K_means_application.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"0\">Hierarchical Clustering</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href=\"#0\">Read the dataset</a>\n",
    "2. <a href=\"#2\">Data investigation</a>\n",
    "3. <a href=\"#3\">Data preprocessing </a>\n",
    "4. <a href=\"#4\">Features transformation </a>\n",
    "5. <a href=\"#5\">K means</a>\n",
    "6. <a href=\"#5\">Training datasets</a>\n",
    "7. <a href=\"#6\">Improvement ideas</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. <a name=\"1\">Read the dataset</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "First dowmload the data set from this link https://www.kaggle.com/code/sadkoktaybicici/credit-card-data-clustering-k-mean/data\n",
    "then import it in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the dataset is: (8950, 18)\n"
     ]
    }
   ],
   "source": [
    "#read the data\n",
    "\n",
    "data_path = 'CC GENERAL.csv'  #the path where you downloaded the data\n",
    "df = pd.read_csv(data_path)\n",
    "\n",
    "print('The shape of the dataset is:', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. <a name=\"2\">Data investigation</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n",
    "\n",
    "in this part you need to check the data quality and assess any issues in the data as:\n",
    "- null values in each column \n",
    "- each column has the proper data type\n",
    "- outliers\n",
    "- duplicate rows\n",
    "- distribution for each column (skewness)\n",
    "<br>\n",
    "\n",
    "**comment each issue you find** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8950 entries, 0 to 8949\n",
      "Data columns (total 18 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   CUST_ID                           8950 non-null   object \n",
      " 1   BALANCE                           8950 non-null   float64\n",
      " 2   BALANCE_FREQUENCY                 8950 non-null   float64\n",
      " 3   PURCHASES                         8950 non-null   float64\n",
      " 4   ONEOFF_PURCHASES                  8950 non-null   float64\n",
      " 5   INSTALLMENTS_PURCHASES            8950 non-null   float64\n",
      " 6   CASH_ADVANCE                      8950 non-null   float64\n",
      " 7   PURCHASES_FREQUENCY               8950 non-null   float64\n",
      " 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64\n",
      " 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64\n",
      " 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64\n",
      " 11  CASH_ADVANCE_TRX                  8950 non-null   int64  \n",
      " 12  PURCHASES_TRX                     8950 non-null   int64  \n",
      " 13  CREDIT_LIMIT                      8949 non-null   float64\n",
      " 14  PAYMENTS                          8950 non-null   float64\n",
      " 15  MINIMUM_PAYMENTS                  8637 non-null   float64\n",
      " 16  PRC_FULL_PAYMENT                  8950 non-null   float64\n",
      " 17  TENURE                            8950 non-null   int64  \n",
      "dtypes: float64(14), int64(3), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Let's see the data types and non-null values for each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CUST_ID                             0.00\n",
       "BALANCE                             0.00\n",
       "BALANCE_FREQUENCY                   0.00\n",
       "PURCHASES                           0.00\n",
       "ONEOFF_PURCHASES                    0.00\n",
       "INSTALLMENTS_PURCHASES              0.00\n",
       "CASH_ADVANCE                        0.00\n",
       "PURCHASES_FREQUENCY                 0.00\n",
       "ONEOFF_PURCHASES_FREQUENCY          0.00\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY    0.00\n",
       "CASH_ADVANCE_FREQUENCY              0.00\n",
       "CASH_ADVANCE_TRX                    0.00\n",
       "PURCHASES_TRX                       0.00\n",
       "CREDIT_LIMIT                        0.01\n",
       "PAYMENTS                            0.00\n",
       "MINIMUM_PAYMENTS                    3.50\n",
       "PRC_FULL_PAYMENT                    0.00\n",
       "TENURE                              0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.isnull().sum(axis=0)*100/df.shape[0],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8949.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8637.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "      <td>8950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1564.474828</td>\n",
       "      <td>0.877271</td>\n",
       "      <td>1003.204834</td>\n",
       "      <td>592.437371</td>\n",
       "      <td>411.067645</td>\n",
       "      <td>978.871112</td>\n",
       "      <td>0.490351</td>\n",
       "      <td>0.202458</td>\n",
       "      <td>0.364437</td>\n",
       "      <td>0.135144</td>\n",
       "      <td>3.248827</td>\n",
       "      <td>14.709832</td>\n",
       "      <td>4494.449450</td>\n",
       "      <td>1733.143852</td>\n",
