FRESCO RETAIL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a50475e3",
   "metadata": {},
   "source": [
    "# FRESCO RETAIL\n",
    "#### Problem description: \n",
    "- The data analyses Fresco Retail’s customers’ transaction data to predict return decision using\n",
    "  various data points like customer background, payment modes, store types, product nature, etc. \n",
    "#### Objective: \n",
    "- To study the influence of different transaction data points and their impact on a customer’s final decision to return the\n",
    "  product(s) bought in a transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d03af03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the library \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82635675",
   "metadata": {},
   "source": [
    "### Import the dataset and lets look first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f7b6eea",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>...</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>CustID</th>\n",
       "      <th>Income</th>\n",
       "      <th>city_code</th>\n",
       "      <th>Return</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Education_code</th>\n",
       "      <th>Profession_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41453307</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14644</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49441287</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14569</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>426787191</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14356</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>525041466</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12776</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1167308476</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1101</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  Month_code  prod_cat_code  prod_subcat_code  Qty  Rate  \\\n",
       "0        41453307           1              2                 6    3   359   \n",
       "1        49441287           1              3                 3    5  1129   \n",
       "2       426787191           1              6                 1    4  1327   \n",
       "3       525041466           1              5                 4    5  1325   \n",
       "4      1167308476           1              3                 3    3  1237   \n",
       "\n",
       "   Amount      Tax  Delivery_chrgs     Payment_mode  ... Reviews  CustID  \\\n",
       "0    1795  188.475            90.0  Mobile Payments  ...     1.0   14644   \n",
       "1    5645  592.725           250.0      Credit Card  ...     1.0   14569   \n",
       "2    6635  696.675             NaN       Debit Card  ...     1.0   14356   \n",
       "3    6625  695.625             NaN       Debit Card  ...     4.0   12776   \n",
       "4    6185  649.425             NaN       Debit Card  ...     1.0    1101   \n",
       "\n",
       "     Income  city_code  Return        DOB Gender Marital_status  \\\n",
       "0   67501.0          8    True 1985-12-31      M              0   \n",
       "1  102738.0          2    True 1985-12-31      M              1   \n",
       "2  104013.0          5    True 1985-12-31      M              1   \n",
       "3   92580.0          8    True 1985-12-31      M              1   \n",
       "4  101763.0          3    True 1985-12-31      M              0   \n",
       "\n",
       "   Education_code  Profession_Code  \n",
       "0               4                3  \n",
       "1               3                2  \n",
       "2               1                2  \n",
       "3               5                3  \n",
       "4               1                3  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_excel('Fresco_LargeRetailer.xlsx',sheet_name= 'Transaction_Data')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae8fd28",
   "metadata": {},
   "source": [
    "### Approach:\n",
    "- Understand the data variables properly.\n",
    "- Clean the data\n",
    "- Conduct EDA (Exploratory Data Analysis) on the cleaned Data\n",
    "- Uni-variate and Bi-variate Analysis\n",
    "- Feature Engineering\n",
    "- Identify the most important variables (or data parameters) that affect the final decision\n",
    "- Develop and Validate Samples\n",
    "- Model Building\n",
    "- Improving model accuracy\n",
    "- Model Comparison\n",
    "- Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f098fab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['Month_code'].replace({1:\"January\",2:\"February\",3:\"March\",4:\"April\",5:\"May\",6:\"June\",7:\"July\",8:\"August\",9:\"September\",\n",
    "           10:\"October\",11:\"November\",12:\"December\"},inplace=True)\n",
    "\n",
    "df['prod_cat_code'].replace({1:\"Bags\",2:\"Books\",3:\"Clothing\",4:'Electronics',5:'Footwear',6:'Home and kitchen'},inplace=True)\n",
    "\n",
    "df['prod_subcat_code'].replace({1:'Bath',2:'Academic',3:'Women',4:'Mens',5:'Tools',6:'Fiction',7:'DIY',8:'Kitchen',9:'Non-Fiction',\n",
    "                                10:'Kids',11:'Mobiles',12:'Audio and video',13:'Comics',14:'Computers',15:'Furnishing',\n",
    "                                16:'Children',17:'Personal Appliances',18:'Cameras'}, inplace=True)\n",
    "\n",
    "df['Education_code'].replace({1:'Basic',2:'Graduation',3:'Master',4:'PhD',5:'Others'}, inplace=True)\n",
    "\n",
    "df['Profession_Code'].replace({1:'Self Employed',2:'Salaried',3:'Others'},inplace=True)\n",
    "\n",
    "df['city_code'].replace({1:'Mumbai',2:'Bangalore',3:'New Delhi',4:'Gurgaon',5:'Kolkata',6:'Chennai',\n",
    "                         7:'Pune',8:'Hyderabad',9:'Vishakhapatnam',10:'Ahmedabad'}, inplace=True)\n",
    "\n",
    "df['Return'].replace({True:'Yes',False: 'No'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bedd87cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>...</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>CustID</th>\n",
       "      <th>Income</th>\n",
       "      <th>city_code</th>\n",
       "      <th>Return</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Education_code</th>\n",
       "      <th>Profession_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41453307</td>\n",
       "      <td>January</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14644</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49441287</td>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14569</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>426787191</td>\n",
       "      <td>January</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Bath</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14356</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>525041466</td>\n",
       "      <td>January</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12776</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1167308476</td>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1101</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id Month_code     prod_cat_code prod_subcat_code  Qty  Rate  \\\n",
       "0        41453307    January             Books          Fiction    3   359   \n",
       "1        49441287    January          Clothing            Women    5  1129   \n",
       "2       426787191    January  Home and kitchen             Bath    4  1327   \n",
       "3       525041466    January          Footwear             Mens    5  1325   \n",
       "4      1167308476    January          Clothing            Women    3  1237   \n",
       "\n",
       "   Amount      Tax  Delivery_chrgs     Payment_mode  ... Reviews  CustID  \\\n",
       "0    1795  188.475            90.0  Mobile Payments  ...     1.0   14644   \n",
       "1    5645  592.725           250.0      Credit Card  ...     1.0   14569   \n",
       "2    6635  696.675             NaN       Debit Card  ...     1.0   14356   \n",
       "3    6625  695.625             NaN       Debit Card  ...     4.0   12776   \n",
       "4    6185  649.425             NaN       Debit Card  ...     1.0    1101   \n",
       "\n",
       "     Income  city_code Return        DOB Gender Marital_status  \\\n",
       "0   67501.0  Hyderabad    Yes 1985-12-31      M              0   \n",
       "1  102738.0  Bangalore    Yes 1985-12-31      M              1   \n",
       "2  104013.0    Kolkata    Yes 1985-12-31      M              1   \n",
       "3   92580.0  Hyderabad    Yes 1985-12-31      M              1   \n",
       "4  101763.0  New Delhi    Yes 1985-12-31      M              0   \n",
       "\n",
       "   Education_code Profession_Code  \n",
       "0             PhD          Others  \n",
       "1          Master        Salaried  \n",
       "2           Basic        Salaried  \n",
       "3          Others          Others  \n",
       "4           Basic          Others  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d244f045",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>...</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>CustID</th>\n",
       "      <th>Income</th>\n",
       "      <th>city_code</th>\n",
       "      <th>Return</th>\n",
       "      <th>DOB</th>\n",
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       "      <th>Marital_status</th>\n",
       "      <th>Education_code</th>\n",
       "      <th>Profession_Code</th>\n",
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       "      <th>0</th>\n",
       "      <td>41453307</td>\n",
       "      <td>January</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14644</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49441287</td>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14569</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>426787191</td>\n",
       "      <td>January</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Bath</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14356</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>525041466</td>\n",
       "      <td>January</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12776</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1167308476</td>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1101</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id Month_code     prod_cat_code prod_subcat_code  Qty  Rate  \\\n",
       "0        41453307    January             Books          Fiction    3   359   \n",
       "1        49441287    January          Clothing            Women    5  1129   \n",
       "2       426787191    January  Home and kitchen             Bath    4  1327   \n",
       "3       525041466    January          Footwear             Mens    5  1325   \n",
       "4      1167308476    January          Clothing            Women    3  1237   \n",
       "\n",
       "   Amount      Tax  Delivery_chrgs     Payment_mode  ... Reviews  CustID  \\\n",
       "0    1795  188.475            90.0  Mobile Payments  ...     1.0   14644   \n",
       "1    5645  592.725           250.0      Credit Card  ...     1.0   14569   \n",
       "2    6635  696.675             0.0       Debit Card  ...     1.0   14356   \n",
