Black Friday EDA and Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d27ac376",
   "metadata": {},
   "source": [
    "# Black Friday Dataset EDA and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f79680e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing requried Modules and Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1072e52e",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.\n",
    "\n",
    "Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17eee4de",
   "metadata": {},
   "source": [
    "## Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7c20e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the train Dataset\n",
    "df_train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20b63c39",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
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       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>A</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057</td>\n",
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       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>550063</th>\n",
       "      <td>1006033</td>\n",
       "      <td>P00372445</td>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>13</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550064</th>\n",
       "      <td>1006035</td>\n",
       "      <td>P00375436</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550065</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00375436</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550066</th>\n",
       "      <td>1006038</td>\n",
       "      <td>P00375436</td>\n",
       "      <td>F</td>\n",
       "      <td>55+</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365</td>\n",
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       "    <tr>\n",
       "      <th>550067</th>\n",
       "      <td>1006039</td>\n",
       "      <td>P00371644</td>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>550068 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0       1000001  P00069042      F   0-17          10             A   \n",
       "1       1000001  P00248942      F   0-17          10             A   \n",
       "2       1000001  P00087842      F   0-17          10             A   \n",
       "3       1000001  P00085442      F   0-17          10             A   \n",
       "4       1000002  P00285442      M    55+          16             C   \n",
       "...         ...        ...    ...    ...         ...           ...   \n",
       "550063  1006033  P00372445      M  51-55          13             B   \n",
       "550064  1006035  P00375436      F  26-35           1             C   \n",
       "550065  1006036  P00375436      F  26-35          15             B   \n",
       "550066  1006038  P00375436      F    55+           1             C   \n",
       "550067  1006039  P00371644      F  46-50           0             B   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               2               0                   1   \n",
       "2                               2               0                  12   \n",
       "3                               2               0                  12   \n",
       "4                              4+               0                   8   \n",
       "...                           ...             ...                 ...   \n",
       "550063                          1               1                  20   \n",
       "550064                          3               0                  20   \n",
       "550065                         4+               1                  20   \n",
       "550066                          2               0                  20   \n",
       "550067                         4+               1                  20   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  Purchase  \n",
       "0                      NaN                 NaN      8370  \n",
       "1                      6.0                14.0     15200  \n",
       "2                      NaN                 NaN      1422  \n",
       "3                     14.0                 NaN      1057  \n",
       "4                      NaN                 NaN      7969  \n",
       "...                    ...                 ...       ...  \n",
       "550063                 NaN                 NaN       368  \n",
       "550064                 NaN                 NaN       371  \n",
       "550065                 NaN                 NaN       137  \n",
       "550066                 NaN                 NaN       365  \n",
       "550067                 NaN                 NaN       490  \n",
       "\n",
       "[550068 rows x 12 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "#\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4c553eb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
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       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
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       "      <th>Marital_Status</th>\n",
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       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
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       "      <td>P00069042</td>\n",
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       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>A</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>14.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
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       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
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      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN      8370  \n",
       "1                 6.0                14.0     15200  \n",
       "2                 NaN                 NaN      1422  \n",
       "3                14.0                 NaN      1057  \n",
       "4                 NaN                 NaN      7969  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c49701d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of rows and columns in dataset\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d1f3ada",
   "metadata": {},
   "outputs": [
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       "      <th>count</th>\n",
       "      <td>5.500680e+05</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>376430.000000</td>\n",
       "      <td>166821.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.076707</td>\n",
       "      <td>0.409653</td>\n",
       "      <td>5.404270</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>9263.968713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727592e+03</td>\n",
       "      <td>6.522660</td>\n",
       "      <td>0.491770</td>\n",
       "      <td>3.936211</td>\n",
       "      <td>5.086590</td>\n",
       "      <td>4.125338</td>\n",
       "      <td>5023.065394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001516e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003077e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004478e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23961.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  5.500680e+05  550068.000000   550068.000000       550068.000000   \n",
       "mean   1.003029e+06       8.076707        0.409653            5.404270   \n",
       "std    1.727592e+03       6.522660        0.491770            3.936211   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001516e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003077e+06       7.000000        0.000000            5.000000   \n",
       "75%    1.004478e+06      14.000000        1.000000            8.000000   \n",
       "max    1.006040e+06      20.000000        1.000000           20.000000   \n",
       "\n",
       "       Product_Category_2  Product_Category_3       Purchase  \n",
       "count       376430.000000       166821.000000  550068.000000  \n",
       "mean             9.842329           12.668243    9263.968713  \n",
       "std              5.086590            4.125338    5023.065394  \n",
       "min              2.000000            3.000000      12.000000  \n",
       "25%              5.000000            9.000000    5823.000000  \n",
       "50%              9.000000           14.000000    8047.000000  \n",
       "75%             15.000000           16.000000   12054.000000  \n",
       "max             18.000000           18.000000   23961.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the dataset\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30c28583",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     550068 non-null  int64  \n",
      " 1   Product_ID                  550068 non-null  object \n",
      " 2   Gender                      550068 non-null  object \n",
      " 3   Age                         550068 non-null  object \n",
      " 4   Occupation                  550068 non-null  int64  \n",
      " 5   City_Category               550068 non-null  object \n",
      " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
      " 7   Marital_Status              550068 non-null  int64  \n",
      " 8   Product_Category_1          550068 non-null  int64  \n",
      " 9   Product_Category_2          376430 non-null  float64\n",
      " 10  Product_Category_3          166821 non-null  float64\n",
      " 11  Purchase                    550068 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 50.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# More information about the dataset\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78f7e0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns in datset\n",
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ded7cbd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000001, 'P00069042', 'F', ..., nan, nan, 8370],\n",
       "       [1000001, 'P00248942', 'F', ..., 6.0, 14.0, 15200],\n",
       "       [1000001, 'P00087842', 'F', ..., nan, nan, 1422],\n",
       "       ...,\n",
       "       [1006036, 'P00375436', 'F', ..., nan, nan, 137],\n",
       "       [1006038, 'P00375436', 'F', ..., nan, nan, 365],\n",
       "       [1006039, 'P00371644', 'F', ..., nan, nan, 490]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Values in the dataset\n",
    "df_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a51b93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=550068, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range of index in dataset\n",
    "df_train.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b072ecfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            173638\n",
       "Product_Category_3            383247\n",
