stage z - pre-processing Black Friday.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing -1- Black Friday:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize': [10, 10]}, font_scale=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <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",
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       "  </thead>\n",
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       "      <td>1000001</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
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       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>12</td>\n",
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       "      <td>55+</td>\n",
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       "      <td>8</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
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       "      <th>5</th>\n",
       "      <td>1000003</td>\n",
       "      <td>P00193542</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00184942</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>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19215</td>\n",
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       "      <th>7</th>\n",
       "      <td>1000004</td>\n",
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       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
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       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15854</td>\n",
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       "      <th>8</th>\n",
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       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
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       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15686</td>\n",
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       "      <td>1000005</td>\n",
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       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
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      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F   0-17          10             A   \n",
       "1  1000001  P00248942      F   0-17          10             A   \n",
       "2  1000001  P00087842      F   0-17          10             A   \n",
       "3  1000001  P00085442      F   0-17          10             A   \n",
       "4  1000002  P00285442      M    55+          16             C   \n",
       "5  1000003  P00193542      M  26-35          15             A   \n",
       "6  1000004  P00184942      M  46-50           7             B   \n",
       "7  1000004  P00346142      M  46-50           7             B   \n",
       "8  1000004   P0097242      M  46-50           7             B   \n",
       "9  1000005  P00274942      M  26-35          20             A   \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",
       "5                          3               0                   1   \n",
       "6                          2               1                   1   \n",
       "7                          2               1                   1   \n",
       "8                          2               1                   1   \n",
       "9                          1               1                   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  \n",
       "5                 2.0                 NaN     15227  \n",
       "6                 8.0                17.0     19215  \n",
       "7                15.0                 NaN     15854  \n",
       "8                16.0                 NaN     15686  \n",
       "9                 NaN                 NaN      7871  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('black_friday.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     550068 non-null  int64  \n",
      " 1   Product_ID                  550068 non-null  object \n",
      " 2   Gender                      550068 non-null  object \n",
      " 3   Age                         550068 non-null  object \n",
      " 4   Occupation                  550068 non-null  int64  \n",
      " 5   City_Category               550068 non-null  object \n",
      " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
      " 7   Marital_Status              550068 non-null  int64  \n",
      " 8   Product_Category_1          550068 non-null  int64  \n",
      " 9   Product_Category_2          376430 non-null  float64\n",
      " 10  Product_Category_3          166821 non-null  float64\n",
      " 11  Purchase                    550068 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 50.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1- Data Cleaning:\n",
    "no need for data cleaning since the vales are considered to be acceptable and logical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Marital_Status</th>\n",
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       "      <th>count</th>\n",
       "      <td>5.500680e+05</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>376430.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.076707</td>\n",
       "      <td>0.409653</td>\n",
       "      <td>5.404270</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>9263.968713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727592e+03</td>\n",
       "      <td>6.522660</td>\n",
       "      <td>0.491770</td>\n",
       "      <td>3.936211</td>\n",
       "      <td>5.086590</td>\n",
       "      <td>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>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001516e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003077e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>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>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>23961.000000</td>\n",
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      ],
      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  5.500680e+05  550068.000000   550068.000000       550068.000000   \n",
       "mean   1.003029e+06       8.076707        0.409653            5.404270   \n",
       "std    1.727592e+03       6.522660        0.491770            3.936211   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001516e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003077e+06       7.000000        0.000000            5.000000   \n",
       "75%    1.004478e+06      14.000000        1.000000            8.000000   \n",
       "max    1.006040e+06      20.000000        1.000000           20.000000   \n",
       "\n",
       "       Product_Category_2       Purchase  \n",
       "count       376430.000000  550068.000000  \n",