       "3    6625  695.625             0.0       Debit Card  ...     4.0   12776   \n",
       "4    6185  649.425             0.0       Debit Card  ...     1.0    1101   \n",
       "\n",
       "     Income  city_code Return        DOB Gender Marital_status  \\\n",
       "0   67501.0  Hyderabad    Yes 1985-12-31      M              0   \n",
       "1  102738.0  Bangalore    Yes 1985-12-31      M              1   \n",
       "2  104013.0    Kolkata    Yes 1985-12-31      M              1   \n",
       "3   92580.0  Hyderabad    Yes 1985-12-31      M              1   \n",
       "4  101763.0  New Delhi    Yes 1985-12-31      M              0   \n",
       "\n",
       "   Education_code Profession_Code  \n",
       "0             PhD          Others  \n",
       "1          Master        Salaried  \n",
       "2           Basic        Salaried  \n",
       "3          Others          Others  \n",
       "4           Basic          Others  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filling NaN with zero, it means there is no charge of delivery\n",
    "df['Delivery_chrgs']= df['Delivery_chrgs'].fillna(0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d352322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>CustID</th>\n",
       "      <th>Income</th>\n",
       "      <th>Marital_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.959700e+04</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19597.00000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19546.000000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19554.000000</td>\n",
       "      <td>19597.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.304610e+10</td>\n",
       "      <td>3.098178</td>\n",
       "      <td>777.154718</td>\n",
       "      <td>2302.11563</td>\n",
       "      <td>241.722141</td>\n",
       "      <td>65.102312</td>\n",
       "      <td>3.882329</td>\n",
       "      <td>9799.000000</td>\n",
       "      <td>70539.063977</td>\n",
       "      <td>0.52901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.724848e+10</td>\n",
       "      <td>1.445638</td>\n",
       "      <td>414.308709</td>\n",
       "      <td>1742.62635</td>\n",
       "      <td>182.975767</td>\n",
       "      <td>81.027200</td>\n",
       "      <td>1.414998</td>\n",
       "      <td>5657.310948</td>\n",
       "      <td>37428.491760</td>\n",
       "      <td>0.49917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.145331e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>7.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7157.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.943273e+10</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>915.00000</td>\n",
       "      <td>96.075000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "      <td>37962.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.332340e+10</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>1826.00000</td>\n",
       "      <td>191.730000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9799.000000</td>\n",
       "      <td>69316.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.684265e+10</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1136.000000</td>\n",
       "      <td>3348.00000</td>\n",
       "      <td>351.540000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14698.000000</td>\n",
       "      <td>99548.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.998755e+10</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>7500.00000</td>\n",
       "      <td>787.500000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>159984.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id           Qty          Rate       Amount           Tax  \\\n",
       "count    1.959700e+04  19597.000000  19597.000000  19597.00000  19597.000000   \n",
       "mean     5.304610e+10      3.098178    777.154718   2302.11563    241.722141   \n",
       "std      2.724848e+10      1.445638    414.308709   1742.62635    182.975767   \n",
       "min      4.145331e+07      1.000000     70.000000     70.00000      7.350000   \n",
       "25%      2.943273e+10      2.000000    418.000000    915.00000     96.075000   \n",
       "50%      5.332340e+10      3.000000    775.000000   1826.00000    191.730000   \n",
       "75%      7.684265e+10      4.000000   1136.000000   3348.00000    351.540000   \n",
       "max      9.998755e+10      5.000000   1500.000000   7500.00000    787.500000   \n",
       "\n",
       "       Delivery_chrgs       Reviews        CustID         Income  \\\n",
       "count    19597.000000  19546.000000  19597.000000   19554.000000   \n",
       "mean        65.102312      3.882329   9799.000000   70539.063977   \n",
       "std         81.027200      1.414998   5657.310948   37428.491760   \n",
       "min          0.000000      1.000000      1.000000    7157.000000   \n",
       "25%          0.000000      3.000000   4900.000000   37962.000000   \n",
       "50%         50.000000      4.000000   9799.000000   69316.000000   \n",
       "75%         90.000000      5.000000  14698.000000   99548.000000   \n",
       "max        250.000000      5.000000  19597.000000  159984.000000   \n",
       "\n",
       "       Marital_status  \n",
       "count     19597.00000  \n",
       "mean          0.52901  \n",
       "std           0.49917  \n",
       "min           0.00000  \n",
       "25%           0.00000  \n",
       "50%           1.00000  \n",
       "75%           1.00000  \n",
       "max           1.00000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check for summary of data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7e8340",
   "metadata": {},
   "source": [
    "**By looking at summary of table we conclude that**\n",
    "- The rate of the product ranging from Rs 70 to Rs 1500 which quite okay, There is not such expensive items  \n",
    "- The Maximum delivery charges is Rs 250 and average charge is Rs 65\n",
    "- The average prodcuts review is 3.8 which is quite good\n",
    "- We could say that most of customers  quantity of bought product is on average 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fb998c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19597 entries, 0 to 19596\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   transaction_id    19597 non-null  int64         \n",
      " 1   Month_code        19597 non-null  object        \n",
      " 2   prod_cat_code     19597 non-null  object        \n",
      " 3   prod_subcat_code  19597 non-null  object        \n",
      " 4   Qty               19597 non-null  int64         \n",
      " 5   Rate              19597 non-null  int64         \n",
      " 6   Amount            19597 non-null  int64         \n",
      " 7   Tax               19597 non-null  float64       \n",
      " 8   Delivery_chrgs    19597 non-null  float64       \n",
      " 9   Payment_mode      19597 non-null  object        \n",
      " 10  Store_type        19597 non-null  object        \n",
      " 11  Reviews           19546 non-null  float64       \n",
      " 12  CustID            19597 non-null  int64         \n",
      " 13  Income            19554 non-null  float64       \n",
      " 14  city_code         19597 non-null  object        \n",
      " 15  Return            19597 non-null  object        \n",
      " 16  DOB               19597 non-null  datetime64[ns]\n",
      " 17  Gender            19597 non-null  object        \n",
      " 18  Marital_status    19597 non-null  int64         \n",
      " 19  Education_code    19597 non-null  object        \n",
      " 20  Profession_Code   19597 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(4), int64(6), object(10)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Lets look at the datatype \n",
    "df. info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830869e8",
   "metadata": {},
   "source": [
    "#### Dropping the useless columns\n",
    "- The transaction id, customer id, and DOB would not help us to build the model so, we will drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "418ce12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['transaction_id','CustID','DOB'], inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4efb511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Income</th>\n",
       "      <th>city_code</th>\n",
       "      <th>Return</th>\n",
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       "      <th>Marital_status</th>\n",
       "      <th>Education_code</th>\n",
       "      <th>Profession_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>January</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Bath</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month_code     prod_cat_code prod_subcat_code  Qty  Rate  Amount      Tax  \\\n",
       "0    January             Books          Fiction    3   359    1795  188.475   \n",
       "1    January          Clothing            Women    5  1129    5645  592.725   \n",
       "2    January  Home and kitchen             Bath    4  1327    6635  696.675   \n",
       "3    January          Footwear             Mens    5  1325    6625  695.625   \n",
       "4    January          Clothing            Women    3  1237    6185  649.425   \n",
       "\n",
       "   Delivery_chrgs     Payment_mode Store_type  Reviews    Income  city_code  \\\n",
       "0            90.0  Mobile Payments     e-Shop      1.0   67501.0  Hyderabad   \n",
       "1           250.0      Credit Card   TeleShop      1.0  102738.0  Bangalore   \n",
       "2             0.0       Debit Card        MBR      1.0  104013.0    Kolkata   \n",
       "3             0.0       Debit Card        MBR      4.0   92580.0  Hyderabad   \n",
       "4             0.0       Debit Card        MBR      1.0  101763.0  New Delhi   \n",
       "\n",
       "  Return Gender  Marital_status Education_code Profession_Code  \n",
       "0    Yes      M               0            PhD          Others  \n",
       "1    Yes      M               1         Master        Salaried  \n",
       "2    Yes      M               1          Basic        Salaried  \n",
       "3    Yes      M               1         Others          Others  \n",
       "4    Yes      M               0          Basic          Others  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f72da4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month_code           0\n",
       "prod_cat_code        0\n",
       "prod_subcat_code     0\n",
       "Qty                  0\n",
       "Rate                 0\n",
       "Amount               0\n",
       "Tax                  0\n",
       "Delivery_chrgs       0\n",
       "Payment_mode         0\n",
       "Store_type           0\n",
       "Reviews             51\n",
       "Income              43\n",
       "city_code            0\n",
       "Return               0\n",
       "Gender               0\n",
       "Marital_status       0\n",
       "Education_code       0\n",
