0.000000        0.000000   \n",
       "75%            111.000000       8.000000        0.000000        0.000000   \n",
       "max            754.000000     162.000000       49.000000       59.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count    1014.000000      1014.000000                 1014.000000   \n",
       "mean       41.290000         1.380000                    0.210000   \n",
       "std        51.200000         5.680000                    1.150000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%        17.500000         0.000000                    0.000000   \n",
       "75%        69.000000         0.000000                    0.000000   \n",
       "max       314.000000        79.000000                   19.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                   1014.000000            1014.000000   \n",
       "mean                       0.030000               0.880000   \n",
       "std                        0.370000               3.560000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        8.000000              51.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count           1014.000000        1014.000000      1014.000000   \n",
       "mean               0.750000           0.000000         0.130000   \n",
       "std                0.180000           0.000000         0.650000   \n",
       "min                0.010000           0.000000         0.000000   \n",
       "25%                0.650000           0.000000         0.000000   \n",
       "50%                0.780000           0.000000         0.000000   \n",
       "75%                0.880000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         8.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count      1014.000000  \n",
       "mean        139.750000  \n",
       "std          72.820000  \n",
       "min          60.000000  \n",
       "25%          97.000000  \n",
       "50%         120.000000  \n",
       "75%         160.000000  \n",
       "max         772.000000  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp3[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "38d83c40-7138-4876-91f1-3044ada9a346",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist3 =kmp3['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f719dcbe-a5f7-40bd-9db4-4657397123e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp3 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist3)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cf1e3c8e-8b16-4802-858c-96e5bc35f294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp3, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "96ae33a2-fc2a-40cb-9259-85088b58cb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp3, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7fa9dadb-5425-4d09-a430-a69b3514a8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      409\n",
       "23-27      256\n",
       "28-34      122\n",
       "0-17        83\n",
       "35-44       67\n",
       "45-59       64\n",
       "60-150      12\n",
       "unknown      1\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp3['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ec811423-deba-481c-b102-e0dcd61d8cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1014"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(subkmp3['USER_AGE_GROUP_cleaned'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f51f35f8-ace6-47a7-a779-c7fb56c8b307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a3554eea-e59e-4884-b31c-57a0ac2b723c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a8622335-dc43-4415-a174-b78e0e304536",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp4 = df[df['k_means_Clusters_PCA']==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f5855bc2-9039-4f76-aa38-188722bff0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.260000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>118.520000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>151.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.280000</td>\n",
       "      <td>18.120000</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>71.280000</td>\n",
       "      <td>10.020000</td>\n",
       "      <td>14.880000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>99.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>697.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count           62.000000      62.000000       62.000000       62.000000   \n",
       "mean            13.260000       5.400000        1.950000      118.520000   \n",
       "std             23.280000      18.120000        6.450000       71.280000   \n",
       "min              0.000000       0.000000        0.000000       68.000000   \n",
       "25%              0.000000       0.000000        0.000000       81.500000   \n",
       "50%              3.500000       0.000000        0.000000       96.000000   \n",
       "75%             15.750000       3.000000        0.000000      120.500000   \n",
       "max            113.000000     123.000000       38.000000      515.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count      62.000000        62.000000                   62.000000   \n",
       "mean        2.770000         2.650000                    0.110000   \n",
       "std        10.020000        14.880000                    0.550000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        63.000000       114.000000                    4.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                     62.000000              62.000000   \n",
       "mean                       0.020000               0.290000   \n",
       "std                        0.130000               1.480000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        1.000000              11.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count             62.000000          62.000000        62.000000   \n",
       "mean               0.490000           0.000000         0.400000   \n",
       "std                0.250000           0.000000         3.180000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.330000           0.000000         0.000000   \n",
       "50%                0.520000           0.000000         0.000000   \n",
       "75%                0.640000           0.000000         0.000000   \n",
       "max                1.000000           0.000000        25.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count        62.000000  \n",
       "mean        151.810000  \n",
       "std          99.900000  \n",
       "min          71.000000  \n",
       "25%         101.000000  \n",
       "50%         122.000000  \n",
       "75%         171.000000  \n",
       "max         697.000000  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp4[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "32637d0b-1684-4834-8650-2f47bc55af1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist4 =kmp4['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "723d372a-e6bc-47aa-a399-2ecb89933001",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp4 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist4)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9fd633ba-aaa9-4f61-b69e-6bcfed1a183e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp4, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8c9452f2-fcb3-4464-9681-8ce913668154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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enew7adIknTx5Uv369VNgYKBCQkLctn///feNMhwAAMDZeBQyixYtauQxAAAAzp9HITN58uTGngMAAOC8eXSOjCTt379fs2bNUnJysg4fPixJeuedd7Rnz55GGw4AAOBsPAqZ/Px89enTR1u3btXLL7+sEydOSPrxTxTMmTOnUQcEAACoj0ch8+c//1nz5s3Te++9p8DAQNf6iBEjVFBQ0GjDAQAAnI1HIbNr1y7dfPPNtdbbtWunI0eOXPBQAAAADeFRyEREROjQoUO11nfu3KnLLrvsgocCAABoCI9C5tZbb9XMmTNVWloqm82mmpoabdq0STNmzNCkSZMae0YAAIA6eRQy8+fPV3x8vGJjY3XixAn16tVLQ4cO1eDBgzVr1qzGnhEAAKBOHn2PTGBgoFauXKnZs2dr165dqqysVP/+/dWtW7fGng8AAKBeHoWMJK1atUpZWVnau3evJKl79+6aPn267rjjjkYbDgAA4Gw8CpnZs2dr4cKFSk1NVWJioiRpy5YtSktLk8Ph0Ny5cxt1SAAAgLp4FDLLly/XypUrlZyc7Fr77W9/q759+yo1NZWQAQAAF4VHJ/ueOnVKv/zlL2utJyQk6PTp0xc8FAAAQEN4FDITJ07U8uXLa60/++yzuu222y54KAAAgIa4oJN93333XQ0aNEiStHXrVjkcDk2aNEnp6emu/RYuXHjhUwIAANTBo5DZvXu3BgwYIOnHv4ItSW3atFGbNm20e/du1342m60RRgQAAKibRyGTl5fX2HMAAACcN4/OkQEAAPAFhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAY3k1ZDIzM3XVVVcpLCxM7dq100033aSioiK3faqqqpSSkqKoqCiFhoYqKSlJZWVlXpoYAAD4Eq+GTH5+vlJSUlRQUKD33ntPp06d0ujRo1VZWenaJy0tTW+88YbWr1+v/Px8lZSUaPz48V6cGgAA+Ap/b975hg0b3K6vWbNG7dq1U2Fhoa655hqVl5dr1apVysnJ0YgRIyRJq1evVs+ePVVQUKBBgwbVOqbT6ZTT6XRdr6ioaNofAgAAeI1PnSNTXl4uSYqMjJQkFRYW6tSpUxo5cqRrn/j4eHXq1Elbtmyp8xiZmZmy2+2uS2xsbNMPDgAAvMJnQqampkbTp0/Xr371K/Xu3VuSVFpaqsDAQEVERLjtGx0drdLS0jqPk5GRofLycteluLi4qUcHAABe4tW3ln4uJSVFu3fv1scff3xBxwkKClJQUFAjTQUAAHyZT7wi86c//Ulvvvmm8vLy1LFjR9d6TEyMqqurdezYMbf9y8rKFBMTc5GnBAAAvsarIWNZlv70pz/plVde0QcffKC4uDi37QkJCQoICFBubq5rraioSA6HQ4mJiRd7XAAA4GO8+tZSSkqKcnJy9NprryksLMx13ovdbldISIjsdrumTp2q9PR0RUZGKjw8XKmpqUpMTKzzE0sAAODS4tWQWb58uSRp+PDhbuurV6/WlClTJElZWVny8/NTUlKSnE6nxowZo2XLll3kSQEAgC/yashYlnXOfYKDg5Wdna3s7OyLMBEAADCJT5zsCwAA4AlCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADG8mrIfPTRRxo3bpw6dOggm82mV1991W27ZVmaPXu22rdvr5CQEI0cOVJ79+71zrAAAMDneDVkKisr1a9fP2VnZ9e5fcGCBVqyZIlWrFihrVu3qlWrVhozZoyqqqou8qQAAMAX+XvzzseOHauxY8fWuc2yLC1atEizZs3SjTfeKElau3atoqOj9eqrr+rWW2+9mKMCAAAf5LPnyBw4cEClpaUaOXKka81ut2vgwIHasmVLvbdzOp2qqKhwuwAAgObJZ0OmtLRUkhQdHe22Hh0d7dpWl8zMTNntdtclNja2SecEAADe47Mh46mMjAyVl5e7LsXFxd4eCQAANBGfDZmYmBhJUllZmdt6WVmZa1tdgoKCFB4e7nYBAADNk8+GTFxcnGJiYpSbm+taq6io0NatW5WYmOjFyQAAgK/w6qeWTpw4oX379rmuHzhwQJ9++qkiIyPVqVMnTZ8+XfPmzVP37t0VFxenRx55RB06dNBNN93kvaEBAIDP8GrIbN++Xddee63renp6uiRp8uTJWrNmjR566CFVVlbqrrvu0rFjxzRkyBBt2LBBwcHB3hoZAAD4EK+GzPDhw2VZVr3bbTab5s6dq7lz517EqQAAgCl89hwZAACAcyFkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsI0ImOztbXbp0UXBwsAYOHKhPPvnE2yMBAAAf4PMh869//Uvp6emaM2eOduzYoX79+mnMmDE6fPiwt0cDAABe5vMhs3DhQt155526/fbb1atXL61YsUItW7bUc8895+3RAACAl/l7e4Czqa6uVmFhoTIyMlxrfn5+GjlypLZs2VLnbZxOp5xOp+t6eXm5JKmiosLjOc44/+vxbdE8XcjzqbHwvMTP+cJzEmhMPz2nLcs6634+HTJHjhzRmTNnFB0d7bYeHR2tL7/8ss7bZGZm6rHHHqu1Hhsb2yQz4tJkXzrN2yMAbnhOork6fvy47HZ7vdt9OmQ8kZGRofT0dNf1mpoaff/994qKipLNZvPiZOarqKhQbGysiouLFR4e7u1xAJ6T8Dk8JxuPZVk6fvy4OnTocNb9fDpk2rRpoxYtWqisrMxtvaysTDExMXXeJigoSEFBQW5rERERTTXiJSk8PJxfUPgUnpPwNTwnG8fZXon5iU+f7BsYGKiEhATl5ua61mpqapSbm6vExEQvTgYAAHyBT78iI0np6emaPHmyfvnLX+rqq6/WokWLVFlZqdtvv93bowEAAC/z+ZCZMGGCvvvuO82ePVulpaW68sortWHDhlonAKPpBQUFac6cObXeugO8heckfA3PyYvPZp3rc00AAAA+yqfPkQEAADgbQgYAABiLkAEAAMYiZJohy7J01113KTIyUjabTZ9++qlX5jh48KBX7x+XrilTpuimm27y9hgALgKf/9QSzt+GDRu0Zs0affjhh+ratavatGnj7ZEAAGgShEwztH//frVv316DBw/29igAADQp3lpqZqZMmaLU1FQ5HA7ZbDZ16dJFNTU1yszMVFxcnEJCQtSvXz+99NJLrtt8+OGHstls2rhxo/r376+QkBCNGDFChw8f1jvvvKOePXsqPDxcf/jDH3Ty5EnX7TZs2KAhQ4YoIiJCUVFR+s1vfqP9+/efdb7du3dr7NixCg0NVXR0tCZOnKgjR4402eMB3zd8+HClpqZq+vTpat26taKjo7Vy5UrXF1+GhYWpW7dueueddyRJZ86c0dSpU13P5x49emjx4sVnvY9z/Q4AMBch08wsXrxYc+fOVceOHXXo0CFt27ZNmZmZWrt2rVasWKE9e/YoLS1Nf/zjH5Wfn+9220cffVR/+9vftHnzZhUXF+uWW27RokWLlJOTo7feekvvvvuuli5d6tq/srJS6enp2r59u3Jzc+Xn56ebb75ZNTU1dc527NgxjRgxQv3799f27du1YcMGlZWV6ZZbbmnSxwS+7/nnn1ebNm30ySefKDU1Vffcc49+//vfa/DgwdqxY4dGjx6tiRMn6uTJk6qpqVHHjh21fv16ffHFF5o9e7Yefvhhvfjii/Uev6G/AwAMZKHZycrKsjp37mxZlmVVVVVZLVu2tDZv3uy2z9SpU63k5GTLsiwrLy/PkmS9//77ru2ZmZmWJGv//v2utbvvvtsaM2ZMvff73XffWZKsXbt2WZZlWQcOHLAkWTt37rQsy7Ief/xxa/To0W63KS4utiRZRUVFHv+8MNuwYcOsIUOGuK6fPn3aatWqlTVx4kTX2qFDhyxJ1pYtW+o8RkpKipWUlOS6PnnyZOvGG2+0LKthvwMAzMU5Ms3cvn37dPLkSY0aNcptvbq6Wv3793db69u3r+vf0dHRatmypbp27eq29sknn7iu7927V7Nnz9bWrVt15MgR1ysxDodDvXv3rjXLZ599pry8PIWGhtbatn//fl1++eWe/ZAw3s+fey1atFBUVJT69OnjWvvpT5IcPnxYkpSdna3nnntODodD//3vf1VdXa0rr7yyzmOfz+8AAPMQMs3ciRMnJElvvfWWLrvsMrdt//u3QAICAlz/ttlsbtd/Wvv520bjxo1T586dtXLlSnXo0EE1NTXq3bu3qqur651l3Lhxeuqpp2pta9++/fn9YGhW6nqu/e/zUfrxXJd169ZpxowZ+utf/6rExESFhYXp6aef1tatW+s89vn8DgAwDyHTzPXq1UtBQUFyOBwaNmxYox336NGjKioq0sqVKzV06FBJ0scff3zW2wwYMED//ve/1aVLF/n789SDZzZt2qTBgwfr3nvvda2d7STzpvodAOAb+K9JMxcWFqYZM2YoLS1NNTU1GjJkiMrLy7Vp0yaFh4dr8uTJHh23devWioqK0rPPPqv27dvL4XDoz3/+81lvk5KSopUrVyo5OVkPPfSQIiMjtW/fPq1bt05///vf1aJFC49mwaWle/fuWrt2rTZu3Ki4uDj94x//0LZt2xQXF1fn/k31OwDANxAyl4DHH39cbdu2VWZmpr7++mtFRERowIABevjhhz0+pp+fn9atW6f77rtPvXv3Vo8ePbRkyRINHz683tt06NBBmzZt0syZMzV69Gg5nU517txZ119/vfz8+AAdGubuu+/Wzp07NWHCBNlsNiUnJ+vee+91fTy7Lk3xOwDAN9gsy7K8PQQAAIAn+N9gAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBrhEDR8+XNOnT6+1vmbNGkVEREiSTp48qYyMDP3iF79QcHCw2rZtq2HDhum1115zO47NZqt1mTZtmmufn6+Hh4frqquucjtGQ1RXV+vpp5/WgAED1KpVK9ntdvXr10+zZs1SSUmJa78pU6bUOc/111/v2qdLly6y2WwqKChwu4/p06e7/ZmNRx991HV7f39/tWnTRtdcc40WLVokp9NZ6/G8GI8DAHeEDIB6TZs2TS+//LKWLl2qL7/8Uhs2bNDvfvc7HT161G2/O++8U4cOHXK7LFiwwG2f1atX69ChQ9q+fbt+9atf6Xe/+5127drVoDmcTqdGjRql+fPna8qUKfroo4+0a9cuLVmyREeOHNHSpUvd9r/++utrzfPCCy+47RMcHKyZM2ee876vuOIKHTp0SA6HQ3l5efr973+vzMxMDR48WMePH7+ojwOA2vijkQDq9frrr2vx4sW64YYbJP34SkZCQkKt/Vq2bKmYmJizHisiIkIxMTGKiYnR448/rsWLFysvL099+vQ55xxZWVn6+OOPtX37dvXv39+13qlTJw0bNkz/+yfjgoKCzjnPXXfdpRUrVujtt992/Xx18ff3dx2rQ4cO6tOnj0aNGqV+/frpqaee0rx581z7NvXjAKA2XpEBUK+YmBi9/fbbtV55uBCnT5/WqlWrJEmBgYENus0LL7ygUaNGuUXMz9lstvOeIy4uTtOmTVNGRoZqamrO67bx8fEaO3asXn755fO+35948jgAqI2QAVCvZ599Vps3b1ZUVJSuuuoqpaWladOmTbX2W7ZsmUJDQ90u//znP932SU5OVmhoqIKCgpSWlqYuXbrolltuadAcX331lXr06OG2dvPNN7vua/DgwW7b3nzzzVrzzJ8/v9ZxZ82apQMHDtSatSHi4+N18OBBt7WmfhwA1MZbSwDqdc011+jrr79WQUGBNm/erNzcXC1evFiPPfaYHnnkEdd+t912m/7yl7+43TY6OtrtelZWlkaOHKmvv/5aaWlpWrJkiSIjIz2ebdmyZaqsrNSSJUv00UcfuW279tprtXz5cre1uu6rbdu2mjFjhmbPnq0JEyac1/1bllXrlSBvPA7ApY6QAS5R4eHhKi8vr7V+7Ngx2e121/WAgAANHTpUQ4cO1cyZMzVv3jzNnTtXM2fOdL0lYrfb1a1bt7PeX0xMjLp166Zu3bpp9erVuuGGG/TFF1+oXbt255y1e/fuKioqcltr3769pLoDpVWrVuec5yfp6elatmyZli1b1qD9f/Kf//xHcXFxbmtN/TgAqI23loBLVI8ePbRjx45a6zt27NDll19e7+169eql06dPq6qqyuP7vvrqq5WQkKAnnniiQfsnJyfrvffe086dOz2+z/qEhobqkUce0RNPPNHgc4F++gRXUlLSBd33+T4OAGojZIBL1D333KOvvvpK9913nz7//HMVFRVp4cKFeuGFF/TAAw9I+vG7UZ555hkVFhbq4MGDevvtt/Xwww/r2muvVXh4uOtYJ0+eVGlpqdvlhx9+OOv9T58+Xc8884y+/fbbc86alpamxMREXXfddVq8eLF27NihAwcOaOPGjXrnnXfUokULt/2dTmeteY4cOVLv8e+66y7Z7Xbl5OTU2nb69GmVlpaqpKREu3bt0tKlSzVs2DBdeeWVevDBB932berHAUAdLACXrE8++cQaNWqU1bZtW8tut1sDBw60XnnlFdf2+fPnW4mJiVZkZKQVHBxsde3a1brvvvusI0eOuPYZNmyYJanWZcyYMa59JLkd17Isq6amxoqPj7fuueeeBs1aVVVlPfnkk1a/fv2skJAQKygoyIqPj7fS0tIsh8Ph2m/y5Ml1ztOjRw/XPp07d7aysrLcjp+Tk2NJsoYNG+ZamzNnjuv2LVq0sCIjI60hQ4ZYWVlZVlVVldvtL9bjAMCdzbL+5wsYAAAADMFbSwAAwFiEDACvu+KKK2p9/0p938MCAD/HW0sAvO6bb77RqVOn6twWHR2tsLCwizwRAFMQMgAAwFi8tQQAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWP8P2fVKo+pMUCEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp4, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a420f8e7-ab3c-4f1b-8ce4-a847832c7719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22    26\n",
       "23-27    17\n",
       "28-34     9\n",
       "45-59     5\n",
       "0-17      3\n",
       "35-44     2\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp4['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f77381ee-e99c-410f-90b1-002a294261de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(subkmp4['USER_AGE_GROUP_cleaned'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "2920fb71-4323-4557-b5a3-15e279166cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "615a16b1-4ee1-4b97-9f37-221f39c72fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AVatUmDl48KCSkpLUuHFj3XrrrcrJyZEkDRo0SH/605+qtEAAAIBzqVSYGTFihPz9/bVz506FhIS42vv27avFixdXWXEAAADnU6k1Mx9//LGWLFmi2NhYt/ZGjRrphx9+qJLCAAAAyqNSV2by8/PdrsicdujQIQUGBl5wUQAAAOVVqTBz4403avbs2a7HPj4+Ki4u1nPPPaebbrqpyooDAAA4n0q9zfTcc88pKSlJq1ev1okTJ/TYY49pw4YNOnTokFasWFHVNQIAAJSpUldmWrRooe+//1433HCDevbsqfz8fPXu3Vtr1qxRgwYNqrpGAACAMlXqyowkhYWF6YknnqjKWgAAACqsUldmZs2apfnz55donz9/vt54440LLgoAAKC8KhVmUlJSVKdOnRLtkZGRevbZZy+4KAAAgPKqVJjZuXOn4uPjS7TXr19fO3fuvOCiAAAAyqtSYSYyMlJZWVkl2r/99ltFRERccFEAAADlVakw079/f/3xj3/UsmXLVFRUpKKiIn366ad65JFH1K9fv6quEQAAoEyV+jTTX/7yF+3YsUNJSUny8zs1RHFxsQYMGMCaGQAAcElVOMyYmfbt26fU1FT99a9/1dq1axUcHKyWLVuqfv36F6NGAACAMlUqzDRs2FAbNmxQo0aN1KhRo4tRFwAAQLlUeM2Mr6+vGjVqpIMHD16MegAAACqkUguAJ0yYoFGjRmn9+vVVXQ8AAECFVGoB8IABA3Ts2DG1atVKAQEBCg4Odtt/6NChKikOAADgfCoVZl588cUqLgMAAKByKhVmBg4cWNV1AAAAVEql1sxI0tatWzVmzBj1799fBw4ckCR99NFH2rBhQ7nHSElJ0a9+9SvVrFlTkZGR6tWrl7Kzs936HD9+XEOGDFFERIRq1KihPn36aP/+/ZUtGwAA/MxUKswsX75cLVu21KpVq/TOO+/o6NGjkk7dzmDs2LEVGmfIkCHKyMjQJ598osLCQt1yyy3Kz8939RkxYoQ++OADzZ8/X8uXL9fevXvVu3fvypQNAAB+hir1NtPjjz+uv/71rxo5cqRq1qzpau/cubP+/ve/l3ucxYsXuz1OTU1VZGSkMjMz1aFDB+Xm5mrmzJmaO3euOnfuLEmaNWuWrrrqKmVkZOi6666rTPkAAOBnpFJXZtatW6fbb7+9RHtkZKT+97//VbqY3NxcSVJ4eLgkKTMzU4WFherSpYurT9OmTVWvXj2tXLmy0ucBAAA/H5W6MlOrVi3l5OQoPj7erX3NmjW6/PLLK1VIcXGxhg8fruuvv14tWrSQJO3bt08BAQGqVauWW9+oqCjt27ev1HEKCgpUUFDgepyXl1epegAAgDNU6spMv379NHr0aO3bt08+Pj4qLi7WihUr9Oijj2rAgAGVKmTIkCFav3695s2bV6njT0tJSVFYWJhri4uLu6DxAACAd6tUmHn22WfVtGlTxcXF6ejRo2rWrJluvPFGtW/fXmPGjKnweEOHDtWiRYu0bNkyxcbGutqjo6N14sQJHT582K3//v37FR0dXepYycnJys3NdW27du2qcD0AAMA5KvU2U0BAgGbMmKGnnnpK69atU35+vq6++mo1bNiwQuOYmYYNG6Z3331X6enpJd62atOmjfz9/ZWWlqY+ffpIkrKzs7Vz504lJiaWOmZgYKACAwMrMy0AAOBAlQozkjRz5kxNnjxZmzdvliQ1atRIw4cP1+9///tyjzFkyBDNnTtX77//vmrWrOlaBxMWFqbg4GCFhYVp0KBBGjlypMLDwxUaGqphw4YpMTGRTzIBAABJlQwzTz31lCZNmuQKFpK0cuVKjRgxQjt37tT48ePLNc60adMkSZ06dXJrnzVrlu69915J0uTJk+Xr66s+ffqooKBAXbt21SuvvFKZsgEAwM9QpcLMtGnTNGPGDPXv39/V9pvf/EYJCQkaNmxYucOMmZ23T1BQkKZOnaqpU6dWplQAAPAzV6kFwIWFhbr22mtLtLdp00YnT5684KIAAADKq1Jh5p577nG9RXSmf/zjH7rrrrsuuCgAAIDyuqAFwB9//LFrIe6qVau0c+dODRgwQCNHjnT1mzRp0oVXCQAAUIZKhZn169frmmuukXTq7tmSVKdOHdWpU0fr16939fPx8amCEgEAAMpWqTCzbNmyqq4DAACgUiq1ZgYAAMBbEGYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjeTTMfPbZZ+rRo4fq1q0rHx8fvffee2777733Xvn4+Lht3bp180yxAADAK3k0zOTn56tVq1aaOnVqmX26deumnJwc1/bWW29dwgoBAIC38/Pkybt3767u3bufs09gYKCio6MvUUUAAMBpvH7NTHp6uiIjI9WkSRM9/PDDOnjwoKdLAgAAXsSjV2bOp1u3burdu7fi4+O1detW/fnPf1b37t21cuVKVatWrdRjCgoKVFBQ4Hqcl5d3qcoFAAAe4NVhpl+/fq5/t2zZUgkJCWrQoIHS09OVlJRU6jEpKSkaN27cpSoRAAB4mNe/zXSmK6+8UnXq1NGWLVvK7JOcnKzc3FzXtmvXrktYIQAAuNS8+srM2Xbv3q2DBw8qJiamzD6BgYEKDAy8hFUBAABP8miYOXr0qNtVlu3bt2vt2rUKDw9XeHi4xo0bpz59+ig6Olpbt27VY489poYNG6pr164erBoAAHgTj4aZ1atX66abbnI9HjlypCRp4MCBmjZtmrKysvTGG2/o8OHDqlu3rm655Rb95S9/4coLUAFtRs32dAlVIvP5AZ4uAYCX8miY6dSpk8yszP1Lliy5hNUAAAAnctQCYAAAgLMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKMRZgAAgKN5NMx89tln6tGjh+rWrSsfHx+99957bvvNTE899ZRiYmIUHBysLl26aPPmzZ4pFgAAeCWPhpn8/Hy1atVKU6dOLXX/c889p5deekmvvvqqVq1aperVq6tr1646fvz4Ja4UAAB4Kz9Pnrx79+7q3r17qfvMTC+++KLGjBmjnj17SpJmz56tqKgovffee+rXr9+lLBUAAHgpr10zs337du3bt09dunRxtYWFhaldu3ZauXKlBysDAADexKNXZs5l3759kqSoqCi39qioKNe+0hQUFKigoMD1OC8v7+IUCAAAvILXXpmprJSUFIWFhbm2uLg4T5cEAAAuIq8NM9HR0ZKk/fv3u7Xv37/fta80ycnJys3NdW27du26qHUCAADP8towEx8fr+joaKWlpbna8vLytGrVKiUmJpZ5XGBgoEJDQ902AADw8+XRNTNHjx7Vli1bXI+3b9+utWvXKjw8XPXq1dPw4cP117/+VY0aNVJ8fLyefPJJ1a1bV7169fJc0QAAwKt4NMysXr1aN910k+vxyJEjJUkDBw5UamqqHnvsMeXn52vw4ME6fPiwbrjhBi1evFhBQUGeKhkAAHgZj4aZTp06yczK3O/j46Px48dr/Pjxl7AqAADgJF67ZgYAAKA8CDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRPHo7AwBA1WozaranS6gSmc8P8HQJcBCuzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEfz6jDz9NNPy8fHx21r2rSpp8sCAABexM/TBZxP8+bNtXTpUtdjPz+vLxkAAFxCXp8M/Pz8FB0d7ekyAACAl/Lqt5kkafPmzapbt66uvPJK3XXXXdq5c6enSwIAAF7Eq6/MtGvXTqmpqWrSpIlycnI0btw43XjjjVq/fr1q1qxZ6jEFBQUqKChwPc7Ly7tU5QIAAA/w6jDTvXt3178TEhLUrl071a9fX//61780aNCgUo9JSUnRuHHjLlWJAADAw7w6zJytVq1aaty4sbZs2VJmn+TkZI0cOdL1OC8vT3FxcaX2bTNqdpXX6AmZzw/wdAkAAHiM16+ZOdPRo0e1detWxcTElNknMDBQoaGhbhsAAPj58uow8+ijj2r58uXasWOHvvzyS91+++2qVq2a+vfv7+nSAACAl/Dqt5l2796t/v376+DBg7rssst0ww03KCMjQ5dddpmnSwMAAF7Cq8PMvHnzPF0CAADwcl79NhMAAMD5EGYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjEWYAAICjefVfAAaAymozaranS6gSmc8P8HQJgNfjygwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0wgwAAHA0R4SZqVOn6oorrlBQUJDatWunr776ytMlAQAAL+H1Yebtt9/WyJEjNXbsWH3zzTdq1aqVunbtqgMHDni6NAAA4AW8PsxMmjRJDzzwgO677z41a9ZMr776qkJCQvT66697ujQAAOAFvDrMnDhxQpmZmerSpYurzdfXV126dNHKlSs9WBkAAPAWfp4u4Fz+97//qaioSFFRUW7tUVFR+u6770o9pqCgQAUFBa7Hubm5kqS8vLwSfYsKfqrCaj2ntLmdC/N2NuZdPszb2So6b/z8nP4eMLPzdzYvtmfPHpNkX375pVv7qFGjrG3btqUeM3bsWJPExsbGxsbG9jPYdu3add684NVXZurUqaNq1app//79bu379+9XdHR0qcckJydr5MiRrsfFxcU6dOiQIiIi5OPjc1HrPVteXp7i4uK0a9cuhYaGXtJzexLzZt6/BMybef8SeHLeZqYjR46obt265+3r1WEmICBAbdq0UVpamnr16iXpVDhJS0vT0KFDSz0mMDBQgYGBbm21atW6yJWeW2ho6C/qm/805v3Lwrx/WZj3L4un5h0WFlaufl4dZiRp5MiRGjhwoK699lq1bdtWL774ovLz83Xfffd5ujQAAOAFvD7M9O3bV//973/11FNPad++fWrdurUWL15cYlEwAAD4ZfL6MCNJQ4cOLfNtJW8WGBiosWPHlnjb6+eOeTPvXwLmzbx/CZwybx+z8nzmCQAAwDt59R/NAwAAOB/CDAAAcDTCDAAAvwDp6eny8fHR4cOHPV1KlSPMVMDUqVN1xRVXKCgoSO3atdNXX311zv7PPPOM2rdvr5CQkFL/1k1qaqp8fHxK3TxxV/DPPvtMPXr0UN26deXj46P33nvPbf/Ro0c1dOhQxcbGKjg42HXjz3PZsWOHBg0apPj4eAUHB6tBgwYaO3asTpw44eqTnp6unj17KiYmRtWrV1fr1q01Z86cizHFUqWkpOhXv/qVatasqcjISPXq1UvZ2dlufR588EE1aNBAwcHBuuyyy9SzZ88yb6lxWnnm1alTp1K//rfddluVz/Ns5Zn3vn37dM899yg6OlrVq1fXNddcowULFpxz3IMHD6pbt26qW7euAgMDFRcXp6FDh5b55+lXrFghPz8/tW7duqqmdk7Tpk1TQkKC6+9mJCYm6qOPPnLtL+1r8tBDD5V7/C1btqhmzZrn/PtW8+bNk4+Pj+vvZ3nChAkT5OPjo+HDh7vaKjP3HTt2lPo9nJGR4epTWFio8ePHq0GDBgoKClKrVq20ePHiizKvPXv26O6771ZERISCg4PVsmVLrV692rXfzPTUU08pJiZGwcHB6tKlizZv3nzecf/4xz+qTZs2CgwMLPV7tTzPgyTNnz9fTZs2VVBQkFq2bKkPP/zwgucMwky5vf322xo5cqTGjh2rb775Rq1atVLXrl3PGTpOnDih3/3ud3r44YdL3d+3b1/l5OS4bV27dlXHjh0VGRl5saZSpvz8fLVq1UpTp04tdf/IkSO1ePFivfnmm9q0aZOGDx+uoUOHauHChWWO+d1336m4uFjTp0/Xhg0bNHnyZL366qv685//7Orz5ZdfKiEhQQsWLFBWVpbuu+8+DRgwQIsWLaryOZZm+fLlGjJkiDIyMvTJJ5+osLBQt9xyi/Lz81192rRpo1mzZmnTpk1asmSJzEy33HKLioqKyhy3PPN655133L7+69evV7Vq1fS73/3uos5ZKt+8BwwYoOzsbC1cuFDr1q1T7969dccdd2jNmjVljuvr66uePXtq4cKF+v7775WamqqlS5eW+qJ4+PBhDRgwQElJSRdljqWJjY3VhAkTlJmZqdWrV6tz587q2bOnNmzY4OrzwAMPuH1dnnvuuXKNXVhYqP79++vGG28ss8+OHTv06KOPnrPPxfb1119r+vTpSkhIKLGvsnNfunSp23Ft2rRx7RszZoymT5+ul19+WRs3btRDDz2k22+//ZzfR5Xx448/6vrrr5e/v78++ugjbdy4URMnTlTt2rVdfZ577jm99NJLevXVV7Vq1SpVr15dXbt21fHjx887/v3336++ffues8+5nocvv/xS/fv316BBg7RmzRr16tVLvXr10vr16ys/aZxyofdP+qVo27atDRkyxPW4qKjI6tataykpKec9dtasWRYWFnbefgcOHDB/f3+bPXv2hZRaJSTZu+++69bWvHlzGz9+vFvbNddcY0888USFxn7uuecsPj7+nH1uvfVWu++++yo0blU5cOCASbLly5eX2efbb781SbZly5YKjX2+eU2ePNlq1qxpR48erdC4VaG0eVevXr3E92N4eLjNmDGjQmNPmTLFYmNjS7T37dvXxowZY2PHjrVWrVpVqu6qULt2bXvttdfMzKxjx472yCOPVGqcxx57zO6+++4y/8+fPHnS2rdvb6+99poNHDjQevbsWfmiK+nIkSPWqFEj++STT0rMtTJz3759u0myNWvWlNknJibG/v73v7u19e7d2+66664Knet8Ro8ebTfccEOZ+4uLiy06Otqef/55V9vhw4ctMDDQ3nrrrXKdo6zv1fI8D3fccYfddtttbm3t2rWzBx98sFznrl+/vk2ePNmtrVWrVjZ27FgzO/Vze8aMGdarVy8LDg62hg0b2vvvv+/qu2zZMpNkP/74o5mZ5efnW7du3ax9+/b2448/uuawYMEC69SpkwUHB1tCQkKJ+yP++9//tmbNmllAQIDVr1/fXnjhBde+l19+2Zo3b+56/O6775okmzZtmqstKSnJ9bpx+vmcPXu21a9f30JDQ61v376Wl5dXrufkNK7MlMOJEyeUmZmpLl26uNp8fX3VpUsXrVy5ssrOM3v2bIWEhOi3v/1tlY1Zldq3b6+FCxdqz549MjMtW7ZM33//vW655ZYKjZObm6vw8PAL7nOxnL7Telnnz8/P16xZsxQfH6+4uLgKj32uec2cOVP9+vVT9erVKzRuVSht3u3bt9fbb7+tQ4cOqbi4WPPmzdPx48fVqVOnco+7d+9evfPOO+rYsaNb+6xZs7Rt2zaNHTu2SuqvjKKiIs2bN0/5+flKTEx0tc+ZM0d16tRRixYtlJycrGPHjp13rE8//VTz588v88qmJI0fP16RkZEaNGhQldRfGUOGDNFtt93m9vPsTJWZuyT95je/UWRkpG644YYSV2sLCgoUFBTk1hYcHKwvvviicpMow8KFC3Xttdfqd7/7nSIjI3X11VdrxowZrv3bt2/Xvn373OYeFhamdu3aVdnP8nM9DytXrizxvHft2rVKX0fGjRunO+64Q1lZWbr11lt111136dChQyX6HT58WDfffLOKi4v1ySefuL0t+sQTT+jRRx/V2rVr1bhxY/Xv318nT56UJGVmZuqOO+5Qv379tG7dOj399NN68sknlZqaKknq2LGjNm7cqP/+97+STl0BrlOnjtLT0yWdunq5cuVKt58hW7du1XvvvadFixZp0aJFWr58uSZMmFCxiVco+vxCVebu3Wcq75WZq666yh5++OHKllmlVMqVmePHj9uAAQNMkvn5+VlAQIC98cYbFRp38+bNFhoaav/4xz/K7PP2229bQECArV+/vjKlX5CioiK77bbb7Prrry+xb+rUqVa9enWTZE2aNKnwVZnzzWvVqlUmyVatWlWp2i9EWfP+8ccf7ZZbbnF9zUNDQ23JkiXlGrNfv34WHBxskqxHjx72008/ufZ9//33FhkZadnZ2WZW9m+7F0tWVpZVr17dqlWrZmFhYfaf//zHtW/69Om2ePFiy8rKsjfffNMuv/xyu/3228853v/+9z+Li4tzXdUq7f/8559/bpdffrn997//NTPzyJWZt956y1q0aOH6Wpx9JaYyc//vf/9rEydOtIyMDPvqq69s9OjR5uPj43ZFoH///tasWTP7/vvvraioyD7++GMLDg62gICAKp1fYGCgBQYGWnJysn3zzTc2ffp0CwoKstTUVDMzW7FihUmyvXv3uh33u9/9zu64445ynaOs79XyPA/+/v42d+5ct+OmTp1qkZGR5Tp3ea7MjBkzxrXv6NGjJsk++ugjM/u/KzObNm2yhIQE69OnjxUUFLj6n74yc/oqpZnZhg0bXMeYmd1555128803u9UwatQoa9asmZmduvoVERFh8+fPNzOz1q1bW0pKikVHR5uZ2RdffGH+/v6Wn59vZqeez5CQELcrMaNGjbJ27dqV6zk5jTBTDucLMw8++KBVr17dtZ2tPGHmyy+/NEm2evXqqiy90koLM88//7w1btzYFi5caN9++629/PLLVqNGDfvkk0/MzM77POzevdsaNGhggwYNKvO8n376qYWEhFQ4JFWVhx56yOrXr1/qLecPHz5s33//vS1fvtx69Ohh11xzjetFoVmzZq55d+vWrcSx5ZnX4MGDrWXLllU3mQooa95Dhw61tm3b2tKlS23t2rX29NNPW1hYmGVlZZmZWbdu3VzzPv3D7LScnBzbtGmTvf/++9asWTNXUD958qRde+21bpedL3WYKSgosM2bN9vq1avt8ccftzp16tiGDRtK7ZuWlub2lmJpX+vbb7/dRo8e7Trm7P/zeXl5dsUVV9iHH37oarvUYWbnzp0WGRlp3377ravtfG8rlWfupbnnnnvc3u45cOCA9ezZ03x9fa1atWrWuHFj+8Mf/mBBQUEXPrEz+Pv7W2JiolvbsGHD7LrrrjOz8oWZc31Pm1Xse/Xs5+FShJl//etfbvtDQ0NdP3dOh5nY2Fjr3bu3nTx50q3v6TDz1VdfudoOHTrk9vbz1VdfbU8//bTbce+99575+/u7xrv99tttyJAh9uOPP1pAQIDl5uZa7dq1bdOmTfbMM89Y+/btXceOHTu2xPM8adKk8y5FOBthphwKCgqsWrVqJV7cBwwYYL/5zW9s//79tnnzZtd2tvKEmfvvv99at25dhVVfmLPDzLFjx8zf398WLVrk1m/QoEHWtWtXM7NzPg979uyxRo0a2T333GNFRUWlnjM9Pd2qV69u06dPr9rJlNOQIUMsNjbWtm3bdt6+BQUFFhIS4vrBtGPHDte8d+/e7da3PPM6evSohYaG2osvvnhhk6iEsua9ZcsWk1TiSlJSUpLrPf7du3e75r1jx44yz/H555+7XkR+/PFHk2TVqlVzbT4+Pq62tLS0qp/keSQlJdngwYNL3Xf6t9vFixebWelf67CwMLf5+Pr6uuYzc+ZMW7NmTalz9vHxsWrVqlX4Kl9lnF67cGYNklw1nP3CVt65l+bvf/+76zfxM/3000+2e/duKy4utscee6zUsHAh6tWrV+KXpVdeecXq1q1rZmZbt24tdV1Lhw4d7I9//KOZnf97uiJh5uznIS4urkQYeeqppywhIaFc48XHx9ukSZPc2po1a+YWZs5+nQoLC7NZs2aZ2f+FmQcffNDq1Knj+qXktNLW/Zz+/7ps2TIzK1+YmTJlijVv3twWLlzousLSs2dPmzZtmt1yyy2WnJzsOra053Py5MlWv379cj0npzni3kyeFhAQoDZt2igtLc31Ucri4mKlpaVp6NChioyMvKBPHx09elT/+te/lJKSUkUVV73CwkIVFhbK19d9mVW1atVUXFwsSWU+D3v27NFNN93k+kTQ2WNIpz7G/Otf/1p/+9vfNHjw4IsziTKYmYYNG6Z3331X6enpio+PL9cxZqaCggJJUv369UvtV955zZ8/XwUFBbr77rsrN4lKON+8T6+VONfX/PLLLy/XuU73LygoUFRUlNatW+e2/5VXXtGnn36qf//73+V6/qtacXGx62t5trVr10qSYmJiJJX+tV65cqXbJ9vef/99/e1vf9OXX36pyy+/XMHBwSXmPGbMGB05ckRTpkyp8NqrykhKSipRw3333aemTZtq9OjRqlatWoljyjP30qxdu9Z1zJmCgoJ0+eWXq7CwUAsWLNAdd9xRwVmc2/XXX1/izwt8//33rrrj4+MVHR2ttLQ018er8/LytGrVKtenTsv7PV0eZz8PiYmJSktLc/s4/CeffOK2XutcLrvsMuXk5Lge5+Xlafv27RWua8KECapRo4aSkpKUnp6uZs2alfvYq666SitWrHBrW7FihRo3buz6HurYsaOGDx+u+fPnu9bGdOrUSUuXLtWKFSv0pz/9qcI1n1eFos8v2Lx58ywwMNBSU1Nt48aNNnjwYKtVq5bt27evzGN++OEHW7NmjY0bN85q1Khha9assTVr1tiRI0fc+r322msWFBTkWmHuKUeOHHHVKMkmTZpka9assR9++MHMTl2Sbt68uS1btsy2bdtms2bNsqCgIHvllVfKHHP37t3WsGFDS0pKst27d1tOTo5rO+30WzDJyclu+w8ePHjR52xm9vDDD1tYWJilp6e7nf/YsWNmduq3uWeffdZWr15tP/zwg61YscJ69Ohh4eHhtn///jLHrci8brjhBuvbt+9Fm2NpzjfvEydOWMOGDe3GG2+0VatW2ZYtW+yFF14wHx8ftzUmZ/vPf/5jr7/+uq1bt862b99uixYtsquuuqrUdUinXcq3mR5//HFbvny5bd++3bKysuzxxx83Hx8f+/jjj23Lli02fvx4W716tW3fvt3ef/99u/LKK61Dhw4VOkd5rsZ66tNMZzrzbabKzj01NdXmzp1rmzZtcr2N4Ovra6+//rqrT0ZGhi1YsMC2bt1qn332mXXu3Nni4+Or/GfeV199ZX5+fvbMM8/Y5s2bbc6cORYSEmJvvvmmq8+ECROsVq1a9v7771tWVpb17NnT4uPj3dZ0lWbz5s22Zs0ae/DBB61x48aun5Wn15yU53lYsWKF+fn52QsvvGCbNm2ysWPHmr+/v61bt65c83v88cctOjraPvvsM8vKyrJevXpZjRo1Knxl5vTzPnz4cIuKinKthynPlZnMzEzz9fW18ePHW3Z2tqWmplpwcLDrHGan1s2Eh4dbtWrVXOt11qxZY9WqVTM/Pz+3T2tW1ZUZwkwFvPzyy1avXj0LCAiwtm3bWkZGxjn7Dxw40CSV2E5/U5yWmJhod95550WsvHxOf6OfvQ0cONDMTq2BuPfee61u3boWFBRkTZo0sYkTJ1pxcXGZY86aNavUMc/M0WU9Tx07drzIMz6lrPpO/+fcs2ePde/e3SIjI83f399iY2PtzjvvtO++++6c45Z3Xt99951Jso8//vgizbB055u32amFur1797bIyEgLCQmxhISE8/7pgE8//dQSExMtLCzMgoKCrFGjRjZ69OhzvnBdyjBz//33W/369S0gIMAuu+wyS0pKcj33O3futA4dOlh4eLgFBgZaw4YNbdSoUZabm1uhczgxzFR27qmpqXbVVVdZSEiIhYaGWtu2bV2LP09LT0+3q666ygIDAy0iIsLuuece27Nnz0WZ0wcffGAtWrSwwMBAa9q0aYkPGxQXF9uTTz5pUVFRFhgYaElJSa6F6OfSsWPHUv+/bN++3czK9zyYmf3rX/+yxo0bW0BAgDVv3vycvxicLTc31/r27WuhoaEWFxdnqampJdbMVCTMmJ1aUxQTE2PZ2dnlCjNm//fRbH9/f6tXr57bR91P69mzp/n5+bl+eS8qKrLatWu71i+dVlVhhrtmAwAAR+PvzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzACAB6WmpqpWrVqeLqNcduzYIR8fH9c9mwBvQZgBqkCnTp3cbh532pkvVMeOHVNycrIaNGigoKAgXXbZZerYsaPef/99t3F8fHxKbA899JCrz5ntoaGh+tWvfuU2Rnn99NNPCg8PV506dcq8yeKCBQvUuXNn1a5dW8HBwWrSpInuv/9+rVmzxm2OpdUcFBRU7lr27dunRx55RA0bNlRQUJCioqJ0/fXXa9q0aa4bXkrSFVdc4Ro/JCRELVu21GuvvVZivKKiIk2ePFktW7ZUUFCQateure7du5e4Qd7TTz/tuuHgmc5+0U5PT3ebW1RUlPr06aNt27aVe44ALh7CDHCJPPTQQ3rnnXf08ssv67vvvtPixYv129/+VgcPHnTr98ADDygnJ8dte+6559z6zJo1Szk5OVq9erWuv/56/fa3vy1xR+TzWbBggZo3b66mTZvqvffeK7F/9OjR6tu3r1q3bq2FCxcqOztbc+fO1ZVXXqnk5GS3vqGhoSVq/uGHH8pVx7Zt23T11Vfr448/1rPPPqs1a9Zo5cqVeuyxx7Ro0SItXbrUrf/48eOVk5Oj9evX6+6779YDDzygjz76yLXfzNSvXz+NHz9ejzzyiDZt2qT09HTFxcWpU6dOpc61vLKzs7V3717Nnz9fGzZsUI8ePdzulg3AQyp0JycApTrzhn1nOvOGg2FhYZaamlqpcc6ks24ml5eXZ5JsypQpFaq5U6dO9uqrr9q0adPs5ptvdtu3cuXKc4555s1Fy3NTxXPp2rWrxcbGut1Jt6xz1a9f3yZPnuy2Pzw83EaMGOF6PG/ePJNkCxcuLDFW7969LSIiwnWusm5wefYN90q7Qd+cOXNM0nlvOGp26mZ9gwcPtsjISAsMDLTmzZvbBx98YGalP3/vvfeeXX311RYYGGjx8fH29NNPW2FhoWv/xIkTrUWLFhYSEmKxsbH28MMPu27od+aYixcvtqZNm1r16tWta9eutnfvXrfzzJgxw5o2bWqBgYHWpEkTmzp1qtv+VatWWevWrS0wMNDatGlj77zzTokbEQLegCszwCUSHR2tDz/8UEeOHKmyMU+ePKmZM2dKkgICAsp93NatW7Vy5UrdcccduuOOO/T555+7XUl56623VKNGDf3hD38o9XgfH58LK/z/O3jwoD7++GMNGTJE1atXr9C5iouLtWDBAv34449uc587d64aN26sHj16lDjmT3/6kw4ePKhPPvnkgmsPDg6WJJ04ceKc/YqLi11vcb355pvauHGjJkyYoGrVqpXa//PPP9eAAQP0yCOPaOPGjZo+fbpSU1P1zDPPuPr4+vrqpZde0oYNG/TGG2/o008/1WOPPeY2zrFjx/TCCy/on//8pz777DPt3LlTjz76qGv/nDlz9NRTT+mZZ57Rpk2b9Oyzz+rJJ5/UG2+8IUk6evSofv3rX6tZs2bKzMzU008/7XY84FU8naaAn4PyXJlZvny5xcbGmr+/v1177bU2fPhw++KLL0qM4+/vb9WrV3fb3nzzTVcfSRYUFGTVq1c3X19fk2RXXHGFHTx4sNz1/vnPf7ZevXq5Hvfs2dPGjh3retytWzdLSEhwO2bixIluNR0+fNg1R0klau7Wrdt568jIyDBJ9s4777i1R0REuMZ57LHHXO3169e3gIAAq169uvn5+ZkkCw8Pt82bN7v6NG3a1Hr27Fnq+Q4dOmSS7G9/+5uZVf7KzN69e619+/Z2+eWXW0FBwTnnuGTJEvP19bXs7OxS9599ZSYpKcmeffZZtz7//Oc/LSYmpsxzzJ8/3yIiItzGlGRbtmxxtU2dOtWioqJcjxs0aGBz5851G+cvf/mLJSYmmpnZ9OnTLSIiwn766SfX/mnTpnFlBl7Jz1MhCvil6dChg7Zt26aMjAx9+eWXSktL05QpUzRu3Dg9+eSTrn533XWXnnjiCbdjo6Ki3B5PnjxZXbp00bZt2zRixAi99NJLCg8PL1cdRUVFeuONNzRlyhRX2913361HH31UTz31lHx9S79ge//99+s3v/mNVq1apbvvvltm5tpXs2ZNffPNN279T1+5qIyvvvpKxcXFuuuuu0osTh41apTuvfde5eTkaNSoUfrDH/6ghg0buvU5s7aqFBsbKzPTsWPH1KpVKy1YsOC8V8TWrl2r2NhYNW7cuFzn+Pbbb7VixQq3KzFFRUU6fvy4jh07ppCQEC1dulQpKSn67rvvlJeXp5MnT7rtl6SQkBA1aNDANUZMTIwOHDggScrPz9fWrVs1aNAgPfDAA64+J0+eVFhYmCRp06ZNSkhIcFvInZiYWK45AJcaYQaoAqGhocrNzS3RfvjwYdeLgyT5+/vrxhtv1I033qjRo0frr3/9q8aPH6/Ro0e7XhTDwsJKvDifLTo6Wg0bNlTDhg01a9Ys3Xrrrdq4caMiIyPPW+uSJUu0Z88e9e3b1629qKhIaWlpuvnmm9WoUSN98cUXKiwslL+/vySpVq1aqlWrlnbv3l1iTF9f3/PWXJqGDRvKx8dH2dnZbu1XXnmlpNIDUZ06dVxznz9/vlq2bKlrr71WzZo1kyQ1btxYmzZtKvV8p9tPB4tzfd0kuX3tpFNvAYWGhioyMlI1a9Ys1xwrGuqOHj2qcePGqXfv3iX2BQUFaceOHfr1r3+thx9+WM8884zCw8P1xRdfaNCgQTpx4oQrzJz+up3m4+PjCnlHjx6VJM2YMUPt2rVz61fW21+AN2PNDFAFmjRpUuLKhCR988035/yNvFmzZq7fqiurbdu2atOmjdtv8ucyc+ZM9evXT2vXrnXb+vXr51p/079/fx09elSvvPJKpesqj4iICN188836+9//rvz8/AofHxcXp759+7p9uqpfv37avHmzPvjggxL9J06c6DqndOrrtnv3bu3fv9+t3zfffKOgoCDVq1fPrT0+Pl4NGjQod5CRpISEBO3evVvff/99ufpfc801ys7OdgW2MzdfX19lZmaquLhYEydO1HXXXafGjRtr79695a5HOnWlr27dutq2bVuJc8THx0uSrrrqKmVlZbl9b2ZkZFToPMAl49l3uYCfh61bt1pQUJANGzbMvv32W/vuu+9s4sSJ5ufnZx999JGZnVoP8+qrr9rq1att+/bt9p///MeaNGlinTt3do3TsWNHe+CBBywnJ8dtO3TokKuPzvo0k5nZhx9+aIGBgbZ79+5z1nngwAHz9/d31VTaGKfX3vzpT3+yatWq2YgRI+zzzz+3HTt22MqVK+3uu+82Hx8fy83NNbNT6zNCQ0NL1JyTk2NFRUXnfe62bNliUVFR1rRpU5s3b55t3LjRvvvuO/vnP/9pUVFRNnLkSFff0j7NtGHDBvPx8bGvv/7azE59+un222+32rVr22uvvWbbt2+3b7/91gYPHmx+fn5uz11hYaE1b97cbrrpJluxYoVt3brV5s+fbzExMTZ69GhXv9I+zVQRnTp1shYtWtjHH39s27Ztsw8//ND1NTh7zczixYvNz8/Pnn76aVu/fr1t3LjR3nrrLXviiSfMzGzt2rUmyV588UXbunWrzZ492y6//HK3+kr7hNS7775rZ/7InzFjhgUHB9uUKVMsOzvbsrKy7PXXX7eJEyeamdmRI0esTp06dvfdd9uGDRvsP//5jzVs2JA1M/BKhBmginz11Vd2880322WXXWZhYWHWrl07txfOZ5991hITEy08PNyCgoLsyiuvtD/+8Y/2v//9z9WnY8eOJqnE1rVrV1ef0sJMcXGxNW3a1B5++OFz1vjCCy9YrVq17MSJEyX2FRQUWK1atdw+jv32229bp06dLCwszPz9/S02NtbuvPNOy8jIcPU5vdi0tC0nJ6dcz93evXtt6NChFh8fb/7+/lajRg1r27atPf/885afn+/qV1qYMTv18e7u3bu7HhcWFtrzzz9vzZs3t4CAAAsNDbWuXbuWWHBtZrZnzx4bOHCg1atXz4KDg61Zs2Y2YcIEt+foQsPMwYMH7b777rOIiAgLCgqyFi1a2KJFi8ys9OCxePFia9++vQUHB1toaKi1bdvW/vGPf7j2T5o0yWJiYiw4ONi6du1qs2fPrnCYMTv18fLWrVtbQECA1a5d2zp06OC2GHvlypXWqlUrCwgIsNatW9uCBQsIM/BKPmYXaaUcAADAJcCaGQAA4GiEGeBnpnnz5qpRo0ap25w5cy5pLTt37iyzlho1amjnzp2XtJ6LYc6cOWXOr3nz5p4uD/hF4G0m4Gfmhx9+UGFhYan7oqKiKvRJnAt18uRJ7dixo8z9V1xxhfz8nP0XIo4cOVLi01Cn+fv7q379+pe4IuCXhzADAAAcjbeZAACAoxFmAACAoxFmAACAoxFmAACAoxFmAACAoxFmAACAoxFmAACAoxFmAACAo/0/nJwZHOcGS3sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5de647a8-989d-4521-ac95-56471d9b4605",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp5 = df[df['k_means_Clusters_PCA']==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "49f28375-b74a-4151-a4de-fecbe32ef18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.980000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>29.780000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>40.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.980000</td>\n",
       "      <td>12.190000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>4.860000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>25.340000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>34.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>233.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count         1309.000000    1309.000000     1309.000000     1309.000000   \n",
       "mean             2.980000       4.090000        0.100000        0.210000   \n",
       "std             10.980000      12.190000        0.950000        1.430000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        0.000000   \n",
       "75%              0.000000       2.000000        0.000000        0.000000   \n",
       "max            233.000000     135.000000       19.000000       23.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count    1309.000000      1309.000000                 1309.000000   \n",
       "mean        0.660000         0.220000                    0.140000   \n",
       "std         4.860000         1.750000                    0.700000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        94.000000        22.000000                    9.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                   1309.000000            1309.000000   \n",
       "mean                       0.060000              29.780000   \n",
       "std                        0.590000              25.340000   \n",
       "min                        0.000000              12.000000   \n",
       "25%                        0.000000              18.000000   \n",
       "50%                        0.000000              22.000000   \n",
       "75%                        0.000000              33.000000   \n",
       "max                       13.000000             492.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count           1309.000000        1309.000000      1309.000000   \n",
       "mean               0.860000           0.000000         0.180000   \n",
       "std                0.180000           0.000000         0.990000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.770000           0.000000         0.000000   \n",
       "50%                0.930000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000        13.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count      1309.000000  \n",
       "mean         40.240000  \n",
       "std          34.280000  \n",
       "min          10.000000  \n",
       "25%          21.000000  \n",
       "50%          30.000000  \n",
       "75%          46.000000  \n",
       "max         498.000000  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp5[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "93dd80e3-21d6-4c91-93a7-7c4f0b92703c",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist5 =kmp5['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9744b098-7eb4-480e-a97e-70e92da65b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp5 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist5)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "49a20b86-cc51-429c-9901-51f11f11d907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp5, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5fd5a8a5-d2e8-41f2-8bff-2a616fd8decb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp5, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "003fcb4b-a1c1-448d-ac63-2f666794d820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      313\n",
       "28-34      278\n",
       "23-27      254\n",
       "45-59      184\n",
       "35-44      175\n",
       "0-17        71\n",
       "60-150      31\n",
       "unknown      3\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp5['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c42598ff-9fee-4e42-ae2a-4978f7f8e19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1309"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(subkmp5['USER_AGE_GROUP_cleaned'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3d3fa070-5b0c-4f29-b68b-42f2c0206353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp5, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "58e3d015-8328-4659-a316-ce90eb8474f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp5, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4696aea8-935d-43be-9089-41290dd6ceec",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp6 = df[df['k_means_Clusters_PCA']==6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a8e80b9d-6389-40f7-8f0a-7d0a40300ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>6.590000</td>\n",
       "      <td>2.270000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>120.780000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>152.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.760000</td>\n",
       "      <td>15.390000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>2.370000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>62.130000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>88.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>407.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>671.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count          153.000000     153.000000      153.000000      153.000000   \n",
