Black Friday-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c07345dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a857f805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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</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</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</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",
       "    </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  \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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train=pd.read_csv('train.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d637472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "801923e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge\n",
    "df = pd.concat([df_train, df_test])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2d72b29",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aabbef25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08c3bc65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4367c907",
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46356e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['User_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afde483b",
   "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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78d18d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            F      M\n",
       "0        True  False\n",
       "1        True  False\n",
       "2        True  False\n",
       "3        True  False\n",
       "4       False   True\n",
       "...       ...    ...\n",
       "233594   True  False\n",
       "233595   True  False\n",
       "233596   True  False\n",
       "233597   True  False\n",
       "233598   True  False\n",
       "\n",
       "[783667 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4961218",
   "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",
       "    <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>P00118942</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>P00254642</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>P00031842</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>P00124742</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>P00316642</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 11 columns</p>\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",
       "233594  P00118942       0  26-35          15             B   \n",
       "233595  P00254642       0  26-35          15             B   \n",
       "233596  P00031842       0  26-35          15             B   \n",
       "233597  P00124742       0  46-50           1             C   \n",
       "233598  P00316642       0  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",
       "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  \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  \n",
       "...                    ...                 ...       ...  \n",
       "233594                 NaN                 NaN       NaN  \n",
       "233595                 8.0                 NaN       NaN  \n",
       "233596                 5.0                12.0       NaN  \n",
       "233597                16.0                 NaN       NaN  \n",
       "233598                 5.0                 NaN       NaN  \n",
       "\n",
       "[783667 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'] = df['Gender'].map({'F': 0, 'M': 1})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d97c5e6",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0db59b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5796ffc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0-17</th>\n",
       "      <th>18-25</th>\n",
       "      <th>26-35</th>\n",
       "      <th>36-45</th>\n",
       "      <th>46-50</th>\n",