       "Profession_Code      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check for missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b07f060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19597, 18)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the shape \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb25d683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since our data are around 20,000 and we can drop NaN values, it wont affect the data, which less than 0.01%\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41651fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19504, 18)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ab384a",
   "metadata": {},
   "source": [
    "#### EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5d79fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Return', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Return'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f5badd",
   "metadata": {},
   "source": [
    "**So its seems like Most of the products would'nt returend, which is quite good for the FRESCO Retail. But Here data is imbalace we have to balanced it beofre going to build the model, otherwise our model will be baised toward maxmimum data points.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87a2f46c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month_code', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.countplot(df['Month_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1e8458f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='prod_cat_code', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.countplot(df['prod_cat_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a09f00",
   "metadata": {},
   "source": [
    "**By looking at graph we could say that maximum returns are from these two category footwear and clothing, or we could customers more likely to buy these product footwear, clothing and kitchen & home** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "02304cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='prod_subcat_code', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(df['prod_subcat_code'],orient= 'horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b8ab754",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Qty', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(df['Qty'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac6ba18e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rate'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Rate'],kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1fb9674e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Tax'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Tax'],kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c599f45c",
   "metadata": {},
   "source": [
    "**Maximum Tax of the product is between 0 to 300**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d435aa09",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Payment_mode', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Payment_mode'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7363983f",
   "metadata": {},
   "source": [
    "**The maximum transaction is done trhough mobile payments and credict and debi card**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "685da578",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Store_type', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Store_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65558db",
   "metadata": {},
   "source": [
    "**The Maximum no.of product was bought online**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad52af19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reviews', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Reviews'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68477055",
   "metadata": {},
   "source": [
    "**Customers are higly statisfied with the products**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91a3895a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='city_code', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XVTcCC4AdkmwIrF9V36uqAo4fOEeSJEmSNEErGgJXWWro5iNZ8Y3mBz2mqm4DaL8+um3fCLhl4LiFbdtG7f2l28eV5KAk85PMX7Ro0STKkyRJkqTRtqJB7sPAhUm+BBTN/MAjprCO8eb51XLax1VVRwNHA8ybN2+Zx0mSJElSX61QCKyq45PMB3ahCWZ7VtU1k/j3bk+yYVXd1g71vKNtXwhsMnDcxsCtbfvG47RLkiRJkiZhRYeDUlXXVNXHq+rISQZAaDacP6C9fwDw1YH2fZKsmWQzmgVgLm6HjN6TZMd2VdD9B86RJEmSJE3QZOb1rZAkJwHPBTZIshB4L/CvwClJDgR+AuwNUFVXJzkFuAZYDLy5qpa0L/UmmpVG1wbOam+SJEmSpEmYthBYVfsu46nnL+P4IxhnnmFVzQe2msLSJEmSJKm3Vng4qCRJkiRp+BkCJUmSJKlHDIGSJEmS1COGQEmSJEnqEUOgJEmSJPWIIVCSJEmSesQQKEmSJEk9YgiUJEmSpB4xBEqSJElSjxgCJUmSJKlHDIGSJEmS1COGQEmSJEnqEUOgJEmSJPWIIVCSJEmSesQQKEmSJEk9YgiUJEmSpB4xBEqSJElSjxgCJUmSJKlHDIGSJEmS1COGQEmSJEnqEUOgJEmSJPWIIVCSJEmSesQQKEmSJEk9YgiUJEmSpB4xBEqSJElSjxgCJUmSJKlHDIGSJEmS1COGQEmSJEnqkRkPgUmenOTygdvdSd6e5LAkPx1o/8uBcw5NsiDJdUl2nemaJUmSJGlUrDbT/2BVXQdsC5BkVeCnwGnA64B/r6oPDR6fZAtgH2BL4LHAfyd5UlUtmcm6JUmSJGkUdD0c9PnA9VV183KO2R04uaruq6obgQXADjNSnSRJkiSNmK5D4D7ASQOP35LkiiTHJHlE27YRcMvAMQvbNkmSJEnSBHUWApOsAbwM+GLbdBTwBJqhorcBHx47dJzTaxmveVCS+UnmL1q0aGoLliRJkqQR0GVP4IuBS6vqdoCqur2qllTV/cBneGDI50Jgk4HzNgZuHe8Fq+roqppXVfPmzJkzjaVLkiRJ0nDqMgTuy8BQ0CQbDjz3cuCq9v7pwD5J1kyyGbA5cPGMVSlJkiRJI2TGVwcFSPInwAuBgweaP5BkW5qhnjeNPVdVVyc5BbgGWAy82ZVBJUmSJGlyOgmBVfVb4FFLtb1mOccfARwx3XVJkiRJ0qjrenVQSZIkSdIMMgRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6pFOQmCSm5JcmeTyJPPbtkcmOTvJj9uvjxg4/tAkC5Jcl2TXLmqWJEmSpFHQZU/g86pq26qa1z5+J3BOVW0OnNM+JskWwD7AlsCLgE8mWbWLgiVJkiRp2M2m4aC7A8e1948D9hhoP7mq7quqG4EFwA4zX54kSZIkDb+uQmAB30xySZKD2rbHVNVtAO3XR7ftGwG3DJy7sG2TJEmSJE3Qah39uztV1a1JHg2cneRHyzk247TVuAc2gfIggE033XTlq5QkSZKkEdNJT2BV3dp+vQM4jWZ45+1JNgRov97RHr4Q2GTg9I2BW5fxukdX1byqmjdnzpzpKl+SJEmShtaMh8Ak6yRZb+w+8BfAVcDpwAHtYQcAX23vnw7sk2TNJJsBmwMXz2zVkiRJkjQauhgO+hjgtCRj//7nq+rrSX4AnJLkQOAnwN4AVXV1klOAa4DFwJurakkHdUuSJEnS0JvxEFhVNwDbjNP+C+D5yzjnCOCIaS5NkiRJkkbebNoiQpIkSZI0zQyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRJkqQeMQRKkiRJUo8YAiVJkiSpRwyBkiRJktQjhkBJkiRJ6hFDoCRJkiT1iCFQkiRJknpkxkNgkk2SnJvk2iRXJzmkbT8syU+TXN7e/nLgnEOTLEhyXZJdZ7pmSZIkSRoVq3Xwby4G3lFVlyZZD7gkydntc/9eVR8aPDjJFsA+wJbAY4H/TvKkqloyo1VLkiRJ0giY8Z7Aqrqtqi5t798DXAtstJxTdgdOrqr7qupGYAGww/RXKkmSJEmjp9M5gUnmAtsBF7VNb0lyRZJjkjyibdsIuGXgtIUsIzQmOSjJ/CTzFy1aNF1lS5IkSdLQ6iwEJlkXOBV4e1XdDRwFPAHYFrgN+PDYoeOcXuO9ZlUdXVXzqmrenDlzpr5oSZIkSRpynYTAJKvTBMATq+rLAFV1e1Utqar7gc/wwJDPhcAmA6dvDNw6k/VKkiRJ0qjoYnXQAJ8Frq2qjwy0bzhw2MuBq9r7pwP7JFkzyWbA5sDFM1WvJEmSJI2SLlYH3Ql4DXBlksvbtncB+ybZlmao503AwQBVdXWSU4BraFYWfbMrg0qSJEnS5Mx4CKyqCxh/nt+ZyznnCOCIaStKkiRJknqi09VBJUmSJEkzyxAoSZIkST1iCJQkSZKkHjEESpIkSVKPGAIlSZIkqUcMgZIkSZLUI4ZASZIkSeoRQ6AkSZIk9YghUJIkSZJ6xBAoSZIkST1iCJQkSZKkHjEESpIkSVKPGAIlSZIkqUcMgZIkSZLUI4ZASZIkSeoRQ6AkSZIk9YghUJIkSZJ6xBAoSZIkST1iCJQkSZKkHjEESpIkSVKPGAIlSZIkqUcMgZIkSZLUI4ZASZIkSeoRQ6AkSZIk9YghUJIkSZJ6xBAoSZIkST1iCJQkSZKkHjEESpIkSVKPDE0ITPKiJNclWZDknV3XI0mSJEnDaChCYJJVgU8ALwa2APZNskW3VUmSJEnS8BmKEAjsACyoqhuq6vfAycDuHdckSZIkSUNnWELgRsAtA48Xtm2SJEmSpAlIVXVdw0NKsjewa1W9oX38GmCHqnrrUscdBBzUPnwycN2MFjo5GwA/77qIEeF7ObV8P6eW7+fU8b2cWr6fU8v3c+r4Xk4t38+pNSzv5+Oqas7Sjat1UckkLAQ2GXi8MXDr0gdV1dHA0TNV1FRIMr+q5nVdxyjwvZxavp9Ty/dz6vheTi3fz6nl+zl1fC+nlu/n1Br293NYhoP+ANg8yWZJ1gD2AU7vuCZJkiRJGjpD0RNYVYuTvAX4BrAqcExVXd1xWZIkSZI0dIYiBAJU1ZnAmV3XMQ2GavjqLOd7ObV8P6eW7+fU8b2cWr6fU8v3c+r4Xk4t38+pNdTv51AsDCNJkiRJmhrDMidQkiRJkjQFDIGSJEmS1COGQEmSJEnqkaFZGEZaliRrA5tW1XVd1yIluaCqdk5yDzA46TpAVdX6HZU2lJI8pap+lOTp4z1fVZfOdE2jwt+dmq2SrAo8hoHPqVX1k+4qGm5J1ufB7+WdHZajWcKFYWbQOB8KH8QPhxOXZDfgQ8AaVbVZkm2Bw6vqZd1WNpySzAH+CdgCWGusvap26awo9VqSo6vqoCTnjvN0+b05Of7uXHlJ9lze81X15ZmqZZQkeSvwXuB24P62uapq6+6qGk5JDgYOB+7lgc+fVVWP766q4TSKF3gNgR1IcjjwM+AEmm+e/YD1quoDnRY2hJJcAuwCfLuqtmvbrvCPxeQk+SbwBeDvgTcCBwCLquqfOi1sSHk1W7OVvztXXpJj27uPBp4JfKt9/Dya93W5IVHjS7IA+POq+kXXtQy7JD8GnlFVP++6Fs0+Dgftxq5V9ecDj49KchFgCJy4xVV1V5Ku6xgVj6qqzyY5pKrOA85Lcl7XRQ2jZV3NBvyQPUlJtuKPe6mP766ioebvzpVUVa8DSPJfwBZVdVv7eEPgE13WNuRuAe7quogRcT3w266LGEVJHs2D/xYN3QVeQ2A3liTZDziZ5kPhvsCSbksaWlcl+Stg1SSbA28DLuy4pmH2v+3X25K8BLgV2LjDeobZIcCTvZo9NZK8F3guTQg8E3gxcAFgCJwcf3dOnbljAbB1O/CkrooZATcA307yNeC+scaq+kh3JQ2tQ4EL246Gwffybd2VNNySvAz4MPBY4A7gccC1wJZd1jUZrg7ajb8CXknzh+J2YO+2TRP3VpofvPuAz9NcPXx7lwUNuf+b5GHAO2iGhP4Hvp+T5dXsqbUX8HzgZ20PzDbAmt2WNNQGf3eeBNyNP+uT9e0k30jy2iQHAF8DxpvDqhXzE+BsYA1gvYGbJu7TNMOUvw9cMnDT5L0f2BH4n6rajObv0ne7LWlynBOoodXOt/pGVb2g61pGRZKdquq7D9WmZUvyd+3dLYEn03wg9Gr2SkpycVXt0M5lex5wD3BVVQ3d1VeNnnaRmGe1D8+vqtO6rGcUJFmPZsGNX3ddy7BKcmFVPbPrOkZJkvlVNS/JD4Htqur+sb9PXdc2UQ4H7UCStYADaT4kDo4nfn1nRQ2hqlqS5LdJHlZV9rhMjSOBpZfiH69NyzZ2xfon7W2N9qaVMz/Jw4HP0FzJ/jVwcacVDbEkT6Lp7Z/LgxcucrXVSWhXAnU10CnQzv09AXhk+/jnwP5VdXWnhQ2nc5McBJzBgy9GukXE5P0qybrAd4ATk9wBLO64pkmxJ7ADSb4I/IhmCOjhNKuDXltVh3Ra2BBKcgpNt/zZwG/G2h3vPjFJnkGzut3bgX8feGp94OVVtU0XdUnjSTIXWL+qrui6lmHVXsX+FE2g/sOc9KpyqNgEJdmR5mLZU2ku+KwK/GYYl4yfDZJcCLy7qs5tHz8X+Bd7tCYuyY3jNLtFxEpIsg7wOx5Y3f9hwInDOP/fnsBuPLGq9k6ye1Udl+TzwDe6LmpIfa29aeWsAaxL8zthcO7F3TRzsbSCkpzB8vcDdR+2SUqyEc0k/NXax8+uqvO7rWpoLa6qo7ouYkR8HNgH+CIwD9gfeGKnFQ23dcYCIEBVfbv94K0JauesaQpV1W+S/CmwA3AnzbSkoQuAYAjsytgKjL9qhz38jGZIjiaoDdFr8MBKbNdV1f8u7xz9sYHtID5XVTd3Xc+Q+1DXBYyiJP8GvAq4hgd6rgowBE7OGUn+BjgNh4mttKpakGTVqloCHNv2Zmlybkjyf2iGhAK8GhivR0srwK11plaSNwDvoVlwJ8CRSQ6vqmO6rWziHA7agfYb6FSa/cKOpemB+T9V9elOCxtC7TCR44CbaH4YNwEOsHdgcpLMAf6RP56v6jyhSUiyNrBpVV3XdS3DLsl1wNZVdd9DHqyH5DCxqZPkfOAFNKsp/wy4DXitw+gnJ8kjgPcBO9P8XT8fOKyqftlpYUNoWVvrVJUjfCap/Vv0zLHevySPAi6sqid3W9nE2RPYgar6j/bueYB/cFfOh4G/GPuQ3S52cBKwfadVDa8TgS8ALwXeCBwALOq0oiGVZDeaXsE1gM2SbAsc7nDQSbsBWJ2BXitNnsPEptRraLbcegvwtzQXI1/RaUVDrA17zuufGnvRbKdzWVW9LsljaC5WaPIW0qxOPeYemi2hho4hsAPtVYPDgJ1ohjN9B3j/sI4p7tjqg70sVfU/SVbvsqAh96iq+mySQwaGiJ7XdVFD6jCaOQPfBqiqy9sFTTQ5vwUuT3IObnq80pLsP167w8QmrqpubqclzKVZIfS6qvp9t1UNL0ekTKl72y0MFidZn2ZzczsfJmFg+6efAhcl+SrNZ/jdGdKVqg2B3TiZZnjD2JXC/Wh6X9zvbuLmJ/ksD8wd2A83Ql0ZY/Mpb0vyEuBWYOMO6xlmi6vqriRd1zEqTm9vmhp/NnB/LZoNjy8FDIET1P6u/BRwPc3wxc2SHFxVZ3Vb2dByRMrUcWudqTO2aN717W3MVzuoZUo4J7ADSS6pqu2XaptfVfO6qmlYJVkTeDMPnjvwSecNTU6Sl9L0TG9Cs+T5+jRzMc7otLAh1F6cOAd4J80Fn7fR9Fy/sdPCpHEkeRhwgsOVJy7Jj4CXVtWC9vETgK9V1VO6rWw4jX1GSnJFVW3dtp1XVc/purZh5tY6Wpo9gd04N8k+wCnt471wm4NJacPeR9qbVt5VVXUXcBfwPIAkf7b8U7QMbwXeTTN08SSabWDe32lFQyzJTjRDbMe2iAguZDKVfgts3nURQ+qOsQDYuoFm2J0mxxEpU8itdaZWknk0f9v/8J4CjF2wGCb2BM6gJPfQjB8OsA5wf/vUKsCv3Vh2xSW5kuXvxTZ0P4yzQZJLgJdV1U/bx88GPlFVT+u2MvVd29vyt/zx5ubOpZ6EpfazXIVm9cBTquqd3VU1XJLs2d59Ic0HwlNo3tO9aeYFvqOr2obZMkakvK+qHA4+QcvaWsce/8lrVwf9B+BKHvgczzBur2UI1FBK8rjlPT+MP4yzQdvr90lgN+DpwL8Au1XVUK581ZUkBwCHAGNLRl8LfMxFNyYvyUVV9edd1zEqkgwOrVsM3FxVC7uqZxglOXY5T1dVvX7GipHG4dY6Uy/JBVW1c9d1TAVDYEfafXA258ErX9k9r84leQbwaeB3wEuqygn5E9Cuuvi3wN/RLLQRmkD9QeCjBsHJSfKvwKo0qy8Org56aWdFDaEka9EstvFEmivZn62qxd1WJT0gycfGab4LmF9VQ7sIRxeSnAXsXVW/7rqWUZHk+cC+NHP+B/8WfbmzoibJENiBdrP4Q2jGuF8O7Ah8z+WPJy7JjjTDRZ5Ksx/bqsBvHFo7MUsNDYNmaNhtwC8BHDqy4pJ8H9inqm5aqn0ucHJV7dhFXcMuybnjNJe/NycmyRdo5lx9h2bj6Jur6pBuqxpubbA+kD/e0sCewElIcjTwFOCLbdMrgKtphofeUFVv76i0oZPkVJp9At1aZ4ok+U+a78+reWA46FD2/LswTDcOoVme+/tV9bwkTwHe13FNw+rjwD40fyzmAfvTXOHWxHyo6wJGyPpLB0CAqrqp3adJk1BVz+u6hhGxxdgc33YFW5eLX3knAD8CdgUOp9mq6NpOKxpuTwR2GeuhTnIU8E2auZdXdlnYEBpvax17f1bONqOyToIhsBu/q6rfJSHJmlX1oyRPfujTNJ6qWpBk1apaAhyb5MKuaxo27cbwmhr3TvI5LUeSx9DMUX1sVb04yRbAM6rqsx2XNmzGVl6kqha7j+WUeGJV7Z1k96o6LsnnaVYD1uRsRLN43l3t43Vofu6XJHFu28Q8vKo+OtiQxJ7/lfP9JFtU1TVdF7KyDIHdWNhu3vkV4Owkv6RZAlkT99skawCXJ/kAzRDGdTquaegMrFz7hyYeWMm2HF47IU9NMt4+TAHczmDyPgccS7M0N8D/0GwobQicmG2S3N3eD7B2+9if9ckbC9a/SrIV8DNgbnflDL0P0PxN/zbN9+WzgX9Jsg7w310WNoQOAD66VNtrx2nTitsZOCDJjTRDbMd+dw7dqvTOCexYu