parallel_score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "data = pd.read_csv(r\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>user_id</th>\n",
       "      <th>Channel</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>Product_ServiceProvider</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1191</td>\n",
       "      <td>8.0</td>\n",
       "      <td>532700</td>\n",
       "      <td>137</td>\n",
       "      <td>30</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Jos Electric</td>\n",
       "      <td>Postpaid</td>\n",
       "      <td>Electricity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1287</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1635552</td>\n",
       "      <td>131</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>MTN</td>\n",
       "      <td>Airtime</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1168</td>\n",
       "      <td>8.0</td>\n",
       "      <td>548300</td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Jos Electric</td>\n",
       "      <td>Postpaid</td>\n",
       "      <td>Electricity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1119</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12200</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>Airtel</td>\n",
       "      <td>Data</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1168</td>\n",
       "      <td>8.0</td>\n",
       "      <td>211700</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Jos Electric</td>\n",
       "      <td>Prepaid</td>\n",
       "      <td>Electricity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53400</td>\n",
       "      <td>106</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>MTN</td>\n",
       "      <td>Airtime</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1183</td>\n",
       "      <td>8.0</td>\n",
       "      <td>276700</td>\n",
       "      <td>134</td>\n",
       "      <td>30</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Jos Electric</td>\n",
       "      <td>Prepaid</td>\n",
       "      <td>Electricity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75100</td>\n",
       "      <td>319</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>Glo</td>\n",
       "      <td>Airtime</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97350</td>\n",
       "      <td>265</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>MTN</td>\n",
       "      <td>Airtime</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1437</td>\n",
       "      <td>2.0</td>\n",
       "      <td>900</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>Glo</td>\n",
       "      <td>Data</td>\n",
       "      <td>Telco</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  Channel  TotalTransactionAmount  Tx_Count  DaysSinceLastTrans  \\\n",
       "0     1191      8.0                  532700       137                  30   \n",
       "1     1287      1.0                 1635552       131                  30   \n",
       "2     1168      8.0                  548300       114                  30   \n",
       "3     1119      2.0                   12200        13                  30   \n",
       "4     1168      8.0                  211700        75                  30   \n",
       "5     1119      1.0                   53400       106                  30   \n",
       "6     1183      8.0                  276700       134                  30   \n",
       "7      919      1.0                   75100       319                  30   \n",
       "8      919      1.0                   97350       265                  30   \n",
       "9     1437      2.0                     900         9                  30   \n",
       "\n",
       "   reseller_id  superdealer_id Product_ServiceProvider Product_Type  \\\n",
       "0       1130.0            18.0            Jos Electric     Postpaid   \n",
       "1          0.0            18.0                     MTN      Airtime   \n",
       "2       1130.0            18.0            Jos Electric     Postpaid   \n",
       "3          0.0           447.0                  Airtel         Data   \n",
       "4       1130.0            18.0            Jos Electric      Prepaid   \n",
       "5          0.0           447.0                     MTN      Airtime   \n",
       "6       1130.0            18.0            Jos Electric      Prepaid   \n",
       "7          0.0           447.0                     Glo      Airtime   \n",
       "8          0.0           447.0                     MTN      Airtime   \n",
       "9          0.0           447.0                     Glo         Data   \n",
       "\n",
       "  Product_Category  \n",
       "0      Electricity  \n",
       "1            Telco  \n",
       "2      Electricity  \n",
       "3            Telco  \n",
       "4      Electricity  \n",
       "5            Telco  \n",
       "6      Electricity  \n",
       "7            Telco  \n",
       "8            Telco  \n",
       "9            Telco  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the top of our data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "543"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check for missing values\n",
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's drop empty values\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "# Let's check missing values\n",
    "data.isnull().sum().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check for duplicates\n",
    "data.duplicated().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's drop duplicates\n",
    "data.drop_duplicates(inplace=True)\n",
    "\n",
    "# Let's check for duplicates again\n",
    "data.duplicated().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2452, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of our data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Our dataset has 2452 observations and 10 features/variables.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2452 entries, 0 to 2634\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   user_id                  2452 non-null   int64  \n",
      " 1   Channel                  2452 non-null   float64\n",
      " 2   TotalTransactionAmount   2452 non-null   int64  \n",
      " 3   Tx_Count                 2452 non-null   int64  \n",
      " 4   DaysSinceLastTrans       2452 non-null   int64  \n",
      " 5   reseller_id              2452 non-null   float64\n",
      " 6   superdealer_id           2452 non-null   float64\n",
      " 7   Product_ServiceProvider  2452 non-null   object \n",
      " 8   Product_Type             2452 non-null   object \n",
      " 9   Product_Category         2452 non-null   object \n",
      "dtypes: float64(3), int64(4), object(3)\n",
      "memory usage: 210.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Preview Columns name and info\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>user_id</th>\n",
       "      <th>Channel</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2.452000e+03</td>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1275.206770</td>\n",
       "      <td>1.674551</td>\n",
       "      <td>5.540556e+04</td>\n",
       "      <td>90.733687</td>\n",
       "      <td>114.360930</td>\n",
       "      <td>107.151713</td>\n",
       "      <td>396.193312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>655.443053</td>\n",
       "      <td>1.846882</td>\n",
       "      <td>5.649720e+05</td>\n",
       "      <td>1250.864387</td>\n",
       "      <td>71.197708</td>\n",
       "      <td>376.064464</td>\n",
       "      <td>269.796894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>838.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1147.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1632.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2725.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.086921e+07</td>\n",
       "      <td>46531.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>2557.000000</td>\n",
       "      <td>2494.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id      Channel  TotalTransactionAmount      Tx_Count  \\\n",
       "count  2452.000000  2452.000000            2.452000e+03   2452.000000   \n",
       "mean   1275.206770     1.674551            5.540556e+04     90.733687   \n",
       "std     655.443053     1.846882            5.649720e+05   1250.864387   \n",
       "min       3.000000     1.000000            1.000000e+00      1.000000   \n",
       "25%     838.000000     1.000000            2.000000e+02      1.000000   \n",
       "50%    1147.000000     1.000000            1.000000e+03      3.000000   \n",
       "75%    1632.000000     1.000000            5.000000e+03      9.000000   \n",
       "max    2725.000000    10.000000            2.086921e+07  46531.000000   \n",
       "\n",
       "       DaysSinceLastTrans  reseller_id  superdealer_id  \n",
       "count         2452.000000  2452.000000     2452.000000  \n",
       "mean           114.360930   107.151713      396.193312  \n",
       "std             71.197708   376.064464      269.796894  \n",
       "min             30.000000     0.000000        0.000000  \n",
       "25%             54.000000     0.000000      447.000000  \n",
       "50%            105.000000     0.000000      447.000000  \n",
       "75%            145.000000     0.000000      447.000000  \n",
       "max            605.000000  2557.000000     2494.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's describe the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id 683\n",
      "[1191 1287 1168 1119 1183  919 1437 1009 1189 1768  922 1130 1285 1188\n",
      " 1180 1572 1176 1273  951 1199]\n",
      "\n",
      "\n",
      "Channel 7\n",
      "[ 8.  1.  2.  6.  5. 10.  4.]\n",
      "\n",
      "\n",
      "DaysSinceLastTrans 276\n",
      "[30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n",
      "\n",
      "\n",
      "reseller_id 10\n",
      "[1130.    0.  553.  910.   56. 1802.  464. 2557.  841. 2379.]\n",
      "\n",
      "\n",
      "superdealer_id 16\n",
      "[  18.  447.  501.  741. 2379.   56.  417.  430.    0. 2494.  384.  466.\n",
      "  172.   40.   86. 1642.]\n",
      "\n",
      "\n",
      "Product_ServiceProvider 13\n",
      "['Jos Electric' 'MTN' 'Airtel' 'Glo' '9mobile' 'DSTV' 'GOTV'\n",
      " 'Ibadan Electric' 'Ikeja Electric' 'Portharcourt Electric' 'Eko Electric'\n",
      " 'Jamb' 'Startimes']\n",
      "\n",
      "\n",
      "Product_Type 7\n",
      "['Postpaid' 'Airtime' 'Data' 'Prepaid' 'Pay TV' 'Voucher Pin' 'E-facility']\n",
      "\n",
      "\n",
      "Product_Category 5\n",
      "['Electricity' 'Telco' 'Cable TV' 'Universal Pin' 'Education']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Are there any unique values in each column entry in the dataset?\n",
    "for i in data.columns:\n",
    "    if i not in ['TotalTransactionAmount','Tx_Count']:\n",
    "        print(i, len(data[i].unique()))\n",
    "        print(data[i].unique()[:20])\n",
    "        print (\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers \n",
    "data.boxplot(figsize=(15, 6), fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2060\n",
       "True      392\n",
       "Name: TotalTransactionAmount, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the ouliers present in the TotalTransactionAmount clearly \n",
    "# as the boxplot is not quite as clear.\n",
    "\n",
    "# Checking for outliers in the TotalTransactionAmount column\n",
    "\n",
    "Q1 = data[\"TotalTransactionAmount\"].quantile(0.25)\n",
    "Q3 = data[\"TotalTransactionAmount\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "outliers = ((data[\"TotalTransactionAmount\"] < Q1 - 1.5 * IQR) | (data[\"TotalTransactionAmount\"] > Q3 + 1.5 * IQR))\n",
    "outliers.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Product_ServiceProvider</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_ServiceProvider</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9mobile</th>\n",
       "      <td>354</td>\n",
       "      <td>6880980</td>\n",
       "      <td>14915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtel</th>\n",
       "      <td>700</td>\n",
       "      <td>25826487</td>\n",
       "      <td>52628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSTV</th>\n",
       "      <td>12</td>\n",
       "      <td>978685</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eko Electric</th>\n",
       "      <td>33</td>\n",
       "      <td>12238240</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOTV</th>\n",
       "      <td>9</td>\n",
       "      <td>94740</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glo</th>\n",
       "      <td>421</td>\n",
       "      <td>8721806</td>\n",
       "      <td>21182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibadan Electric</th>\n",
       "      <td>13</td>\n",
       "      <td>503973</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ikeja Electric</th>\n",
       "      <td>18</td>\n",
       "      <td>929501</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamb</th>\n",
       "      <td>42</td>\n",
       "      <td>19046753</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jos Electric</th>\n",
       "      <td>32</td>\n",
       "      <td>3706721</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTN</th>\n",
       "      <td>813</td>\n",
       "      <td>56573750</td>\n",
       "      <td>126170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portharcourt Electric</th>\n",
       "      <td>3</td>\n",
       "      <td>343500</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Startimes</th>\n",
       "      <td>2</td>\n",
       "      <td>9300</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Product_ServiceProvider  TotalTransactionAmount  \\\n",
       "Product_ServiceProvider                                                    \n",
       "9mobile                                      354                 6880980   \n",
       "Airtel                                       700                25826487   \n",
       "DSTV                                          12                  978685   \n",
       "Eko Electric                                  33                12238240   \n",
       "GOTV                                           9                   94740   \n",
       "Glo                                          421                 8721806   \n",
       "Ibadan Electric                               13                  503973   \n",
       "Ikeja Electric                                18                  929501   \n",
       "Jamb                                          42                19046753   \n",
       "Jos Electric                                  32                 3706721   \n",
       "MTN                                          813                56573750   \n",
       "Portharcourt Electric                          3                  343500   \n",
       "Startimes                                      2                    9300   \n",
       "\n",
       "                         Tx_Count  \n",
       "Product_ServiceProvider            \n",
       "9mobile                     14915  \n",
       "Airtel                      52628  \n",
       "DSTV                           63  \n",
       "Eko Electric                  328  \n",
       "GOTV                           56  \n",
       "Glo                         21182  \n",
       "Ibadan Electric               280  \n",
       "Ikeja Electric                203  \n",
       "Jamb                         4824  \n",
       "Jos Electric                 1653  \n",
       "MTN                        126170  \n",
       "Portharcourt Electric         174  \n",
       "Startimes                       3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GROUPING OF Product_ServiceProvider\n",
    "Product_ServiceProvider  = pd.DataFrame(data.groupby('Product_ServiceProvider').agg({'Product_ServiceProvider':'count',\n",
    "                                                                                       'TotalTransactionAmount':'sum', \n",
    "                                                                                        'Tx_Count':'sum'}))\n",
    "Product_ServiceProvider                                                                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Product_Category</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cable TV</th>\n",
       "      <td>23</td>\n",
       "      <td>1082725</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>42</td>\n",
       "      <td>19046753</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity</th>\n",
       "      <td>99</td>\n",
       "      <td>17721935</td>\n",
       "      <td>2638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telco</th>\n",
       "      <td>2274</td>\n",
       "      <td>94643272</td>\n",
       "      <td>179426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal Pin</th>\n",
       "      <td>14</td>\n",
       "      <td>3359751</td>\n",
       "      <td>35469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Product_Category  TotalTransactionAmount  Tx_Count\n",
       "Product_Category                                                    \n",
       "Cable TV                        23                 1082725       122\n",
       "Education                       42                19046753      4824\n",
       "Electricity                     99                17721935      2638\n",
       "Telco                         2274                94643272    179426\n",
       "Universal Pin                   14                 3359751     35469"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GROUPING OFProduct_Category\n",
    "Product_Category  = pd.DataFrame(data.groupby('Product_Category').agg({'Product_Category':'count',\n",
    "                                                                                       'TotalTransactionAmount':'sum', \n",
    "                                                                                        'Tx_Count':'sum'}))\n",
    "Product_Category "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Product_Type</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Airtime</th>\n",
       "      <td>1876</td>\n",
       "      <td>85016497</td>\n",
       "      <td>164996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <td>398</td>\n",
       "      <td>9626775</td>\n",
       "      <td>14430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E-facility</th>\n",
       "      <td>42</td>\n",
