data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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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>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Z_CostContact</th>\n",
       "      <th>Z_Revenue</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5524</td>\n",
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       "      <td>Single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04-09-2012</td>\n",
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       "      <th>1</th>\n",
       "      <td>2174</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>08-03-2014</td>\n",
       "      <td>38</td>\n",
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       "      <th>2</th>\n",
       "      <td>4141</td>\n",
       "      <td>1965</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-08-2013</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6182</td>\n",
       "      <td>1984</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10-02-2014</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5324</td>\n",
       "      <td>1981</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19-01-2014</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0  5524        1957  Graduation         Single  58138.0        0         0   \n",
       "1  2174        1954  Graduation         Single  46344.0        1         1   \n",
       "2  4141        1965  Graduation       Together  71613.0        0         0   \n",
       "3  6182        1984  Graduation       Together  26646.0        1         0   \n",
       "4  5324        1981         PhD        Married  58293.0        1         0   \n",
       "\n",
       "  Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  AcceptedCmp3  \\\n",
       "0  04-09-2012       58       635  ...                  7             0   \n",
       "1  08-03-2014       38        11  ...                  5             0   \n",
       "2  21-08-2013       26       426  ...                  4             0   \n",
       "3  10-02-2014       26        11  ...                  6             0   \n",
       "4  19-01-2014       94       173  ...                  5             0   \n",
       "\n",
       "   AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
       "0             0             0             0             0         0   \n",
       "1             0             0             0             0         0   \n",
       "2             0             0             0             0         0   \n",
       "3             0             0             0             0         0   \n",
       "4             0             0             0             0         0   \n",
       "\n",
       "   Z_CostContact  Z_Revenue  Response  \n",
       "0              3         11         1  \n",
       "1              3         11         0  \n",
       "2              3         11         0  \n",
       "3              3         11         0  \n",
       "4              3         11         0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_data: pd.DataFrame = pd.read_csv('./data/marketing_campaign.csv', sep='\\t')\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2240"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding number of records\n",
    "consumer_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ID                   2240 non-null   int64  \n",
      " 1   Year_Birth           2240 non-null   int64  \n",
      " 2   Education            2240 non-null   object \n",
      " 3   Marital_Status       2240 non-null   object \n",
      " 4   Income               2216 non-null   float64\n",
      " 5   Kidhome              2240 non-null   int64  \n",
      " 6   Teenhome             2240 non-null   int64  \n",
      " 7   Dt_Customer          2240 non-null   object \n",
      " 8   Recency              2240 non-null   int64  \n",
      " 9   MntWines             2240 non-null   int64  \n",
      " 10  MntFruits            2240 non-null   int64  \n",
      " 11  MntMeatProducts      2240 non-null   int64  \n",
      " 12  MntFishProducts      2240 non-null   int64  \n",
      " 13  MntSweetProducts     2240 non-null   int64  \n",
      " 14  MntGoldProds         2240 non-null   int64  \n",
      " 15  NumDealsPurchases    2240 non-null   int64  \n",
      " 16  NumWebPurchases      2240 non-null   int64  \n",
      " 17  NumCatalogPurchases  2240 non-null   int64  \n",
      " 18  NumStorePurchases    2240 non-null   int64  \n",
      " 19  NumWebVisitsMonth    2240 non-null   int64  \n",
      " 20  AcceptedCmp3         2240 non-null   int64  \n",
      " 21  AcceptedCmp4         2240 non-null   int64  \n",
      " 22  AcceptedCmp5         2240 non-null   int64  \n",
      " 23  AcceptedCmp1         2240 non-null   int64  \n",
      " 24  AcceptedCmp2         2240 non-null   int64  \n",
      " 25  Complain             2240 non-null   int64  \n",
      " 26  Z_CostContact        2240 non-null   int64  \n",
      " 27  Z_Revenue            2240 non-null   int64  \n",
      " 28  Response             2240 non-null   int64  \n",
      "dtypes: float64(1), int64(25), object(3)\n",
      "memory usage: 507.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Finding general information about the columns\n",