       "mean             9.842329    9263.968713  \n",
       "std              5.086590    5023.065394  \n",
       "min              2.000000      12.000000  \n",
       "25%              5.000000    5823.000000  \n",
       "50%              9.000000    8047.000000  \n",
       "75%             15.000000   12054.000000  \n",
       "max             18.000000   23961.000000  "
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2- Removing Missing Data:\n",
    "## a) counting NaNs and finding if other words were used to present missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:Product_ID -  0 missing data\n",
      "\n",
      "Category:Gender -  0 missing data\n",
      "\n",
      "Category:Age -  0 missing data\n",
      "\n",
      "Category:City_Category -  0 missing data\n",
      "\n",
      "Category:Stay_In_Current_City_Years -  0 missing data\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cat_att=['Product_ID', 'Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years']\n",
    "for cat in cat_att:\n",
    "    notFound=0\n",
    "    for ex in df[cat]:\n",
    "        if ('x'or'xx'or'unknown'or'not found' or 'missing') in ex.lower():\n",
    "            notFound +=1\n",
    "    print(f'Category:{cat} -  {notFound} missing data\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            173638\n",
       "Purchase                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Product_Category_3 should be removed more than half the data is already missing if computed with any mean it will bias towards that value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     550068 non-null  int64  \n",
      " 1   Product_ID                  550068 non-null  object \n",
      " 2   Gender                      550068 non-null  object \n",
      " 3   Age                         550068 non-null  object \n",
      " 4   Occupation                  550068 non-null  int64  \n",
      " 5   City_Category               550068 non-null  object \n",
      " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
      " 7   Marital_Status              550068 non-null  int64  \n",
      " 8   Product_Category_1          550068 non-null  int64  \n",
      " 9   Product_Category_2          376430 non-null  float64\n",
      " 10  Purchase                    550068 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(5)\n",
      "memory usage: 46.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop('Product_Category_3', axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test for outliers to specify strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nada Abbas\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "C:\\Users\\Nada Abbas\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb589179fc8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Product_Category_2'])\n",
    "sns.stripplot(df['Product_Category_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "# mean is not appropriate for float values\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "df['Product_Category_2'] = imputer.fit_transform(df[['Product_Category_2']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                       0\n",
       "Product_ID                    0\n",
       "Gender                        0\n",
       "Age                           0\n",
       "Occupation                    0\n",
       "City_Category                 0\n",
       "Stay_In_Current_City_Years    0\n",
       "Marital_Status                0\n",
       "Product_Category_1            0\n",
       "Product_Category_2            0\n",
       "Purchase                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3- working with the categorical attributes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "most probably the product ID is not a highly effective attribute (Feature Engineering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n",
      "******************************\n",
      "['0-17' '55+' '26-35' '46-50' '51-55' '36-45' '18-25']\n",
      "******************************\n",
      "['A' 'C' 'B']\n",
      "******************************\n",
      "['2' '4+' '3' '1' '0']\n",
      "******************************\n"
     ]
    }
   ],
   "source": [
    "cat_att = ['Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years']\n",
    "for att in cat_att:\n",
    "    print(df[att].unique())\n",
    "    print('*'*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can nottice here that all of the attributes are ordinal so we can simply use label encoding except for thegender attribute which is nominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</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>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</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>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</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>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</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>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</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    1          10              1   \n",
       "1  1000001  P00248942      F    1          10              1   \n",
       "2  1000001  P00087842      F    1          10              1   \n",
       "3  1000001  P00085442      F    1          10              1   \n",
       "4  1000002  P00285442      M    7          16              3   \n",
       "\n",
       "   Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                           3               0                   3   \n",
       "1                           3               0                   1   \n",
       "2                           3               0                  12   \n",
       "3                           3               0                  12   \n",
       "4                           5               0                   8   \n",
       "\n",
       "   Product_Category_2  Purchase  \n",
       "0                 9.0      8370  \n",
       "1                 6.0     15200  \n",
       "2                 9.0      1422  \n",
       "3                14.0      1057  \n",
       "4                 9.0      7969  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_dict = {'0-17':1, '18-25':2, '26-35':3, '36-45':4, '46-50':5, '51-55':6, '55+':7}\n",
    "CityCategory_dict = {'A':1, 'B':2, 'C':3}\n",
    "Stay_In_Current_City_Years_dict = {'0': 1, '1':2, '2':3, '3':4, '4+':5}\n",
    "\n",
    "dicts=[Age_dict, CityCategory_dict, Stay_In_Current_City_Years_dict]\n",
    "original = ['Age', 'City_Category', 'Stay_In_Current_City_Years']\n",
    "\n",
    "for att, att_dict in zip(original, dicts):\n",