       "      <td>19046753</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pay TV</th>\n",
       "      <td>23</td>\n",
       "      <td>1082725</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postpaid</th>\n",
       "      <td>28</td>\n",
       "      <td>12532751</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prepaid</th>\n",
       "      <td>71</td>\n",
       "      <td>5189184</td>\n",
       "      <td>2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voucher Pin</th>\n",
       "      <td>14</td>\n",
       "      <td>3359751</td>\n",
       "      <td>35469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Product_Type  TotalTransactionAmount  Tx_Count\n",
       "Product_Type                                                \n",
       "Airtime               1876                85016497    164996\n",
       "Data                   398                 9626775     14430\n",
       "E-facility              42                19046753      4824\n",
       "Pay TV                  23                 1082725       122\n",
       "Postpaid                28                12532751       504\n",
       "Prepaid                 71                 5189184      2134\n",
       "Voucher Pin             14                 3359751     35469"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GROUPING OF Product_Type\n",
    "Product_Type  = pd.DataFrame(data.groupby('Product_Type').agg({'Product_Type':'count',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum'}))\n",
    "Product_Type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27338</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>30</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>10050</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>39</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>32</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>35</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <td>30</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>683 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "user_id                                                    \n",
       "3                       351          0.0             0.0   \n",
       "14                      231          0.0             0.0   \n",
       "18                       35          0.0            18.0   \n",
       "19                      605          0.0             0.0   \n",
       "32                      132          0.0             0.0   \n",
       "...                     ...          ...             ...   \n",
       "2682                     30       2557.0           501.0   \n",
       "2684                     39        464.0           501.0   \n",
       "2690                     32        464.0           501.0   \n",
       "2694                     35        464.0           501.0   \n",
       "2725                     30       1802.0           447.0   \n",
       "\n",
       "         TotalTransactionAmount  Tx_Count  \n",
       "user_id                                    \n",
       "3                           100         1  \n",
       "14                           51         2  \n",
       "18                        27338        70  \n",
       "19                          100         1  \n",
       "32                         7800         2  \n",
       "...                         ...       ...  \n",
       "2682                      10050        40  \n",
       "2684                       1200         2  \n",
       "2690                       1400         3  \n",
       "2694                        300         1  \n",
       "2725                        200         1  \n",
       "\n",
       "[683 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#uNIQUE ID GROUPBY\n",
    "user_id  = pd.DataFrame(data.groupby('user_id').agg({'DaysSinceLastTrans':'min','reseller_id':'max','superdealer_id':'max',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum'}))\n",
    "user_id "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>683 distinct customers</br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\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>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superdealer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>91</td>\n",
       "      <td>3529227</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>332</td>\n",
       "      <td>31532455</td>\n",
       "      <td>11495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>12</td>\n",
       "      <td>15647983</td>\n",
       "      <td>48069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>6</td>\n",
       "      <td>11300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86.0</th>\n",
       "      <td>33</td>\n",
       "      <td>49515383</td>\n",
       "      <td>101313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172.0</th>\n",
       "      <td>23</td>\n",
       "      <td>3630798</td>\n",
       "      <td>3410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384.0</th>\n",
       "      <td>5</td>\n",
       "      <td>28075</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417.0</th>\n",
       "      <td>28</td>\n",
       "      <td>1537950</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430.0</th>\n",
       "      <td>13</td>\n",
       "      <td>7175</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447.0</th>\n",
       "      <td>1440</td>\n",
       "      <td>5984706</td>\n",
       "      <td>14207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501.0</th>\n",
       "      <td>386</td>\n",
       "      <td>20812975</td>\n",
       "      <td>6464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741.0</th>\n",
       "      <td>55</td>\n",
       "      <td>178948</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642.0</th>\n",
       "      <td>8</td>\n",
       "      <td>3357950</td>\n",
       "      <td>35450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379.0</th>\n",
       "      <td>1</td>\n",
       "      <td>21171</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494.0</th>\n",
       "      <td>18</td>\n",
       "      <td>57340</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                superdealer_id  TotalTransactionAmount  Tx_Count\n",
       "superdealer_id                                                  \n",
       "0.0                         91                 3529227       962\n",
       "18.0                       332                31532455     11495\n",
       "40.0                        12                15647983     48069\n",
       "56.0                         6                   11300         7\n",
       "86.0                        33                49515383    101313\n",
       "172.0                       23                 3630798      3410\n",
       "384.0                        5                   28075        13\n",
       "417.0                       28                 1537950       244\n",
       "430.0                       13                    7175        28\n",
       "447.0                     1440                 5984706     14207\n",
       "466.0                        1                    1000         1\n",
       "501.0                      386                20812975      6464\n",
       "741.0                       55                  178948       673\n",
       "1642.0                       8                 3357950     35450\n",
       "2379.0                       1                   21171        10\n",
       "2494.0                      18                   57340       133"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GROUPING OF superdealer_id\n",
    "superdealer_id  = pd.DataFrame(data.groupby('superdealer_id').agg({'superdealer_id':'count',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum'}))\n",
    "superdealer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>reseller_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reseller_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>2230</td>\n",
       "      <td>130970800</td>\n",
       "      <td>217731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>11</td>\n",
       "      <td>408900</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464.0</th>\n",
       "      <td>9</td>\n",
       "      <td>5700</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553.0</th>\n",
       "      <td>16</td>\n",
       "      <td>198724</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841.0</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910.0</th>\n",
       "      <td>3</td>\n",
       "      <td>1400</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130.0</th>\n",
       "      <td>139</td>\n",
       "      <td>4042922</td>\n",
       "      <td>3948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802.0</th>\n",
       "      <td>28</td>\n",
       "      <td>93140</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379.0</th>\n",
       "      <td>2</td>\n",
       "      <td>81300</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557.0</th>\n",
       "      <td>13</td>\n",
       "      <td>51400</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             reseller_id  TotalTransactionAmount  Tx_Count\n",
       "reseller_id                                               \n",
       "0.0                 2230               130970800    217731\n",
       "56.0                  11                  408900        77\n",
       "464.0                  9                    5700        18\n",
       "553.0                 16                  198724       244\n",
       "841.0                  1                     150         2\n",
       "910.0                  3                    1400        14\n",
       "1130.0               139                 4042922      3948\n",
       "1802.0                28                   93140       309\n",
       "2379.0                 2                   81300        63\n",
       "2557.0                13                   51400        73"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GROUPING OF reseller_id\n",
    "reseller_id  = pd.DataFrame(data.groupby('reseller_id').agg({'reseller_id':'count',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum'}))\n",
    "reseller_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_ServiceProvider</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Cable TV</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Pay TV</th>\n",
       "      <th>DSTV</th>\n",
       "      <td>12</td>\n",
       "      <td>978685</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOTV</th>\n",
       "      <td>9</td>\n",
       "      <td>94740</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Startimes</th>\n",
       "      <td>2</td>\n",
       "      <td>9300</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <th>E-facility</th>\n",
       "      <th>Jamb</th>\n",
       "      <td>42</td>\n",
       "      <td>19046753</td>\n",
       "      <td>4824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Electricity</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Postpaid</th>\n",
       "      <th>Eko Electric</th>\n",
       "      <td>13</td>\n",
       "      <td>10550140</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibadan Electric</th>\n",
       "      <td>2</td>\n",
       "      <td>164400</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ikeja Electric</th>\n",
       "      <td>7</td>\n",
       "      <td>618811</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jos Electric</th>\n",
       "      <td>5</td>\n",
       "      <td>1188300</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portharcourt Electric</th>\n",
       "      <td>1</td>\n",
       "      <td>11100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Prepaid</th>\n",
       "      <th>Eko Electric</th>\n",
       "      <td>20</td>\n",
       "      <td>1688100</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibadan Electric</th>\n",
       "      <td>11</td>\n",
       "      <td>339573</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ikeja Electric</th>\n",
       "      <td>11</td>\n",
       "      <td>310690</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jos Electric</th>\n",
       "      <td>27</td>\n",
       "      <td>2518421</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portharcourt Electric</th>\n",
       "      <td>2</td>\n",
       "      <td>332400</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Telco</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">Airtime</th>\n",
       "      <th>9mobile</th>\n",
       "      <td>290</td>\n",
       "      <td>5728780</td>\n",
       "      <td>10992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtel</th>\n",
       "      <td>594</td>\n",
       "      <td>22429037</td>\n",
       "      <td>39240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glo</th>\n",
       "      <td>359</td>\n",
       "      <td>7478131</td>\n",
       "      <td>17238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTN</th>\n",
       "      <td>633</td>\n",
       "      <td>49380549</td>\n",
       "      <td>97526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Data</th>\n",
       "      <th>9mobile</th>\n",
       "      <td>61</td>\n",
       "      <td>959200</td>\n",
       "      <td>1835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtel</th>\n",
       "      <td>102</td>\n",
       "      <td>2427600</td>\n",
       "      <td>3239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glo</th>\n",
       "      <td>59</td>\n",
       "      <td>1049575</td>\n",
       "      <td>1917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTN</th>\n",
       "      <td>176</td>\n",
       "      <td>5190400</td>\n",
       "      <td>7439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Universal Pin</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">Voucher Pin</th>\n",
       "      <th>9mobile</th>\n",
       "      <td>3</td>\n",
       "      <td>193000</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airtel</th>\n",
       "      <td>4</td>\n",
       "      <td>969850</td>\n",
       "      <td>10149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glo</th>\n",
       "      <td>3</td>\n",
       "      <td>194100</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTN</th>\n",
       "      <td>4</td>\n",
       "      <td>2002801</td>\n",
       "      <td>21205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       user_id  \\\n",
       "Product_Category Product_Type Product_ServiceProvider            \n",
       "Cable TV         Pay TV       DSTV                          12   \n",
       "                              GOTV                           9   \n",
       "                              Startimes                      2   \n",
       "Education        E-facility   Jamb                          42   \n",
       "Electricity      Postpaid     Eko Electric                  13   \n",
       "                              Ibadan Electric                2   \n",
       "                              Ikeja Electric                 7   \n",
       "                              Jos Electric                   5   \n",
       "                              Portharcourt Electric          1   \n",
       "                 Prepaid      Eko Electric                  20   \n",
       "                              Ibadan Electric               11   \n",
       "                              Ikeja Electric                11   \n",
       "                              Jos Electric                  27   \n",
       "                              Portharcourt Electric          2   \n",
       "Telco            Airtime      9mobile                      290   \n",
       "                              Airtel                       594   \n",
       "                              Glo                          359   \n",
       "                              MTN                          633   \n",
       "                 Data         9mobile                       61   \n",
       "                              Airtel                       102   \n",
       "                              Glo                           59   \n",
       "                              MTN                          176   \n",
       "Universal Pin    Voucher Pin  9mobile                        3   \n",
       "                              Airtel                         4   \n",
       "                              Glo                            3   \n",
       "                              MTN                            4   \n",
       "\n",
       "                                                       TotalTransactionAmount  \\\n",
       "Product_Category Product_Type Product_ServiceProvider                           \n",
       "Cable TV         Pay TV       DSTV                                     978685   \n",
       "                              GOTV                                      94740   \n",
       "                              Startimes                                  9300   \n",
       "Education        E-facility   Jamb                                   19046753   \n",
       "Electricity      Postpaid     Eko Electric                           10550140   \n",
       "                              Ibadan Electric                          164400   \n",
       "                              Ikeja Electric                           618811   \n",
       "                              Jos Electric                            1188300   \n",
       "                              Portharcourt Electric                     11100   \n",
       "                 Prepaid      Eko Electric                            1688100   \n",
       "                              Ibadan Electric                          339573   \n",
       "                              Ikeja Electric                           310690   \n",
       "                              Jos Electric                            2518421   \n",
       "                              Portharcourt Electric                    332400   \n",
       "Telco            Airtime      9mobile                                 5728780   \n",
       "                              Airtel                                 22429037   \n",
       "                              Glo                                     7478131   \n",
       "                              MTN                                    49380549   \n",
       "                 Data         9mobile                                  959200   \n",