       "Purchase                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in dataset\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "517b0e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='User_ID', ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XzJtGSfbk+L0apMlGTciOylY2lDdz1cK8kK5Gt9uMzETK6jsxk2TR4LZjLeSmxlOQ5gp3KCdJdMby7FfO5atPbue/Xz0IwNLidK5fdurTXf0pzU4iLSGOnZWtUZNs6jt6eXZrFVUtPZyRl8JdV53BosI0Ul1xtPX088j6I2wsb+bX68r5xNJClpdMjlED0GSjJuiXbx0m1RXLikn0ywAwPSuR7ZWttHT3hzuU09bQ4aaypYerFuZF5IytrOR4HvvCSo40dpGf5uLFHcfHPmmIGBGWFKXx97LGUadSR4rK5m4eWX+EGBFWf6iY712/6JT/l4rmbi6cncMTm47x/PZqHDEyaWatabJR43agtp21+2q54+LZxE+ypzAO3repaB65bI5d7Fg5vr2yBQGWRPC7fkeM+H18eCCWFKfzzqFG9lRHdtXuqpZuHn73CMnxsdx2fikZSU7+uKlyxGOdsTHcfM4MHl53hJd21XD31WeSmRTeKevBoMlGjYvXa7j7+d2kJ8TxhQtKeXWSPRhrWoqLOIdQ1RL+GWmnsz6jf8DL1mMtzM1NITXIteQiSX5aArmp8RGzwHOk/7OO3n7+sLGCxDgHt184M6DFz7ExMaxaWsjP3jzEA68c4IEbFtsRbkhpsolyoa6l9PiGY2yvaOUHn14yKd5tDeeIEQrSEiIi2YDvD5UrzhHQk0+Hen1fHR29HlacNbmGOUeypCidv+6ro7K529YqExNhjOGpzZV093n48kWzxlVlIy/Vxbkzs3h6ayVf/egcCtOD81iNcInO+YIqLN46WM99L+3jork5fGJp+Gps2a0oI4Gath48A+F72FdVSze/evsw33vlAN99eT9/2l6F2zMQ8Pm/t2YKToWnYy4pSgdgzc7x3fMJhW0VrZQ3dnHNogIKJpAszp2VjTHw/NbImLZ+OrRno8ZkjK9W1Tf/tJu5uSn87KalEXnDOVgKMxLpP9zEofpOzsxPDfn1mzrd/G79UWJjhI+eOY2Wrn62HmuhurWHW84tGfP8svoO3i1r4rL5uZNqpqA/GUlOZmQm8sL2av7XxbNO+dkMtPc/nnJIgYwedLs9vLKnhhmZiSwvmdjU88wkJ+fMzOTZbVXcecnsqP6902SjRlXb1ss3/7SbNw/Uc/aMDH5589mT+h4A+Ho2ALuqWkOebDwDXh7bcAxj4IsXziTbej7QwsJU/ripkkfePcqNK6eP+n/wo9cPkeh08KFJNlNwNEuK01mz8zj7azqYXxD6NwgjeXVvLb39A6w6q/C0kv6nzi7mX5/ZyaYjzawc8nC/aHscgQ6jqRH5xporuOwHb7P+cCP/ee18nv7yuWF5FkqoZSY5ccXFsLMq9DOcNh9tpqHDzaeXF51INADz8lK5+ZwZ1Hf0cscfttHvZ4jvQG07L+2q4dbzS0iepCvRR7Ko0Fe+5uktI8/wCrWjjV1sOdbCBbOzyTvNNU5XLcojIc7By7trghRdeGiyUado7e7jyh+t49+f201Wcjx3XDybhDjHpHyo2EhiRChMT2BXVWtIr9vn8fLWwQZKs5OYm3vqvZbZ05L5xNIi1h1q5JvP7z5l4anXa7j/L/tJiY/lSxfODFXYESEpPpZrFuXz7Naq015z09bTT01bz4Sfl+PxenlhRzXpCXFcEoQK4i9sP05RRgKv7KnliY0VJz6izdR566MCUlbfwa2/28zxll6uXZzPOTOzTgwBROMP+EQVZSSy/nAjvf0DuEK0lmjTkSY63B5Wr5jud2z+7BkZtHT38czWKmraernSquBw08rp/PTNMtYdauS+jy8M+6MEwuGW80p4YcdxnttaxS3nlYz7/OrWHl7aeZxjVrmiOIdwybxpXDg3Z1zDYO+WNVHf4eaz58wIWs22mdlJrN1XR6fbE7U91uiMWtniQG07n/n1RkSEL10088QCx6moMD2B/gHDgdqOkJRCMcaw8UgzM7ISKR2jJtalZ0yju2+Av5c1Utvey7LpGew53sYfN1Vw/bJCbg7TuH2434wsnZ7BkuJ0frf+KDefM2NcPfHHNxzjl28dJineweXzc0lPdLK7uo21++qobOnhxhXTA2qvpauPNw/UMT8/Naj3+0pzkoE6jjR2Be1xHqGmw2iTgMfrpb6jl47eiZdY2Xu8jRsf2kCsQ3jqy+dM6UQDJ08SCIUjjV00dfUFVP5HRPjYkgJWnVVAZXM3T2+p5JktlXz2nBl89xOnlkCZSr5y0UyONHbx5ObAEp8xhh/89SD/+cIeZk9L5quXzOHiedM4qzidm1dO5+pF+eyraefFHdVj1sszxrBm53EE4drF+cF4OScUpifgjI2hPIqfJKs9myi3p7qNZ7ZW0j9giBGYX5DG+bOzmDGOEvm7q9q4+eGNJDkdPPGlcyjJTmJjebONUUe+tIQ4spOd7Kxsg3Ptv96WYy244mJYUBD4u9aVpVksn5FJXXsvnz13BrmpkVVsMxyuXJjHipJM/t9f3+faxaM/qmDAa7jnxT38YWMF/7C8mIWFaSf1XkSEC2Zn093n4a2DDeSkxHPhHP/PBFq7t46DdR1cvTAv6MOYjhihJCuR8sbQllEKJu3ZRLGy+k6e2lJJbqqLG84u4vxZ2Ryq6+DKH63j0fVH8QZwg3NHZSs3/WYDyfGxPPXlcyddWfOJEhEWF6Wzu7rV9mu19fSzp7qNs4rTxz3G74gRCtITNNFYRIR7rptPS3cf97y4x+/vQJfbw51PbOMPGyv4yodn8f1PLvI7THbZmbksKEhl7d5avz2Lpk4331qzh/w0F+fOCs4j0ocrzU6mocNNdxQUHR2JrclGRK4UkYMiUiYid42wP15EnrL2bxSRkiH77ra2HxSRK8ZqU0TutLYZEckesv1iEWkTkR3Wxz02vuSQGfAant1aSXayk1vPK2XZ9AyuWpTP1z46lxWlmXxrzV5u+s0GKpr8P5vlld01fObXG8hIdPLUl8+JuFIf4baoMI2y+k66bP7lXru3Fo/XsLQ4sp45E60WFqbxr5fN5cUdx/neK/vxDhv+qmzu5hMPvsvavbX8xzVnctdVZ4w69Cgi3LCsiKykeJ7YVEF9e+9J+we8hq89tYOW7n4+uazItlmbBem+NxQ1w64fLWwbRhMRB/Bz4DKgCtgsImuMMfuGHHYb0GKMmS0iq4EHgH8QkfnAamABUAC8LiJzrXP8tfku8BLw1gjhrDPGXBv0FxlGe4630d7r4eNLC096JHNaQhy/u/VDPLOlivte2seVP36Hr310Dp9eXnyia1/R1M2P3zjEc9uqWDo9nV/efLa+Mx7BkuI0vMY3VDl0MV2wvbSrhozEuBP3idTpu+Mjs6lt7+XX646Qn+ZiSVE6jhihrL6Tg3UdZCfH8/htKzl/dmC9kPg4B587dwYPvVPOb989wuUL8phfkEpP3wB3P7+LdYca+f71i5jgbOmA5Fm/o7VtvczKmXil7HCx857NCqDMGFMOICJPAquAoclmFXCv9fmzwM/E9xZjFfCkMcYNHBGRMqs9/LVpjNlubbPxJUWO9w43kZnkHHE9hojw6Q8Vc8GcbO56fjffffkA/3ftwRMzrKpbe3DGxvDli2by9cvnEh87uR4TECyLCtMB2FVlX7Jp6nTzblkjF8zOnjI/u6EgIty3aiErS7O458U9vLrXV508IzGOj8ybxk9vWjruKcRZyfHcekEpv1lXzjU/XceKkkwqm7upae/l3y6fyz98qNjvYwOCIcUVR1J8LLXaszlFITD0O18FrPR3jDHGIyJtQJa1fcOwcwcrP47V5kjOFZGdwHHg34wxe4cfICK3A7cDTJ8euSUfAI639lDR3M01i/JHnf9fkJ7AY19Ywd7jbfxlVw2VLT0YY7jlvBlct6SA/DR9Jz2anBTfEy532fislFf31jLgNSwuis7prJFMRLhuSQHtPf24PV76BrwnyvxMdK1KXqqLr182l4YON+8ebmRBYRrfvX4RF8+bFszQ/cpPdVHbpskmUm0DZhhjOkXkauAFYM7wg4wxDwEPASxfvjyinwm8v6YdwfcY3UAsKEjzO8sp3GsjIt3ionRbpz//eedxZuUknRgiUcEnIrjiHEFbnJvojOXuq8NToSEvzcWG8iYGvCbqKnrYmWyqgeIhXxdZ20Y6pkpEYoE0oGmMc8dq8yTGmPYhn78sIg+KSLYxpnEcryWilDd2kZ/uInGc7840sYzf4uI0Xt1bS2t3X9Cns9a197LxSDP/fOkcHUJTAclLdeHxGpq63ExLia43KHbORtsMzBGRUhFx4rvhv2bYMWuAW6zPbwDeNL6VU2uA1dZstVJ8PZFNAbZ5EhHJs+4DISIr8L3mpqC8wjDoH/BS0dzNzOzou0EYjQaflbLbhqG0l3fXYAxcu7gg6G2ryWmwqGc0DqXZlmyMMR7gTmAtsB942hizV0S+LSIfsw57GMiyJgB8HbjLOncv8DS+yQSvAncYYwb8tQkgIl8VkSp8vZ1dIvIb6xo3AHusezY/AVabsZYCR7BjTd0MeA2zcnQ9TCgstEqD7LKhAvSfdx7nzPxUZk/TNw4qMDkp8cRIdCYbW+/ZGGNeBl4etu2eIZ/3Ap/yc+79wP2BtGlt/wm+ZDJ8+8+An4039khV3tBJjEDJKBUCdLgsOAa/j1lJTv6yq4aMRGfQnhdS1dLNtopWvnHFvKC0p6aGOEcMmUlOGjrd4Q5l3KbCBIFJpbyxi8L0BOJDVIlY+eqkHWnsGrM21ni8sN13q3HVWTqEFixT5U1WZpKT5q6+cIcxblquJop4jeF4a8+UL5IZatOzkmjv9dDaPfFCp0MZY3h+WzXnzMykKEP/L9X4ZCbF09zVF9Q3P6GgySaKNHX24fGa037ynxqfkixfQjjSFJwiiDsqWylv7OL6ZUVBaU9NLVlJTtweL119A+EOZVx0GC2KDK4czkvVxZihlJvqwhUXw9EgVdx9dmsVrrgYrlqYF5T2os1UGe6yS2aSbwp+c1dfVD1ILXoiVdS19yLAtNT4MY9VwRMjQklWEkeCkGzaevr50/ZqrllUQIrLf/n78dI/4FNH1olk446qIXUdRositW29ZCU7iXPof1uolWYn0dTVR33H6U05fWZLJd19A9x6fklwAlNTTkaSEwGaomySgPZsokhtey8Fer8mLAanmm860jzhRZgDXsPv1h9lRUnmifU7KjyiuScY54ghNSGO5k5NNsoGfR4vLV19LJ2eHu5QpqSC9ATiY2NY937jhJPNn3cep6qlh4vm5ET1HzsVfplJTu3ZKHvUtfdiYMoUbIy0P8aOGGFubgpvHKibUBHE3v4B/u/agxSku5hfkGpTlGqqyExycrC2I9xhjIsO/keJuhMz0aZGsolE8/NTaezsY0dl67jPffy9Y1S39nDlgtEfC6FUILKSnHS6Pbg90TP9WXs2UaKpq48YIeiVh1Xg5uamEBsjvL6/jrNnfPAIZ3+9sMHSNuUNnfzgtff5yLwcrYM2SYS7551hzUhr7e4nNzU6qolozyZKNHf5StxH2zMsJpMEp4MVpZm8tq8u4NXbfR4vX3tqB/FxMXzv+sU2R6imijRr2nxbT3CqWoSCJpso0dzVd2IxlwqfaxbnU1bfybaKljGP9XoNdz23i11VbXz/+kVa+UEFTVqiJhtlE002keETSwtJS4jj1+8cGfU4Ywz3v7yf57dX86+XzeXKhfkhilBNBamuOIRJmGxE5HkRuUZENDmFQVtPPz39A2Tq/ZqwS3TGcvM501m7r5ZjfmqleY3hhR3VPPz3I3z+vBLuvGR2iKNUk50jRkh2xU6+ZAM8CNwEHBKR74uIPoQjhCqbuwG0ZxMhPnduCbExwn0v7cPrPfneTbfbw+/ePcrmoy3c+ZHZfOu6+frIZ2WLtIS4qEo2Ac1GM8a8DrwuImnAjdbnlcCvgd8bY6LnFUeB4TNdBh9JrMkmMuSmuvjm1WfyX3/exwNrD1CUnogI7Klu4+XdNXT1DXD90kL+TR+MpmyUlhBHfUf0PEQt4KnPIpIF3Ax8FtgO/AG4ALgFuNiO4JTP4IOSNNlEjs+fV8KBmg5+9XY5SU4HA8bQ2+8lP83FzefM0OfUKNulJcRxqL4TY0xU9J4DSjYi8idgHvA4cJ0xpsba9ZSIbLErOOXT3NVHotOBS5/OGTFEhO9dv4iPzs/lp28ewumIYda0ZBYVpp1YtBnutRhqcktLiKPP48Xt8UbF34ZAeza/Nsa8PHSDiMQbY9zGmOU2xKWGaO5ynygrriJHTIxw2fxcGqJoKENNHmkJvunPrT395EVBsgl0gsB3Rtj2XjADUf41d/WdWDGslFLwQbJpj5JJAqP2bEQkDygEEkRkKTA4MJgK6KB0CHiNoa2nn8U67VkpNcRgsmnrngTJBrgC+DxQBPxgyPYO4Js2xaSG6HR78JoPfrCUUgogZXBhZ+8kSDbGmEeBR0Xkk8aY50IUkxpi8F2LJhul1FCOGCHFFTs5ejYicrMx5vdAiYh8ffh+Y8wPRjhNBdHgoi1NNkqp4VJccbRPhp4NkGT9q3XRw0STjVLKn5QoKlkz1jDar6x//ys04ajh2nv6iY0REp2RP7VRKRVaKa44Klt6wh1GQAItxPnfIpIqInEi8oaINIjIzXYHp3xz6NMS4qJihbBSKrRSXLF0uz0MeAN7vlI4BbrO5nJjTDtwLXAUmA18w66g1Afae/pJ1SE0pdQIUlyxGHyzViNdoMlmcLjtGuAZY0ybTfGoYdp6+knXZKOUGkFKvO9vQ2fv5Ek2L4nIAeBs4A0RyQF67QtLgW9BZ3uv9myUUiNLcfn6AR1RMCMtoGRjjLkLOA9Ybj1OoAtYZWdgyvduRRd0KqX8+SDZRH7PJuBHDABn4FtvM/Scx4IcjxpCpz0rpUaTbCWbdnfk92wCfcTA48AsYAcwYG02aLKxlSabyKOPDVCRJDYmhkSnIyru2QTas1kOzDfGRP78uklEk41SaizJ8bFRMYwW6ASBPUCenYGoU3X0enCILuhUSvmX6oqLigkCgfZssoF9IrIJOPGkKGPMx2yJSgG+GSbJrlhd0KmU8ivFFUtjY+Q/wC/QZHOvnUGokXW4PSdmmyil1EiSXbF0uD1E+l2OgP6SGWPeFpEZwBxjzOsikgjo2I7NOns9+oROpdSoUlxxDHgNPf0DYx8cRoHWRvsS8CzwK2tTIfBCAOddKSIHRaRMRO4aYX+8iDxl7d8oIiVD9t1tbT8oIleM1aaI3GltMyKSPWS7iMhPrH27RGRZIK85ErT39pMSrz0bpZR/0bLWJtAJAncA5wPtAMaYQ8C00U4QEQfwc+AqYD5wo4jMH3bYbUCLMWY28EPgAevc+cBqYAFwJfCgiDjGaPNd4KPAsWHXuAqYY33cDvwiwNccVh6vl+6+AR1GU0qNKtl6Qxrp9dECTTZuY0zf4BfWws6xBghXAGXGmHLr3Cc5terAKuBR6/NngUvFdzd8FfCkMcZtjDkClFnt+W3TGLPdGHN0hDhWAY8Znw1AuojkB/i6w6bL7esSp7h02rNSyr/B0Y/J0rN5W0S+CSSIyGXAM8CfxzinEKgc8nWVtW3EY4wxHqANyBrl3EDanEgciMjtIrJFRLY0NDSM0aT9Bqcyas9GKTWawSoCk6VncxfQAOwGvgy8DPyHXUGFgzHmIWPMcmPM8pycnHCHc+JdiiYbpdRoEuIcOEQivopAoLPRvCLyAvCCMSbQt/3VQPGQr4usbSMdU2UNzaUBTWOcO1abE4kj4nyQbHQYTSnln4iQFO+I7p6NNZPrXhFpBA4CB62ndN4TQNubgTkiUioiTnw3/NcMO2YNcIv1+Q3Am1ZJnDXAamu2Wim+m/ubAmxzuDXA56zXcg7QZoypCSD+sBocRkuK1xnmSqnRpbji6IzwYpxjDaP9C75ZaB8yxmQaYzKBlcD5IvIvo51o3YO5E1gL7AeeNsbsFZFvi8hg5YGHgSwRKQO+jm+4DmPMXuBpYB/wKnCHMWbAX5sAIvJVEanC13PZJSK/sa7xMlCOb5LBr4H/Fcg3Jtw63B4SnQ5iYwId6VRKTVXJ8bFRP4z2WeAyY0zj4AZjTLmI3Az8Fd90Zb+MMS/j+2M/dNs9Qz7vBT7l59z7gfsDadPa/hPgJyNsN/imbkeVjl6tHqCUCkxyfCw1bT3hDmNUY71tjhuaaAZZ9230ZoKNOnr79X6NUiogya5YOt0evN7ILVkzVrLpm+A+dZo6ez1aPUApFZDk+Fi85oPHkkSisf6aLRGR9hG2C+CyIR4FGGO0CKdSKmCDa20aOt0RW09x1L9mxhidChUGPX0DDHiNDqMppQIyWLKmscPN3NyUMEczMp3qFIE63LqgUykVuMFk09AZuc+10WQTgQYXdCZrslFKBWDwjWljZ+TeStdkE4EGF3SmxuswmlJqbIMlaxo6tGejxkF7Nkqp8RgsWdOow2hqPDp6+4lzCPGx+t+jlApMsitWk40aH9+05zh8j/ZRSqmxpcTHabJR49OhCzqVUuOUHB+r92zU+GhdNKXUeCW7Ymnq7IvYkjWabCKQ1kVTSo1XcnwsHq+J2JI1mmwiTE/fAG6PV3s2SqlxST6x1iYyh9I02USYwTFXTTZKqfE4UUUgQu/baLKJMPUdvYA+DlopNT6RXrJGk02EGXxXkqyz0ZRS4zA4gzVSS9Zosokw9TqMppSaAJfTQZxD9J6NCkx9Ry8CJGnPRik1DjEiZCXF06j3bFQgGjrcJLtiidHqAUqpccpOceo9GxWY+g63DqEppSYkOzleh9FUYOrb3aToowWUUhOQkxxPY4dOEFABaOjUno1SamKyU+Jp6nJHZMkaTTYRZMBraNJko5SaoOzkePoHIrNkjSabCNLU5cZrIFkXdCqlJiA72QlEZskaTTYRpL7dWmOj056VUhOQkxIPRGYVAU02EWSwekCqDqMppSYgJ9mXbCKxioAmmwhyolSNDqMppSYg20o2kViMU5NNBPmgCKf2bJRS45eWEEdsTGSWrNFkE0EGF3TGOfS/RSk1fjEx4lvYqT0bNZqGDjfTrBt8Sik1EdkpTu3ZqNHVd7iZluIKdxhKqSiWnRyvs9HU6Oo7ek9MXVRKqYnIjtCSNZpsIoQxRofRlFKnLccqWWNMZJWs0WQTIdp7PfT2e7Vno5Q6LZFaskaTTYSob/dNe85L03s2SqmJi9SSNZpsIkStlWxyUzXZKKUmbrCKQH2ETX/WZBMhatusno0mG6XUaRgcio+0kjWabCJEnQ6jKaWCYLBkTaQt7LQ12YjIlSJyUETKROSuEfbHi8hT1v6NIlIyZN/d1vaDInLFWG2KSKnVRpnVptPa/nkRaRCRHdbHF+18zRNV295LWkIcrjhHuENRSkWxSC1ZY1uyEREH8HPgKmA+cKOIzB922G1AizFmNvBD4AHr3PnAamABcCXwoIg4xmjzAeCHVlstVtuDnjLGnGV9/MaGl3va6trdOoSmlDptMTFCVrIz4opx2tmzWQGUGWPKjTF9wJPAqmHHrAIetT5/FrhURMTa/qQxxm2MOQKUWe2N2KZ1ziVWG1htfty+lxZ8de295OoQmlIqCHJS4qdOzwYoBCqHfF1lbRvxGGOMB2gDskY519/2LKDVamOka31SRHaJyLMiUnw6L8outW295OoaG6VUEOQkx+tstDD4M1BijFkMvMYHPamTiMjtIrJFRLY0NDSENEDPgJfGTrdODlBKBUVeWsKJSUeRws5kUw0M7UUUWdtGPEZEYoE0oGmUc/1tbwLSrTZOupYxpskYM5jifwOcPVKwxpiHjDHLjTHLc3JyxvEyT19Dpxuv0TU2SqngyE9z0djZh9szEO5QTrAz2WwG5lizxJz4bvivGXbMGuAW6/MbgDeNr6DPGmC1NVutFJgDbPLXpnXO36w2sNp8EUBE8odc72PA/iC/ztNW1+7LhTpBQCkVDIOjJPXtkTOUZtsjIY0xHhG5E1gLOIDfGmP2isi3gS3GmDXAw8DjIlIGNONLHljHPQ3sAzzAHcaYAYCR2rQu+e/AkyLyHWC71TbAV0XkY1Y7zcDn7XrNE3ViQacOoymlgmDwjWtNWy/FmYlhjsbH1ucPG2NeBl4etu2eIZ/3Ap/yc+79wP2BtGltL8c3W2349ruBu8cbeyjVaakapVQQ5VtvXGsj6L7NVJggEPFq23uJjRGykpzhDkUpNQkMjpLUtvWEOZIPaLKJADWtPeSmuoiJkXCHopSaBFJccSQ5HdS0ac9GDVHV0kNRRkK4w1BKTSJ5aa4T94MjgSabCFDd2kOhJhulVBDlpyXoPRv1gT6Pl9r2XooyImPGiFJqctCejTpJbVsvxkBRuvZslFLBk5fqor7DzYDXhDsUQJNN2FW1dAPoPRulVFDlpbkY8JqIKcipySbMqlp9UxP1no1SKpgG19pEyow0TTZhVt3Sg4jvZp5SSgXL4N+U6pbIWGujySbMqlp6yE1x4YzV/wqlVPAUZ/qSTaU1VB9u+hcuzKpbu3UITSkVdCmuODKTnFQ0a7JR6IJOpZR9ijMTqWjSZDPlDXgNtW29FOq0Z6WUDWZkJmrPRkFNWw8er9EFnUopW0zPTKS6tQfPgDfcoWiyCafyhi4AZuYkhTkSpdRkND0zkQGviYjpz5pswuhwQyegyUYpZY/BB6cdi4D7Nppswqi8oYsUVyw5yfHhDkUpNQnNyPIlm0i4b6PJJowON3QyMycZEX2OjVIq+HJTXTgdMZpsprryhi5m6RCaUsomjhihKCOBiuaucIeiySZcOt0eatt7mZWTHO5QlFKT2PSsRL1nM5UdsWaiac9GKWWn2TnJlNV3hv1RA5pswuSDmWjas1FK2eeM/FTcHi9Hm8I7lKbJJkzKGzqJkQ9miyillB3OyEsB4EBNR1jj0GQTJgfrOpiRlUR8rCPcoSilJrHZ05KJEThY2x7WODTZhMmuqjYWFaaFOwyl1CTninMwMyeZ/bXas5ly6tt7qWnrZXGRJhullP3m5aVwQHs2U8/OqjYAzipOD28gSqkp4cy8FCqbe+h0e8IWgyabMNhV1YojRlhQoD0bpZT95uWlAnAwjENpmmzCYGdVG3NzU0hw6uQApZT9FhT4ks3OytawxaDJJsSMMeyqamWJ3q9RSoVIQXoCxZkJbChvClsMmmxC7EhjF63d/SwuSg93KEqpKeS8mdlsKG8KWyUBTTYh9uaBegAunJMd5kiUUlPJubOyaO/1sL8mPLPSNNmE2Bv765mbm3zioUZKKRUK587KAmD94cawXF+TTQi1dfez6WgzHz0zN9yhKKWmmNxUFzNzknjvcHju22iyCaG33q9nwGu4VJONUioMLpqTw/rDTbR194f82ppsQuiV3bVkJTl1MadSKiw+tbwIt8fLCzuqQ35tTTYhUt7Qydp9tXxqeTGOGH0MtFIq9BYUpLG4KI0/bqrAmNDOStNkEyK/erscpyOG2y4oDXcoSqkp7MYV0zlQ28G2ipaQXleTTQgcbezi+e1VfHp5MTkp8eEORyk1hV23pICsJCf3rtmHZ8AbsutqsrFZd5+Hr/x+K4nOWP7x4lnhDkcpNcUlx8dy38cXsru6jV+9Ux6y69qabETkShE5KCJlInLXCPvjReQpa/9GESkZsu9ua/tBEblirDZFpNRqo8xq0znWNezW0OHmy49v5f26Dn5641IK0hNCdWmllPLr6kX5XLM4n//560F+9fbhkNy/ibWrYRFxAD8HLgOqgM0issYYs2/IYbcBLcaY2SKyGngA+AcRmQ+sBhYABcDrIjLXOsdfmw8APzTGPCkiv7Ta/oW/a9j1utu6+3m/voM39tfz1OYKuvoG+O4nFnHR3By7LqmUUuP2PzcsAeB7rxzglT213LRiOmeXZFCalUSMDZOYbEs2wAqgzBhTDiAiTwKrgKHJZhVwr/X5s8DPRESs7U8aY9zAEREps9pjpDZFZD9wCXCTdcyjVru/8HcNY0Mqf3FHNf/85A4AHDHCh+fmcPdVZzAnNyXYl1JKqdOS4HTwsxuXct6sLB5ed4T//dwuAG49v4RvXbcg6NezM9kUApVDvq4CVvo7xhjjEZE2IMvavmHYuYXW5yO1mQW0GmM8Ixzv7xon1WwQkduB260vO0XkYMCv1I9y4JGJnZrNsPiigMYcGhpzaERbzNlA42eC0NC9fPDufAJm+NthZ7KJKsaYh4CHwh0HgIhsMcYsD3cc46Exh4bGHBrRFnM0xGvnBIFqoHjI10XWthGPEZFYIA1oGuVcf9ubgHSrjeHX8ncNpZRSIWJnstkMzLFmiTnx3fBfM+yYNcAt1uc3AG9a91LWAKutmWSlwBxgk782rXP+ZrWB1eaLY1xDKaVUiNg2jGbdH7kTWAs4gN8aY/aKyLeBLcaYNcDDwOPWBIBmfMkD67in8U0m8AB3GGMGAEZq07rkvwNPish3gO1W2/i7RoSLiOG8cdKYQ0NjDo1oizni4xV9k6+UUspuWkFAKaWU7TTZKKWUsp0mG5uJyG9FpF5E9vjZLyLyE6uczi4RWTZk3y0icsj6uGXI9rNFZLd1zk+shbCRHvP9IlIpIp3BjNWumEUkUUT+IiIHRGSviHw/0mO2tr8qIjutmH9pVfKI6JiH7F/jr91IildE3hJfyawd1se0KIjZKSIPicj71s/0J4MZc0CMMfph4wdwEbAM2ONn/9XAK4AA5wAbre2Z+NaFZgIZ1ucZ1r5N1rFinXtVFMR8DpAPdEbD9xlIBD5iHeME1kXJ9znV+leA54DVkR6ztf964Al/7UZSvMBbwHI7fo5tjPm/gO9Yn8cA2XbF7+9DezY2M8a8g28WnD+rgMeMzwZ864XygSuA14wxzcaYFuA14EprX6oxZoPx/eQ8Bnw8kmO22txgjKkJZpx2xmyM6TbG/M1quw/Yhm/9VsTGbLXZbp0biy9JBnUGkB0xi0gy8HXgO8GM1a547WZTzF8Avme17zXGhLw6giab8BuprE/hGNurRtgeSuONORJMOGYRSQeuA96wN8RTTChmEVkL1AMd+OoBhtJEYr4P+H9AdygCHGaiPxePWENo/ykS3GHsAIwrZuvnF+A+EdkmIs+ISG5IIh1Ck41SoxBf1Yk/Aj8xVgHYSGeMuQLfkGU8vgK1EUtEzgJmGWP+FO5YxuEzxphFwIXWx2fDHM9YYvH1ytcbY5YB7wH/E+ogNNmE33hL81Rz8nDOSGWA7DbemCPBRGN+CDhkjPmR3QGOYMLfZ2NML74qGqtsjnG48cZ8LrBcRI4CfwfmishbIYnUZ9zfY2PM4L8d+O4zrSC0JlLOqxt43tr+DL57QqEV6ptEU/EDKMH/zb5rOPlm3ybzwc2+I/hu9GVYn2da+4ZPELg60mMecq4tEwRs+j5/B99N9phoiBlIBvKtY2KBp4A7IznmQNuNlHit72u2dUwcvmHKr0RyzNa+J4FLrM8/Dzxj18+039cU6gtOtQ98QzA1QD++MdTbgK8M/oBaPzA/Bw4DuxkyywXfTb0y6+PWIduXA3usc36GVQkiwmP+b6str/XvvZEcM753hQbYD+ywPr4Y4THn4qsfuMv6+fgpEBvJMQ9ru4Tgz0YL9vc4CdhqfY/3Aj8GHJEcs7V9BvCOFfcbwPRgxhzIh5arUUopZTu9Z6OUUsp2mmyUUkrZTpONUkop22myUUopZTtNNkopNcWNVfxzhOM/LSL7rIKvTwR0js5GU0qpqU1ELgI68dVcWzjGsXOAp/Gt22kRkWnGmPqxrqE9G6VCQERKhr9rFJF7ReTf7LyWiFwsIm0ist0qi/+OiFwb7Guq6GZGKP4pIrOsR1ZsFZF1InKGtetLwM+Nr9gngSQa8K2GVUpFIRGJNcZ4Ajh0nTHmWuucs4AXRKTHGBPqwqIqujyEbyHpIRFZCTyIr9beXAAReRdw4Fug/epYjWmyUSrMROSr+FaIe4B9xpjVIpKErwLAQnxlUe41xrwoIp/H9+yXZHy/6B8ez7WMMTtE5NvAnYS+irWKEtZjH84DnhlS1Dre+jcWmANcjK/SxjsissgY0zpam5pslAq/u4BSY4x7SDn4/wO8aYz5grVtk4i8bu1bBiw2xoz2zJPRbAO+cToBq0kvBmg1xpw1wr4qfA9s6weOiMj7+JLP5rEaVErZz99MHIOvXtUfRORmfL0bgMuBu0RkB74nQ7qA6da+104j0YCvtpZSfhnfQ/iOiMin4MSjqJdYu1/A16tBRLLxDauN+fgNTTZKhUYTvkq8Q2UCjfiq+P4cX49ls/UMHQE+aYw5y/qYbozZb53XdZqxLMVXYFQpAETkj/ieczNPRKpE5DbgM8BtIrITX9HRwcdVrAWaRGQf8DfgG8aYprGuocNoSoWAMaZTRGpE5BJjzJsikonvkb0/BoqNMX8Tkb8Dq/Hdj1kL/JOI/JMxxojIUmPM9tONQ0QWA/8JfPF021KThzHmRj+7TnkUtvGtl/m69REwTTZKhc7ngJ+LyA+sr/8LqAD+JiJp+HozPzHGtIrIfcCPgF0iEoPv2SQTnbJ8oYhsBxLxPS76qzoTTYWaLupUSillO71no5RSynY6jKZUlBKRRcDjwza7jTErwxGPUqPRYTSllFK202E0pZRSttNko5RSynaabJRSStlOk41SSinb/X+gI3EUmJ4GowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_train['User_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fcc31391",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Occupation', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_train['Occupation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a75d16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marital_Status', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_train['Marital_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1ea9caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_1', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_train['Product_Category_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1cf1b969",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Purchase', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_train['Purchase'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60203f96",
   "metadata": {},
   "source": [
    "## 1. Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990869d5",
   "metadata": {},
   "source": [
    "Occupation column is a numerical column has unique values from 0 to 20. I don't know the meaning of these values, But let's show it 😊."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ded100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "list(df_train['Occupation'].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c48c49",
   "metadata": {},
   "source": [
    "### Question 1.1 : Are purchase value clear vary based on occupation value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71550db2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When occupation =  0  mean purchase value =  9124.428587839973\n",
      "------------------------------------------------------------\n",
      "When occupation =  1  mean purchase value =  8953.193269514612\n",
      "------------------------------------------------------------\n",
      "When occupation =  2  mean purchase value =  8952.481683466225\n",
      "------------------------------------------------------------\n",
      "When occupation =  3  mean purchase value =  9178.593087818697\n",
      "------------------------------------------------------------\n",
      "When occupation =  4  mean purchase value =  9213.980251147868\n",
      "------------------------------------------------------------\n",
      "When occupation =  5  mean purchase value =  9333.149297856615\n",
      "------------------------------------------------------------\n",
      "When occupation =  6  mean purchase value =  9256.535691476296\n",
      "------------------------------------------------------------\n",
      "When occupation =  7  mean purchase value =  9425.728222819745\n",
      "------------------------------------------------------------\n",
      "When occupation =  8  mean purchase value =  9532.592496765847\n",
      "------------------------------------------------------------\n",
      "When occupation =  9  mean purchase value =  8637.74376092831\n",
      "------------------------------------------------------------\n",
      "When occupation =  10  mean purchase value =  8959.355375096675\n",
      "------------------------------------------------------------\n",
      "When occupation =  11  mean purchase value =  9213.84584843777\n",
      "------------------------------------------------------------\n",
      "When occupation =  12  mean purchase value =  9796.640238622149\n",
      "------------------------------------------------------------\n",
      "When occupation =  13  mean purchase value =  9306.351061076604\n",
      "------------------------------------------------------------\n",
      "When occupation =  14  mean purchase value =  9500.702771979933\n",
      "------------------------------------------------------------\n",
      "When occupation =  15  mean purchase value =  9778.891163173037\n",
      "------------------------------------------------------------\n",
      "When occupation =  16  mean purchase value =  9394.46434905995\n",
      "------------------------------------------------------------\n",
      "When occupation =  17  mean purchase value =  9821.478235896411\n",
      "------------------------------------------------------------\n",
      "When occupation =  18  mean purchase value =  9169.655844155845\n",
      "------------------------------------------------------------\n",
      "When occupation =  19  mean purchase value =  8710.62723082378\n",
      "------------------------------------------------------------\n",
      "When occupation =  20  mean purchase value =  8836.49490495203\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "labels = []\n",
    "values = []\n",
    "\n",
    "for uniqueOccupationValue in df_train['Occupation'].sort_values().unique():\n",
    "    OccPurchaserData = df_train.loc[df_train['Occupation'] == uniqueOccupationValue]\n",
    "    OccPurchaserMean = np.mean(OccPurchaserData['Purchase'])\n",
    "    labels.append(uniqueOccupationValue)\n",
    "    values.append(OccPurchaserMean)\n",
    "    \n",
    "    print(\"When occupation = \",uniqueOccupationValue,\" mean purchase value = \",OccPurchaserMean)\n",
    "    print(\"------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e320f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Graphically) 📈\n",
    "plt.bar(labels,values, width=.9, facecolor='b', edgecolor='w', alpha=.5)\n",
    "plt.text(-1,10800,'Average purchase in black friday based on Occupation')\n",
    "         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6825b436",
   "metadata": {},
   "source": [
    "### Answer : Purchase value don't effect with occupation value. Because mean values of purchase at each occupation value nearest to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1d4233",
   "metadata": {},
   "source": [
    "## 2. Stay_In_Current_City_Years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35523c9b",
   "metadata": {},
   "source": [
    "Stay_In_Current_City_Years is a categorical column has unique values, '0', '1', '2', and '4+'. It represent number of years a purchaser stay in a city.\n",
    "Let's show it 😊."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e3bb6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0', '1', '2', '3', '4+']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "list(df_train['Stay_In_Current_City_Years'].sort_values().unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e97d404",
   "metadata": {},
   "source": [
    "Now we will convert '4+' value to 4 to can convert Stay_In_Current_City_Years column from string to int. 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7265bcc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stay_In_Current_City_Years converted to int successfully\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "df_train.loc[df_train['Stay_In_Current_City_Years'] == '4+','Stay_In_Current_City_Years'] = '4'\n",
    "df_train['Stay_In_Current_City_Years'] = pd.to_numeric(df_train['Stay_In_Current_City_Years'])\n",
    "print(\"Stay_In_Current_City_Years converted to int successfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46fcbc82",
   "metadata": {},
   "source": [
    "### Question 2.1 : Are people who stay more years buy less than people who stay less ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a61ce9bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean purchase of people who stay  0  years =  9180.075122987177\n",
      "Mean purchase of people who stay  1  years =  9250.145923300364\n",
      "Mean purchase of people who stay  2  years =  9320.429810090536\n",
      "Mean purchase of people who stay  3  years =  9286.904119221284\n",
      "Mean purchase of people who stay more than  4  years =  9275.59887165687\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "labels = []\n",
    "values = []\n",
    "yearsCountData = []\n",
    "\n",
    "for uniqueYearsValue in df_train['Stay_In_Current_City_Years'].sort_values().unique():\n",
    "    CityYearsPurchaserData = df_train.loc[df_train['Stay_In_Current_City_Years'] == uniqueYearsValue]\n",
    "    CityYearsPurchaserMean = np.mean(CityYearsPurchaserData['Purchase'])\n",
    "    labels.append(uniqueYearsValue)\n",
    "    values.append(CityYearsPurchaserMean)\n",
    "    yearsCountData.append(CityYearsPurchaserData.shape[0])\n",
    "    \n",
    "    if uniqueYearsValue != 4:\n",
    "        print(\"Mean purchase of people who stay \",uniqueYearsValue,\" years = \",CityYearsPurchaserMean)\n",
    "    elif uniqueYearsValue == 4:\n",
    "        print(\"Mean purchase of people who stay more than \",uniqueYearsValue,\" years = \",CityYearsPurchaserMean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b7c1769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploratory Data Analysis (Graphically) 📈\n",
    "plt.bar(labels,values, width=.9, facecolor='b', edgecolor='w', alpha=.5)\n",
    "plt.text(-0.7,10800,'Average purchase in black friday based on Stay In Current City Years')\n",
    "         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eef04b9",
   "metadata": {},
   "source": [
    "### Answer: No. Because the mean purchases of people despite the varying years of stay very close"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a745af7d",
   "metadata": {},
   "source": [
    "### Question 2.2 : Do people who stay longer in city have more action towards buying ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64636dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of purchasers who stay  0  years =  74398\n",
      "Number of purchasers who stay  1  years =  193821\n",
      "Number of purchasers who stay  2  years =  101838\n",
      "Number of purchasers who stay  3  years =  95285\n",
      "Number of purchasers who stay more than  4  years =  84726\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "for uniqueYearsValue in df_train['Stay_In_Current_City_Years'].sort_values().unique():\n",
    "    CityYearsPurchaserData = df_train.loc[df_train['Stay_In_Current_City_Years'] == uniqueYearsValue]\n",
    "    if uniqueYearsValue != 4:\n",
    "        print(\"Number of purchasers who stay \",uniqueYearsValue,\" years = \",CityYearsPurchaserData.shape[0])\n",
    "    elif uniqueYearsValue == 4:\n",
    "        print(\"Number of purchasers who stay more than \",uniqueYearsValue,\" years = \",CityYearsPurchaserData.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "858ffc10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Graphically) 📈\n",
    "values = yearsCountData\n",
    "labels = list(df_train['Stay_In_Current_City_Years'].sort_values().unique())\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.pie(values, labels=labels,\n",
    "              explode=[0,0.1,0,0,0],\n",
    "              autopct='%1.1f%%',\n",
    "              shadow=True,\n",
    "              startangle=0,\n",
    "              labeldistance=1.1,\n",
    "              pctdistance=.6)\n",
    "\n",
    "plt.legend(labels)\n",
    "plt.title('Number of purchasers in black friday based on Stay_In_Current_City_Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d54a36",
   "metadata": {},