    "    df[att] = df[att].map(att_dict)\n",
    "    \n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n",
      "******************************\n",
      "[1 7 3 5 6 4 2]\n",
      "******************************\n",
      "[1 3 2]\n",
      "******************************\n",
      "[3 5 4 2 1]\n",
      "******************************\n"
     ]
    }
   ],
   "source": [
    "cat_att = ['Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years']\n",
    "for att in cat_att:\n",
    "    print(df[att].unique())\n",
    "    print('*'*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>Purchase</th>\n",
       "      <th>Gender_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1422</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7969</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID  Age  Occupation  City_Category  \\\n",
       "0  1000001  P00069042    1          10              1   \n",
       "1  1000001  P00248942    1          10              1   \n",
       "2  1000001  P00087842    1          10              1   \n",
       "3  1000001  P00085442    1          10              1   \n",
       "4  1000002  P00285442    7          16              3   \n",
       "\n",
       "   Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                           3               0                   3   \n",
       "1                           3               0                   1   \n",
       "2                           3               0                  12   \n",
       "3                           3               0                  12   \n",
       "4                           5               0                   8   \n",
       "\n",
       "   Product_Category_2  Purchase  Gender_M  \n",
       "0                 9.0      8370         0  \n",
       "1                 6.0     15200         0  \n",
       "2                 9.0      1422         0  \n",
       "3                14.0      1057         0  \n",
       "4                 9.0      7969         1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['Gender'], drop_first=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 4, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 16, 15,  7, 20,  9,  1, 12, 17,  0,  3,  4, 11,  8, 19,  2, 18,\n",
       "        5, 14, 13,  6], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Occupation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Occupation'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4- Feature Scaling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>Purchase</th>\n",
       "      <th>Gender_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.500680e+05</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>3.496430</td>\n",
       "      <td>8.076707</td>\n",
       "      <td>2.042640</td>\n",
       "      <td>2.858418</td>\n",
       "      <td>0.409653</td>\n",
       "      <td>5.404270</td>\n",
       "      <td>9.576434</td>\n",
       "      <td>9263.968713</td>\n",
       "      <td>0.753105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727592e+03</td>\n",
       "      <td>1.353632</td>\n",
       "      <td>6.522660</td>\n",
       "      <td>0.760211</td>\n",
       "      <td>1.289443</td>\n",
       "      <td>0.491770</td>\n",
       "      <td>3.936211</td>\n",
       "      <td>4.226025</td>\n",
       "      <td>5023.065394</td>\n",
       "      <td>0.431205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001516e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5823.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003077e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004478e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>12054.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23961.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID            Age     Occupation  City_Category  \\\n",
       "count  5.500680e+05  550068.000000  550068.000000  550068.000000   \n",
       "mean   1.003029e+06       3.496430       8.076707       2.042640   \n",
       "std    1.727592e+03       1.353632       6.522660       0.760211   \n",
       "min    1.000001e+06       1.000000       0.000000       1.000000   \n",
       "25%    1.001516e+06       3.000000       2.000000       1.000000   \n",
       "50%    1.003077e+06       3.000000       7.000000       2.000000   \n",
       "75%    1.004478e+06       4.000000      14.000000       3.000000   \n",
       "max    1.006040e+06       7.000000      20.000000       3.000000   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "count               550068.000000   550068.000000       550068.000000   \n",
       "mean                     2.858418        0.409653            5.404270   \n",
       "std                      1.289443        0.491770            3.936211   \n",
       "min                      1.000000        0.000000            1.000000   \n",
       "25%                      2.000000        0.000000            1.000000   \n",
       "50%                      3.000000        0.000000            5.000000   \n",
       "75%                      4.000000        1.000000            8.000000   \n",
       "max                      5.000000        1.000000           20.000000   \n",
       "\n",
       "       Product_Category_2       Purchase       Gender_M  \n",
       "count       550068.000000  550068.000000  550068.000000  \n",
       "mean             9.576434    9263.968713       0.753105  \n",
       "std              4.226025    5023.065394       0.431205  \n",
       "min              2.000000      12.000000       0.000000  \n",
       "25%              8.000000    5823.000000       1.000000  \n",
       "50%              9.000000    8047.000000       1.000000  \n",
       "75%             14.000000   12054.000000       1.000000  \n",
       "max             18.000000   23961.000000       1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the attributes ranges are very near each other so no nead for feature scaling here though I would simply drop the user ID and Product ID since they are not necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5- Split to Train and Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = df.drop('Purchase', axis=1)\n",
    "y = df['Purchase']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: 440054  -   x_test: 110014  -  y_train: 440054  -  y_test:110014\n"
     ]
    }
   ],
   "source": [
    "print(f'x_train: {len(x_train)}  -   x_test: {len(x_test)}  -  y_train: {len(y_train)}  -  y_test:{len(y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This was exactly what we learnt in this session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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