55. Superstore Marketing Campaign.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c91c84bb",
   "metadata": {},
   "source": [
    "# Superstore Marketing Campaign"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd0146f",
   "metadata": {},
   "source": [
    "## Getting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "35dfcef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6de2e89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>...</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>Response</th>\n",
       "      <th>Complain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1826</td>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>84835.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/16/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>189</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</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>1961</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>57091.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/15/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>464</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10476</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>67267.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5/13/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1386</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>32474.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11/5/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5371</td>\n",
       "      <td>1989</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>21474.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8/4/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0   1826        1970  Graduation       Divorced  84835.0        0         0   \n",
       "1      1        1961  Graduation         Single  57091.0        0         0   \n",
       "2  10476        1958  Graduation        Married  67267.0        0         1   \n",
       "3   1386        1967  Graduation       Together  32474.0        1         1   \n",
       "4   5371        1989  Graduation         Single  21474.0        1         0   \n",
       "\n",
       "  Dt_Customer  Recency  MntWines  ...  MntFishProducts  MntSweetProducts  \\\n",
       "0   6/16/2014        0       189  ...              111               189   \n",
       "1   6/15/2014        0       464  ...                7                 0   \n",
       "2   5/13/2014        0       134  ...               15                 2   \n",
       "3   11/5/2014        0        10  ...                0                 0   \n",
       "4    8/4/2014        0         6  ...               11                 0   \n",
       "\n",
       "   MntGoldProds  NumDealsPurchases  NumWebPurchases  NumCatalogPurchases  \\\n",
       "0           218                  1                4                    4   \n",
       "1            37                  1                7                    3   \n",
       "2            30                  1                3                    2   \n",
       "3             0                  1                1                    0   \n",
       "4            34                  2                3                    1   \n",
       "\n",
       "   NumStorePurchases  NumWebVisitsMonth  Response  Complain  \n",
       "0                  6                  1         1         0  \n",
       "1                  7                  5         1         0  \n",
       "2                  5                  2         0         0  \n",
       "3                  2                  7         0         0  \n",
       "4                  2                  7         1         0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/proj55/superstore_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1468925c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1826</td>\n",
       "      <td>1</td>\n",
       "      <td>10476</td>\n",
       "      <td>1386</td>\n",
       "      <td>5371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_Birth</th>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>1958</td>\n",
       "      <td>1967</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Single</td>\n",
       "      <td>Married</td>\n",
       "      <td>Together</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>84835.0</td>\n",
       "      <td>57091.0</td>\n",
       "      <td>67267.0</td>\n",
       "      <td>32474.0</td>\n",
       "      <td>21474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teenhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dt_Customer</th>\n",
       "      <td>6/16/2014</td>\n",
       "      <td>6/15/2014</td>\n",
       "      <td>5/13/2014</td>\n",
       "      <td>11/5/2014</td>\n",
       "      <td>8/4/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recency</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntWines</th>\n",
       "      <td>189</td>\n",
       "      <td>464</td>\n",
       "      <td>134</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFruits</th>\n",
       "      <td>104</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <td>379</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFishProducts</th>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntGoldProds</th>\n",
       "      <td>218</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0           1           2           3  \\\n",
       "Id                         1826           1       10476        1386   \n",
       "Year_Birth                 1970        1961        1958        1967   \n",
       "Education            Graduation  Graduation  Graduation  Graduation   \n",
       "Marital_Status         Divorced      Single     Married    Together   \n",