       "                              Airtel                                  2427600   \n",
       "                              Glo                                     1049575   \n",
       "                              MTN                                     5190400   \n",
       "Universal Pin    Voucher Pin  9mobile                                  193000   \n",
       "                              Airtel                                   969850   \n",
       "                              Glo                                      194100   \n",
       "                              MTN                                     2002801   \n",
       "\n",
       "                                                       Tx_Count  \n",
       "Product_Category Product_Type Product_ServiceProvider            \n",
       "Cable TV         Pay TV       DSTV                           63  \n",
       "                              GOTV                           56  \n",
       "                              Startimes                       3  \n",
       "Education        E-facility   Jamb                         4824  \n",
       "Electricity      Postpaid     Eko Electric                  139  \n",
       "                              Ibadan Electric                28  \n",
       "                              Ikeja Electric                 45  \n",
       "                              Jos Electric                  290  \n",
       "                              Portharcourt Electric           2  \n",
       "                 Prepaid      Eko Electric                  189  \n",
       "                              Ibadan Electric               252  \n",
       "                              Ikeja Electric                158  \n",
       "                              Jos Electric                 1363  \n",
       "                              Portharcourt Electric         172  \n",
       "Telco            Airtime      9mobile                     10992  \n",
       "                              Airtel                      39240  \n",
       "                              Glo                         17238  \n",
       "                              MTN                         97526  \n",
       "                 Data         9mobile                      1835  \n",
       "                              Airtel                       3239  \n",
       "                              Glo                          1917  \n",
       "                              MTN                          7439  \n",
       "Universal Pin    Voucher Pin  9mobile                      2088  \n",
       "                              Airtel                      10149  \n",
       "                              Glo                          2027  \n",
       "                              MTN                         21205  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine  = pd.DataFrame(data.groupby(['Product_Category','Product_Type','Product_ServiceProvider']).agg({'user_id':'count',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum'}))\n",
    "combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Telco</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>Telco</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">18</th>\n",
       "      <th>Cable TV</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telco</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <th>Telco</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <th>Telco</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <th>Telco</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <th>Telco</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <th>Telco</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          user_id\n",
       "user_id Product_Category         \n",
       "3       Telco                   1\n",
       "14      Telco                   1\n",
       "18      Cable TV                1\n",
       "        Electricity             2\n",
       "        Telco                   9\n",
       "...                           ...\n",
       "2682    Telco                   5\n",
       "2684    Telco                   2\n",
       "2690    Telco                   1\n",
       "2694    Telco                   1\n",
       "2725    Telco                   1\n",
       "\n",
       "[777 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#uNIQUE ID GROUPBY\n",
    "user  = pd.DataFrame(data.groupby(['user_id','Product_Category']).agg({'user_id':'count'}))\n",
    "\n",
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "user2 = user.unstack()['user_id']\n",
    "user2.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Product_Category</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2725</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>683 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Product_Category  Cable TV  Education  Electricity  Telco  Universal Pin\n",
       "user_id                                                                 \n",
       "3                      0.0        0.0          0.0    1.0            0.0\n",
       "14                     0.0        0.0          0.0    1.0            0.0\n",
       "18                     1.0        0.0          2.0    9.0            0.0\n",
       "19                     0.0        0.0          0.0    1.0            0.0\n",
       "32                     0.0        0.0          0.0    2.0            0.0\n",
       "...                    ...        ...          ...    ...            ...\n",
       "2682                   0.0        0.0          0.0    5.0            0.0\n",
       "2684                   0.0        0.0          0.0    2.0            0.0\n",
       "2690                   0.0        0.0          0.0    1.0            0.0\n",
       "2694                   0.0        0.0          0.0    1.0            0.0\n",
       "2725                   0.0        0.0          0.0    1.0            0.0\n",
       "\n",
       "[683 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat([user_id,user2], axis=1, join='inner')\n",
    "result.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means clustering with Customer Segmentation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27338</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7800</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>2682</td>\n",
       "      <td>30</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>10050</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>2684</td>\n",
       "      <td>39</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>2690</td>\n",
       "      <td>32</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>2694</td>\n",
       "      <td>35</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2725</td>\n",
       "      <td>30</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>683 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "0          3                 351          0.0             0.0   \n",
       "1         14                 231          0.0             0.0   \n",
       "2         18                  35          0.0            18.0   \n",
       "3         19                 605          0.0             0.0   \n",
       "4         32                 132          0.0             0.0   \n",
       "..       ...                 ...          ...             ...   \n",
       "678     2682                  30       2557.0           501.0   \n",
       "679     2684                  39        464.0           501.0   \n",
       "680     2690                  32        464.0           501.0   \n",
       "681     2694                  35        464.0           501.0   \n",
       "682     2725                  30       1802.0           447.0   \n",
       "\n",
       "     TotalTransactionAmount  Tx_Count  Cable TV  Education  Electricity  \\\n",
       "0                       100         1       0.0        0.0          0.0   \n",
       "1                        51         2       0.0        0.0          0.0   \n",
       "2                     27338        70       1.0        0.0          2.0   \n",
       "3                       100         1       0.0        0.0          0.0   \n",
       "4                      7800         2       0.0        0.0          0.0   \n",
       "..                      ...       ...       ...        ...          ...   \n",
       "678                   10050        40       0.0        0.0          0.0   \n",
       "679                    1200         2       0.0        0.0          0.0   \n",
       "680                    1400         3       0.0        0.0          0.0   \n",
       "681                     300         1       0.0        0.0          0.0   \n",
       "682                     200         1       0.0        0.0          0.0   \n",
       "\n",
       "     Telco  Universal Pin  \n",
       "0      1.0            0.0  \n",
       "1      1.0            0.0  \n",
       "2      9.0            0.0  \n",
       "3      1.0            0.0  \n",
       "4      2.0            0.0  \n",
       "..     ...            ...  \n",
       "678    5.0            0.0  \n",
       "679    2.0            0.0  \n",
       "680    1.0            0.0  \n",
       "681    1.0            0.0  \n",
       "682    1.0            0.0  \n",
       "\n",
       "[683 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reseller(row):\n",
    "    if row['reseller_id'] > 0 :\n",
    "        return 1\n",
    "    else:\n",
    "        return 0  \n",
    "\n",
    "def superdealer_id(row):\n",
    "    if row['superdealer_id'] > 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def superdealer(row):\n",
    "    if row['superdealer_id'] == row['user_id']:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def check(row,column):\n",
    "    if row[column] == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "             \n",
    "\n",
    "result['used_reseller'] = result.apply(lambda row: reseller(row), axis=1)\n",
    "result['used_superdealer'] = result.apply(lambda row: superdealer_id(row), axis=1)\n",
    "result['superdealer'] = result.apply(lambda row: superdealer(row), axis=1)\n",
    "result['Cable TV_'] = result.apply(lambda row: check(row,'Cable TV'), axis=1)\n",
    "result['Education_'] = result.apply(lambda row: check(row,'Education'), axis=1)\n",
    "result['Electricity_'] = result.apply(lambda row: check(row,'Electricity'), axis=1)\n",
    "result['Telco_'] = result.apply(lambda row: check(row,'Telco'), axis=1)\n",
    "result['Universal Pin_'] = result.apply(lambda row: check(row,'Universal Pin'), axis=1)\n",
    "\n",
    "result['services'] = result['Cable TV_'] + result['Education_'] + result['Electricity_'] + result['Telco_'] + result['Universal Pin_']\n",
    "result.drop(['Education_','Cable TV_','Telco_','Electricity_','Universal Pin_'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Electricity</th>\n",
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       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>86</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>49515383</td>\n",
       "      <td>101313</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "11       86                  30          0.0            86.0   \n",
       "\n",
       "    TotalTransactionAmount  Tx_Count  Cable TV  Education  Electricity  Telco  \\\n",
       "11                49515383    101313       0.0        0.0         10.0   23.0   \n",
       "\n",
       "    Universal Pin  used_reseller  used_superdealer  superdealer  services  \n",
       "11            0.0              0                 1            1         2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['Tx_Count'] == 101313 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.drop(11,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27338</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7800</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>2682</td>\n",
       "      <td>30</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>10050</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>2684</td>\n",
       "      <td>39</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>2690</td>\n",
       "      <td>32</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>2694</td>\n",
       "      <td>35</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2725</td>\n",
       "      <td>30</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "0          3                 351          0.0             0.0   \n",
       "1         14                 231          0.0             0.0   \n",
       "2         18                  35          0.0            18.0   \n",
       "3         19                 605          0.0             0.0   \n",
       "4         32                 132          0.0             0.0   \n",
       "..       ...                 ...          ...             ...   \n",
       "678     2682                  30       2557.0           501.0   \n",
       "679     2684                  39        464.0           501.0   \n",
       "680     2690                  32        464.0           501.0   \n",
       "681     2694                  35        464.0           501.0   \n",
       "682     2725                  30       1802.0           447.0   \n",
       "\n",
       "     TotalTransactionAmount  Tx_Count  Cable TV  Education  Electricity  \\\n",
       "0                       100         1       0.0        0.0          0.0   \n",
       "1                        51         2       0.0        0.0          0.0   \n",
       "2                     27338        70       1.0        0.0          2.0   \n",
       "3                       100         1       0.0        0.0          0.0   \n",
       "4                      7800         2       0.0        0.0          0.0   \n",
       "..                      ...       ...       ...        ...          ...   \n",
       "678                   10050        40       0.0        0.0          0.0   \n",
       "679                    1200         2       0.0        0.0          0.0   \n",
       "680                    1400         3       0.0        0.0          0.0   \n",
       "681                     300         1       0.0        0.0          0.0   \n",
       "682                     200         1       0.0        0.0          0.0   \n",
       "\n",
       "     Telco  Universal Pin  used_reseller  used_superdealer  superdealer  \\\n",
       "0      1.0            0.0              0                 0            0   \n",
       "1      1.0            0.0              0                 0            0   \n",
       "2      9.0            0.0              0                 1            1   \n",
       "3      1.0            0.0              0                 0            0   \n",
       "4      2.0            0.0              0                 0            0   \n",
       "..     ...            ...            ...               ...          ...   \n",
       "678    5.0            0.0              1                 1            0   \n",
       "679    2.0            0.0              1                 1            0   \n",
       "680    1.0            0.0              1                 1            0   \n",
       "681    1.0            0.0              1                 1            0   \n",
       "682    1.0            0.0              1                 1            0   \n",
       "\n",
       "     services  \n",
       "0           1  \n",
       "1           1  \n",
       "2           3  \n",
       "3           1  \n",
       "4           1  \n",
       "..        ...  \n",
       "678         1  \n",
       "679         1  \n",
       "680         1  \n",
       "681         1  \n",
       "682         1  \n",
       "\n",
       "[682 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_data_full = result.copy()\n",
    "cluster_data = cluster_data_full.drop(['user_id','Education',\n",
    "                            'Cable TV','Telco','Electricity','Universal Pin',\n",
    "                           'reseller_id','superdealer_id','used_reseller','used_superdealer','superdealer','services'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>DaysSinceLastTrans</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>351</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>231</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>27338</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>605</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132</td>\n",
       "      <td>7800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>30</td>\n",
       "      <td>10050</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>39</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>32</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>35</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>30</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DaysSinceLastTrans  TotalTransactionAmount  Tx_Count\n",
       "0                   351                     100         1\n",
       "1                   231                      51         2\n",
       "2                    35                   27338        70\n",
       "3                   605                     100         1\n",
       "4                   132                    7800         2\n",
       "..                  ...                     ...       ...\n",
       "678                  30                   10050        40\n",
       "679                  39                    1200         2\n",
       "680                  32                    1400         3\n",
       "681                  35                     300         1\n",
       "682                  30                     200         1\n",
       "\n",
       "[682 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>DaysSinceLastTrans</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039836</td>\n",
       "      <td>-0.066729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <td>-0.039836</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.697227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tx_Count</th>\n",
       "      <td>-0.066729</td>\n",
       "      <td>0.697227</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DaysSinceLastTrans  TotalTransactionAmount  Tx_Count\n",
       "DaysSinceLastTrans                1.000000               -0.039836 -0.066729\n",
       "TotalTransactionAmount           -0.039836                1.000000  0.697227\n",
       "Tx_Count                         -0.066729                0.697227  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking thier correlation\n",