       "      <th>51-55</th>\n",
       "      <th>55+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0-17  18-25  26-35  36-45  46-50  51-55    55+\n",
       "0        True  False  False  False  False  False  False\n",
       "1        True  False  False  False  False  False  False\n",
       "2        True  False  False  False  False  False  False\n",
       "3        True  False  False  False  False  False  False\n",
       "4       False  False  False  False  False  False   True\n",
       "...       ...    ...    ...    ...    ...    ...    ...\n",
       "233594  False  False   True  False  False  False  False\n",
       "233595  False  False   True  False  False  False  False\n",
       "233596  False  False   True  False  False  False  False\n",
       "233597  False  False  False  False   True  False  False\n",
       "233598  False  False  False  False   True  False  False\n",
       "\n",
       "[783667 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0901426",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    </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",
       "    </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",
       "    <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>P00118942</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>P00254642</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>P00031842</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>P00124742</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>P00316642</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 11 columns</p>\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",
       "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  \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  \n",
       "...                    ...                 ...       ...  \n",
       "233594                 NaN                 NaN       NaN  \n",
       "233595                 8.0                 NaN       NaN  \n",
       "233596                 5.0                12.0       NaN  \n",
       "233597                16.0                 NaN       NaN  \n",
       "233598                 5.0                 NaN       NaN  \n",
       "\n",
       "[783667 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3fc98b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'C', 'B'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['City_Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db8964a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_city=pd.get_dummies(df['City_Category'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91221329",
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            B      C\n",
       "0       False  False\n",
       "1       False  False\n",
       "2       False  False\n",
       "3       False  False\n",
       "4       False   True\n",
       "...       ...    ...\n",
       "233594   True  False\n",
       "233595   True  False\n",
       "233596   True  False\n",
       "233597  False   True\n",
       "233598   True  False\n",
       "\n",
       "[783667 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ede38fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.concat([df,df_city],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "938eb981",
   "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",
       "      <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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>True</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  False  False  \n",
       "1                 6.0                14.0   15200.0  False  False  \n",
       "2                 NaN                 NaN    1422.0  False  False  \n",
       "3                14.0                 NaN    1057.0  False  False  \n",