0Lbw4CvV9Xvu65n2LSrhN4BrE6zGMfDaDaLX7DcE6Vp4sq10yPJD6rqz5JcVlXbtW2XV9W2HZemnmvn+Z8KbE1zoWJd4D1V9alOCxtCSVahWSfhRmAHmg/YF1eVF8onIMm+wF/RBJbvDDy1HrCkql7QSWEjYFl/44fxb7shcIa1v+CuqKqtuq5FGk+SbYBntQ/Pr6rxerWkGdX2CrwCOLuqnt4uCvVvVfWc5Z8paZgk+V5VPaPrOoZZG1Q2A/4fMLj35z00n0FdEXiSkmw6XntV/WSma1lZDgedYVV1f5IfJtl0GL9hZptlbBp/FzAf+L9uJD0x7VyBv6ZZhh/gxCRHV9WRHZYlQbPlxunAE5J8F5gD7NVtSeqzJH+3vOer6iMzVcuI+WaSVwBfLnsqJqXtlboZMExPva/xwHSZtWjC9nU0qwMPFXsCO5DkWzSrg14M/Gas3WX4J66dB7gE+HzbtA/ND+ZdwM5VtVtXtQ2jdi7bM6rqN+3jdWi2Lxm6se4aPUlWA55M8zN+XVX970OcIk2bJO9t7z6Z5m/62CqMu9GMonhDJ4UNuXaO+jrAYpr9ap2vOkluozX9kjwdOLiqDu66lomyJ7AbbgcxdXaqqp0GHl+Z5LtVtVOSV3dW1fAKTages6Rt0wQl2YVmG5jfdl3LCNmBZsGN1YCnJ6Gqju+2JPVVVb0PIMk3gadX1T3t48N4YI87TVBVrdd1DSPEbbSmWVVdmuTPuq5jMgyBHaiq89rx2ptX1X8n+ROaqzOauHWT/HlVXQSQZAeaSfnQXEXUxBwLXJTktPbxHrj64mS9FvhUkl/QTMz/DnBBVf2y06qGVJITgCcAl/PAhYoCDIHq2qbA4MJuv8fVQSctybPHa6+q82e6llHgNlpTa6lh4KsATwcWdVTOSjEEdiDJXwMHAY+k+VCzEfAp4Pld1jWk3gAck2Rdmh6ru4E3tMMY/1+nlQ2RJBtX1cKq+ki7AMfONO/n64CNOy1uSFXV/gBJHkszd+0TwGPx9+5kzaPZ6Nw5DJptTgAubi+eFfByvDixMv5h4P5aNCMALgF26aacoeY2WlNvsKd6Mc0cwVM7qmWlOCewA0kup/mldtHAUudXVtXTOi1siCV5GM3386+6rmUYJbkO2LWqblqq/fXAu6vqCZ0UNsTa4cjPAp4G/By4APhOVX2v08KGVJIvAm+rqtu6rkVaWjsvaHBV5cu6rGeUJNkE+EBV7dt1LcOmHXV2O818QLfR0oN4Rbob91XV75NmqlW72IFpfBKSrEmzbPxcYLWx97SqDu+wrGH0t8DZSf6yqn4MkOSdwH6AS/BPzv8HXE/Ty3/u0gFbKybJGTS/H9cDrklyMc0GvYALamnW+BPg7qo6NsmcJJtV1Y1dFzUiFgJuqzUJVXVz2xM4l2bV7+vck3pykpy+vOeH8W+RIbAb5yV5F7B2khcCfwOc0XFNw+qrNCuBXsLAB0NNTFWdmeQ+4Kwke9AMs/0z4NnOYZucqtogyZbAs4EjkmxO8wf4NR2XNmxOBx7Dgzc8hubixE9nvhzpwdpVQufRrBJ6LLA68J/ATss7T+NLciQPXBhfBdgO+GF3FQ2vJC+huRB5Pc0Uj82SHFxVZ3Vb2VB6BnALcBJwESOwaJ7DQTvQbhh/IPAXNN9E3wD+w7kuE5fkqqryCuEUSbIz8BXgQuCVVfW7bisaXknWp/kQ+ByaYWIb0KwWekCnhQ2ZJP8FvKuqrliqfR7wXreBUdfaKR7bAZcOTPG4wq11JifJ2O/IoplzdVNVuZjJJCT5EfDSseGfSZ4AfK2qntJtZcMnyarAC4F9ga1p5gKeVFVXd1rYSrAnsANVdT/wmfamlXNhkqdV1ZVdFzLM2n2ZxjY/XZNmkaI70oyvdX+myblg4PbxqlrYcT3Dau7SARCgquYnmdtBPdLSfl9VlaTgD/uraoKS7A5sXFWfaB9fDMwBKsk/VtWXOi1wON2x1Py/G4A7uipmmLWrq34d+Ho7FWlf4NtJDq+qI7utbnIMgTMoyZUsZ+6fVw0nZWfgtUlupBkOOhZafC8nwH2Zpt7Y92CSdarqN13XM8TWWs5za89YFdKynZLk08DD29W/X48XeSfjH2n2tBuzBrA9zbZPxwKGwIm7OsmZwCk0nz/3Bn6QZE+Aqvpyl8UNmzb8vYQmAM4FPkYz13IoGQJn1kvbr29uv57Qft0PcEPpyXlx1wVI40nyDJo9FtcFNk2yDXBwVf1Nt5UNnR8k+euqetCH6iQH0swFljpVVR9q5/ffTTMv8D1VdXbHZQ2jNarqloHHF1TVncCd9q5O2lo0q4OOLfC2iGZ7st1oQuHQBpiZluQ4mgWKzgLeV1VXdVzSSnNOYAeSfLeqdnqoNq24JI9moMegqn7SYTkSSS6i2R/w9IF5Qs5hnaAkjwFOo9mAeyz0zaPpJXh5Vf2sq9qkQe084D9cXG8DjFZQkgVV9cRlPHe9WxWpS0nuB8ZG9QyGp6GdNmNPYDfWSbJzVV0AkOSZuHnnpCR5GfBhmk247wAeB1wLbNllXRJAVd0ytm1Ja0lXtQyrqrodeGaS5/HAMvFfq6pvdViW9AdJDgYOB+4F7qf9UAg8vsu6htBFy+j1Pxi4uKOahlqStWgWItySB18of31nRQ2pqlql6xqmmiGwGwcCx7QbnBfNFgf+QE7O+4Edgf+uqu3aD4puKKvZ4Jb2Ak+1+zS9jeYChSahqs4Fzu26Dmkcfw9sWVU/77qQIfe3wFeS/BVwadu2Pc1iZXt0VdSQOwH4EbArzYWK/fDvkFoOB+1AklWrakk7dCRVdVfXNQ2rJPOral6SHwLbVdX9SS6uqh26rk39lmQD4KPAC2h6Br4JHFJVv+i0MElTKsnXgT2ryrn9UyDJLjwwmudqe/0nL8ll7QXyK6pq6ySrA9+oql26rk3dsyewGwuSfAk4pqq8IrNyfpVkXeB84MQkd9DsKyR1qu0V2K/rOiRNu0Nptiu6iGaVagCq6m3dlTS82tBn8Jsa/9t+/VWSrYCf0axqKdkT2IUk69Esg/w6YBXgGODkqrq708KGULti2L007+N+wMOAE+1tUVeSvGc5T1dVvX/GipE07dr97C4ArqSZEwhAVR3XWVESkOQNwKk0m5sfS7Na9Xuq6lOdFqZZwRDYsSTPBk4CHk6zB877l9rYUyuoHX73i/KbWh1K8o5xmtehmQv8qKpad4ZLkjSNklxYVc/sug5JmghDYAeSrEqz2eTraLrlTwBOBJ4F/EtVPam76oZDkh2BfwXupFkc5gRgA5oewf2r6usdlicBf+j1P4QmAJ4CfLiq7ui2KklTKckRwM3AGTx4OKhbRKhT7ebmr6D5rDm4fcnhXdWk2cM5gd34Mc0qdx+sqgsH2r/U9gzqoX0ceBfN8M9vAS+uqu8neQpNz6ohUJ1J8kjg72iGKB8HPL2qftltVZKmyV+1Xw8daHOLCM0GX6VZgf4SBi5QSGBPYCeSrFtVv+66jmGW5PKq2ra9f21VPXXgucvGNueWZlqSDwJ7AkcDn/BnXZLUhSRXVdVWD32k+siewBmU5Eiaq4MstYE04EpiE3T/wP17l3rOKxvq0jtorrj+M/DugZ/10CwMs35XhUmaHu2eoHN58JC74zsrSGpcmORpVXVl14Vo9jEEzqz5A/ffB7y3q0JGwDZJ7qb5YL12e5/28VrdlaW+q6pVuq5B0sxJcgLwBOByYEnbXIAhUJ1IciXN9+BqwOuS3EBzcXLsYuTWXdan2cHhoB1xyKIkScMvybXAFq5MrdkiyeOW93xV3TxTtWj28op1d/xjIUnS8LsK+NOui5DGVNXNbdB7wdj9gbY3dV2fZgeHg0qSJE1QkjNoLuiuB1zTbho/tgJjVdXunRUnNfZK8ruqOhEgySeBNTuuSbOEIXAGJbmHB3oA/2SpeWwuGCFJ0vD40DhtAXYG9p3hWqTx7AmcnuR+4MXAnVX1Nx3XpFnCOYGSJEkrIcm2NPsFvhK4EfhyVR3ZaVHqrXav2jHrAV8Bvgu8B6Cq7uygLM0yhkBJkqQJSvIkYB+aXr9fAF8A/r6qlrsohzTdktxIM/IsA1/HVFU9vpPCNKsYAiVJkiaoHWL3HeDAqlrQtt3gB2xJw8A5gZIkSRP3CpqewHOTfB04mQf3uEidS7IVsAUDeyhXlXtYyp5ASZKkyUqyDrAHzbDQXYDjgNOq6ptd1iUleS/wXJoQeCbN4jAXVNVeXdal2cEQKEmSNAXaBTn2Bl5VVbt0XY/6LcmVwDbAZVW1TZLHAP9RVbt1XJpmATeLlyRJmgJVdWdVfdoAqFni3qq6H1icZH3gDsA5qwKcEyhJkiSNovlJHg58BrgE+DVwcacVadZwOKgkSZI0wpLMBdavqiu6rkWzgyFQkiRJGkFJNgIex8Dov6o6v7uKNFs4HFSSJEkaMUn+DXgVcA2wpG0uwBAoewIlSZKkUZPkOmDrqrqv61o0+7g6qCRJkjR6bgBW77oIzU4OB5UkSZJGRJIjaYZ9/ha4PMk5wB96A6vqbV3VptnDEChJkiSNjvnt10uA07ssRLOXcwIlSZKkEZbkEcAmbhGhMc4JlCRJkkZMkm8nWT/JI4EfAscm+UjXdWl2MARKkiRJo+dhVXU3sCdwbFVtD7yg45o0SxgCJUmSpNGzWpINgVcC/9V1MZpdDIGSJEnS6Dkc+AawoKp+kOTxwI87rkmzhAvDSJIkSVKPuEWEJEmSNCKS/GNVfWBgv8AHcZ9AgSFQkiRJGiUHJ/kuD+wXKP0Rh4NKkiRJIyLJ24FXARsCXwBOqqrLu6xJs48hUJIkSRoxSR4H7NPe1gJOogmELg4jQ6AkSZI0ypJsBxwDbF1Vq3Zdj7rnFhGSJEnSiEmyepLdkpwInAX8D/CKjsvSLGFPoCRJkjQikrwQ2Bd4CXAxcDLwlar6TaeFaVYxBEqSJEkjIsm5wOeBU6vqzq7r0exkCJQkSZKkHnFOoCRJkiT1iCFQkiRJknrEEChJkiRJPWIIlCRpQJI3Jtm/vf/aJI/toIabkmww0/+uJKkfVuu6AEmSZpOq+tTAw9cCVwG3dlONJElTz55ASVKvJdk/yRVJfpjkhCSHJfn7JHsB84ATk1ye5CVJThs474VJvryc131Rkkvb1z2nbXtkkq+0/973k2zdtj8qyTeTXJbk00AGXufVSS5ua/h0klWn7c2QJPWCIVCS1FtJtgTeDexSVdsAh4w9V1VfAuYD+1XVtsCZwFOTzGkPeR1w7DJedw7wGeAV7evu3T71PuCyqtoaeBdwfNv+XuCCqtoOOB3YtH2dpwKvAnZqa1gC7Lfy/3NJUp85HFSS1Ge7AF+qqp8DVNWdScY9sKoqyQnAq5McCzwD2H8Zr7sjcH5V3Tj2um37zsAr2rZvtT2ADwOeDezZtn8tyS/b458PbA/8oK1rbeCOlfj/SpJkCJQk9VqAmsDxxwJnAL8DvlhViyf4uuMlzFrq69LHH1dVh06gRkmSlsvhoJKkPjsHeGWSR0EzZ2+p5+8B1ht7UFW30iwS88/A55bzut8DnpNks6Ve93za4ZxJngv8vKruXqr9xcAjBurbK8mjx14nyeMm8f+UJOkP7AmUJPVWVV2d5AjgvCRLgMuAmwYO+RzwqST3As+oqnuBE4E5VXXNcl53UZKDgC8nWYVmCOcLgcOAY5NcAfwWOKA95X3ASUkuBc4DftK+zjVJ/hn4Zvs6/wu8Gbh5Kv7/kqR+StVERsFIktRvST5Os7jLZ7uuRZKkyTAESpK0gpJcAvwGeGFV3dd1PZIkTYYhUJKklZDkImDNpZpfU1VXdlGPJEkPxRAoSZIkST3i6qCSJEmS1COGQEmSJEnqEUOgJEmSJPWIIVCSJEmSesQQKEmSJEk98v8DS2jw1HyRe5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(df.city_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8641e821",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Profession_Code', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(df.Profession_Code,hue=df.Return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9a608f1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month_code', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets do Bi-variate analysis\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(df.Month_code,hue=df.Return)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0e09b8",
   "metadata": {},
   "source": [
    "**In Months of January,may, October,and November are maximumn returns products**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff1084b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reviews', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Reviews'],hue=df.Return)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b97a15",
   "metadata": {},
   "source": [
    "**Almost all product of rating 1 was returned** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "309a0090",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohit Kumar (Prince)\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Store_type', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Store_type'],hue=df.Return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "772a5f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rate', ylabel='Return'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Rate',y='Return',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "537d9044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Tax', ylabel='Return'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZRrkIvg0cGxFHV01/NnBtw5mgm+EN1fIb6M7Nz46fHRGLI+Jo4FjgxmGFiIgA/ha4NTM/1qZ8EXFYRBxcLS+h+8ewoelsmXlhZh6ZmSvoPp++lpmvazrXrIhYFhEHzC7TnUte34Z8mfkT4EcRcVw19GLgB23I1uOPeHJaaDZD09k2Ay+IiKXV3+yLgVsHnm3YO1+avAAvp/tumB8CFzWw/s/Tndd7jG5Tnwf8Et2djRurr4f03P+iKuttwJlDzvabdDcZbwHWVZeXtyEfcCLwnSrbeuB91Xjj2XrWdzpP7ixuRS668/DfrS7fn33Otyjf84G11e/1n4FntijbUuB+4KCesbZkez/d/witBz5L9x1BA83mISYkqXCjPDUkSeqDRSBJhbMIJKlwFoEkFc4ikKTCPaPpANJCERGzb9kD+BVgB93DJgCcmpmPNhJM2ku+fVSah4i4BHg4Mz/SdBZpbzk1JO2FiHhLRHw7uudO+FJELK3Gr4mIP66W3xoRVzWbVHp6FoG0d67OzF/LzJPofvT/vGr8fOB9EfFbwLuAyaYCSrvjPgJp7zwvIv6K7klW9qd7/Hcy856IeB/wdeD3M/OB5iJKu+YWgbR3/g74k8w8ge4xYfbrue0EuseveVYDuaS+WQTS3jkAuLs6pPc5s4MRcSrdwxqfDLy7OhKk1EoWgbR3/oLumd1uoHuESCJiMfBJ4E3ZPT/Au4ArqsMIS63j20clqXBuEUhS4SwCSSqcRSBJhbMIJKlwFoEkFc4ikKTCWQSSVLj/B5LIXQmFAtAJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Tax',y='Return',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6ddab3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Amount', ylabel='Return'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Amount',y='Return',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b2ad3a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e5103f4910>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for the numerical features we plot pairs plot see the how the data points are located\n",
    "sns.pairplot(df[['Rate','Amount','Tax','Delivery_chrgs','Income']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dfb6fd",
   "metadata": {},
   "source": [
    "### Handling categorical variables\n",
    "One can find many ways to handle categorical data. Some of them categorical data are,\n",
    "\n",
    "- **Nominal data** --> data are not in any order --> **OneHotEncoder** is used in this case\n",
    "- **Ordinal data** --> data are in order --> **LabelEncoder** is used in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d7dff4c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19504 entries, 0 to 19596\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Month_code        19504 non-null  object \n",
      " 1   prod_cat_code     19504 non-null  object \n",
      " 2   prod_subcat_code  19504 non-null  object \n",
      " 3   Qty               19504 non-null  int64  \n",
      " 4   Rate              19504 non-null  int64  \n",
      " 5   Amount            19504 non-null  int64  \n",
      " 6   Tax               19504 non-null  float64\n",
      " 7   Delivery_chrgs    19504 non-null  float64\n",
      " 8   Payment_mode      19504 non-null  object \n",
      " 9   Store_type        19504 non-null  object \n",
      " 10  Reviews           19504 non-null  float64\n",
      " 11  Income            19504 non-null  float64\n",
      " 12  city_code         19504 non-null  object \n",
      " 13  Return            19504 non-null  object \n",
      " 14  Gender            19504 non-null  object \n",
      " 15  Marital_status    19504 non-null  int64  \n",
      " 16  Education_code    19504 non-null  object \n",
      " 17  Profession_Code   19504 non-null  object \n",
      "dtypes: float64(4), int64(4), object(10)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "850e7c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Qty']=df['Qty'].astype('str')\n",
    "df['Marital_status']= df['Marital_status'].astype('str')\n",
    "df['Reviews']= df['Reviews'].astype('str')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d60a4d3",
   "metadata": {},
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      ],
      "text/plain": [