    "consumer_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' \n",
    "We see that the non null count of \"Income\" is least and reduces the dataset\n",
    "by only 1% i,e 24 rows, so we do either of the 2 things\n",
    "1. We can drop the records directly\n",
    "2. We can fill the missing values\n",
    "\n",
    "Since we do not have a lot of data, we prefer to retain the records and fill them with mean income value\n",
    "'''\n",
    "consumer_data['Income'].fillna(consumer_data['Income'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Dropping Duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2240"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We directly drop duplicates as they do not contribute uniquely to our models\n",
    "consumer_data.drop_duplicates(inplace=True)\n",
    "consumer_data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Format Standardizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "We standardize formatting of numberical values, floating point precisions and formatting the date column\n",
    "'''\n",
    "consumer_data['Dt_Customer'] = pd.to_datetime(consumer_data['Dt_Customer'], format='%d-%m-%Y')\n",
    "consumer_data['Income'] = consumer_data['Income'].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Structural Consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education column value =  ['Graduation' 'PhD' 'Master' 'Basic' '2n Cycle']\n",
      "Maritial status column value =  ['Single' 'Together' 'Married' 'Divorced' 'Widow' 'Alone' 'Absurd' 'YOLO']\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "We make sure the labels of categorical data are consistent, so that we do not face encoding\n",
    "challenged later\n",
    "'''\n",
    "print('Education column value = ', consumer_data['Education'].unique())\n",
    "print('Maritial status column value = ',consumer_data['Marital_Status'].unique())\n",
    "\n",
    "consumer_data['Education'] = consumer_data['Education'].str.lower()\n",
    "consumer_data['Marital_Status'] = consumer_data['Marital_Status'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1984</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2014-02-10</td>\n",
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       "      <td>married</td>\n",
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       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Year_Birth Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0  5524        1957      grad         single  58138.0        0         0   \n",
       "1  2174        1954      grad         single  46344.0        1         1   \n",
       "2  4141        1965      grad        married  71613.0        0         0   \n",
       "3  6182        1984      grad        married  26646.0        1         0   \n",
       "4  5324        1981  postgrad        married  58293.0        1         0   \n",
       "\n",
       "  Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  AcceptedCmp3  \\\n",
       "0  2012-09-04       58       635  ...                  7             0   \n",
       "1  2014-03-08       38        11  ...                  5             0   \n",
       "2  2013-08-21       26       426  ...                  4             0   \n",
       "3  2014-02-10       26        11  ...                  6             0   \n",
       "4  2014-01-19       94       173  ...                  5             0   \n",
       "\n",
       "   AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
       "0             0             0             0             0         0   \n",
       "1             0             0             0             0         0   \n",
       "2             0             0             0             0         0   \n",
       "3             0             0             0             0         0   \n",
       "4             0             0             0             0         0   \n",
       "\n",
       "   Z_CostContact  Z_Revenue  Response  \n",
       "0              3         11         1  \n",
       "1              3         11         0  \n",
       "2              3         11         0  \n",
       "3              3         11         0  \n",
       "4              3         11         0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Here we see 2n cycle and master correspond to the same education level\n",
    "Similarly to reduce unnecessary complexity, \n",
    "    we mark single, alone, absurd, widow, divorced, yolo as single and \n",
    "    together, married as married\n",
    "'''\n",
    "education_mapping = {\n",
    "    'graduation': 'grad',\n",
    "    'phd': 'postgrad',\n",
    "    'master': 'postgrad',\n",
    "    'basic': 'grad',\n",
    "    '2n cycle': 'postgrad'\n",
    "}\n",
    "\n",
    "maritial_status_mapping = {\n",
    "    'single': 'single',\n",
    "    'together': 'married',\n",
    "    'married': 'married',\n",
    "    'divorced': 'single',\n",
    "    'widow': 'single',\n",
    "    'alone': 'single',\n",
    "    'absurd': 'single',\n",
    "    'yolo': 'single',\n",
    "}\n",
    "consumer_data['Education'] = consumer_data['Education'].replace(education_mapping)\n",
    "consumer_data['Marital_Status'] = consumer_data['Marital_Status'].replace(maritial_status_mapping)\n",