       "      <td>864.206542</td>\n",
       "      <td>0.153715</td>\n",
       "      <td>11.517318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2081.531879</td>\n",
       "      <td>0.236904</td>\n",
       "      <td>2136.634782</td>\n",
       "      <td>1659.887917</td>\n",
       "      <td>904.338115</td>\n",
       "      <td>2097.163877</td>\n",
       "      <td>0.401371</td>\n",
       "      <td>0.298336</td>\n",
       "      <td>0.397448</td>\n",
       "      <td>0.200121</td>\n",
       "      <td>6.824647</td>\n",
       "      <td>24.857649</td>\n",
       "      <td>3638.815725</td>\n",
       "      <td>2895.063757</td>\n",
       "      <td>2372.446607</td>\n",
       "      <td>0.292499</td>\n",
       "      <td>1.338331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>128.281915</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>39.635000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</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>1600.000000</td>\n",
       "      <td>383.276166</td>\n",
       "      <td>169.123707</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>873.385231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>361.280000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>856.901546</td>\n",
       "      <td>312.343947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2054.140036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1110.130000</td>\n",
       "      <td>577.405000</td>\n",
       "      <td>468.637500</td>\n",
       "      <td>1113.821139</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>1901.134317</td>\n",
       "      <td>825.485459</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19043.138560</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49039.570000</td>\n",
       "      <td>40761.250000</td>\n",
       "      <td>22500.000000</td>\n",
       "      <td>47137.211760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>50721.483360</td>\n",
       "      <td>76406.207520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BALANCE  BALANCE_FREQUENCY     PURCHASES  ONEOFF_PURCHASES  \\\n",
       "count   8950.000000        8950.000000   8950.000000       8950.000000   \n",
       "mean    1564.474828           0.877271   1003.204834        592.437371   \n",
       "std     2081.531879           0.236904   2136.634782       1659.887917   \n",
       "min        0.000000           0.000000      0.000000          0.000000   \n",
       "25%      128.281915           0.888889     39.635000          0.000000   \n",
       "50%      873.385231           1.000000    361.280000         38.000000   \n",
       "75%     2054.140036           1.000000   1110.130000        577.405000   \n",
       "max    19043.138560           1.000000  49039.570000      40761.250000   \n",
       "\n",
       "       INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "count             8950.000000   8950.000000          8950.000000   \n",
       "mean               411.067645    978.871112             0.490351   \n",
       "std                904.338115   2097.163877             0.401371   \n",
       "min                  0.000000      0.000000             0.000000   \n",
       "25%                  0.000000      0.000000             0.083333   \n",
       "50%                 89.000000      0.000000             0.500000   \n",
       "75%                468.637500   1113.821139             0.916667   \n",
       "max              22500.000000  47137.211760             1.000000   \n",
       "\n",
       "       ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "count                 8950.000000                       8950.000000   \n",
       "mean                     0.202458                          0.364437   \n",
       "std                      0.298336                          0.397448   \n",
       "min                      0.000000                          0.000000   \n",
       "25%                      0.000000                          0.000000   \n",
       "50%                      0.083333                          0.166667   \n",
       "75%                      0.300000                          0.750000   \n",
       "max                      1.000000                          1.000000   \n",
       "\n",
       "       CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "count             8950.000000       8950.000000    8950.000000   8949.000000   \n",
       "mean                 0.135144          3.248827      14.709832   4494.449450   \n",
       "std                  0.200121          6.824647      24.857649   3638.815725   \n",
       "min                  0.000000          0.000000       0.000000     50.000000   \n",
       "25%                  0.000000          0.000000       1.000000   1600.000000   \n",
       "50%                  0.000000          0.000000       7.000000   3000.000000   \n",
       "75%                  0.222222          4.000000      17.000000   6500.000000   \n",