       "Income                  84835.0     57091.0     67267.0     32474.0   \n",
       "Kidhome                       0           0           0           1   \n",
       "Teenhome                      0           0           1           1   \n",
       "Dt_Customer           6/16/2014   6/15/2014   5/13/2014   11/5/2014   \n",
       "Recency                       0           0           0           0   \n",
       "MntWines                    189         464         134          10   \n",
       "MntFruits                   104           5          11           0   \n",
       "MntMeatProducts             379          64          59           1   \n",
       "MntFishProducts             111           7          15           0   \n",
       "MntSweetProducts            189           0           2           0   \n",
       "MntGoldProds                218          37          30           0   \n",
       "NumDealsPurchases             1           1           1           1   \n",
       "NumWebPurchases               4           7           3           1   \n",
       "NumCatalogPurchases           4           3           2           0   \n",
       "NumStorePurchases             6           7           5           2   \n",
       "NumWebVisitsMonth             1           5           2           7   \n",
       "Response                      1           1           0           0   \n",
       "Complain                      0           0           0           0   \n",
       "\n",
       "                              4  \n",
       "Id                         5371  \n",
       "Year_Birth                 1989  \n",
       "Education            Graduation  \n",
       "Marital_Status           Single  \n",
       "Income                  21474.0  \n",
       "Kidhome                       1  \n",
       "Teenhome                      0  \n",
       "Dt_Customer            8/4/2014  \n",
       "Recency                       0  \n",
       "MntWines                      6  \n",
       "MntFruits                    16  \n",
       "MntMeatProducts              24  \n",
       "MntFishProducts              11  \n",
       "MntSweetProducts              0  \n",
       "MntGoldProds                 34  \n",
       "NumDealsPurchases             2  \n",
       "NumWebPurchases               3  \n",
       "NumCatalogPurchases           1  \n",
       "NumStorePurchases             2  \n",
       "NumWebVisitsMonth             7  \n",
       "Response                      1  \n",
       "Complain                      0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77241daf",
   "metadata": {},
   "source": [
    "## Cleaning Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c0e7bab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                      0\n",
       "Year_Birth              0\n",
       "Education               0\n",
       "Marital_Status          0\n",
       "Income                 24\n",
       "Kidhome                 0\n",
       "Teenhome                0\n",
       "Dt_Customer             0\n",
       "Recency                 0\n",
       "MntWines                0\n",
       "MntFruits               0\n",
       "MntMeatProducts         0\n",
       "MntFishProducts         0\n",
       "MntSweetProducts        0\n",
       "MntGoldProds            0\n",
       "NumDealsPurchases       0\n",
       "NumWebPurchases         0\n",
       "NumCatalogPurchases     0\n",
       "NumStorePurchases       0\n",
       "NumWebVisitsMonth       0\n",
       "Response                0\n",
       "Complain                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "46010507",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Income\"].fillna(np.mean(df[\"Income\"]), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d3ce956e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                     0\n",
       "Year_Birth             0\n",
       "Education              0\n",
       "Marital_Status         0\n",
       "Income                 0\n",
       "Kidhome                0\n",
       "Teenhome               0\n",
       "Dt_Customer            0\n",
       "Recency                0\n",
       "MntWines               0\n",
       "MntFruits              0\n",
       "MntMeatProducts        0\n",
       "MntFishProducts        0\n",
       "MntSweetProducts       0\n",
       "MntGoldProds           0\n",
       "NumDealsPurchases      0\n",
       "NumWebPurchases        0\n",
       "NumCatalogPurchases    0\n",
       "NumStorePurchases      0\n",
       "NumWebVisitsMonth      0\n",
       "Response               0\n",
       "Complain               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10f0256",
   "metadata": {},
   "source": [
    "## Exploring Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7ce3b4f6",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1826</td>\n",
       "      <td>1</td>\n",
       "      <td>10476</td>\n",
       "      <td>1386</td>\n",
       "      <td>5371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_Birth</th>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>1958</td>\n",
       "      <td>1967</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Single</td>\n",
       "      <td>Married</td>\n",
       "      <td>Together</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>84835.0</td>\n",
       "      <td>57091.0</td>\n",
       "      <td>67267.0</td>\n",
       "      <td>32474.0</td>\n",
       "      <td>21474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teenhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dt_Customer</th>\n",
       "      <td>6/16/2014</td>\n",
       "      <td>6/15/2014</td>\n",
       "      <td>5/13/2014</td>\n",