       "mean            13.370000       6.590000        2.270000        0.590000   \n",
       "std             45.760000      15.390000        6.670000        2.370000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       1.000000        0.000000        0.000000   \n",
       "75%              7.000000       5.000000        0.000000        0.000000   \n",
       "max            407.000000     118.000000       41.000000       21.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count     153.000000       153.000000                  153.000000   \n",
       "mean        1.050000       120.780000                    0.560000   \n",
       "std         4.810000        62.130000                    1.850000   \n",
       "min         0.000000        65.000000                    0.000000   \n",
       "25%         0.000000        83.000000                    0.000000   \n",
       "50%         0.000000        99.000000                    0.000000   \n",
       "75%         0.000000       137.000000                    0.000000   \n",
       "max        47.000000       523.000000                   15.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                    153.000000             153.000000   \n",
       "mean                       0.100000               0.480000   \n",
       "std                        0.920000               2.440000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                       11.000000              22.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count            153.000000         153.000000       153.000000   \n",
       "mean               0.820000           0.000000         0.240000   \n",
       "std                0.140000           0.000000         0.840000   \n",
       "min                0.270000           0.000000         0.000000   \n",
       "25%                0.760000           0.000000         0.000000   \n",
       "50%                0.860000           0.000000         0.000000   \n",
       "75%                0.920000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         7.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count       153.000000  \n",
       "mean        152.390000  \n",
       "std          88.540000  \n",
       "min          76.000000  \n",
       "25%          97.000000  \n",
       "50%         133.000000  \n",
       "75%         168.000000  \n",
       "max         671.000000  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp6[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e8dbcdf3-5dd2-45b7-8122-dd23160c8c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist6 =kmp6['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "471e8dd4-9e1d-4c0c-baea-ec9a1fea69ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp6 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist6)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f5a02de1-d9f0-4bec-b7ad-bbe95f349537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp6, x='USER_GENDER', order=['female','male'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "689ea500-201f-47d5-bee9-27a8c302dc8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp6, x='USER_GENDER', order=['female','male'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5c9f6482-4ddb-4246-855d-ac0e799ab1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27     40\n",
       "18-22     39\n",
       "28-34     35\n",
       "35-44     17\n",
       "45-59     17\n",
       "0-17       4\n",
       "60-150     1\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp6['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6a3fe4e2-d84f-4d53-bb27-0a04780f1d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp6, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7103f13f-004c-4388-853a-acdef11bb100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp6, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8c80233f-35d1-4921-8b85-6a8053694cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmp7 = df[df['k_means_Clusters_PCA']==7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a6c1480e-c3f4-4149-ab1e-c007e3ce5c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "      <td>2277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.330000</td>\n",
       "      <td>3.490000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>17.340000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>31.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.870000</td>\n",
       "      <td>6.370000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>21.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count         2277.000000    2277.000000     2277.000000     2277.000000   \n",
       "mean             5.330000       3.490000        0.660000       17.340000   \n",
       "std             10.870000       6.370000        2.800000        9.990000   \n",
       "min              0.000000       0.000000        0.000000        6.000000   \n",
       "25%              0.000000       0.000000        0.000000       11.000000   \n",
       "50%              0.000000       1.000000        0.000000       14.000000   \n",
       "75%              6.000000       4.000000        0.000000       20.000000   \n",
       "max            105.000000      75.000000       35.000000       68.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count    2277.000000      2277.000000                 2277.000000   \n",
       "mean        0.790000         0.240000                    0.080000   \n",
       "std         3.560000         1.710000                    0.470000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        45.000000        39.000000                    9.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                   2277.000000            2277.000000   \n",
       "mean                       0.050000               0.280000   \n",
       "std                        0.390000               1.500000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        8.000000              21.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count           2277.000000        2277.000000      2277.000000   \n",
       "mean               0.480000           0.000000         0.060000   \n",
       "std                0.240000           0.000000         0.460000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.310000           0.000000         0.000000   \n",
       "50%                0.500000           0.000000         0.000000   \n",
       "75%                0.670000           0.000000         0.000000   \n",
       "max                1.000000           0.000000        11.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count      2277.000000  \n",
       "mean         31.460000  \n",
       "std          21.910000  \n",
       "min          10.000000  \n",
       "25%          17.000000  \n",
       "50%          25.000000  \n",
       "75%          39.000000  \n",
       "max         212.000000  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(kmp7[numeric_cols].describe().round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "7454a4c6-3456-492a-aa8e-614e6112eb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmplist7 =kmp7['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e3e2bae4-a293-43aa-a3fe-0dbdd1d02c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "subkmp7 = users_cleaned[users_cleaned['USER_ID'].isin(kmplist7)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "483d4b63-b7f7-4213-801d-0bdb2c2f597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp7, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4bd1b5c8-ff62-4b1c-b05d-e4393908e44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp7, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "4e1da450-4c7f-4b32-a77d-0d329ab1a5a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      870\n",
       "23-27      556\n",
       "28-34      275\n",
       "0-17       197\n",
       "45-59      182\n",
       "35-44      173\n",
       "60-150      22\n",
       "unknown      2\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subkmp7['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6588ac48-b659-4f2d-b94c-e516e154dfa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2277"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(subkmp7['USER_AGE_GROUP_cleaned'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5e0d39ab-0410-47c6-9b43-fff9955a6399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp7, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6576281a-7e77-4f59-ba3e-fd6f1a674f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subkmp7, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat='percent')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b526e492-f735-4468-9f36-069400283428",
   "metadata": {},
   "source": [
    "# Gaussian Mixture Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b097635d-da50-4125-85cf-4caf854bb04b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#GaussianMixture, BayesianGaussianMixture\n",
    "\n",
    "df_for_params = df[numeric_cols].astype(float)\n",
    "\n",
    "def gmm_bic_score(estimator, df_for_params):\n",
    "    # Callable to pass to GridSearchCV that will use the BIC score\n",
    "    return -estimator.bic(df_for_params)\n",
    "\n",
    "param_grid = {\n",
    "    \"n_components\": range(1, 7),\n",
    "    \"covariance_type\": [\"spherical\", \"tied\", \"diag\", \"full\"],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    GaussianMixture(),\n",
    "    param_grid=param_grid,\n",
    "    scoring=gmm_bic_score\n",
    ")\n",
    "grid_search.fit(df_for_params)\n",
    "\n",
    "# Best model parameters and estimator\n",
    "best_gmm = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c9df41b7-cc2c-44b6-b866-a8b288b79a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianMixture(covariance_type=&#x27;diag&#x27;, n_components=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianMixture<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.mixture.GaussianMixture.html\">?<span>Documentation for GaussianMixture</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianMixture(covariance_type=&#x27;diag&#x27;, n_components=6)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianMixture(covariance_type='diag', n_components=6)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_gmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d8583e13-78c5-4132-9e13-4299118ec999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_gmm.n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f2a86e36-1a2c-40f9-aaf5-383a96dc8189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_types = [\"spherical\", \"tied\", \"diag\", \"full\"]\n",
    "bic_scores = np.array(grid_search.cv_results_[\"mean_test_score\"]).reshape(len(cv_types), -1)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "for i, cv_type in enumerate(cv_types):\n",
    "    plt.plot(range(1, 7), bic_scores[i], label=cv_type)\n",
    "\n",
    "plt.xlabel(\"Number of components\")\n",
    "plt.ylabel(\"BIC score\")\n",
    "plt.title(\"BIC score per model\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "7e1fdace-649a-4108-9799-ac07b9d7b0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = best_gmm.n_components\n",
    "gmm = GaussianMixture(n_components=n_components, random_state=1981).fit(df_for_params)\n",
    "\n",
    "# Step 5: Predict cluster assignments\n",
    "labels = gmm.predict(df_for_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "1a397c54-7131-49dc-9e38-68e53b9319d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gmm_clusters'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "0488c46c-322d-45b3-a6a8-afbf3ada5d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    737018\n",
       "2    100785\n",
       "1     95578\n",
       "3     12310\n",
       "4      9137\n",
       "5         1\n",
       "Name: gmm_clusters, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gmm_clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06950b37-3fbb-4356-848c-31860f7eca43",
   "metadata": {},
   "source": [
    "## Cluster descriptive analysis GMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a2fe5c0b-6819-456e-a7b2-39c56728da88",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = df[df['gmm_clusters']==0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "692a302f-58c2-4b1c-a0a4-e0486126d7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmmlist =gmm['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e7100638-cfd0-45ed-8650-ec93bf6d495d",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgmm = users_cleaned[users_cleaned['USER_ID'].isin(gmmlist)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3f140ae9-9975-42de-b2f7-4ef747d55be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "      <td>737018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.719328</td>\n",
       "      <td>0.149600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045349</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092572</td>\n",
       "      <td>0.677287</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.409982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.272426</td>\n",
       "      <td>0.418394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.343885</td>\n",
       "      <td>0.403376</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.240443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       737018.000000  737018.000000   737018.000000   737018.000000   \n",
       "mean             1.719328       0.149600        0.000000        0.020710   \n",
       "std              2.272426       0.418394        0.000000        0.142413   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              2.000000       0.000000        0.000000        0.000000   \n",
       "max             18.000000       3.000000        0.000000        1.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  737018.000000    737018.000000               737018.000000   \n",
       "mean        0.000000         0.000000                    0.045349   \n",
       "std         0.000000         0.000000                    0.208914   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max         0.000000         0.000000                    2.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 737018.000000          737018.000000   \n",
       "mean                       0.000000               0.092572   \n",
       "std                        0.000000               0.343885   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        0.000000               3.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         737018.000000      737018.000000    737018.000000   \n",
       "mean               0.677287           0.000000         0.000000   \n",
       "std                0.403376           0.000000         0.000000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.400000           0.000000         0.000000   \n",
       "50%                1.000000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         0.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    737018.000000  \n",
       "mean          2.409982  \n",
       "std           2.240443  \n",
       "min           1.000000  \n",
       "25%           1.000000  \n",
       "50%           2.000000  \n",
       "75%           3.000000  \n",
       "max          20.000000  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "43c0dc9d-ed6e-441c-aac2-46ebf37433df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "8987bbf7-04de-425e-961a-3ebbc9d84768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "101eb7bd-0d28-461a-b0f3-d82bf7def7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      242899\n",
       "18-22      197568\n",
       "28-34      152053\n",
       "35-44       62382\n",
       "45-59       45828\n",
       "0-17        27136\n",
       "60-150       8255\n",
       "unknown       897\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a5c12884-d10a-48fd-ad9b-5c6316b3753d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "16dda4d7-dd08-4967-9194-f50a7849bdde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8bfe754-840c-45f4-856d-06b0a64244cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "d2442260-a739-4bde-ac94-0ba339267386",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm1 = df[df['gmm_clusters']==1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6194f6f8-1515-4cff-9f9b-d96b7344bf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmmlist1 =gmm1['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "495429f2-386a-401c-a921-b12fe8113258",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgmm1 = users_cleaned[users_cleaned['USER_ID'].isin(gmmlist1)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "72ffe9a9-7724-4f40-85d2-31d33475539a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "      <td>95578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.867679</td>\n",
       "      <td>0.563456</td>\n",
       "      <td>0.541443</td>\n",
       "      <td>0.794545</td>\n",
       "      <td>1.045596</td>\n",
       "      <td>0.695087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000837</td>\n",
       "      <td>0.610320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.486922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.927191</td>\n",
       "      <td>1.454168</td>\n",
       "      <td>1.190008</td>\n",
       "      <td>2.011399</td>\n",
       "      <td>3.342641</td>\n",
       "      <td>2.527559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.580688</td>\n",
       "      <td>0.344755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.465267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count        95578.000000   95578.000000    95578.000000    95578.000000   \n",
       "mean             2.867679       0.563456        0.541443        0.794545   \n",
       "std              6.927191       1.454168        1.190008        2.011399   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        0.000000   \n",
       "75%              2.000000       0.000000        1.000000        1.000000   \n",
       "max             68.000000      13.000000       11.000000       20.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count   95578.000000     95578.000000                95578.000000   \n",
       "mean        1.045596         0.695087                    0.000000   \n",