       "max                  1.500000        123.000000     358.000000  30000.000000   \n",
       "\n",
       "           PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT       TENURE  \n",
       "count   8950.000000       8637.000000       8950.000000  8950.000000  \n",
       "mean    1733.143852        864.206542          0.153715    11.517318  \n",
       "std     2895.063757       2372.446607          0.292499     1.338331  \n",
       "min        0.000000          0.019163          0.000000     6.000000  \n",
       "25%      383.276166        169.123707          0.000000    12.000000  \n",
       "50%      856.901546        312.343947          0.000000    12.000000  \n",
       "75%     1901.134317        825.485459          0.142857    12.000000  \n",
       "max    50721.483360      76406.207520          1.000000    12.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will print basic statistics for numerical columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feel free to investigate what you want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CUST_ID                              object\n",
       "BALANCE                             float64\n",
       "BALANCE_FREQUENCY                   float64\n",
       "PURCHASES                           float64\n",
       "ONEOFF_PURCHASES                    float64\n",
       "INSTALLMENTS_PURCHASES              float64\n",
       "CASH_ADVANCE                        float64\n",
       "PURCHASES_FREQUENCY                 float64\n",
       "ONEOFF_PURCHASES_FREQUENCY          float64\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY    float64\n",
       "CASH_ADVANCE_FREQUENCY              float64\n",
       "CASH_ADVANCE_TRX                      int64\n",
       "PURCHASES_TRX                         int64\n",
       "CREDIT_LIMIT                        float64\n",
       "PAYMENTS                            float64\n",
       "MINIMUM_PAYMENTS                    float64\n",
       "PRC_FULL_PAYMENT                    float64\n",
       "TENURE                                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BALANCE                              2.393386\n",
       "BALANCE_FREQUENCY                   -2.023266\n",
       "PURCHASES                            8.144269\n",
       "ONEOFF_PURCHASES                    10.045083\n",
       "INSTALLMENTS_PURCHASES               7.299120\n",
       "CASH_ADVANCE                         5.166609\n",
       "PURCHASES_FREQUENCY                  0.060164\n",
       "ONEOFF_PURCHASES_FREQUENCY           1.535613\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY     0.509201\n",
       "CASH_ADVANCE_FREQUENCY               1.828686\n",
       "CASH_ADVANCE_TRX                     5.721298\n",
       "PURCHASES_TRX                        4.630655\n",
       "CREDIT_LIMIT                         1.522464\n",
       "PAYMENTS                             5.907620\n",
       "MINIMUM_PAYMENTS                    13.622797\n",
       "PRC_FULL_PAYMENT                     1.942820\n",
       "TENURE                              -2.943017\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'BALANCE'}>,\n",
       "        <AxesSubplot:title={'center':'BALANCE_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES'}>,\n",
       "        <AxesSubplot:title={'center':'ONEOFF_PURCHASES'}>],\n",
       "       [<AxesSubplot:title={'center':'INSTALLMENTS_PURCHASES'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'ONEOFF_PURCHASES_FREQUENCY'}>],\n",
       "       [<AxesSubplot:title={'center':'PURCHASES_INSTALLMENTS_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE_TRX'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES_TRX'}>],\n",
       "       [<AxesSubplot:title={'center':'CREDIT_LIMIT'}>,\n",
       "        <AxesSubplot:title={'center':'PAYMENTS'}>,\n",
       "        <AxesSubplot:title={'center':'MINIMUM_PAYMENTS'}>,\n",
       "        <AxesSubplot:title={'center':'PRC_FULL_PAYMENT'}>],\n",
       "       [<AxesSubplot:title={'center':'TENURE'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (20 , 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. <a name=\"3\">Data preprocessing</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define below all the issues that you had found in the previous part\n",
    "1- We have 2 features have null values it's CREDIT_LIMIT , MINIMUM_PAYMENTS   <br>\n",
    "2- We have some features have large scale which affect the model accuracy        <br>\n",
    "3- We have CUST_ID feature of type object         <br>\n",
    "4- Data have skweness "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a copy for the original dataset\n",
    "df_copy=df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for each issue adapt this methodology \n",
    "- start by defining the solution\n",
    "- apply this solution onn the data\n",
    "- test the solution to make sure that you have solved the issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First issue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solution is we can fill the missing value with the mean of each feature \n",