       "      <td>11/5/2014</td>\n",
       "      <td>8/4/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recency</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntWines</th>\n",
       "      <td>189</td>\n",
       "      <td>464</td>\n",
       "      <td>134</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFruits</th>\n",
       "      <td>104</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <td>379</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFishProducts</th>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntGoldProds</th>\n",
       "      <td>218</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0           1           2           3  \\\n",
       "Id                         1826           1       10476        1386   \n",
       "Year_Birth                 1970        1961        1958        1967   \n",
       "Education            Graduation  Graduation  Graduation  Graduation   \n",
       "Marital_Status         Divorced      Single     Married    Together   \n",
       "Income                  84835.0     57091.0     67267.0     32474.0   \n",
       "Kidhome                       0           0           0           1   \n",
       "Teenhome                      0           0           1           1   \n",
       "Dt_Customer           6/16/2014   6/15/2014   5/13/2014   11/5/2014   \n",
       "Recency                       0           0           0           0   \n",
       "MntWines                    189         464         134          10   \n",
       "MntFruits                   104           5          11           0   \n",
       "MntMeatProducts             379          64          59           1   \n",
       "MntFishProducts             111           7          15           0   \n",
       "MntSweetProducts            189           0           2           0   \n",
       "MntGoldProds                218          37          30           0   \n",
       "NumDealsPurchases             1           1           1           1   \n",
       "NumWebPurchases               4           7           3           1   \n",
       "NumCatalogPurchases           4           3           2           0   \n",
       "NumStorePurchases             6           7           5           2   \n",
       "NumWebVisitsMonth             1           5           2           7   \n",
       "Response                      1           1           0           0   \n",
       "Complain                      0           0           0           0   \n",
       "\n",
       "                              4  \n",
       "Id                         5371  \n",
       "Year_Birth                 1989  \n",
       "Education            Graduation  \n",
       "Marital_Status           Single  \n",
       "Income                  21474.0  \n",
       "Kidhome                       1  \n",
       "Teenhome                      0  \n",
       "Dt_Customer            8/4/2014  \n",
       "Recency                       0  \n",
       "MntWines                      6  \n",
       "MntFruits                    16  \n",
       "MntMeatProducts              24  \n",
       "MntFishProducts              11  \n",
       "MntSweetProducts              0  \n",
       "MntGoldProds                 34  \n",
       "NumDealsPurchases             2  \n",
       "NumWebPurchases               3  \n",
       "NumCatalogPurchases           1  \n",
       "NumStorePurchases             2  \n",
       "NumWebVisitsMonth             7  \n",
       "Response                      1  \n",
       "Complain                      0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "657a6851",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f42041d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2219,   21])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Complain\"].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fb02b7c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=[0, 1], y=df[\"Complain\"].value_counts().values, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3d7a5b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Income', ylabel='NumStorePurchases'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Income\", y=\"NumStorePurchases\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "329221b1",
   "metadata": {},
   "source": [
    "## Pre-Processing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1500c673",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1826</td>\n",
       "      <td>1</td>\n",
       "      <td>10476</td>\n",
       "      <td>1386</td>\n",
       "      <td>5371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_Birth</th>\n",
       "      <td>1970</td>\n",
       "      <td>1961</td>\n",
       "      <td>1958</td>\n",
       "      <td>1967</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Graduation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>Single</td>\n",
       "      <td>Married</td>\n",
       "      <td>Together</td>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>84835.0</td>\n",
       "      <td>57091.0</td>\n",
       "      <td>67267.0</td>\n",
       "      <td>32474.0</td>\n",
       "      <td>21474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teenhome</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dt_Customer</th>\n",
       "      <td>6/16/2014</td>\n",
       "      <td>6/15/2014</td>\n",
       "      <td>5/13/2014</td>\n",
       "      <td>11/5/2014</td>\n",
       "      <td>8/4/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recency</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntWines</th>\n",
       "      <td>189</td>\n",
       "      <td>464</td>\n",
       "      <td>134</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFruits</th>\n",