       "std         3.342641         2.527559                    0.000000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        33.000000        25.000000                    0.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                  95578.000000           95578.000000   \n",
       "mean                       0.000000               1.000837   \n",
       "std                        0.000000               2.580688   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        0.000000              25.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count          95578.000000       95578.000000     95578.000000   \n",
       "mean               0.610320           0.000000         0.000000   \n",
       "std                0.344755           0.000000         0.000000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.375000           0.000000         0.000000   \n",
       "50%                0.666667           0.000000         0.000000   \n",
       "75%                0.944444           0.000000         0.000000   \n",
       "max                1.000000           0.000000         0.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count     95578.000000  \n",
       "mean          8.486922  \n",
       "std           9.465267  \n",
       "min           1.000000  \n",
       "25%           2.000000  \n",
       "50%           5.000000  \n",
       "75%          11.000000  \n",
       "max          85.000000  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm1[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "cdf81aa3-4459-448d-9b3c-b86ef1ce7c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm1, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "934c16d7-41f3-4da1-b8fe-a377bf865cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm1, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c6d0d1ae-fa00-4833-9fcb-617a46b8d439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      26865\n",
       "18-22      26556\n",
       "28-34      19763\n",
       "35-44       9195\n",
       "45-59       7157\n",
       "0-17        4698\n",
       "60-150      1251\n",
       "unknown       93\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm1['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c778d1ce-ebbe-430d-a8d3-36bedf11e366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "95ec575d-c560-4dea-be60-cc780298ad78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4a43e7-04b4-4275-a03d-b2ab0030200f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "01bf2b67-c1fd-4b76-883f-c1e2c55f9ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm2 = df[df['gmm_clusters']==2].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "08e5de3d-2641-461d-8475-c91deca08b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmmlist2 =gmm2['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "9505a715-e354-4c61-af00-d9b08276390f",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgmm2 = users_cleaned[users_cleaned['USER_ID'].isin(gmmlist2)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "3c03e0ce-40d3-4f32-95ac-14e89e5930d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "      <td>100785.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.336151</td>\n",
       "      <td>1.423337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.481748</td>\n",
       "      <td>0.271003</td>\n",
       "      <td>0.009624</td>\n",
       "      <td>0.733044</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173131</td>\n",
       "      <td>10.035293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.318489</td>\n",
       "      <td>2.835438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.258314</td>\n",
       "      <td>0.688574</td>\n",
       "      <td>0.097632</td>\n",
       "      <td>0.299429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.545807</td>\n",
       "      <td>11.334642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>126.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       100785.000000  100785.000000   100785.000000   100785.000000   \n",
       "mean             5.336151       1.423337        0.000000        0.000000   \n",
       "std             10.318489       2.835438        0.000000        0.000000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        0.000000   \n",
       "75%              6.000000       2.000000        0.000000        0.000000   \n",
       "max            116.000000      27.000000        0.000000        0.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  100785.000000    100785.000000               100785.000000   \n",
       "mean        0.000000         0.000000                    0.481748   \n",
       "std         0.000000         0.000000                    1.258314   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max         0.000000         0.000000                   13.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 100785.000000          100785.000000   \n",
       "mean                       0.271003               0.009624   \n",
       "std                        0.688574               0.097632   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        7.000000               1.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         100785.000000      100785.000000    100785.000000   \n",
       "mean               0.733044           0.000000         0.173131   \n",
       "std                0.299429           0.000000         0.545807   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.583333           0.000000         0.000000   \n",
       "50%                0.818182           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         5.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    100785.000000  \n",
       "mean         10.035293  \n",
       "std          11.334642  \n",
       "min           0.000000  \n",
       "25%           2.000000  \n",
       "50%           6.000000  \n",
       "75%          15.000000  \n",
       "max         126.000000  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm2[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "94b9a07d-61d1-4127-85f9-e8c5d45dcca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm2, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "1d576bb5-096c-4cc9-a95b-6aca1a198d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm2, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "572143b7-ffbe-40d1-a223-9cc57240900c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      31105\n",
       "18-22      26681\n",
       "28-34      21252\n",
       "35-44      10148\n",
       "45-59       6631\n",
       "0-17        3697\n",
       "60-150      1161\n",
       "unknown      110\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm2['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "f407b513-e4d3-4c13-a32f-2709ee2f8b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "c073886d-24b0-407e-8d7b-48cdc6950597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c85c68c1-b060-4f32-aee8-0fd55dfdbc90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "df387867-44a0-4e1d-8737-684d55ecda1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm3 = df[df['gmm_clusters']==3].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "6e1b10aa-0dcc-4cb5-812f-f504500a3483",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmmlist3 =gmm3['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "7d89066e-f933-46d0-9891-73ca91a18581",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgmm3 = users_cleaned[users_cleaned['USER_ID'].isin(gmmlist3)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "bff10ba2-16bb-4e98-81f1-a00e181a0cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.316166</td>\n",
       "      <td>3.971974</td>\n",
       "      <td>4.650366</td>\n",
       "      <td>0.463607</td>\n",
       "      <td>0.479285</td>\n",
       "      <td>0.081478</td>\n",
       "      <td>0.531275</td>\n",
       "      <td>0.103899</td>\n",
       "      <td>0.704143</td>\n",
       "      <td>0.705222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.550447</td>\n",
       "      <td>15.333063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.751094</td>\n",
       "      <td>8.561588</td>\n",
       "      <td>9.546884</td>\n",
       "      <td>1.149454</td>\n",
       "      <td>1.544220</td>\n",
       "      <td>0.329116</td>\n",
       "      <td>0.974342</td>\n",
       "      <td>0.347935</td>\n",
       "      <td>1.654259</td>\n",
       "      <td>0.261015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.885214</td>\n",
       "      <td>15.171413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>242.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count        12310.000000   12310.000000    12310.000000    12310.000000   \n",
       "mean             4.316166       3.971974        4.650366        0.463607   \n",
       "std              8.751094       8.561588        9.546884        1.149454   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        0.000000   \n",
       "75%              5.000000       3.000000        5.000000        0.000000   \n",
       "max            242.000000      63.000000       80.000000       11.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count   12310.000000     12310.000000                12310.000000   \n",
       "mean        0.479285         0.081478                    0.531275   \n",
       "std         1.544220         0.329116                    0.974342   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    1.000000   \n",
       "max        11.000000         2.000000                    6.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                  12310.000000           12310.000000   \n",
       "mean                       0.103899               0.704143   \n",
       "std                        0.347935               1.654259   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        2.000000              11.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count          12310.000000       12310.000000     12310.000000   \n",
       "mean               0.705222           0.000000         1.550447   \n",
       "std                0.261015           0.000000         4.885214   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.545455           0.000000         0.000000   \n",
       "50%                0.760000           0.000000         0.000000   \n",
       "75%                0.913043           0.000000         1.000000   \n",
       "max                1.000000           0.000000       241.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count     12310.000000  \n",
       "mean         15.333063  \n",
       "std          15.171413  \n",
       "min           0.000000  \n",
       "25%           4.000000  \n",
       "50%          11.000000  \n",
       "75%          22.000000  \n",
       "max         103.000000  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm3[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "442532a8-aa2f-4a54-9c19-9b50a5a700f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm3, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "998c8cab-3139-4518-9eee-526f1bbf1599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm3, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "a3cc2663-795c-4cc7-b8ec-478867e2a53c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      3896\n",
       "18-22      3212\n",
       "28-34      2640\n",
       "35-44      1172\n",
       "45-59       815\n",
       "0-17        430\n",
       "60-150      127\n",
       "unknown      18\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm3['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "d01171c7-77d8-4d99-8068-6d35bde7c404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "ebfde0f2-659f-4a56-ab20-fed03c7e5a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a173413-4ac2-403e-8dc5-a7e28770e7a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "4da3bba4-74e3-4a15-9275-937bc3c67ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm4 = df[df['gmm_clusters']==4].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "22d39ae4-8244-4db4-aff8-323a243cd7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmmlist4 =gmm4['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "7bd152cc-ce63-4631-bfc9-09e751ca48fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgmm4 = users_cleaned[users_cleaned['USER_ID'].isin(gmmlist4)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "2df51cc4-acff-4b21-8ff4-0d39355432a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>9137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.291781</td>\n",
       "      <td>7.168874</td>\n",
       "      <td>3.567035</td>\n",
       "      <td>3.970121</td>\n",
       "      <td>10.116997</td>\n",
       "      <td>8.170953</td>\n",
       "      <td>1.616395</td>\n",
       "      <td>1.124220</td>\n",
       "      <td>3.765021</td>\n",
       "      <td>0.743310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.474116</td>\n",
       "      <td>61.176316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>37.032128</td>\n",
       "      <td>20.925569</td>\n",
       "      <td>16.416626</td>\n",
       "      <td>14.050544</td>\n",
       "      <td>35.061623</td>\n",
       "      <td>21.228592</td>\n",
       "      <td>6.879709</td>\n",
       "      <td>5.911305</td>\n",
       "      <td>14.263449</td>\n",
       "      <td>0.212839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.018915</td>\n",
       "      <td>62.415076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794872</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.907216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>754.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>1418.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count         9137.000000    9137.000000     9137.000000     9137.000000   \n",
       "mean            14.291781       7.168874        3.567035        3.970121   \n",
       "std             37.032128      20.925569       16.416626       14.050544   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       1.000000        0.000000        0.000000   \n",
       "75%              9.000000       5.000000        0.000000        1.000000   \n",
       "max            754.000000     534.000000      347.000000      515.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count    9137.000000      9137.000000                 9137.000000   \n",
       "mean       10.116997         8.170953                    1.616395   \n",
       "std        35.061623        21.228592                    6.879709   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         4.000000         6.000000                    1.000000   \n",
       "max      1287.000000       523.000000                  372.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                   9137.000000            9137.000000   \n",
       "mean                       1.124220               3.765021   \n",
       "std                        5.911305              14.263449   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                      223.000000             492.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count           9137.000000        9137.000000      9137.000000   \n",
       "mean               0.743310           0.000000         0.474116   \n",
       "std                0.212839           0.000000         3.018915   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.625000           0.000000         0.000000   \n",
       "50%                0.794872           0.000000         0.000000   \n",
       "75%                0.907216           0.000000         0.000000   \n",
       "max                1.000000           0.000000       151.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count      9137.000000  \n",
       "mean         61.176316  \n",
       "std          62.415076  \n",
       "min           0.000000  \n",
       "25%          27.000000  \n",
       "50%          45.000000  \n",
       "75%          76.000000  \n",
       "max        1418.000000  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm4[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6c3f7a64-70f1-4ea2-b5a1-41028b50e590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm4, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "f90fb3c6-d5b9-4d15-892d-8e7270c93a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm4, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "bbb7dd96-5d30-4a67-8ac4-e61aafe82be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      2763\n",
       "23-27      2561\n",
       "28-34      1627\n",
       "35-44       832\n",
       "45-59       741\n",
       "0-17        471\n",
       "60-150      125\n",
       "unknown      17\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgmm4['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "bdfe6183-8c08-492c-bdb0-6af704d81ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "74244fd3-67cf-4c00-b613-14a91e55fa5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subgmm4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "4a935d45-4280-4976-ab7a-9cd63d7f357d",
   "metadata": {},
   "outputs": [],
   "source": [
    "subusers = users_cleaned[['USER_ID','USER_GENDER', 'USER_AGE_GROUP_cleaned']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "120dab98-6e61-4c67-a9f8-ac6682616223",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = df.merge(subusers, on='USER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc21a30-51bf-4cf3-9d92-e94e6021a6d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "6d9f5fb3-fea8-4973-9149-d326167211c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features1 = merged[['USER_GENDER']]\n",
    "categorical_features2 = merged[['USER_AGE_GROUP_cleaned']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "e7934a62-3eb8-4392-9f8b-4e8b73a70a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/impr001/opt/anaconda/anaconda3/lib/python3.11/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/impr001/opt/anaconda/anaconda3/lib/python3.11/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "/Users/impr001/opt/anaconda/anaconda3/lib/python3.11/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/impr001/opt/anaconda/anaconda3/lib/python3.11/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
     ]
    },
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      "text/plain": [
       "         index  USER_GENDER   index  USER_AGE_GROUP_cleaned  \\\n",
       "0            0            2       0                       3   \n",