    "\n",
    "fill_missing_value = {\"CREDIT_LIMIT\" : df_copy[\"CREDIT_LIMIT\"].mean() , \n",
    "                     \"MINIMUM_PAYMENTS\" : df_copy[\"MINIMUM_PAYMENTS\"].mean()}\n",
    "\n",
    "df_copy = df_copy.fillna(value=fill_missing_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8950 entries, 0 to 8949\n",
      "Data columns (total 18 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   CUST_ID                           8950 non-null   object \n",
      " 1   BALANCE                           8950 non-null   float64\n",
      " 2   BALANCE_FREQUENCY                 8950 non-null   float64\n",
      " 3   PURCHASES                         8950 non-null   float64\n",
      " 4   ONEOFF_PURCHASES                  8950 non-null   float64\n",
      " 5   INSTALLMENTS_PURCHASES            8950 non-null   float64\n",
      " 6   CASH_ADVANCE                      8950 non-null   float64\n",
      " 7   PURCHASES_FREQUENCY               8950 non-null   float64\n",
      " 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64\n",
      " 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64\n",
      " 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64\n",
      " 11  CASH_ADVANCE_TRX                  8950 non-null   int64  \n",
      " 12  PURCHASES_TRX                     8950 non-null   int64  \n",
      " 13  CREDIT_LIMIT                      8950 non-null   float64\n",
      " 14  PAYMENTS                          8950 non-null   float64\n",
      " 15  MINIMUM_PAYMENTS                  8950 non-null   float64\n",
      " 16  PRC_FULL_PAYMENT                  8950 non-null   float64\n",
      " 17  TENURE                            8950 non-null   int64  \n",
      "dtypes: float64(14), int64(3), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#test \n",
    "df_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Second issue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solution We need to scale the features to make it all have same range\n",
    "# It will be implemented in features transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Third issue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solution we can drop column or make it as index for data frame\n",
    "df_copy = df_copy.set_index(\"CUST_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUST_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>C10001</th>\n",
       "      <td>40.900749</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>201.802084</td>\n",
       "      <td>139.509787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10002</th>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6442.945483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>4103.032597</td>\n",
       "      <td>1072.340217</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10003</th>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>622.066742</td>\n",
       "      <td>627.284787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10004</th>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>864.206542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10005</th>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>678.334763</td>\n",
       "      <td>244.791237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "CUST_ID                                                                \n",
       "C10001     40.900749           0.818182      95.40              0.00   \n",
       "C10002   3202.467416           0.909091       0.00              0.00   \n",
       "C10003   2495.148862           1.000000     773.17            773.17   \n",
       "C10004   1666.670542           0.636364    1499.00           1499.00   \n",
       "C10005    817.714335           1.000000      16.00             16.00   \n",
       "\n",
       "         INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "CUST_ID                                                              \n",
       "C10001                     95.4      0.000000             0.166667   \n",
       "C10002                      0.0   6442.945483             0.000000   \n",
       "C10003                      0.0      0.000000             1.000000   \n",
       "C10004                      0.0    205.788017             0.083333   \n",
       "C10005                      0.0      0.000000             0.083333   \n",
       "\n",
       "         ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "CUST_ID                                                                 \n",
       "C10001                     0.000000                          0.083333   \n",
       "C10002                     0.000000                          0.000000   \n",
       "C10003                     1.000000                          0.000000   \n",
       "C10004                     0.083333                          0.000000   \n",
       "C10005                     0.083333                          0.000000   \n",