    "cluster_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution transactionamount\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(cluster_data['TotalTransactionAmount'])\n",
    "plt.title('Distribution of TotalTransactionAmount', fontsize = 20)\n",
    "plt.xlabel('Range of TotalTransactionAmount')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution transactionamount\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.set(style = 'whitegrid')\n",
    "sns.distplot(cluster_data['Tx_Count'])\n",
    "plt.title('Distribution of Tx_Count', fontsize = 20)\n",
    "plt.xlabel('Range of Tx_Count')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features before PCA: 3\n",
      "Number of features after PCA: 2\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(cluster_data)\n",
    "\n",
    "\n",
    "# reduce dimensionality with PCA\n",
    "print('Number of features before PCA: {}'.format(len(scaled_data[0])))\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(scaled_data)\n",
    "print('Number of features after PCA: {}'.format(len(X_pca[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we calculate the Within Cluster Sum of Squared Errors (WSS) for different values of k.\n",
    "wcss = []\n",
    "for k in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=k, init=\"k-means++\")\n",
    "    kmeans.fit(X_pca)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.figure(figsize=(12,6))    \n",
    "plt.plot(range(1,11),wcss, linewidth=2, color=\"red\", marker =\"8\")\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "prediction = kmeans.fit_predict(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first PCA component\n",
    "pca1 = [val[0] for val in X_pca]\n",
    "# second PCA component\n",
    "pca2 = [val[1] for val in X_pca]\n",
    "\n",
    "# add the cluster and PCA components to the dataframe\n",
    "cluster_data_full['Cluster'] = prediction\n",
    "cluster_data_full['PCA 1'] = pca1\n",
    "cluster_data_full['PCA 2'] = pca2\n",
    "\n",
    "# plot scatter plot\n",
    "sns.scatterplot(data=cluster_data_full, x='PCA 1', y='PCA 2', hue='Cluster')\n",
    "plt.title('Customer Segmentation With K-Means')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>PCA 1</th>\n",
       "      <th>PCA 2</th>\n",
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       "  </thead>\n",
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       "      <td>19</td>\n",
       "      <td>605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-1.014243</td>\n",
       "      <td>7.980769</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>678</th>\n",
       "      <td>2682</td>\n",
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       "      <td>501.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-0.027073</td>\n",
       "      <td>-1.060771</td>\n",
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       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>2684</td>\n",
       "      <td>39</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-0.061209</td>\n",
       "      <td>-0.921130</td>\n",
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       "    <tr>\n",
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       "      <td>2690</td>\n",
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       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.048965</td>\n",
       "      <td>-1.031179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>2694</td>\n",
       "      <td>35</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.055535</td>\n",
       "      <td>-0.984166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2725</td>\n",
       "      <td>30</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.047209</td>\n",
       "      <td>-1.062817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "0          3                 351          0.0             0.0   \n",
       "1         14                 231          0.0             0.0   \n",
       "2         18                  35          0.0            18.0   \n",
       "3         19                 605          0.0             0.0   \n",
       "4         32                 132          0.0             0.0   \n",
       "..       ...                 ...          ...             ...   \n",
       "678     2682                  30       2557.0           501.0   \n",
       "679     2684                  39        464.0           501.0   \n",
       "680     2690                  32        464.0           501.0   \n",
       "681     2694                  35        464.0           501.0   \n",
       "682     2725                  30       1802.0           447.0   \n",
       "\n",
       "     TotalTransactionAmount  Tx_Count  Cable TV  Education  Electricity  \\\n",
       "0                       100         1       0.0        0.0          0.0   \n",
       "1                        51         2       0.0        0.0          0.0   \n",
       "2                     27338        70       1.0        0.0          2.0   \n",
       "3                       100         1       0.0        0.0          0.0   \n",
       "4                      7800         2       0.0        0.0          0.0   \n",
       "..                      ...       ...       ...        ...          ...   \n",
       "678                   10050        40       0.0        0.0          0.0   \n",
       "679                    1200         2       0.0        0.0          0.0   \n",
       "680                    1400         3       0.0        0.0          0.0   \n",
       "681                     300         1       0.0        0.0          0.0   \n",
       "682                     200         1       0.0        0.0          0.0   \n",
       "\n",
       "     Telco  Universal Pin  used_reseller  used_superdealer  superdealer  \\\n",
       "0      1.0            0.0              0                 0            0   \n",
       "1      1.0            0.0              0                 0            0   \n",
       "2      9.0            0.0              0                 1            1   \n",
       "3      1.0            0.0              0                 0            0   \n",
       "4      2.0            0.0              0                 0            0   \n",
       "..     ...            ...            ...               ...          ...   \n",
       "678    5.0            0.0              1                 1            0   \n",
       "679    2.0            0.0              1                 1            0   \n",
       "680    1.0            0.0              1                 1            0   \n",
       "681    1.0            0.0              1                 1            0   \n",
       "682    1.0            0.0              1                 1            0   \n",
       "\n",
       "     services  Cluster     PCA 1     PCA 2  \n",
       "0           1        2 -0.587103  3.985858  \n",
       "1           1        2 -0.385038  2.098517  \n",
       "2           3        0 -0.011956 -0.979474  \n",
       "3           1        2 -1.014243  7.980769  \n",
       "4           1        0 -0.212142  0.542309  \n",
       "..        ...      ...       ...       ...  \n",
       "678         1        0 -0.027073 -1.060771  \n",
       "679         1        0 -0.061209 -0.921130  \n",
       "680         1        0 -0.048965 -1.031179  \n",
       "681         1        0 -0.055535 -0.984166  \n",
       "682         1        0 -0.047209 -1.062817  \n",
       "\n",
       "[682 rows x 18 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>PCA 1</th>\n",
       "      <th>PCA 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.00000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>5.960000e+02</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.0</td>\n",
       "      <td>596.000000</td>\n",
       "      <td>596.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1678.875839</td>\n",
       "      <td>78.52349</td>\n",
       "      <td>112.177852</td>\n",
       "      <td>428.124161</td>\n",
       "      <td>6.420014e+04</td>\n",
       "      <td>51.011745</td>\n",
       "      <td>0.020134</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.100671</td>\n",
       "      <td>3.332215</td>\n",
       "      <td>0.010067</td>\n",
       "      <td>0.087248</td>\n",
       "      <td>0.986577</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>1.100671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.060482</td>\n",
       "      <td>-0.291293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>572.614527</td>\n",
       "      <td>34.35227</td>\n",
       "      <td>395.838128</td>\n",
       "      <td>211.884608</td>\n",
       "      <td>4.009832e+05</td>\n",
       "      <td>216.648012</td>\n",
       "      <td>0.162741</td>\n",
       "      <td>0.135126</td>\n",
       "      <td>0.409877</td>\n",
       "      <td>3.367222</td>\n",
       "      <td>0.129249</td>\n",
       "      <td>0.282436</td>\n",
       "      <td>0.115173</td>\n",
       "      <td>0.081716</td>\n",
       "      <td>0.375645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.403755</td>\n",
       "      <td>0.533242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+01</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.248742</td>\n",
       "      <td>-1.062828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1219.500000</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.178511</td>\n",
       "      <td>-0.826421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1588.500000</td>\n",
       "      <td>79.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>1.050000e+03</td>\n",
       "      <td>4.000000</td>\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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.119603</td>\n",
       "      <td>-0.290084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2164.250000</td>\n",
       "      <td>111.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>1.259500e+04</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.048971</td>\n",
       "      <td>0.211137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2725.000000</td>\n",
       "      <td>150.00000</td>\n",
       "      <td>2557.000000</td>\n",
       "      <td>2494.000000</td>\n",
       "      <td>7.553011e+06</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.177860</td>\n",
       "      <td>0.900403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "count   596.000000           596.00000   596.000000      596.000000   \n",
       "mean   1678.875839            78.52349   112.177852      428.124161   \n",
       "std     572.614527            34.35227   395.838128      211.884608   \n",
       "min      18.000000            30.00000     0.000000        0.000000   \n",
       "25%    1219.500000            44.00000     0.000000      447.000000   \n",
       "50%    1588.500000            79.00000     0.000000      447.000000   \n",
       "75%    2164.250000           111.00000     0.000000      447.000000   \n",
       "max    2725.000000           150.00000  2557.000000     2494.000000   \n",
       "\n",
       "       TotalTransactionAmount     Tx_Count    Cable TV   Education  \\\n",
       "count            5.960000e+02   596.000000  596.000000  596.000000   \n",
       "mean             6.420014e+04    51.011745    0.020134    0.015101   \n",
       "std              4.009832e+05   216.648012    0.162741    0.135126   \n",
       "min              1.000000e+01     1.000000    0.000000    0.000000   \n",
       "25%              1.000000e+02     1.000000    0.000000    0.000000   \n",
       "50%              1.050000e+03     4.000000    0.000000    0.000000   \n",
       "75%              1.259500e+04    30.000000    0.000000    0.000000   \n",
       "max              7.553011e+06  3410.000000    2.000000    2.000000   \n",
       "\n",
       "       Electricity       Telco  Universal Pin  used_reseller  \\\n",
       "count   596.000000  596.000000     596.000000     596.000000   \n",
       "mean      0.100671    3.332215       0.010067       0.087248   \n",
       "std       0.409877    3.367222       0.129249       0.282436   \n",
       "min       0.000000    0.000000       0.000000       0.000000   \n",
       "25%       0.000000    1.000000       0.000000       0.000000   \n",
       "50%       0.000000    2.000000       0.000000       0.000000   \n",
       "75%       0.000000    4.000000       0.000000       0.000000   \n",
       "max       4.000000   19.000000       2.000000       1.000000   \n",
       "\n",
       "       used_superdealer  superdealer    services  Cluster       PCA 1  \\\n",
       "count        596.000000   596.000000  596.000000    596.0  596.000000   \n",
       "mean           0.986577     0.006711    1.100671      0.0   -0.060482   \n",
       "std            0.115173     0.081716    0.375645      0.0    0.403755   \n",
       "min            0.000000     0.000000    1.000000      0.0   -0.248742   \n",
       "25%            1.000000     0.000000    1.000000      0.0   -0.178511   \n",
       "50%            1.000000     0.000000    1.000000      0.0   -0.119603   \n",
       "75%            1.000000     0.000000    1.000000      0.0   -0.048971   \n",
       "max            1.000000     1.000000    4.000000      0.0    7.177860   \n",
       "\n",
       "            PCA 2  \n",
       "count  596.000000  \n",
       "mean    -0.291293  \n",
       "std      0.533242  \n",
       "min     -1.062828  \n",
       "25%     -0.826421  \n",
       "50%     -0.290084  \n",
       "75%      0.211137  \n",
       "max      0.900403  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_full[cluster_data_full['Cluster'] == 0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
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       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
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       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>PCA 1</th>\n",
       "      <th>PCA 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>701.333333</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>9.885444e+06</td>\n",
       "      <td>28304.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.786561</td>\n",
       "      <td>1.141576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>836.732534</td>\n",
       "      <td>45.610671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>931.330947</td>\n",
       "      <td>6.180623e+06</td>\n",
       "      <td>24144.032810</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.154701</td>\n",
       "      <td>6.244998</td>\n",
       "      <td>4.618802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.700612</td>\n",
       "      <td>0.867558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.357950e+06</td>\n",
       "      <td>1394.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.060601</td>\n",
       "      <td>0.174145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>231.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.004175e+06</td>\n",
       "      <td>18422.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.343162</td>\n",
       "      <td>0.787119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>422.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.065040e+07</td>\n",
       "      <td>35450.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.625723</td>\n",
       "      <td>1.400092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1032.000000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>841.000000</td>\n",
       "      <td>1.314919e+07</td>\n",
       "      <td>41759.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.649542</td>\n",
       "      <td>1.625291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1642.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1642.000000</td>\n",
       "      <td>1.564798e+07</td>\n",
       "      <td>48069.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.673360</td>\n",
       "      <td>1.850490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "count     3.000000            3.000000          3.0        3.000000   \n",
       "mean    701.333333           56.333333          0.0      566.666667   \n",
       "std     836.732534           45.610671          0.0      931.330947   \n",
       "min      40.000000           30.000000          0.0       18.000000   \n",
       "25%     231.000000           30.000000          0.0       29.000000   \n",
       "50%     422.000000           30.000000          0.0       40.000000   \n",
       "75%    1032.000000           69.500000          0.0      841.000000   \n",
       "max    1642.000000          109.000000          0.0     1642.000000   \n",
       "\n",
       "       TotalTransactionAmount      Tx_Count  Cable TV  Education  Electricity  \\\n",
       "count            3.000000e+00      3.000000       3.0        3.0     3.000000   \n",
       "mean             9.885444e+06  28304.333333       0.0        0.0     0.666667   \n",
       "std              6.180623e+06  24144.032810       0.0        0.0     1.154701   \n",
       "min              3.357950e+06   1394.000000       0.0        0.0     0.000000   \n",
       "25%              7.004175e+06  18422.000000       0.0        0.0     0.000000   \n",
       "50%              1.065040e+07  35450.000000       0.0        0.0     0.000000   \n",
       "75%              1.314919e+07  41759.500000       0.0        0.0     1.000000   \n",
       "max              1.564798e+07  48069.000000       0.0        0.0     2.000000   \n",
       "\n",
       "           Telco  Universal Pin  used_reseller  used_superdealer  superdealer  \\\n",
       "count   3.000000       3.000000            3.0               3.0     3.000000   \n",
       "mean    7.000000       2.666667            0.0               1.0     0.666667   \n",
       "std     6.244998       4.618802            0.0               0.0     0.577350   \n",
       "min     0.000000       0.000000            0.0               1.0     0.000000   \n",
       "25%     4.500000       0.000000            0.0               1.0     0.500000   \n",
       "50%     9.000000       0.000000            0.0               1.0     1.000000   \n",
       "75%    10.500000       4.000000            0.0               1.0     1.000000   \n",
       "max    12.000000       8.000000            0.0               1.0     1.000000   \n",
       "\n",
       "       services  Cluster      PCA 1     PCA 2  \n",
       "count  3.000000      3.0   3.000000  3.000000  \n",
       "mean   1.333333      1.0  16.786561  1.141576  \n",