       "1            1            2       1                       3   \n",
       "2            2            1       2                       1   \n",
       "3            3            1       3                       0   \n",
       "4            4            1       4                       1   \n",
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       "954825  954825            2  954825                       2   \n",
       "954826  954826            1  954826                       5   \n",
       "954827  954827            2  954827                       2   \n",
       "954828  954828            2  954828                       3   \n",
       "\n",
       "        COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "0                       37              0               0               0   \n",
       "1                        1              0               0               0   \n",
       "2                        5              2               0              17   \n",
       "3                        0              1               0               0   \n",
       "4                        4              0               0               0   \n",
       "...                    ...            ...             ...             ...   \n",
       "954824                   1              0               0               0   \n",
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       "954827                   0              0               0               6   \n",
       "954828                   1              0               0               0   \n",
       "\n",
       "        ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "0                   0                0                           0   \n",
       "1                   0                0                           0   \n",
       "2                   0                0                           0   \n",
       "3                   0                0                           0   \n",
       "4                   0                0                           0   \n",
       "...               ...              ...                         ...   \n",
       "954824              0                0                           0   \n",
       "954825              0                0                           0   \n",
       "954826              0                0                           0   \n",
       "954827              0                0                           0   \n",
       "954828              0                0                           0   \n",
       "\n",
       "        THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "0                                  0                      0   \n",
       "1                                  0                      0   \n",
       "2                                  0                      0   \n",
       "3                                  0                      0   \n",
       "4                                  0                      0   \n",
       "...                              ...                    ...   \n",
       "954824                             0                      0   \n",
       "954825                             0                      0   \n",
       "954826                             0                      0   \n",
       "954827                             0                      0   \n",
       "954828                             0                      0   \n",
       "\n",
       "        USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "0                   0.842105                  0                0   \n",
       "1                   1.000000                  0                0   \n",
       "2                   0.500000                  0                0   \n",
       "3                   1.000000                  0                0   \n",
       "4                   0.666667                  0                3   \n",
       "...                      ...                ...              ...   \n",
       "954824              1.000000                  0                0   \n",
       "954825              1.000000                  0                0   \n",
       "954826              0.000000                  0                0   \n",
       "954827              0.000000                  0                0   \n",
       "954828              1.000000                  0                0   \n",
       "\n",
       "        CATALOG_STREAMS  \n",
       "0                    37  \n",
       "1                     1  \n",
       "2                    30  \n",
       "3                     1  \n",
       "4                     1  \n",
       "...                 ...  \n",
       "954824                1  \n",
       "954825                1  \n",
       "954826                1  \n",
       "954827                6  \n",
       "954828                1  \n",
       "\n",
       "[954829 rows x 17 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "\n",
    "encoder.fit(categorical_features1)\n",
    "encoded1 = encoder.transform(categorical_features1)\n",
    "X_categorical1 = pd.DataFrame(encoded1, columns = categorical_features1.columns)\n",
    "\n",
    "encoder.fit(categorical_features2)\n",
    "encoded2 = encoder.transform(categorical_features2)\n",
    "X_categorical2 = pd.DataFrame(encoded2, columns = categorical_features2.columns)\n",
    "\n",
    "table = pd.concat([X_categorical1.reset_index(), X_categorical2.reset_index(), merged[numeric_cols]], axis=1)\n",
    "\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "8edb7763-55c3-46a1-a397-2186c493e6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmm2 = BayesianGaussianMixture(covariance_type='diag', n_components=5, reg_covar=1e-6, random_state=1981).fit(table)\n",
    "\n",
    "# Step 5: Predict cluster assignments\n",
    "labels_bgmm2 = bgmm2.predict(table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "d9169109-1230-4f70-9fd5-35be575fd019",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bgmm2_clusters'] = labels_bgmm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "2f3b4dd4-f80e-4009-8bc2-cb7a959a4733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    730018\n",
       "1    101752\n",
       "4     73123\n",
       "0     33204\n",
       "2     16732\n",
       "Name: bgmm2_clusters, dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bgmm2_clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd2e9e3-def9-4e96-a1a8-b93b691028a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "d6f95d07-f921-4409-b95b-2a24efb992da",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmcat = df[df['bgmm2_clusters']==0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "14d57993-99b4-4caa-b8c8-94e8021deec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmlistcat =bgmmcat['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "0fbfbea9-0126-4d62-8e42-efd621acb450",
   "metadata": {},
   "outputs": [],
   "source": [
    "subbgmmcat = users_cleaned[users_cleaned['USER_ID'].isin(bgmmlistcat)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "c4750ba5-4bbf-4b37-9c3e-e9112f3e43e0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.259336</td>\n",
       "      <td>1.022437</td>\n",
       "      <td>0.317974</td>\n",
       "      <td>2.331797</td>\n",
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       "      <td>2.220615</td>\n",
       "      <td>0.957282</td>\n",
       "      <td>3.183112</td>\n",
       "      <td>1.672376</td>\n",
       "      <td>4.753313</td>\n",
       "      <td>0.406742</td>\n",
       "      <td>1.267970</td>\n",
       "      <td>0.368601</td>\n",
       "      <td>0.301398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090319</td>\n",
       "      <td>10.652953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>306.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count        33204.000000   33204.000000    33204.000000    33204.000000   \n",
       "mean             4.259336       1.022437        0.317974        2.331797   \n",
       "std              7.892964       2.220615        0.957282        3.183112   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        2.000000   \n",
       "75%              5.000000       1.000000        0.000000        3.000000   \n",
       "max            185.000000      79.000000       71.000000       49.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count   33204.000000     33204.000000                33204.000000   \n",
       "mean        0.432448         1.889019                    0.104114   \n",
       "std         1.672376         4.753313                    0.406742   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max       121.000000        94.000000                   23.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                  33204.000000           33204.000000   \n",
       "mean                       0.122214               0.098603   \n",
       "std                        1.267970               0.368601   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                      124.000000              12.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count          33204.000000       33204.000000     33204.000000   \n",
       "mean               0.559198           0.000000         0.002078   \n",
       "std                0.301398           0.000000         0.090319   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.333333           0.000000         0.000000   \n",
       "50%                0.600000           0.000000         0.000000   \n",
       "75%                0.800000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         9.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count     33204.000000  \n",
       "mean         12.171967  \n",
       "std          10.652953  \n",
       "min           2.000000  \n",
       "25%           4.000000  \n",
       "50%           9.000000  \n",
       "75%          17.000000  \n",
       "max         306.000000  "
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "98b7412d-585f-4392-aeac-301728678146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "1533a137-f5c9-4b6f-90d0-6c7a46311912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "096d050c-c766-4791-9866-12875f973f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      10874\n",
       "23-27       9386\n",
       "28-34       5537\n",
       "35-44       2512\n",
       "45-59       2490\n",
       "0-17        1976\n",
       "60-150       403\n",
       "unknown       26\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "25acd18d-a513-481a-a2c6-c78d565e826f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "b7b6f64d-1bb7-4d43-bfe7-6adb47c8e979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fcfa69b-3c98-4ce5-a87c-9321e8b0abca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "050422b0-cec5-4526-982e-334640c15072",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmcat1 = df[df['bgmm2_clusters']==1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "0e7f4f77-d82c-4b88-8ef0-8593287c914e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmlistcat1 =bgmmcat1['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "9e2dc656-2046-4abd-a601-70c70f9257c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "subbgmmcat1 = users_cleaned[users_cleaned['USER_ID'].isin(bgmmlistcat1)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "c625eba5-92b3-4435-8715-37b0922cc556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "      <td>101752.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.186375</td>\n",
       "      <td>1.350303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.703043</td>\n",
       "      <td>0.140105</td>\n",
       "      <td>0.453347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.711498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.832770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.670584</td>\n",
       "      <td>2.788633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.634879</td>\n",
       "      <td>0.474556</td>\n",
       "      <td>1.216274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.649224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       101752.000000  101752.000000   101752.000000   101752.000000   \n",
       "mean             7.186375       1.350303        0.000000        0.000000   \n",
       "std              9.670584       2.788633        0.000000        0.000000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              3.000000       0.000000        0.000000        0.000000   \n",
       "75%             11.000000       2.000000        0.000000        0.000000   \n",
       "max             82.000000      28.000000        0.000000        0.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  101752.000000    101752.000000               101752.000000   \n",
       "mean        0.703043         0.140105                    0.453347   \n",
       "std         2.634879         0.474556                    1.216274   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max        26.000000         4.000000                   12.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 101752.000000          101752.000000   \n",
       "mean                       0.000000               0.000000   \n",
       "std                        0.000000               0.000000   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        0.000000               0.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         101752.000000      101752.000000    101752.000000   \n",
       "mean               0.711498           0.000000         0.000000   \n",
       "std                0.280058           0.000000         0.000000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.555556           0.000000         0.000000   \n",
       "50%                0.777778           0.000000         0.000000   \n",
       "75%                0.952381           0.000000         0.000000   \n",
       "max                1.000000           0.000000         0.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    101752.000000  \n",
       "mean         11.832770  \n",
       "std          10.649224  \n",
       "min           1.000000  \n",
       "25%           4.000000  \n",
       "50%          10.000000  \n",
       "75%          15.000000  \n",
       "max         115.000000  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat1[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "57698212-1cad-4cf2-bf9b-b1255f5e25fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat1, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "a1aa50c4-fa0a-4c30-a0bf-917f621396b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat1, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "11b1efa8-3fb2-456f-b0a7-9ab2066db61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      30772\n",
       "18-22      29143\n",
       "28-34      20137\n",
       "35-44       9153\n",
       "45-59       6886\n",
       "0-17        4330\n",
       "60-150      1215\n",
       "unknown      116\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat1['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "b6b80805-eb36-4710-98c9-c84ed0e2a339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "5444f276-294d-4439-b333-7de00848bb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat1, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6bb05ed-8cc0-4980-9bbf-690181a7cf5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "56d740e6-2669-46fc-a805-4f94e3f4e45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmcat2 = df[df['bgmm2_clusters']==2].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "cf6096fa-18f8-4d5f-beb4-459632521cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmlistcat2 =bgmmcat2['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "9fcbb8a8-d4f6-4106-b273-c2352da07d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "subbgmmcat2 = users_cleaned[users_cleaned['USER_ID'].isin(bgmmlistcat2)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "05039563-d5dd-4ab6-9bef-daac42e57852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "      <td>16732.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.092816</td>\n",
       "      <td>6.636326</td>\n",
       "      <td>4.491274</td>\n",
       "      <td>2.276357</td>\n",
       "      <td>6.716471</td>\n",
       "      <td>3.891764</td>\n",
       "      <td>1.160650</td>\n",
       "      <td>0.017093</td>\n",
       "      <td>2.732608</td>\n",
       "      <td>0.734401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.215037</td>\n",
       "      <td>44.278389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.286719</td>\n",
       "      <td>16.923239</td>\n",
       "      <td>14.339843</td>\n",
       "      <td>10.393835</td>\n",
       "      <td>26.512294</td>\n",
       "      <td>15.916187</td>\n",
       "      <td>5.206627</td>\n",
       "      <td>0.163839</td>\n",
       "      <td>10.741761</td>\n",
       "      <td>0.221132</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.667975</td>\n",
       "      <td>58.705927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>754.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>1287.000000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>372.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>492.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>3665.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count        16732.000000   16732.000000    16732.000000    16732.000000   \n",
       "mean            13.092816       6.636326        4.491274        2.276357   \n",
       "std             30.286719      16.923239       14.339843       10.393835   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       1.000000        0.000000        0.000000   \n",
       "75%             12.000000       6.000000        1.000000        1.000000   \n",
       "max            754.000000     534.000000      347.000000      515.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count   16732.000000     16732.000000                16732.000000   \n",
       "mean        6.716471         3.891764                    1.160650   \n",
       "std        26.512294        15.916187                    5.206627   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         1.000000         0.000000                    1.000000   \n",
       "max      1287.000000       523.000000                  372.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                  16732.000000           16732.000000   \n",
       "mean                       0.017093               2.732608   \n",