       "\n",
       "         CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  \\\n",
       "CUST_ID                                                            \n",
       "C10001                 0.000000                 0              2   \n",
       "C10002                 0.250000                 4              0   \n",
       "C10003                 0.000000                 0             12   \n",
       "C10004                 0.083333                 1              1   \n",
       "C10005                 0.000000                 0              1   \n",
       "\n",
       "         CREDIT_LIMIT     PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
       "CUST_ID                                                                         \n",
       "C10001         1000.0   201.802084        139.509787          0.000000      12  \n",
       "C10002         7000.0  4103.032597       1072.340217          0.222222      12  \n",
       "C10003         7500.0   622.066742        627.284787          0.000000      12  \n",
       "C10004         7500.0     0.000000        864.206542          0.000000      12  \n",
       "C10005         1200.0   678.334763        244.791237          0.000000      12  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fourth issue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solution take sqrt of features\n",
    "df_copy = np.sqrt(df_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'BALANCE'}>,\n",
       "        <AxesSubplot:title={'center':'BALANCE_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES'}>,\n",
       "        <AxesSubplot:title={'center':'ONEOFF_PURCHASES'}>],\n",
       "       [<AxesSubplot:title={'center':'INSTALLMENTS_PURCHASES'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'ONEOFF_PURCHASES_FREQUENCY'}>],\n",
       "       [<AxesSubplot:title={'center':'PURCHASES_INSTALLMENTS_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE_FREQUENCY'}>,\n",
       "        <AxesSubplot:title={'center':'CASH_ADVANCE_TRX'}>,\n",
       "        <AxesSubplot:title={'center':'PURCHASES_TRX'}>],\n",
       "       [<AxesSubplot:title={'center':'CREDIT_LIMIT'}>,\n",
       "        <AxesSubplot:title={'center':'PAYMENTS'}>,\n",
       "        <AxesSubplot:title={'center':'MINIMUM_PAYMENTS'}>,\n",
       "        <AxesSubplot:title={'center':'PRC_FULL_PAYMENT'}>],\n",
       "       [<AxesSubplot:title={'center':'TENURE'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test\n",
    "df_copy.hist(figsize = (20 , 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. <a name=\"4\">Features transformation</a>\n",
    "(<a href=\"#0\">Go to top</a>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What is the feature scaling technique that would use and why?* <br>\n",
    "*return to this section again and try another technique and see how that will impact your result*<br>\n",
    "for more details on different methods for scaling check these links\n",
    "- https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing\n",
    "- https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing\n",
    "- https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([137.99687881,   1.        , 221.44879769, 201.89415544,\n",
       "       150.        , 217.11105859,   1.        ,   1.        ,\n",
       "         1.        ,   1.22474487,  11.09053651,  18.92088793,\n",
       "       173.20508076, 225.21430541, 276.416728  ,   1.        ,\n",
       "         3.46410162])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scalar_object = preprocessing.MinMaxScaler()\n",
    "df_copy_normalized = pd.DataFrame(Scalar_object.fit_transform(df_copy) , columns=df_copy.columns)\n",
    "\n",
    "#test solution\n",
    "Scalar_object.data_min_\n",
    "Scalar_object.data_max_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# after trying the 2 techniques MinMax scaling will be butter for our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = StandardScaler()\n",
    "# print(scaler.fit(df_copy_normalized))\n",
    "# print(scaler.transform(df_copy_normalized))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. <a name=\"5\">K Means</a>\n",
    "(<a href=\"#5\">Go to top</a>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1- Use the k means class that you implemented in the previous task to cluster this data\n",
    "2- Use http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html and see if the difference in the result\n",
    "3- Use elbow method to determine the K (plot the result using two plot one for distorion and another for inertia)\n",
    "4- (Optionally) make a method that pick the best number of clusters for you \n",
    "5- Using different techniques for scaling and comment on the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calling My Implementation for Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdelrahman\\GitHub_Partition\\Machine-Learning-II-Baysien-Theorm\\Algoritms Implementaion\\k-mean\n"
     ]
    }
   ],