       "4                 NaN                 NaN    7969.0  False   True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d96a43fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('City_Category',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd09c9ec",
   "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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>True</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  False  False  \n",
       "1   15200.0  False  False  \n",
       "2    1422.0  False  False  \n",
       "3    1057.0  False  False  \n",
       "4    7969.0  False   True  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "942ac0ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad168812",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c92e4bb1",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Focus on replacing the missing values\n",
    "df['Product_Category_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e13f3bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Category_2\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d056a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2c337e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Product_Category_2']=df['Product_Category_2'].fillna(df['Product_Category_2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b947d80a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b4537120",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_3'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21cecdb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Category_3\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "321284db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Product_Category_3']=df['Product_Category_3'].fillna(df['Product_Category_3'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ad094d52",
   "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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase      B      C  \n",
       "0    8370.0  False  False  \n",
       "1   15200.0  False  False  \n",
       "2    1422.0  False  False  \n",
       "3    1057.0  False  False  \n",
       "4    7969.0  False   True  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5036aabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "22ef7551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4+', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2eecdabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Stay_In_Current_City_Years']=df['Stay_In_Current_City_Years'].str.replace('+','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7af087e",
   "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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase      B      C  \n",
       "0    8370.0  False  False  \n",
       "1   15200.0  False  False  \n",
       "2    1422.0  False  False  \n",
       "3    1057.0  False  False  \n",
       "4    7969.0  False   True  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "082b7077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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  783667 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  bool   \n",
      " 11  C                           783667 non-null  bool   \n",
      "dtypes: bool(2), float64(3), int64(5), object(2)\n",
      "memory usage: 67.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6477ce1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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  783667 non-null  int32  \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  bool   \n",
      " 11  C                           783667 non-null  bool   \n",
      "dtypes: bool(2), float64(3), int32(1), int64(5), object(1)\n",
      "memory usage: 64.3+ MB\n"
     ]
    }
   ],
   "source": [
    "## Conversion of  object into integers\n",