       "std    0.577350      0.0   9.700612  0.867558  \n",
       "min    1.000000      1.0   9.060601  0.174145  \n",
       "25%    1.000000      1.0  11.343162  0.787119  \n",
       "50%    1.000000      1.0  13.625723  1.400092  \n",
       "75%    1.500000      1.0  20.649542  1.625291  \n",
       "max    2.000000      1.0  27.673360  1.850490  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_full[cluster_data_full['Cluster'] == 1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>PCA 1</th>\n",
       "      <th>PCA 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>83.00000</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>680.60241</td>\n",
       "      <td>226.26506</td>\n",
       "      <td>0.674699</td>\n",
       "      <td>426.024096</td>\n",
       "      <td>2.219209e+05</td>\n",
       "      <td>70.481928</td>\n",
       "      <td>0.132530</td>\n",
       "      <td>0.397590</td>\n",
       "      <td>0.325301</td>\n",
       "      <td>2.939759</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.915663</td>\n",
       "      <td>0.036145</td>\n",
       "      <td>1.385542</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.172441</td>\n",
       "      <td>2.050431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>259.01158</td>\n",
       "      <td>71.77371</td>\n",
       "      <td>6.146799</td>\n",
       "      <td>183.906258</td>\n",
       "      <td>6.062870e+05</td>\n",
       "      <td>165.119717</td>\n",
       "      <td>0.512552</td>\n",
       "      <td>0.492375</td>\n",
       "      <td>0.898623</td>\n",
       "      <td>3.347184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109764</td>\n",
       "      <td>0.279582</td>\n",
       "      <td>0.187784</td>\n",
       "      <td>0.621394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.582128</td>\n",
       "      <td>1.119320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>153.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+01</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>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.014243</td>\n",
       "      <td>0.884893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>549.50000</td>\n",
       "      <td>187.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>1.200000e+03</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>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.384738</td>\n",
       "      <td>1.429325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>773.00000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>1.120000e+04</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.297940</td>\n",
       "      <td>1.802451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>841.00000</td>\n",
       "      <td>237.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>1.187750e+05</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.216568</td>\n",
       "      <td>2.202437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1095.00000</td>\n",
       "      <td>605.00000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>3.826750e+06</td>\n",
       "      <td>1068.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.159443</td>\n",
       "      <td>7.980769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "count    83.00000            83.00000    83.000000       83.000000   \n",
       "mean    680.60241           226.26506     0.674699      426.024096   \n",
       "std     259.01158            71.77371     6.146799      183.906258   \n",
       "min       3.00000           153.00000     0.000000        0.000000   \n",
       "25%     549.50000           187.50000     0.000000      447.000000   \n",
       "50%     773.00000           211.00000     0.000000      501.000000   \n",
       "75%     841.00000           237.50000     0.000000      501.000000   \n",
       "max    1095.00000           605.00000    56.000000      741.000000   \n",
       "\n",
       "       TotalTransactionAmount     Tx_Count   Cable TV  Education  Electricity  \\\n",
       "count            8.300000e+01    83.000000  83.000000  83.000000    83.000000   \n",
       "mean             2.219209e+05    70.481928   0.132530   0.397590     0.325301   \n",
       "std              6.062870e+05   165.119717   0.512552   0.492375     0.898623   \n",
       "min              1.000000e+01     1.000000   0.000000   0.000000     0.000000   \n",
       "25%              1.200000e+03     5.000000   0.000000   0.000000     0.000000   \n",
       "50%              1.120000e+04    14.000000   0.000000   0.000000     0.000000   \n",
       "75%              1.187750e+05    55.000000   0.000000   1.000000     0.000000   \n",
       "max              3.826750e+06  1068.000000   3.000000   1.000000     5.000000   \n",
       "\n",
       "           Telco  Universal Pin  used_reseller  used_superdealer  superdealer  \\\n",
       "count  83.000000           83.0      83.000000         83.000000    83.000000   \n",
       "mean    2.939759            0.0       0.012048          0.915663     0.036145   \n",
       "std     3.347184            0.0       0.109764          0.279582     0.187784   \n",
       "min     0.000000            0.0       0.000000          0.000000     0.000000   \n",
       "25%     1.000000            0.0       0.000000          1.000000     0.000000   \n",
       "50%     2.000000            0.0       0.000000          1.000000     0.000000   \n",
       "75%     4.000000            0.0       0.000000          1.000000     0.000000   \n",
       "max    16.000000            0.0       1.000000          1.000000     1.000000   \n",
       "\n",
       "        services  Cluster      PCA 1      PCA 2  \n",
       "count  83.000000     83.0  83.000000  83.000000  \n",
       "mean    1.385542      2.0  -0.172441   2.050431  \n",
       "std     0.621394      0.0   0.582128   1.119320  \n",
       "min     1.000000      2.0  -1.014243   0.884893  \n",
       "25%     1.000000      2.0  -0.384738   1.429325  \n",
       "50%     1.000000      2.0  -0.297940   1.802451  \n",
       "75%     2.000000      2.0  -0.216568   2.202437  \n",
       "max     4.000000      2.0   3.159443   7.980769  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data_full[cluster_data_full['Cluster'] == 2].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMF ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_data = result[['TotalTransactionAmount','Tx_Count','DaysSinceLastTrans']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-4e38c10aabbe>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  customer_data[\"RecencyScore\"] = pd.cut(customer_data[\"DaysSinceLastTrans\"],\n"
     ]
    }
   ],
   "source": [
    "customer_data[\"RecencyScore\"] = pd.cut(customer_data[\"DaysSinceLastTrans\"], \n",
    "                                       bins=[-1, \n",
    "                                         np.percentile(customer_data[\"DaysSinceLastTrans\"], 25), \n",
    "                                             np.percentile(customer_data[\"DaysSinceLastTrans\"], 50), \n",
    "                                             np.percentile(customer_data[\"DaysSinceLastTrans\"], 75), \n",
    "                                             customer_data[\"DaysSinceLastTrans\"].max()], \n",
    "                                       labels=[4, 3, 2, 1]).astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-22df6ec1664e>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  customer_data[\"FrequencyScore\"] = pd.cut(customer_data[\"Tx_Count\"],\n"
     ]
    }
   ],
   "source": [
    "customer_data[\"FrequencyScore\"] = pd.cut(customer_data[\"Tx_Count\"], \n",
    "                                       bins=[-1, \n",
    "                                         np.percentile(customer_data[\"Tx_Count\"], 25), \n",
    "                                             np.percentile(customer_data[\"Tx_Count\"], 50), \n",
    "                                             np.percentile(customer_data[\"Tx_Count\"], 75), \n",
    "                                             customer_data[\"Tx_Count\"].max()], \n",
    "                                       labels=[1, 2, 3, 4]).astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-46-0781ff34ea94>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  customer_data[\"MonetaryScore\"] = pd.cut(customer_data[\"TotalTransactionAmount\"],\n"
     ]
    }
   ],
   "source": [
    "customer_data[\"MonetaryScore\"] = pd.cut(customer_data[\"TotalTransactionAmount\"], \n",
    "                                       bins=[-1, \n",
    "                                         np.percentile(customer_data[\"TotalTransactionAmount\"], 25), \n",
    "                                             np.percentile(customer_data[\"TotalTransactionAmount\"], 50), \n",
    "                                             np.percentile(customer_data[\"TotalTransactionAmount\"], 75), \n",
    "                                             customer_data[\"TotalTransactionAmount\"].max()], \n",
    "                                       labels=[1, 2, 3, 4]).astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>RecencyScore</th>\n",
       "      <th>FrequencyScore</th>\n",
       "      <th>MonetaryScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RecencyScore  FrequencyScore  MonetaryScore\n",
       "0               1               1              1\n",
       "1               1               2              1\n",
       "2               4               4              4\n",
       "3               1               1              1\n",
       "4               1               2              3\n",
       "..            ...             ...            ...\n",
       "678             4               4              3\n",
       "679             4               2              2\n",
       "680             4               2              2\n",
       "681             4               1              2\n",
       "682             4               1              2\n",
       "\n",
       "[682 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data = customer_data[['RecencyScore','FrequencyScore','MonetaryScore']]\n",
    "customer_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_recency = customer_data.RecencyScore.value_counts().sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(customer_recency.index, customer_recency.values, alpha=0.8)\n",
    "plt.title('customer_recency_score')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Degree of Recency ', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_FrequencyScore = customer_data.FrequencyScore.value_counts().sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(customer_FrequencyScore.index, customer_FrequencyScore.values, alpha=0.8)\n",
    "plt.title('customer_FrequencyScore')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Degree of Frequency ', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_MonetaryScore = customer_data.MonetaryScore.value_counts().sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(customer_MonetaryScore.index, customer_MonetaryScore.values, alpha=0.8)\n",
    "plt.title('customer_MonetaryScore')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Degree of MonetaryScore ', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for k in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=k, init=\"k-means++\")\n",
    "    kmeans.fit(customer_data)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.figure(figsize=(12,6))    \n",
    "plt.plot(range(1,11),wcss, linewidth=2, color=\"red\", marker =\"8\")\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "prediction = kmeans.fit_predict(customer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_data['Cluster'] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function to generate pie charts for a given categorical feature\n",
    "def cluster_demographic(var):\n",
    "    \n",
    "    # Create subsets for each cluster\n",
    "    df_0 = customer_data[customer_data['Cluster']==0]\n",
    "    df_1 = customer_data[customer_data['Cluster']==1]\n",
    "    df_2 = customer_data[customer_data['Cluster']==2]\n",
    "    df_3 = customer_data[customer_data['Cluster']==3]\n",
    "\n",
    "    fig, ax = plt.subplots(2, 2)\n",
    "\n",
    "\n",
    "    ax[0,0].pie(df_0[var].value_counts(), labels=df_0[var].value_counts().index)\n",
    "    ax[0,0].title.set_text('Cluster 0')\n",
    "    ax[0,1].pie(df_1[var].value_counts(), labels=df_1[var].value_counts().index)\n",
    "    ax[0,1].title.set_text('Cluster 1')\n",
    "    ax[1,0].pie(df_2[var].value_counts(), labels=df_2[var].value_counts().index)\n",
    "    ax[1,0].title.set_text('Cluster 2')\n",
    "    ax[1,1].pie(df_3[var].value_counts(), labels=df_3[var].value_counts().index)\n",
    "    ax[1,1].title.set_text('Cluster 3')\n",
    "    plt.suptitle(var)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing clusters based on recency\n",
    "cluster_demographic('RecencyScore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i3ySly+Tk8ai4FA9HUnL5fMMRfjuYgcdPUUaF2xjRpxmJHepiNEoYRJNPqAKX4sGjqvyyP52PvzvEyfRCv8QhSdC1ZSz3DGhBVHiQLpOU7pJTqUPhdFYR85fu5NjpfH+HU6ZudAgPDmtL0/rhWHX4RQr6prg9uD0q3/9yiqXrfyczt9TfIZXp3CKGB4e1JVRnNSndJCeny43T5ebNZbvYvDPV3+FcUtsmtXh4eDvCQ626+iIF/Sp1KBw5lcfcj34lp8Du73AuSpYl+natz5hBrXTTea55cioqKmLEiBG89dZb1K1bt0KvcTgVtu5J460Veyj2QZ9SVckS3HZDQ+6+tSVmkxzQt2sF73G7PTgVD2+v2M26n0/5O5wKiaxhZdp93YmrFez3TnNNk9OuXbuYOnUqx48f55tvvqlQcrI7Ff718W9s3ZOmVRg+06hODZ4d143QIDNm0WEunMfuVMjMKeH5d7bpqglXEUaDxN+GtKFX53p+TVCa1t2WLl3KtGnTiI6OvuJzFbeHgmInk97YHJCJCeDo6XweenkDh07kYnco/g5H0Am7U2Hjbyn8/V8bAy4xAShulX8v2828T3dg/2OAsz94pc+pV69eLFq06JI1J4fLTVZuCc/8e4tu2+BXQ5Zg3G2t6du9gd+rwoJ/OZwK763ax+otyf4ORRN1o0N4/m8J1Ay1+Lx14PNeL7tTYe+RM/zj1Y3VIjEBeFT478q9rNp0TNSgrmF2p8LiNQerTWICSMks4pG5/+N0VhFOxe3TY/s0OTmcCnuPZjPjvZ9wOH37Rn1h0eoDbPjlpEhQ1yC7Q+Hz9Yf54oej/g5Fc6UOhclvbiYrtxTXFaaGaclnycnpcpOcVsjM97f7rQ3rC/9evodte9NEgrqG2B0KqzYf49N1v/s7FK8ptis8NX8TuYV23B7fJCifJCe3x0NuoYPn3t7i08zrL68u+Y1dR7KwO0WCqu7sDoXvtp9g0eoD/g7F6wqKnUxZ8CN2h29aPT4ZhFlidzHxle/JyCnx9qF0w2iQePUfidSLCcWggwFtgvYcTjc/709n9oe/+DsUn2p5XQQzxidg8fLNH6//auxOhZc++PmaSkxw9nbsPxduv+LyLULgKra7mLd0p7/D8Ln9x3N4Z+VeSr3cdeHV5OR0uflpXzo7f8/y5mF0KyOnhDc+2yn6n6ohh1Nh5vvbvf4D1atvtp3gdFaRV/uPvZqcXIqHt5bt9uYhdO+HHafZdyz7muhru1bYnQqrtyRz6IRvljnRq1eX/ObV89pryanUofDm57t8sv6S3r326Q5cPh4jInhPqUPho28O+jsMvzuZXsi3PyV7bViQV5KT4vZw5FQem3ae9kbxASev0MGS7w6J5l01YHcqvP7JDhwucbEB+HD1ARwu75zXXklOHo/K65/u8EbRAWvNlmTc+lidRqiCoyn5/How099h6Ibd6eaNz3Z55cKreXJyuz38tC/9mrs7dyUOl5tlGw6LsU8BrNTuYmk1HmhZWVv3pHE8rUDzcjVPTopb5eNvRXv8Yr7afBxV9IsHrBKHwo7fRa3pYj5de4gSu7b9y5onp8OncknJLNK62Gqh1KHwxQ9HquW8wurO7lBY/v0RRMv84n47lIld4/Na0+RUYnex7H9HtCyy2lm58Shi4czAI0mwbvtJf4ehW6oKX2w8qmm3habJSXF7+O1ghpZFVjvFdoX9x7P9HYbwJ7Nnz2bSpEkXfczt9vDDjtOU2EV/4eX875dTyBpeeTVLTh6PyrY9aX7bwimQfPfTSc3b50Llbd26lRUrVlzyccXt4YuN1W8pFK3lFTk4nqrdjkmaJadSh8L2/aLWVBG/HMjAZBSTgfUgLy+PV199lQceeOCSzykqdXEywz/7ywWab7ad0GxKj2a/ELPJwO4jZ7QqrlordSicSBMnux4899xzPProo4SFhV30cVVVr9m5oZXx8/50DLI2TTvNktPprMJrdhJkZWzedRqnGGXsV5999hlxcXEkJCRc8jmlDoVfRD9qheUXOTVbiUOTBVkUxcPWPelaFHXNOJCcg0vxiC2l/Gj16tVkZWUxePBg8vPzKSkpYebMmUyZMqXsOUaDzP5jOX6MMvCcyiikRXxElcvRJDk5XG52HxZV36txOqtI9Dv52cKFC8v+e/ny5Wzfvr1cYgJwuT3VZiMOXzl4PJtm9cORq9i80+TXYTTKYuDlVcovcqKTneCFyziZLvoGr9bhlHxNxjtpUnOSpLO3EYWrk5lbSr2YUH+HIQDDhg1j2LBh5f7m8agcEGPSrtrx1Hwkqt4prknNKa9QJKbKELen9U3xeMjOF026q5V6phijBl0WIjn50bGUPBS3mAmsVx6PKu6oVoLHo5KvQUtKk+SUWyiuLpVRUOLCLZKTbqkqOFzi+6kMLZbv1SQ5FZaIqRiVobg9YrqPjnlUUXOqLC2WpdakQ9yi47E6cx7sSr2oIH+HcVEGWUJW3Wj0NQhaU9F1cnrnqRsIserzt2exVP2c1uRXYfXy5npVcV1cCEVbl+PM0t9yF7br2hHWsY+/wxAuQQUcOt6YIjmzlM7NIpFQKfjlG0pP7PV3SGVq9f8bWKtWKdAmOVn0mb0BSuxu3EW5lB7b6e9QLmCsEYWPdoQXKkFC3zWnf77/C1azkQeGtiaxYz9sTbqQu/FjSn7/GX8vuaq6nFUuQ5NfhtWs3+SUWaBgDI/1dxgXJRnNoNEkSUF7KmDTcasAzu4G89qnO7n92bWs2l1K+K0PU++Rtwht3xsM/otdtgZXvQwN4tD1/LCUMyWYo+r5O4yLMthCkGR9n/zXMoMsERVu83cYFeLxwAer9zP8uXUs3JCK7Ya7afD3d6mRMATJ7Pv3IAdVfXCxJskp1GbWohivSE4rwBhZx99hXJSlTjMkWTTr9MpiNhAVrs+bKZezatMxRj6/gbmfHYB2t9Hg7+8Q0eseDME1fHJ8yWxDkqp+Xmty2a4RYsZokHU5oPDgiRxMfeL9HcZFmaMb+DsE4TJkSaJ+AE8v2rwrlc27UmnVMJKHh95Avc79Kdr/I3mbP0fJ894yMMawSFTFifSnZuXrr7/Ot99+iyRJDB8+nDFjxly+HC2Ccbjc1IkK5oQOJ0keOZmLbLEhGUyobv2Mx5LMVgy2EH+HIVxBfO2LL0IXSPYdy+ahVzZRLyaECcPb0mx8T0qTd5P7wyc4049rfjxTeOzZDrvzbN++nW3btvHll1+iKAoDBgwgMTGRhg0bXrIczdoUDev4psp4tRQPuJ0OjDWj/R1KOeao+nhcYtqP3kWHB1V56Q+9OJVRxFNvbuHeFzewz1mbuFH/JO6emVjj22h6HEudpkhmS7m/de3alUWLFmE0GsnOzsbtdhMUdPkmsybJyWYx0rphpBZFeYXD4cIYHuPvMMoxx8SLzvAA4HS5qV2r6nee9CS30MEL7/3MiGnr2JxmJfr2p6l7/+sEN+8OGvQV2eLbIMkX3iQzmUzMmzePpKQkEhISiIm5/G9Sk+QkSRKtGtXSoiivyC1xY6qpr+Rkqd0E+U9XF0F/ZFmifdMof4fhFXanm3mf7uT2Z79j5a4SavZ/iPoT/kNo+z6VH4YgyZhj4i/58MSJE9m6dStpaWksXbr0skVp1qyLqmkjPFSfP7bUHAemyLr+DqMcm8ZVacE7rGYjfbtV7xsXHg8sWn2A4c+t4711KVh7/pUG/3iXGglDkSxXd7fSUqcJuC9caO7o0aMcOHAAAJvNRt++fTl06NBly9Ju3zpV5cYO+rxlfzK9CFOUfpKTOa4RBlvg3gW61tSJCqFWTau/w/CJVZuPc9eMDbz8yX7UtgNpMPG/RPQejSG4ZoVeH9omEcl0YSUlJSWFqVOn4nQ6cTqdrF+/nk6dOl22LM06PaxmI/0T4ln5wzGtitTMkZRcTJ2v83cYZcI69kUymPwdhlBBqqrSs30dvvj+2tlYc8ueNLbsSaPldRE8PKwn9Tr2o/jAVnI3f4aSe4nNTCSZ4JY9L9rflJiYyO7duxkyZAgGg4G+ffuSlJR02RgkVcOFrB1OhYmvfE/qmWKtitREeKiFD57tzfFZd/o7FCSDiQaPvY9svjauxNVFSkYhD768wd9h+E3d6BAm3NGG5vVqUJq8949hCOUrItb6LYn9yxRkizYj0jUdnizLEr0662+qSG6hA9XjxhBS09+hENSks98nZQpXLyo8iNjIwBstrpWUzCKefnMro1/8H3sdscSNeoG40TOxxbcte05ohz4XbdJVlqbJyWQ00LdbA13OZXU5nBh1cMcurMsA5KvsZBT8T5Lgpo766bf0l7xCB/9c+Asjpq3lhxQLtW5/krr3zyOk7c0EN+uu6XQszSd2Wc0GerbXX8d4kV05O3LVjwyhEVhqN/ZrDELlmE0GhiQ2xqLjFTh8ye708MZnuxj+7FpW7Cgi/JYxmrcINE9ONquJewe20l3tKSPf/8kpsvcYTbbMEfzDYJAYfGMjf4ehKx4PrNh4FLfBonk/qlemxIfYTNyss76nlDMlmKLq++34lrrNCGrSCcko7tIFKqvZyPBeTQi2ie/wfMN7NUaStL/oeiU52SxG7rutta4WoTt+Oh9TRJx/Di7JRA+agKxhZ6HgH7IsMXpAC3+HoRsRYVYG9mzoleau1xYTMhllRvRp5q3ir9rBEzkYw/wzxSasUz8MIeF+ObagLYvJQK/O9bmuGqxWUFWSBJNHd8Fo8E4a8VpyspiNDOzZkObx+vhRHkvNRzZZzy6N60NyUBgRN/9VjGuqRkxGmUdHdsQLLZmAMvSmxjSICwu85ARnVxKcOrYboUH+b6N7POB22n0+nKBW33Fi9YFqRpYl4iKDubv/tdu8a1inBnf1bY5Ngy2gLsXra8QGWYxMuqeLLq4ydoeCyYdLp4S2u4WgJp1FJ3g1ZLUYGXxjQ25oX9vfoficxWTgmTFdMZu8mz68npxMRgNN64czvFcTbx/qinKK3T6rOVnqNiOy37hyzbk33niDpKQkkpKSePnll30Sh+A9FrORiXd2oEm9mv4OxafuH9qGGsFmr9yhO59PVte3Wozc2bsZbfy85tPpHDumWt4f5WusEU3snVPK3Z3bsmULmzdvZsWKFXzxxRfs27ePtWvXej0WwbusZiPP/y2BiLBro0/xli71uKFDHSw+2DLLZ1t/WMwGnh3Xza8d5CfTCzHX8u74K0NITWqPfhH5T9vxREVFMWnSJMxmMyaTiUaNGpGamurVWATfsFmNvPhADyw63iJNCzd2qMODw9r6bIdvn+5LZLMYmTG+h9+qwYdT8ry6lrhsDaH2PS8iB4VdsGxEkyZNaN++PQDJycmsWbOGxMREr8Ui+I7RIBMVHsQzY7piMlbPrb56tIlj4l86+KTGdI7PP0mbxciLD17vlybe/uPZGINrgBemkBjDY6kz9mWMoZHIl1ni9PDhw4wdO5annnqK+Ph4zeMQ/MNiNtDyukhmP9Kz2o0g79O1Po/d1cnn8wr9kuZtFiPP3deNHm18O2K7sNiFx61gCNW2aWlr1IG64+ZirBF12Ttzv/76K/feey+PP/44Q4cO1TQGwf8sZgPxcWHMe+ymgNkp+Er+cksTxg9t45cJz36rg1rNRh69qyMT7miH2YdVYZfThammVhOAJWre8Bdibn/y7N54l1kuIi0tjYcffpi5c+decQVAIXCZjAYia1iZ/8TNtGsSuBsjBFuNPHl3J+7o3dRnfUx/pulKmJXhcCrkFTl44d2ffLIp53uTbkTd9hFFu/9XpXIks42YYU9grde8QqO///nPf7Js2TLq1///yccjRoxg5MiRVYpD0C+HU+HzDYf5dN3v+PdXdnU6NY/msbs6YTUbMPuxk9/vyQnA41FxKW4++Ho/qzZrvwPp+WY+2IO6GT+Q+/3HlS7D2qA10YMeQQ6qgWzy7XQYIbDYHQoZOSW88flODibn+jucywq2GnloeDu6tor1W23pfLpITufYHQqnMgv597LdHD6V55VjPDS8HTdGZpLx2eyrfq05Jp7IvuOwxDYUc+WEClNVFYfLzZ4jZ/jPij1k5JT4O6QLdG4Rw2MjO2Lxc23pfLpKTvD/taidh7P44OsDnMrQtql3a0I84xIjOP32Pyr8GmONaCJ6jyaoUQckg0nTpUiFa4fi9uD2qHy37QQffXOAYvuF+7v5WrMG4dzdvwXN48N1UVs6n+6S0zlutwfFrbL32Bk+/vYQv5/UpkocHxfG6xO6kzz37is+11gjmpo9hhLS5iaQ5csOERCEinI43bg9Hj5bf5h1P58kr9Dh0+PLskSXFjHc1a85tWsFYzYZkPW2dC06Tk7neDwqTpebUqfC97+msHFHCkdT8qtU5pcvJ5H8r9GoTvsFj5ki6xDcogehbRIxhEaCJCGLibuCFzicCpIkcehkLl9vPs6vBzOwO91eO15MRBD9E+Lp370BsiwRZNX3ea375HQ+xe3BpXiwOxU2/pbC9n0ZHD2dR8lVVo+X/7M3GYufxZl54uze7tH1CW7Rg5DWN2IICvsjIYmObsF3SuwujEaZwyfz+P7XU+w7nkPamWIUd+U2DZBliQaxoTRvEEGHZlE0bxBBsM2EJJ0d7hAIAio5nU9xe3A43VhMBgpLnJzMKOR4agGnMgopKHbi9nhwu1WUP/7f7fEQbDURWcPG3wY0gsJMDLZQjKERqIoLyWAUS5sIulDqUFBVFYvJQFGpi/TsEk6k5ZOcXkhqVtHZZqAEsiQhSxI2q5GQIBOhNjPREUG0a1KLBrFhKG4Psizpri+pogI2OV2Mqqp/tOdVQEUtm6aicm7KilGWMJsNyHpYYEoQroLT5capeC6YfKWiIksSBlnCZNRn/1FlVKvkJAhC9SHuiQuCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEsiOQmCoEu6SU5ut5uFCxcybNgwBg8ezIABA5gzZw5OpxOASZMm8e6771a6/LFjx5KTk1OlGHNycnjkkUcYNGgQAwYMYPbs2Xg8lVtGVbg2BMJ5nZaWxtixY7ntttsYOHAgK1asqFJ5WtFNcpo+fTo7duzggw8+YOXKlXz++eccP36cZ555RpPyf/zxxyqXMXPmTBo1asSqVatYsWIFu3fvZvny5RpEJ1RXgXBeP//889x44418+eWXvP/++7zwwgukp6drEF3V6GJx4ZSUFFatWsXmzZsJCQkBICgoiOeff57ffvvtguc3a9aMrVu3EhERUe7fFouFyZMnc+LECWRZplWrVsyYMaPsRBg9ejRvv/02siwzY8YM0tLScLlcJCUl8cADD5CSksJf//pXGjVqxOnTp/nwww+Jjo4uO26fPn3o2LEjABaLhSZNmpCamurtj0cIUIFyXi9YsIBzC+KmpqZiNBqxWCze/niuSBc1p3379tG4ceOyL/CcqKgo+vXrV+Fy1q5dS3FxcdkVCuDUqVO89NJLAHzwwQfExcXx5JNPcvvtt7N8+XI+//xztmzZwurVqwFIT0/noYce4ttvvy33BQL069ePqKgoAPbv389XX31Fnz59Kv2+heotUM5rWZYxGAyMGjWKESNGMHz4cMLDw6vy1jWhi5qTLMua9N106tSJV199lVGjRtGjRw9Gjx5NgwYNyj2npKSEn3/+mfz8fF5//fWyvx08eJC2bdtiNBpp3779ZY+zadMmnnzySaZOnUqLFi2qHLdQPQXaef3hhx+Sk5PDmDFjWLZsGbfffnuVY68KXdSc2rZty7FjxygqKir394yMDMaPH4/dfuHml+ec61gEqFevHmvXrmX8+PEUFRUxZswYNmzYUO75Ho8HVVX55JNPWLlyJStXruTTTz/l/vvvB8BsNmM0XjpnL1y4kKeeeop//etfDBkypBLvVrhWBMp5/c0335TFGBERQe/evdm/f3+l3rOWdJGcYmJiGDRoEFOmTCn7kIqKipg+fTo1a9bEarWWe35ERAR79uwB4Kuvvir7+8cff8zkyZPp2bMnTz75JD179iz7kA0GA4qiEBISQvv27Vm4cCEABQUFjBw5kvXr118xzo8++oiPPvqIpUuX0qNHD03eu1B9Bcp5vWTJEhYvXgxAYWEh69evp3v37lX/AKpIF806gGnTprFgwQJGjBiBwWDA6XTSu3dvJkyYcMFzp06dyowZMwgLC6NHjx5l/UBDhgxh+/btDBgwAJvNRlxcHKNGjQKgf//+jBo1ivnz5zN37lxeeOEFBg0ahNPpZODAgdx2222kpKRcMj6n08ncuXMJCQnhkUceKft7//79efDBBzX+NITqQu/nNcCsWbN47rnnGDRoEAB/+ctfdNGXKvatEwRBl3TRrBMEQfgzkZwEQdAlkZwEQdAlkZwEQdAlkZwEQdAlkZwEQdClgE9Or7/+OgMGDCApKalsAJogCIFPN4MwK2P79u1s27aNL7/8EkVRGDBgAImJiTRs2NDfoQmCUEUBXXPq2rUrixYtwmg0kp2djdvtJigoyN9hCcIliZp+xQV0cgIwmUzMmzePpKQkEhISiImJ8XdIgnBR59f0ly1bxocffsixY8f8HZZuBXxyApg4cSJbt24lLS2NpUuX+jscQbgoUdO/OgGdnI4ePcqBAwcAsNls9O3bl0OHDvk5KkG4NFHTr7iATk4pKSlMnToVp9OJ0+lk/fr1dOrUyd9hCcJliZp+xQT03brExER2797NkCFDMBgM9O3bl6SkJNweFZfi/mNdZAkJQAKjLGM0BnQ+FgLY0aNHcTqdtGjRotI1fbfHg9PlAVQkJCQJJEnCYJAwyNXr3A7oJVMcLjeK4sFiNpBX6OB0VhFpZ4rJzC2hsNiJw+VBliUM8tkvMMhqonatYGrXCqZWuI1aNWwo7rPPsZoDOk8LAWDjxo3MmzePJUuWAPDAAw9w++23k5SUdMFzSx0KALIkkZ5TTHaenazcEjJyS8gtdOB2qxgNEkbD2QuuxWSgdlQwdaJCiKsVTGiQGYfLjcVkwGgIzKQVUL9IVVUpcSiYDDIHknPYvi+dIyl5HDudj93pvuryZAnqx4bRPD6Cdo1r0a5pFAZZwhzAX6igX5eq6QM4FTcej0pxqYvdh8+w++gZDibncDqriMpUH8xGmcb1atKhWTTdW8dSJyoUp+Im2GrS+F15T0DUnOwOBUmC7fvS2bjjNDt/z8LhuvpkVBFN6tUksWNdenWqh8EgE2QNqPwtBBCPx4PD5aHErvDdT8n8sOM0KZlFV35hJdgsRto1iWLQDdfRvMHZrafMJoNXjqUVXSenUrtCicPFsg2HWffzqbKqri9IEnRtGctd/ZpTu1YwZpMBWZZ8dnyh+nJ7PCiKyt5jZ/hk7SEOJuf69PiRNaz0696ApOuvw2Q0YLPo8wKsy+RU6lDIKbDz7sq9/HIwo1LVWi01qx/OX/s3p8V1EaJvSqg0t9uD4lbZeTiTD1cf4ER6oV/jkWWJ3l3qce/AVpgMMladJSldJSeHU8Hh8vD2it38sPO035PSn7VuFMnf7+xAzRCL7r5IQd9KHQpHU/J48/NdXmu6VZbFZGDoTY24/eYmyH/0ueqBbpKTw6mwbvtJFn69H0clOrd9RZYlBvSI554BLTEbZQyi41y4DJfixqV4eGv5bv736+V3QfG3sGAzj9zRjg5No3Vx8fV7cnIpbkodbmZ/+DO7D5/xZyhXJSYiiGfHdSMmIkg09YSLsjsVdv2exfzPdpJf5LzyC3SiR9s4/n5nBywmg18vvn5NTnaHwv7j2cxZ/CtFpS5/hVFpBllizMBW9E9ogEUkKOE8Dqeb97/ex1ebj/s7lEqJqmnj2XHdiKsV7LeLr9+Sk92p8N22E7zz5V7d9S1drW6tYnni7k6iBiXg8XiwO93MfP9ndh3O8nc4VWI0SDx9TxfaN4nySzPPL8nJ4VR4/6v9fPVjYF5VLqZp/XBm3J+AzWwUQw6uUS7FTX6Rkyn//pG0M8X+DkcTkgQP3t6OmzvW9XmC8nlycjjdvPLxr2zdk+bLw/pE/ZhQZj3Sk2CrSSSoa4zi9pCTb+fx138gr8jh73A0N6JPM27v1dinrQOf9nbZnQoLPt9ZLRMTwMmMQia9sRm703eDRQX/c3s85BU6eHxe9UxMAJ+sPcTn6w9j9+FAaJ8lJ7tTYfGag2zQ+e3UqjqZUciMd38SCeoaoaoqxaUKT7+xibzC6pmYzvl03e9s3Zvms3PbJ8nJ7lD4dusJVv5w1BeH87t9x7J57ZPfdD1eS9CG0+Xm2f9sITO31N+h+MTrn+zgaEo+Ti/NbT2f15OTonhITi/gva/2eftQuvLjrjRWbzkmalDVmN2hsPCr/Rw7ne/vUHzG7VF5/p1t5BU68HZ3tdeTk92l8OLC7Xg8AT5eoBI++PoAGTkluD0ef4ciaMzpcrP7yBm+rkZ3nCuq1KEw84Ptfyx65z1eTU52p8Lcxb9W+7b4pbg9Kv987ydcXv4SBd8rLnXxyse/+jsMvzmaks/KH456tYPca8lJcXvYc+QMvx7M9NYhAkJ6dgkLv9rn0+VeBO+yOxVe+fg3SuzX9nf68bcHOZNf6rVWkVeT04Jlu7xVfED5ZtuJanuL+Vrjdns4mJwb8KO/teD2qMxe9AsuxTstA68kp1KHwmfrfudMnt0bxQccj0fljaU7fTpGRPAOxa2Ki+55ktMK2LY3zSsJyivJye32sPz7a2PYQEXtPnKGgydy8IjO8YDlcLn57qcT1WZqilYWfrUPjxfu3GmenOwOhWX/O4LiFj/CP3v/q/04vVQFFrxP4uxIaaG87Hw7G389hUvRduyTV2pO1+Lt1Yo4ejqfE2kF/g5DqAS3x8NPe9MoKA6cdZl8acnaQ5qvLqJpcnIpbr7ZmizuTF3G0vWHKbEH3tpV1zqX4mHZ90f8HYZuncmz8/tJbTdq0DQ5eTwq3/50Qssiq51f9qfjdl97A1IDXXp2CUdTrp2R4JXx1ebjml54NU1OOQV23S3erjceFTbtPI1b9MkFjFKHwhcbxQ2eK9m+Px1Z0m6pIM2S09k7GSe1Kq5a+2Hnaa9tCipoz2iQ2L6vei7zoyWX4mHzrlTNpmtpWnPatPO0lsVVWweOZ/s7BOEqnMooorBE9BNWxOZdp7E7tLnwapacSkpdZOSUaFVcteZR4dcD1/a0nkDhUtxs2Z3q7zACxoHkHM32vdMsOe05GjjbOunB7iNZYsR4AHApHnb8LqaqVFSJXSErV5tKiibJqdShsCuA9pzTg99P5XllVK2gLbPJQHKquEt3NXb8nqnJZGBNkpOqqhxJydOiqGvGibQCTEaxW7DeFRY7xaj+q7T3aLYmiyxq8uswmwxivtFVcnvUa2Zp10B2KrPQ3yEEnPTsEk1Gi2uSnBTFI0aFV8KZPJGc9MzjUTl8Ks/fYQScrLwSTVoFmiSnnEKxNEplZGSLu5t65nS5Sc0Sg4qvVn6RU5N9GzVJTmdE86RS0nOKxUhxHfOo6jW/2mVlFRRVfYK0JsnJLrZAqpT8IofobNUxVRXndmUVllY9OWmyt7BLx1f/+rGh2Hy4hfLVqFXT5u8QhMtQUXW9tVerhhEoij6Hoxgu06wrKipixIgRvPXWW9StW/eSz9PkV6vo4OpvlKFd02g6tYiheb0a1A43YbNZcUkqsiRjV/S3hrdRNoLqRqOvQdCYhKTLjVHDQy28/vce1KgRjCxfejS2y638cd77PoEFmS5+4d21axdTp04lOTn5imVo8qtw+3hPuiCrke6t42jXJIqmtYOpFWbCYrPhLi3CkZGM4/hPFGw7xpn04wQ16UJQ71E8umYGBQ593RZOjO/O2I53+jsM4TK06NjVUsdm0Uy9pz32wz+R/J9/I1tDsNZugiW2Iaaoukg1o/EEhyGbbZhMVqxGM/n2AjKLs0ktzCS1MJ3MomyySrLJLM6m2OmdmzKz+07huvB6F/x96dKlTJs2jaeeeuqKZWiSnMKCzVoUc1HR4TYSWtemdeNIGsdaqRFsxmi24CrIxpl2FPveg+SkH8eRmYzquPCDLtzxHbZ2NzGh+728uHG+1+KsDKNsRNJwiQlBe2FB3ju3r9bd/ZtzR2I82esWUrhjLQCe4jxKDv9MyeGfL/oaOTQSW52mxMVeR/3IOkixHfEEhSKbbZhNFlRU8koLyCrO5nRhBqmFGWQVZ5NZfIbM4mxKXZW7E281Xvxze/HFFytchibJKTzUokUxNK1fk64tY2kRH06DWmZCgixIBiOunFTsp/fi3PY7GRnHcWadAnfF+wIyP55B04lvc9N1CXx/fKsmsWoh3BaGWTb5OwzhEmQZwkL8n5xkGV56IIFmcTbSPpqGI/VwhV/rKcym+OBWig9e/Lw31ojCUqcptWOuIz6yDtTu/EfysmIxWXF7POSW5v1R8zo/eWWTVZx9ye4Sm7Hq/al+qTkZZejQPIaOzaJpXr8GcTXP9g+pbgVn1kkcKVuw7zlKfvpxlNx0qtxmdtrJX7WAsYMnsi/jEFklOVUrTyN1QmORZTGFRa+MBplQP9ecosNtvDahB6bidFLenoS7WNt5fkp+Fkp+FsX7f7zo48bwWCx1mlE/pgGNIutA3evwBIVgMFkxmywoboVcez4ZRWc4XZBOelEWmcVnCDbrJDmFXOYLDLEa6dY6jvZNo2hSO5haoSbMNhvukkIcmck4jm2jIP0YZzKO4y7K0yKciyo5tI3gE714rMffmLLuZVQ/dBL+WWxItL9DEC7DZDRo1iqojITWsTx1VzuK924k9bt3weP7znklNx0lN53ivRd/3BhZB2udpsRHx9MksjZq/SYQFIridmEyVK1VoElyMhpkQmwmgm0mEtrG0bphJI2irdQIOdc/dAZH6hEcuw+SnZGMIyMZ1en7gZtZn71M3D/+y6Dmffjy4Hc+P/6fRQbV9HcIwhU0rlvTL8f92+BWDOxelzNr3qZo70a/xFARSvZpirLLLzJpqdOM2BHPVLlsbe7WuZwsfvZmJNmAMycVx+k9OMr6h1LAo5OxIh6F3M/mcMddU9mRtpdT+f5bRMwoGwmxBPvt+ELF1I8N8+nxjEaZVx7uQf0ImdRFz+DMSPbp8bVgiqyNdIXuig0bNlyxHE2Sk9kkk7/tS3I3foI/xlRcDcep/dj3beGJHuN57NsXcPuhqgzQKKIBTrfr7FgnQbeCbSZCbCaKSr2/TG+dqBBeebgbcu4pUt6ehac0MOf1Wes1RzJVvTmsSW+sbDRjjolH74npnOyv3qCGZGRkm9v8FkOr6KaYxJ063XO63DStH+714yR2rMObj12Pa98G0hY/F7CJCSCocSckSSerEgDY6rcEDQLylewlL9CvcSJNIq/zy/E71W6DySBqTXpnNRvo0CzKq8eYcEc7HrujNWe+nE/O+g9A9f+Mi8oyhsciW4I0KUvTbGKt21zL4rzKlXWKku2rebzHeCwG394uNkgyDWpeek6RoB8Gg8yNHbzzXVnNMgsev4GbW4ZweuHTFB/a5pXj+FJQw/ZotS+5ZslJMlkIadVTq+J8Ivd/i7HaS3w+haRNTAsUvdwkEK7IZjESH6dtx3h8XBiLpvYi0n6KlLcfxXUmRdPy/SW4RQKy2apJWdolJ9lAcMseQGBNx8hcPJ0e9TrSPraVz47Zv0kiNqM2X6DgfUaDRM/2tTUrr1/3Brw2MQH7b6tJX/LCRaddBSLJaMZSp6lm5WnarJNkI5Y6TbQs0us8hdkUfr+Eid3HEGL2/q39YHMQrWOaizl1AcRkNHBL5/qalPX03R156LZmZC5/hdwf9H93+2qEtEnUdKCotsnJZKFmj2FaFukTBdu/gpwMHup6j9ePdUP9rngCuMPzWhVsNdGhaeU7xoOsRv77dCLd4s2kvPsEpUd/0zA6PZAI7zkcWYNpK+doXHOSsV3XDmPNGC2L9YkzH0+nVVRjetbv4tXjJDXrhdX4/2NA3njjDZKSkkhKSuLll1/26rGFyrNZjfy1f+Vu+DSrH86iZ24mNO8wKf997I/5otWLrVEHZI0HFWt+71+SZWpef7vWxXqdx15Mweq3+Vvnu4iw1fTKMTrXbkuYJbTs31u2bGHz5s2sWLGCL774gn379rF27VqvHFuougZxYTSqW+OqXjP4xkbMfrAbRduWk/HZLNRKLkGid+E33IFs0XZlV+2Tk8FISKsbMARf3ZeoB8X7NuE+/Tv/SBiHpHHHvoTEPe1vx2b6/47wqKgoJk2ahNlsxmQy0ahRI1JT/TelRrg8s1Hmrr4Vrz09N6YLY/o1JOOzl8jfusKLkfmXOSYec3QDzcv1zqhJCSJuHuWVor0tc8mLNAiNoX+TmzQtt2eDLtSwlr8d3aRJE9q3bw9AcnIya9asITExUdPjCtqRZZm2TWrRIj7iss8LCzbz/pSbaBfr4fR/H8OevMdHEfqDRNSAB5GquALBxXglOclGM8Ete2Cp08wbxXuXRyFv+avc1XYwcaHa9J1ZDGZGdxhertZ0vsOHDzN27Fieeuop4uPjNTmm4B1Ws5FH7+p4yQX82zSKZOHkRMzpezj97pMoBVk+jtC3QtvfgqlW3StO9K0Mr803kU0Woof8HQJwYmvp8V04fv+FJ64fj6zBlJyxHe/Earj4RMhff/2Ve++9l8cff5yhQ4dW+ViC99UMsTAksdEFf7+zd1P+eV8XCn74mKwvXkVVqr49kp4ZgmsS2ftezQZd/plXJ8MZgmpQM2GINw/hNWdW/ItIo43hrZKqVE772Fb0qN8J80XWVE5LS+Phhx9m7ty5JCVV7TiC79gsRkb0bUZU+NkOYFmGFx/ozsib6pG25HkKflnj5wh9o9aAB5CM3qt8eDU5yWYrNa8fhjnGP5Nrqyrnk5kMatabhuGVG4AXYg5mYsIYLMaL15reffddHA4Hs2bNYvDgwQwePJglS5ZUJWTBR4wGmaljuhEdbmPRM71oFlpCytv/wJFyyN+h+URQk87Y4tt4pa/pHElVNZqldwmqquIuziflP3/HYw+8ZSAi+oxFadWdv695Hpe74mv6SEhMvvGRs0ujiNUHqiW7U8GEQvH+Hzmz5m39LKroZaaIOOqMnaP50IE/8/oaJ5IkYbAGE3vnZLjMBoB6lbP2PYJcbka3H35VrxvT8S80j2okElM1ZjUbkQD7qYPXTGKSLUHE3TVdk8Xkrngsrx8BkIwmzNHxRA18yBeH09yZj6aT2KAbraMrdvdxULM+3HRdQrmR4EL1JJut1Op/H9Z6LfwditdJBhOxI59DDg7zyt25P/PZ6nCy2Upws+7UunW8rw6pGSUvg6LNn/OPhHGX3Gb5nIS6HflL64EiMV1DZJOF2BFTsdb33coWPifJRA9/EnN0feRLbJipNZ8uXSmbrYS0TiTqtokBtWomQP6W5RgKcri/y18v+ZwbGnTjoW6jsfjoyxP0QzZbiR3xDEGNO/k7FM1JRjOxf5mMrX4rZB80587xeYY4W4PqRsywJwKuDyrro+l0iGlJt7odLnhsWMtbGd95pEhM1zDZZCF62OMEtwysRRcvxxBcgzpjZmNt0Mpr45kuxS/VF9lsxdaoHXF/nY5sC73yC3TCU1JAwXfv8WDXUdT8YyqKJEk80OVuhrTod8khA8K1QzZZiBr4EGGdb/V3KFVmiqxDnfv+hSmytk9rTOd4fSjB5aiKC4/TTsbnL2M/td9fYVy16FEvcMJi4PVt7/FYj/HEh9cVfUxCOR6nndITe8laNT8gd1KxxbclZvhTSGaLJjupVIZfk9M5HpeD/G0ryd30WWDsPGG0EPePd5ANRgyyLPaeEy5KVVx4XHYyv3iN0mM7/R1OhciWICL7jSO4eYJfakvn00VygrNXGiUvg6yv/40j9bC/w7kkOSiMqFsfwNawnc/b4EJg8rgcFO3bRPa37+p6vl1w84Q/pqSYkU3+7zvVTXKCs6PJVcVJ6dGdnFn7Hu6CM/4OqYxktlEzYQg1ug0EyYBsFBtiChXncTlQFSe5PyylcMda1KuYbeBtxhrRRA18CEvtJrq64OoqOZ2juhVUj5uCX78l/6eVuIvy/BaLZDQT1mUg4dcPA1n2e1VXCGwepx3VrZC76Y8k5cealKV2Y2pePxzbde3Onts6m82gy+Q0e/ZscnNzmfnCDJCg5MhvFGz/CvupAz6LwRx7HWHtexPSOhEkSVdXFCHweZylqB43eT+uoGjv9767AEsyQU27EH7DXzCFxyIZzT4Z7V0ZuktOW7du5dFHH+Wmm25i1qxZAKgeD6riwOMooWjPD5Qc+Q376d81n89krBlDcLNuhHXujyGoBpLRhBRgY7GEwOJxOUCScGacoHDXekqO/Iq7MEfbg8gGrPVbEtI8geAWCUgGo2ZbhnuTrpJTXl4e48ePZ8CAARw8eLAsOZ1PdSt4XE4koxHH6cMUH/oJZ+YJXDlpf3ypFXs7ktGMqVYdrHVbENSoA5a6zZD+qNaKppvgDx6nHWQZd0E2pSf34Uw/hjPrFM6skxUfjiAbMYZFYqwZjSWuEcFNumCOawhuBclk4c0F/2bNmrPrTSUmJvLUU0958R1Vja4amc899xyPPvooaWlpl3yOZDBi+COJ2Bq0wlK7MapbQTIYkWQD7qJcXPlZqC4HqscNbgXVrYAkYQgJxxBUA0NITWSzFdXlANkgkpGgC+e6DuSIOEwRcWc70d0KstGMqrhw5WWgus72WZ39nwtVOXvuG2tGYwyLRLYEoSpOVI8HyWj6/3lwRnO53X4kSeK+++5j7dq19OnTx4/v+tJ0k5w+++wz4uLiSEhIYPny5RV+nWyywHnJxVgjCmONim1+KAVA1Va4dp1/bktGE5bYii3aKF1iY8vzd/sBdL/bj26adWPGjCErKwuDwUB+fj4lJSUMGTKEKVOm+Ds0Qah2kpOTGTlyJEuWLNHtphq6SU7nW758Odu3b79on5MgCFVz+PBh7r//fiZMmKDrTTX0eQ9REASvCKTdfnRZcxIEQXtpaWkMHTqUV199lYSEBH+Hc0W66RAXBMG7zt/t55wRI0YwcuRIP0Z1aaLmJAiCLok+J0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdEkkJ0EQdOn/AGYZadhbdh4SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_demographic('FrequencyScore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kllBU6jrn++1WMw3jwmjZKIr2LWJp0yya+OgQ3B4vIXaLHz6BIJzK6626sFpkM1n55aQdL2LvkQIOZRRzNLMEp9t71vc7bDIJMSEk1gulfmwozRtE0DEpjlC7jAbYrcH35x40EVc6PciyRNrxItZsTWfz7izyi521Ksvp9nLwRDEHTxTz3U9HAbDKEp1axzOgWxM6t47H61VFshJ8TlW16lrS2l/TWfPLCfYdLUCtxTKSlS6FI5lVF+o/SogJoWubeK65ognNEiPwqlUtimBguBU1+/fvz/vvv0+jRo1QVQ23x0tesZPPV+9nw45MKl2Kz2OwyhJd2iQwpFcz2jaLRTabMJuDv7osGIfLrWAymdi6N5vFaw+z81Ae/vhLjAi1clWnhowa0Bqb1Wz4RGW4BAVVVxWP4mX/sSI+/HYPuw/rs7VzbTRJCOe2wW3o0jYBs2RCFolKuAiKouJVNZZtPMxnK/ZTVukJSBxmyUTfro0Yc11bQmwWHHZjJirDJahKl0JWfjmvfrqNgyeKAx1OtfqxIdw6qA09LmuA1SKdsj+eIJxN1UVXZcvuLP67eBe5RZWBDgkAyQRXdWrI7antCA+1Gq5GZZgE5Va8KIrKO4t2snLLMb9Ud2ujVaMoHvprF+pFOQz3YwrG5HQpHM4s4fUvtnM4o+TcbwgAkwkGdm/Kndd3wCJLhmkpGCJBOd0KP+3M4s0vd1BaEZgq74WQTDCyfxJ/uTYZi2xGkkRtSjiV16viVlRe+fQXNuzIDHQ45yUuysHfb+tKiwaRhhiCE9AEpWkaLo+X/yzYzppfgm/+XdP64Tx1dw8iQq1YZHOgwxEMxOlWyMqv4Jm5m8gpNEZz7nyZTHBTv1a/d6QHNkkFLEF5FC9llR6efGujYau95yM8xML0u3rQpH54UI4zEfTndCms35HBawt+RfEGvIFSa62bRPPE+O6EOiyYpcA0+QKSoFxuhcMZJTw9d1NQNOnORTabeOCWzlzZIdEQ1WIhcFxuhXlLd7N47eFAh6KLhJgQXpp0FeEh1oAMtfF7gnK5FQ6kF/H4mxvxKKo/D+1zt1ybzMhrkkRNqg755z//SWFhITNmzDjna11uhdnzf+WHbSf8EJn/xEU5eGnS1USEWf3eee7Xo7k8Xg5nlPBEHUxOAJ+t3M836w/j9MNgUsH3Nm7cyJdffnler3W6Fd5dsrvOJSeA3KJKHnzlB4pKXShe//7d+i1BuTxejmaWMO2NDbjrYHI66b0lu1m3/QROt0hSwayoqIiZM2cyYcKEc77W6VZY+P0BvllfN5p1p1NQ4uTBV34gv9jp1yTllwTlVVXyiyt57PX1uDxnn/BYF8ye/yu/7s8VSSqIPfHEEzz44INERESc9XVOl8LKzcf45Lt9fooscIpKXTwye61fWwh+SVAut5cn3tx4ztnYdYWqwT/f/5m8okrU2sz6FAJqwYIFJCYm0qNHj7O+zuX28su+HN788jc/RRZ4BSVOnvnvT7j8dPH1eSe5063wr/d/ZsuebF8expAaxoXxykN9RKd5kBk3bhy5ubmYzWaKi4upqKhg+PDhTJ06tcbrCoqd3DNj5SVz4f2jEX1bMmpgG5/PpvBpgnK6Fb7/+Thzvtjhq0MY3qArmzL++g5i+EGQWrhwIZs3bz7lLp7L7WXq6+vZf6wwQJEFlskEM+7rTVLjKJ8OUvZpE6/SqfD2op2+PIThLd90lJ2H8musgCgEN6dbYemGw5dscgLQfu/G8PXdeJ/VoCpdCv/+eCubdmb5ovigEhlm5Z2pA0Qtqo4oKHFy13Mr6vTd6POV2rMZfxva3mdNPZ/UoFRV5WhWiUhOvysuc/Ppin1ifFQd4HQpzPzkF5GcfvfdT0d9erfaJwnKo2jMnv+rL4oOWot+PHRJDLGo6zLzy/l1f26gwzAMxavx3pLdVDp9k6R0T1Ber8rG3zI4llWqd9FBTfGqzPtmt1+WLBZ8o9Lp4aNv9wY6DMNZs/U4ZZVun5Ste4LyeFU+X52md7F1wqqfj9fJKT6XinKnwubdotviz1QN5n69yye1KN0TVHpOGUdF7em0VFVj2YbD4o5eEKp0KXzy3V7DrvQaaBt+y6C4/Nxbvl0oXRNUhdPDglX79Syyzln++zZXQnBRVY3vtwbfoor+ommweO0h3TvMdU1QXlXjJ3Hn7qxyCys5kF4U6DCEC+BRvHyz/rBonp/Dj9tOIOm8mYhuCUpVNTZsz8Ar5p6d09c/HqLCGfwL9V0qFK/Guu11bxkVvRWVuXTfiUm3BFXpUti0S9Sezscv+3LEGuZBxPP7OmbCuX3/83Fdx/vplqCsFonfDubpVVydVulSyMwrC3QYwnnQNI3Nuy+9ie61tWVPlq67HOmWoA6dKMZ1Cc7qrq0tu7PxqqJPw+gqXQpb94oEdb7yipwUlep3N0+XBOVWvGz8LTj2/TKKbftzcbpEQjc62Syx82B+oMMIKocz9OuH0iVBeTyqobYpDwZ7jxRgt4p+KKOrcCoUlek/vqcuSztehFenZYF1SVAWWRJTWy6Qy+Ol3EfzlwT95BcH16abRnA8u1S3Rfz06YMyVS1BIVwYcfIbX3ZBRaBDCDrHc8rQaziULgmqsERUgWsjM6880CEIZ6FpGhm54je6UJl5Zdgs+nRf6JKgRO2pdo5nl55xU4VXX32V1NRUhgwZwrvvvuvnyAQAt8dLTqGoQV0oxatRXK7P6ga6LIPnFsMLaiWv2Ilb8Z6yqcLmzZvZtGkTX3/9NYqikJqaSp8+fWjRokWAIr00KV6NvCLRDK8NvZYV0m2YgXDhPIp62tnx3bp14/3330eWZfLz8/F6vYSEhPg/QIFCHcf0XEoUneYt6lODMugkyvAQC/95+CpCQ2x4NRVV01A1FRWtOjFoaFD9b2r++w+v8wWLbEbj9N+dxWJh1qxZ/Pe//2Xw4MEkJCT4LhDhjDSMObc0KszKa/+vJ7Lf9ga/MDad1ijXpRSPAdc3kiWY81BvrAWHKN99ELMtDJPdgWQNQbbaUK0ONIsFzWwBswxmGZNkxiSZkSQJs8mMWar673RUTcWluHEpLioVF5UeJ07FifP3x5yn/a/qeafiQtW89G7SjW6NOp3xM0yaNIm77rqLCRMmMH/+fG655RYffVvCmchmY2YAs1kiIiKU3KVv4C0z3u4y9QbfDfaLr/XrkqDMBvwRZz14FfbyTDI+ex7Ui2gPSzJSaCRyaCTmkAgkRzhmRziSIwzJForZHkq41U6kzQEWO6o1FGzRaLIMkgxmM9LJxGeSMEtmZElG1VTMkhmXcuoNhoMHD+J2u2nbti0Oh4OBAweyb1/d31rbaDQ0LAatouQXO8nILcERGU/Zju8DHc4pNMVAneT1ohx6FKObp8ZfQYLdzYn/PntxyQlAVVBL83GX6jvdQbKHEtVzBJHdhp7yXHp6OrNmzeKTTz4BYNWqVdx00026Hl84NxMmQu2WQIdxRh8sP8Dkv1xH0boFoBmrm8Uk6/O96XJ5iA636VGMLv7vxstIaRpG5oePo7mMe4tYdZajASbzqdeIPn360LdvX4YPH85NN91E586dGTJkiP+DvMRJkomIUGugwzij9Tsy8XhNhCRfHuhQTmEOjdKlHF1qUEb5EYf3bcngKxI5MW8K3tKCQIdzTpao+DM+N3HiRCZOnOjHaIQ/s8iSYc7tM1n5aw7XXHkjFfs2BzqUapIjHNMZ+m4vuCw9CrFbZcw6rgFTGz0vS2TcoCSy5r+AJ/d4QGM5X7b6YlyTkclmieQm0YEO46zmfbMHW0JTLDENAh1KNUtMA1Sd+qB0SVAut0LD+DA9iqqV5CZRPPLXFHK/mYPz2K6AxXFBJBk5ol6goxDOoXVTYycop1shLb2EiCuM0wVgiW2AyaTPzQWdJgubaNM0RpeiLlRclJ0Z93SjaN3nlO9eF5AYasMa10i3Ox2C70SE2gi16zOmx1feWbKH8JS+mCzG6Au2xDbEZNUnFl0SlMMm0yk5To+iLojdKvPag72p2PUjxRu/9PvxL4Y1oTkQ2GaxcG4uj5ckgzfz9h4ppKzCTVj73oEOBQB7o9YGq0EB7Zr7twYlSfDG33ujZe0lf/k7fj22HmwNkzFZ7YEOQzgHu8VMa4MnKIBFG04Q2ePGQIeBSbZib5CkW3m6JaiIUKtf73j8e2IvQj355HzxkuHGgJyPsNbdMem8h5igP1mW6Jjk/9bBhfr8+/1IoVHYdEwOteFonqJbBznomKAUr0a39vX1Ku6sHhvblSaRGlkfPRWU/Ti2Bq0wWYx9+1r4n+Sm0dgMvjyzqsK2A4VEXnlDQOMIbdsLyabfwG3dEpTDJjOwe1O9ijujcUPbckVSFJkfPI7qDM6tm8I69MEkiwQVLLxelW7t/HPxvRhvL9pFSFJXJEd4gCIwEZLUVbf+J9B56/OWDSOJifBdv0pqz2YM79WEzI+moxTn+uw4vmUirMNVug1kE3wvxG7hup7NAh3GOWXmV5BbWEF4p2sDcnxbg1a6JifQOUFpGlx7RWM9i6x2edt47hnWhuzP/4U7+7BPjuEPtkbJmCRj37YWTtW6STT1oox/U+OTlYeq5nfqnCjOR2T363VvGej6KWxWM8OuaqH7qPJmiRFMG9OZvOXvUHl4u65l+1t0r5tE/1OQGtLL+CP/V/18HK9kwdGik1+PK0fEEZJ0OSazvi0D3dOszSpzjY61qOhwGy/f152Sn76mbMdq3coNBGt8U+xNO4jmXRCyWsxc16OZYZdf+aMffssjqsdwvx4zqvdNmHww3U33b9thk7k9tR2y+eKDtcoScx7qjSvtJwrXfqZDdIEV0/92TGbjLt8hnJ1ZMjGyX2Bv45+P95bswtYgCTnyzJPR9SSFRBDW4WqfnNs+uRxYLWZd7ujN+ftVSPmHyV0yR4eoAsua0Ax7k7aYJONfgYXTs9tkRvRvRWyksfuiSis8HM0qJeKK6/xyvKgrb8BXsyJ88tfisMncNrjtRe2N9eJ9PYjSSshe8EJQDsT8s5hrxoraUx0gmyXuufGyQIdxTnOX7iO80wCfn3PmiHpEXD4YyUf9qj67nFstEnde375W7314dGdaxclkfvQkmif4d9UISboce8NkUXuqA2SzROfW8X6f2nWhtu/PxelSCG3bw6fHiR92f9XS1n+g556OPvuLsVll+l3emMtaXtiSIn8d2JqrOsSS8cHjqBUlPorOfyR7GHHDJiKJeXd1ht0qM+kvnQnwEmjn9M3mTCJ92Fke2q43tgZJSH9YFfaPezp+8cUXfPDBBxw6dKjWx/DpJd1ulXnk9stxnOcWNNdc3phb+jUj65NnUAqzfBma38QNu18MK6iDYiPt/OXa5ECHcVafrNiHHJXw+8oZ+jKHxxCXOuGUC6/eezr6vM0RYpO5b2THc74upVU9Jt3UnpyvZuLKSPN1WH4R3ulaHM0uQxLTWuocu01mZP8kUpKMu+igoqjsPFxMZPdh+hZskki4aTKcYWOEk3s6DhkyhB49elzUno4+T1BWi5nuHeozom+rM76mYVwYT93RhfxV86hI+9nXIfmFLbElsQPvEE27OsxmlZk6thvx0cba1eiP3l68i9A2VyLZ9NuZut6Q/8Ma16RG0+7PJk2axMaNG8nMzGT+/Pm1PpZfem3tVpm/DmpN746nrpscHmLhlUk9KPtlOaW/LPdHOD5nqdeIxFunIxlkhUPBd+xWM8/f29uwq24ezSylsMRJWEo/XcqL7jOKsLY9z3jhPXjwIHv27AHQZU9Hv91WslllHhjVucbdD1mWeP3vV6Ec2UbB6g/8FYpPyZHxNBjzrFiM7hJhNktER9iYfncPXQYn+8L8NUd0WYYlvMsgIrsNO2urID09nWnTpuF2u3G73axatYquXbvW+ph+ve9tt8o8Of5KWjaKBOC1/9cba0k6OYtm+TMMnzGHRtFg7HNI9hDdZ3ULxmWVzTRLjOD5e3ud9w0hf1q64QhYHNib1X78Vkjr7sReO/acXRZ67+lo0jRNq/W7a6nSpZCRU0zjEBcn/vsImrvS3yHoTo6MI/G2pzCHx561bS7UXW6Pl7ziSqb8Zz0FJaduaR9Ik2/tQtfIPLI/ffaC3xuW0p96g8cHpMsiIJd5h02mef1wSretqBPJyd6kPY3G/xs5op5ITpcwq8VMfHQIsx7uS5OEQC0ad3rvfL0LR5P2mMMvYICpSSJ20HjqDbozYP2pAWuHSBYr0X1GEdN/TEDWrtFLxOWp1B/1WFWzTqxScMmTzRLhIVZenHQVHVrEBjqcaoWlLk7klhHRddB5vV6yh5J429OEp/QL6J3ogDTx/kh1O1GKssle+DKe/BOBDOWCmGQr9VInENq6uxhKIJyWy+1l4ZoDfLZiH141oH9mAHTvUJ8po9pz9JU7QPWe8XWWuMYkjpqGFBKJdIaxTv4S8AQFoKkqmtdD0fovKNrwpeEnB4ckX0G91AlIVocYSiCcldOtkFdUyYx5WziaVRrocJg/vT8l371J+d6NpzxnstqJ7vtXIjpdi8lsMcTcUUMkqJNUtxOlJI+cr2bizj4S6HBOIUfXJ27IvdgSW4pak3DeVFXDo6is2nKMeUt3U+FUAhbL+Bs6MKiVRsa7/6jxeGi7XtQbfBcm2Wqoi66hEhSApqloigfn8b0U/vCJIaa9SI4woq4cTsQVqZjMsuhrEmrF5fbi8arMX7mP7zYdpTwAicpulfjsqYGcePcRPHnpWOs3p97ge7DGNUKyGm9EvOES1EmaqqIpbty5xylY8xHOI7/5PQZLTAMie9xAWPurQIM5b73NsmXLgKrxHo888ojfYxKCn9OlYJJMrN12goVrDnA8279Nv3/d15OmpgzM9jAs9RoZpjl3OoZNUH+kuivxVpRQ+tuPVOzdiDvnqM+OJdnDCGnVlcjuQ7HENsQkmTGZZTZs2MCsWbN4//33MZlMjB8/nttuu40BAwb4LBahbvN6VRSvypHMUj5fvZ+te3PwKL7rf02sF8qgK5sy6Mqm2ExeLHbj1Zj+LCgS1EmaoqCpCqrHSfmejZTv3YQr8wCau