    "\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</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>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Z_CostContact</th>\n",
       "      <th>Z_Revenue</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5592.159821</td>\n",
       "      <td>1968.805804</td>\n",
       "      <td>52247.251339</td>\n",
       "      <td>0.444196</td>\n",
       "      <td>0.506250</td>\n",
       "      <td>2013-07-10 10:01:42.857142784</td>\n",
       "      <td>49.109375</td>\n",
       "      <td>303.935714</td>\n",
       "      <td>26.302232</td>\n",
       "      <td>166.950000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.316518</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.074554</td>\n",
       "      <td>0.072768</td>\n",
       "      <td>0.064286</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.149107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1893.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012-07-30 00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2828.250000</td>\n",
       "      <td>1959.000000</td>\n",
       "      <td>35538.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013-01-16 00:00:00</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5458.500000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>51741.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013-07-08 12:00:00</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8427.750000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>68289.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2013-12-30 06:00:00</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>504.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11191.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>666666.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2014-06-29 00:00:00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3246.662198</td>\n",
       "      <td>11.984069</td>\n",
       "      <td>25037.797168</td>\n",
       "      <td>0.538398</td>\n",
       "      <td>0.544538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.962453</td>\n",
       "      <td>336.597393</td>\n",
       "      <td>39.773434</td>\n",
       "      <td>225.715373</td>\n",
       "      <td>...</td>\n",
       "      <td>2.426645</td>\n",
       "      <td>0.259813</td>\n",
       "      <td>0.262728</td>\n",
       "      <td>0.259813</td>\n",
       "      <td>0.245316</td>\n",
       "      <td>0.114976</td>\n",
       "      <td>0.096391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID   Year_Birth         Income      Kidhome     Teenhome  \\\n",
       "count   2240.000000  2240.000000    2240.000000  2240.000000  2240.000000   \n",
       "mean    5592.159821  1968.805804   52247.251339     0.444196     0.506250   \n",
       "min        0.000000  1893.000000    1730.000000     0.000000     0.000000   \n",
       "25%     2828.250000  1959.000000   35538.750000     0.000000     0.000000   \n",
       "50%     5458.500000  1970.000000   51741.500000     0.000000     0.000000   \n",
       "75%     8427.750000  1977.000000   68289.750000     1.000000     1.000000   \n",
       "max    11191.000000  1996.000000  666666.000000     2.000000     2.000000   \n",
       "std     3246.662198    11.984069   25037.797168     0.538398     0.544538   \n",
       "\n",
       "                         Dt_Customer      Recency     MntWines    MntFruits  \\\n",
       "count                           2240  2240.000000  2240.000000  2240.000000   \n",
       "mean   2013-07-10 10:01:42.857142784    49.109375   303.935714    26.302232   \n",
       "min              2012-07-30 00:00:00     0.000000     0.000000     0.000000   \n",
       "25%              2013-01-16 00:00:00    24.000000    23.750000     1.000000   \n",
       "50%              2013-07-08 12:00:00    49.000000   173.500000     8.000000   \n",
       "75%              2013-12-30 06:00:00    74.000000   504.250000    33.000000   \n",
       "max              2014-06-29 00:00:00    99.000000  1493.000000   199.000000   \n",
       "std                              NaN    28.962453   336.597393    39.773434   \n",
       "\n",
       "       MntMeatProducts  ...  NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  \\\n",
       "count      2240.000000  ...        2240.000000   2240.000000   2240.000000   \n",
       "mean        166.950000  ...           5.316518      0.072768      0.074554   \n",
       "min           0.000000  ...           0.000000      0.000000      0.000000   \n",
       "25%          16.000000  ...           3.000000      0.000000      0.000000   \n",
       "50%          67.000000  ...           6.000000      0.000000      0.000000   \n",
       "75%         232.000000  ...           7.000000      0.000000      0.000000   \n",
       "max        1725.000000  ...          20.000000      1.000000      1.000000   \n",
       "std         225.715373  ...           2.426645      0.259813      0.262728   \n",
       "\n",
       "       AcceptedCmp5  AcceptedCmp1  AcceptedCmp2     Complain  Z_CostContact  \\\n",
       "count   2240.000000   2240.000000   2240.000000  2240.000000         2240.0   \n",