   "source": [
    "cd ./k-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Closest centroids for the first 3 examples:\n",
      "[[0]\n",
      " [2]\n",
      " [1]]\n",
      "(the closest centroids should be 0, 2, 1 respectively)\n",
      "Centroids computed after initial finding of closest centroids:\n",
      "[[2.42830111 3.15792418]\n",
      " [5.81350331 2.63365645]\n",
      " [7.11938687 3.6166844 ]]\n",
      "\n",
      "The centroids should be\n",
      "   [ 2.428301 3.157924 ]\n",
      "   [ 5.813503 2.633656 ]\n",
      "   [ 7.119387 3.616684 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run Kmeans.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Object of my implementation and use fit method to get my centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_for_model = df_copy_normalized.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "My_object = K_means(Data_for_model, 3 , 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kmeans_cluester_centroids = My_object.k_kmeans_fit()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "plt.scatter(Data_for_model[:,0], Data_for_model[:,1])\n",
    "\n",
    "# Plot the clusters \n",
    "plt.scatter(Kmeans_cluester_centroids[:, 0], Kmeans_cluester_centroids[:, 1], s=200,c='red')                        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Object of sklearn fit method to get sklearn centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.27561565, 0.978857  , 0.20359768, 0.18088252, 0.14412765,\n",
       "        0.07042872, 0.89892139, 0.74356843, 0.62127085, 0.16031905,\n",
       "        0.08519104, 0.2871395 , 0.40459324, 0.20764167, 0.08871564,\n",
       "        0.25856813, 0.95922739],\n",
       "       [0.26264866, 0.89718598, 0.03488778, 0.03473588, 0.00611108,\n",
       "        0.13408137, 0.18068708, 0.1525801 , 0.03359788, 0.3090437 ,\n",
       "        0.15505891, 0.03858585, 0.31557311, 0.1403222 , 0.08966231,\n",
       "        0.07573229, 0.90316468],\n",
       "       [0.14211749, 0.90016835, 0.10472577, 0.01756158, 0.14630039,\n",
       "        0.03031457, 0.85469525, 0.08143074, 0.82455236, 0.07027965,\n",
       "        0.03447108, 0.18481693, 0.28494325, 0.12213971, 0.07415627,\n",
       "        0.35456378, 0.91893662]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ready_kmeans = KMeans(n_clusters=3, random_state=0 , max_iter=30).fit(df_copy_normalized.to_numpy())\n",
    "Ready_kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3907.736030624756"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get inertia from sklearn\n",
    "Ready_kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "plt.scatter(df_for_kmean[:,0], df_for_kmean[:,1])\n",
    "\n",
    "# Plot the clusters \n",
    "plt.scatter(Ready_kmeans.cluster_centers_[:, 0], Ready_kmeans.cluster_centers_[:, 1], s=200,c='red')                        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Draw my elbow to get insight about selecting K and Choosing Prefect K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suitable K is 2\n"
     ]
    }
   ],
   "source": [
    "suitable_K = My_object.elbow()\n",
    "print(\"Suitable K is {}\".format(suitable_K))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# draw elbow from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suitable K is 2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "inertia_list = []\n",
    "prefect_K = 0\n",
    "max_slope = 0\n",
    "for k in range(1,10):\n",
    "    kmean_object = KMeans(n_clusters=k, random_state=0 , max_iter=30).fit(df_copy_normalized.to_numpy())\n",
    "    inertia_list.append(kmean_object.inertia_)\n",
    "    if  k-1 >1 and abs(inertia_list[k-1]-inertia_list[k-2]) > max_slope :\n",
    "        max_slope = abs(inertia_list[k-1]-inertia_list[k-2])\n",
    "        prefect_K = k-1\n",
    "plt.plot(inertia_list) \n",
    "plt.xlabel(\"num of K\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.show()\n",
    "print(\"Suitable K is {}\".format(prefect_K))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. <a name=\"5\">Training and hyperparamter tuning</a>\n",
    "(<a href=\"#0\">Go to top</a>)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start the training process we need to specify 3 paramters:<br>\n",
    "1- Linkage criteria : The linkage criterion determines the distance between two clusters\n",
    "    - Complete-Linkage Clustering\n",
    "    - Single-Linkage Clustering\n",
    "    - Average-Linkage Clustering\n",
    "    - Centroid Linkage Clustering\n",
    "2- Distance function:\n",
    "    - Euclidean Distance \n",
    "    - Manhattan Distance \n",
    "    - Mahalanobis distance \n",
    "3- Number of clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Number of clusters*\n",
    "Use Dendograms to specify the optimum number of clusters\n",
    "- Compare how changing linkage criteria or distance function would affect the optimum number of clusters\n",