   "source": [
    "### Answer : No. Because people who stay longer in city have less action towards buying than others. Decision : We must take care of people who stay in range 1 year in city because they have more action towards buying than others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8e48d4",
   "metadata": {},
   "source": [
    "## 3. Marital_Status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd22c51",
   "metadata": {},
   "source": [
    "Marital_Status is a numerical column has two value 0 and 1. This column describe if purchaser married or not. Let's show it 😊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc9506db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "list(df_train['Marital_Status'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc49c1d",
   "metadata": {},
   "source": [
    "### Question 3.1 : Are people who married buy more than else ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9db63c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean purchase of people who marital Status is  0  =  9265.907618921507\n",
      "Mean purchase of people who marital Status is  1  =  9261.174574082374\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "labels = []\n",
    "values = []\n",
    "maritalStatusCount = []\n",
    "\n",
    "for maritalStatusValue in df_train['Marital_Status'].unique():\n",
    "    maritalStatusPurchaserData = df_train.loc[df_train['Marital_Status'] == maritalStatusValue]\n",
    "    maritalStatusPurchaserMean = np.mean(maritalStatusPurchaserData['Purchase'])\n",
    "    labels.append(maritalStatusValue)\n",
    "    values.append(maritalStatusPurchaserMean)\n",
    "    maritalStatusCount.append(maritalStatusPurchaserData.shape[0])\n",
    "    \n",
    "    print(\"Mean purchase of people who marital Status is \",maritalStatusValue,\" = \",maritalStatusPurchaserMean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2204d181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Graphically) 📈\n",
    "plt.bar(labels,values, width=.9, facecolor='b', edgecolor='w', alpha=.5)\n",
    "plt.text(-0.7,10800,'Average purchase in black friday based on Marital_Status')\n",
    "         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd74acc",
   "metadata": {},
   "source": [
    "### Answer : No. Because purchasers who married or not, have almost same average of purchase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd2477b",
   "metadata": {},
   "source": [
    "### Question 3.2 : Do people who married have more action towards buying ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc8efab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of purchasers who marital status is  0  =  324731\n",
      "Number of purchasers who marital status is  1  =  225337\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "for maritalStatusValue in df_train['Marital_Status'].unique():\n",
    "    maritalStatusPurchaserData = df_train.loc[df_train['Marital_Status'] == maritalStatusValue]\n",
    "    print(\"Number of purchasers who marital status is \",maritalStatusValue,\" = \",maritalStatusPurchaserData.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "95dcdf65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "values = maritalStatusCount\n",
    "labels = list(df_train['Marital_Status'].unique())\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.pie(values, labels=labels,\n",
    "              explode=[0,0.1],\n",
    "              autopct='%1.1f%%',\n",
    "              shadow=True,\n",
    "              startangle=0,\n",
    "              labeldistance=1.1,\n",
    "              pctdistance=.6)\n",
    "\n",
    "plt.legend(labels)\n",
    "plt.title('Number of purchasers in black friday based on Marital_Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da93f1c0",
   "metadata": {},
   "source": [
    "### Answer : Yes. Because 59% of purchasers are married.\n",
    "Decision : We must take care of married than singles in the next marketing campaigns. where the ratio is 59 to 41 percent respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af2b1b2",
   "metadata": {},
   "source": [
    "## 4. product of category"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26cc91c",
   "metadata": {},
   "source": [
    "### Question 4.1 : Are Profit from product of category 1 more or from product of category 2 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b17bd56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profit from product of category 1 =  2972716\n",
      "Profit from product of category 2 =  3704948.0\n"
     ]
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Numerically) 💰\n",
    "Product_Category_1_sum = df_train['Product_Category_1'].sum()\n",
    "Product_Category_2_sum = df_train['Product_Category_2'].sum()\n",
    "\n",
    "print(\"Profit from product of category 1 = \",Product_Category_1_sum)\n",
    "print(\"Profit from product of category 2 = \",Product_Category_2_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fd102d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "labels = ['Product_Category_1','Product_Category_2']\n",
    "values = [Product_Category_1_sum,Product_Category_2_sum]\n",
    "\n",
    "plt.bar(labels,values, width=.9, facecolor='b', edgecolor='w', alpha=.5)\n",
    "plt.text(-0.7,6000000,'Compar between number of boughts of category_1 and category_2 in black friday')\n",
    "         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7915c309",
   "metadata": {},
   "source": [
    " ### Answer : Profit from product of category 2 more than product of category 1.\n",
    "Decision : We must take care of product of category 2 in the next marketing campaigns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d332acd4",
   "metadata": {},
   "source": [
    "## Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f78f5333",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the test Dataset\n",
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd21df62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00118942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00254642</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00031842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>1006037</td>\n",
       "      <td>P00124742</td>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>1006039</td>\n",
       "      <td>P00316642</td>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233599 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0       1000004  P00128942      M  46-50           7             B   \n",
       "1       1000009  P00113442      M  26-35          17             C   \n",
       "2       1000010  P00288442      F  36-45           1             B   \n",
       "3       1000010  P00145342      F  36-45           1             B   \n",
       "4       1000011  P00053842      F  26-35           1             C   \n",
       "...         ...        ...    ...    ...         ...           ...   \n",
       "233594  1006036  P00118942      F  26-35          15             B   \n",
       "233595  1006036  P00254642      F  26-35          15             B   \n",
       "233596  1006036  P00031842      F  26-35          15             B   \n",
       "233597  1006037  P00124742      F  46-50           1             C   \n",
       "233598  1006039  P00316642      F  46-50           0             B   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               1                   1   \n",
       "1                               0               0                   3   \n",
       "2                              4+               1                   5   \n",
       "3                              4+               1                   4   \n",
       "4                               1               0                   4   \n",
       "...                           ...             ...                 ...   \n",
       "233594                         4+               1                   8   \n",
       "233595                         4+               1                   5   \n",
       "233596                         4+               1                   1   \n",
       "233597                         4+               0                  10   \n",
       "233598                         4+               1                   4   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  \n",
       "0                     11.0                 NaN  \n",
       "1                      5.0                 NaN  \n",
       "2                     14.0                 NaN  \n",
       "3                      9.0                 NaN  \n",
       "4                      5.0                12.0  \n",
       "...                    ...                 ...  \n",
       "233594                 NaN                 NaN  \n",
       "233595                 8.0                 NaN  \n",
       "233596                 5.0                12.0  \n",
       "233597                16.0                 NaN  \n",
       "233598                 5.0                 NaN  \n",
       "\n",
       "[233599 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d693c1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  \n",
       "0                11.0                 NaN  \n",
       "1                 5.0                 NaN  \n",
       "2                14.0                 NaN  \n",
       "3                 9.0                 NaN  \n",
       "4                 5.0                12.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4cedc10f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233599, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of rows and columns in dataset\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f53189a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns in datset\n",
    "df_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ac127087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000004, 'P00128942', 'M', ..., 1, 11.0, nan],\n",
       "       [1000009, 'P00113442', 'M', ..., 3, 5.0, nan],\n",
       "       [1000010, 'P00288442', 'F', ..., 5, 14.0, nan],\n",
       "       ...,\n",
       "       [1006036, 'P00031842', 'F', ..., 1, 5.0, 12.0],\n",
       "       [1006037, 'P00124742', 'F', ..., 10, 16.0, nan],\n",
       "       [1006039, 'P00316642', 'F', ..., 4, 5.0, nan]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Values in the dataset\n",
    "df_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b702ba6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 233599 entries, 0 to 233598\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     233599 non-null  int64  \n",
      " 1   Product_ID                  233599 non-null  object \n",
      " 2   Gender                      233599 non-null  object \n",
      " 3   Age                         233599 non-null  object \n",
      " 4   Occupation                  233599 non-null  int64  \n",
      " 5   City_Category               233599 non-null  object \n",
      " 6   Stay_In_Current_City_Years  233599 non-null  object \n",
      " 7   Marital_Status              233599 non-null  int64  \n",
      " 8   Product_Category_1          233599 non-null  int64  \n",
      " 9   Product_Category_2          161255 non-null  float64\n",
      " 10  Product_Category_3          71037 non-null   float64\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 19.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# More information about the dataset\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b38f1b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.335990e+05</td>\n",
       "      <td>233599.000000</td>\n",
       "      <td>233599.000000</td>\n",
       "      <td>233599.000000</td>\n",
       "      <td>161255.000000</td>\n",
       "      <td>71037.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.085407</td>\n",
       "      <td>0.410070</td>\n",
       "      <td>5.276542</td>\n",
       "      <td>9.849586</td>\n",
       "      <td>12.669454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.726505e+03</td>\n",
       "      <td>6.521146</td>\n",
       "      <td>0.491847</td>\n",
       "      <td>3.736380</td>\n",
       "      <td>5.094943</td>\n",