       "mean       0.072768      0.064286      0.013393     0.009375            3.0   \n",
       "min        0.000000      0.000000      0.000000     0.000000            3.0   \n",
       "25%        0.000000      0.000000      0.000000     0.000000            3.0   \n",
       "50%        0.000000      0.000000      0.000000     0.000000            3.0   \n",
       "75%        0.000000      0.000000      0.000000     0.000000            3.0   \n",
       "max        1.000000      1.000000      1.000000     1.000000            3.0   \n",
       "std        0.259813      0.245316      0.114976     0.096391            0.0   \n",
       "\n",
       "       Z_Revenue     Response  \n",
       "count     2240.0  2240.000000  \n",
       "mean        11.0     0.149107  \n",
       "min         11.0     0.000000  \n",
       "25%         11.0     0.000000  \n",
       "50%         11.0     0.000000  \n",
       "75%         11.0     0.000000  \n",
       "max         11.0     1.000000  \n",
       "std          0.0     0.356274  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We plot statistics of the dataset to find more about data\n",
    "'''\n",
    "consumer_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "1. ID is just a unique field which does not have any signifance to predicting or clustering, we can use index instead of the id\n",
    "2. Z_CostContact and Z_Revenue have same values throughout the dataset, hence they are redundant\n",
    "3. Income might have outlier becasue the max income is really far away from mean as well as 3rd quantile\n",
    "4. Year_Birth might also have outlier since mean=1968, std=12 and min value=1893"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Dropping unwanted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
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       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>...</th>\n",
       "      <th>NumCatalogPurchases</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
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       "      <th>AcceptedCmp2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>grad</td>\n",
       "      <td>single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>grad</td>\n",
       "      <td>single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-08</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-08-21</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>postgrad</td>\n",
       "      <td>married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year_Birth Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0        1957      grad         single  58138.0        0         0   \n",
       "1        1954      grad         single  46344.0        1         1   \n",
       "2        1965      grad        married  71613.0        0         0   \n",
       "3        1984      grad        married  26646.0        1         0   \n",
       "4        1981  postgrad        married  58293.0        1         0   \n",
       "\n",
       "  Dt_Customer  Recency  MntWines  MntFruits  ...  NumCatalogPurchases  \\\n",
       "0  2012-09-04       58       635         88  ...                   10   \n",
       "1  2014-03-08       38        11          1  ...                    1   \n",
       "2  2013-08-21       26       426         49  ...                    2   \n",
       "3  2014-02-10       26        11          4  ...                    0   \n",
       "4  2014-01-19       94       173         43  ...                    3   \n",
       "\n",
       "   NumStorePurchases  NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  \\\n",
       "0                  4                  7             0             0   \n",
       "1                  2                  5             0             0   \n",
       "2                 10                  4             0             0   \n",
       "3                  4                  6             0             0   \n",
       "4                  6                  5             0             0   \n",
       "\n",
       "   AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  Response  \n",
       "0             0             0             0         0         1  \n",
       "1             0             0             0         0         0  \n",
       "2             0             0             0         0         0  \n",
       "3             0             0             0         0         0  \n",
       "4             0             0             0         0         0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_data.drop(columns=['Z_CostContact', 'Z_Revenue', 'ID'], inplace=True)\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Finding Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(0, consumer_data.shape[0])]\n",
    "plt.scatter(x, consumer_data['Income'])\n",
    "plt.ylabel('Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(0, consumer_data.shape[0])]\n",
    "plt.scatter(x, consumer_data['Year_Birth'])\n",
    "plt.ylabel('Birth year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2229"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We remove the outliers from income and year_birth column\n",
    "For income, we remove the few local outliers, hence we cap income to 140000\n",