       "   August  December  February  January  July  June  March  May  November  \\\n",
       "0       0         0         0        1     0     0      0    0         0   \n",
       "1       0         0         0        1     0     0      0    0         0   \n",
       "2       0         0         0        1     0     0      0    0         0   \n",
       "3       0         0         0        1     0     0      0    0         0   \n",
       "4       0         0         0        1     0     0      0    0         0   \n",
       "\n",
       "   October  September  \n",
       "0        0          0  \n",
       "1        0          0  \n",
       "2        0          0  \n",
       "3        0          0  \n",
       "4        0          0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Months = pd.get_dummies(df['Month_code'],drop_first=True)\n",
    "Months.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c4e9e318",
   "metadata": {},
   "outputs": [
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       "   Books  Clothing  Electronics  Footwear  Home and kitchen\n",
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      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_cat= pd.get_dummies(df['prod_cat_code'],drop_first=True)\n",
    "product_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be7028a8",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Audio and video  Bath  Cameras  Computers  DIY  Fiction  Furnishing  Kids  \\\n",
       "0                0     0        0          0    0        1           0     0   \n",
       "1                0     0        0          0    0        0           0     0   \n",
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       "3                0     0        0          0    0        0           0     0   \n",
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       "\n",
       "   Kitchen  Mens  Mobiles  Non-Fiction  Personal Appliances  Tools  Women  \n",
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       "3        0     1        0            0                    0      0      0  \n",
       "4        0     0        0            0                    0      0      1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_subcat= pd.get_dummies(df['prod_subcat_code'],drop_first=True)\n",
    "product_subcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "757da526",
   "metadata": {},
   "outputs": [
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       "   2  3  4  5\n",
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    "qty= pd.get_dummies(df['Qty'],drop_first=True)\n",
    "qty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "58ed1b68",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Credit Card  Debit Card  Mobile Payments\n",
       "0            0           0                1\n",
       "1            1           0                0\n",
       "2            0           1                0\n",
       "3            0           1                0\n",
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      ]
     },
     "execution_count": 39,
     "metadata": {},
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   "source": [
    "payment_mode= pd.get_dummies(df['Payment_mode'],drop_first=True)\n",
    "payment_mode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "54aed4dd",
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MBR</th>\n",
       "      <th>TeleShop</th>\n",
       "      <th>e-Shop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MBR  TeleShop  e-Shop\n",
       "0    0         0       1\n",
       "1    0         1       0\n",
       "2    1         0       0\n",
       "3    1         0       0\n",
       "4    1         0       0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_type= pd.get_dummies(df['Store_type'],drop_first=True)\n",
    "store_type.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "305ab90e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bangalore</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Gurgaon</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Kolkata</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>New Delhi</th>\n",
       "      <th>Pune</th>\n",
       "      <th>Vishakhapatnam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bangalore  Chennai  Gurgaon  Hyderabad  Kolkata  Mumbai  New Delhi  Pune  \\\n",
       "0          0        0        0          1        0       0          0     0   \n",
       "1          1        0        0          0        0       0          0     0   \n",
       "2          0        0        0          0        1       0          0     0   \n",
       "3          0        0        0          1        0       0          0     0   \n",
       "4          0        0        0          0        0       0          1     0   \n",
       "\n",
       "   Vishakhapatnam  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "City_code=  pd.get_dummies(df['city_code'],drop_first=True)\n",
    "City_code.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ba49c78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   M\n",
       "0  1\n",
       "1  1\n",
       "2  1\n",
       "3  1\n",
       "4  1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender=  pd.get_dummies(df['Gender'],drop_first=True)\n",
    "gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8373df70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Others        4111\n",
       "Graduation    3993\n",
       "Master        3920\n",
       "Basic         3885\n",
       "PhD           3595\n",
       "Name: Education_code, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Education_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f74fcaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Income</th>\n",
       "      <th>city_code</th>\n",
       "      <th>Return</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Education_code</th>\n",
       "      <th>Profession_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>January</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Bath</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Salaried</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month_code     prod_cat_code prod_subcat_code Qty  Rate  Amount      Tax  \\\n",
       "0    January             Books          Fiction   3   359    1795  188.475   \n",
       "1    January          Clothing            Women   5  1129    5645  592.725   \n",
       "2    January  Home and kitchen             Bath   4  1327    6635  696.675   \n",
       "3    January          Footwear             Mens   5  1325    6625  695.625   \n",
       "4    January          Clothing            Women   3  1237    6185  649.425   \n",
       "\n",
       "   Delivery_chrgs     Payment_mode Store_type Reviews    Income  city_code  \\\n",
       "0            90.0  Mobile Payments     e-Shop     1.0   67501.0  Hyderabad   \n",
       "1           250.0      Credit Card   TeleShop     1.0  102738.0  Bangalore   \n",
       "2             0.0       Debit Card        MBR     1.0  104013.0    Kolkata   \n",
       "3             0.0       Debit Card        MBR     4.0   92580.0  Hyderabad   \n",
       "4             0.0       Debit Card        MBR     1.0  101763.0  New Delhi   \n",
       "\n",
       "  Return Gender Marital_status  Education_code Profession_Code  \n",
       "0    Yes      M              0               4          Others  \n",
       "1    Yes      M              1               3        Salaried  \n",
       "2    Yes      M              1               1        Salaried  \n",
       "3    Yes      M              1               0          Others  \n",
       "4    Yes      M              0               1          Others  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education_code'].replace({'Others':0,'Basic':1 ,'Graduation':2, 'Master':3,'PhD':4}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef343aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salaried</th>\n",
       "      <th>Self Employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Salaried  Self Employed\n",
       "0         0              0\n",
       "1         1              0\n",
       "2         1              0\n",
       "3         0              0\n",
       "4         0              0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Profession= pd.get_dummies(df['Profession_Code'],drop_first=True)\n",
    "Profession.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e39083e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    9516\n",
       "4.0    4688\n",
       "1.0    2484\n",
       "2.0    1537\n",
       "3.0    1279\n",
       "Name: Reviews, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reviews.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9c9ab4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   2.0  3.0  4.0  5.0\n",
       "0    0    0    0    0\n",
       "1    0    0    0    0\n",
       "2    0    0    0    0\n",
       "3    0    0    1    0\n",
       "4    0    0    0    0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews= pd.get_dummies(df['Reviews'],drop_first=True)\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "57f43fa5",
   "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>Month_code</th>\n",
       "      <th>prod_cat_code</th>\n",
       "      <th>prod_subcat_code</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Payment_mode</th>\n",
       "      <th>Store_type</th>\n",
       "      <th>...</th>\n",
       "      <th>September</th>\n",
       "      <th>Books</th>\n",
       "      <th>Clothing</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>Footwear</th>\n",
       "      <th>Home and kitchen</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>Books</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>3</td>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>188.475</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Mobile Payments</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>5</td>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>592.725</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>January</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Bath</td>\n",
       "      <td>4</td>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>696.675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Mens</td>\n",
       "      <td>5</td>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>695.625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>3</td>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>649.425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>MBR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month_code     prod_cat_code prod_subcat_code Qty  Rate  Amount      Tax  \\\n",
       "0    January             Books          Fiction   3   359    1795  188.475   \n",
       "1    January          Clothing            Women   5  1129    5645  592.725   \n",
       "2    January  Home and kitchen             Bath   4  1327    6635  696.675   \n",
       "3    January          Footwear             Mens   5  1325    6625  695.625   \n",
       "4    January          Clothing            Women   3  1237    6185  649.425   \n",