       "      <td>4.125944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001527e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003070e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004477e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  2.335990e+05  233599.000000   233599.000000       233599.000000   \n",
       "mean   1.003029e+06       8.085407        0.410070            5.276542   \n",
       "std    1.726505e+03       6.521146        0.491847            3.736380   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001527e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003070e+06       7.000000        0.000000            5.000000   \n",
       "75%    1.004477e+06      14.000000        1.000000            8.000000   \n",
       "max    1.006040e+06      20.000000        1.000000           18.000000   \n",
       "\n",
       "       Product_Category_2  Product_Category_3  \n",
       "count       161255.000000        71037.000000  \n",
       "mean             9.849586           12.669454  \n",
       "std              5.094943            4.125944  \n",
       "min              2.000000            3.000000  \n",
       "25%              5.000000            9.000000  \n",
       "50%              9.000000           14.000000  \n",
       "75%             15.000000           16.000000  \n",
       "max             18.000000           18.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the dataset\n",
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f65289b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=233599, step=1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range of index in dataset\n",
    "df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7a7e05d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2             72344\n",
       "Product_Category_3            162562\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in dataset\n",
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "df1f59d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Occupation', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_test['Occupation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "42a29174",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marital_Status', ylabel='Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df_test['Marital_Status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d515572",
   "metadata": {},
   "source": [
    "## Merge both the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f7b57843",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0p/_vy9x_j55gd1trtzdrx_dnqr0000gn/T/ipykernel_8134/3477338726.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df_train.append(df_test)\n"
     ]
    }
   ],
   "source": [
    "# MErge both train and test data\n",
    "df = df_train.append(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c9077207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                          4               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e905ae3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of rows and columns in dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6de86731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns in datset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dd13e968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000001, 'P00069042', 'F', ..., nan, nan, 8370.0],\n",
       "       [1000001, 'P00248942', 'F', ..., 6.0, 14.0, 15200.0],\n",
       "       [1000001, 'P00087842', 'F', ..., nan, nan, 1422.0],\n",
       "       ...,\n",
       "       [1006036, 'P00031842', 'F', ..., 5.0, 12.0, nan],\n",
       "       [1006037, 'P00124742', 'F', ..., 16.0, nan, nan],\n",
       "       [1006039, 'P00316642', 'F', ..., 5.0, nan, nan]], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ceea639d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     783667 non-null  int64  \n",
      " 1   Product_ID                  783667 non-null  object \n",
      " 2   Gender                      783667 non-null  object \n",
      " 3   Age                         783667 non-null  object \n",
      " 4   Occupation                  783667 non-null  int64  \n",
      " 5   City_Category               783667 non-null  object \n",
      " 6   Stay_In_Current_City_Years  783667 non-null  object \n",
      " 7   Marital_Status              783667 non-null  int64  \n",
      " 8   Product_Category_1          783667 non-null  int64  \n",
      " 9   Product_Category_2          537685 non-null  float64\n",
      " 10  Product_Category_3          237858 non-null  float64\n",
      " 11  Purchase                    550068 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 77.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# More information about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b7339185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.836670e+05</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>537685.000000</td>\n",
       "      <td>237858.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.079300</td>\n",
       "      <td>0.409777</td>\n",
       "      <td>5.366196</td>\n",
       "      <td>9.844506</td>\n",
       "      <td>12.668605</td>\n",
       "      <td>9263.968713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727267e+03</td>\n",
       "      <td>6.522206</td>\n",
       "      <td>0.491793</td>\n",
       "      <td>3.878160</td>\n",
       "      <td>5.089093</td>\n",
       "      <td>4.125510</td>\n",
       "      <td>5023.065394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001519e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003075e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004478e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23961.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  7.836670e+05  783667.000000   783667.000000       783667.000000   \n",
       "mean   1.003029e+06       8.079300        0.409777            5.366196   \n",
       "std    1.727267e+03       6.522206        0.491793            3.878160   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001519e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003075e+06       7.000000        0.000000            5.000000   \n",
       "75%    1.004478e+06      14.000000        1.000000            8.000000   \n",
       "max    1.006040e+06      20.000000        1.000000           20.000000   \n",
       "\n",
       "       Product_Category_2  Product_Category_3       Purchase  \n",
       "count       537685.000000       237858.000000  550068.000000  \n",
       "mean             9.844506           12.668605    9263.968713  \n",
       "std              5.089093            4.125510    5023.065394  \n",
       "min              2.000000            3.000000      12.000000  \n",
       "25%              5.000000            9.000000    5823.000000  \n",
       "50%              9.000000           14.000000    8047.000000  \n",
       "75%             15.000000           16.000000   12054.000000  \n",
       "max             18.000000           18.000000   23961.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3cb247e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In dataset the 'User_ID' is useless for the upcoming operations\n",
    "df.drop(['User_ID'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59726bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042      F  0-17          10             A   \n",
       "1  P00248942      F  0-17          10             A   \n",
       "2  P00087842      F  0-17          10             A   \n",
       "3  P00085442      F  0-17          10             A   \n",
       "4  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                          4               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now this column is gone and dataset is update successfully\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "94fa3b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            245982\n",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "590d584e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Occupation', ylabel='Density'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m4mOimOznYgre0UxzclJ5dEslHd09IY5ueIoONTE3J/WEPhXwdlTHRUdRe6wjDJH172RqwjPmZOJaghCRaOB24FJgLnCNiMztc1gZ8EnggT7npgM/AFYCK4AfiMg4t2Idrn01x8jPSiY66sSLaa9leeM42trFc0U1IYxseFSVfdXHmD3B/x1RlAiZKXEcaY6MBOFR5Ykdh7jtmT00tnWFOxxjTjlu3kGsAIpVtURVO4G1wBW+B6hqqapuB/r2el4CPKuq9ap6FHgWWO1irMOyr/oYM8b773/oVZCdzMS0BNZuPHGST6Q53NTOsY5uCvppMgPISo7nSITcQax9q4zXimtpbO3i39uqwh2OMaccNxPEJKDc53mFsy1o54rIDSJSKCKFR44cGXagw3Gso5vKhrZ++x96RYnw8TOm8uq+Wl7cE9l3EfuqB+50B8hKiaehtYu2zvA2me2tbmZnVRPnzcri4rnjKTrUxNuHTp7BAMacDE7qTmpVvVNVl6nqsqysrJC+d+8IpoIBLqa9Pn3WVPKzkvjeozvDfmEdyN7qZmCwBOHtqC6pPRaSmPrz721VCN5yIGfNyGLcmFjeOnByV9A1JtK4mSAqgck+z3OdbW6fGxIlR7wXyPyswRNEfEw0P/vAAiqOtnHNn9+ktLbF7fCGpbjmGOlJcWQkx/d7TJazb/+R8P03qCrrtlWRn51MSkIs0VHCjPEplNa10N1jczSMCRY3E8RGYIaITBOROOBqYF2A5z4NXCwi45zO6YudbRGjrL4VEcgdF1gxvpXTM7j92iWUHDnGBb98mb+tP0BZXWQlin01xwa9I8pIjkOA/TXhu4PYUdnIwbpWFvpM5puemURHt4cdlSfXzHVjIplr1VxVtVtEbsJ7YY8G7lbVXSJyC1CoqutEZDnwCDAOeJ+I/EhV56lqvYj8GG+SAbhFVSOq/aCsvpUJqQlDWjXusoU5LMkby31vHOS+Nw9yxyslrJqVzYVzsv0OKw0l7wimZtYsnjjgcbHRUYxLimP/EXcSRCAVO5/ZVU1MlDBvok+CcO7k1u+v47QpETfgzZiTkqvlvlX1CeCJPtu+7/N4I97mI3/n3g3c7WZ8I1Fe39rv/IeB5KQl8s3Vs8lMjuff26p4cU8NWSnxLJ48NvhBDsGR5g6a2ruZkT1wpzt4m5nC2cT0Vmk98yalkRj3TnJOjo9hfGo8b5bUceN5BWGLzZhTyUndSR1OZfWtfmcbByohNpoPLc1l8rhE/r2tiqb28I7j3zdI2RBfWSnxHKg9hscT+glqnd0etpU3sCzvxLuE6VnJbCytP2kmJRoT6SxBDEN7Vw/VTR0jShDgHQL74aWT6erx8Pzb1UGKbnj2OSOYCgaZ1wHeO4j2Lg9VYajJtLOqkY5uD8unnpggpmYk0d7lYc/h5pDHZcypyBLEMFQc9a7xMNIEAZCZEs9pU8aytbyBhtbOEb/ecO2tOUZaYuzxUUoDyUwJ30imTaVHAVial37Cvpw07xDc3YcsQRgTDJYghqG83vvLeTh9EP6cPj2Drh7ln4XhK+pXXH2MGdnJAXWWZ/UmiDCMZNpYWs/UjDHHY/CVnhRHYmw0bx+2CXPGBIMliGEoqw/eHQR4O66nZiRx75ulYWnXV1X21jQzY5BZ4b2S4qIZOybWtZFM/VFVNh086vfuAbxNdjMnpNgdhDFBYgliGMrqW0mMjSYzOS5or7lyWjrl9W1sLA39aN66lk4aWrsC6qAGEBHys5JDniAO1LZQ19Lpt/+h15wJKew+fHIt0mRMpHJ1mOupqncEUzDnLszJSSUxNpp/b69i5fSMoL0uDD634HgNpgA6qHvlZyXx4p7Q1r8qPOjtf1g2QIKYPSGFtRvLqWnuYHxqQqhCM+aUZHcQwzDcORADiYuJ4sK543lix+GQl4sorumtwRRYExN4S4wcae4IaZntwtJ6xo6JZXpm/4lsdo53JTwr3GfMyFmCGCJVHfEciP68b2EO9S2dvL6/LuivPZC91cdIcSaaBaq3BlVJCJuZCg8eZVneOKIGWH+jdy2L3TbU1ZgRswQxRHUtnbR29jAlPbAaTENx7qwsUhJiQr62wb6aZgrGBzaCqVe+018RqqGudcc6KDnS0m8Hda+xY+LISUtgt91BGDNiliCG6PgIpozg30HEx0RzybwJPL3zcMhmA6squw83MyvAEUy9Jo9LJDZaQtZRvcnpfxiog7pXQXby8ZnhxpjhswQxROVBHuLa1/sWTaS5o5uXQ9QBXNnQRkNrF/N8KqMGIiY6iryMpJDNhdhYWk9cdBTzA4izINs7wiocQ4ZN+HV2eysT/OKZPTxYWE6PfQ+GzRLEEJXVeRNE7jh3EsSZ+RmkJ8Xx7+2HXHn9vnZWepti5k9MHfK5+VlJIbuD2HCgnsVTxgZUPbcgO5n2Lg+VDaEvBWLC77Gtlbywu4bEuGi2ljfw8OYKPDbseVgsQQxRWX0r41Pjh1Tmeyhio6O4dP4EniuqpqWj25X38LWrqpHoKGFOznASRDIH61rpcnnUVVN7FzsrGzk9wOG/BU4HenGI52mY8Htq52G2lDdw3uxsvriqgAvmZLOlvMHqcw2TJYghKqtvZbJLdw+9PrhkEm1dPTy21f3O6h2VjczITh5WwsvPSqbbo8f7ZdxSWFqPR+H06QN3UPfqXfQonIsamdBr6+zhe4/tZGJaAufNygZg1cxsUhNi2HAgtCMDTxWWIIao4miba/0PvZZMGcecnFTufaPU1RnBqsrOysZ3LbwzFPkhuhC/WeLtf1gS4EJAGcnxjBsTe3zdcDM63PtGKUeaO7h84USinaHQ0VHC8qnp7K0+xsEIW8HxZGAJYgg6u70lroM9Sa4vEeHjp+ex+3Azm8uOuvY+Nc0d1B7rZP6koTcvAUzPSgLcH+r6ZkldwP0PvQqyky1BjCLHOrr508v7OXtGJlMzk961b/nUdKIE7vdTUcAMzBLEEFQ2tKHq3ggmX1csnkhKfAx/ernEtffYUeFdvzmQkUH+pCbEkp0S72pHdVtnj7f/YVpgzUu9CrKTKT5yzGoyjRL3rC/laGsXX7to5gn7UhNjmTk+hce3H7LvwxBZghgCN+dA9JUUH8PnV+XzbFE1L+2pceU9NpbWExstzBvGCKZebhft2324CY/CebOzh3ReflYyDa1d1LWEb40NExpN7V3c+UoJ58/O7nc98jkTUqlsaGNvtd1VDoUliCEoc9owQ3EHAfCZs6cxPTOJH6zb5cqIptf313LalHGMiRt+zcb87CSKa9z7pb6rqokJqQksyh07pPN6O6qtmenU99fXSmls83/30GumU4Llhd3u/Ng6VVmCGILSOm+Z72w/i9W4IT4mmp+8fz7l9a186m8bg5okGlo72VXVxJn5I6scO29iGs3t3RysC/5Ips5uD/tqmrl43vgB6y/5E2kJ4oENZSf8MSNX2dDGn18t4eK54wdsKk1LjGVuTiovRkiC6L3ruf3FYm59cnfENn1ZghiCg3Ut5GUEt8z3YM4syOTXV59GYWk9H/7TG8fXjh6pN0vqUIX3FGSO6HUW5nr/UW6raAhCVO+2r6aZrh5l9bwJQz53YloiibHREZMgQqHHo2w6WE9HV2jKtISbR5WvP7gNVeW7l80d9PjzZ2ezqexoWJf2Bej2eHhgQxmVDa3ERgt/enl/WFeTHIir60GIyGrgN0A0cJeq3tpnfzxwL7AUqAOuUtVSEZkKvA3scQ59U1U/72asgSitaz0+CSuU1iyaSFJcNN94aDuX/e41vnnJLBJio4kaQaJav7+OxNjoITfd9DVzfArxMVFsr2jkisWTBl17Yii2ljcwJi6aFUPsoAaIihLys0M30zvc9lY3842HtrOtvIHk+BguX5jDwhH+vw22YH43PKo8tfMwb5TUcesHFwTUL3je7Gx+/2IxrxXXcvnCicN632B44e0ayupbuXr5ZOZPSmPd1ip+8ngRq2ZnkZ0SWWuYuHYHISLRwO3ApcBc4BoR6ZvmrweOqmoB8Cvgf3z27VfVxc6fsCeHHo9SVtdKXmZo+h/6umDOeJ7+yjmcMyOTnzz+Nve9cXDYM5hVldf21bJ8WjpxMSP7CsRGRzFvYirbg3wHcbSlk6KqJpZPTScmengxFmSdnENdh9oc1dbZw/X3bKSivpXvXT6XsWNiebCwnMON7SGKOLRqmzt4YEMZrxXX8rHTp3DV8skBnbcoN42U+BheL651OcL+dXT18OaBOhZMSmNh7liiRPjvDy2grauHO10csThcbjYxrQCKVbVEVTuBtcAVfY65ArjHefwQcIGEsv1mCA43tdPZ42FqRtLgB7skKyWeP39iGT9aM4891c3831tlwypEVtXYTkltCxfNGdrIoP4szB3LzsqmoC50tH5/LSIEXF7Dn4LsZA41tnMsBCVLwul3L+yjvL6N2z+6hOvPmsYnz5hKQmw067ZVRmzb9nCU1bVw35sH+dVze9lb3cyl8yfw4yvmB9zkGxMdxen5GbwWxgSxuewo7V0ezvJp2s3PSua8Wdk8tq0q5IuFDcbNBDEJKPd5XuFs83uMqnYDjUDvFWGaiGwRkZdF5Gx/byAiN4hIoYgUHjnibvXT0lrvCKa8EAxxHYiIcN2ZU1mzaCK7DzfzzK7DQ36NzQePEhcdxZpFff93DM+iyWm0dfUErfZRa2c3hQePsjB3LGmJscN+ndFQcuNAbQt3vlLCh5fmHk+mY+JjuGTeBErrWtlR2ejae7d39YSkv6Orx8N/P/E2f3qlhNLaFlbNyuIbl8zi7BlZQ+4PPHtGJuX1bWGZVe1RZf3+OiaPSzxhsu0HTpvEkeYO1od4sbDBRGon9SFgiqqeBnwNeEBEThisr6p3quoyVV2WlZXlakClzhcqnHcQvk6fnsHKaem8Wlw7pEJk3T0etpY3cNG88aSNGf7F11dvW/f28uBcjJ7ZVU1Xj4dzZozs/2mkjWRyw12vlhAVJXxz9ex3bV+aN46MpDhXLjjtXT3818M7WPrjZ/nZk2/z8p4a10pqezzKTQ9s5o5XSlg5LZ1vrZ7NRXMnkJIwvO9u76CMcNxFHKhtoa6lkzPyTxwYcv4cb82oR7ZUhjyugbiZICoB38bBXGeb32NEJAZIA+pUtUNV6wBUdROwH+h/kHMIHKxrJT4migmpkdOJ9N4FOUxITeChzRUBD4F9+3AzbV09XLk0N2hxTMtIIj0pjvX7R/6Prry+lY2l9ZwxPYMJaSP7rPMykoiJklO2qmt9Syf/2lzBBxZPIqvP0OsoEU6fnkFZfStVQS57/v3HdvJ/b5Xx3gU5zMhO4emiav6z3Z3Ckr9+bi9P76rmu5fN4YrFk0bcZzY9M4mJaQm8ti/0CWJrWQPxMVF+J6bGx0Rz2cKJPLXzMO0RNArNzQSxEZghItNEJA64GljX55h1wHXO4yuBF1RVRSTL6eRGRKYDM4Cw9uCU1nqHuA51PL6bYqOj+MiyybR39vDvAP6B9niUZ4uqyUyO4+wRDm/1FRUlnDszi1f21Y6o7n5bZw//2lxBSkIMF84ZP+K4YqOjyMsYc8reQdz/5kHauzxcf/Y0v/uXTBlHbLTwZknw7iL+WVjOg4UVfOn8Am778CI+dnoeZxVksuFAPUVVwW3OemN/Hb99oZiPLMvl+rP8/zcOlYjwnoJM1u+vC+lCQu1dPeys8hbGjO1n0MXF88bT1tXDhgP1IYtrMK4lCKdP4SbgabxDVh9U1V0icouIrHEO+wuQISLFeJuSbna2nwNsF5GteDuvP6+qYf3UDta1khchzUu+JqQlcN7sbLZXNA66lvWGA3XUHuvgvfNzhj0yqD+rZmVR39JJ5dHh/1r9yeNF1DR38KElucQHab2N2Tmp7D586q1P3dHdwz1vHOTcmVnM7Ge52MQ47zDmbRUNNLV3jfg927t6uO3pPSzNG8dXLnznhv7ieeOZNDaRh7dU0tYZnF+/Hd09fOfRHUxOT+RHawLviA7EWTMyaWzrYleQE9pAnnu7mo5uD4snj+33mDOmZxAfExWy1SQD4WofhKo+oaozVTVfVX/qbPu+qq5zHrer6odVtUBVV6hqibP9X6o6zxniukRV/+1mnIPp7vFwoK6FaZmRlyAAzp2ZxZT0MfzXwzs4UOu/8626qZ3n3q6mICuZWROGtv50ILwdht7x+MPx1M7D3L+hjLMLMpkxxPWxBzI3J5Xy+jYa20Z+gYwkj22tovZYB589e/qAxy2fmk5Xjw764yEQ/9pcQU1zB1+7aObxctoAMVFR3jVMOnt4MUh1w+569QAlR1r48RXzSYwL7uJcZzp9AK+GsJnp0S1VpCbEHK+A7E9CbDQrp2fw0t7ImO0NkdtJHVEO1rfS2e3p95dauEVHCVcvn0xMtHDDvYXUNL17/HtjWxf3vFFKbFQUHzhtkiszwdOT4liUO3ZYCeJQYxs3P7ydBZPSuGjeyJuWfM112nt3Hzp17iJUlb+8eoDZE1J4T8HAw4BzxyUyPjWeBzeWD3jcYLp7PPzp5f0smjzWb3mWnLREluaN4439df3+SAlUdVM7v3+hmNXzJrBqVnCGYvvKSoln9oSUkM2HqG/p5KU9NSxy5j0MZNXMLEqOtFDu8iJcgbIEEYC9ziihWRGaIADGjonjDx9dQmVDGx/843qe2nmIsrpW1r5Vxm+f30drRw+fOGMq45LiXIvhgtnZVBxto34IFVR7PMpX1m6ls9vDb685jZio4H4l5zlLqRadQgnipb1H2FPdzGfOnj5oshcRluWls62icURNbY/vOER5fRs3rsrv9z0vmjue6Gjh1iffHvb7APzv03vo8Sjffu+cEb3OQM6ekUlh6dGgNYkN5PEdh+j2KIsGaF7qde4s78i9l/dGRjOTJYgA7K0+hsg7wyYj1Zn5may94XR6PMrn/76Zc257kZsf3kFmchxfPC+fSeMSXX3/K5flIgIbhtAp+seXitlwoJ5brpjvShNeVko8mclxFFWdGglCVfnNc/uYNDaRNYsCKxexePJY4qKj+Mcw7yI8HuUPL+5n5vjkAQcPpCTEsmpmFk/vquaNYQ6v3V7RwEObK/jUe6a6Wlb/PQWZdPZ4RrwUqb9Z731nvj+6pZKZ45PJCWBU3vTMJHLHJfJShPRDWIIIwN7qZqakjwl6W6gbFuaO5dVvnsffr1/Jzz6wgEe+eCafOzc/JDVectISmZuTSuHBo3R2Dz4jdNPBo/zquX2sWTSRDy0JzqS9vkSEOTmpp8wdxMt7j7C1vIGbzi8IeMhnUnwMF80bzyNbKunoHvov5ud317CnupkvrioYdBTfewoymTQ2kZ88XjTkWcE9HuXbj+wgKzmeG88vGHKcQ3H69AzGxEXzTFG1q+9zsK6FTQeP8v4Am3ZFhFWzsli/vzagf0NuswQRgD3VzRHb/+BPTHQUZ83I5NqVUzhtyrgRFfUbqjPyM2nr6hl0qdSm9i6+vHYLOWkJ/OQDwR2l0tfcnFT2VR8bdu2qSNHd4+G2p/cwaWwiH1oytHksVy2bTENrF8/sevcFcbBfv6rK7S8WMzk9kcsX5gz6PrHRUXznsjnsqmrijy/tH1KM96wvZWdlE9+7fC6pw5wIF6iE2GjOm53NM7sOuzrc9f4NZURHCR88LfD/X+fOzKa1s4fC0vAPd7UEMYiO7h4O1LZEdP9DJJmaMYa8jDE8U3S435FDHo/yrYe2c6ixnd9ec5rrF4O5E1Pp7PGc9PMh7nilhF1VTXz3sjlDnjB2lvPL/sHCoTUzvVFSx9byBj53Tn7AQ6PfuyCHNYsm8pvn97G1vCGgc7aUHeXWJ3ezalZWQIkoGC6dP4HaY52uXYjbu3p4sLCcS+aNH9KkzzPzM4iNlojoh7AEMYgDtS30ePT4ilRmYCLCh5bk0uNRHtlSccKvM1Xlx48X8eTOw9y8ejZL+lkiMph6S5oPdlcTyXZWNvKb5/Zx2YIcLl0w9AtoVJTwkWWTeXVf7ZBGmv3hxf1kpcQPeeb9j6+Yz/jUBD7117cGnW9wsK6Fz/99E+PT4vnVRxaHbL2V82ZlExcTxVPDqGcWiHXbqmho7eLjp08d0nlJ8TEsn5puCeJk0FvnaOb4yO6gjiSZyfFcOj+HvdXHuPeNUuqOdQDe4bZf/cdW/vp6KdefNY3P9DMDONjyMsaQlRLPWxE0Q3Uoapra+cTdb5GZHMcP18wb9ut84ow8kuKi+e3z+wI6ftPBel4rruWzZ08jYYgTF9PGxPLAZ1eSGBvN1Xe8yV9eO3BCE1+PR3lsayXv+91rtHd5uONjy1wdZddXUnwM587M4j/bD50Q20hXAOzxKHe9WsLM8cmcPn3o65mcOzOL3YebOdQY3DIpQ+XqgkGngh0VjcTFRDE90xLEUJw+PYOYKOHRrZWs+NnzTMtMoqyulR5VvnrhTL50fkHIfimKCCumpbNxBAkimIvdDMWuqkYe3lxJckIM93/29BNqLg3FuKQ4rjtzKn98eT9frm4ecEJij0f54boiJqQm8NGVecN6v7yMJP7xuTP49iM7+PF/ioiNFnLHjSEpPoaOrp7jpdjn5qRyx8eXnlDhNBSuXTGFZ4uqeXrX4aAuIvSvTRXsrT7GHz66ZFjf81WzsvnvJ3fzyt4jXLXc/e9ZfyxBDGJT2VEW5aaNuEjYaLRsajq56WPweJSiQ01cMCeb9y2cOODawW5ZMTWdx7cfouJoK7njwluyPRBHmjt4etdhig41MWlsIn//zMqgDAP+zNnTuWd9KT9Yt4u/X7+y3+MeLCxnR2Ujv7l6MUnxw79MTE4fw72fXsEb++v43YvFVB5to7qx3fujKyuJL50/g/NmZQW99EugeqsQ3Lv+YNASRGe3h9++vJ/Fk8dy6fyhL5cL3haLCakJvLTHEkTEau/qYWdlI58OUqGw0WhCakJIfmkPpnfZ0rcO1IcsQbR39fDAhjJe2nuEXZWNpCTEkjsukQWT0sjtZ07K5rKj3L/hIEVVTcTGRHHR3PGcPSMzaHNE0pPi+N7lc7n54R385bUDfi/+RVVN/OQ/RayYmh7wXIuBiAhnFmRSWnfi7OCL5gZ35vxQRUUJnzgjj588/jY7KxuD8uPl8R2HONzkHYAx3Lvk3uGujzvNX/0V+HObJYgB7KxspKtHWZY39DZEE1lmjU8hNSGGjaX1fHCIQ0SH4/XiWr7yj60cae5g9oQUctISaGrv5o2SOl4rriU9KY6D9a3Mm5hKlAiltS08t7uGbeUNJMRGce7MLM4syCR5BL/e+3PV8sm8sLuG/3lqN2sWTWTZ1He+39VN7Vx/z0ZSEmJHdIE7mXx46WR+90IxP3vibe7/zMoR/TdvKTvKxtJ6vrgqf1hrqfs6d2YWazeWs7W8geVTw3MNsgQxgMKD3lEvS6aMDW8gZsSiooSV0zN4ZW8tqurahU9V+fOrJdz65G4KspO5/dolrJiWfrwPo62zh6JDjWyraOSvrx+gq+edUV4LJqXx/cvnIuJdH8AtIsL/fmQRN96/mYe3VLKzqpHpmcnUt3SyqewoKQkx3P+ZlSNej+NkkTYmlq9fPJPvPbaLx3ccGnZT0/aKBv61uYJpmUl87aKRL19zZkEm0VHCi7trLEFEok0HjzItM4mM5OF3DJrIcen8CTxbVM3msgaW5gV/eG1rZzfffGg7/9l+iPcumMBtVy46oQknMS6apXnpLM1L58qluZTWtSB4S4KMHeMdwTPUETPDkZoQy18/uZzP3beJTWVH2Vt9jLjoKBZMSuOu65aROcq+89euzGPtxnJ+uK7o+LDoQHlUeWXvEZ4tqiYvYwwfPz0vKH0qaYmxnDE9g8d3HOIbl8wKy92cJYh+eDzK5oNHjxfPMie/C+eOJy46iv9srwp6gjhY18Ln7tvE3upmvrV6Np8/d/BCenExUWGdoR8THcUFc8Zz/mzvzN0xcdGIyKhLDuCtiPzrqxbzoT+u57q/vsXVy6cE1LxXd6yDR7ZUUlLbwoJJaXxoSW5QB7SsWTyRbz60nW0VjQOuJeEWG5rTj01lR6lr6eTcmZYgThWpCbGcOyuLJ3YcwhOk8gptnT386eX9rP71qxxqbOdvn1rBFwaoeBqJRISk+JiTKmY3zBifwl3XLafiaBu3v1hMSW3/M+8b27r4+VO7+c3z+7wVlE+bxNXLJwd9tOPq+ROIi4ni0TCtVW13EP14Ysch4mKiOH928OvRm/C5fGEOzxZV80ZJ3fEF7Ieq2+OhtLaVH67bxSNbKmls6+KiueP5wfvmnhRDaE3/VkxL5+EvnMkn7n6Lu149QEF2MotyxzJxbALFNc2UHGnhpb1HeGxLJS2dPSzKTePS+TmkJrpTLiY1IZbzZ2Xzn+1VfPeyOSEfDmwJwg+PR3lyx2HOmZFFist1gkxoXTx3Atkp8fzq2b2cmZ8R8K/mrh4Puw83U1TVyJ7qZtq7PMTFRHHhnGw+ccZUTp8+8MI95uQxf1IaXzqvgA0H6nl9fy3/2lwBwO9eKAYgPiaKyxdO5NNnTWVbufvLln5oaS5P7TrMU0GezBcISxB+bCk/yuGmdr516axwh2KCLDEumi9fOIPvPLKTF3bXcMEA6xsAtHR089fXD/DHl/bT4rTTz8tJY05OKt++bDZj4uyf0KkoPjaac2ZmcdaMTGqaO6hpaueM/Axyx41h3sTU46VHQpEgLpidzfSsJP740n4uW5AT0qZA+3b7cf+GMuJjoga9eJiT00eWTebPr5Rwy3+KWJg71m/5iq4eD2s3lvOb5/ZRe6yDWeNTeE9BJtOzko6XT7fkcOqLEmFCagITUhO4YrE7a5YMGkOU8Plz8/nmQ9t5ZV9tSPtFrZO6j+KaZh7dUsnHT89zvQy1CY/Y6Ch+8ZHF1DR18PG/bKDUZw3lts4e1r5VxkW/fJnvPbqT6ZlJPPzFM7nuzKkUZCeHdG0NY3q9f/EkctISuPXJ3SFdSMh+AvXxi2f2khgbzRdW5Yc7FOOipXnjuGbFFO59o5Tz/vclJo1LRIDqpg46ezzMn5TKXZ9YxgVzshERdh8KvES2McEWFxPFj9bM44b7NvHb5/fx9UtC0/xtCcLH3988yJM7D/OVC2fY5LhRoCA7ma9fPIsNB+ooq29FFa47M48L5oxn5bT0UT/s00SWi+dN4MqlufzhpWJm56SEpMPa1QQhIquB3wDRwF2qemuf/fHAvcBSoA64SlVLnX3/BVwP9AD/n6o+7VacHo9y/1tl/OCxnVwwO5ubznN3PVwTOVITY7lo7jsVNyOhsKAx/fnhmnkcrGvhS/+3hfL6Nj591lRXy7K4liBEJBq4HbgIqAA2isg6VS3yOex64KiqFojI1cD/AFeJyFzgamAeMBF4TkRmqurQV1wfRGVDG1+8fzPbyhs4qyCT31+7JGylh40xZiDJ8THc++mVfOUfW/ifp3bz9zcP8r5FEzl/dvaIiwP64+aVcAVQrKolqtoJrAWu6HPMFcA9zuOHgAvEe19/BbBWVTtU9QBQ7Lxe0GUkxRETJfzqqkXcd/0KEuPcy8bGGDNSiXHR/OljS7n30yvIyxjDXa+W8NMn3nblvUQ1OCUHTnhhkSuB1ar6Gef5x4GVqnqTzzE7nWMqnOf7gZXAD4E3VfXvzva/AE+q6kN93uMG4Abn6Sxgjyv/MYHLBGrDHEOksM/i3ezzeId9Fu8W7s8jT1X9jp09qTupVfVO4M5wx9FLRApVdVm444gE9lm8m30e77DP4t0i+fNws4mpEpjs8zzX2eb3GBGJAdLwdlYHcq4xxhgXuZkgNgIzRGSaiMTh7XRe1+eYdcB1zuMrgRfU2+a1DrhaROJFZBowA3jLxViNMcb04VoTk6p2i8hNwNN4h7neraq7ROQWoFBV1wF/Ae4TkWKgHm8SwTnuQaAI6AZudGMEkwsiprkrAthn8W72ebzDPot3i9jPw7VOamOMMSc3G/BvjDHGL0sQxhhj/LIEESQislpE9ohIsYjcHO54wklESkVkh4hsFZHCcMcTaiJyt4jUOPN8ereli8izIrLP+Tu4i2JHqH4+ix+KSKXz/dgqIu8NZ4yhIiKTReRFESkSkV0i8mVne8R+NyxBBIFPWZFLgbnANU65kNHsPFVdHKnju132N2B1n203A8+r6gzgeef5aPA3TvwsAH7lfD8Wq+oTIY4pXLqB/19V5wKnAzc614mI/W5YggiOQMqKmFFCVV/BOyrPl29ZmXuA94cypnDp57MYlVT1kKpudh43A28Dk4jg74YliOCYBJT7PK9wto1WCjwjIpuccigGxqvqIefxYWC0L1d4k4hsd5qgIqZJJVREZCpwGrCBCP5uWIIwbjhLVZfgbXK7UUTOCXdAkcSZDDqax5f/EcgHFgOHgF+ENZoQE5Fk4F/AV1S1yXdfpH03LEEEh5UG8aGqlc7fNcAjuFSJ9yRTLSI5AM7fNWGOJ2xUtVpVe1TVA/yZUfT9EJFYvMnhflV92Nkcsd8NSxDBEUhZkVFBRJJEJKX3MXAxsHPgs0YF37Iy1wGPhTGWsOq9GDo+wCj5fjhLGfwFeFtVf+mzK2K/GzaTOkicoXq/5p2yIj8Nb0ThISLT8d41gLeUywOj7bMQkf8DVuEt41wN/AB4FHgQmAIcBD6iqqd8520/n8UqvM1LCpQCn/Npgz9lichZwKvADsDjbP423n6IiPxuWIIwxhjjlzUxGWOM8csShDHGGL8sQRhjjPHLEoQxxhi/LEEYY4zxyxKEGbVEJFdEHnOqaO4Xkd8481jCFc/7fYs8isgtInJhuOIxxhKEGZWcSUsPA486VTRnAslAOOdsvB9vNWAAVPX7qvpc+MIxo50lCDNanQ+0q+pfAZw1z78KfNqZDf6/IrLTKSj3JQARWS4i60Vkm4i8JSIpIvJJEfl974uKyH9EZJXz+JiI/Mqp/f+8iGQ52z8rIhud1/mXiIwRkTOBNcBtzhoJ+SLyNxG50jnnAhHZ4qyzcbeIxDvbS0XkRyKy2dk3O2SfoDnlWYIwo9U8YJPvBqdwWhnwGWAqsFhVFwL3O01P/wC+rKqLgAuBtkHeIwkoVNV5wMt4ZxEDPKyqy53XeRu4XlXX4y258A1njYT9vS8iIgl411W4SlUX4J2h/gWf96l1iiP+Efj60D4GY/pnCcKYE60C7lDVbgCn7MEs4JCqbnS2NfXuH4AHb1IB+DtwlvN4voi8KiI7gI/iTVYDmQUcUNW9zvN7AN8Kub1F3zbhTWzGBIUlCDNaFQFLfTeISCreejhD0c27/x0lDHBsb12bvwE3OXcDPxrknEB0OH/34L27MCYoLEGY0ep5YIyIfAKOLxv7C7wX76eBz4lIjLMvHdgD5IjIcmdbirO/FFgsIlEiMpl3l66OAq50Hl8LvOY8TgEOOaWfP+pzfLOzr689wFQRKXCefxxvk5UxrrIEYUYlZ2GWDwAfFpF9wF6gHW91zbvw9kVsF5FtwLXOUrJXAb9ztj2L95f/68ABvHckvwU2+7xNC7BCRHbi7RS/xdn+PbwVPF8Hdvscvxb4htMZne8TazvwKeCfTrOUB/hTsD4LY/pj1VyNcYmIHFPV5HDHYcxw2R2EMcYYv+wOwhhjjF92B2GMMcYvSxDGGGP8sgRhjDHGL0sQxhhj/LIEYYwxxq//B7qc6ycDBDlIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df['Occupation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9a9afbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Marital_Status', ylabel='Density'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df['Marital_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a2cee49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_1', ylabel='Density'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "sns.distplot(df['Product_Category_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba150be",
   "metadata": {},
   "source": [
    "# Data Pre-processing\n",
    "1. In our data we also have some chategorical feature or characterize data eg: Age, Gender, City_Category, etc\n",
    "   but our model is not gonna understand this. So we have to convert it into numerical values similar to others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36628da6",
   "metadata": {},
   "source": [
    "### 1. Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "741810d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method 1\n",
    "# Female --> 1  and Male --> 0\n",
    "##     df['Gender']=pd.get_dummies(df['Gender'],drop_first=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260bfcc8",
   "metadata": {},
   "source": [
    "In this method, we have to create one column by using the dataset 'Age' column and this inbuilt function. Then have to delete 'Age' column and add this new values to original dataset.\n",
    "\n",
    "This is kind of large process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dafcfd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method 2\n",
    "# Using map()\n",
    "\n",
    "df['Gender'] = df['Gender'].map({'F':0,'M':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4e2175dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042       0  0-17          10             A   \n",
       "1  P00248942       0  0-17          10             A   \n",
       "2  P00087842       0  0-17          10             A   \n",
       "3  P00085442       0  0-17          10             A   \n",
       "4  P00285442       1   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                          4               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb484232",
   "metadata": {},
   "source": [
    "In this method we do operations in runtime while we encounter with 'F' and 'M'. So this method is more efficient than first"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a36aca4",
   "metadata": {},
   "source": [
    "## 2. Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8671f635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique Values in dataset\n",
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "32758f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['Age'].map({'0-17' : 1, '18-25' : 2, '26-35' : 3, '36-45': 4, '46-50': 5, '51-55': 6, '55+': 7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db07d280",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
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       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
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       "      <td>10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0  P00069042       0    1          10             A   \n",
       "1  P00248942       0    1          10             A   \n",
       "2  P00087842       0    1          10             A   \n",
       "3  P00085442       0    1          10             A   \n",
       "4  P00285442       1    7          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                          4               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the changes in Age column \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b691988c",
   "metadata": {},
   "source": [
    "Here we can see the Age column is not containing the range of ages, now it is containing the numerical value that indicates its range of age where it lies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c00d1c8",
   "metadata": {},
   "source": [
    "## 3. City_Category\n",
    " Fixing categorical city_category column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "35884579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'C', 'B'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in dataset\n",
    "df['City_Category'].unique()\n",
    "# Here we have 3 values and 2 values are sufficient for future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "75063760",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save it in new variable for further operations\n",
    "df_city = pd.get_dummies(df['City_Category'],drop_first = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "09a6ef78",
   "metadata": {},
   "outputs": [
    {
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       "   B  C\n",
       "0  0  0\n",
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   ],
   "source": [
    "# Top Values\n",
    "df_city.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8f3d8e73",
   "metadata": {},
   "outputs": [
    {
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       "<p>783667 rows × 13 columns</p>\n",
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      "text/plain": [
       "       Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0       P00069042       0    1          10             A   \n",
       "1       P00248942       0    1          10             A   \n",
       "2       P00087842       0    1          10             A   \n",
       "3       P00085442       0    1          10             A   \n",
       "4       P00285442       1    7          16             C   \n",
       "...           ...     ...  ...         ...           ...   \n",
       "233594  P00118942       0    3          15             B   \n",
       "233595  P00254642       0    3          15             B   \n",
       "233596  P00031842       0    3          15             B   \n",
       "233597  P00124742       0    5           1             C   \n",
       "233598  P00316642       0    5           0             B   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               2               0                   1   \n",
       "2                               2               0                  12   \n",
       "3                               2               0                  12   \n",
       "4                               4               0                   8   \n",
       "...                           ...             ...                 ...   \n",
       "233594                         4+               1                   8   \n",
       "233595                         4+               1                   5   \n",
       "233596                         4+               1                   1   \n",
       "233597                         4+               0                  10   \n",
       "233598                         4+               1                   4   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  Purchase  B  C  \n",
       "0                      NaN                 NaN    8370.0  0  0  \n",
       "1                      6.0                14.0   15200.0  0  0  \n",
       "2                      NaN                 NaN    1422.0  0  0  \n",
       "3                     14.0                 NaN    1057.0  0  0  \n",
       "4                      NaN                 NaN    7969.0  0  1  \n",
       "...                    ...                 ...       ... .. ..  \n",
       "233594                 NaN                 NaN       NaN  1  0  \n",
       "233595                 8.0                 NaN       NaN  1  0  \n",
       "233596                 5.0                12.0       NaN  1  0  \n",
       "233597                16.0                 NaN       NaN  0  1  \n",
       "233598                 5.0                 NaN       NaN  1  0  \n",
       "\n",
       "[783667 rows x 13 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat to original dataset\n",
    "pd.concat([df,df_city],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b199806b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0  P00069042       0    1          10             A   \n",
       "1  P00248942       0    1          10             A   \n",
       "2  P00087842       0    1          10             A   \n",
       "3  P00085442       0    1          10             A   \n",
       "4  P00285442       1    7          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                          4               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  B  C  \n",
       "0                 NaN                 NaN    8370.0  0  0  \n",
       "1                 6.0                14.0   15200.0  0  0  \n",
       "2                 NaN                 NaN    1422.0  0  0  \n",
       "3                14.0                 NaN    1057.0  0  0  \n",
       "4                 NaN                 NaN    7969.0  0  1  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save it into original dataset\n",
    "df = pd.concat([df,df_city],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b44f43fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we dont need City_Category column, SO we can drop it.\n",
    "df.drop('City_Category',axis = 1, inplace = True)\n",
    "# inplace for make changes permanent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c86452b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                          4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 NaN                 NaN   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 NaN                 NaN   \n",
       "3               0                  12                14.0                 NaN   \n",
       "4               0                   8                 NaN                 NaN   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279b0d23",
   "metadata": {},
   "source": [
    "Here we drop City_Category column from dataset and add two new columns to dataset in terms of B and C."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9193b886",
   "metadata": {},
   "source": [
    "## Null Values in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f9a49fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            245982\n",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e1fef6",
   "metadata": {},
   "source": [
    "Product_Category_2, Product_Category_3 and Purchase having so many null or missing values. We have to do something of them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d056ed",
   "metadata": {},
   "source": [
    "## 1. Product_Category_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6487448a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  6., 14.,  2.,  8., 15., 16., 11.,  5.,  3.,  4., 12.,  9.,\n",
       "       10., 17., 13.,  7., 18.])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in Product_Category_2\n",
    "df['Product_Category_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "57652ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0     91317\n",
       "14.0    78834\n",
       "2.0     70498\n",
       "16.0    61687\n",
       "15.0    54114\n",
       "5.0     37165\n",
       "4.0     36705\n",
       "6.0     23575\n",
       "11.0    20230\n",
       "17.0    19104\n",
       "13.0    15054\n",
       "9.0      8177\n",
       "12.0     7801\n",
       "10.0     4420\n",
       "3.0      4123\n",
       "18.0     4027\n",
       "7.0       854\n",
       "Name: Product_Category_2, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different values with their count in dataset\n",
    "df['Product_Category_2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe46e8ec",
   "metadata": {},
   "source": [
    "nan --> None value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7b3f6de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8.0\n",
       "Name: Product_Category_2, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values with mode \n",
    "df['Product_Category_2'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5d0764c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dc91f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace it\n",
    "df['Product_Category_2'] = df['Product_Category_2'].fillna(df['Product_Category_2']).mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5959d1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check again for null values\n",
    "df['Product_Category_2'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1987f7de",
   "metadata": {},
   "source": [
    "## 2. Product_Category_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cf73db0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 14., 17.,  5.,  4., 16., 15.,  8.,  9., 13.,  6., 12.,  3.,\n",
       "       18., 11., 10.])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in Product_Category_3\n",
    "df['Product_Category_3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "64f95ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0    46469\n",
       "15.0    39968\n",
       "14.0    26283\n",
       "17.0    23818\n",
       "5.0     23799\n",
       "8.0     17861\n",
       "9.0     16532\n",
       "12.0    13115\n",
       "13.0     7849\n",
       "6.0      6888\n",
       "18.0     6621\n",
       "4.0      2691\n",
       "11.0     2585\n",
       "10.0     2501\n",
       "3.0       878\n",
       "Name: Product_Category_3, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different values with their count in dataset\n",
    "df['Product_Category_3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "12f52ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16.0\n",
       "Name: Product_Category_3, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values with mode \n",
    "df['Product_Category_3'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ed9b1fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_3'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6e74301e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace it\n",
    "df['Product_Category_3'] = df['Product_Category_3'].fillna(df['Product_Category_3']).mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bb594053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check again for null values\n",
    "df['Product_Category_3'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b8a23129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
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       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                          4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 8.0                16.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                 8.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "22277f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2                 0\n",