/aD4kyyFXujNjhadCIk+XIskfFoXuWU3VHT0tIoLy+nU6dOADz99NM0bdqUsWPHXsxHEgQAKpweZLNE2vEi1u/IYNehfI5klqBexN2/RvFhtGseS5fWcbRvUQ+HXUYygUUOni6KoEpQf6SpXjS3E5PFhreyFHfuMdzZR/EUZOAtL0ZTFfAqaF4vmlcBNOSwGOTIesgxiVhjGiBHxiFH1ENV3EhW+3n3LR05coTRo0fzySef0KxZM59+TuHS43IreFUNiyxxPLuMjLwy8gorKSh1UlTqprjMRXGZC49XxWGVsdtkwkMsxETYqRfloHmDSJKbRGEymTBRtTRMsAraBHU6mqaheVxoZxnjIclWTBcxtiMtLY177rmHiRMncuONN9a6HEG4UB7Fi6JoqL//yZpMVP1bA5NkwmKWsF7EPgBGVKcSlK9t3bqVSZMmMXXq1IuaACkIwvkRCeo8ZWZmcuONNzJz5kx69PDtQvSCIFQJ3sapn82dOxeXy8WMGTOqHxs1ahSjR48OYFSCULeJGpQgCIZlzNFZgiAIiAQlCIKBiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhGSZBeb1e3n33XUaMGMENN9xAamoqL774Im63G4BHH32UuXPn1rr8O+64g4KCgouKsaCggPvvv59hw4aRmprKP//5T1TV2Hv4CYEVDOd1ZmYmd9xxB9dffz1Dhw7lyy+/vKjy9GSYBDV9+nS2bdvGvHnzWLRoEZ9//jmHDx/mscce06X89evXX3QZzz//PC1btmTx4sV8+eWX7Nixg4ULF+oQnVBXBcN5/dRTT3H11Vfz9ddf89577/HMM8+QlZWlQ3QXzxDrQaWnp7N48WLWrVtHWFgYACEhITz11FP88ssvp7y+devWbNy4kZiYmBr/b7PZmDJlCkePHkWSJNq3b8/TTz9dfTKMHTuWt956C0mSePrpp8nMzMTj8TBkyBAmTJhAeno6t956Ky1btuTEiRN88MEHxMfHVx93wIABdOnSBQCbzUZSUhIZGRm+/nqEIBUs5/WcOXM4uepSRkYGsixjsxlj805D1KB27dpFq1atqn/Ek+Li4hg0aNB5l7NixQrKy8urr1QAx48f54UXXgBg3rx5JCYmMnnyZG666SYWLlzI559/zoYNG1i6dCkAWVlZ3HvvvSxfvrzGjwgwaNAg4uLiANi9ezdLliwR204JZxQs57UkSZjNZsaMGcOoUaMYOXIk0dHRF/PRdWOIGpQkSbr05XTt2pWZM2cyZswYevbsydixY2natGmN11RUVLBlyxaKi4t59dVXqx/bu3cvKSkpyLJcvbXUmaxdu5bJkyczbdo02rZte9FxC3VTsJ3XH3zwAQUFBYwbN44vvviCm2666aJjv1iGqEGlpKRw6NAhysrKajyenZ3N3XffjdN55n3vTnY2AjRu3JgVK1Zw9913U1ZWxrhx41i9enWN16uqiqZpfPrppyxatIhFixbx2Wefcc899wBgtVqR5TPn7XfffZdHHnmEf//73wwfPrwWn1a4VATLef3tt99WxxgTE8O1117L7t27a/WZ9WaIBJWQkMCwYcOYOnVq9RdVVlbG9OnTiYqKwm6313h9TEwMv/1WtRX6kiVLqh//+OOPmTJlCr1792by5Mn07t27+os2m80oikJYWBidOnXi3XffBaCkpITRo0ezatWqc8b50Ucf8dFHHzF//nx69uypy2cX6q5gOa8/+eQTPvzwQwBKS0tZtWoVV1555cV/ATowRBMP4Mknn2TOnDmMGjUKs9mM2+3m2muvZeLEiae8dtq0aTz99NNERETQs2fP6n6h4cOHs3nzZlJTU3E4HCQmJjJmzBgABg8ezJgxY5g9ezYvvfQSzzzzDMOGDcPtdjN06FCuv/560tPTzxif2+3mpZdeIiwsjPvvv7/68cGDB/N///d/On8bQl1h9PMaYMaMGTzxxBMMGzYMgL/85S+G6VsVmyYIgmBYhmjiCYIgnI5IUIIgGJZIUIIgGJZIUIIgGJZIUIIgGJZIUIIgGNYll6BeffVVUlNTGTJkSPWgNkEwGnGeVjHMQE1/2Lx5M5s2beLrr79GURRSU1Pp06cPLVq0CHRoglBNnKf/c0nVoLp168b777+PLMvk5+fj9XoJCQkJdFiCUIM4T//nkkpQABaLhVmzZjFkyBB69OhBQkJCoEMShFOI87TKJTvVpbKykgkTJpCamsott9wS6HAE4bQu9fP0kqpBHTx4kD179gDgcDgYOHAg+/btC3BUglCTOE//55JKUOnp6UybNg23243b7WbVqlV07do10GEJQg3iPP2fOnkXz6uquN0qGhpgwoSGSTLR+6qr2LFjB8OHD8dsNjNw4ECGDBkS6HAFoYY+ffqc8Tz1KF4Ub81eGckEFtmMJJkCEa5PBXUflNOt4PVqyGYJkwR5hZUczy7lQHoR2QUVqFrVj2cymbDKEmEhVqLDbURH2GmWGEFivVBcbi82qxnZfElVJgWDq3R5ABOSyURWfjmZ+eVk5JZRUOKkwqnwxz9ai1miXpSd+rGhxEeHEB8TQniIFbfHi81iRpaD99wOuhpUpUvBLJnYeTCfn3Zlkp5TRnpO1Q93oWxWM62bRNOhZSxd2yTQLDECxasiyxJW2eyD6AXh9DyKiuJVcboVtuzOZufBfNKOF3Iit4zaVCFC7TKtGkfTpmk0XdrE06pxFIqiEmK36B+8DwVFDcrlVjCZTBzLKmXphsNs2JFBuVPR/ThmyUTzBpH0TElkSK/mmEwmHLagy+FCkNA0DafbS3mlh++3Hmftryc4nFHik2PZrWa6tklgQPcmXNayHpqmYbMa/9w2dIJyuhSKylx8u/EIa35JJ7/4wmtJtWWzmOl/RWNuuTYZh00OuiuPYFyKV8Wrauw9UsBnK/bz28E8vx4/ItTK0N7Nuf6qlkgmEw67cROVIROU061Q4VR4bcGvbNmdHdBYTCa4ol19/jqwNQ3jw7DW0c5Iwfe8qori1Vi//QTzV6ZxIrfs3G/yIYss0adLI25PbYvDJmM3YI3KUAlK8ap4vSqfrz7AF9+n4VEufk8xPSU1jmLSXzpRv16oIX9MwbicLoUD6UX85/PtpOcENjH9mUWWGNG3FSOvScIsSVgM1KlumATldCtsT8vljYU7yCvyX1PuQkmSidEDW3Njn1bYrKIjXTg7j+LF6fLyymfb2LwrK9DhnFVMhJ17bryMLq3jsRuk7zXgCcrt8VJU5mLmJ7+w82B+IEO5IMlNopn6tysIC7FgsxjjxxSMxelS2Hu0gBc/3EpJufvcbzCIvl0acd/IjlgsEmYpsLWpgCYop0thz9ECXnhvC5Uu/e/K+ZrdaubekR3pcVmiaPIJNbjcCu99s5sl6w4HOpRaqR8bwuN3dCc+JiSg53bAEpTTrbBswxHeXbKrVuM8jOTKDvV56K9dxZAEAVXVcLoVnnhzI/uOFQY6nIsim0089NeuXNE2IWBNvoAkKJdb4e1FO1m+6ai/D+0zLRtG8vy9vXDYZEwmcZfvUqR4Vcoq3Dz6n/UBv0Onp78NaceQ3s0DUpPye4JyuhVmfbaNtb9m+POwftEsMYIZ9/XGYZPFUIRLjOJVySuq5B+vravVrAajG9G3FaMHtfZ7kvJrD5jL7eVf7/9cJ5MTwJHMEh55bS1Od/D1pwm1p6oapeVuJs9eWyeTE8DCNQf46oeDOP3cV+y3BOV0KcxbuostewI78NLXjmWVMv3tTSJJXUIqXQqP/mcdRaWuQIfiUx99u5d12zP8mqT8kqDcHi/b03JZvDY472hcqD1Hqm4tu0SSqvOcboXH39xARl55oEPxi9nzt7HnSAEuj9cvx/N5glJVjeIyFy9//IuvD2Uom3dl8cX3B/xeJRb8x+lSeP3z7aQdLwp0KH6javDCvC2UV3r8cjyfJyi34mX625uCcpzTxVqwaj+FZS4MMlhf0JHL7eWn3Vms3poe6FD8rtKl8Ox/f8Ll9n0tyqcJyulS+M+C7RzLLvXlYQxL8Wq8+MHPuD3GmlMoXLwKp4f/LNge6DACJu14EZ+t3OfziofPEpTb42XjzkzW/HLpXWH+KO14ESs2HxX9UXWI063w0sdbL8lWwR99vjqN3MIKn7YQfJagNA3++/UuXxUfVN77ZjcVPlhgT/A/r1dl9+F8dqT5dw0nI9I0mDX/V9w+7DD3SYJye7x899NRisrq9m3X8+Vye3np461i6EEdoHg15ny+I9BhGMa+o4X8vCcHxUdLI/kkQWkazF+53xdFB60daXls35+LqooO82Dl9nj5duNhsgsqAh2Kobz11W94fdTM0z1BidrTmS1cc8Bv40cE3/h89YFAh2A4BSVOfth63CcLTOqeoETt6cx2Hy6gNIjWBRL+x+tV2bQzU1x4z2D+qjSfdJbrmqDcHi8rNova09l89YMYvBmMFK8qLrxnkV1Qwa5D+bonKd1rUJ+vTtO7yDpl9c/HxUoHQSg9p4yjWZfmeL7z9eUPB3QfeqFrgioocfp1a6hgVO5U+GlnJqoqBm8GC6dLYcXmY4EOw/B2pOUh6bwWmm4JyquqbPwtU6/i6rRFPx7CJUaXBw1JMolz+zx4VY0te7J1bebplqCcLm/A97ALFvuOFfplHpOgjxO5ZXV2nSe9fb/1uK7NPN0SlEWW2HMkeHZlCbSD6UWBDkE4Dx7Fy9ptJwIdRtD4dX8uVlm/7dh0S1B7jxSgeMUgxPO161C+4TYmFU7l9qjsPCQuvOfLo6hk5uu3NpYuCcrpVli/o24u4+srB9KLfDqHSdCHzWrmgKjtXpCdOg430CVBaRrsOCAmT16IgyeKsVqMs8W0cHp5RZWipnuBdh/K160fSpe/EKtFIitfzE+6ECXlbipdogZldEczSwIdQtA5nFGsW1k6NfG8KF5xlblQR3T8IQXfuFTWGtdTXrETi6xP60CXUhRFpW+XRrRuGk14iEWPIi8Je44UiNUNDMzt8ZJbWBnoMIJOeaVHt81rddmFL9yiMjG1IWZ7KJI1BNBQFQ9eRcGtqDjdKqVOlYJyhaJSFwUlTnIKK8kpqOBEbhm5RRVcigOrSyvceFUVSdLvtqygH49XJb9YJKjaKC13Ex1hP+Pz//znPyksLGTGjBlnLUeXBFV5dCfZC/53IJPVjtkRgTk0AikkArMjgoiQCKLDojGHx2COj8YcEoPZ0QTJFoLJbEHzViU0xePFpaiUuVSKyr0UlbspKHGSW1RJbkEFmXkVZOSV4nQHf0Zze1RRgzIwTdMoEatP1EpJxZkT1MaNG/nyyy/p27fvOcvRJUGprprtdM3tRHE7UYpzzq8AScYcEoY5JPL3hBaOPTSShiGRNAmPwZwYjblVFGZHApI9FMlqR1O9v9fSvLgVlQqXl2KnSlG5QlGpk7wiJzmFFeQXV1LpNGZndGyUHZGfDEwzoRp0Rx5Zlri8TRwuV9X5b7QxiOYzTIgvKipi5syZTJgwgb17956zHJ0S1EXewVMVvGVFeMuKzvMNJiR7COaQCKTfa2rmkAhiHREkhMdgjozB3CAKc0h9TOExWKxVmdypuPB4/bOf1/mQJRlN86LTzyDUYW2bRdMrpQGXtYgmMcqCPSQETBL83tejV5/PH2mqiupR0BQP2gXeBLNEhJ/28SeeeIIJEyYwffp0OnbseM5ydPnLkOynD8Z3NFRnOaqzHDjXJE6JyB7XY+86CEtYFL9l72NZ2mr25R3yR6Bn1a95D8Z1uSXQYQgGEx/t4OrODemUFEeLeBuhoXY0r4Ir8yDOAz9ReGI/rqxDaK4KpNAo7A2SsCW2xBLfBCkqHjU0ArPFjizbsJj/9yeuqApOxYVb8eBW3KiKB0kzYTGZkU1mzCYJs2RGkiQkScIkmZHMZiSHAzQNVVFQPR5Ut7vqP5cbr8uJt9KJ6nTirazEW1GBUl5B45tvOuVzLViwAFmWmTlzJnl55zduUpcEJUfW06MYH1Ep3vgVxRu/Qo5tSEr/2+l81X2UeSpZlvY9aw5voswdmFvJFrMFCbE2lGGZQDb7djCt3SrTKyWRy9sm0KZRCFFhNiRZxp2bjvPYJsp37KUgIw1vacFp36+WF1GRtoWKtC2nfV4Kj8XRMBlr/eZYYhtiiorHEhKOzRqCNdSGhkZRZQmZ5flklGaTUZpNbnk+OeV55JTnU+mpmiRtNVuJtIUTbgslzBZGmMVBqDWUUKuDEKudkFAHdtmOQ47CJluJ97iwWWw1Ylm6dCk7d+4kKioKqOqLev7555k6deoZvx+TpsOYdE9hFsfn3HexxfhVRNfrcHQfgiWiHtszd7M07Xt25+xHw39t+SHJ1/DXlOE1rnKCcVQ4Pcz5fDs/6DRZ2GSCjklx9OiQSIdmkSREWbDaHXhK8nAd34vz2G6cGfvx5J0AzT83geTIOGwNk7EmNMca2xCi4lBDwpGsdmwWO15VpbCyiJxTElg+ueX5OJXTr5771g3/JMoeccbjdu/enW7dujF79uyzx3dRn+535pAzB2JUJVuXUbJ1GXJkHG2uuZ32Pe/CpSp8m7aG1Yc3UOz0/QjimJAo5NMMMXjttddYtmwZAH369OGRRx7xeSzCqSyymZhIR63f3zghjKs6NqRjq1ia1rPiCHWgupy4MtNw7vmRvBP7cWcfRvMEbolspTgXpTiX8t3rT/u8HF0fW8PWNEloSsvYhtCoOWpIGGaLHavFhuJVKHQWk12Wx4mSLLLKcskpzyfUEqJLfLokKJPFXtVh56esryelOJfchS8DEJbSn+t73sBN7VPZk5PGN/tXsz17t892Tm0R3eSUzs0NGzawbt06vvzyS0wmE+PHj2fFihUMGDDAJzEIZ2aRJRKizy9BhYdYuKpTQ7q2iSepvoOIMBsmScKVfQTn0R8p/XkfeZkH8JYH1+wBpTALpTCL8p2nf16ObYi9YTLN4puRFNsArUkShISjeN1nbRmEhobyj3/845zH1yVBaV4P5tBIvGWFehQXMGU7VlO2YzVSWAzN+93K/+s+FgVYcXAtKw+uI79S38/XIDzhlMfi4uJ49NFHsVqtALRs2ZKMDLFSRKA0iA875TFZgq7t6nNl+/q0axJBvQgLFpsdT2EWlcd34lq3h4yM/XgKssCPXQaBoOSfoCy/ZhPY1qg19W95TJfydUpQCnJEvaBPUCepZQXkLa5qG4e27cng3iMZ1mYAB/IP883+1fyS8Rvei6wtWiSZCNupJ39SUlL1v48cOcKyZcv45JNPLupYQu01T4ygVaNIrurUkA7No2kca8XmcKBWluI8sR/n9hXkZqThyj4CXrFbD4A1thEmnWZH6NJJrnrcFP74GcWbvtIhJGOS7GHE9L8Nc5tuYLaw6tB6vjvwIznltVtmJim2OY/1mUiI5fRNiLS0NO655x4mTpzIjTfeeDGhCxfB66wEE7izDlF5dCeuE/txZRxAdZYFOjTDih14JxGXX6fL2CxdEhSAM+MAGe+eu01ZFzhadSX86luwxjXmaNEJluxfxZYT21HU87+CDk2+htEpN2Axnzq5euvWrUyaNImpU6cyZMgQPUMXLpDXWU7uolepOLA10KEEjcb3v4ElMk6XsnS7v22Lb4rJYkfz1P3F5SsPbKXywFaw2onr81fu7jiSCVfcxprDG1h+4EcySs+9eURK/banTU6ZmZncd999zJw5kx49evgifOECSFYH9uYpIkGdJzk6Ude7+rolKFXx4GjWgYq0n/Uq0vjcTgpW/BdW/Bd708u4uu9o+g+cQkZpDkv2rWRT+rbTTq2RTBLJ9Vqctsi5c+ficrlqzPIeNWoUo0eP9tnHEM7MJEmEtOxMwYp3Ax1KUAht3U3X8nRr4mmaSun278n7Zo4exQUv2Ur0VX/BmtIHsz2UdUe38G3aGo4V/+9OR8f67Xiw5/gz9j8JxqJ63KS//RBKodgb71wa3vVvbPFNdStPtwQF4C0v5ugrd+hVXNCzNWxNVP9bMSe2JK+ikCX7V7H+2M/c330s3Rp28skET0F/mtdD8dbvqmrLwhlJIRE0nfgWJlm/RSt1TVCq20nGh0/gzjyoV5F1gyQT1WsEtk7XIIdEYDZJyGJ6S1BRXZUcnTkOzUCrYRhNeMdriB04DsmqX8tA15mQJtlKTN+/6llk3aAqFK2dT/bseyjf+DWSasz1qYSzC21zZaBDMLSIy6/TNTmB3glKkrA3boslrrGexdYhJiI69kX60yxvwfgkm4PIK28IdBiG5WjeEUtMou7l6r6WhMksi1rUGTiapyDZQgMdhlBLltgGWOqJi+/pVDXtzrwGeW3pn6Ak8+/ZtIHeRQc3k0TsoDuRbOLOXdCSzMT0FcM9/iwk6XLkCN+sCeeb1bgkM9F9Rvmk6GAVcUUqcnhsoMMQLoJklnE074ij+bmXqr10mIgdoG/H+B/5JEFJZpmQpCtELep35vAYYvqM9kkVWPAvyWon7vqJmGRroEMxhNC2PTCHRvqsfJ+tZ2oyy8Tf9PeqdaIucXGp/4dJDCuoMyRbiGghACZbCLED7/BZ7Ql8maAkCUtUAlG9Tl08/VLiaNUFe5N2IkHVIZLFRkTXwVjimgQ6lICKH/6gz2/6+LR6I1ntRPW8EWtiS18exrBMFjtxQ+8TTbs6yGS2EH/DA3CJbnoR0f16HE3aIVl829T1eftLsthIvOUxJMepi7PVaZKZ+n+Z8vtW8EJdY5IkLNH1iR00PtCh+J2tYTIxfUb55cLrlw4iyRZCwsh/XFL9UfHDJmJr0MrnVxghcCSrnfCUvkT1HBHoUPxGcoRXXXj9NNjYLxnDJFuw1W9B3JD/41KoEkf3u42Q5CtE0+4SIFntRPUeSVhKv0CH4nsmiYSRj/i0U/zP/Falkax2Qtv2JP7Gh+p0TSri8lQiL79OJKdLiGSxUW/wXThadgl0KL4jyST85VFs9VvoulrBOQ/rtyNRlaRCkrqQ8JdHQap7d7VCWl9JTP/bRHK6BEkWGwkjHsbWIOncLw42Zpn6ox7D0bS9389tXZdbOV+qx4Ur4wBZnz6Lprj9fXifiOg+rGowppgIfElT3ZVkL3yZyoPbAh2KLiRbCPVHTcOa0Cwg53ZAEhRUJSl3zjEyP56O5g7edcxNFjvxwx/A0SxF1JwEoOrcrtrlaFGgQ7kockQciWOewhwWjRSgkfMBS1BQtZSq6iwj58uZOI/vDlQYtWaJaUD90Y9jDo0Sd+uEGlS3k8pDv5KzeHZQXoAdLTr9PhDTodsed7UR0AR1kupxUfbbD+SvfC+g+9RfiNDWV1bPyTJJdbfTX6g9VXGjVpaRtWBG0KwyK4VEEHfdBBwtOhqiRWCIBAVVSUp1VZLz1UycR8+wEbwBmEOjiLlmLKFtuov+JuGcNE1DU9yU/PIdRes+N/SGn2GX9aXeoDsxmS1+vVN3NoZJUCepHhdlu9dTsGoeaqVxfkyTbCXyyhuI6jEck2Q2zA8oBAfV4wJNo3jT1xRtWmSo/SPlqATib3gAa3xTQ9Sa/shwCQp+/zExUb53I0Ubv8KTeyxgsZgsNiK6DCKq902YJNlwP6AQXFSPC7xeCtZ+RsnWb8F7/rtR683WIInIK28gpFVXTGZzQPuazsSQCeokzetFUxXcuccpWv9F1aagmuqXY1vjmxLarieRl6eCSRKJSdCV6naiKW6KNi6ifO9GlKJz70atB5NsJaz9VUT2vBE5LNrwfaiGTlB/pLoq0RQ3xT8vpeLgNtxZh/VNViYJW8Nkwtr1IrRtTySrXTTlBJ9Tf78p5C0ronTnj1Ts3Yg756iuxzDJVmyJLQlt15vwlL6gaUGz9HTQJKiTZrzwPIUFBbzw3LM40/dSeXgHrsyDuLMOoboqzq8Qk4QcEYsclYAlKh570w6EJl9R9ZTFZsiqrlD3aYoHTfWiuisp37MRV9YhlKJsPIVZeEsLgfP7U5WjErA1TMLRtAP2Ju2xRMWjelxIFlvQrUsWVNFu3LiRrxZ9Td++faumzbTohKNJe1TFjWSxoXpcaL//p7qdqO5KVFcFqrMMkyRjiUlEjqiH5AirPhlMklSVlOrw/EAhOJhkCyYsSFY7EV0HoykuNFXFZJYxSWa85cV4irL/1xyUqvqNJFsIb81fzHc/rAdJos/VjZl83T2YrI7q3avNQZaYTgqaqIuKipg5cyYTJkxg79691Y+bZAvm35thZrMM9vNb4c9kFbUkwbhMkoTpT6sGyBGxyBGx0KRdjcc3bNjApu27+errxZhMJsaPH8+qH9czYMAAf4bsE0FTbXjiiSd48MEHiYiICHQogmAocXFxPProo1itViwWCy1btiQjIyPQYekiKBLUggULSExMpEePHoEORRAMJykpiU6dOgFw5MgRli1bRp8+fQIblE6CopN83Lhx5ObmYjabKS4upqKiguHDhzN16tRAhyYIhpGWlsY999zDxIkTufHGGwMdji6CIkH90cKFC9m8eTMzZswIdCiCYBhbt25l0qRJTJ06lSFDhgQ6HN0ETSe5IAinl5mZyX333cfMmTPrXDdI0NWgBEGo6dlnn+WLL76gSZP/7dM3atQoRo8eHcCo9CESlCAIhhUUd/EEQbg0iQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJhiQQlCIJh/X9lKGOxIwztlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_demographic('MonetaryScore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(50,20))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(customer_data.MonetaryScore[customer_data.Cluster == 0], customer_data[\"FrequencyScore\"][customer_data.Cluster == 0], customer_data[\"RecencyScore\"][customer_data.Cluster == 0], c='blue', s=60)\n",
    "ax.scatter(customer_data.MonetaryScore[customer_data.Cluster == 1], customer_data[\"FrequencyScore\"][customer_data.Cluster == 1], customer_data[\"RecencyScore\"][customer_data.Cluster == 1], c='red', s=60)\n",
    "ax.scatter(customer_data.MonetaryScore[customer_data.Cluster == 2], customer_data[\"FrequencyScore\"][customer_data.Cluster == 2], customer_data[\"RecencyScore\"][customer_data.Cluster == 2], c='green', s=60)\n",
    "ax.scatter(customer_data.MonetaryScore[customer_data.Cluster == 3], customer_data[\"FrequencyScore\"][customer_data.Cluster == 3], customer_data[\"RecencyScore\"][customer_data.Cluster == 3], c='orange', s=60)\n",
    "\n",
    "ax.view_init(30, 185)\n",
    "plt.xlabel(\"MonetaryScore\")\n",
    "plt.ylabel(\"FrequencyScore\")\n",
    "ax.set_zlabel('RecencyScore')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>RecencyScore</th>\n",
       "      <th>FrequencyScore</th>\n",
       "      <th>MonetaryScore</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RecencyScore  FrequencyScore  MonetaryScore  Cluster\n",
       "0               1               1              1        0\n",
       "1               1               2              1        0\n",
       "2               4               4              4        1\n",
       "3               1               1              1        0\n",
       "4               1               2              3        2\n",
       "..            ...             ...            ...      ...\n",
       "678             4               4              3        1\n",
       "679             4               2              2        3\n",
       "680             4               2              2        3\n",
       "681             4               1              2        3\n",
       "682             4               1              2        3\n",
       "\n",
       "[682 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label(row):\n",
    "    if row['Cluster'] == 0:\n",
    "        return 'lost'\n",
    "    elif row['Cluster'] == 1 :\n",
    "        return 'loyal/core'\n",
    "    elif row['Cluster'] == 2 :\n",
    "        return 'slipping'\n",
    "    elif row['Cluster'] == 3 :\n",
    "        return 'Faithful'\n",
    "             \n",
    "result['label'] = customer_data.apply(lambda row: label(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>reseller_id</th>\n",
       "      <th>superdealer_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>Cable TV</th>\n",
       "      <th>Education</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Telco</th>\n",
       "      <th>Universal Pin</th>\n",
       "      <th>used_reseller</th>\n",
       "      <th>used_superdealer</th>\n",
       "      <th>superdealer</th>\n",
       "      <th>services</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>lost</td>\n",
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       "      <td>14</td>\n",
       "      <td>231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27338</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>loyal/core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7800</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>slipping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>2682</td>\n",
       "      <td>30</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>10050</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>loyal/core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>2684</td>\n",
       "      <td>39</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Faithful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>2690</td>\n",
       "      <td>32</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Faithful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>2694</td>\n",
       "      <td>35</td>\n",
       "      <td>464.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Faithful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2725</td>\n",
       "      <td>30</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Faithful</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>682 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  DaysSinceLastTrans  reseller_id  superdealer_id  \\\n",
       "0          3                 351          0.0             0.0   \n",
       "1         14                 231          0.0             0.0   \n",
       "2         18                  35          0.0            18.0   \n",
       "3         19                 605          0.0             0.0   \n",
       "4         32                 132          0.0             0.0   \n",
       "..       ...                 ...          ...             ...   \n",
       "678     2682                  30       2557.0           501.0   \n",
       "679     2684                  39        464.0           501.0   \n",
       "680     2690                  32        464.0           501.0   \n",
       "681     2694                  35        464.0           501.0   \n",
       "682     2725                  30       1802.0           447.0   \n",
       "\n",
       "     TotalTransactionAmount  Tx_Count  Cable TV  Education  Electricity  \\\n",
       "0                       100         1       0.0        0.0          0.0   \n",
       "1                        51         2       0.0        0.0          0.0   \n",
       "2                     27338        70       1.0        0.0          2.0   \n",
       "3                       100         1       0.0        0.0          0.0   \n",
       "4                      7800         2       0.0        0.0          0.0   \n",
       "..                      ...       ...       ...        ...          ...   \n",
       "678                   10050        40       0.0        0.0          0.0   \n",
       "679                    1200         2       0.0        0.0          0.0   \n",
       "680                    1400         3       0.0        0.0          0.0   \n",
       "681                     300         1       0.0        0.0          0.0   \n",
       "682                     200         1       0.0        0.0          0.0   \n",
       "\n",
       "     Telco  Universal Pin  used_reseller  used_superdealer  superdealer  \\\n",
       "0      1.0            0.0              0                 0            0   \n",
       "1      1.0            0.0              0                 0            0   \n",
       "2      9.0            0.0              0                 1            1   \n",
       "3      1.0            0.0              0                 0            0   \n",
       "4      2.0            0.0              0                 0            0   \n",
       "..     ...            ...            ...               ...          ...   \n",
       "678    5.0            0.0              1                 1            0   \n",
       "679    2.0            0.0              1                 1            0   \n",
       "680    1.0            0.0              1                 1            0   \n",
       "681    1.0            0.0              1                 1            0   \n",
       "682    1.0            0.0              1                 1            0   \n",
       "\n",
       "     services       label  \n",
       "0           1        lost  \n",
       "1           1        lost  \n",
       "2           3  loyal/core  \n",
       "3           1        lost  \n",
       "4           1    slipping  \n",
       "..        ...         ...  \n",
       "678         1  loyal/core  \n",
       "679         1    Faithful  \n",
       "680         1    Faithful  \n",
       "681         1    Faithful  \n",