    "df['Stay_In_Current_City_Years']=df['Stay_In_Current_City_Years'].astype(int)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c269b892",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['B']=df['B'].astype(int)\n",
    "df['C']=df['C'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7e9f5bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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  783667 non-null  int32  \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  int32  \n",
      " 11  C                           783667 non-null  int32  \n",
      "dtypes: float64(3), int32(3), int64(5), object(1)\n",
      "memory usage: 68.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "907354de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Purchase'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualisation Age vs Purchased\n",
    "sns.barplot(x='Age',y='Purchase',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "13891f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Purchase'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAG0CAYAAAAvjxMUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuwElEQVR4nO3dfXQU9b3H8c+SJ0IMCwkmIdeAtOZGJKAIFAIiWCCgBFRaUcEIioDlMQq1pdQaPZIoXgGbVASkBA0Qe1pQvNVI8CEt8mg0V0BEekQISAzVZSEYEkjm/mGd45KAP0LITuD9OmfO2fnNd3e+MwePn/xmZtdlWZYlAAAAnFUzfzcAAADQFBCaAAAADBCaAAAADBCaAAAADBCaAAAADBCaAAAADBCaAAAADBCaAAAADBCaAAAADBCaAAAADPg1NP3jH//QsGHDFBsbK5fLpVdffdVnu2VZSk9PV2xsrEJDQ9W/f3/t3LnTp6ayslJTp05VmzZtFBYWpuHDh+vAgQM+NR6PR6mpqXK73XK73UpNTdWRI0d8avbv369hw4YpLCxMbdq00bRp01RVVXUhDhsAADRBgf7c+fHjx3Xttdfqvvvu0y9+8Yta2+fOnat58+YpJydH//3f/60nn3xSgwYN0u7duxUeHi5JSktL0+uvv668vDxFRkZqxowZSklJUVFRkQICAiRJo0aN0oEDB5Sfny9JmjBhglJTU/X6669LkqqrqzV06FBdfvnl2rBhg77++muNGTNGlmUpKyvL+Hhqamr05ZdfKjw8XC6X63xPDwAAaASWZenYsWOKjY1Vs2ZnmU+yHEKStWbNGnu9pqbGiomJsZ566il77MSJE5bb7bZeeOEFy7Is68iRI1ZQUJCVl5dn1xw8eNBq1qyZlZ+fb1mWZX3yySeWJGvz5s12zaZNmyxJ1qeffmpZlmW98cYbVrNmzayDBw/aNatWrbJCQkIsr9drfAwlJSWWJBYWFhYWFpYmuJSUlJz1//N+nWk6m71796q0tFTJycn2WEhIiPr166eNGzdq4sSJKioq0smTJ31qYmNjlZiYqI0bN2rw4MHatGmT3G63evbsadf06tVLbrdbGzduVEJCgjZt2qTExETFxsbaNYMHD1ZlZaWKiop000031dljZWWlKisr7fXvsp9UUlKili1bNti5AAAAF87Ro0cVFxdnX8U6E8eGptLSUklSdHS0z3h0dLT27dtn1wQHB6t169a1ar5/f2lpqaKiomp9flRUlE/N6ftp3bq1goOD7Zq6ZGZm6vHHH6813rJlS0ITAABNzI/dWuP4p+dOPwDLsn70oE6vqau+PjWnmzVrlrxer72UlJSctS8AANB0OTY0xcTESFKtmZ6ysjJ7VigmJkZVVVXyeDxnrfnqq69qff7hw4d9ak7fj8fj0cmTJ2vNQP1QSEiIPavE7BIAABc3x4amDh06KCYmRgUFBfZYVVWVCgsL1bt3b0lSt27dFBQU5FNz6NAh7dixw65JSkqS1+vV1q1b7ZotW7bI6/X61OzYsUOHDh2ya9atW6eQkBB169btgh4nAABoGvx6T1N5ebn+9a9/2et79+5VcXGxIiIi1K5dO6WlpSkjI0Px8fGKj49XRkaGWrRooVGjRkmS3G63xo0bpxkzZigyMlIRERGaOXOmOnfurIEDB0qSOnbsqCFDhmj8+PFatGiRpO++ciAlJUUJCQmSpOTkZF1zzTVKTU3VM888o2+++UYzZ87U+PHjmT0CAADfMX6e/gJ4991363zkb8yYMZZlffe1A4899pgVExNjhYSEWDfeeKO1fft2n8+oqKiwpkyZYkVERFihoaFWSkqKtX//fp+ar7/+2ho9erQVHh5uhYeHW6NHj7Y8Ho9Pzb59+6yhQ4daoaGhVkREhDVlyhTrxIkT53Q8Xq/XknROX1MAAAD8y/T/3y7L+s9z8jhvR48eldvtltfrZYYKAIAmwvT/3469pwkAAMBJCE0AAAAGCE0AAAAGCE0AAAAGCE0AAAAGCE0AAAAGCE0AAAAG/PqN4AAAXIymT5+uw4cPS5Iuv/xyPffcc37uCA2B0AQAQAM7fPhwnT8Wj6aNy3MAAAAGCE0AAAAGuDwHALhkzLnnl42yH++/vT94fbjR9itJs3P/2mj7utQw0wQAAGCAmSYAABpYSDOXvp+X+O41vteUnywkNAEA0MC6tWnp7xYcqyk/WUhoAoAf0ZT/MgbQcAhNAPAjmvJfxgAaDjeCAwAAGGCmCYCkpncJqvDGfo22rxOBAZLru5t5T5SWNtq++/2jsFH2A0jSrjnvNMp+TnpP+LxurP1KUsfZPz+v9xOaAEjiEhTqp6mFbeB8EJoA4Ee0tCTJ+sFrfI+wjUsJoQmXlKb2V3GfrD6Ntq+QoyFy6btLUKVHSxtt3+9Pfb9R9nM+7quu9ncLwEWjVUh4na+bAkITLin8VQwA/vVwtzH+bqHeCE0AJElWqFXnazQ92TNeb7R9HfvmW5/XjbXvKc8Oa5T9AD9EaIIj7H+ic6Ps59SRSEkB/3n9ZaPtt90ftjfKfs5H1Y1V/m4BAByN0AQAqLfmweF1vgYuRoQmXFIiQqrrfA2gfvrFj/R3C0CjITRdhJraE2KN6Xddj/i7BQBAE0VougjxhBgAAA2P354DAAAwQGgCAAAwQGgCAAAwQGgCAAAwwI3gjajbr19qlP209JTbafiQp7zR9lv0zL2Nsh8AAPyBmSYAAAADhCYAAAADhCYAAAAD3NN0EaoJCqvzNQAAqD9C00WoPOFmf7cAAMBFh8tzAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABghNAAAABhwdmk6dOqXf//736tChg0JDQ/WTn/xETzzxhGpqauway7KUnp6u2NhYhYaGqn///tq5c6fP51RWVmrq1Klq06aNwsLCNHz4cB04cMCnxuPxKDU1VW63W263W6mpqTpy5EhjHCYAAGgCHB