       "\n",
       "   Delivery_chrgs     Payment_mode Store_type  ... September  Books Clothing  \\\n",
       "0            90.0  Mobile Payments     e-Shop  ...         0      1        0   \n",
       "1           250.0      Credit Card   TeleShop  ...         0      0        1   \n",
       "2             0.0       Debit Card        MBR  ...         0      0        0   \n",
       "3             0.0       Debit Card        MBR  ...         0      0        0   \n",
       "4             0.0       Debit Card        MBR  ...         0      0        1   \n",
       "\n",
       "  Electronics Footwear Home and kitchen  2.0 3.0  4.0  5.0  \n",
       "0           0        0                0    0   0    0    0  \n",
       "1           0        0                0    0   0    0    0  \n",
       "2           0        0                1    0   0    0    0  \n",
       "3           0        1                0    0   0    1    0  \n",
       "4           0        0                0    0   0    0    0  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.concat([df,Profession,gender,City_code,store_type,payment_mode,product_subcat,qty,Months,product_cat,reviews],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d60fb4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have drop these varibles\n",
    "df.drop(['Month_code','prod_cat_code','prod_subcat_code','Qty','Payment_mode','Store_type','city_code','Gender',\n",
    "        'Education_code','Profession_Code', 'Reviews'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c01e2a",
   "metadata": {},
   "source": [
    "### Features selections\n",
    "- Correlation\n",
    "- chi2\n",
    "- Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c934002a",
   "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>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Tax</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.695217</td>\n",
       "      <td>0.695217</td>\n",
       "      <td>0.420517</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>0.695217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609654</td>\n",
       "      <td>-0.006164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax</th>\n",
       "      <td>0.695217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.609654</td>\n",
       "      <td>-0.006164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <td>0.420517</td>\n",
       "      <td>0.609654</td>\n",
       "      <td>0.609654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.000127</td>\n",
       "      <td>-0.006164</td>\n",
       "      <td>-0.006164</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Rate    Amount       Tax  Delivery_chrgs    Income\n",
       "Rate            1.000000  0.695217  0.695217        0.420517  0.000127\n",
       "Amount          0.695217  1.000000  1.000000        0.609654 -0.006164\n",
       "Tax             0.695217  1.000000  1.000000        0.609654 -0.006164\n",
       "Delivery_chrgs  0.420517  0.609654  0.609654        1.000000  0.006873\n",
       "Income          0.000127 -0.006164 -0.006164        0.006873  1.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_col=df[['Rate','Amount','Tax','Delivery_chrgs','Income']]\n",
    "corr= cont_col.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bdbb7ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(corr,annot = True, cmap = \"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cc8e6615",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold=0.90\n",
    "# find and remove correlated features\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fb2c2bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Tax'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(cont_col,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "04134046",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Tax',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "432e0c7a",
   "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>Rate</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Delivery_chrgs</th>\n",
       "      <th>Income</th>\n",
       "      <th>Return</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Salaried</th>\n",
       "      <th>Self Employed</th>\n",
       "      <th>M</th>\n",
       "      <th>Bangalore</th>\n",
       "      <th>...</th>\n",
       "      <th>September</th>\n",
       "      <th>Books</th>\n",
       "      <th>Clothing</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>Footwear</th>\n",
       "      <th>Home and kitchen</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359</td>\n",
       "      <td>1795</td>\n",
       "      <td>90.0</td>\n",
       "      <td>67501.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1129</td>\n",
       "      <td>5645</td>\n",
       "      <td>250.0</td>\n",
       "      <td>102738.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1327</td>\n",
       "      <td>6635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104013.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1325</td>\n",
       "      <td>6625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92580.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1237</td>\n",
       "      <td>6185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101763.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rate  Amount  Delivery_chrgs    Income Return Marital_status  Salaried  \\\n",
       "0   359    1795            90.0   67501.0    Yes              0         0   \n",
       "1  1129    5645           250.0  102738.0    Yes              1         1   \n",
       "2  1327    6635             0.0  104013.0    Yes              1         1   \n",
       "3  1325    6625             0.0   92580.0    Yes              1         0   \n",
       "4  1237    6185             0.0  101763.0    Yes              0         0   \n",
       "\n",
       "   Self Employed  M  Bangalore  ...  September  Books  Clothing  Electronics  \\\n",
       "0              0  1          0  ...          0      1         0            0   \n",
       "1              0  1          1  ...          0      0         1            0   \n",
       "2              0  1          0  ...          0      0         0            0   \n",
       "3              0  1          0  ...          0      0         0            0   \n",
       "4              0  1          0  ...          0      0         1            0   \n",
       "\n",
       "   Footwear  Home and kitchen  2.0  3.0  4.0  5.0  \n",
       "0         0                 0    0    0    0    0  \n",
       "1         0                 0    0    0    0    0  \n",
       "2         0                 1    0    0    0    0  \n",
       "3         1                 0    0    0    1    0  \n",
       "4         0                 0    0    0    0    0  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5c6462e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop('Return', axis=1) #independent features\n",
    "y= df['Return'] #depedndent featues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "80ad9b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= y.replace({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "884ef961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rate', 'Amount', 'Delivery_chrgs', 'Income', 'Marital_status',\n",
       "       'Salaried', 'Self Employed', 'M', 'Bangalore', 'Chennai', 'Gurgaon',\n",
       "       'Hyderabad', 'Kolkata', 'Mumbai', 'New Delhi', 'Pune', 'Vishakhapatnam',\n",
       "       'MBR', 'TeleShop', 'e-Shop', 'Credit Card', 'Debit Card',\n",
       "       'Mobile Payments', 'Audio and video', 'Bath', 'Cameras', 'Computers',\n",
       "       'DIY', 'Fiction', 'Furnishing', 'Kids', 'Kitchen', 'Mens', 'Mobiles',\n",
       "       'Non-Fiction', 'Personal Appliances', 'Tools', 'Women', '2', '3', '4',\n",
       "       '5', 'August', 'December', 'February', 'January', 'July', 'June',\n",
       "       'March', 'May', 'November', 'October', 'September', 'Books', 'Clothing',\n",
       "       'Electronics', 'Footwear', 'Home and kitchen', '2.0', '3.0', '4.0',\n",
       "       '5.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "64abaea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "chi_catcols= X[['Marital_status',\n",
    "       'Salaried', 'Self Employed', 'M', 'Bangalore', 'Chennai', 'Gurgaon',\n",
    "       'Hyderabad', 'Kolkata', 'Mumbai', 'New Delhi', 'Pune', 'Vishakhapatnam',\n",
    "       'MBR', 'TeleShop', 'e-Shop', 'Credit Card', 'Debit Card',\n",
    "       'Mobile Payments', 'Audio and video', 'Bath', 'Cameras', 'Computers',\n",
    "       'DIY', 'Fiction', 'Furnishing', 'Kids', 'Kitchen', 'Mens', 'Mobiles',\n",
    "       'Non-Fiction', 'Personal Appliances', 'Tools', 'Women', '2', '3', '4',\n",
    "       '5', 'August', 'December', 'February', 'January', 'July', 'June',\n",
    "       'March', 'May', 'November', 'October', 'September', 'Books', 'Clothing',\n",
    "       'Electronics', 'Footwear', 'Home and kitchen', '2.0', '3.0', '4.0',\n",
    "       '5.0']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d3f8ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#chi score\n",
    "chi_scores = chi2(chi_catcols,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c2fabdd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.11213507e-01, 2.22746001e-01, 5.28335092e-01, 4.98231006e-03,\n",
       "        2.40903092e+00, 6.01279795e-02, 2.26469989e+00, 1.17802205e+00,\n",
       "        5.71560542e-01, 6.70888979e-01, 2.34011589e+00, 3.83912789e-02,\n",
       "        1.05520300e+00, 1.70381595e+00, 1.33927770e+00, 2.22642377e-01,\n",
       "        8.61386973e-01, 1.77347206e+00, 2.54329395e+00, 2.24403457e+01,\n",
       "        5.88165980e+00, 1.89325055e+01, 2.07026964e-01, 5.86842043e-01,\n",
       "        2.27443166e+01, 3.60239380e+01, 4.78553946e+01, 2.91170289e+01,\n",
       "        1.27210760e+02, 4.31011890e+00, 2.24802071e+01, 2.03783370e+00,\n",
       "        8.60343559e+00, 3.57082576e+02, 2.18932843e+02, 2.24761670e+02,\n",
       "        8.90389382e+01, 9.59987260e+02, 6.10691365e+01, 8.91066974e+01,\n",
       "        7.87195872e+01, 8.87609086e+01, 7.15801803e+01, 4.36787607e+01,\n",
       "        6.45415537e+01, 1.25009435e+02, 1.08831102e+02, 1.22462848e+02,\n",
       "        7.79150745e+01, 5.40249707e+01, 5.54852890e-01, 1.62979890e+01,\n",
       "        8.76386479e+00, 1.02268259e+01, 4.28345262e+02, 9.75487401e+00,\n",
       "        3.33129164e+02, 9.09619195e+02]),\n",
       " array([6.45817928e-001, 6.36955522e-001, 4.67307664e-001, 9.43727666e-001,\n",
       "        1.20637026e-001, 8.06293778e-001, 1.32351849e-001, 2.77759035e-001,\n",
       "        4.49639464e-001, 4.12741840e-001, 1.26080174e-001, 8.44659535e-001,\n",
       "        3.04311824e-001, 1.91789689e-001, 2.47161387e-001, 6.37033893e-001,\n",
       "        3.53350848e-001, 1.82952967e-001, 1.10763496e-001, 2.16772325e-006,\n",
       "        1.52993926e-002, 1.35425421e-005, 6.49107686e-001, 4.43642886e-001,\n",
       "        1.85050614e-006, 1.94908303e-009, 4.58842813e-012, 6.81354580e-008,\n",