    "'''\n",
    "consumer_data = consumer_data[(consumer_data['Income'] < 140000) & (consumer_data['Year_Birth'] > 1920)]\n",
    "consumer_data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Response</th>\n",
       "      <th>Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
       "      <td>grad</td>\n",
       "      <td>single</td>\n",
       "      <td>58138.0</td>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>58</td>\n",
       "      <td>635</td>\n",
       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>172</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>grad</td>\n",
       "      <td>single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>2014-03-08</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>71613.0</td>\n",
       "      <td>2013-08-21</td>\n",
       "      <td>26</td>\n",
       "      <td>426</td>\n",
       "      <td>49</td>\n",
       "      <td>127</td>\n",
       "      <td>111</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>postgrad</td>\n",
       "      <td>married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year_Birth Education Marital_Status   Income Dt_Customer  Recency  \\\n",
       "0        1957      grad         single  58138.0  2012-09-04       58   \n",
       "1        1954      grad         single  46344.0  2014-03-08       38   \n",
       "2        1965      grad        married  71613.0  2013-08-21       26   \n",
       "3        1984      grad        married  26646.0  2014-02-10       26   \n",
       "4        1981  postgrad        married  58293.0  2014-01-19       94   \n",
       "\n",
       "   MntWines  MntFruits  MntMeatProducts  MntFishProducts  ...  \\\n",
       "0       635         88              546              172  ...   \n",
       "1        11          1                6                2  ...   \n",
       "2       426         49              127              111  ...   \n",
       "3        11          4               20               10  ...   \n",
       "4       173         43              118               46  ...   \n",
       "\n",
       "   NumStorePurchases  NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  \\\n",
       "0                  4                  7             0             0   \n",
       "1                  2                  5             0             0   \n",
       "2                 10                  4             0             0   \n",
       "3                  4                  6             0             0   \n",
       "4                  6                  5             0             0   \n",
       "\n",
       "   AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  Response  Children  \n",
       "0             0             0             0         0         1         0  \n",
       "1             0             0             0         0         0         2  \n",
       "2             0             0             0         0         0         0  \n",
       "3             0             0             0         0         0         1  \n",
       "4             0             0             0         0         0         1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We can reduce some columns like \"Kidhome\" and \"Teenhome\" by combining them\n",
    "'''\n",
    "consumer_data['Children'] = consumer_data['Kidhome'] + consumer_data['Teenhome']\n",
    "consumer_data.drop(columns=['Kidhome', 'Teenhome'], inplace=True)\n",
    "consumer_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Column Label Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Dt_Customer</th>\n",
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       "      <th>Fruits</th>\n",
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       "      <th>Fish</th>\n",
       "      <th>...</th>\n",
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       "      <th>Web_Visits</th>\n",
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       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Response</th>\n",
       "      <th>Children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1957</td>\n",
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       "      <td>88</td>\n",
       "      <td>546</td>\n",
       "      <td>172</td>\n",
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       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>grad</td>\n",
       "      <td>single</td>\n",
       "      <td>46344.0</td>\n",
       "      <td>2014-03-08</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>1965</td>\n",
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       "      <td>71613.0</td>\n",
       "      <td>2013-08-21</td>\n",
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       "      <td>49</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>postgrad</td>\n",
       "      <td>married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year_Birth Education Marital_Status   Income Dt_Customer  Last_Purchase  \\\n",
       "0        1957      grad         single  58138.0  2012-09-04             58   \n",
       "1        1954      grad         single  46344.0  2014-03-08             38   \n",
       "2        1965      grad        married  71613.0  2013-08-21             26   \n",