    "- you can use silhouette_score or any other evalution method to help you determine the optimum number of clusters\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just trying different prameters to get which better than other in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.title(\"Counters Dendograms\")\n",
    "dend = shc.dendrogram(shc.linkage(y=df_copy_normalized , method='centroid'\n",
    "                                  ,metric='euclidean'),orientation='right') #fill y with your dataframe\n",
    "                                                                                      #and method with linkage criteria\n",
    "                                                                                      #and metric with distance function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get silhouette score to help me selecting number of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "slah_list = []\n",
    "for i in range(2,10):\n",
    "    slah_list.append(silhouette_score(df_copy_normalized.to_numpy() , k_kmeans(df_copy_normalized.to_numpy() , i ,30)[2]))\n",
    "    \n",
    "plt.plot(range(2,10) , slah_list) \n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"silhouette_score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "clustering = AgglomerativeClustering().fit(df_copy_normalized.to_numpy())\n",
    "clustering.labels_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. <a name=\"6\">improvement ideas</a>\n",
    "(<a href=\"#0\">Go to top</a>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Try to use PCA to reduce the number of features and compare how this will affect the clustring process\n",
    "- Try to run your code again but with different tranformation technique\n",
    "- Implement gap statistics method and use it as evaluation metric and compare the result with what you did before https://www.datanovia.com/en/lessons/determining-the-optimal-number-of-clusters-3-must-know-methods/#gap-statistic-method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. DBScan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from plotnine.data import economics\n",
    "\n",
    "from plotnine import ggplot, aes, geom_line , geom_point , theme_minimal , scale_color_discrete , theme  , element_blank , labs\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = df_copy_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.046344</td>\n",
       "      <td>0.904534</td>\n",
       "      <td>0.044106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074744</td>\n",
       "      <td>0.147783</td>\n",
       "      <td>0.063076</td>\n",
       "      <td>0.042251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.410084</td>\n",
       "      <td>0.953463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.369709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.180334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.461043</td>\n",
       "      <td>0.284418</td>\n",
       "      <td>0.118027</td>\n",
       "      <td>0.471404</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.361975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125564</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.183083</td>\n",
       "      <td>0.478719</td>\n",
       "      <td>0.110745</td>\n",
       "      <td>0.090153</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.295839</td>\n",
       "      <td>0.797724</td>\n",
       "      <td>0.174835</td>\n",
       "      <td>0.191768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066074</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235702</td>\n",
       "      <td>0.090167</td>\n",
       "      <td>0.052852</td>\n",
       "      <td>0.478719</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105904</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.207220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018063</td>\n",
       "      <td>0.019812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052852</td>\n",
       "      <td>0.165950</td>\n",
       "      <td>0.115645</td>\n",
       "      <td>0.056130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0  0.046344           0.904534   0.044106          0.000000   \n",
       "1  0.410084           0.953463   0.000000          0.000000   \n",
       "2  0.361975           1.000000   0.125564          0.137725   \n",
       "3  0.295839           0.797724   0.174835          0.191768   \n",
       "4  0.207220           1.000000   0.018063          0.019812   \n",
       "\n",
       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                0.065115      0.000000             0.408249   \n",
       "1                0.000000      0.369709             0.000000   \n",
       "2                0.000000      0.000000             1.000000   \n",
       "3                0.000000      0.066074             0.288675   \n",
       "4                0.000000      0.000000             0.288675   \n",
       "\n",
       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                    0.000000                          0.288675   \n",