       "std                        0.163839              10.741761   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               1.000000   \n",
       "max                        4.000000             492.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count          16732.000000       16732.000000     16732.000000   \n",
       "mean               0.734401           0.000000         1.215037   \n",
       "std                0.221132           0.000000         4.667975   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.615385           0.000000         0.000000   \n",
       "50%                0.785714           0.000000         0.000000   \n",
       "75%                0.903226           0.000000         1.000000   \n",
       "max                1.000000           0.000000       241.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count     16732.000000  \n",
       "mean         44.278389  \n",
       "std          58.705927  \n",
       "min           0.000000  \n",
       "25%          15.000000  \n",
       "50%          32.000000  \n",
       "75%          56.000000  \n",
       "max        3665.000000  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat2[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "29a5af4b-f509-451d-a109-9c4fb108caf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='CATALOG_STREAMS', ylabel='Density'>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(subbgmmcat2['CATALOG_STREAMS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "96e0b34f-e8d7-4020-8e1c-5d6ad0ff6484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat2, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "24d7ab07-c37c-441d-a288-3a95d1f81928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat2, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "045e9db1-7dcf-46a4-9dba-e56fe55f691f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18-22      4985\n",
       "23-27      4944\n",
       "28-34      3053\n",
       "35-44      1445\n",
       "45-59      1215\n",
       "0-17        853\n",
       "60-150      209\n",
       "unknown      28\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat2['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "e55d5575-3219-42af-9722-3262358cfd3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mvB6IzpeTk6OMjAw1atRI/v7+Wrx4sdOWkpKi3bt3Ky4uTpIUFxenDRs26NChQ07NokWLFBoaqtjYWKfm3D5ya3L7yE9gYKDzUQC5GwAA+PXy6jlEw4YN07333qsqVaro2LFjmjFjhpYuXaoFCxYoLCxMvXv31qBBgxQeHq7Q0FA9+eSTiouLU9OmTSVJbdq0UWxsrB599FGNHTtWqampeuaZZ5SQkKDAwEBJUt++ffX6669ryJAh6tWrl5YsWaIPPvhA8+bN8+bUAQBAMeLVQHTo0CH16NFDBw4cUFhYmOrVq6cFCxbo7rvvliSNHz9evr6+6tSpkzIyMhQfH6833njDeb6fn5/mzp2rfv36KS4uTiEhIerZs6dGjRrl1MTExGjevHkaOHCgJkyYoOjoaE2dOpVL7gEAgMOrgeitt966aHtQUJAmTpyoiRMnXrCmatWq+uyzzy7aT4sWLbRu3bpCjREAAPz6FbtziAAAAK41AhEAAHA9AhEAAHC9QgWili1b5vl8IOnsJzrn3ncMAADgelGoQLR06VJlZmbm2X/69Gl9/fXXVzwoAACAa+myrjJbv3698+/Nmzc79wuTzt6cdf78+brhhhuKbnQAAADXwGUFogYNGsjHx0c+Pj75vjUWHBys1157rcgGBwAAcC1cViDauXOnzEw33XSTVq9erQoVKjhtAQEBioiIkJ+fX5EPEgAA4Gq6rEBUtWpVSWfvNwYAAPBrUehPqt62bZu+/PJLHTp0KE9AGj58+BUPDAAA4FopVCB688031a9fP5UvX15RUVHy8fFx2nx8fAhEAADgulKoQPSPf/xDzz//vIYOHVrU4wEAALjmCvU5REeOHNFDDz1U1GMBAADwikIFooceekgLFy4s6rEAAAB4RaHeMqtevbqeffZZrVy5UnXr1pW/v79H+5/+9KciGRwAAMC1UKhA9K9//UulSpXSsmXLtGzZMo82Hx8fAhEAALiuFCoQ7dy5s6jHAQAA4DWFOocIAADg16RQK0S9evW6aPvbb79dqMEAAAB4Q6EC0ZEjRzweZ2VlaePGjTp69Gi+N30FAAAozgoViD766KM8+3JyctSvXz9Vq1btigcFAABwLRXZOUS+vr4aNGiQxo8fX1RdAgAAXBNFelL19u3bdebMmaLsEgAA4Kor1FtmgwYN8nhsZjpw4IDmzZunnj17FsnAAAAArpVCBaJ169Z5PPb19VWFChU0bty4S16BBgAAUNwUKhB9+eWXRT0OAAAArylUIMr1888/KyUlRZJUs2ZNVahQoUgGBQAAcC0V6qTqEydOqFevXqpYsaKaNWumZs2aqVKlSurdu7dOnjxZ1GMEAAC4qgoViAYNGqRly5bp008/1dGjR3X06FF98sknWrZsmf785z8X9RgBAACuqkK9ZTZ79mz997//VYsWLZx99913n4KDg9W5c2dNmjSpqMYHAABw1RVqhejkyZOKjIzMsz8iIoK3zAAAwHWnUIEoLi5OI0aM0OnTp519p06d0nPPPae4uLgiGxwAAMC1UKi3zF555RXdc889io6OVv369SVJ33//vQIDA7Vw4cIiHSAAAMDVVqhAVLduXW3btk3Tp0/X1q1bJUkPP/ywunXrpuDg4CIdIAAAwNVWqEA0evRoRUZGqk+fPh773377bf38888aOnRokQwOAADgWijUOURTpkxRrVq18uyvU6eOJk+efMWDAgAAuJYKFYhSU1NVsWLFPPsrVKigAwcOXPGgAAAArqVCBaLKlStr+fLlefYvX75clSpVuuJBAQAAXEuFOoeoT58+GjBggLKystSyZUtJ0uLFizVkyBA+qRoAAFx3ChWIBg8erMOHD+uPf/yjMjMzJUlBQUEaOnSohg0bVqQDBAAAuNoKFYh8fHz0z3/+U88++6y2bNmi4OBg3XzzzQoMDCzq8QEAAFx1hQpEuUqVKqXf/va3RTUWAAAAryjUSdUAAAC/JgQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgegQiAADgel4NRKNHj9Zvf/tblS5dWhEREerQoYNSUlI8ak6fPq2EhASVK1dOpUqVUqdOnXTw4EGPmt27d6tt27YqWbKkIiIiNHjwYJ05c8ajZunSpWrYsKECAwNVvXp1JSYmXu3pAQCA64RXA9GyZcuUkJCglStXatGiRcrKylKbNm104sQJp2bgwIH69NNPNWvWLC1btkz79+9Xx44dnfbs7Gy1bdtWmZmZWrFihd555x0lJiZq+PDhTs3OnTvVtm1b3XXXXUpOTtaAAQP0hz/8QQsWLLim8wUAAMVTCW8efP78+R6PExMTFRERobVr16pZs2ZKS0vTW2+9pRkzZqhly5aSpGnTpql27dpauXKlmjZtqoULF2rz5s364osvFBkZqQYNGujvf/+7hg4dqpEjRyogIECTJ09WTEyMxo0bJ0mqXbu2vvnmG40fP17x8fHXfN4AAKB4KVbnEKWlpUmSwsPDJUlr165VVlaWWrdu7dTUqlVLVapUUVJSkiQpKSlJdevWVWRkpFMTHx+v9PR0bdq0yak5t4/cmtw+zpeRkaH09HSPDQAA/HoVm0CUk5OjAQMG6Pbbb9ctt9wiSUpNTVVAQIDKlCnjURsZGanU1FSn5twwlNue23axmvT0dJ06dSrPWEaPHq2wsDBnq1y5cpHMEQAAFE/FJhAlJCRo48aNmjlzpreHomHDhiktLc3Z9uzZ4+0hAQCAq8ir5xDl6t+/v+bOnauvvvpK0dHRzv6oqChlZmbq6NGjHqtEBw8eVFRUlFOzevVqj/5yr0I7t+b8K9MOHjyo0NBQBQcH5xlPYGCgAgMDi2RuAACg+PPqCpGZqX///vroo4+0ZMkSxcTEeLQ3atRI/v7+Wrx4sbMvJSVFu3fvVlxcnCQpLi5OGzZs0KFDh5yaRYsWKTQ0VLGxsU7NuX3k1uT2AQAA3M2rK0QJCQmaMWOGPvnkE5UuXdo55ycsLEzBwcEKCwtT7969NWjQIIWHhys0NFRPPvmk4uLi1LRpU0lSmzZtFBsbq0cffVRjx45VamqqnnnmGSUkJDirPH379tXrr7+uIUOGqFevXlqyZIk++OADzZs3z2tzBwAAxYdXV4gmTZqktLQ0tWjRQhUrVnS2999/36kZP3687r//fnXq1EnNmjVTVFSUPvzwQ6fdz89Pc+fOlZ+fn+Li4tS9e3f16NFDo0aNcmpiYmI0b948LVq0SPXr19e4ceM0depULrkHAACSvLxCZGaXrAkKCtLEiRM1ceLEC9ZUrVpVn3322UX7adGihdatW3fZYwQAAL9+xeYqMwAAAG8hEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcr4e0BAEBx1mjwu94ewhVb+2IPbw8BKPZYIQIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5XwtsDAAAUP40Gv+vtIVyxtS/28PYQcB1hhQgAALgegQgAALgegQgAALgegQgAALgegQgAALgegQgAALgegQgAALgegQgAALgegQgAALieVwPRV199pXbt2qlSpUry8fHRxx9/7NFuZho+fLgqVqyo4OBgtW7dWtu2bfOo+eWXX9StWzeFhoaqTJky6t27t44fP+5Rs379et15550KCgpS5cqVNXbs2Ks9NQAAcB3xaiA6ceKE6tevr4kTJ+bbPnbsWL366quaPHmyVq1apZCQEMXHx+v06dNOTbdu3bRp0yYtWrRIc+fO1VdffaXHH3/caU9PT1ebNm1UtWpVrV27Vi+++KJGjhypf/3rX1d9fgAA4Prg1XuZ3Xvvvbr33nvzbTMzvfLKK3rmmWfUvn17SdK7776ryMhIffzxx+ratau2bNmi+fPn69tvv9Wtt94qSXrttdd033336aWXXlKlSpU0ffp0ZWZm6u2331ZAQIDq1Kmj5ORkvfzyyx7BCQAAuFexPYdo586dSk1NVevWrZ19YWFhatKkiZKSkiRJSUlJKlOmjBOGJKl169by9fXVqlWrnJpmzZopICDAqYmPj1dKSoqOHDlyjWYDAACKs2J7t/vU1FRJUmRkpMf+yMhIpy01NVUREREe7SVKlFB4eLhHTUxMTJ4+ctvKli2b59gZGRnKyMhwHqenp1/hbAAAQHFWbFeIvGn06NEKCwtztsqVK3t7SAAA4CoqtoEoKipKknTw4EGP/QcPHnTaoqKidOjQIY/2M2fO6JdffvGoya+Pc49xvmHDhiktLc3Z9uzZc+UTAgAAxVaxDUQxMTGKiorS4sWLnX3p6elatWqV4uLiJElxcXE6evSo1q5d69QsWbJEOTk5atKkiVPz1VdfKSsry6lZtGiRatasme/bZZIUGBio0NBQjw0AAPx6eTUQHT9+XMnJyUpOTpZ09kTq5ORk7d69Wz4+PhowYID+8Y9/aM6cOdqwYYN69OihSpUqqUOHDpKk2rVr65577lGfPn20evVqLV++XP3791fXrl1VqVIlSdIjjzyigIAA9e7dW5s2bdL777+vCRMmaNCgQV6aNQAAKG68elL1mjVrdNdddzmPc0NKz549lZiYqCFDhujEiRN6/PHHdfToUd1xxx2aP3++goKCnOdMnz5d/fv3V6tWreTr66tOnTrp1VdfddrDwsK0cOFCJSQkqFGjRipfvryGDx/OJfcAAMDh1UDUokULmdkF2318fDRq1CiNGjXqgjXh4eGaMWPGRY9Tr149ff3114UeJwAA+HUrtucQAQAAXCsEIgAA4HoEIgAA4HoEIgAA4HrF9tYd15NGg9/19hCu2NoXe3h7CAAAeA0rRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPUIRAAAwPVKeHsAAAAUF40Gv+vtIVyxtS/28PYQrkusEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANcjEAEAANdzVSCaOHGibrzxRgUFBalJkyZavXq1t4cEAACKAdcEovfff1+DBg3SiBEj9N1336l+/fqKj4/XoUOHvD00AADgZa4JRC+//LL69Omjxx57TLGxsZo8ebJKliypt99+29tDAwAAXuaKm7tmZmZq7dq1GjZsmLPP19dXrVu3VlJSkhdHBgCA93FTW5cEov/973/Kzs5WZGSkx/7IyEht3bo1T31GRoYyMjKcx2lpaZKk9PT0fPvPzjhVhKP1jgvN7WKY9/WLeRcc875+Me+C+7XOO3efmV26A3OBffv2mSRbsWKFx/7Bgwdb48aN89SPGDHCJLGxsbGxsbH9CrY9e/ZcMiu4YoWofPny8vPz08GDBz32Hzx4UFFRUXnqhw0bpkGDBjmPc3Jy9Msvv6hcuXLy8fG56uM9V3p6uipXrqw9e/YoNDT0mh7bm5g383YD5s283cCb8zYzHTt2TJUqVbpkrSsCUUBAgBo1aqTFixerQ4cOks6GnMWLF6t///556gMDAxUYGOixr0yZMtdgpBcWGhrqqh+gXMzbXZi3uzBvd/HWvMPCwgpU54pAJEmDBg1Sz549deutt6px48Z65ZVXdOLECT322GPeHhoAAPAy1wSiLl266Oeff9bw4cOVmpqqBg0aaP78+XlOtAYAAO7jmkAkSf3798/3LbLiLDAwUCNGjMjzFt6vHfNm3m7AvJm3G1wv8/YxK8i1aAAAAL9ervmkagAAgAshEAEAANcjEAEAgAJZunSpfHx8dPToUW8PpcgRiLxg4sSJuvHGGxUUFKQmTZpo9erVF61//vnnddttt6lkyZL5fh5SYmKifHx88t0OHTp0lWZxYV999ZXatWunSpUqycfHRx9//LFH+/Hjx9W/f39FR0crODjYudnuxezatUu9e/dWTEyMgoODVa1aNY0YMUKZmZlOzdKlS9W+fXtVrFhRISEhatCggaZPn341ppjH6NGj9dvf/lalS5dWRESEOnTooJSUFI+aJ554QtWqVVNwcLAqVKig9u3b53vrmHMVZE4tWrTI92vftm3bIp/n+Qoy79TUVD366KOKiopSSEiIGjZsqNmzZ1+038OHD+uee+5RpUqVFBgYqMqVK6t///4XvCXB8uXLVaJECTVo0KCopnZJkyZNUr169ZzPVomLi9Pnn3/utOf3denbt2+B+//xxx9VunTpi34G2syZM+Xj4+N8vtq1NmbMGPn4+GjAgAHOvsLMe9euXfl+D69cudKpycrK0qhRo1StWjUFBQWpfv36mj9//tWamvbt26fu3burXLlyCg4OVt26dbVmzRqn3cw0fPhwVaxYUcHBwWrdurW2bdt2yX7/9Kc/qVGjRgoMDMz3+7Ugr4UkzZo1S7Vq1VJQUJDq1q2rzz777Irn7HYEomvs/fff16BBgzRixAh99913ql+/vuLj4y8aXDIzM/XQQw+pX79++bZ36dJFBw4c8Nji4+PVvHlzRUREXK2pXNCJEydUv359TZw4Md/2QYMGaf78+Xrvvfe0ZcsWDRgwQP3799ecOXMu2OfWrVuVk5OjKVOmaNOmTRo/frwmT56sv/71r07NihUrVK9ePc2ePVvr16/XY489ph49emju3LlFPsfzLVu2TAkJCVq5cqUWLVqkrKwstWnTRidOnHBqGjVqpGnTpmnLli1asGCBzExt2rRRdnb2BfstyJw+/PBDj6/9xo0b5efnp4ceeuiqzlkq2Lx79OihlJQUzZkzRxs2bFDHjh3VuXNnrVu37oL9+vr6qn379pozZ45++OEHJSYm6osvvsj3D+vRo0fVo0cPtWrV6qrM8UKio6M1ZswYrV27VmvWrFHLli3Vvn17bdq0yanp06ePx9dm7NixBeo7KytLDz/8sO68884L1uzatUtPP/30RWuupm+//VZTpkxRvXr18rQVdt5ffPGFx/MaNWrktD3zzDOaMmWKXnvtNW3evFl9+/bVAw88cNHvo8I6cuSIbr/9dvn7++vzzz/X5s2bNW7cOJUtW9apGTt2rF599VVNnjxZq1atUkhIiOLj43X69OlL9t+rVy916dLlojUXey1WrFihhx9+WL1799a6devUoUMHdejQQRs3biz8pCFX3MusOGncuLElJCQ4j7Ozs61SpUo2evToSz532rRpFhYWdsm6Q4cOmb+/v7377rtXMtQiIck++ugjj3116tSxUaNGeexr2LCh/e1vf7usvseOHWsxMTEXrbnvvvvsscceu6x+i8KhQ4dMki1btuyCNd9//71Jsh9//PGy+r7UnMaPH2+lS5e248ePX1a/RSG/eYeEhOT5XgwPD7c333zzsvqeMGGCRUdH59nfpUsXe+aZZ2zEiBFWv379Qo27qJQtW9amTp1qZmbNmze3p556qlD9DBkyxLp3737Bn/kzZ87YbbfdZlOnTrWePXta+/btCz/oQjh27JjdfPPNtmjRojzzLMy8d+7caZJs3bp1F6ypWLGivf766x77OnbsaN26dbusYxXE0KFD7Y477rhge05OjkVFRdmLL77o7Dt69KgFBgbaf/7znwId40LfrwV5LTp37mxt27b12NekSRN74oknLnncqlWr2vjx4z321a9f30aMGGFmZ39nv/nmm9ahQwcLDg626tWr2yeffOLUfvnllybJjhw5YmZmJ06csHvuucduu+02O3LkiDP+2bNnW4sWLSw4ONjq1auX516i//3vfy02NtYCAgKsatWq9tJLLzltr732mtWpU8d5/NFHH5kkmzRpkrOvVatWzt+M3Nfy3XfftapVq1poaKh16dLF0tPTL/l6nIsVomsoMzNTa9euVevWrZ19vr6+at26tZKSkorsOO+++65KliypBx98sMj6LEq33Xab5syZo3379snM9OWXX+qHH35QmzZtLquftLQ0hYeHX3HN1ZCWliZJFzz2iRMnNG3aNMXExKhy5cqX3ffF5vTWW2+pa9euCgkJuax+i0J+877tttv0/vvv65dfflFOTo5mzpyp06dPq0WLFgXud//+/frwww/VvHlzj/3Tpk3Tjh07NGLEiCIZf2FlZ2dr5syZOnHihOLi4pz906dPV/ny5XXLLbdo2LBhOnny5CX7WrJkiWbNmnXBFVZJGjVqlCIiItS7d+8iGf/lSkhIUNu2bT1+l52rMPOWpN/97neKiIjQHXfckWfFOCMjQ0FBQR77goOD9c033xRuEhcxZ84c3XrrrXrooYcUERGh3/zmN3rzzTed9p07dyo1NdVj/mFhYWrSpEmR/S6/2GuRlJSU57WPj48vsmM/99xz6ty5s9avX6/77rtP3bp10y+//JKn7ujRo7r77ruVk5OjRYsWeby9+7e//U1PP/20kpOTVaNGDT388MM6c+aMJGnt2rXq3Lmzunbtqg0bNmjkyJF69tlnlZiYKElq3ry5Nm/erJ9//lnS2ZXo8uXLa+nSpZLOrqAmJSV5/A7Zvn27Pv74Y82dO1dz587VsmXLNGbMmMub+GXFJ1yRffv2maQ8SXnw4MHWuHHjSz6/oCtEtWvXtn79+hV2mEVK+awQnT592nr06GGSrESJEhYQEGDvvPPOZfW7bds2Cw0NtX/9618XrHn//fctICDANm7cWJihF1p2dra1bdvWbr/99jxtEydOtJCQEJNkNWvWvOzVoUvNadWqVSbJVq1aVaixX4kLzfvIkSPWpk0b5+sdGhpqCxYsKFCfXbt2teDgYJNk7dq1s1OnTjltP/zwg0VERFhKSoqZXfh/3FfT+vXrLSQkxPz8/CwsLMzmzZvntE2ZMsXmz59v69evt/fee89uuOEGe+CBBy7a3//+9z+rXLmys8KW38/8119/bTfccIP9/PPPZmbXfIXoP//5j91yyy3O1+L8FaHCzPvnn3+2cePG2cqVK2316tU2dOhQ8/Hx8ViZePjhhy02NtZ++OEHy87OtoULF1pwcLAFBAQU+RwDAwMtMDDQhg0bZt99951NmTLFgoKCLDEx0czMli9fbpJs//79Hs976KGHrHPnzgU6xoW+XwvyWvj7+9uMGTM8njdx4kSLiIi45HELskL0zDPPOG3Hjx83Sfb555+b2f+tEG3ZssXq1atnnTp1soyMDKc+d4Uod6XUzGzTpk3Oc8zMHnnkEbv77rs9xjB48GCLjY01s7MrcOXKlbNZs2aZmVmDBg1s9OjRFhUVZWZm33zzjfn7+9uJEyfM7OxrWbJkSY8VocGDB1uTJk0u+Xqci0B0DV0qED3xxBMWEhLibOcrSCBasWKFSbI1a9YU5dALLb9A9OKLL1qNGjVszpw59v3339trr71mpUqVskWLFpmZXfJ12Lt3r1WrVs169+59weMuWbLESpYsedlBqyj07dvXqlatanv27MnTdvToUfvhhx9s2bJl1q5dO2vYsKHzhyU2NtaZ8z333JPnuQWZ0+OPP25169YtuslchgvNu3///ta4cWP74osvLDk52UaOHGlhYWG2fv16MzO75557nHnn/kLMdeDAAduyZYt98sknFhsb6wT9M2fO2K233uqxhO6NQJSRkWHbtm2zNWvW2F/+8hcrX768bdq0Kd/axYsXe7xFmt/X+4EHHrChQ4c6zzn/Zz49Pd1uvPFG++yzz5x91zIQ7d692yIiIuz777939l3qLbKCzDs/jz76qMfbVocOHbL27dubr6+v+fn5WY0aNeyPf/yjBQUFXfnEzuPv729xcXEe+5588klr2rSpmRUsEF3s+9rs8r5fz38trnYg+uCDDzzaQ0NDnd87uYEoOjraOnbsaGfOnPGozQ1Eq1evdvb98ssvHm+l/+Y3v7GRI0d6PO/jjz82f39/p78HHnjAEhIS7MiRIxYQEGBpaWlWtmxZ27Jliz3//PN22223Oc8dMWJEntf45ZdfvuQpFecjEF1DGRkZ5ufnlycg9OjRw373u9/ZwYMHbdu2bc52voIEol69elmDBg2KcNRX5vxAdPLkSfP397e5c+d61PXu3dvi4+PNzC76Ouzbt89uvvlme/TRRy07OzvfYy5dutRCQkJsypQpRTuZAkhISLDo6GjbsWPHJWszMjKsZMmSzi+2Xbt2OXPeu3evR21B5nT8+HELDQ21V1555comUQgXmvePP/5okvKsaLVq1co532Hv3r3OvHft2nXBY3z99dfOH6EjR46YJPPz83M2Hx8fZ9/ixYuLfpIF0KpVK3v88cfzbcv9n/b8+fPNLP+vd1hYmMecfH19nTm99dZbtm7dunzn7ePjY35+fpe94ni5cs/lOPf4kpzjn//HsaDzzs/rr7/urAic69SpU7Z3717LycmxIUOG5Bs2rlSVKlXy/IfrjTfesEqVKpmZ2fbt2/M9z6dZs2b2pz/9ycwu/X19OYHo/NeicuXKeULN8OHDrV69epfsKyYmxl5++WWPfbGxsR6B6Py/UWFhYTZt2jQz+79A9MQTT1j58uWd/9jkyu8cqNyf1y+//NLMChaIJkyYYHXq1LE5c+Y4Kz3t27e3SZMmWZs2bWzYsGHOc/N7LcePH29Vq1a95OtxLlfdy8zbAgIC1KhRIy1evNi5TDYnJ0eLFy9W//79FRERcUVXhR0/flwffPCBRo8eXUQjLnpZWVnKysqSr6/n6Wt+fn7KycmRpAu+Dvv27dNdd93lXK11fh/S2cvU77//fv3zn//U448/fnUmkQ8z05NPPqmPPvpIS5cuVUxMTIGeY2bKyMiQJFWtWjXfuoLOadasWcrIyFD37t0LN4lCuNS8c88dudjX+4YbbijQsXLrMzIyFBkZqQ0bNni0v/HGG1qyZIn++9//Fuj1vxpycnKcr+f5kpOTJUkVK1aUlP/XOykpyeOqw08++UT//Oc/tWLFCt1www0KDg7OM+9nnnlGx44d04QJEy77fLTL1apVqzzHf+yxx1SrVi0NHTpUfn5+eZ5TkHnnJzk52XnOuYKCgnTDDTcoKytLs2fPVufOnS9zFpd2++235/n4iB9++MEZe0xMjKKiorR48WLn0vn09HStWrXKuRq4oN/XBXH+axEXF6fFixd7fNzBokWLPM5fu5AKFSrowIEDzuP09HTt3Lnzssc0ZswYlSpVSq1atdLSpUsVGxtb4OfWrl1by5cv99i3fPly1ahRw/keat68uQYMGKBZs2Y55wq1aNFCX3zxhZYvX64///nPlz3mS7qs+IQrNnPmTAsMDLTExETbvHmzPf7441amTBlLTU294HN++uknW7dunT333HNWqlQpW7duna1bt86OHTvmUTd16lQLCgpyzv73lmPHjjljlGQvv/yyrVu3zn766SczO7vEXqdOHfvyyy9tx44dNm3aNAsKCrI33njjgn3u3bvXqlevbq1atbK9e/fagQMHnC1X7ltKw4YN82g/fPjwVZ9zv379LCwszJYuXepx7JMnT5rZ2f9RvvDCC7ZmzRr76aefbPny5dauXTsLDw+3gwcPXrDfy5nTHXfcYV26dLlqc8zPpeadmZlp1atXtzvvvNNWrVplP/74o7300kvm4+Pjcb7N+ebNm2dvv/22bdiwwXbu3Glz58612rVr53teVq5r/ZbZX/7yF1u2bJnt3LnT1q9fb3/5y1/Mx8fHFi5caD/++KONGjXK1qxZYzt37rRPPvnEbrrpJmvWrNllHaMgq8LeuMrsXOe+ZVbYeScmJtqMGTNsy5Ytzlsivr6+9vbbbzs1K1eutNmzZ9v27dvtq6++spYtW1pMTMxV+X23evVqK1GihD3//PO2bds2mz59upUsWdLee+89p2bMmDFWpkwZ++STT2z9+vXWvn17i4mJ8TjPLT/btm2zdevW2RNPPGE1atRwflfmnodTkNdi+fLlVqJECXvppZdsy5YtNmLECPP397cNGzZccm5/+ctfLCoqyr766itbv369dejQwUqVKnXZK0S5r/uAAQMsMjLSOT+oICtEa9euNV9fXxs1apSlpKRYYmKiBQcHO8cwO3seUXh4uPn5+TnnL61bt878/PysRIkSHlfRFtUKEYHIC1577TWrUqWKBQQEWOPGjW3lypUXre/Zs6dJyrPlfnPliouLs0ceeeQqjrxgcn9gzt969uxpZmfPC/n9739vlSpVsqCgIKtZs6aNGzfOcnJyLtjntGnT8u3z3Ex/odepefPmV3nGdsGx5f6A79u3z+69916LiIgwf39/i46OtkceecS2bt160X4LOqetW7eaJFu4cOFVmmH+LjVvs7MnP3fs2NEiIiKsZMmSVq9evUt+JMSSJUssLi7OwsLCLCgoyG6++WYbOnToRf/4XetA1KtXL6tataoFBARYhQoVrFWrVs7rv3v3bmvWrJmFh4dbYGCgVa9e3QYPHmxpaWmXdYzrLRAVdt6JiYlWu3ZtK1mypIWGhlrjxo2dE2pzLV261GrXrm2BgYFWrlw5e/TRR23fvn1Xa1r26aef2i233GKBgYFWq1atPBdw5OTk2LPPPmuRkZEWGBhorVq1ck7wv5jmzZvn+zOzc+dOMyvYa2Fm9sEHH1iNGjUsICDA6tSpc9H/YJwrLS3NunTpYqGhoVa5cmVLTEzMcw7R5QQis7PnV1WsWNFSUlIKFIjM/u+ye39/f6tSpYrHRxjkat++vZUoUcL5z392draVLVvWOZcrV1EFIu52DwAAXI/PIQIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAIAAK5HIAKAX4HExESVKVPG28MokF27dsnHx8e5zxlQHBCIgGKkRYsWHjdszHXuH7uTJ09q2LBhqlatmoKCglShQgU1b95cn3zyiUc/Pj4+eba+ffs6NefuDw0N1W9/+1uPPgrq1KlTCg8PV/ny5S94Y9PZs2erZcuWKlu2rIKDg1WzZk316tVL69at85hjfmMOCgoq8FhSU1P11FNPqXr16goKClJkZKRuv/12TZo0ybnRrCTdeOONTv8lS5ZU3bp1NXXq1Dz9ZWdna/z48apbt66CgoJUtmxZ3XvvvXluTDly5EjnJp/nOv8P/9KlSz3mFhkZqU6dOmnHjh0FniOAq4NABFxn+vbtqw8//FCvvfaatm7dqvnz5+vBBx/U4cOHPer69OmjAwcOeGxjx471qJk2bZoOHDigNWvW6Pbbb9eDDz6Y527mlzJ79mzVqVNHtWrV0scff5ynfejQoerSpYsaNGigOXPmKCUlRTNmzNBNN92kYcOGedSGhobmGfNPP/1UoHHs2LFDv/nNb7Rw4UK98MILWrdunZKSkjRkyBDNnTtXX3zxhUf9qFGjdODAAW3cuFHdu3dXnz599PnnnzvtZqauXbtq1KhReuqpp7RlyxYtXbpUlStXVosWLfKda0GlpKRo//79mjVrljZt2qR27dp53OUegBdc1p3PAFxV594o81zn3uQzLCzMEhMTC9XPuXTeTRzT09NNkk2YMOGyxtyiRQubPHmyTZo0ye6++26PtqSkpIv2ee4NfQtyI9OLiY+Pt+joaI+7YF/oWFWrVrXx48d7tIeHh9vAgQOdxzNnzjRJNmfOnDx9dezY0cqVK+cc60I3lj3/Rpf53Rhz+vTpJumSN/o1O3uTzMcff9wiIiIsMDDQ6tSpY59++qmZ5f/6ffzxx/ab3/zGAgMDLSYmxkaOHGlZWVlO+7hx4+yWW26xkiVLWnR0tPXr18+5kea5fc6fP99q1aplISEhFh8fb/v37/c4zptvvmm1atWywMBAq1mzpk2cONGjfdWqVdagQQMLDAy0Ro0a2YcffpjnBqCAt7FCBFxnoqKi9Nlnn+nYsWNF1ueZM2f01ltvSZICAgIK/Lzt27crKSlJnTt3VufOnfX11197rOj85z//UalSpfTHP/4x3+f7+Phc2cD/v8OHD2vhwoVKSEhQSEjIZR0rJydHs2fP1pEjRzzmPmPGDNWoUUPt2rXL85w///nPOnz4sBYtWnTFYw8ODpYkZWZmXrQuJyfHebvuvffe0+bNmzVmzBj5+fnlW//111+rR48eeuqpp7R582ZNmTJFiYmJev75550aX19fvfrqq9q0aZPeeecdLVmyREOGDPHo5+TJk3rppZf073//W1999ZV2796tp59+2mmfPn26hg8frueff15btmzRCy+8oGeffVbvvPOOJOn48eO6//77FRsbq7Vr12rkyJEezweKDW8nMgD/pyArRMuWLbPo6Gjz9/e3W2+91QYMGGDffPNNnn78/f0tJCTEY3vvvfecGkkWFBRkISEh5uvra5LsxhtvtMOHDxd4vH/961+tQ4cOzuP27dvbiBEjnMf33HOP1atXz+M548aN8xjT0aNHnTlKyjPme+6555LjWLlypUmyDz/80GN/uXLlnH6GDBni7K9ataoFBARYSEiIlShRwiRZeHi4bdu2zampVauWtW/fPt/j/fLLLybJ/vnPf5pZ4VeI9u/fb7fddpvdcMMNlpGRcdE5LliwwHx9fS0lJSXf9vNXiFq1amUvvPCCR82///1vq1ix4gWPMWvWLCtXrpxHn5Lsxx9/dPZNnDjRIiMjncfVqlWzGTNmePTz97//3eLi4szMbMqUKVauXDk7deqU0z5p0iRWiFDslPBWEANQOM2aNdOOHTu0cuVKrVixQosXL9aECRP03HPP6dlnn3XqunXrpr/97W8ez42MjPR4PH78eLVu3Vo7duzQwIED9eqrryo8PLxA48jOztY777yjCRMmOPu6d++up59+WsOHD5evb/4L0L169dLvfvc7rVq1St27d5eZOW2lS5fWd99951Gfu4JSGKtXr1ZOTo66deuW54TvwYMH6/e//70OHDigwYMH649//KOqV6/uUXPu2IpSdHS0zEwnT55U/fr1NXv27EuuzCUnJys6Olo1atQo0DG+//57LV++3GNFKDs7W6dPn9bJkydVsmRJffHFFxo9erS2bt2q9PR0nTlzxqNdkkqWLKlq1ao5fVSsWFGHDh2SJJ04cULbt29X79691adPH6fmzJkzCgsLkyRt2bJF9erV8zg5Pi4urkBzAK4lAhFQjISGhiotLS3P/qNHjzp/YCTJ399fd955p+68804NHTpU//jHPzRq1CgNHTrU+cMaFhaW5w/8+aKiolS9enVVr15d06ZN03333afNmzcrIiLikmNdsGCB9u3bpy5dunjsz87O1uLFi3X33Xfr5ptv1jfffKOsrCz5+/tLksqUKaMyZcpo7969efr09fW95JjzU716dfn4+CglJcVj/0033SQp/1BVvnx5Z+6zZs1S3bp1deuttyo2NlaSVKNGDW3ZsiXf4+Xuzw0nF/u6SfL42kln384KDQ1VRESESpcuXaA5Xm4wPH78uJ577jl17NgxT1tQUJB27dql+++/X/369dPzzz+v8PBwffPNN+rdu7cyMzOdQJT7dcvl4+PjBMXjx49Lkt588001adLEo+5Cb+UBxRXnEAHFSM2aNfOskEjSd999d9GVgdjYWOd/94XVuHFjNWrUyGNF4WLeeustde3aVcnJyR5b165dnfORHn74YR0/flxvvPFGocdVEOXKldPdd9+t119/XSdOnLjs51euXFldunTxuOqta9eu2rZtmz799NM89ePGjXOOKZ39uu3du1cHDx70qPvuu+8UFBSkKlWqeOyPiYlRtWrVChyGJKlevXrau3evfvjhhwLVN2zYUCkpKU7oO3fz9fXV2rVrlZOTo3Hjxqlp06aqUaOG9u/fX+DxSGdXHCtVqqQdO3bkOUZMTIwkqXbt2lq/fr3H9+bKlSsv6zjANeHdd+wAnGv79u0WFBRkTz75pH3//fe2detWGzdunJUoUcI+//xzMzt7ftDkyZNtzZo1tnPnTps3b57VrFnTWrZs6fTTvHlz69Onjx04cMBj++WXX5wanXeVmZnZZ599ZoGBgbZ3796LjvPQoUPm7+/vjCm/PnLPRfrzn/9sfn5+NnDgQPv6669t165dlpSUZN27dzcfHx9LS0szs7Pnq4SGhuYZ84EDByw7O/uSr92PP/5okZGRVqtWLZs5c6Zt3rzZtm7dav/+978tMjLSBg0a5NTmd5XZpk2bzMfHx7799lszO3tV2gMPPGBly5a1qVOn2s6dO+3777+3xx9/3EqUKOHx2mVlZVmdOnXsrrvusuXLl9v27dtt1qxZVrFiRRs6dKhTl99VZpejRYsWdsstt9jChQttx44d9tlnnzlfg/PPIZo/f76VKFHCRo4caRs3brTNmzfbf/7zH/vb3/5mZmbJyckmyV555RXbvn27vfvuu3bDDTd4jC+/K9c++ugjO/dPx5tvvmnBwcE2YcIES0lJsfXr19vbb79t48aNMzOzY8eOWfny5a179+62adMmmzdvnlWvXp1ziFDsEIiAYmb16tV29913W4UKFSwsLMyaNGni8cf3hRdesLi4OAsPD7egoCC76aab7E9/+pP973//c2qaN29ukvJs8fHxTk1+gSgnJ8dq1apl/fr1u+gYX3rpJStTpoxlZmbmacvIyLAyZcp4XGr//vvvW4sWLSwsLMz8/f0tOjraHnnkEVu5cqVTk3sCb37bgQMHCvTa7d+/3/r3728xMTHm7+9vpUqVssaNG9uLL75oJ06ccOryC0RmZy/dv/fee53HWVlZ9uKLL1qdOnUsICDAQkNDLT4+Ps9J7GZm+/bts549e1qVKlUsODjYYmNjbcyYMR6v0ZUGosOHD9tjjz1m5cqVs6CgILvlllts7ty5ZpZ/eJk/f77ddtttFhwcbKGhoda4cWP717/+5bS//PLLVrFiRQsODrb4+Hh79913LzsQmZ396IAGDRpYQECAlS1b1po1a+ZxgntSUpLVr1/fAgICrEGDBjZ79mwCEYodH7OrdNYgAADAdYJziAAAgOsRiADkq06dOipVqlS+2/Tp06/pWHbv3n3BsZQqVUq7d+++puO5GqZPn37B+dWpU8fbwwN+9XjLDEC+fvrpJ2VlZeXbFhkZeVlXSF2pM2fOaNeuXRdsv/HGG1WixPX9KSLHjh3Lc5VaLn9/f1WtWvUajwhwFwIRAABwPd4yAwAArkcgAgAArkcgAgAArkcgAgAArkcgAgAArkcgAgAArkcgAgAArkcgAgAArvf/AD/Hfj7kTEZgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "090d2695-8245-4049-9d4e-8d59bac5e87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5XegiPT1dXbp0ydacmeTkZNWqVUtPPPGE2rZtm2mbFi1aaMqUKa7HfMMwAAC4VLbDjJnpyJEjiouL0yuvvKJ169YpODhYNWrUUJkyZbLVV8uWLdWyZcsrtgkMDFRkZGR2ywQAAH8TOQoz5cuX1+bNm1WhQgVVqFDhetTlsmTJEoWHh6tIkSJq0qSJXnnllSvOy0lJSVFKSorrcVJS0nWtDwAAeFe258z4+vqqQoUKOn78+PWox02LFi00depUxcfH6/XXX9fSpUvVsmVLpaWlZbnPiBEjFBoa6lqio6Ove50AAMB7cjQBeOTIkerXr582bdqU2/W46dixo+6//37VqFFDbdq00bx58/Tzzz9ryZIlWe4zcOBAJSYmupYDBw5c1xoBAIB35WgCcJcuXXTmzBnVqlVLAQEBCg4Odtt+4sSJXCnucjfddJOKFSumXbt2qWnTppm2CQwMZJIwAAB/IzkKM2+99VYul+GZgwcP6vjx4ypRooRXjg8AAPKeHIWZrl275srBT58+rV27drke7927V+vWrVNYWJjCwsI0bNgwtWvXTpGRkdq9e7f69++v8uXLq3nz5rlyfAAA4Hw5mjMjSbt379agQYPUqVMnHTt2TJL0zTffaPPmzR73sXr1atWpU0d16tSRJPXt21d16tTR4MGDlS9fPm3YsEH333+/KlasqG7duqlu3br6/vvv+RgJAAC45OjMzMWriho2bKhly5bp1VdfVXh4uNavX6/Jkyfrf//7n0f9NG7cWGaW5fYFCxbkpDwAAPA3kqMzMy+++KJeeeUVLVq0SAEBAa71TZo00cqVK3OtOAAAgKvJUZjZuHGjHnjggQzrw8PD9fvvv19zUQAAAJ7KUZgpXLiwEhISMqxfu3atSpUqdc1FAQAAeCpHYaZjx44aMGCAjhw5Ih8fH6Wnp2v58uV64YUX1KVLl9yuEQAAIEs5CjOvvfaaKleurOjoaJ0+fVpVq1bVHXfcoQYNGmjQoEG5XSMAAECWcnQ1U0BAgCZNmqTBgwdr48aNSk5OVp06dVS+fPncrg8AAOCKchRmJGny5MkaO3asdu7cKUmqUKGCevfurX/+85+5VhwAAMDV5CjMDB48WGPGjFGvXr0UGxsrSVqxYoX69Omj/fv3a/jw4blaJAAAQFZyFGYmTJigSZMmqVOnTq51999/v2rWrKlevXoRZgAAwA2TownAqampuuWWWzKsr1u3rs6fP3/NRQEAAHgqR2Hm0Ucf1YQJEzKsf//99/XII49cc1EAAACeuqYJwAsXLtRtt90mSVq1apX279+vLl26qG/fvq52Y8aMufYqAQAAspCjMLNp0ybdfPPNki7cPVuSihUrpmLFimnTpk2udj4+PrlQIgAAQNZyFGa+++673K4DAAAgR3I0ZwYAACCvIMwAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABHI8wAAABH82qYWbZsmVq1aqWSJUvKx8dHs2fPdttuZho8eLBKlCih4OBgNWvWTDt37vROsQAAIE/yaphJTk5WrVq1NH78+Ey3jxo1Sm+//bbee+89rVq1SgUKFFDz5s119uzZG1wpAADIq/y8efCWLVuqZcuWmW4zM7311lsaNGiQWrduLUmaOnWqIiIiNHv2bHXs2PFGlgoAAPKoPDtnZu/evTpy5IiaNWvmWhcaGqr69etrxYoVWe6XkpKipKQktwUAAPx15dkwc+TIEUlSRESE2/qIiAjXtsyMGDFCoaGhriU6Ovq61gkAALwrz4aZnBo4cKASExNdy4EDB7xdEgAAuI7ybJiJjIyUJB09etRt/dGjR13bMhMYGKiQkBC3BQAA/HXl2TATExOjyMhIxcfHu9YlJSVp1apVio2N9WJlAAAgL/Hq1UynT5/Wrl27XI/37t2rdevWKSwsTKVLl1bv3r31yiuvqEKFCoqJidHLL7+skiVLqk2bNt4rGgAA5CleDTOrV6/WXXfd5Xrct29fSVLXrl0VFxen/v37Kzk5Wd27d9fJkyd1++23a/78+QoKCvJWyQAAII/xaphp3LixzCzL7T4+Pho+fLiGDx9+A6sCAABOkmfnzAAAAHiCMAMAAByNMAMAAByNMAMAAByNMAMAAByNMAMAAByNMAMAAByNMAMAAByNMAMAAByNMAMAABzNq7czAIDrpW6/qd4uIVeseaOLt0sA8jzOzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEfz83YBAIDcU7ffVG+XkCvWvNHF2yXAQTgzAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHI0wAwAAHC1Ph5mhQ4fKx8fHbalcubK3ywIAAHmIn7cLuJpq1app8eLFrsd+fnm+ZAAAcAPl+WTg5+enyMhIb5cBAADyqDwfZnbu3KmSJUsqKChIsbGxGjFihEqXLp0rfdftNzVX+vG2NW908XYJAAB4TZ4OM/Xr11dcXJwqVaqkhIQEDRs2THfccYc2bdqkQoUKZbpPSkqKUlJSXI+TkpJuVLkAAMAL8nSYadmypevfNWvWVP369VWmTBl99tln6tatW6b7jBgxQsOGDbtRJQIAAC/L01czXa5w4cKqWLGidu3alWWbgQMHKjEx0bUcOHDgBlYIAABuNEeFmdOnT2v37t0qUaJElm0CAwMVEhLitgAAgL+uPB1mXnjhBS1dulT79u3Tjz/+qAceeED58uVTp06dvF0aAADII/L0nJmDBw+qU6dOOn78uIoXL67bb79dK1euVPHixb1dGgAAyCPydJiZMWOGt0sAAAB5XJ7+mAkAAOBqCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDRCDMAAMDR/LxdAAAA16puv6neLiFXrHmji7dLcCTOzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEcjzAAAAEdzRJgZP368ypYtq6CgINWvX18//fSTt0sCAAB5RJ4PM59++qn69u2rIUOG6JdfflGtWrXUvHlzHTt2zNulAQCAPCDPh5kxY8boySef1OOPP66qVavqvffeU/78+fXhhx96uzQAAJAH5OkbTZ47d05r1qzRwIEDXet8fX3VrFkzrVixwouVAQDgfdxg84I8HWZ+//13paWlKSIiwm19RESEtm3bluk+KSkpSklJcT1OTEyUJCUlJWVom5byZy5W6z2Zje1KGLezMW7PMG5nY9ye+SuP++I6M7t6B5aHHTp0yCTZjz/+6La+X79+Vq9evUz3GTJkiEliYWFhYWFh+QssBw4cuGpeyNNnZooVK6Z8+fLp6NGjbuuPHj2qyMjITPcZOHCg+vbt63qcnp6uEydOqGjRovLx8bmu9V4uKSlJ0dHROnDggEJCQm7osb2JcTPuvwPGzbj/Drw5bjPTqVOnVLJkyau2zdNhJiAgQHXr1lV8fLzatGkj6UI4iY+PV8+ePTPdJzAwUIGBgW7rChcufJ0rvbKQkJC/1Q//RYz774Vx/70w7r8Xb407NDTUo3Z5OsxIUt++fdW1a1fdcsstqlevnt566y0lJyfr8ccf93ZpAAAgD8jzYaZDhw767bffNHjwYB05ckS1a9fW/PnzM0wKBgAAf095PsxIUs+ePbP8WCkvCwwM1JAhQzJ87PVXx7gZ998B42bcfwdOGbePmSfXPAEAAORNef4bgAEAAK6EMAMAAByNMAMAwN/AkiVL5OPjo5MnT3q7lFxHmMmG8ePHq2zZsgoKClL9+vX1008/XbH9q6++qgYNGih//vyZftdNXFycfHx8Ml28cVfwZcuWqVWrVipZsqR8fHw0e/Zst+2nT59Wz549FRUVpeDgYNeNP69k37596tatm2JiYhQcHKxy5cppyJAhOnfunKvNkiVL1Lp1a5UoUUIFChRQ7dq1NW3atOsxxEyNGDFCt956qwoVKqTw8HC1adNG27dvd2vz1FNPqVy5cgoODlbx4sXVunXrLG+pcZEn42rcuHGmr/99992X6+O8nCfjPnLkiB599FFFRkaqQIECuvnmmzVr1qwr9nv8+HG1aNFCJUuWVGBgoKKjo9WzZ88sv6Z9+fLl8vPzU+3atXNraFc0YcIE1axZ0/W9GbGxsfrmm29c2zN7TZ5++mmP+9+1a5cKFSp0xe+3mjFjhnx8fFzfn+UNI0eOlI+Pj3r37u1al5Ox79u3L9Of4ZUrV7rapKamavjw4SpXrpyCgoJUq1YtzZ8//7qM69ChQ+rcubOKFi2q4OBg1ahRQ6tXr3ZtNzMNHjxYJUqUUHBwsJo1a6adO3detd9nn31WdevWVWBgYKY/q548D5I0c+ZMVa5cWUFBQapRo4a+/vrrax4zCDMe+/TTT9W3b18NGTJEv/zyi2rVqqXmzZtfMXScO3dODz30kJ555plMt3fo0EEJCQluS/PmzdWoUSOFh4dfr6FkKTk5WbVq1dL48eMz3d63b1/Nnz9fH3/8sbZu3arevXurZ8+emjt3bpZ9btu2Tenp6Zo4caI2b96ssWPH6r333tP//d//udr8+OOPqlmzpmbNmqUNGzbo8ccfV5cuXTRv3rxcH2Nmli5dqh49emjlypVatGiRUlNTdc899yg5OdnVpm7dupoyZYq2bt2qBQsWyMx0zz33KC0tLct+PRnX559/7vb6b9q0Sfny5dNDDz10XccseTbuLl26aPv27Zo7d642btyotm3bqn379lq7dm2W/fr6+qp169aaO3euduzYobi4OC1evDjTN8WTJ0+qS5cuatq06XUZY2aioqI0cuRIrVmzRqtXr1aTJk3UunVrbd682dXmySefdHtdRo0a5VHfqamp6tSpk+64444s2+zbt08vvPDCFdtcbz///LMmTpyomjVrZtiW07EvXrzYbb+6deu6tg0aNEgTJ07UO++8oy1btujpp5/WAw88cMWfo5z4448/1LBhQ/n7++ubb77Rli1bNHr0aBUpUsTVZtSoUXr77bf13nvvadWqVSpQoICaN2+us2fPXrX/J554Qh06dLhimys9Dz/++KM6deqkbt26ae3atWrTpo3atGmjTZs25XzQuOBa75/0d1GvXj3r0aOH63FaWpqVLFnSRowYcdV9p0yZYqGhoVdtd+zYMfP397epU6deS6m5QpJ98cUXbuuqVatmw4cPd1t3880320svvZStvkeNGmUxMTFXbHPvvffa448/nq1+c8uxY8dMki1dujTLNuvXrzdJtmvXrmz1fbVxjR071goVKmSnT5/OVr+5IbNxFyhQIMPPY1hYmE2aNClbfY8bN86ioqIyrO/QoYMNGjTIhgwZYrVq1cpR3bmhSJEi9sEHH5iZWaNGjey5557LUT/9+/e3zp07Z/l//vz589agQQP74IMPrGvXrta6deucF51Dp06dsgoVKtiiRYsyjDUnY9+7d69JsrVr12bZpkSJEvaf//zHbV3btm3tkUceydaxrmbAgAF2++23Z7k9PT3dIiMj7Y033nCtO3nypAUGBtonn3zi0TGy+ln15Hlo37693XfffW7r6tevb0899ZRHxy5TpoyNHTvWbV2tWrVsyJAhZnbh9/akSZOsTZs2FhwcbOXLl7c5c+a42n733Xcmyf744w8zM0tOTrYWLVpYgwYN7I8//nCNYdasWda4cWMLDg62mjVrZrg/4v/+9z+rWrWqBQQEWJkyZezNN990bXvnnXesWrVqrsdffPGFSbIJEya41jVt2tT1vnHx+Zw6daqVKVPGQkJCrEOHDpaUlOTRc3IRZ2Y8cO7cOa1Zs0bNmjVzrfP19VWzZs20YsWKXDvO1KlTlT9/fj344IO51mduatCggebOnatDhw7JzPTdd99px44duueee7LVT2JiosLCwq65zfVy8U7rWR0/OTlZU6ZMUUxMjKKjo7Pd95XGNXnyZHXs2FEFChTIVr+5IbNxN2jQQJ9++qlOnDih9PR0zZgxQ2fPnlXjxo097vfw4cP6/PPP1ahRI7f1U6ZM0Z49ezRkyJBcqT8n0tLSNGPGDCUnJys2Nta1ftq0aSpWrJiqV6+ugQMH6syZM1ft69tvv9XMmTOzPLMpScOHD1d4eLi6deuWK/XnRI8ePXTfffe5/T67VE7GLkn333+/wsPDdfvtt2c4W5uSkqKgoCC3dcHBwfrhhx9yNogszJ07V7fccoseeughhYeHq06dOpo0aZJr+969e3XkyBG3sYeGhqp+/fq59rv8Ss/DihUrMjzvzZs3z9X3kWHDhql9+/basGGD7r33Xj3yyCM6ceJEhnYnT57U3XffrfT0dC1atMjtY9GXXnpJL7zwgtatW6eKFSuqU6dOOn/+vCRpzZo1at++vTp27KiNGzdq6NChevnllxUXFydJatSokbZs2aLffvtN0oUzwMWKFdOSJUskXTh7uWLFCrffIbt379bs2bM1b948zZs3T0uXLtXIkSOzN/BsRZ+/qZzcvftSnp6ZqVKlij3zzDM5LTNXKZMzM2fPnrUuXbqYJPPz87OAgAD76KOPstXvzp07LSQkxN5///0s23z66acWEBBgmzZtyknp1yQtLc3uu+8+a9iwYYZt48ePtwIFCpgkq1SpUrbPylxtXKtWrTJJtmrVqhzVfi2yGvcff/xh99xzj+s1DwkJsQULFnjUZ8eOHS04ONgkWatWrezPP/90bduxY4eFh4fb9u3bzSzrv3avlw0bNliBAgUsX758Fhoaal999ZVr28SJE23+/Pm2YcMG+/jjj61UqVL2wAMPXLG/33//3aKjo11ntTL7P//9999bqVKl7LfffjMz88qZmU8++cSqV6/uei0uPxOTk7H/9ttvNnr0aFu5cqX99NNPNmDAAPPx8XE7I9CpUyerWrWq7dixw9LS0mzhwoUWHBxsAQEBuTq+wMBACwwMtIEDB9ovv/xiEydOtKCgIIuLizMzs+XLl5skO3z4sNt+Dz30kLVv396jY2T1s+rJ8+Dv72/Tp09322/8+PEWHh7u0bE9OTMzaNAg17bTp0+bJPvmm2/M7P+dmdm6davVrFnT2rVrZykpKa72F8/MXDxLaWa2efNm1z5mZg8//LDdfffdbjX069fPqlatamYXzn4VLVrUZs6caWZmtWvXthEjRlhkZKSZmf3www/m7+9vycnJZnbh+cyfP7/bmZh+/fpZ/fr1PXpOLiLMeOBqYeapp56yAgUKuJbLeRJmfvzxR5Nkq1evzs3ScyyzMPPGG29YxYoVbe7cubZ+/Xp75513rGDBgrZo0SIzs6s+DwcPHrRy5cpZt27dsjzut99+a/nz5892SMotTz/9tJUpUybTW86fPHnSduzYYUuXLrVWrVrZzTff7HpTqFq1qmvcLVq0yLCvJ+Pq3r271ahRI/cGkw1Zjbtnz55Wr149W7x4sa1bt86GDh1qoaGhtmHDBjMza9GihWvcF3+ZXZSQkGBbt261OXPmWNWqVV1B/fz583bLLbe4nXa+0WEmJSXFdu7caatXr7YXX3zRihUrZps3b860bXx8vNtHipm91g888IANGDDAtc/l/+eTkpKsbNmy9vXXX7vW3egws3//fgsPD7f169e71l3tYyVPxp6ZRx991O3jnmPHjlnr1q3N19fX8uXLZxUrVrR//etfFhQUdO0Du4S/v7/Fxsa6revVq5fddtttZuZZmLnSz7RZ9n5WL38ebkSY+eyzz9y2h4SEuH7vXAwzUVFR1rZtWzt//rxb24th5qeffnKtO3HihNvHz3Xq1LGhQ4e67Td79mzz9/d39ffAAw9Yjx497I8//rCAgABLTEy0IkWK2NatW+3VV1+1Bg0auPYdMmRIhud5zJgxV52KcDnCjAdSUlIsX758Gd7cu3TpYvfff78dPXrUdu7c6Vou50mYeeKJJ6x27dq5WPW1uTzMnDlzxvz9/W3evHlu7bp162bNmzc3M7vi83Do0CGrUKGCPfroo5aWlpbpMZcsWWIFChSwiRMn5u5gPNSjRw+LioqyPXv2XLVtSkqK5c+f3/WLad++fa5xHzx40K2tJ+M6ffq0hYSE2FtvvXVtg8iBrMa9a9cuk5ThTFLTpk1dn/EfPHjQNe59+/ZleYzvv//e9Sbyxx9/mCTLly+fa/Hx8XGti4+Pz/1BXkXTpk2te/fumW67+Nft/PnzzSzz1zo0NNRtPL6+vq7xTJ482dauXZvpmH18fCxfvnzZPsuXExfnLlxagyRXDZe/sXk69sz85z//cf0lfqk///zTDh48aOnp6da/f/9Mw8K1KF26dIY/lt59910rWbKkmZnt3r0703ktd955pz377LNmdvWf6eyEmcufh+jo6AxhZPDgwVazZk2P+ouJibExY8a4ratatapbmLn8fSo0NNSmTJliZv8vzDz11FNWrFgx1x8lF2U27+fi/9fvvvvOzDwLM+PGjbNq1arZ3LlzXWdYWrdubRMmTLB77rnHBg4c6No3s+dz7NixVqZMGY+ek4sccW8mbwsICFDdunUVHx/vupQyPT1d8fHx6tmzp8LDw6/p6qPTp0/rs88+04gRI3Kp4tyXmpqq1NRU+fq6T7PKly+f0tPTJSnL5+HQoUO66667XFcEXd6HdOEy5n/84x96/fXX1b179+sziCyYmXr16qUvvvhCS5YsUUxMjEf7mJlSUlIkSWXKlMm0nafjmjlzplJSUtS5c+ecDSIHrjbui3MlrvSalypVyqNjXWyfkpKiiIgIbdy40W37u+++q2+//Vb/+9//PHr+c1t6errrtbzcunXrJEklSpSQlPlrvWLFCrcr2+bMmaPXX39dP/74o0qVKqXg4OAMYx40aJBOnTqlcePGZXvuVU40bdo0Qw2PP/64KleurAEDBihfvnwZ9vFk7JlZt26da59LBQUFqVSpUkpNTdWsWbPUvn37bI7iyho2bJjh6wV27NjhqjsmJkaRkZGKj493XV6dlJSkVatWua469fRn2hOXPw+xsbGKj493uxx+0aJFbvO1rqR48eJKSEhwPU5KStLevXuzXdfIkSNVsGBBNW3aVEuWLFHVqlU93rdKlSpavny527rly5erYsWKrp+hRo0aqXfv3po5c6Zrbkzjxo21ePFiLV++XM8//3y2a76qbEWfv7EZM2ZYYGCgxcXF2ZYtW6x79+5WuHBhO3LkSJb7/Prrr7Z27VobNmyYFSxY0NauXWtr1661U6dOubX74IMPLCgoyDXD3FtOnTrlqlGSjRkzxtauXWu//vqrmV04JV2tWjX77rvvbM+ePTZlyhQLCgqyd999N8s+Dx48aOXLl7emTZvawYMHLSEhwbVcdPEjmIEDB7ptP378+HUfs5nZM888Y6GhobZkyRK34585c8bMLvw199prr9nq1avt119/teXLl1urVq0sLCzMjh49mmW/2RnX7bffbh06dLhuY8zM1cZ97tw5K1++vN1xxx22atUq27Vrl7355pvm4+PjNsfkcl999ZV9+OGHtnHjRtu7d6/NmzfPqlSpkuk8pItu5MdML774oi1dutT27t1rGzZssBdffNF8fHxs4cKFtmvXLhs+fLitXr3a9u7da3PmzLGbbrrJ7rzzzmwdw5Ozsd66mulSl37MlNOxx8XF2fTp023r1q2ujxF8fX3tww8/dLVZuXKlzZo1y3bv3m3Lli2zJk2aWExMTK7/zvvpp5/Mz8/PXn31Vdu5c6dNmzbN8ufPbx9//LGrzciRI61w4cI2Z84c27Bhg7Vu3dpiYmLc5nRlZufOnbZ27Vp76qmnrGLFiq7flRfnnHjyPCxfvtz8/PzszTfftK1bt9qQIUPM39/fNm7c6NH4XnzxRYuMjLRly5bZhg0brE2bNlawYMFsn5m5+Lz37t3bIiIiXPNhPDkzs2bNGvP19bXhw4fb9u3bLS4uzoKDg13HMLswbyYsLMzy5cvnmq+zdu1ay5cvn/n5+bldrZlbZ2YIM9nwzjvvWOnSpS0gIMDq1atnK1euvGL7rl27mqQMy8UfiotiY2Pt4Ycfvo6Ve+biD/rlS9euXc3swhyIxx57zEqWLGlBQUFWqVIlGz16tKWnp2fZ55QpUzLt89IcndXz1KhRo+s84guyqu/if85Dhw5Zy5YtLTw83Pz9/S0qKsoefvhh27Zt2xX79XRc27ZtM0m2cOHC6zTCzF1t3GYXJuq2bdvWwsPDLX/+/FazZs2rfnXAt99+a7GxsRYaGmpBQUFWoUIFGzBgwBXfuG5kmHniiSesTJkyFhAQYMWLF7emTZu6nvv9+/fbnXfeaWFhYRYYGGjly5e3fv36WWJiYraO4cQwk9Oxx8XFWZUqVSx//vwWEhJi9erVc03+vGjJkiVWpUoVCwwMtKJFi9qjjz5qhw4dui5j+vLLL6169eoWGBholStXznCxQXp6ur388ssWERFhgYGB1rRpU9dE9Ctp1KhRpv9f9u7da2aePQ9mZp999plVrFjRAgICrFq1alf8w+ByiYmJ1qFDBwsJCbHo6GiLi4vLMGcmO2HG7MKcohIlStj27ds9CjNm/+/SbH9/fytdurTbpe4XtW7d2vz8/Fx/vKelpVmRIkVc85cuyq0ww12zAQCAo/E9MwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwAAwNEIMwDgRXFxcSpcuLC3y/DIvn375OPj47pnE5BXEGaAXNC4cWO3m8dddOkb1ZkzZzRw4ECVK1dOQUFBKl68uBo1aqQ5c+a49ePj45Nhefrpp11tLl0fEhKiW2+91a0PT/35558KCwtTsWLFsrzJ4qxZs9SkSRMVKVJEwcHBqlSpkp544gmtXbvWbYyZ1RwUFORxLUeOHNFzzz2n8uXLKygoSBEREWrYsKEmTJjguuGlJJUtW9bVf/78+VWjRg198MEHGfpLS0vT2LFjVaNGDQUFBalIkSJq2bJlhhvkDR061HXDwUtd/qa9ZMkSt7FFRESoXbt22rNnj8djBHD9EGaAG+Tpp5/W559/rnfeeUfbtm3T/Pnz9eCDD+r48eNu7Z588kklJCS4LaNGjXJrM2XKFCUkJGj16tVq2LChHnzwwQx3RL6aWbNmqVq1aqpcubJmz56dYfuAAQPUoUMH1a5dW3PnztX27ds1ffp03XTTTRo4cKBb25CQkAw1//rrrx7VsWfPHtWpU0cLFy7Ua6+9prVr12rFihXq37+/5s2bp8WLF7u1Hz58uBISErRp0yZ17txZTz75pL755hvXdjNTx44dNXz4cD333HPaunWrlixZoujoaDVu3DjTsXpq+/btOnz4sGbOnKnNmzerVatWbnfLBuAl2bqTE4BMXXrDvktdesPB0NBQi4uLy1E/l9JlN5NLSkoySTZu3Lhs1dy4cWN77733bMKECXb33Xe7bVuxYsUV+7z05qKe3FTxSpo3b25RUVFud9LN6lhlypSxsWPHum0PCwuzPn36uB7PmDHDJNncuXMz9NW2bVsrWrSo61hZ3eDy8hvuZXaDvmnTppmkq95w1OzCzfq6d+9u4eHhFhgYaNWqVbMvv/zSzDJ//mbPnm116tSxwMBAi4mJsaFDh1pqaqpr++jRo6169eqWP39+i4qKsmeeecZ1Q79L+5w/f75VrlzZChQoYM2bN7fDhw+7HWfSpElWuXJlCwwMtEqVKtn48ePdtq9atcpq165tgYGBVrduXfv8888z3IgQyAs4MwPcIJGRkfr666916tSpXOvz/Pnzmjx5siQpICDA4/12796tFStWqH379mrfvr2+//57tzMpn3zyiQoWLKh//etfme7v4+NzbYX//44fP66FCxeqR48eKlCgQLaOlZ6erlmzZumPP/5wG/v06dNVsWJFtWrVKsM+zz//vI4fP65FixZdc+3BwcGSpHPnzl2xXXp6uusjro8//lhbtmzRyJEjlS9fvkzbf//99+rSpYuee+45bdmyRRMnTlRcXJxeffVVVxtfX1+9/fbb2rx5sz766CN9++236t+/v1s/Z86c0Ztvvqn//ve/WrZsmfbv368XXnjBtX3atGkaPHiwXn31VW3dulWvvfaaXn75ZX300UeSpNOnT+sf//iHqlatqjVr1mjo0KFu+wN5irfTFPBX4MmZmaVLl1pUVJT5+/vbLbfcYr1797YffvghQz/+/v5WoEABt+Xjjz92tZFkQUFBVqBAAfP19TVJVrZsWTt+/LjH9f7f//2ftWnTxvW4devWNmTIENfjFi1aWM2aNd32GT16tFtNJ0+edI1RUoaaW7RocdU6Vq5caZLs888/d1tftGhRVz/9+/d3rS9TpowFBARYgQIFzM/PzyRZWFiY7dy509WmcuXK1rp160yPd+LECZNkr7/+upnl/MzM4cOHrUGDBlaqVClLSUm54hgXLFhgvr6+tn379ky3X35mpmnTpvbaa6+5tfnvf/9rJUqUyPIYM2fOtKJFi7r1Kcl27drlWjd+/HiLiIhwPS5XrpxNnz7drZ9///vfFhsba2ZmEydOtKJFi9qff/7p2j5hwgTOzCBP8vNWiAL+bu68807t2bNHK1eu1I8//qj4+HiNGzdOw4YN08svv+xq98gjj+ill15y2zciIsLt8dixY9WsWTPt2bNHffr00dtvv62wsDCP6khLS9NHH32kcePGudZ17txZL7zwggYPHixf38xP2D7xxBO6//77tWrVKnXu3Flm5tpWqFAh/fLLL27tL565yImffvpJ6enpeuSRRzJMTu7Xr58ee+wxJSQkqF+/fvrXv/6l8uXLu7W5tLbcFBUVJTPTmTNnVKtWLc2aNeuqZ8TWrVunqKgoVaxY0aNjrF+/XsuXL3c7E5OWlqazZ8/qzJkzyp8/vxYvXqwRI0Zo27ZtSkpK0vnz5922S1L+/PlVrlw5Vx8lSpTQsWPHJEnJycnavXu3unXrpieffNLV5vz58woNDZUkbd26VTVr1nSbyB0bG+vRGIAbjTAD5IKQkBAlJiZmWH/y5EnXm4Mk+fv764477tAdd9yhAQMG6JVXXtHw4cM1YMAA15tiaGhohjfny0VGRqp8+fIqX768pkyZonvvvVdbtmxReHj4VWtdsGCBDh06pA4dOritT0tLU3x8vO6++25VqFBBP/zwg1JTU+Xv7y9JKly4sAoXLqyDBw9m6NPX1/eqNWemfPny8vHx0fbt293W33TTTZIyD0TFihVzjX3mzJmqUaOGbrnlFlWtWlWSVLFiRW3dujXT411cfzFYXOl1k+T22kkXPgIKCQlReHi4ChUq5NEYsxvqTp8+rWHDhqlt27YZtgUFBWnfvn36xz/+oWeeeUavvvqqwsLC9MMPP6hbt246d+6cK8xcfN0u8vHxcYW806dPS5ImTZqk+vXru7XL6uMvIC9jzgyQCypVqpThzIQk/fLLL1f8i7xq1aquv6pzql69eqpbt67bX/JXMnnyZHXs2FHr1q1zWzp27Oiaf9OpUyedPn1a7777bo7r8kTRokV199136z//+Y+Sk5OzvX90dLQ6dOjgdnVVx44dtXPnTn355ZcZ2o8ePdp1TOnC63bw4EEdPXrUrd0vv/yioKAglS5d2m19TEyMypUr53GQkaSaNWvq4MGD2rFjh0ftb775Zm3fvt0V2C5dfH19tWbNGqWnp2v06NG67bbbVLFiRR0+fNjjeqQLZ/pKliypPXv2ZDhGTEyMJKlKlSrasGGD28/mypUrs3Uc4Ibx7qdcwF/D7t27LSgoyHr16mXr16+3bdu22ejRo83Pz8+++eYbM7swH+a9996z1atX2969e+2rr76ySpUqWZMmTVz9NGrUyJ588klLSEhwW06cOOFqo8uuZjIz+/rrry0wMNAOHjx4xTqPHTtm/v7+rpoy6+Pi3Jvnn3/e8uXLZ3369LHvv//e9u3bZytWrLDOnTubj4+PJSYmmtmF+RkhISEZak5ISLC0tLSrPne7du2yiIgIq1y5ss2YMcO2bNli27Zts//+978WERFhffv2dbXN7GqmzZs3m4+Pj/38889mduHqpwceeMCKFCliH3zwge3du9fWr19v3bt3Nz8/P7fnLjU11apVq2Z33XWXLV++3Hbv3m0zZ860EiVK2IABA1ztMruaKTsaN25s1atXt4ULF9qePXvs66+/dr0Gl8+ZmT9/vvn5+dnQoUNt06ZNtmXLFvvkk0/spZdeMjOzdevWmSR76623bPfu3TZ16lQrVaqUW32ZXSH1xRdf2KW/8idNmmTBwcE2btw42759u23YsME+/PBDGz16tJmZnTp1yooVK2adO3e2zZs321dffWXly5dnzgzyJMIMkEt++uknu/vuu6148eIWGhpq9evXd3vjfO211yw2NtbCwsIsKCjIbrrpJnv22Wft999/d7Vp1KiRScqwNG/e3NUmszCTnp5ulStXtmeeeeaKNb755ptWuHBhO3fuXIZtKSkpVrhwYbfLsT/99FNr3LixhYaGmr+/v0VFRdnDDz9sK1eudLW5ONk0syUhIcGj5+7w4cPWs2dPi4mJMX9/fytYsKDVq1fP3njjDUtOTna1yyzMmF24vLtly5aux6mpqfbGG29YtWrVLCAgwEJCQqx58+YZJlybmR06dMi6du1qpUuXtuDgYKtataqNHDnS7Tm61jBz/Phxe/zxx61o0aIWFBRk1atXt3nz5plZ5sFj/vz51qBBAwsODraQkBCrV6+evf/++67tY8aMsRIlSlhwcLA1b97cpk6dmu0wY3bh8vLatWtbQECAFSlSxO688063ydgrVqywWrVqWUBAgNWuXdtmzZpFmEGe5GN2nWbKAQAA3ADMmQEAAI5GmAH+YqpVq6aCBQtmukybNu2G1rJ///4saylYsKD2799/Q+u5HqZNm5bl+KpVq+bt8oC/BT5mAv5ifv31V6Wmpma6LSIiIltX4lyr8+fPa9++fVluL1u2rPz8nP0NEadOncpwNdRF/v7+KlOmzA2uCPj7IcwAAABH42MmAADgaIQZAADgaIQZAADgaIQZAADgaIQZAADgaIQZAADgaIQZAADgaIQZAADgaP8fMCDxM2/+8eEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat2, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9cdb90-2e47-4d20-95b7-055b5227e0f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "01a11820-4951-43be-bf89-e9d9e21aa08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmcat3 = df[df['bgmm2_clusters']==3].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "9a61eaeb-4e22-4904-ad0d-d6366c7e1a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmlistcat3 =bgmmcat3['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "62a309dc-4884-46b5-bd31-f5a43565ce18",
   "metadata": {},
   "outputs": [],
   "source": [
    "subbgmmcat3 = users_cleaned[users_cleaned['USER_ID'].isin(bgmmlistcat3)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "97499b1e-76d6-4648-94f2-c17f9351681d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "      <td>730018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.450603</td>\n",
       "      <td>0.150691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092580</td>\n",
       "      <td>0.676889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.264755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.706753</td>\n",
       "      <td>0.419012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153702</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344296</td>\n",
       "      <td>0.405041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.858093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count       730018.000000  730018.000000   730018.000000   730018.000000   \n",
       "mean             1.450603       0.150691        0.000000        0.024210   \n",
       "std              1.706753       0.419012        0.000000        0.153702   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              1.000000       0.000000        0.000000        0.000000   \n",
       "75%              2.000000       0.000000        0.000000        0.000000   \n",
       "max             11.000000       3.000000        0.000000        1.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count  730018.000000    730018.000000               730018.000000   \n",
       "mean        0.000000         0.000000                    0.046962   \n",
       "std         0.000000         0.000000                    0.212971   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max         0.000000         0.000000                    2.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                 730018.000000          730018.000000   \n",
       "mean                       0.000000               0.092580   \n",