       "3        1984      grad        married  26646.0  2014-02-10             26   \n",
       "4        1981  postgrad        married  58293.0  2014-01-19             94   \n",
       "\n",
       "   Wines  Fruits  Meat  Fish  ...  Store_Purchases  Web_Visits  AcceptedCmp3  \\\n",
       "0    635      88   546   172  ...                4           7             0   \n",
       "1     11       1     6     2  ...                2           5             0   \n",
       "2    426      49   127   111  ...               10           4             0   \n",
       "3     11       4    20    10  ...                4           6             0   \n",
       "4    173      43   118    46  ...                6           5             0   \n",
       "\n",
       "   AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  Response  \\\n",
       "0             0             0             0             0         0         1   \n",
       "1             0             0             0             0         0         0   \n",
       "2             0             0             0             0         0         0   \n",
       "3             0             0             0             0         0         0   \n",
       "4             0             0             0             0         0         0   \n",
       "\n",
       "   Children  \n",
       "0         0  \n",
       "1         2  \n",
       "2         0  \n",
       "3         1  \n",
       "4         1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Column label renaming helps us the make column name more intuitive, concise and consistent\n",
    "'''\n",
    "column_name_map = {\n",
    "    'Recency': 'Last_Purchase',\n",
    "    'MntWines': 'Wines',\n",
    "    'MntFruits': 'Fruits',\n",
    "    'MntMeatProducts': 'Meat',\n",
    "    'MntFishProducts': 'Fish',\n",
    "    'MntSweetProducts': 'Sweet',\n",
    "    'MntGoldProds': 'Gold',\n",
    "    'NumDealsPurchases': 'Deal_Purchases',\n",
    "\t'NumWebPurchases': 'Web_Purchases',\n",
    "\t'NumCatalogPurchases': 'Catalog_Purchases',\n",
    "\t'NumStorePurchases': 'Store_Purchases',\n",
    "\t'NumWebVisitsMonth': 'Web_Visits',\n",
    "}\n",
    "consumer_data.rename(columns=column_name_map, inplace=True)\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>grad</td>\n",
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       "      <td>46344.0</td>\n",
       "      <td>2014-03-08</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>grad</td>\n",
       "      <td>married</td>\n",
       "      <td>26646.0</td>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
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       "      <td>39</td>\n",
       "      <td>3247</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>postgrad</td>\n",
       "      <td>married</td>\n",
       "      <td>58293.0</td>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>94</td>\n",
       "      <td>173</td>\n",
       "      <td>43</td>\n",
       "      <td>118</td>\n",
       "      <td>46</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>3269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year_Birth Education Marital_Status   Income Dt_Customer  Last_Purchase  \\\n",
       "0        1957      grad         single  58138.0  2012-09-04             58   \n",
       "1        1954      grad         single  46344.0  2014-03-08             38   \n",
       "2        1965      grad        married  71613.0  2013-08-21             26   \n",
       "3        1984      grad        married  26646.0  2014-02-10             26   \n",
       "4        1981  postgrad        married  58293.0  2014-01-19             94   \n",
       "\n",
       "   Wines  Fruits  Meat  Fish  ...  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  \\\n",
       "0    635      88   546   172  ...             0             0             0   \n",
       "1     11       1     6     2  ...             0             0             0   \n",
       "2    426      49   127   111  ...             0             0             0   \n",
       "3     11       4    20    10  ...             0             0             0   \n",
       "4    173      43   118    46  ...             0             0             0   \n",
       "\n",
       "   AcceptedCmp1  AcceptedCmp2  Complain  Response  Children  Age  \\\n",
       "0             0             0         0         1         0   66   \n",
       "1             0             0         0         0         2   69   \n",
       "2             0             0         0         0         0   58   \n",
       "3             0             0         0         0         1   39   \n",
       "4             0             0         0         0         1   42   \n",
       "\n",
       "   Customer_From  \n",
       "0           3771  \n",
       "1           3221  \n",
       "2           3420  \n",
       "3           3247  \n",
       "4           3269  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We scale Dt_customer in this block by anchoring it to 1st January 2023\n",
    "'''\n",
    "consumer_data['Age'] = 2023 - consumer_data['Year_Birth']\n",
    "consumer_data['Customer_From'] = (pd.to_datetime('2023-01-01') - consumer_data['Dt_Customer']).dt.days\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1957</td>\n",