       "682         1    Faithful  \n",
       "\n",
       "[682 rows x 16 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_segment  = pd.DataFrame(result.groupby('label').agg({'user_id':'count',\n",
    "                                                                'TotalTransactionAmount':'sum', \n",
    "                                                                'Tx_Count':'sum',\n",
    "                                                             'DaysSinceLastTrans':'mean'}))\n",
    "customer_segment['%Customers'] = (customer_segment['user_id'] / sum(customer_segment['user_id'] ))*100\n",
    "customer_segment['%Revenue'] = (customer_segment['TotalTransactionAmount'] / sum(customer_segment['TotalTransactionAmount'] ))*100\n",
    "customer_segment['%Tx_Count'] = (customer_segment['Tx_Count'] / sum(customer_segment['Tx_Count'] ))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>TotalTransactionAmount</th>\n",
       "      <th>Tx_Count</th>\n",
       "      <th>DaysSinceLastTrans</th>\n",
       "      <th>%Customers</th>\n",
       "      <th>%Revenue</th>\n",
       "      <th>%Tx_Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Faithful</th>\n",
       "      <td>150</td>\n",
       "      <td>596353</td>\n",
       "      <td>1519</td>\n",
       "      <td>57.520000</td>\n",
       "      <td>21.994135</td>\n",
       "      <td>0.690711</td>\n",
       "      <td>1.253652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lost</th>\n",
       "      <td>271</td>\n",
       "      <td>68296</td>\n",
       "      <td>387</td>\n",
       "      <td>112.450185</td>\n",
       "      <td>39.736070</td>\n",
       "      <td>0.079102</td>\n",
       "      <td>0.319397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loyal/core</th>\n",
       "      <td>144</td>\n",
       "      <td>54288721</td>\n",
       "      <td>110650</td>\n",
       "      <td>42.208333</td>\n",
       "      <td>21.114370</td>\n",
       "      <td>62.878523</td>\n",
       "      <td>91.320998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slipping</th>\n",
       "      <td>117</td>\n",
       "      <td>31385683</td>\n",
       "      <td>8610</td>\n",
       "      <td>175.803419</td>\n",
       "      <td>17.155425</td>\n",
       "      <td>36.351665</td>\n",
       "      <td>7.105954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  TotalTransactionAmount  Tx_Count  DaysSinceLastTrans  \\\n",
       "label                                                                       \n",
       "Faithful        150                  596353      1519           57.520000   \n",
       "lost            271                   68296       387          112.450185   \n",
       "loyal/core      144                54288721    110650           42.208333   \n",
       "slipping        117                31385683      8610          175.803419   \n",
       "\n",
       "            %Customers   %Revenue  %Tx_Count  \n",
       "label                                         \n",
       "Faithful     21.994135   0.690711   1.253652  \n",
       "lost         39.736070   0.079102   0.319397  \n",
       "loyal/core   21.114370  62.878523  91.320998  \n",
       "slipping     17.155425  36.351665   7.105954  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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Vbd++XX/729/07rvvql69et7IBgAAAACoJLflbubMmcrJydGcOXN0ySWXKDMzUzNmzPBGNgAAAABAJZndPaFNmzZ68skndejQITmdTs2YMYOVOwAAAACoYdyu3H3zzTcaOHCgxowZo9zcXPXr1087d+70RjYAAAAAQCW5LXfJycl644031LhxYzVv3lzJycmaOXOmN7IBAAAAACrJbbk7e/as2rVr59oODw9XSUmJR0MBAAAAAKrGbbkzm80qKCiQyWSSJB04cMDjoQAAAAAAVeP2hioPP/yw7r77bv3yyy96/PHH9eWXX2ratGneyAYAAAAAqCS35a5///5q06aNvvzySzkcDo0dO1Zt27b1RjYAAAAAQCW5PS3z559/1uuvv6677rpL1157rZ577jnl5+d7IxsAAAAAoJLclrvJkyerTZs2kqSWLVuqV69eSkhI8HgwAAAAAEDluS13v/76q+655x5JksVi0b333svKHQAAAADUMG7LXUlJiXJzc13bv/zyi5xOp0dDAQAAAACqxu0NVe69914NHz5c119/vUwmkzIyMjRx4kRvZAMAAAAAVJLbcjdixAiFhobqq6++kr+/v/7xj3+oQ4cO3sgGAAAAAKgkt+VOkho2bKhevXrJ6XTKbrdrz5496ty5s6ezAQAAAAAqyW25mzdvnl577TU1adLEtc9kMmnjxo0eDQYAAAAAqDy35W7VqlXasGGDmjVr5o08AAAAAIDz4PZumS1atKDYAQAAAEAN53blLiwsTMnJybrhhhtUt25d136uuQMAAACAmsNtuVuxYoUkad26da59XHMHAAAAADWL23L36aefeiMHAAAAAOACuL3m7vTp05o2bZr+/ve/68SJE5o6dapOnz7tjWwAAAAAgEpyW+5mzJihhg0b6tixY7JYLCosLNTUqVMrNfiCBQsUGRmpyMhIJScnS5Li4+MVERGhm2++WTfffLM+/vjjC3sHAAAAAAD3p2Xu3btXzzzzjDZv3qx69eopJSVFw4YNcztwRkaGtmzZopUrV8pkMumBBx7Qxx9/rKysLL399tsKDg6uljcAAAAAAKjEyp2fX+mnlJSUlNl3LkFBQZo8ebICAgJUp04dtW3bVkeOHNGRI0eUkJCgqKgovfDCC3I4HOefHgAAAAAgqRLlrmfPnnr22Wd19uxZffHFF4qLi1Pv3r3dDty+fXt169ZNkpSTk6OPPvpI119/vfr06aNZs2Zp6dKl2rFjh5YvX37BbwIAAAAAajuT0+l0VvQEu92ul19+WZ999plKSkp0/fXX65FHHpHFYqnUBN9//73GjBmjuLg43XLLLaUe+/jjj5WWlqaFCxe6HcdqtSorK6tScwIAAKD26BASoq2zk30dAygjbPJEZR86VO3jhoaGnrOPub3m7oUXXtD48eM1duzYKk+amZmpcePGKSEhQZGRkdq3b59ycnI0ePBgSZLT6ZTZ7DZCKeW9EQAAANRO1pOnFBhY39cxgDICAizq0aNHtY3nbsHL7WmZn3322XlNfPToUY0dO1YpKSmKjIyU9FuZmzVrlgoKCmS327VkyRINGjTovMYHAAAAAPzB7bJZq1atdP/996t79+6qX/+Pv4jcd999Fb4uNTVVVqtVs2fPdu0bOXKkRo8erejoaBUXFysiIqJSd94EAAAAAFTMbblr3LixJOmnn36q0sCJiYlKTEw852OjRo2q0lgAAAAAgIq5LXdNmzbV+PHjvZEFAAAAAHCePHbNHQAAAADAezx2zR0AAAAAwHs8ds0dAAAAAMB73Ja7Z555xhs5AAAAAAAXwG25e+ihh865f9GiRdUeBgAAAABwftyWu8GDB7t+ttvtWr9+vUJDQz0aCgAAAABQNW7L3S233FJmOyYmxmOBAAAAAABV5/arEP7M6XQqLy/PE1kAAAAAAOepytfcZWdnq1evXh4LBAAAAACouipdc2cymRQdHa3rrrvOo6EAAAAAAFXj9rTMQYMGKS8vT7fccot69uypLVu2yGq1eiMbAAAAAKCS3Ja7+Ph4nThxQpLUqFEjmUwmPfnkk57OBQAAAACoArflLicnR5MmTZIkNWzYUAkJCfr+++89HgwAAAAAUHluy11xcbEKCwtd26dPn5bT6fRoKAAAAABA1bi9ocrw4cN1++23a8iQITKZTPr444916623eiMbAAAAAKCS3Ja7MWPGqH379srIyJDZbNaECRPUt29fb2QDAAAAAFSS29MyCwsL9f333ysxMVExMTH64osvVFRU5I1sAAAAAIBKcrtyFx8fr1atWkkqfbfM5557zuPhAAAwuiLbGZU4SnwdAzgnfz9/BQbU83UMAJXkttzl5ORo/vz5kv64W+ZNN93k8WAAANQGJY4SPbdhka9jAOc0PuIhX0cAUAXcLRMAAAAADIC7ZQIAAACAAVT5bplPPPGEwsPDvZENAAAAAFBJbsvdL7/8IofDobCwMHXp0kVBQUHeyAUAAAAAqIIKy93//u//avHixWrTpo3sdrsOHz6sUaNG6YknnvBWPgAAAABAJZRb7j744AP9+9//1saNG9WkSRNJUm5ursaPH69ly5bp9ttv91pIADVX8dkiObmNO2ook5+/zHUDfR0DAACvKLfcLVmyRAsWLHAVO0lq1qyZUlJSNHbsWModAEmS01Gi/yx73tcxgHNqd/ujvo4AAIDXlPtVCDabTcHBwWX2N2/eXDabzaOhAAAAAABVU265KyoqKvdFdrvdI2EAAAAAAOen3HIXGhqqVatWldmflpamLl26eDQUAAAAAKBqyr3m7rHHHlNMTIz+85//6JprrlFxcbG2bdumdevWacmSJZUafMGCBfroo48kSeHh4Zo4caIyMjL0zDPPyGq16sYbb9Rjjz1WPe8EAAAAAGqxclfuWrdurXfeeUcFBQVKSUnR888/L6vVqqVLl6pFixZuB87IyNCWLVu0cuVKpaWlac+ePVq7dq0SEhL04osvKj09XVlZWdq8eXO1viEAAAAAqI0q/J67li1batq0aec1cFBQkCZPnqyAgABJUtu2bZWTk6OQkBC1bt1akhQVFaV169YpPDz8vOYAAAAAAPym3JW7C9W+fXt169ZNkpSTk6OPPvpIJpNJQUFBrucEBwcrNzfXUxEAAAAAoNaocOWuOnz//fcaM2aMJk6cKH9/f+Xk5LgeczqdMplMVRovKyurmhMCuBAd2/6lwrvrAr5ks9m0a2+mr2NUKKQdxxBqLqvVquw9+3wdw60OISEqKjrt6xhAGTabVVmZ2V6br9xy98knn2jgwIGy2WyuUyurKjMzU+PGjVNCQoIiIyO1fft25efnux7Pz88/53fpVSQ0NFQWi+W88gCofvaiUwoMDPR1DOCcAgIC1KNHD1/HqNCps4UcQ6ixLBZLjT+GJMl68pQCA+v7OgZQRkBA9R5DVqu1wsWuck/LnDdvniTpzjvvPK+Jjx49qrFjxyolJUWRkZGSpK5du+rgwYM6dOiQSkpKtHbtWvXt2/e8xgcAAAAA/KHclbv69etr8ODBys3NVVRUVJnH16xZU+HAqampslqtmj17tmvfyJEjNXv2bMXFxclqtSo8PFxDhgy5gPgAAAAAAKmCcvfqq69q7969mjJlip588skqD5yYmKjExMRzPrZ69eoqjwcAAAAAKF+55a5Bgwbq2bOn/vWvfyk4OFh79uxRcXGxunTpogYNGngzIwAAAADADbd3yzx16pRiYmLUtGlTlZSUKDc3V4sWLVL37t29kQ8AAAAAUAluy92cOXOUkpKiPn36SJK2bt2q2bNna+nSpR4PBwAAAACoHLdfYn769GlXsZOksLAwnTlzxqOhAAAAAABV47bcmUwm/fTTT67tw4cPy9/f36OhAAAAAABV4/a0zLFjx+rOO+9UWFiYTCaTtmzZoqSkJG9kAwAAAABUkttyN3DgQLVp00ZfffWVHA6HxowZo7Zt23ojGwAAAACgktyWO0lq06aN2rRp4+ksAAAAAIDz5PaaOwAAAABAzUe5AwAAAAADcFvuJk6c6I0cAAAAAIAL4Lbc7d27V06n0xtZAAAAAADnye0NVYKDgxUZGamuXbuqfv36rv2JiYkeDQYAAAAAqDy35e7qq6/W1Vdf7Y0sAAAAAIDz5LbcxcbG6uzZszp06JDat28vq9WqevXqeSMbAAAAAKCS3F5z9+2332rgwIEaM2aM8vLy1K9fP+3cudMb2QAAAAAAleS23M2ZM0dvvPGGGjdurObNmys5OVkzZ870RjYAAAAAQCW5PS3z7NmzateunWs7PDxcc+fO9Wgobzp9xqaSEoevYwBl+Pv7qX69AF/HAAAAwEXCbbkzm80qKCiQyWSSJB04cMDjobyppMShOambfB0DKGPSP/r7OgIAAAAuIm7L3cMPP6y7775b+fn5evzxx/Xll19q2rRp3sgGAAAAAKgkt+Wuf//+atOmjb788ks5HA6NHTtWbdu29UY2AAAAAEAlub2hiiQVFxfL4XDIbDbLbHbbBwEAAAAAXua23H3wwQe65557tHv3bu3YsUOjRo3S+vXrvZENAAAAAFBJbpfh3njjDa1cuVLBwcGSpCNHjmjMmDEaPHiwx8MBAAAAACrH7cpdnTp1XMVOki6//HLVqVPHo6EAAAAAAFVT7srdnj17JElXXnmlpk2bpjvvvFP+/v5asWKFunfv7rWAAAAAAAD3yi13cXFxpbY/++wz188mk0mJiYkeCwUAAAAAqJpyy92nn37qzRwAAAAAgAvg9oYq+fn5WrlypU6cOFFq/8SJEz2VCQAAAABQRW5vqPLwww9r165dcjqdpf4BAAAAANQcblfu7Ha7FixYcF6DFxYWauTIkVq0aJFatWql+Ph4ZWZmql69epKk2NhYDRo06LzGBgAAAAD8wW2569y5s7Kzs9WhQ4cqDfztt98qMTFROTk5rn1ZWVl6++23S321AgAAAADgwrktd927d9fw4cMVFBQks/mPp2/cuLHC1y1dulRJSUmua/POnDmjI0eOKCEhQbm5uRo0aJBiY2Pl5+f2zFAAAAAAgBtuy11qaqpSUlJ0xRVXVGngmTNnltr+5Zdf1KdPHyUlJalhw4YaM2aMli9frjvuuKNqiQEAAAAAZbgtd40aNdLQoUMveKLWrVtr4cKFru2YmBilpaVVudxlZWVdcJb/FvLX9ioqKqrWMYHqYLPZlJm5x9cx3OrY9i8cQ6ixbDabdu3N9HWMCoW04xhCzWW1WpW9Z5+vY7jVISRERUWnfR0DKMNmsyorM9tr87ktd3369NGcOXMUERGhgIAA1/7OnTtXaaJ9+/YpJydHgwcPliQ5nc5Sp3lWVmhoqCwWS5VfV56ThWcVGBhYbeMB1SUgIEA9evTwdQy37EWnOIZQY10Mx9Gps4UcQ6ixLBZLjT+GJMl68pQCA+v7OgZQRkBA9R5DVqu1wsUut+1qzZo1kqT169e79plMJrfX3P2Z0+nUrFmz1KdPHwUGBmrJkiW65ZZbqjQGAAAAAODc3Ja7Tz/9tFom6tixo0aPHq3o6GgVFxcrIiJCw4YNq5axAQAAAKC2c1vuXn/99XPuv++++yo1wX+Xw1GjRmnUqFGVjAYAAAAAqCy35S47+48LAG02m77++muFhYV5NBQAAAAAoGrclrtnnnmm1HZubq6mTJnisUAAAAAAgKqr8jeIN2vWTD/99JMnsgAAAAAAzlOVrrlzOp3KyspSkyZNPBoKAAAAAFA1VbrmTpJatGihiRMneiwQAAAAAKDqqnzNHQAAAACg5im33MXHx5f7IpPJpFmzZnkkEAAAAACg6sotd+3bty+z79dff9Wbb76pli1bejQUAAAAAKBqyi13999/f6ntjIwMTZo0SVFRUUpMTPR4MAAAAABA5bm95q64uFjPPfecVq5cqaefflqDBw/2Ri4AAAAAQBVUWO5ycnL0+OOPq379+kpLS1Pz5s29lQsAAAAAUAXlfon5Bx98oDvuuEODBg3SW2+9RbEDAAAAgBqs3JW7KVOmyM/PTy+//LJeeeUV136n0ymTyaSdO3d6JSAAAAAAwL1yy93GjRu9mQMAAAAAcAHKLXd83QEAAAAAXDzKveYOAAAAAHDxoNwBAAAAgAFQ7gAAAADAACh3AAAAAGAAlDsAAAAAMADKHQAAAAAYAOUOAAAAAAyAcgcAAAAABkC5AwAAAAADoNwBAAAAgAFQ7gAAAADAACh3AAAAAGAAlDsAAAAAMADKHQAAAAAYAOUOAAAAAAzAo+WusLBQw4YN0+HDhyVJGRkZioqKUkREhObOnevJqQEAAACgVvFYufv2228VHR2tnJwcSdLZs2eVkJCgF198Uenp6crKytLmzZs9NT0AAAAA1CoeK3dLly5VUlKSgoODJUm7du1SSEiIWrduLbPZrKioKK1bt85T0wMAAABArWL21MAzZ84stZ2Xl6egoCDXdnBwsHJzcz01PQAAAADUKh4rd3/mcDhkMplc206ns9R2ZWVlZVVnLIX8tb2KioqqdUygOthsNmVm7vF1DLc6tv0LxxBqLJvNpl17M30do0Ih7TiGUHNZrVZl79nn6xhudQgJUVHRaV/HAMqw2azKysz22nxeK3fNmzdXfn6+azs/P991ymZVhIaGymKxVFuuk4VnFRgYWG3jAdUlICBAPXr08HUMt+xFpziGUGNdDMfRqbOFHEOosSwWS40/hiTJevKUAgPr+zoGUEZAQPUeQ1artcLFLq99FULXrl118OBBHTp0SCUlJVq7dq369u3rrekBAAAAwNC8tnJnsVg0e/ZsxcXFyWq1Kjw8XEOGDPHW9AAAAABgaB4vd59++qnr57CwMK1evdrTUwIAAABAreO10zIBAAAAAJ5DuQMAAAAAA6DcAQAAAIABUO4AAAAAwAAodwAAAABgAJQ7AAAAADAAyh0AAAAAGADlDgAAAAAMgHIHAAAAAAZAuQMAAAAAA6DcAQAAAIABUO4AAAAAwAAodwAAAABgAJQ7AAAAADAAyh0AAAAAGADlDgAAAAAMgHIHAAAAAAZAuQMAAAAAA6DcAQAAAIABUO4AAAAAwAAodwAAAABgAJQ7AAAAADAAyh0AAAAAGADlDgAAAAAMgHIHAAAAAAZAuQMAAAAAA6DcAQAAAIABUO4AAAAAwAAodwAAAABgAJQ7AAAAADAAsy8mjYmJ0fHjx2U2/zb9tGnT1LVrV19EAQAAAABD8Hq5czqdysnJ0aZNm1zlDgAAAABwYbx+WuaBAwckSffff79uuukmvf32296OAAAAAACG4/Wls5MnTyosLExPPvmk7Ha77rnnHv31r3/V3/72N29HAQAAAADD8Hq5u/rqq3X11Ve7tkeMGKHNmzdXutxlZWVVa56Qv7ZXUVFRtY4JVAebzabMzD2+juFWx7Z/4RhCjWWz2bRrb6avY1QopB3HEGouq9Wq7D37fB3DrQ4hISoqOu3rGEAZNptVWZnZXpvP6+Vux44dstvtCgsLk/TbNXhVufYuNDRUFoul2vKcLDyrwMDAahsPqC4BAQHq0aOHr2O4ZS86xTGEGutiOI5OnS3kGEKNZbFYavwxJEnWk6cUGFjf1zGAMgICqvcYslqtFS52ef2au1OnTik5OVlWq1WFhYVauXKlBg0a5O0YAAAAAGAoXl+569+/v7799lsNHz5cDodDd911V6nTNAEAAAAAVeeT7yJ49NFH9eijj/piagAAAAAwJK+flgkAAAAAqH6UOwAAAAAwAModAAAAABgA5Q4AAAAADIByBwAAAAAGQLkDAAAAAAOg3AEAAACAAVDuAAAAAMAAKHcAAAAAYACUOwAAAAAwAModAAAAABgA5Q4AAAAADIByBwAAAAAGQLkDAAAAAAOg3AEAAACAAVDuAAAAAMAAKHcAAAAAYACUOwAAAAAwAModAAAAABgA5Q4AAAAADIByBwAAAAAGQLkDAAAAAAOg3AEAAACAAVDuAAAAAMAAKHcAAAAAYACUOwAAAAAwAModAAAAABgA5Q4AAAAADIByBwAAAAAGQLkDAAAAAAPwSblbs2aNhg4dqoiICL3zzju+iAAAAAAAhmL29oS5ubmaO3euVqxYoYCAAI0cOVK9e/dWu3btvB0FAAAAAAzD6yt3GRkZ6tOnjxo3bqzAwEANHjxY69at83YMAAAAADAUr6/c5eXlKSgoyLUdHBysXbt2uX2d0+mUJNlstmrNY7fbFGjh0kPUPHa7TVarydcx3Cq22eWoU8/XMYBzstnscvhbfR2jQnabXfX86/o6BnBOdptdVlPNPoYkyWa3y1SPzyLUPDa7XbJW3zH0exf6vRv9mdfLncPhkMn0x3+wOp3OUtvlsdvtkqTs7OxqzzSkZ9NqHxO4UDkHvvd1hMq7sr+vEwDn9N3+g76OUCkDm4f5OgJwTge/P+DrCJVWN+IGX0cAysjO8cznkN1uV926Zf8w6PVy17x5c+3YscO1nZ+fr+DgYLevq1+/vjp06KA6depUqgwCAAAAgJE4nU7Z7XbVr1//nI97vdxde+21mj9/vo4fP6569eppw4YNmj59utvX+fn5qWHDhl5ICAAAAAA107lW7H7n9XLXrFkzPfbYY7rnnntkt9s1YsQIdenSxdsxAAAAAMBQTM7yrsYDAAAAAFw0uE0kAAAAABgA5Q4AAAAADIByBwAAAAAGQLkDAAAAAAOg3AEAAACAAVDucE4DBgzQ4cOHtXHjRs2bN++8xpg3b542btxYzckA39u2bZtiYmKqbbwrr7zS9fPRo0f1z3/+s9rGBi4mhw8fVmhoqG6++eZS/xw9evScz3/vvff03nvvSZLi4+P1008/SfrjM+zP9uzZo379+mnUqFHlZpg/f77mz59fDe8GqLnO53Ps1KlTGjt2rIcSobp4/XvucHG54YYbdMMNN5zXa/kPVKDqPv/8c/Xt29fXMQCfCQ4O1qpVqyr13OjoaNfP27Ztc/sfnps2bdJNN92kxx9//IIyArVRQUGB9u7d6+sYcINyB/3888964oknVFRUJD8/PyUmJroeW7FihbZv367Zs2drwIABGjJkiDIyMiRJs2bN0lVXXaWYmBh17NhRO3bskNVqVUJCgq677jpNnjxZvXr1Uq9evRQbG6v27dtr7969atKkiebNm6fGjRsrPT1dL7zwggIDA9WpUyeVlJRo9uzZvvpVAFVy8OBBTZ06VSdOnFBgYKCmTJmiNm3a6IYbbtDGjRvVoEEDHT58WKNHj1Z6errmzp2rrVu3qqCgQMHBwZo7d66aNm1aaswvvvjCNeaUKVN04MABBQQEaPLkyQoLC9OmTZv0/PPPy+FwqHXr1po2bZqaNm2qAQMGqEuXLtq7d6/effddffHFF3rzzTflcDjUuXNnJSUlyWKx+Og3BVyY7OxsTZ8+XUVFRTp+/LhGjx6t6Oho1wqbxWJRXl6eRo8erXfeeUeStHDhQu3du1dnzpxRcnKyTpw44VrlCwgI0OHDh9WrVy/deuutkn5bQd+3b59v3iDgI+f6HOvSpYvWrFmjV199Vf7+/mrVqpWeffZZzZgxQ3l5eRo7dqwWLlzo6+goB6dlQsuXL1e/fv20YsUKjRs3TpmZmeU+NzAwUGlpaRo3bpwmTZrk2l9YWKiVK1fqueee0+TJk2Wz2Uq97rvvvtN9992ntWvXqlGjRlqzZo2OHz+uWbNm6c0339Ty5ctVUFDgsfcIeMKECRMUExOjNWvWKD4+Xv/85z8VEBCgfv36ad26dZKktLQ0DR8+XIcOHdKBAwf0/vvva/369WrRooVWr15dajy73a4TJ04oODhY8+bN0xVXXKGPPvpIycnJev7553Xs2DFNnTpVCxcu1Jo1a9S9e3dNmzbN9fq+fftq/fr1On78uJYuXar3339fq1atUpMmTZSamurV3w1wvvLy8kqdkvnqq69q2bJleuSRR/TBBx9o8eLFSk5OLvWa0aNHKzg4WC+//LIuvfRSSVK7du2UlpammJgYpaamKjw8XCNHjtTIkSMVGxvri7cG1Djn+hyz2Wx6/vnn9dprr2nFihVq2bKlDhw4oMTERAUHB1PsajhW7qCwsDDFxcVp7969Cg8P19133+36y+ef3XHHHZJ+u55h8uTJOn78eKn9nTp1UlBQUJm/fjZp0kRXXXWVJKl9+/YqKCjQjh07dPXVV6tZs2aSpOHDh+uTTz7xyHsEqtvp06d1+PBhRURESJK6deumSy65RAcOHNBtt92m+fPna8SIEVq7dq3efPNNNWvWTJMmTdKyZct08OBBffPNN7riiitKjZmZmanu3btLkr7++mulpKRI+m1FYcmSJdq0aZO6dOmiVq1aSZLuvPNOvfzyy67Xd+3aVdJvp6cdOnTIdVza7XbX8QfUdOc6LbOkpERffPGF/vWvfyk7O1tFRUVuxxk4cKCk30re+vXrPZIVuJhV9DnWv39/RUdHa+DAgRo8eLA6dep0zutYUfNQ7qAePXroww8/1Geffab09HStXLmy3OeazX/8X8bhcMjf31+SXP/7+/7/fp6kUqeDmUwmOZ1O+fn5yeFwVNfbALzK6XSec19JSYl69uypvLw8bdiwQa1atVKzZs2UlZWl8ePH695779XgwYPl5+dXZozPP/9cAwYMkPTbsWYymVyP7d+/v8zx4nQ6VVxc7Nr+/TgrKSnRjTfe6DrF+vTp0yopKameNw74wKOPPqpGjRqpf//+Gjp0qNauXev2Nb9/Lv33cfTffv8skn77AwhQ21T0OZaYmKjvvvtOmzdv1oQJExQbG6sePXr4ICWqitMyoeTkZK1evVq33HKLpk6dqv/7v/8r97kffvihJOnjjz9W27Ztdckll0iS0tPTJUm7d+/WyZMn1aFDB7fzdu/eXbt371ZeXp6cTqfS09PL/RAGapoGDRqoVatW2rBhgyTpm2++0S+//KL27dvLZDJp+PDhmjFjhut6nq+//lq9evVSdHS0/vKXv+izzz4rU7j+/e9/q1u3bpKka665xnW87d+/Xw8++KC6du2qb7/91vXX0yVLlqh3795lsvXu3Vsff/yxjh07JqfTqaeeekpvvvmmp34VgMd9+eWXGjdunAYOHKjPP/9cksocP/7+/lX6I0bjxo31n//8R5I4awS1UkWfYxEREbr00ks1ZswY3Xzzzdq7d6/MZnOpPyiiZmLlDoqJidH48eO1YsUK+fv7a86cOaWu4/lvO3fu1PLly1WvXr1SNz758ccfdcstt0iS5s6dW2olrzyXXXaZEhMTdf/99ysgIECtWrVSo0aNqudNAV7w7LPP6qmnntL8+fNVp04dzZ8/XwEBAZKkyMhIvfbaa65Tw4YOHarY2FhFRUVJkkJDQ0ud4vLzzz8rKCjIteo9btw4JSYm6qabbpLZbFZycrKaNm2qadOmKTY2Vna7XZdffrlmzpxZJlfHjh0VGxurv//973I4HOrUqZNGjx7t6V8H4DFxcXG66667ZLFY1LFjR7Vs2bLMKWL9+vXT6NGj9eqrr1ZqzOjoaD366KOKiopSnz59FBQU5InoQI1W3ufYuHHjdP/998tisahJkyaaPXu2GjVqpMsvv1wxMTF66623fB0d5TA5z7UmC5zDgAEDtHjxYtf1Pr+LiYlRbGzsOVcQKvLrr7/qrbfeUmxsrPz8/DRjxgyFhIRU6/eHAb7gcDj03nvv6eDBg6XuPgsAAOBJrNzBZxo3bqyTJ09q2LBh8vf3V+fOnV03gAAuZrGxsTp69Ch3qAQAAF7Fyh0AAAAAGAA3VAEAAAAAA6DcAQAAAIABUO4AAAAAwAAodwAAQ9i5c6f+8Y9/6Oabb1ZUVJRGjx6t7Ozs8x7vxx9/VFxcXDUmBADAs7hbJgDgovf1119rwoQJWrBggUJDQyVJq1evVkxMjD766CNddtllVR7zyJEjOnjwYHVHBQDAY7hbJgDgohcTE6OoqKgyX6eyfv16de/eXQcOHND06dO1du1aSdK2bdtc2/v379eUKVNks9nkdDo1YsQIjRw5UkOGDFFubq569uyp1NRUffLJJ1qwYIEcDofq16+v+Ph4denSRfPnz9cPP/yg3Nxc5efnq3Pnzurdu7fS0tJ0+PBhTZgwQcOGDZMkvfTSS9qwYYMcDodatmyppKQkNWvWTDExMbrkkkt04MABRUdHq1mzZnrppZdkMpnk7++viRMnqmfPnl7/vQIALi6s3AEALnpZWVlKSkoqs3/w4MGSpAMHDpT72tTUVA0YMECjR49Wfn6+Zs2apejoaM2YMUPTp09Xamqq9u/fr6SkJL3//vtq3bq1tm7dqkceeUTr1q2TJGVmZmrVqlWqU6eO+vbtq6ZNm+qdd97RJ598omeffVbDhg1TWlqasrOztWzZMpnNZi1ZskSJiYl65ZVXJEmNGjVSenq6JGngwIFKSUlRt27dtGXLFm3bto1yBwBwi3IHALjo+fn5yeFwnNdrBw0apEmTJmnXrl0KCwtTYmKi/PxKX5L+1VdfqU+fPmrdurUkKSwsTJdddpmysrIkSddee60aNmwoSQoODtb1118vSbriiit04sQJSdKmTZu0e/du3XbbbZIkh8OhM2fOuOa45pprXD9HRkYqNjZW4eHh+tvf/qYHH3zwvN4bAKB24YYqAICLXrdu3fTtt9+W2f/0008rIyNDJpNJ/30Vgt1ud/3cv39/rV+/XjfeeKP27t2rqKgo/fzzz6XGcTgcMplMpfY5nU4VFxdLkgICAko9ZjaX/dupw+HQAw88oFWrVmnVqlX64IMP9N5777keDwwMdP382GOP6d1331VoaKhWrFihUaNGVebXAACo5Sh3AICL3sMPP6wFCxa4VtIkacWKFVq/fr06dOigyy67TEeOHNGxY8fkdDr14Ycfup43fvx4paenKzIyUklJSWrQoIF++OEH+fv7u0pgWFiYtmzZoh9//FGStHXrVh09elRdu3atdMbrrrtOy5cvV2FhoSRp3rx5mjhxYpnnFRcXa8CAATpz5oyio6OVlJSkffv2yWazndfvBgBQe3BaJgDgonfNNddoxowZmjlzpoqKimS323XFFVdo8eLFatq0qZo2baqRI0fqtttuU1BQkPr166fdu3dLkh555BFNmTJFS5Yskb+/vwYOHKiePXuqoKBAFotFI0aM0LJly5SUlKTY2FiVlJSobt26WrRoketUzMq4/fbblZubqzvuuEMmk0ktWrTQ7NmzyzzPbDYrISFBTzzxhMxms0wmk2bNmlVmdRAAgD/jbpkAAAAAYACclgkAAAAABkC5AwAAAAADoNwBAAAAgAFQ7gAAAADAACh3AAAAAGAAlDsAAAAAMADKHQAAAAAYAOUOAAAAAAzg/wF43l28YWFsXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_count = customer_segment['%Customers'].sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(customer_count.index, customer_count.values, alpha=0.8)\n",
    "plt.title('customer_count')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel(' Customers', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transaction_count = customer_segment['%Tx_Count'].sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(transaction_count.index, transaction_count.values, alpha=0.8)\n",
    "plt.title('transaction_count')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel(' Customers', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\OLAMIDE\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Revenue_generated = customer_segment['%Revenue'].sort_values()\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(Revenue_generated.index, Revenue_generated.values, alpha=0.8)\n",
    "plt.title('Revenue_generated')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel(' Customers', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol>\n",
    "<li>    \n",
    "<b>Core/Loyal</b> - Your Best Customers<br>\n",
    "Who They Are: Highly engaged customers who have bought the most recent, the most often, and generated the most revenue.\n",
    "</li>\n",
    "<li>\n",
    "<b>Slipping</b> - Once Loyal, Now Gone<br>\n",
    "Who They Are: Great past customers who haven't bought in awhile.\n",
    "</li>\n",
    "<li>\n",
    "<b>Faithful</b> - The customers having common behaviour across these metrics.<br>\n",
    "Who They Are: Customer who have average metrics across each RFM scores.\n",
    "</li>\n",
    "<li>\n",
    "<b>Lost</b> - The customers with low recency, frequency and monetary value\n",
    "</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('ho.xlsx') as writer:\n",
    "    result.to_excel(writer, sheet_name = 'sheet1', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}