       "      <td>104</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <td>379</td>\n",
       "      <td>64</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntFishProducts</th>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MntGoldProds</th>\n",
       "      <td>218</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumDealsPurchases</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Response</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0           1           2           3  \\\n",
       "Id                         1826           1       10476        1386   \n",
       "Year_Birth                 1970        1961        1958        1967   \n",
       "Education            Graduation  Graduation  Graduation  Graduation   \n",
       "Marital_Status         Divorced      Single     Married    Together   \n",
       "Income                  84835.0     57091.0     67267.0     32474.0   \n",
       "Kidhome                       0           0           0           1   \n",
       "Teenhome                      0           0           1           1   \n",
       "Dt_Customer           6/16/2014   6/15/2014   5/13/2014   11/5/2014   \n",
       "Recency                       0           0           0           0   \n",
       "MntWines                    189         464         134          10   \n",
       "MntFruits                   104           5          11           0   \n",
       "MntMeatProducts             379          64          59           1   \n",
       "MntFishProducts             111           7          15           0   \n",
       "MntSweetProducts            189           0           2           0   \n",
       "MntGoldProds                218          37          30           0   \n",
       "NumDealsPurchases             1           1           1           1   \n",
       "NumWebPurchases               4           7           3           1   \n",
       "NumCatalogPurchases           4           3           2           0   \n",
       "NumStorePurchases             6           7           5           2   \n",
       "NumWebVisitsMonth             1           5           2           7   \n",
       "Response                      1           1           0           0   \n",
       "Complain                      0           0           0           0   \n",
       "\n",
       "                              4  \n",
       "Id                         5371  \n",
       "Year_Birth                 1989  \n",
       "Education            Graduation  \n",
       "Marital_Status           Single  \n",
       "Income                  21474.0  \n",
       "Kidhome                       1  \n",
       "Teenhome                      0  \n",
       "Dt_Customer            8/4/2014  \n",
       "Recency                       0  \n",
       "MntWines                      6  \n",
       "MntFruits                    16  \n",
       "MntMeatProducts              24  \n",
       "MntFishProducts              11  \n",
       "MntSweetProducts              0  \n",
       "MntGoldProds                 34  \n",
       "NumDealsPurchases             2  \n",
       "NumWebPurchases               3  \n",
       "NumCatalogPurchases           1  \n",
       "NumStorePurchases             2  \n",
       "NumWebVisitsMonth             7  \n",
       "Response                      1  \n",
       "Complain                      0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ede6bf5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "Education\n",
      "['2n Cycle' 'Basic' 'Graduation' 'Master' 'PhD']\n",
      "--------------------\n",
      "--------------------\n",
      "Marital_Status\n",
      "['Absurd' 'Alone' 'Divorced' 'Married' 'Single' 'Together' 'Widow' 'YOLO']\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "df.drop(\"Id\", axis=1, inplace=True)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df.drop(\"Dt_Customer\", axis=1, inplace=True)\n",
    "\n",
    "for column in df.keys():\n",
    "    if pd.api.types.is_object_dtype(df[column]):\n",
    "        le = LabelEncoder()\n",
    "        df[column] = le.fit_transform(df[column])\n",
    "        print(\"-\"*20)\n",
    "        print(column)\n",
    "        print(le.classes_)\n",
    "        print(\"-\"*20)\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "X = df.drop(\"Complain\", axis=1)\n",
    "y = df[\"Complain\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb1311b",
   "metadata": {},
   "source": [
    "## Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "deb3bad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0dad56fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888392857142857"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfclf = RandomForestClassifier()\n",
    "rfclf.fit(X_train, y_train)\n",
    "rfclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c3062662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9866071428571429"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kclf = KNeighborsClassifier().fit(X_train, y_train)\n",
    "kclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f3ca6a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9866071428571429"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svclf = SVC().fit(X_train, y_train)\n",
    "svclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a351d265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888392857142857"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbclf = XGBClassifier()\n",
    "xgbclf.fit(X_train.values, y_train.values)\n",
    "xgbclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a776e954",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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