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       "      <td>1954</td>\n",
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       "      <td>3420</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26646.0</td>\n",
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      "text/plain": [
       "   Year_Birth  Education  Marital_Status   Income Dt_Customer  Last_Purchase  \\\n",
       "0        1957          0               1  58138.0  2012-09-04             58   \n",
       "1        1954          0               1  46344.0  2014-03-08             38   \n",
       "2        1965          0               0  71613.0  2013-08-21             26   \n",
       "3        1984          0               0  26646.0  2014-02-10             26   \n",
       "4        1981          1               0  58293.0  2014-01-19             94   \n",
       "\n",
       "   Wines  Fruits  Meat  Fish  ...  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  \\\n",
       "0    635      88   546   172  ...             0             0             0   \n",
       "1     11       1     6     2  ...             0             0             0   \n",
       "2    426      49   127   111  ...             0             0             0   \n",
       "3     11       4    20    10  ...             0             0             0   \n",
       "4    173      43   118    46  ...             0             0             0   \n",
       "\n",
       "   AcceptedCmp1  AcceptedCmp2  Complain  Response  Children  Age  \\\n",
       "0             0             0         0         1         0   66   \n",
       "1             0             0         0         0         2   69   \n",
       "2             0             0         0         0         0   58   \n",
       "3             0             0         0         0         1   39   \n",
       "4             0             0         0         0         1   42   \n",
       "\n",
       "   Customer_From  \n",
       "0           3771  \n",
       "1           3221  \n",
       "2           3420  \n",
       "3           3247  \n",
       "4           3269  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We can encode category labels\n",
    "'''\n",
    "consumer_data['Education'] = consumer_data['Education'].astype(\"category\").cat.codes.to_list()\n",
    "consumer_data['Marital_Status'] = consumer_data['Marital_Status'].astype(\"category\").cat.codes.to_list()\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Data normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3269</td>\n",
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      ],
      "text/plain": [
       "   Year_Birth  Education  Marital_Status  Income Dt_Customer  Last_Purchase  \\\n",
       "0        1957          0               1    0.50  2012-09-04             58   \n",
       "1        1954          0               1    0.40  2014-03-08             38   \n",
       "2        1965          0               0    0.62  2013-08-21             26   \n",
       "3        1984          0               0    0.22  2014-02-10             26   \n",
       "4        1981          1               0    0.51  2014-01-19             94   \n",
       "\n",
       "   Wines  Fruits  Meat  Fish  ...  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  \\\n",
       "0    635      88   546   172  ...             0             0             0   \n",
       "1     11       1     6     2  ...             0             0             0   \n",
       "2    426      49   127   111  ...             0             0             0   \n",
       "3     11       4    20    10  ...             0             0             0   \n",
       "4    173      43   118    46  ...             0             0             0   \n",
       "\n",
       "   AcceptedCmp1  AcceptedCmp2  Complain  Response  Children  Age  \\\n",
       "0             0             0         0         1         0   66   \n",
       "1             0             0         0         0         2   69   \n",
       "2             0             0         0         0         0   58   \n",
       "3             0             0         0         0         1   39   \n",
       "4             0             0         0         0         1   42   \n",
       "\n",
       "   Customer_From  \n",
       "0           3771  \n",
       "1           3221  \n",
       "2           3420  \n",
       "3           3247  \n",
       "4           3269  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We normalize the feature values\n",
    "'''\n",
    "consumer_data['Income'] = ((consumer_data['Income'] - consumer_data['Income'].min()) / (consumer_data['Income'].max() - consumer_data['Income'].min()))\n",
    "\n",
    "consumer_data['Income'] = consumer_data['Income'].round(2)\n",
    "\n",
    "consumer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Data Validation\n",
    "We just visually go through the data and its statistics to confirm if all the parametes look correct to avoid any inconsistencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumer_data.to_csv('./data/clean_consumer_data.csv', index=False)"
   ]
  }
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