       "std                        0.000000               0.344296   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        0.000000               0.000000   \n",
       "max                        0.000000               3.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count         730018.000000      730018.000000    730018.000000   \n",
       "mean               0.676889           0.000000         0.000000   \n",
       "std                0.405041           0.000000         0.000000   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.333333           0.000000         0.000000   \n",
       "50%                1.000000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000         0.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count    730018.000000  \n",
       "mean          2.264755  \n",
       "std           1.858093  \n",
       "min           1.000000  \n",
       "25%           1.000000  \n",
       "50%           1.000000  \n",
       "75%           3.000000  \n",
       "max          17.000000  "
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat3[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "0b091fdf-1e9b-4f9d-b415-ef3de219e247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hWZalyy67TNu3b1dkZKQ9FxQUpI4dOyowMNB4kwAAAE3deYWqLl26SJK8Xm+jNAMAANBcNejuP0n64osvtHHjRh07dqxGyMrIyPjZjQEAADQnDQpVy5cv15QpU9ShQwdFR0fL4XDYcw6Hg1AFAABanAaFqmeeeUbPPvus0tPTTfcDAADQLDXoOVXfffedbr/9dtO9AAAANFsNClW33367Pv74Y9O9AAAANFsN+vqve/fueuKJJ/TJJ5+od+/eat26tc/8ww8/bKQ5AACA5qJBoerll19WWFiY8vLylJeX5zPncDgIVQAAoMVpUKg6dOiQ6T4AAACatQZdUwUAAABfDTpTdd99951z/tVXX21QMwAAAM1Vg0LVd9995/P+9OnT2rdvn0pKSmr9Q8sAAAAXuwaFqvfee6/GmNfr1ZQpU/TLX/7yZzcFAADQ3Bi7piogIEBpaWmaN2+eqVUCAAA0G0YvVP/yyy915swZk6sEAABoFhr09V9aWprPe8uydPToUWVnZyspKclIYwAAAM1Jg0LV7t27fd4HBAQoMjJSL774Yp13BgIAAFyMGhSqNm7caLoPAACAZq1BoapacXGxDh48KEnq0aOHIiMjjTQFAADQ3DToQvXy8nLdd9996tSpkwYPHqzBgwcrJiZGEydO1IkTJ0z3CAAA0OQ1KFSlpaUpLy9PH374oUpKSlRSUqIPPvhAeXl5euSRR0z3CAAA0OQ16Ou/v/71r/rLX/6iG264wR67+eabFRoaqjvuuENLly411R8AAECz0KAzVSdOnFBUVFSN8Y4dO/L1HwAAaJEaFKrcbrdmzZqlU6dO2WMnT57UU089Jbfbbaw5AACA5qJBX//Nnz9fI0eOVOfOndWnTx9J0j/+8Q8FBwfr448/NtogAABAc9CgUNW7d2998cUXevPNN3XgwAFJ0vjx43XXXXcpNDTUaIMAAADNQYNCVWZmpqKiovTAAw/4jL/66qsqLi5Wenq6keYAAACaiwZdU/XSSy+pZ8+eNcavvPJKLVu27Gc3BQAA0Nw0KFR5PB516tSpxnhkZKSOHj36s5sCAABobhoUqmJjY7Vly5Ya41u2bFFMTMzPbgoAAKC5adA1VQ888IBSUlJ0+vRpDRs2TJKUm5ur6dOn80R1AADQIjUoVE2bNk3ffPONHnzwQVVWVkqSQkJClJ6erhkzZhhtEAAAoDloUKhyOBx64YUX9MQTT+jzzz9XaGioLr/8cgUHB5vuDwAAoFloUKiqFhYWpgEDBpjqBQAAoNlq0IXqAAAA8EWoAgAAMIBQBQAAYAChCgAAwAC/hqrMzEwNGDBA7dq1U8eOHTV69GgdPHjQp+bUqVNKTk5W+/btFRYWpjFjxqioqMinprCwUKNGjVKbNm3UsWNHTZs2TWfOnPGp2bRpk/r166fg4GB1795dK1asqNHP4sWL1bVrV4WEhGjgwIHavn37efcCAABaJr+Gqry8PCUnJ+uTTz7R+vXrdfr0aY0YMULl5eV2TWpqqj788EO98847ysvL05EjR3TbbbfZ81VVVRo1apQqKyu1detWvf7661qxYoUyMjLsmkOHDmnUqFEaOnSo9uzZo5SUFN1///1at26dXbN69WqlpaVp1qxZ2rVrl/r06aPExEQdO3as3r0AAICWy2FZluXvJqoVFxerY8eOysvL0+DBg1VaWqrIyEitXLlSY8eOlSQdOHBAV1xxhfLz83Xttddq7dq1uuWWW3TkyBFFRUVJkpYtW6b09HQVFxcrKChI6enpys7O1r59++xt3XnnnSopKVFOTo4kaeDAgRowYIAWLVokSfJ6vYqNjdXUqVP12GOP1auXupSVlcnlcqm0tFROp7NB+yh+2hsNWg4Xr4K5d/u7BY5L+GgKxyRgUn1/fzepa6pKS0slSREREZKkgoICnT59WgkJCXZNz549demllyo/P1+SlJ+fr969e9uBSpISExNVVlam/fv32zVnr6O6pnodlZWVKigo8KkJCAhQQkKCXVOfXn6soqJCZWVlPi8AAHBxajKhyuv1KiUlRb/+9a/Vq1cvSZLH41FQUJDCw8N9aqOiouTxeOyaswNV9Xz13LlqysrKdPLkSR0/flxVVVW11py9jrp6+bHMzEy5XC77FRsbW8+9AQAAmpsmE6qSk5O1b98+rVq1yt+tGDNjxgyVlpbar8OHD/u7JQAA0Eh+1p+pMeWhhx7SmjVrtHnzZnXu3Nkej46OVmVlpUpKSnzOEBUVFSk6Otqu+fFdetV35J1d8+O79IqKiuR0OhUaGqrAwEAFBgbWWnP2Ourq5ceCg4P5e4gAALQQfj1TZVmWHnroIb333nvasGGDunXr5jMfHx+v1q1bKzc31x47ePCgCgsL5Xa7JUlut1t79+71uUtv/fr1cjqdiouLs2vOXkd1TfU6goKCFB8f71Pj9XqVm5tr19SnFwAA0HL59UxVcnKyVq5cqQ8++EDt2rWzr01yuVwKDQ2Vy+XSxIkTlZaWpoiICDmdTk2dOlVut9u+227EiBGKi4vThAkTNGfOHHk8Hs2cOVPJycn2WaLJkydr0aJFmj59uu677z5t2LBBb7/9trKzs+1e0tLSlJSUpP79++uaa67R/PnzVV5ernvvvdfuqa5eAABAy+XXULV06VJJ0g033OAz/tprr+mee+6RJM2bN08BAQEaM2aMKioqlJiYqCVLlti1gYGBWrNmjaZMmSK32622bdsqKSlJs2fPtmu6deum7OxspaamKisrS507d9Yrr7yixMREu2bcuHEqLi5WRkaGPB6P+vbtq5ycHJ+L1+vqBQAAtFxN6jlVFzueU4XG0BSeCcRxibM1hWMSMKlZPqcKAACguSJUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwwK+havPmzbr11lsVExMjh8Oh999/32fesixlZGSoU6dOCg0NVUJCgr744gufmm+//VZ33XWXnE6nwsPDNXHiRP3www8+NZ9++qmuv/56hYSEKDY2VnPmzKnRyzvvvKOePXsqJCREvXv31kcffXTevQAAgJbLr6GqvLxcffr00eLFi2udnzNnjhYsWKBly5Zp27Ztatu2rRITE3Xq1Cm75q677tL+/fu1fv16rVmzRps3b9akSZPs+bKyMo0YMUJdunRRQUGB5s6dqyeffFIvv/yyXbN161aNHz9eEydO1O7duzV69GiNHj1a+/btO69eAABAy+WwLMvydxOS5HA49N5772n06NGS/n1mKCYmRo888ogeffRRSVJpaamioqK0YsUK3Xnnnfr8888VFxenHTt2qH///pKknJwc3Xzzzfrf//1fxcTEaOnSpfrjH/8oj8ejoKAgSdJjjz2m999/XwcOHJAkjRs3TuXl5VqzZo3dz7XXXqu+fftq2bJl9eqlNhUVFaqoqLDfl5WVKTY2VqWlpXI6nQ3aT/HT3mjQcrh4Fcy9298tcFzCR1M4JgGTysrK5HK56vz93WSvqTp06JA8Ho8SEhLsMZfLpYEDByo/P1+SlJ+fr/DwcDtQSVJCQoICAgK0bds2u2bw4MF2oJKkxMREHTx4UN99951dc/Z2qmuqt1OfXmqTmZkpl8tlv2JjYxu6OwAAQBPXZEOVx+ORJEVFRfmMR0VF2XMej0cdO3b0mW/VqpUiIiJ8ampbx9nb+Kmas+fr6qU2M2bMUGlpqf06fPhwHT81AABorlr5u4GLWXBwsIKDg/3dBgAAuACa7Jmq6OhoSVJRUZHPeFFRkT0XHR2tY8eO+cyfOXNG3377rU9Nbes4exs/VXP2fF29AACAlq3Jhqpu3bopOjpaubm59lhZWZm2bdsmt9stSXK73SopKVFBQYFds2HDBnm9Xg0cONCu2bx5s06fPm3XrF+/Xj169NAll1xi15y9neqa6u3UpxcAANCy+TVU/fDDD9qzZ4/27Nkj6d8XhO/Zs0eFhYVyOBxKSUnRM888o7/97W/au3ev7r77bsXExNh3CF5xxRUaOXKkHnjgAW3fvl1btmzRQw89pDvvvFMxMTGSpN/97ncKCgrSxIkTtX//fq1evVpZWVlKS0uz+/jDH/6gnJwcvfjiizpw4ICefPJJ7dy5Uw899JAk1asXAADQsvn1mqqdO3dq6NCh9vvqoJOUlKQVK1Zo+vTpKi8v16RJk1RSUqLrrrtOOTk5CgkJsZd588039dBDD+nGG29UQECAxowZowULFtjzLpdLH3/8sZKTkxUfH68OHTooIyPD51lWgwYN0sqVKzVz5kw9/vjjuvzyy/X++++rV69edk19egEAAC1Xk3lOVUtQ3+dcnAvPA8KPNYVnAnFc4mxN4ZgETGr2z6kCAABoTghVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAwgVAEAABhAqAIAADCAUAUAAGAAoQoAAMAAQhUAAIABhCoAAAADCFUAAAAGEKoAAAAMIFQBAAAYQKgCAAAwgFAFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQAAAAYQqgAAAAxo5e8GAAAwLX7aG/5uAU1Iwdy7L8h2OFMFAABgAKEKAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUnafFixera9euCgkJ0cCBA7V9+3Z/twQAAJoAQtV5WL16tdLS0jRr1izt2rVLffr0UWJioo4dO+bv1gAAgJ8Rqs7Dn/70Jz3wwAO69957FRcXp2XLlqlNmzZ69dVX/d0aAADwM56oXk+VlZUqKCjQjBkz7LGAgAAlJCQoPz+/1mUqKipUUVFhvy8tLZUklZWVNbiPqoqTDV4WF6efczyZwnGJs3FMoqn5ucdk9fKWZZ2zjlBVT8ePH1dVVZWioqJ8xqOionTgwIFal8nMzNRTTz1VYzw2NrZRekTL5Fo42d8tAD44JtHUmDomv//+e7lcrp+cJ1Q1ohkzZigtLc1+7/V69e2336p9+/ZyOBx+7Kz5KysrU2xsrA4fPiyn0+nvdgCOSTQ5HJPmWJal77//XjExMeesI1TVU4cOHRQYGKiioiKf8aKiIkVHR9e6THBwsIKDg33GwsPDG6vFFsnpdPIfCzQpHJNoajgmzTjXGapqXKheT0FBQYqPj1dubq495vV6lZubK7fb7cfOAABAU8CZqvOQlpampKQk9e/fX9dcc43mz5+v8vJy3Xvvvf5uDQAA+Bmh6jyMGzdOxcXFysjIkMfjUd++fZWTk1Pj4nU0vuDgYM2aNavG16uAv3BMoqnhmLzwHFZd9wcCAACgTlxTBQAAYAChCgAAwABCFQAAgAGEKjQqy7I0adIkRUREyOFwaM+ePX7p4+uvv/br9tFy3XPPPRo9erS/2wBwAXD3HxpVTk6OVqxYoU2bNumyyy5Thw4d/N0SAACNglCFRvXll1+qU6dOGjRokL9bAQCgUfH1HxrNPffco6lTp6qwsFAOh0Ndu3aV1+tVZmamunXrptDQUPXp00d/+ctf7GU2bdokh8OhdevW6eqrr1ZoaKiGDRumY8eOae3atbriiivkdDr1u9/9TidOnLCXy8nJ0XXXXafw8HC1b99et9xyi7788stz9rdv3z7ddNNNCgsLU1RUlCZMmKDjx4832v5A03fDDTdo6tSpSklJ0SWXXKKoqCgtX77cfshvu3bt1L17d61du1aSVFVVpYkTJ9rHc48ePZSVlXXObdT1GQDQfBGq0GiysrI0e/Zsde7cWUePHtWOHTuUmZmpN954Q8uWLdP+/fuVmpqq//iP/1BeXp7Psk8++aQWLVqkrVu36vDhw7rjjjs0f/58rVy5UtnZ2fr444+1cOFCu768vFxpaWnauXOncnNzFRAQoN/+9rfyer219lZSUqJhw4bp6quv1s6dO5WTk6OioiLdcccdjbpP0PS9/vrr6tChg7Zv366pU6dqypQpuv322zVo0CDt2rVLI0aM0IQJE3TixAl5vV517txZ77zzjj777DNlZGTo8ccf19tvv/2T66/vZwBAM2QBjWjevHlWly5dLMuyrFOnTllt2rSxtm7d6lMzceJEa/z48ZZlWdbGjRstSdZ//dd/2fOZmZmWJOvLL7+0x37/+99biYmJP7nd4uJiS5K1d+9ey7Is69ChQ5Yka/fu3ZZlWdbTTz9tjRgxwmeZw4cPW5KsgwcPNvjnRfM2ZMgQ67rrrrPfnzlzxmrbtq01YcIEe+zo0aOWJCs/P7/WdSQnJ1tjxoyx3yclJVm/+c1vLMuq32cAQPPFNVW4YP71r3/pxIkTGj58uM94ZWWlrr76ap+xq666yv53VFSU2rRpo8suu8xnbPv27fb7L774QhkZGdq2bZuOHz9un6EqLCxUr169avTyj3/8Qxs3blRYWFiNuS+//FK/+tWvGvZDotk7+9gLDAxU+/bt1bt3b3us+s9SHTt2TJK0ePFivfrqqyosLNTJkydVWVmpvn371rru8/kMAGh+CFW4YH744QdJUnZ2tn7xi1/4zP34b1O1bt3a/rfD4fB5Xz129ld7t956q7p06aLly5crJiZGXq9XvXr1UmVl5U/2cuutt+qFF16oMdepU6fz+8FwUantWPvx8Sj9+9qoVatW6dFHH9WLL74ot9utdu3aae7cudq2bVut6z6fzwCA5odQhQsmLi5OwcHBKiws1JAhQ4yt95tvvtHBgwe1fPlyXX/99ZKkv//97+dcpl+/fvrrX/+qrl27qlUrPgZomC1btmjQoEF68MEH7bFz3SDRWJ8BAE0Dv01wwbRr106PPvqoUlNT5fV6dd1116m0tFRbtmyR0+lUUlJSg9Z7ySWXqH379nr55ZfVqVMnFRYW6rHHHjvnMsnJyVq+fLnGjx+v6dOnKyIiQv/617+0atUqvfLKKwoMDGxQL2hZLr/8cr3xxhtat26dunXrpv/8z//Ujh071K1bt1rrG+szAKBpIFThgnr66acVGRmpzMxMffXVVwoPD1e/fv30+OOPN3idAQEBWrVqlR5++GH16tVLPXr00IIFC3TDDTf85DIxMTHasmWL0tPTNWLECFVUVKhLly4aOXKkAgK4KRb18/vf/167d+/WuHHj5HA4NH78eD344IP2Ixdq0xifAQBNg8OyLMvfTQAAADR3/F9yAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMIBQBQAAYAChCgAAwABCFQAAgAGEKgAAAAMIVQD87oYbblBKSkqN8RUrVig8PFySdOLECc2YMUO//OUvFRISosjISA0ZMkQffPCBz3ocDkeN1+TJk+2as8edTqcGDBjgs476qKys1Ny5c9WvXz+1bdtWLpdLffr00cyZM3XkyBG77p577qm1n5EjR9o1Xbt2lcPh0CeffOKzjZSUFJ8/tfTkk0/ay7dq1UodOnTQ4MGDNX/+fFVUVNTYnxdiPwDwRagC0CxMnjxZ7777rhYuXKgDBw4oJydHY8eO1TfffONT98ADD+jo0aM+rzlz5vjUvPbaazp69Kh27typX//61xo7dqz27t1brz4qKio0fPhwPffcc7rnnnu0efNm7d27VwsWLNDx48e1cOFCn/qRI0fW6Oett97yqQkJCVF6enqd277yyit19OhRFRYWauPGjbr99tuVmZmpQYMG6fvvv7+g+wFATfxBZQDNwt/+9jdlZWXp5ptvlvTvMzzx8fE16tq0aaPo6Ohzris8PFzR0dGKjo7W008/raysLG3cuFG9e/eus4958+bp73//u3bu3Kmrr77aHr/00ks1ZMgQ/fjPqQYHB9fZz6RJk7Rs2TJ99NFH9s9Xm1atWtnriomJUe/evTV8+HD16dNHL7zwgp555hm7trH3A4CaOFMFoFmIjo7WRx99VOOMzM9x5swZ/fnPf5YkBQUF1WuZt956S8OHD/cJVGdzOBzn3Ue3bt00efJkzZgxQ16v97yW7dmzp2666Sa9++67573dag3ZDwBqIlQBaBZefvllbd26Ve3bt9eAAQOUmpqqLVu21KhbsmSJwsLCfF5vvvmmT8348eMVFham4OBgpaamqmvXrrrjjjvq1cc///lP9ejRw2fst7/9rb2tQYMG+cytWbOmRj/PPfdcjfXOnDlThw4dqtFrffTs2VNff/21z1hj7wcANfH1H4BmYfDgwfrqq6/0ySefaOvWrcrNzVVWVpaeeuopPfHEE3bdXXfdpT/+8Y8+y0ZFRfm8nzdvnhISEvTVV18pNTVVCxYsUERERIN7W7JkicrLy7VgwQJt3rzZZ27o0KFaunSpz1ht24qMjNSjjz6qjIwMjRs37ry2b1lWjTNk/tgPQEtHqALgd06nU6WlpTXGS0pK5HK57PetW7fW9ddfr+uvv17p6el65plnNHv2bKWnp9tfW7lcLnXv3v2c24uOjlb37t3VvXt3vfbaa7r55pv12WefqWPHjnX2evnll+vgwYM+Y506dZJUe1hq27Ztnf1US0tL05IlS7RkyZJ61Vf7/PPP1a1bN5+xxt4PAGri6z8AftejRw/t2rWrxviuXbv0q1/96ieXi4uL05kzZ3Tq1KkGb/uaa65RfHy8nn322XrVjx8/XuvXr9fu3bsbvM2fEhYWpieeeELPPvtsva8dq74TcsyYMT9r2+e7HwDURKgC4HdTpkzRP//5Tz388MP69NNPdfDgQf3pT3/SW2+9pUceeUTSv5+99NJLL6mgoEBff/21PvroIz3++OMaOnSonE6nva4TJ07I4/H4vL777rtzbj8lJUUvvfSS/u///q/OXlNTU+V2u3XjjTcqKytLu3bt0qFDh7Ru3TqtXbtWgYGBPvUVFRU1+jl+/PhPrn/SpElyuVxauXJljbkzZ87I4/HoyJEj2rt3rxYuXKghQ4aob9++mjZtmk9tY+8HALWwAKAJ2L59uzV8+HArMjLScrlc1sCBA6333nvPnn/uuecst9ttRUREWCEhIdZll11mPfzww9bx48ftmiFDhliSarwSExPtGkk+67Usy/J6vVbPnj2tKVOm1KvXU6dOWc8//7zVp08fKzQ01AoODrZ69uxppaamWoWFhXZdUlJSrf306NHDrunSpYs1b948n/WvXLnSkmQNGTLEHps1a5a9fGBgoBUREWFdd9111rx586xTp075LH+h9gMAXw7L+tFDVQAAAHDe+PoPAADAAEIVAJzlyiuvrPF8p596zhMAnI2v/wDgLP/zP/+j06dP1zoXFRWldu3aXeCOADQXhCoAAAAD+PoPAADAAEIVAACAAYQqAAAAAwhVAAAABhCqAAAADCBUAQAAGECoAgAAMOD/AfBpl+fUxENTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat3, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "0bd6aae6-9960-474c-a1f5-597dd8b08266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat3, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "2f4fe253-d44c-4eb1-976d-2b8a6f3a0a1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      241411\n",
       "18-22      193876\n",
       "28-34      151938\n",
       "35-44       62074\n",
       "45-59       45373\n",
       "0-17        26267\n",
       "60-150       8190\n",
       "unknown       889\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat3['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "6e399d2c-a1c4-4641-9e4b-b2e1b4fbbc48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "db08d7af-5726-4627-b703-0c355846b4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat3, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4064a2a8-1446-4a90-b19e-5095e3cbd8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "d87fd88c-1c53-4caf-9f1f-f1e106036dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmcat4 = df[df['bgmm2_clusters']==4].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "97225cc1-8e8c-41e4-b83f-b839c6ca475e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgmmlistcat4 =bgmmcat4['USER_ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "a29e676b-f5d2-4a00-8299-1a84e8abcd5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "subbgmmcat4 = users_cleaned[users_cleaned['USER_ID'].isin(bgmmlistcat4)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "fafe2f52-cd42-42bf-93dc-6bcb6638dfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COLLECTION_STREAMS</th>\n",
       "      <th>RADIO_STREAMS</th>\n",
       "      <th>ARTIST_STREAMS</th>\n",
       "      <th>SEARCH_STREAMS</th>\n",
       "      <th>ALBUM_STREAMS</th>\n",
       "      <th>THIS_IS_STREAMS</th>\n",
       "      <th>EDITORIAL_PLAYLIST_STREAMS</th>\n",
       "      <th>THIRD_PARTY_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_PLAYLIST_STREAMS</th>\n",
       "      <th>USER_COMPLETION_RATE</th>\n",
       "      <th>FRONTLINE_STREAMS</th>\n",
       "      <th>MIDLINE_STREAMS</th>\n",
       "      <th>CATALOG_STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "      <td>73123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.533362</td>\n",
       "      <td>0.404469</td>\n",
       "      <td>0.764219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.472081</td>\n",
       "      <td>1.249169</td>\n",
       "      <td>0.676480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.279912</td>\n",
       "      <td>4.756492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.760371</td>\n",
       "      <td>1.160368</td>\n",
       "      <td>1.941078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.082033</td>\n",
       "      <td>2.834586</td>\n",
       "      <td>0.372468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.796957</td>\n",
       "      <td>5.977388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       COLLECTION_STREAMS  RADIO_STREAMS  ARTIST_STREAMS  SEARCH_STREAMS  \\\n",
       "count        73123.000000   73123.000000    73123.000000    73123.000000   \n",
       "mean             1.533362       0.404469        0.764219        0.000000   \n",
       "std              3.760371       1.160368        1.941078        0.000000   \n",
       "min              0.000000       0.000000        0.000000        0.000000   \n",
       "25%              0.000000       0.000000        0.000000        0.000000   \n",
       "50%              0.000000       0.000000        0.000000        0.000000   \n",
       "75%              1.000000       0.000000        1.000000        0.000000   \n",
       "max             43.000000      49.000000       91.000000        0.000000   \n",
       "\n",
       "       ALBUM_STREAMS  THIS_IS_STREAMS  EDITORIAL_PLAYLIST_STREAMS  \\\n",
       "count   73123.000000     73123.000000                73123.000000   \n",
       "mean        0.000000         0.000000                    0.000000   \n",
       "std         0.000000         0.000000                    0.000000   \n",
       "min         0.000000         0.000000                    0.000000   \n",
       "25%         0.000000         0.000000                    0.000000   \n",
       "50%         0.000000         0.000000                    0.000000   \n",
       "75%         0.000000         0.000000                    0.000000   \n",
       "max         0.000000         0.000000                    0.000000   \n",
       "\n",
       "       THIRD_PARTY_PLAYLIST_STREAMS  USER_PLAYLIST_STREAMS  \\\n",
       "count                  73123.000000           73123.000000   \n",
       "mean                       0.472081               1.249169   \n",
       "std                        2.082033               2.834586   \n",
       "min                        0.000000               0.000000   \n",
       "25%                        0.000000               0.000000   \n",
       "50%                        0.000000               0.000000   \n",
       "75%                        1.000000               1.000000   \n",
       "max                      223.000000              36.000000   \n",
       "\n",
       "       USER_COMPLETION_RATE  FRONTLINE_STREAMS  MIDLINE_STREAMS  \\\n",
       "count          73123.000000       73123.000000     73123.000000   \n",
       "mean               0.676480           0.000000         0.279912   \n",
       "std                0.372468           0.000000         0.796957   \n",
       "min                0.000000           0.000000         0.000000   \n",
       "25%                0.500000           0.000000         0.000000   \n",
       "50%                0.800000           0.000000         0.000000   \n",
       "75%                1.000000           0.000000         0.000000   \n",
       "max                1.000000           0.000000        39.000000   \n",
       "\n",
       "       CATALOG_STREAMS  \n",
       "count     73123.000000  \n",
       "mean          4.756492  \n",
       "std           5.977388  \n",
       "min           0.000000  \n",
       "25%           1.000000  \n",
       "50%           3.000000  \n",
       "75%           6.000000  \n",
       "max         193.000000  "
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat4[numeric_cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "55fe432f-f8fe-4b37-8528-43770159ff5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='count'>"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat4, x='USER_GENDER', order=['female','male',''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "367c12ef-bcf8-42e4-92ee-38c266048561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_GENDER', ylabel='percent'>"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat4, x='USER_GENDER', order=['female','male',''], stat='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "dddbf2e8-d1a8-4238-a565-80e255993c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23-27      20813\n",
       "18-22      17903\n",
       "28-34      16670\n",
       "35-44       8545\n",
       "45-59       5208\n",
       "0-17        3006\n",
       "60-150       902\n",
       "unknown       76\n",
       "Name: USER_AGE_GROUP_cleaned, dtype: int64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subbgmmcat4['USER_AGE_GROUP_cleaned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "0bdcc113-3490-47b9-8233-89d843e2b83c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='count'>"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "47934f7f-b221-4e4d-bcc1-dcbe38202bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='USER_AGE_GROUP_cleaned', ylabel='percent'>"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subbgmmcat4, x='USER_AGE_GROUP_cleaned', order=['0-17', '18-22', '23-27', '28-34', '35-44', '45-59', '60-150', 'unknown'], stat ='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35be56f6-667d-42f6-849e-e60a8ac3287c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