       "Product_Category_3                 0\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "714ed61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
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       "      <td>8.0</td>\n",
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       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                          4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 8.0                16.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                 8.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a112e8e",
   "metadata": {},
   "source": [
    "## 4. Stay_In_Current_City_Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "907bb32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 3, 1, 0, '2', '0', '4+', '1', '3'], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in Stay_In_Current_City_Years Column\n",
    "df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119a7b37",
   "metadata": {},
   "source": [
    "Here we have to convert '4+' to '4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a0a86b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0p/_vy9x_j55gd1trtzdrx_dnqr0000gn/T/ipykernel_8134/2154818033.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  df['Stay_In_Current_City_Years'] = df['Stay_In_Current_City_Years'].str.replace('+','')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "         ... \n",
       "233594      4\n",
       "233595      4\n",
       "233596      4\n",
       "233597      4\n",
       "233598      4\n",
       "Name: Stay_In_Current_City_Years, Length: 783667, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace '+' by '' in dataset and save it to original dataset\n",
    "df['Stay_In_Current_City_Years'] = df['Stay_In_Current_City_Years'].str.replace('+','')\n",
    "df['Stay_In_Current_City_Years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2bd0d7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
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       "      <th>Product_Category_2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                        NaN   \n",
       "1  P00248942       0    1          10                        NaN   \n",
       "2  P00087842       0    1          10                        NaN   \n",
       "3  P00085442       0    1          10                        NaN   \n",
       "4  P00285442       1    7          16                        NaN   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 8.0                16.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                 8.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54201e4c",
   "metadata": {},
   "source": [
    "Now we don't have '+4' value in Stay_In_Current_City_Years column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a8bbac3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Product_ID                  783667 non-null  object \n",
      " 1   Gender                      783667 non-null  int64  \n",
      " 2   Age                         783667 non-null  int64  \n",
      " 3   Occupation                  783667 non-null  int64  \n",
      " 4   Stay_In_Current_City_Years  233599 non-null  object \n",
      " 5   Marital_Status              783667 non-null  int64  \n",
      " 6   Product_Category_1          783667 non-null  int64  \n",
      " 7   Product_Category_2          783667 non-null  float64\n",
      " 8   Product_Category_3          783667 non-null  float64\n",
      " 9   Purchase                    550068 non-null  float64\n",
      " 10  B                           783667 non-null  uint8  \n",
      " 11  C                           783667 non-null  uint8  \n",
      "dtypes: float64(3), int64(5), object(2), uint8(2)\n",
      "memory usage: 67.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6661f1",
   "metadata": {},
   "source": [
    "From above result we can see that the DataTypes of 'Stay_In_Current_City_Years' column is 'Object' But, in dataset we have Integer values associated with it.\n",
    "Now we have to convert these 'Object' DataTypes to 'Integer' DataTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b6f1ac44",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [91]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Convert Object to Integer\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStay_In_Current_City_Years\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mStay_In_Current_City_Years\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/generic.py:5912\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5905\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   5906\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m   5907\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[1;32m   5908\u001b[0m     ]\n\u001b[1;32m   5910\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   5911\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 5912\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   5915\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:419\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastype\u001b[39m(\u001b[38;5;28mself\u001b[39m: T, dtype, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/internals/managers.py:304\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    303\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 304\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[1;32m    306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/internals/blocks.py:580\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    563\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m    564\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m    577\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    578\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 580\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    583\u001b[0m newb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_block(new_values)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1292\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1289\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1292\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m   1294\u001b[0m     \u001b[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m   1296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1237\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1234\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m   1236\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1237\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m   1240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/dtypes/cast.py:1154\u001b[0m, in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m   1151\u001b[0m \n\u001b[1;32m   1152\u001b[0m     \u001b[38;5;66;03m# work around NumPy brokenness, #1987\u001b[39;00m\n\u001b[1;32m   1153\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(dtype\u001b[38;5;241m.\u001b[39mtype, np\u001b[38;5;241m.\u001b[39minteger):\n\u001b[0;32m-> 1154\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype_intsafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1156\u001b[0m     \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[1;32m   1157\u001b[0m     \u001b[38;5;66;03m# then coerce to a proper dtype and recall astype_nansafe\u001b[39;00m\n\u001b[1;32m   1159\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m is_datetime64_dtype(dtype):\n",
      "File \u001b[0;32mpandas/_libs/lib.pyx:668\u001b[0m, in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    }
   ],
   "source": [
    "# Convert Object to Integer\n",
    "df['Stay_In_Current_City_Years'] = df['Stay_In_Current_City_Years'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "833e875a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1b2df5",
   "metadata": {},
   "source": [
    "Now 'Stay_In_Current_City_Years' having elements with Integer DataTypes.\n",
    "Similarly 'B' and 'C' columns having DataTypes \"unit8\". So we also have to change it to Intger. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b09edc1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Object to Integer of B column\n",
    "df['B'] = df['B'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40dc5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Object to Integer of B column\n",
    "df['C'] = df['C'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9880f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162d6452",
   "metadata": {},
   "source": [
    "Now 'Stay_In_Current_City_Years', 'B' and 'C' columns having elements with \"Integer\" DataTypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b851217",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6df0e48",
   "metadata": {},
   "source": [
    "## 1. Age VS Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dd0ac8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Barplot : Age VS Purchase\n",
    "sns.barplot('Age','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1466b0e8",
   "metadata": {},
   "source": [
    "Where In Gender,\n",
    "      0 --> Male and\n",
    "      1 --> Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29a6bd53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make diagram more user friendly\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (12,6)\n",
    "sns.barplot('Age','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792f17ba",
   "metadata": {},
   "source": [
    "Observation: For every age range male purchase more products then female"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63dd784",
   "metadata": {},
   "source": [
    "## 2. Occupation VS Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d0ab536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Barplot : Occupation VS Purchase\n",
    "sns.barplot('Occupation','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601483ff",
   "metadata": {},
   "source": [
    "This is kind of Uniform graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067b698a",
   "metadata": {},
   "source": [
    "## 3. Product_Category_1 VS Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "170986b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Barplot : Product_Category_1 VS Purchase\n",
    "sns.barplot('Product_Category_1','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36d933b",
   "metadata": {},
   "source": [
    "## 4. Product_Category_2 VS Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7155948b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Barplot : Product_Category_2 VS Purchase\n",
    "sns.barplot('Product_Category_2','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642a70b0",
   "metadata": {},
   "source": [
    "## 5. Product_Category_3 VS Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab41d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Barplot : Product_Category_3 VS Purchase\n",
    "sns.barplot('Product_Category_3','Purchase',hue = 'Gender',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b168228",
   "metadata": {},
   "source": [
    "# Feacture Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f091eb1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test Dataset\n",
    "df_test = df[df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97826e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Dataset\n",
    "df_train = df[~df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d6f3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    " X = df_train.drop('Purchase',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a8cde53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3416862f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e6013d",
   "metadata": {},
   "outputs": [],
   "source": [
    " y = df_train['Purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afd1a755",
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf7f254",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7fa5767",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4602094b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop('Product_ID',axis = 1,inplace = True)\n",
    "X_test.drop('Product_ID',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27cb9cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  }
 ],
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