       "        1.67056482e-029, 3.78863134e-002, 2.12320107e-006, 1.53428012e-001,\n",
       "        3.35529455e-003, 1.21577109e-079, 1.54582485e-049, 8.27544342e-051,\n",
       "        3.87117362e-021, 8.94893291e-211, 5.51052733e-015, 3.74082381e-021,\n",
       "        7.15822965e-019, 4.45533780e-021, 2.66217358e-017, 3.86948515e-011,\n",
       "        9.45178400e-016, 5.06532717e-029, 1.76720669e-025, 1.82806461e-028,\n",
       "        1.07567326e-018, 1.97957779e-013, 4.56341557e-001, 5.41213580e-005,\n",
       "        3.07257260e-003, 1.38412648e-003, 3.72428770e-095, 1.78847899e-003,\n",
       "        2.00106291e-074, 7.95674826e-200]))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "53330f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "#here first array represents chi square values and second array represnts p-values\n",
    "p_values = pd.Series(chi_scores[1],index = chi_catcols.columns)\n",
    "p_values.sort_values(ascending = False , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8e3a94b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "p_values.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "de59d76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will drop values with higher p- values which more than 0.05\n",
    "X.drop(['Salaried', 'Self Employed', 'M', 'Bangalore', 'Chennai', 'Gurgaon',\n",
    "       'Hyderabad', 'Kolkata', 'Mumbai', 'New Delhi', 'Pune', 'Vishakhapatnam',\n",
    "       'MBR', 'TeleShop', 'e-Shop', 'Credit Card', 'Debit Card',\n",
    "       'Mobile Payments', 'Audio and video', 'Bath', 'Cameras', 'Computers',\n",
    "       'DIY', 'Mens', 'Mobiles'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69aef304",
   "metadata": {},
   "source": [
    "#### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "211a5e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "selection = ExtraTreesClassifier()\n",
    "selection.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eee48108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uS3J2ksdM1p7k1cAwcGbzTsELknyh6X9UkvuT7J5kjyS3NO1PTfLlJBuTXJLkoKb9lUmuSHJNkn9M8vim/T1JNiS5EDijlUmRJEmSpmC+hv49+5b2nN20nQH8z6o6BBgF/nCy9qr6HDACHNO8Y3AZcGjT/+fpvQPwbOA5wBVN+wbgrVX1LOBdwEea9kuB51bVocCngd/tq/NZwFFV9br+4pOsSzKSZGTb1rHZzoUkSZI0KwtieU+S5cB+VfXPTdPHgbMmax8/WFU9mOTmJD8LHAa8H3g+sAS4JMnewPOaMbeftqy5PQD4TJIVwO7ArX1Dn1NV909wfxvovYhg2YpVNd0HL0mSJO1K8/VK/yBcArwM+BHwj8DPNX9fozcP36+qNX1/P9uc99fAh6tqNfDbwB59Y943Z9VLkiRJM7QgQn9VjQH/meTnm6Y3AP88WXuzfQ+wT98wXwNOBL5RVd8FfhI4CLihqu4Gbk3yqwDpeUZz3nJgS7N97C5/cJIkSdKAzdflPRM5FjglyV7ALcDxO2k/vWm/Hzic3tr9x9ML/wDXAXdW1fblN8cAH03yB8Bu9NbvXwu8h96yny3A5cBPD+oBSpIkSYOQhzOvBmHZilW14tiT2y5j3vAXeSVJkgYjycaqGp7o2EK60r8grV65nBGDriRJklq0INb0S5IkSZo5Q78kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcUvbLqDrRreMMXTSeW2XMa9tXr+27RIkSZI6bUFc6U/yU0k+neRbSb6Z5PwkBya5fifnrUny8r799yR51yR9v76r65YkSZLmg3kf+pMEOBu4uKqeWlVPA34fePwUTl8DvHxnnQCq6nkzLlKSJEmax+Z96AdeBPyoqk7Z3lBVm4Bvb99PskeS05KMJrkmyYuS7A68Fzg6yaYkRzfdn5bk4iS3JHlb3xj3NrcvbI5/LslNSc5sXniQ5OVN26VJPpTk3ME/fEmSJGl2FkLoPxjYuJM+bwaoqtXAa4GP03ts7wY+U1VrquozTd+DgP8LOAz4wyS7TTDeocCJwNOApwBHJNkD+FvgZVX1c8D+s3lQkiRJ0lxZCKF/Kn4O+ARAVd0E/Dtw4CR9z6uqB6rqe8CdTLxM6Mqquq2qHgI2AUP0XizcUlW3Nn3+YbJikqxLMpJkZNvWsZk8HkmSJGmXWQih/wbgWTvpk2mM90Df9jYm/gajifpM+T6qakNVDVfV8JK9lk+jNEmSJGnXWwih/6vAsiS/tb0hybOBJ/f1+RpwTHPsQOBJwL8A9wD77KI6bgKekmSo2T96B30lSZKkeWPeh/6qKuBXgJc0X9l5A/Ae4Pa+bh8BliQZBT4DHFdVDwAX0fvgbv8HeWdax/3A/wC+nORS4DuAa3ckSZI07y2IH+eqqtuBX5vg0MHN8R8Ax01w3l3As3cw7sF923s3txcDF/e1v6XvlIuq6qDm23z+BhiZxsOQJEmSWrEgQv888ltJjgV2B66h920+O7R65XJG/MVZSZIktcjQPw1V9QHgA23XIUmSJE3HvF/TL0mSJGl2DP2SJElSxxn6JUmSpI4z9EuSJEkdZ+iXJEmSOs7QL0mSJHWcoV+SJEnqOEO/JEmS1HGGfkmSJKnj/EXeARvdMsbQSee1XYamafP6tW2XIEmStMt4pV+SJEnqOEN/I8kTk1yU5MYkNyQ5YYI+SfKhJDcnuS7JM9uoVZIkSZoOl/c87EHgnVV1dZJ9gI1JvlJV3+zr8zJgVfP3HOCjza0kSZI0b3mlv1FVd1TV1c32PcCNwMpx3Y4Czqiey4H9kqyY41IlSZKkaTH0TyDJEHAocMW4QyuBb/ft38aPvzCQJEmS5hVD/zhJ9gY+D5xYVXePPzzBKTXBGOuSjCQZ2bZ1bBBlSpIkSVNm6O+TZDd6gf/MqvrCBF1uA57Yt38AcPv4TlW1oaqGq2p4yV7LB1OsJEmSNEWG/kaSAH8P3FhV75+k2znAG5tv8XkuMFZVd8xZkZIkSdIM+O09DzsCeAMwmmRT0/b7wJMAquoU4Hzg5cDNwFbg+LkvU5IkSZoeQ3+jqi5l4jX7/X0KePPcVCRJkiTtGob+AVu9cjkj69e2XYYkSZIWMdf0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6bmnbBXTd6JYxhk46r+0yNCCb169tuwRJkqSd8kq/JEmS1HGGfkmSJKnjph36k1SST/TtL03y3STnTnOcJyT5XLO9JsnLp3DOC2dwP0NJXrer+kmSJEkLzUyu9N8HHJxkz2b/JcCW6QyQZGlV3V5Vr26a1gA7Df0zNARMJcxPtZ8kSZK0oMx0ec+XgO2fYHwt8A/bDyQ5LMnXk1zT3P5M035ckrOSfBG4sLmyfn2S3YH3Akcn2ZTk6MnG2JkkL2jG2NScuw+wHvj5pu3tzf1ekuTq5u95zenj+x2X5MN9Y5/bvNOwJMnpTe2jSd4+QR3rkowkGdm2dWz6sytJkiTtQjP99p5PA+9ultocAnwM+Pnm2E3A86vqwSRHAu8DXtUcOxw4pKruSjIEUFU/TPJuYLiq3gKQZN8djLEj7wLeXFWXJdkb+AFwEvCuqnpFM/ZewEuq6gdJVtF7wTI8Qb/jJrmPNcDKqjq46bff+A5VtQHYALBsxaqaQt2SJEnSwMwo9FfVdU1ofy1w/rjDy4GPN4G6gN36jn2lqu6awl3saIwduQx4f5IzgS9U1W1JxvfZDfhwkjXANuDAKY693S3AU5L8NXAecOE0z5ckSZLm1Gy+vecc4C/pW9rT+GPgouZK+CuBPfqO3TfFsXc0xqSqaj3wm8CewOVJDpqg29uB7wDPoHeFf/dJhnuQR87PHs19/Gdz7sXAm4FTp1KbJEmS1JbZ/DjXx4CxqhpN8sK+9uU8/MHe46Y41j3APrMcgyRPrapRYDTJ4cBBwLcnGPu2qnooybHAkklq2Az8jySPAlYChzX38Vjgh1X1+STfAk6fan2SJElSG2Z8pb+qbquqD05w6M+BP01yGQ8H6p25CHja9g/yznAMgBObD9heC9xP7wPH1wEPJrm2+dDtR4Bjk1xOb2nP9ncfxve7DLgVGKX3jsbVTb+VwMVJNtEL/L83jfokSZKkOZcqP2c6SMPDwzUyMtJ2GZIkSeq4JBuraniiY/4iryRJktRxs1nT35okxwMnjGu+rKre3EY9kiRJ0ny2IEN/VZ0GnNZ2HZIkSdJC4PIeSZIkqeMM/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeq4Bfk9/QvJ6JYxhk46r+0y1JLN69e2XYIkSZJX+iVJkqSuM/TPQJIlSa5Jcm7btUiSJEk7Y+ifmROAG9suQpIkSZoKQ/80JTkAWAuc2nYtkiRJ0lQY+qfvZOB3gYcm65BkXZKRJCPbto7NWWGSJEnSRAz905DkFcCdVbVxR/2qakNVDVfV8JK9ls9RdZIkSdLEDP3TcwTwS0k2A58GXpzkk+2WJEmSJO2YoX8aqur3quqAqhoCXgN8tape33JZkiRJ0g4Z+iVJkqSO8xd5Z6iqLgYubrkMSZIkaacM/QO2euVyRtavbbsMSZIkLWIu75EkSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkddzStgvoutEtYwyddF7bZWge2rx+bdslSJKkRWLBXelP8oEkJ/btX5Dk1L79v0ryjlaKkyRJkuahBRf6ga8DzwNI8ijgscDT+44/D7ishbokSZKkeWkhhv7LaEI/vbB/PXBPksckWQb8LLBfkmuSjCb5WNNOks1J3pfkG0lGkjyzeafgW0netP0OkvzfSa5Kcl2SP2rahpLcmOTvktyQ5MIke87tQ5ckSZKmb8GF/qq6HXgwyZPohf9vAFcAhwPDwL8CpwJHV9Vqep9b+J2+Ib5dVYcDlwCnA68Gngu8FyDJS4FVwGHAGuBZSZ7fnLsK+JuqejrwfeBVg3qckiRJ0q6y4EJ/Y/vV/u2h/xt9+1uAW6vqX5u+Hwee33fuOc3tKHBFVd1TVd8FfpBkP+Clzd81wNXAQfTCPs24m5rtjcDQRMUlWde8kzCybevY7B6pJEmSNEsL9dt7tq/rX01vec+3gXcCd9ML6i/ZwbkPNLcP9W1v318KBPjTqvrb/pOSDI3rvw2YcHlPVW0ANgAsW7GqpvKAJEmSpEFZyFf6XwHcVVXbquouYD96S3xOA4aS/Lem7xuAf57G2BcAv55kb4AkK5M8bpdVLkmSJM2xhXqlf5Tet/Z8alzb3lV1W5LjgbOSLAWuAk6Z6sBVdWGSnwW+kQTgXuD19K7sS5IkSQtOqlx9MkjLVqyqFcee3HYZmof8cS5JkrQrJdlYVcMTHVuoV/oXjNUrlzNiuJMkSVKLFuqafkmSJElTZOiXJEmSOs7QL0mSJHWcoV+SJEnqOEO/JEmS1HGGfkmSJKnjDP2SJElSxxn6JUmSpI4z9EuSJEkdZ+iXJEmSOm5p2wV03eiWMYZOOq/tMqSB27x+bdslSJKkSXilX5IkSeo4Q38jyR5JrkxybZIbkvzRBH2S5ENJbk5yXZJntlGrJEmSNB0u73nYA8CLq+reJLsBlyb5UlVd3tfnZcCq5u85wEebW0mSJGne8kp/o3rubXZ3a/5qXLejgDOavpcD+yVZMZd1SpIkSdNl6O+TZEmSTcCdwFeq6opxXVYC3+7bv61pkyRJkuYtQ3+fqtpWVWuAA4DDkhw8rksmOm18Q5J1SUaSjGzbOjaASiVJkqSpM/RPoKq+D1wM/OK4Q7cBT+zbPwC4fYLzN1TVcFUNL9lr+aDKlCRJkqbE0N9Isn+S/ZrtPYEjgZvGdTsHeGPzLT7PBcaq6o65rVSSJEmaHr+952ErgI8nWULvxdBnq+rcJG8CqKpTgPOBlwM3A1uB49sqVpIkSZoqQ3+jqq4DDp2g/ZS+7QLePJd1SZIkSbNl6B+w1SuXM7J+bdtlSJIkaRFzTb8kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeOWtl1A141uGWPopPPaLkPSLrB5/dq2S5AkaUa80i9JkiR1nKFfkiRJ6ridhv4k25JsSnJDkmuTvCPJDs9LMpTk+mZ7OMmHdlXBs5Xk3rZrkCRJkubSVNb0319VawCSPA74FLAc+MOp3EFVjQAjMy1wuyRLq+rB2Y4z3+5LkiRJGrRpLe+pqjuBdcBb0rMkyV8kuSrJdUl+e/w5SV6Y5Nwkj0qyOcl+fcduTvL4JPsn+XwzzlVJjmiOvyfJhiQXAmckuSTJmr7zL0tyyES1Jtk7yWlJRpvaXtV37E+ady0uT/L4pu30JO9PchHwZ0me2hy/Ksl7t79DkGRFkq81735cn+TnJ7jvdUlGkoxs2zo2nSmWJEmSdrlpr+mvqlua8x4H/AYwVlXPBp4N/FaSn57kvIeA/xf4FYAkzwE2V9V3gA8CH2jGeRVwat+pzwKOqqrXNe3HNecfCCyrqusmKfV/NbWtrqpDgK827Y8GLq+qZwBfA36r75wDgSOr6p1NTR9sarq9r8/rgAuadz+eAWya4LFuqKrhqhpestfyScqTJEmS5sZMP8ib5valwBuTbAKuAH4SWLWD8z4DHN1sv6bZBzgS+HAzzjnAvkn2aY6dU1X3N9tnAa9Ishvw68DpO7ivI4G/2b5TVf/ZbP4QOLfZ3ggM9Z1zVlVta7YPb+4PekuatrsKOD7Je4DVVXXPDmqQJEmSWjft7+lP8hRgG3AnvfD/1qq6YFyfoUlO/wbw35LsD/wy8L+b9kcBh/eF++3jANy3fb+qtib5CnAU8GvA8I5KBWqC9h9V1fb2bTxyDu6boP8jVNXXkjwfWAt8IslfVNUZOztPkiRJasu0rvQ3Yf0U4MNNcL4A+J3myjtJDkzy6MnOb845G3g/cGNV/Udz6ELgLX33s2YHZZwKfAi4qqru2kG/8WM+Zgd9J3I5vaVG0HtXYvs4TwburKq/A/4eeOY0x5UkSZLm1FRC/57bv7IT+Ed6YfqPmmOnAt8Erm6+ovNv2fm7B58BXs/DS3sA3gYMNx+4/SbwpslOrqqNwN3AaTu5n/8NPKb5sO21wIt20n+8E4F3JLkSWAFs/0TuC4FNSa6h96Lgg9McV5IkSZpTeXily8KQ5AnAxcBBzYeDB3U/e9H7utJK8hrgtVV11HTHGR4erpGRWX9jqSRJkrRDSTZW1YTL36e9pr9NSd4I/AnwjkEG/saz6H24OMD36X1wWJIkSVpwFlTobz4w+4gPzSY5HjhhXNfLqurNs7yvS+h9JackSZK0oC2o0D+RqjqNna/vlyRJkhatmX5PvyRJkqQFwtAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjlvw39M/341uGWPopPPaLkPSIrZ5/dq2S5AktczQPwNJNgP3ANuAB6tquN2KJEmSpMkZ+mfuRVX1vbaLkCRJknbGNf2SJElSxxn6Z6aAC5NsTLKu7WIkSZKkHXF5z8wcUVW3J3kc8JUkN1XV17YfbF4IrANYsu/+bdUoSZIkAV7pn5Gqur25vRM4Gzhs3PENVTVcVcNL9lreRomSJEnSfzH0T1OSRyfZZ/s28FLg+narkiRJkibn8p7pezxwdhLozd+nqurL7ZYkSZIkTc7QP01VdQvwjLbrkCRJkqbK5T2SJElSx3mlf8BWr1zOyPq1bZchSZKkRcwr/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeMM/ZIkSVLHGfolSZKkjlvadgFdN7pljKGTzmu7DEnqtM3r17ZdgiTNawv2Sn+Se9uuQZIkSVoIFmzolyRJkjQ1Cz70J3lhkouTfC7JTUnOTJLm2LOTfD3JtUmuTLJPkj2SnJZkNMk1SV7U9D0uyf+T5ItJbk3yliTvaPpcnuQnmn5PTfLlJBuTXJLkoDYfvyRJkrQzXVnTfyjwdOB24DLgiCRXAp8Bjq6qq5LsC9wPnABQVaubwH5hkgObcQ5uxtoDuBn4n1V1aJIPAG8ETgY2AG+qqn9L8hzgI8CL5+hxSpIkSdPWldB/ZVXdBpBkEzAEjAF3VNVVAFV1d3P854C/btpuSvLvwPbQf1FV3QPck2QM+GLTPgockmRv4HnAWc2bCQDLxheTZB2wDmDJvvvv0gcqSZIkTVdXQv8Dfdvb6D2uADVB30zQNtE4D/XtP9SM+Sjg+1W1ZkfFVNUGeu8IsGzFqolqkCRJkubMgl/TvwM3AU9I8myAZj3/UuBrwDFN24HAk4B/mcqAzbsFtyb51eb8JHnGIIqXJEmSdpXOhv6q+iFwNPDXSa4FvkJvrf5HgCVJRumt+T+uqh6YfKQfcwzwG82YNwBH7drKJUmSpF0rVa4+GaRlK1bVimNPbrsMSeo0f5xLkiDJxqoanuhYV9b0z1urVy5nxCcjSZIktaizy3skSZIk9Rj6JUmSpI4z9EuSJEkdZ+iXJEmSOs7QL0mSJHWcoV+SJEnqOEO/JEmS1HGGfkmSJKnjDP2SJElSxxn6JUmSpI5b2nYBXTe6ZYyhk85ruwxJ0jyxef3atkuQtAh5pV+SJEnquEUX+pNsS7IpyfVJvphkv530X5Pk5XNUniRJkrTLLbrQD9xfVWuq6mDgLuDNO+m/BjD0S5IkacFajKG/3zeAlQBJDkvy9STXNLc/k2R34L3A0c27A0cneXSSjyW5qul7VKuPQJIkSdqJRftB3iRLgF8A/r5pugl4flU9mORI4H1V9aok7waGq+otzXnvA75aVb/eLA26Msk/VtV9LTwMSZIkaacWY+jfM8kmYAjYCHylaV8OfDzJKqCA3SY5/6XALyV5V7O/B/Ak4MbtHZKsA9YBLNl3/11cviRJkjQ9i3F5z/1VtQZ4MrA7D6/p/2Pgomat/yvphfmJBHhV87mANVX1pKq6sb9DVW2oquGqGl6y1/LBPApJkiRpihZj6AegqsaAtwHvSrIbvSv9W5rDx/V1vQfYp2//AuCtSQKQ5NDBVytJkiTN3KIN/QBVdQ1wLfAa4M+BP01yGbCkr9tFwNO2f5CX3jsCuwHXJbm+2ZckSZLmrUW3pr+q9h63/8q+3QP7tv9Xc/wu4NnjhvntwVQnSZIk7XqLLvTPtdUrlzPiT65LkiSpRYt6eY8kSZK0GBj6JUmSpI4z9EuSJEkdZ+iXJEmSOs7QL0mSJHWcoV+SJEnqOEO/JEmS1HGGfkmSJKnjDP2SJElSxxn6JUmSpI5b2nYBXTe6ZYyhk85ruwxJ0iKxef3atkuQNA95pV+SJEnqOEP/OEmWJLkmybkTHEuSDyW5Ocl1SZ7ZRo2SJEnSdBj6f9wJwI2THHsZsKr5Wwd8dK6KkiRJkmbK0N8nyQHAWuDUSbocBZxRPZcD+yVZMWcFSpIkSTNg6H+kk4HfBR6a5PhK4Nt9+7c1bY+QZF2SkSQj27aO7fIiJUmSpOkw9DeSvAK4s6o27qjbBG31Yw1VG6pquKqGl+y1fJfVKEmSJM2Eof9hRwC/lGQz8GngxUk+Oa7PbcAT+/YPAG6fm/IkSZKkmTH0N6rq96rqgKoaAl4DfLWqXj+u2znAG5tv8XkuMFZVd8x1rZIkSdJ0+ONcO5HkTQBVdQpwPvBy4GZgK3B8i6VJkiRJU2Lon0BVXQxc3Gyf0tdewJvbqUqSJEmaGUP/gK1euZwRfxJdkiRJLXJNvyRJktRxhn5JkiSp4wz9kiRJUscZ+iVJkqSOM/RLkiRJHWfolyRJkjrO0C9JkiR1nKFfkiRJ6jhDvyRJktRxhn5JkiSp45a2XUDXjW4ZY+ik89ouQ5KkeWnz+rVtlyAtCgvuSn+SX0lSSQ5qsYYTk+zV1v1LkiRJ07HgQj/wWuBS4DUt1nAiYOiXJEnSgrCgQn+SvYEjgN+gCf1JXpjkn5N8Nsm/Jlmf5JgkVyYZTfLUpt+Tk/xTkuua2yc17acneXXffdzbN+7FST6X5KYkZ6bnbcATgIuSXDTHUyBJkiRN24IK/cAvA1+uqn8F7kryzKb9GcAJwGrgDcCBVXUYcCrw1qbPh4EzquoQ4EzgQ1O4v0PpXdV/GvAU4Iiq+hBwO/CiqnrRrnhQkiRJ0iAttND/WuDTzfanm32Aq6