2ann76ab3wwgvKzs7Wrl27NHfuXD3zzDPKysqya+bOnat58+YpOztb27ZtU0xMjAYNGqRjx47ZNWlpaVqzZo3y8vK0YcMGlZeXKyUlRdXV1XbNqFGjVFxcrPz8fOXn56u4uFipqamNerwAAMC5Av3dwNls2rRJt956q4YOHSpJuvLKK7Vq1Sp98MEHkr6bZVqwYIFmz56tESNGSJKWL1+u6OhorVy5UhMnTpTX69XSpUv18ssva+DAgZKk3NxcxcXFaf369Ro8eLB27dql/Px8bd68WT179pQkLVmyRElJSdq9e7cSEhL8cPQAAMBJHD3TdMMNN+jtt9/WZ599Jkn6v//7P23YsEG33HKLJGnv3r0qLS1VcnKy/Z6QkBD169dPGzdulCQVFRXp5MmTPjWxsbFKTEy0azZt2iS3220HJknq1auX3G63XQMAAC5tjp5p+s1vfiOv16urr75aAQEBqq6u1pw5c3T33XdLkkpLSyVJ0dHRPu+Ljo7Wvn377Jrg4GC1bt26Vs337y8tLVVUVFSt/UdFRdk1damsrFRlZaW9fvTo0XocJQAAaAocPdP0yiuvKDc3VytXrtSHH36o5cuX63/+53+0fPlynzqXy+WzbllWrbHTnV5TV/2PfU5mZqZ947jb7VZcXJzJYQEAgCbI0aHp17/+tX7729/qrrvuUufOnZWamqqHHnpImZmZkqSYmBhJqjUbVFZWZs8+xcTEqKqqSh6P56w1X331Va39Hz58uNYs1g/NmjVLXq/XXkpKSup/sAAAwNEcHZq+/fZbNWvm22JAQID9lQMdOnRQTEyMCgoK7O1VVVUqLCxU7969JUndunVTUFCQT82hQ4e0Y8cOuyYpKUler1dbt261a7Zs2SKv12vX1CUkJEQtW7b0WQAAwMXJ0fc0DRs2THPmzFG7du3UqVMnffTRR5o3b57uv/9+Sd9dUktLS1NGRobi4+MVHx+vjIwMtWjRQqNGjZIkud1ujRs3TjNmzFBkZKQiIiI0c+ZMde7c2X6armPHjhoyZIjGjx+vRYsWSZImTJiglJQUnpwDAACSHB6asrKy9Oijj2rSpEkqKytTbGysJk6cqD/84Q92zSOPPKKKigpNmjRJHo9HPXv21Lp16xQeHm7XzJ8/X4GBgRo5cqQqKio0YMAA5eTkKCAgwK5ZsWKFpk2bZj9lN3z4cGVnZzfewQIAAEdzdGgKDw/XggULtGDBgjPWuFwupaenKz09/Yw1zZs3V1ZWls+XYp4uIiJCubm559EtAAC4mDn6niYAAACnIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYIDQBAAAYcHxoOnjwoO655x5FRkaqRYsWuu6661RUVGRvtyxL6enpio2NVWhoqPr376+dO3f6fEZlZaWmTp2qNm3aKCwsTMOHD9eBAwd8ajwej1JTU+V2u+V2u5WamqojR440xiECAIAmwNGhyePxqE+fPgoKCtKbb76pTz75RM8++6xatWpl18ydO1fz5s1Tdna2tm3bppiYGA0aNEjHjh2za9LS0rRmzRrl5eVpw4YNKi8vV0pKiqqrq+2aUaNGqbi4WPn5+crPz1dxcbFSU1Mb83ABAICDBfq7gbN5+umnFRcXp2XLltljV155pf3asiwtWLBAs2fP1ogRIyRJy5cvV3R0tFauXKmJEyfK6/Vq6dKlevnllzVw4EBJUm5uruLi4rR+/XoNHjxYu3btUn5+vjZv3qyePXtKkpYsWaKkpCTt3r1bCQkJjXfQAADAkRw907R27Vp1795dd9xxh6KiotS1a1ctWbLE3r53716VlpYqOTnZHgsJCVG/fv20ceNGSVJRUZFOnjzpUxMbG6vExES7ZtOmTXK73XZgkqRevXrJ7XbbNXWprKzU0aNHfRYAAHBxcnRo+vzzz7Vw4ULFx8frrbfe0oMPPqhp06bppZdekiSVlpZKkqKjo33eFx0dbW8rLS1VcHCwWrdufdaaqKioWvuPioqya+qSmZlp3wPldrsVFxdX/4MFAACO5ujQVFNTo+uvv14ZGRnq2rWrJk6cqPHjx2vhwoU+dS6Xy2fdsqxaY6c7vaau+h/7nFmzZsnr9dpLSUmJyWEBAIAmyNGhqW3btrrmmmt8xjp27Kj9+/dLkmJiYiSp1mxQWVmZPfsUExOjqqoqeTyes9Z89dVXtfZ/+PDhWrNYPxQSEqKWLVv6LAAA4OLk6NDUp08f7d6922fss88+U/v27SVJHTp0UExMjAoKCuztVVVVKiwsVO/evSVJ3bp1U1BQkE/NoUOHtGPHDrsmKSlJXq9XW7dutWu2bNkir9dr1wAAgEubo5+ee+ihh9S7d29lZGRo5MiR2rp1qxYvXqzFixdL+u6SWlpamjIyMhQfH6/4+HhlZGSoRYsWGjVqlCTJ7XZr3LhxmjFjhiIjIxUREaGZM2eqc+fO9tN0HTt21JAhQzR+/HgtWrRIkjRhwgSlpKTw5BwAAJDk8NDUo0cPrVmzRrNmzdITTzyhDh06aMGCBRo9erRd88gjj6iiokKTJk2Sx+NRz549tW7dOoWHh9s18+fPV2BgoEaOHKmKigoNGDBAOTk5CggIsGtWrFihadOm2U/ZDR8+XNnZ2Y13sAAAwNEcHZokKSUlRSkpKWfc7nK5lJ6ervT09DPWNG/eXFlZWcrKyjpjTUREhHJzc8+nVQAAcBFz9D1NAAAATlHv0HTq1CmtX79eixYtsn+y5Msvv1R5eXmDNQcAAOAU9bo8t2/fPg0ZMkT79+9XZWWlBg0apPDwcM2dO1cnTpzQCy+80NB9AgAA+FW9ZpqmT5+u7t27y+PxKDQ01B6//fbb9fbbbzdYcwAAAE5Rr5mmDRs26P3331dwcLDPePv27XXw4MEGaQwAAMBJ6jXTVFNTo+rq6lrjBw4c8HnUHwAA4GJRr9A0aNAgLViwwF53uVwqLy/XY489pltuuaWhegMAAHCMel2emz9/vm666SZdc801OnHihEaNGqU9e/aoTZs2WrVqVUP3CAAA4Hf1Ck2xsbEqLi5WXl6eioqKVFNTo3Hjxmn06NE+N4YDAABcLOr9jeChoaG67777dN999zVkPwAAAI5Ur3uali9frr///e/2+iOPPKJWrVqpd+/e2rdvX4M1BwAA4BT1Ck0ZGRn2ZbhNmzYpOztbc+fOVZs2bfTQQw81aIMAAABOUK/LcyUlJbrqqqskSa+++qp++ctfasKECerTp4/69+/fkP0BAAA4Qr1mmi677DJ9/fXXkqR169Zp4MCBkqTmzZuroqKi4boDAABwiHrNNA0aNEgPPPCAunbtqs8++0xDhw6VJO3cuVNXXnllQ/YHAADgCPWaafrTn/6kpKQkHT58WH/7298UGRkpSSoqKtLdd9/doA0CAAA4Qb1mmlq1aqXs7Oxa448//vh5NwQAAOBE9f6eJkn69ttvtX//flVVVfmMd+nS5byaAgAAcJp6habDhw9r7Nixys/Pr3N7XT/mCwAA0JTV656mtLQ0HTlyRJs3b1ZoaKjy8/O1fPlyxcfHa+3atQ3dIwAAgN/Va6bpnXfe0WuvvaYePXqoWbNmat++vQYNGqSWLVsqMzPTfpoOAADgYlGvmabjx48rKipKkhQREaHDhw9Lkjp37qwPP/yw4boDAABwiHqFpoSEBO3evVuSdN1112nRokU6ePCgXnjhBbVt27ZBGwQAAHCCel2eS0tL06FDhyRJjz32mAYPHqwVK1YoODhYOTk5DdkfAACAI9QrNI0ePdp+3bVrV33xxRf69NNP1a5dO7Vp06bBmgMAAHCK8/qepu+1aNFC119/fUN8FAAAgCPVKzRVV1crJydHb7/9tsrKylRTU+Oz/Z133mmQ5gAAAJyiXqFp+vTpysnJ0dChQ5WYmCiXy9XQfQEAADhKvUJTXl6e/vKXv+iWW25p6H4AAAAcqV5fORAcHKyrrrqqoXsBAABwrHqFphkzZui5556TZVkN3Q8AAIAjGV+eGzFihM/6O++8ozfffFOdOnVSUFCQz7bVq1c3THcAAAAOYRya3G63z/rtt9/e4M0AAAA4lXFoWrZs2YXsAwAAwNHqdU/T3r17tWfPnlrje/bs0RdffHG+PQEAADhOvULT2LFjtXHjxlrjW7Zs0dixY8+3JwAAAMepV2j66KOP1KdPn1rjvXr1UnFx8fn2BAAA4Dj1Ck0ul0vHjh2rNe71elVdXX3eTQEAADhNvUJT3759lZmZ6ROQqqurlZmZqRtuuKHBmgMAAHCKev2MytNPP61+/fopISFBffv2lST985//1NGjR/mxXgAAcFGq10xTp06d9PHHH2vkyJEqKyvTsWPHdO+99+rTTz9VYmJiQ/cIAADgd+c803Ty5EklJydr0aJFysjIuBA9AQAAOM45zzQFBQVpx44dcrlcF6IfAAAAR6rX5bl7771XS5cubeheAAAAHKteN4JXVVXpxRdfVEFBgbp3766wsDCf7fPmzWuQ5gAAAJyiXqFpx44duv766yVJn332mc82LtsBAICLUb1C07vvvtvQfQAAADhave5pAgAAuNTUa6bppptuOutlOL7gEgAAXGzqFZquu+46n/WTJ0+quLhYO3bs0JgxYxqiLwAAAEepV2iaP39+nePp6ekqLy8/r4YAAACcqEHvabrnnnv05z//uSE/EgAAwBEaNDRt2rRJzZs3b8iPBAAAcIR6XZ67/fbbfW4EtyxLhw4d0gcffKBHH320wZoDAABwinqFplatWsnlcsmyLElSs2bNlJCQoCeeeELJyckN2iAAAIATnFNo+vbbb/XrX/9ab731lk6dOqUBAwYoKytLbdq0uVD9AQAAOMI53dP02GOPKScnRykpKbr77ru1fv16/epXv7pQvQEAADjGOc00rV69WkuXLtVdd90lSRo9erT69Omj6upqBQQEXJAGAQAAnOCcZppKSkrUt29fe/1nP/uZAgMD9eWXXzZ4YwAAAE5yTqGpurpawcHBPmOBgYE6depUgzYFAADgNOd0ec6yLI0dO1YhISH22IkTJ/Tggw8qLCzMHlu9enXDdQgAAOAA5xSa6vpduXvuuafBmgEAAHCqcwpNy5Ytu1B9GMnMzNTvfvc7TZ8+XQsWLJD03ezX448/rsWLF8vj8ahnz57605/+pE6dOtnvq6ys1MyZM7Vq1SpVVFRowIABev7553XFFVfYNR6PR9OmTdPatWslScOHD1dWVpZatWrVmIcIAAAcqkF/RuVC2rZtmxYvXqwuXbr4jM+dO1fz5s1Tdna2tm3bppiYGA0aNEjHjh2za9LS0rRmzRrl5eVpw4YNKi8vV0pKiqqrq+2aUaNGqbi4WPn5+crPz1dxcbFSU1Mb7fgAAICzNYnQVF5ertGjR2vJkiVq3bq1PW5ZlhYsWKDZs2drxIgRSkxM1PLly/Xtt99q5cqVkiSv16ulS5fq2Wef1cCBA9W1a1fl5uZq+/btWr9+vSRp165dys/P14svvqikpCQlJSVpyZIl+t///V/t3r3bL8cMAACcpUmEpsmTJ2vo0KEaOHCgz/jevXtVWlrq89MtISEh6tevnzZu3ChJKioq0smTJ31qYmNjlZiYaNds2rRJbrdbPXv2tGt69eolt9tt1wAAgEtbvX57rjHl5eXpww8/1LZt22ptKy0tlSRFR0f7jEdHR2vfvn12TXBwsM8M1fc137+/tLRUUVFRtT4/KirKrqlLZWWlKisr7fWjR48aHhUAAGhqHD3TVFJSounTpys3N1fNmzc/Y53L5fJZtyyr1tjpTq+pq/7HPiczM1Nut9te4uLizrpPAADQdDk6NBUVFamsrEzdunVTYGCgAgMDVVhYqD/+8Y8KDAy0Z5hOnw0qKyuzt8XExKiqqkoej+esNV999VWt/R8+fLjWLNYPzZo1S16v115KSkrO63gBAIBzOTo0DRgwQNu3b1dxcbG9dO/eXaNHj1ZxcbF+8pOfKCYmRgUFBfZ7qqqqVFhYqN69e0uSunXrpqCgIJ+aQ4cOaceOHXZNUlKSvF6vtm7datds2bJFXq/XrqlLSEiIWrZs6bMAAICLk6PvaQoPD1diYqLPWFhYmCIjI+3xtLQ0ZWRkKD4+XvHx8crIyFCLFi00atQoSZLb7da4ceM0Y8YMRUZGKiIiQjNnzlTnzp3tG8s7duyoIUOGaPz48Vq0aJEkacKECUpJSVFCQkIjHjEAAHAqR4cmE4888ogqKio0adIk+8st161bp/DwcLtm/vz5CgwM1MiRI+0vt8zJyVFAQIBds2LFCk2bNs1+ym748OHKzs5u9OMBAADO1ORC03vvveez7nK5lJ6ervT09DO+p3nz5srKylJWVtYZayIiIpSbm9tAXQIAgIuNo+9pAgAAcApCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFCEwAAgAFHh6bMzEz16NFD4eHhioqK0m233abdu3f71FiWpfT0dMXGxio0NFT9+/fXzp07fWoqKys1depUtWnTRmFhYRo+fLgOHDjgU+PxeJSamiq32y23263U1FQdOXLkQh8iAABoIhwdmgoLCzV58mRt3rxZBQUFOnXqlJKTk3X8+HG7Zu7cuZo3b56ys7O1bds2xcTEaNCgQTp27Jhdk5aWpjVr1igvL08bNmxQeXm5UlJSVF1dbdeMGjVKxcXFys/PV35+voqLi5WamtqoxwsAAJwr0N8NnE1+fr7P+rJlyxQVFaWioiLdeOONsixLCxYs0OzZszVixAhJ0vLlyxUdHa2VK1dq4sSJ8nq9Wrp0qV5++WUNHDhQkpSbm6u4uDitX79egwcP1q5du5Sfn6/NmzerZ8+ekqQlS5YoKSlJu3fvVkJCQuMeOAAAcBxHzzSdzuv1SpIiIiIkSXv37lVpaamSk5PtmpCQEPXr108bN26UJBUVFenkyZM+NbGxsUpMTLRrNm3aJLfbbQcmSerVq5fcbrddU5fKykodPXrUZwEAABenJhOaLMvSww8/rBtuuEGJiYmSpNLSUklSdHS0T210dLS9rbS0VMHBwWrduvVZa6KiomrtMyoqyq6pS2Zmpn0PlNvtVlxcXP0PEAAAOFqTCU1TpkzRxx9/rFWrVtXa5nK5fNYty6o1drrTa+qq/7HPmTVrlrxer72UlJT82GEAAIAmqkmEpqlTp2rt2rV69913dcUVV9jjMTExklRrNqisrMyefYqJiVFVVZU8Hs9Za7766qta+z18+HCtWawfCgkJUcuWLX0WAABwcXJ0aLIsS1OmTNHq1av1zjvvqEOHDj7bO3TooJiYGBUUFNhjVVVVKiwsVO/evSVJ3bp1U1BQkE/NoUOHtGPHDrsmKSlJXq9XW7dutWu2bNkir9dr1wAAgEubo5+emzx5slauXKnXXntN4eHh9oyS2+1WaGioXC6X0tLSlJGRofj4eMXHxysjI0MtWrTQqFGj7Npx48ZpxowZioyMVEREhGbOnKnOnTvbT9N17NhRQ4YM0fjx47Vo0SJJ0oQJE5SSksKTcwAAQJLDQ9PChQslSf379/cZX7ZsmcaOHStJeuSRR1RRUaFJkybJ4/GoZ8+eWrduncLDw+36+fPnKzAwUCNHjlRFRYUGDBignJwcBQQE2DUrVqzQtGnT7Kfshg8fruzs7At7gAAAoMlwdGiyLOtHa1wul9LT05Wenn7GmubNmysrK0tZWVlnrImIiFBubm592gQAAJcAR9/TBAAA4BSEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEJgAAAAOEptM8//zz6tChg5o3b65u3brpn//8p79bAgAADkBo+oFXXnlFaWlpmj17tj766CP17dtXN998s/bv3+/v1gAAgJ8Rmn5g3rx5GjdunB544AF17NhRCxYsUFxcnBYuXOjv1gAAgJ8Rmv6jqqpKRUVFSk5O9hlPTk7Wxo0b/dQVAABwikB/N+AU//73v1VdXa3o6Gif8ejoaJWWltb5nsrKSlVWVtrrXq9XknT06NE666srKxqoW2c603GbOHaiugE7cZ76nptTFacauBPnqe+5OX6Kc3MmFZXfNnAnzlPfc3Pi5MkG7sR56ntuyk8cb+BOnOdM5+b7ccuyzvp+QtNpXC6Xz7plWbXGvpeZmanHH3+81nhcXNwF6c3p3FkP+rsF58p0+7sDx3L/hnNzRm7OzZk88id/d+BcT/6Ffzdn9OTZNx87dkzus/x3R2j6jzZt2iggIKDWrFJZWVmt2afvzZo1Sw8//LC9XlNTo2+++UaRkZFnDFqN5ejRo4qLi1NJSYlatmzp116chnNzZpybM+PcnBnnpm6clzNz2rmxLEvHjh1TbGzsWesITf8RHBysbt26qaCgQLfffrs9XlBQoFtvvbXO94SEhCgkJMRnrFWrVheyzXPWsmVLR/yDdCLOzZlxbs6Mc3NmnJu6cV7OzEnn5mwzTN8jNP3Aww8/rNTUVHXv3l1JSUlavHix9u/frwcf5LITAACXOkLTD9x55536+uuv9cQTT+jQoUNKTEzUG2+8ofbt2/u7NQAA4GeEptNMmjRJkyZN8ncb5y0kJESPPfZYrcuH4NycDefmzDg3Z8a5qRvn5cya6rlxWT/2fB0AAAD4cksAAAAThCYAAAADhCYAAAADhCYAAAADhKaLzD/+8Q8NGzZMsbGxcrlcevXVV/3dkmNkZmaqR48eCg8PV1RUlG677Tbt3r3b32353cKFC9WlSxf7S+aSkpL05ptv+rstR8rMzJTL5VJaWpq/W/G79PR0uVwunyUmJsbfbTnGwYMHdc899ygyMlItWrTQddddp6KiIn+35XdXXnllrX83LpdLkydP9ndrRghNF5njx4/r2muvVXZ2tr9bcZzCwkJNnjxZmzdvVkFBgU6dOqXk5GQdP37x/0jl2VxxxRV66qmn9MEHH+iDDz7Qz3/+c916663auXOnv1tzlG3btmnx4sXq0qWLv1txjE6dOunQoUP2sn37dn+35Agej0d9+vRRUFCQ3nzzTX3yySd69tlnHfeLEf6wbds2n38zBQUFkqQ77rjDz52Z4XuaLjI333yzbr75Zn+34Uj5+fk+68uWLVNUVJSKiop04403+qkr/xs2bJjP+pw5c7Rw4UJt3rxZnTp18lNXzlJeXq7Ro0dryZIlevLJH/nFz0tIYGAgs0t1ePrppxUXF6dly5bZY1deeaX/GnKQyy+/3Gf9qaee0k9/+lP169fPTx2dG2aacMnyer2SpIiICD934hzV1dXKy8vT8ePHlZSU5O92HGPy5MkaOnSoBg4c6O9WHGXPnj2KjY1Vhw4ddNddd+nzzz/3d0uOsHbtWnXv3l133HGHoqKi1LVrVy1ZssTfbTlOVVWVcnNzdf/99/v9R+5NEZpwSbIsSw8//LBuuOEGJSYm+rsdv9u+fbsuu+wyhYSE6MEHH9SaNWt0zTXX+LstR8jLy9OHH36ozMxMf7fiKD179tRLL72kt956S0uWLFFpaal69+6tr7/+2t+t+d3nn3+uhQsXKj4+Xm+99ZYefPBBTZs2TS+99JK/W3OUV199VUeOHNHYsWP93YoxLs/hkjRlyhR9/PHH2rBhg79bcYSEhAQVFxfryJEj+tvf/qYxY8aosLDwkg9OJSUlmj59utatW6fmzZv7ux1H+eFtAJ07d1ZSUpJ++tOfavny5Xr44Yf92Jn/1dTUqHv37srIyJAkde3aVTt37tTChQt17733+rk751i6dKluvvlmxcbG+rsVY8w04ZIzdepUrV27Vu+++66uuOIKf7fjCMHBwbrqqqvUvXt3ZWZm6tprr9Vzzz3n77b8rqioSGVlZerWrZsCAwMVGBiowsJC/fGPf1RgYKCqq6v93aJjhIWFqXPnztqzZ4+/W/G7tm3b1vqDo2PHjtq/f7+fOnKeffv2af369XrggQf83co5YaYJlwzLsjR16lStWbNG7733njp06ODvlhzLsixVVlb6uw2/GzBgQK0nwu677z5dffXV+s1vfqOAgAA/deY8lZWV2rVrl/r27evvVvyuT58+tb7O5LPPPlP79u391JHzfP8gztChQ/3dyjkhNF1kysvL9a9//cte37t3r4qLixUREaF27dr5sTP/mzx5slauXKnXXntN4eHhKi0tlSS53W6Fhob6uTv/+d3vfqebb75ZcXFxOnbsmPLy8vTee+/VetrwUhQeHl7rnrewsDBFRkZe8vfCzZw5U8OGDVO7du1UVlamJ598UkePHtWYMWP83ZrfPfTQQ+rdu7cyMjI0cuRIbd26VYsXL9bixYv93Zoj1NTUaNmyZRozZowCA5tYDLFwUXn33XctSbWWMWPG+Ls1v6vrvEiyli1b5u/W/Or++++32rdvbwUHB1uXX365NWDAAGvdunX+bsux+vXrZ02fPt3fbfjdnXfeabVt29YKCgqyYmNjrREjRlg7d+70d1uO8frrr1uJiYlWSEiIdfXVV1uLFy/2d0uO8dZbb1mSrN27d/u7lXPmsizL8k9cAwAAaDq4ERwAAMAAoQkAAMAAoQkAAMAAoQkAAMAAoQkAAMAAoQkAAMAAoQkAAMAAoQkAAMAAoQnAJW3jxo0KCAjQkCFD/N0KAIfjG8EBXNIeeOABXXbZZXrxxRf1ySefXPK/0QjgzJhpAnDJOn78uP7yl7/oV7/6lVJSUpSTk+Ozfe3atYqPj1doaKhuuukmLV++XC6XS0eOHLFrNm7cqBtvvFGhoaGKi4vTtGnTdPz48cY9EACNgtAE4JL1yiuvKCEhQQkJCbrnnnu0bNkyfT/5/sUXX+iXv/ylbrvtNhUXF2vixImaPXu2z/u3b9+uwYMHa8SIEfr444/1yiuvaMOGDZoyZYo/DgfABcblOQCXrD59+mjkyJGaPn26Tp06pbZt22rVqlUaOHCgfvvb3+rvf/+7tm/fbtf//ve/15w5c+TxeNSqVSvde++9Cg0N1aJFi+yaDRs2qF+/fjp+/LiaN2/uj8MCcIEw0wTgkrR7925t3bpVd911lyQpMDBQd955p/785z/b23v06OHznp/97Gc+60VFRcrJydFll11mL4MHD1ZNTY327t3bOAcCoNEE+rsBAPCHpUuX6tSpU/qv//ove8yyLAUFBcnj8ciyLLlcLp/3nD4xX1NTo4kTJ2ratGm1Pp8byoGLD6EJwCXn1KlTeumll/Tss88qOTnZZ9svfvELrVixQldffbXeeOMNn20ffPCBz/r111+vnTt36qqrrrrgPQPwP+5pAnDJefXVV3XnnXeqrKxMbrfbZ9vs2bP1xhtvaPXq1UpISNBDDz2kcePGqbi4WDNmzNCBAwd05MgRud1uffzxx+rVq5fuu+8+jR8/XmFhYdq1a5cKCgqUlZXlp6MDcKFwTxOAS87SpUs1cODAWoFJ+m6mqbi4WB6PR3/961+1evVqdenSRQsXLrSfngsJCZEkdenSRYWFhdqzZ4/69u2rrl276tFHH1Xbtm0b9XgANA5mmgDA0Jw5c/TCCy+opKTE360A8APuaQKAM3j++efVo0cPRUZG6v3339czzzzDdzABlzBCEwCcwZ49e/Tkk0/qm2++Ubt27TRjxgzNmjXL320B8BMuzwEAABjgRnAAAAADhCYAAAADhCYAAAADhCYAAAADhCYAAAADhCYAAAADhCYAAAADhCYAAAADhCYAAAAD/w/+bvWLHaKfAAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Age', y='Purchase', data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54c9a300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Purchase'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "  sns.barplot(x='Occupation',y='Purchase',hue='Gender',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4f48d48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category_1', ylabel='Purchase'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_1',y='Purchase',hue='Gender',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "55c6a0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category_2', ylabel='Purchase'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_2',y='Purchase',hue='Gender',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1d3ed36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category_3', ylabel='Purchase'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_3',y='Purchase',hue='Gender',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6ae8888e",
   "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>8.0</td>\n",
       "      <td>16.0</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>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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</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>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                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "841a54ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d19a0100",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=df[df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0bcb285b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df[~df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "534eb64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_train.drop('Purchase',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2f8b79e5",
   "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>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>8.0</td>\n",
       "      <td>16.0</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>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>8.0</td>\n",
       "      <td>16.0</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>16.0</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>8.0</td>\n",
       "      <td>16.0</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                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   B  C  \n",
       "0  0  0  \n",
       "1  0  0  \n",
       "2  0  0  \n",
       "3  0  0  \n",
       "4  0  1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4441b76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068, 11)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d6ce67c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df_train['Purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dab9656c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "378c8297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          8370.0\n",
       "1         15200.0\n",
       "2          1422.0\n",
       "3          1057.0\n",
       "4          7969.0\n",
       "           ...   \n",
       "550063      368.0\n",
       "550064      371.0\n",
       "550065      137.0\n",
       "550066      365.0\n",
       "550067      490.0\n",
       "Name: Purchase, Length: 550068, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "87998523",
   "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": 62,
   "id": "71353f98",
   "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": 63,
   "id": "6575cf38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2edce507",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}