       "1                    0.000000                          0.000000   \n",
       "2                    1.000000                          0.000000   \n",
       "3                    0.288675                          0.000000   \n",
       "4                    0.288675                          0.000000   \n",
       "\n",
       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "0                0.000000          0.000000       0.074744      0.147783   \n",
       "1                0.408248          0.180334       0.000000      0.461043   \n",
       "2                0.000000          0.000000       0.183083      0.478719   \n",
       "3                0.235702          0.090167       0.052852      0.478719   \n",
       "4                0.000000          0.000000       0.052852      0.165950   \n",
       "\n",
       "   PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
       "0  0.063076          0.042251          0.000000     1.0  \n",
       "1  0.284418          0.118027          0.471404     1.0  \n",
       "2  0.110745          0.090153          0.000000     1.0  \n",
       "3  0.000000          0.105904          0.000000     1.0  \n",
       "4  0.115645          0.056130          0.000000     1.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_value = 0.4\n",
    "min_samples_value = 30\n",
    "db1 = DBSCAN(eps = eps_value, min_samples = min_samples_value).fit(d1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (129514225366)>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labsList = [\"Noise\"]\n",
    "labsList = labsList  + [\"Cluster \" + str(i) for i in range(1,len(set(db1.labels_)))]\n",
    "\n",
    "d1[\"assignments\"] = db1.labels_\n",
    "\n",
    "(ggplot(d1, aes(x = \"BALANCE\", y = \"PURCHASES\", color = \"factor(assignments)\")) +\n",
    " geom_point() +\n",
    " theme_minimal() +\n",
    " scale_color_discrete(name = \"Cluster Assignment\",\n",
    "                      labels = labsList) +\n",
    "theme(panel_grid_major = element_blank()) +\n",
    "labs(title = \"DBSCAN with eps = {}, min_samples = {}\".format(eps_value , min_samples_value)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    6447\n",
       " 1    1995\n",
       "-1     488\n",
       " 2      20\n",
       "Name: assignments, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy_normalized.assignments.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.17152508882124096"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab only clustered data points\n",
    "d1_clustered = d1.loc[(d1.assignments >= 0)]\n",
    "\n",
    "silhouette_score(d1_clustered[[\"BALANCE\", \"PURCHASES\"]], d1_clustered[\"assignments\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.13763755, 0.16402718, ..., 0.2568836 , 0.2599082 ,\n",
       "        0.26207949],\n",
       "       [0.        , 0.20809134, 0.20862077, ..., 0.28621369, 0.28930752,\n",
       "        0.29469591],\n",
       "       [0.        , 0.06939312, 0.12912063, ..., 0.23823409, 0.238569  ,\n",
       "        0.26002411],\n",
       "       ...,\n",
       "       [0.        , 0.15766245, 0.16289556, ..., 0.41951963, 0.4195724 ,\n",
       "        0.42306264],\n",
       "       [0.        , 0.28011295, 0.29470174, ..., 0.70873107, 0.74018212,\n",
       "        0.74349511],\n",
       "       [0.        , 0.32399157, 0.36863562, ..., 0.7057177 , 0.70774342,\n",
       "        0.72466278]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we ask for mins + 1 nearest, because the data point it self (distance = 0) is included\n",
    "mins = min_samples_value\n",
    "nn = NearestNeighbors(mins + 1)\n",
    "\n",
    "nn.fit(d1)\n",
    "\n",
    "distances, neighbors = nn.kneighbors(d1)\n",
    "\n",
    "distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort the distances\n",
    "distances = np.sort(distances[:, mins], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (129392790110)>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the distances\n",
    "distances_df = pd.DataFrame({\"distances\": distances,\n",
    "                             \"index\": list(range(0,len(distances)))})\n",
    "plt = (ggplot(distances_df, aes(x = \"index\", y = \"distances\")) +\n",
    " geom_line(color = \"white\", size = 2) + theme_minimal() +\n",
    " labs(title = \"Elbow Method for Choosing eps\") +\n",
    " theme(panel_grid_minor = element_blank(),\n",
    "      rect = element_rect(fill = \"#202124ff\"),\n",
    "      axis_text = element_text(color = \"white\"),\n",
    "      axis_title = element_text(color = \"white\"),\n",
    "      plot_title = element_text(color = \"white\"),\n",
    "      panel_border = element_line(color = \"darkgray\"),\n",
    "      plot_background = element_rect(fill = \"#202124ff\")\n",
    "      ))\n",
    "ggsave(plot=plt, filename='elbow.png', dpi=300)\n",
    "\n",
    "plt"
   ]
  },
  {
   "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": 2
}