rqjqp6APgWcGHTPgoMNduHA59qtj8B/NwU7u/Kqrqtqh4CNvWNtUNJ1iUZSTKybevYVE6RJEmSBmbBfHtPkp8EXgwcnKSAJUAB5wMP9HV9qG//ISZ/jNXcPkjz4idJgN37+vSPu20HYz1y4KoNwAaAZStW1U66S5IkSQO1kK70v5re8pwnV9VQVT0RuJWpXbEH+DoPf/j3GHofBgbYDDyr2T4K2G0KY90D7DPF+5UkSZJatZBC/2uBs8e1fR543RTPfxtwfJLr6K37P6Fp/zvgBUmuBJ4D3DeFsTYAX/KDvJIkSVoIUuXqk0FatmJVrTj25LbLkCRpXvLHuaRdJ8nGqhqe6NhCutIvSZIkaQYWzAd5F6rVK5cz4lUMSZIktcgr/ZIkSVLHGfolSZKkjjP0S5IkSR1n6JckSZI6ztAvSZIkdZyhX5IkSeo4Q78kSZLUcYZ+SZIkqeMM/ZIkSVLH+Yu8Aza6ZYyhk85ruwxJkiQN2Ob1a9suYVJe6ZckSZI6ztDfJ8nmJKNJNiUZmeB4knwoyc1JrkvyzDbqlCRJkqbD5T0/7kVV9b1Jjr0MWNX8PQf4aHMrSZIkzVte6Z+eo4AzqudyYL8kK9ouSpIkSdoRQ/8jFXBhko1J1k1wfCXw7b7925o2SZIkad5yec8jHVFVtyd5HPCVJDdV1df6jmeCc2p8Q/OCYR3Akn33H0ylkiRJ0hR5pb9PVd3e3N4JnA0cNq7LbcAT+/YPAG6fYJwNVTVcVcNL9lo+qHIlSZKkKTH0N5I8Osk+27eBlwLXj+t2DvDG5lt8nguMVdUdc1yqJEmSNC0u73nY44Gzk0BvXj5VVV9O8iaAqjoFOB94OXAzsBU4vqVaJUmSpCkz9Deq6hbgGRO0n9K3XcCb57IuSZIkabYM/QO2euVyRubxTzJLkiSp+1zTL0mSJHWcoV+SJEnqOEO/JEmS1HGGfkmSJKnjDP2SJElSx6X3LZQalCT3AP/Sdh0d91jge20X0WHO72A5v4PnHA+W8ztYzu/gdWmOn1xV+090wK/sHLx/qarhtovosiQjzvHgOL+D5fwOnnM8WM7vYDm/g7dY5tjlPZIkSVLHGfolSZKkjjP0D96GtgtYBJzjwXJ+B8v5HTzneLCc38FyfgdvUcyxH+SVJEmSOs4r/ZIkSVLHGfpnIckvJvmXJDcnOWmC40nyoeb4dUmeOdVzNfP5TfLEJBcluTHJDUlOmPvq57/Z/Pttji9Jck2Sc+eu6oVllv+P2C/J55Lc1PxbPnxuq5//Zjm/b2/+/3B9kn9IssfcVj//TWF+D0ryjSQPJHnXdM5Vz0zn2Oe5qZnNv+HmeLee56rKvxn8AUuAbwFPAXYHrgWeNq7Py4EvAQGeC1wx1XMX+98s53cF8Mxmex/gX53fXTe/fcffAXwKOLftxzMf/2Y7x8DHgd9stncH9mv7Mc2nv1n+P2IlcCuwZ7P/WeC4th/TfPqb4vw+Dng28CfAu6Zzrn+znmOf5wY4v33HO/U855X+mTsMuLmqbqmqHwKfBo4a1+co4IzquRzYL8mKKZ672M14fqvqjqq6GqCq7gFupPckr4fN5t8vSQ4A1gKnzmXRC8yM5zjJvsDzgb8HqKofVtX357D2hWBW/4bp/U7NnkmWAnsBt89V4QvETue3qu6sqquAH033XAGzmGOf56ZkNv+GO/k8Z+ifuZXAt/v2b+PH/4ObrM9Uzl3sZjO//yXJEHAocMWuL3FBm+38ngz8LvDQgOrrgtnM8VOA7wKnNW8tn5rk0YMsdgGa8fxW1RbgL4H/A9wBjFXVhQOsdSGazfOUz3FTs0vmyee5Sc12fk+mY89zhv6ZywRt478KabI+Uzl3sZvN/PYOJnsDnwdOrKq7d2FtXTDj+U3yCuDOqtq468vqlNn8G14KPBP4aFUdCtwHuC76kWbzb/gx9K74/TTwBODRSV6/i+tb6GbzPOVz3NTMep58ntuhGc9vV5/nDP0zdxvwxL79A/jxt4cn6zOVcxe72cwvSXaj9z/CM6vqCwOsc6GazfweAfxSks303i59cZJPDq7UBWu2/4+4raq2X7n7HL0XAXrYbOb3SODWqvpuVf0I+ALwvAHWuhDN5nnK57ipmdU8+Ty3U7OZ304+zxn6Z+4qYFWSn06yO/Aa4Jxxfc4B3th8g8Rz6b2FfMcUz13sZjy/SUJvLfSNVfX+uS17wZjx/FbV71XVAVU11Jz31aryKumPm80c/3/At5P8TNPvF4BvzlnlC8Ns/h/8f4DnJtmr+f/FL9BbE62HzeZ5yue4qZnxPPk8NyUznt+uPs8tbbuAhaqqHkzyFuACep8Q/1hV3ZDkTc3xU4Dz6X17xM3AVuD4HZ3bwsOYt2Yzv/Reob8BGE2yqWn7/ao6fw4fwrw2y/nVFOyCOX4rcGbzZHULzv8jzPL/wVck+RxwNfAgcA2L5Bc5p2oq85vkp4ARYF/goSQn0vt2lLt9jtu52cwxcAg+z+3QbP8Nt1X3IPmLvJIkSVLHubxHkiRJ6jhDvyRJktRxhn5JkiSp4wz9kiRJUscZ+iVJkqSOM/RLkiRJHWfolyRJkjrO0C9JkiR13P8PwoLFmceujHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e3ab36",
   "metadata": {},
   "source": [
    "## Fitting model using Logistic Regression\n",
    "- Split dataset into train and test set in order to prediction w.r.t X_test\n",
    "- Scaling is required in logistic regression\n",
    "- Oversampling with smote\n",
    "- Import model\n",
    "- Fit the data\n",
    "- Predict w.r.t X_test\n",
    "- In classification we check accuracy Score\n",
    "- Plot error grap\n",
    "- Model comparision \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bb8220c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#Split train-test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4dc1f978",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### standarisation: We use the Standardscaler from sklearn library\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "### fit vs fit_transform\n",
    "X_train_scaled=scaler.fit_transform(X_train) #alway give parameter in fit_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2235d009",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4bfc118f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import SMOTE oversampling and other necessary libraries \n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "74474c4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before oversampling:  Counter({0: 12221, 1: 1431})\n",
      "After oversampling:  Counter({0: 12221, 1: 12221})\n"
     ]
    }
   ],
   "source": [
    "# summarize class distribution\n",
    "print(\"Before oversampling: \",Counter(y_train))\n",
    "\n",
    "# define oversampling strategy\n",
    "SMOTE = SMOTE()\n",
    "\n",
    "# fit and apply the transform\n",
    "X_train_SMOTE, y_train_SMOTE = SMOTE.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "# summarize class distribution\n",
    "print(\"After oversampling: \",Counter(y_train_SMOTE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9f1b77de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf=LogisticRegression()\n",
    "clf.fit(X_train_SMOTE,y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c5811852",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the model\n",
    "y_pred=clf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b15fab84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4751  485]\n",
      " [  77  539]]\n",
      "The Model accuracy is 0.9039644565960355\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.91      0.94      5236\n",
      "           1       0.53      0.88      0.66       616\n",
      "\n",
      "    accuracy                           0.90      5852\n",
      "   macro avg       0.76      0.89      0.80      5852\n",
      "weighted avg       0.94      0.90      0.91      5852\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print('The Model accuracy is',accuracy)\n",
    "report= classification_report(y_test,y_pred)\n",
    "print(report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f41f963b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1450312",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3dd3fbb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.89492568 0.89492568 0.91235264 0.90722706 0.90102564 0.89487179\n",
      " 0.89487179 0.89538462 0.90461538 0.89538462]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8995584907148209"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score= cross_val_score(clf,X,y,cv=10)\n",
    "print(score)\n",
    "np.mean(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bdb899",
   "metadata": {},
   "source": [
    "### Model2: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b7395f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# building model by Random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier=RandomForestClassifier()\n",
    "classifier.fit(X_train_SMOTE,y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "887c751d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0d8d3edc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5085  151]\n",
      " [ 173  443]]\n",
      "The Model accuracy is 0.9446343130553657\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97      5236\n",
      "           1       0.75      0.72      0.73       616\n",
      "\n",
      "    accuracy                           0.94      5852\n",
      "   macro avg       0.86      0.85      0.85      5852\n",
      "weighted avg       0.94      0.94      0.94      5852\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print('The Model accuracy is',accuracy)\n",
    "report= classification_report(y_test,y_pred)\n",
    "print(report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f6b93e",
   "metadata": {},
   "source": [
    "**If we compare logistic model with random forest the accurcay and precision is quite much better than logistic model. It mean random forest model is better**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda27ff7",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning\n",
    "#### The main parameters used by a Random Forest Classifier are:\n",
    "- criterion = the function used to evaluate the quality of a split.\n",
    "- max_depth = maximum number of levels allowed in each tree.\n",
    "- max_features = maximum number of features considered when splitting a node.\n",
    "- min_samples_leaf = minimum number of samples which can be stored in a tree leaf.\n",
    "- min_samples_split = minimum number of samples necessary in a node to cause node splitting.\n",
    "- n_estimators = number of trees in the ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d3cfbc",
   "metadata": {},
   "source": [
    "#### Randomized Search Cv\n",
    "In the Randomised SearchCv it will work on no of itiration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "68939371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [200, 314, 428, 542, 657, 771, 885, 1000], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth': [10, 37, 64, 91, 118, 145, 172, 200], 'min_samples_split': [2, 5, 7, 9, 10], 'min_samples_leaf': [1, 2, 3, 4, 5], 'criterion': ['entropy', 'gini']}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop =1000, num = 8)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt','log2']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 200,8)]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 7,9,10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2,3, 4,5]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "              'criterion':['entropy','gini']}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cd1825dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building Randomised SearchCv model\n",
    "rf_randomcv=RandomizedSearchCV(estimator=classifier,param_distributions=random_grid,n_iter=10,cv=5,verbose=2,\n",
    "                               random_state=100,n_jobs=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1ce271bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['entropy', 'gini'],\n",
       "                                        'max_depth': [10, 37, 64, 91, 118, 145,\n",
       "                                                      172, 200],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 4, 5],\n",
       "                                        'min_samples_split': [2, 5, 7, 9, 10],\n",
       "                                        'n_estimators': [200, 314, 428, 542,\n",
       "                                                         657, 771, 885, 1000]},\n",
       "                   random_state=100, verbose=2)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fit the randomized model\n",
    "rf_randomcv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e7d1a76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 428,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 37,\n",
       " 'criterion': 'entropy'}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the best parameter we should give in \n",
    "rf_randomcv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba43f89",
   "metadata": {},
   "source": [
    "**After getting the best Parameter for random forest classifier , we will now predict the model**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1e8c82f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5087  149]\n",
      " [ 168  448]]\n",
      "0.9458304853041695\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97      5236\n",
      "           1       0.75      0.73      0.74       616\n",
      "\n",
      "    accuracy                           0.95      5852\n",
      "   macro avg       0.86      0.85      0.85      5852\n",
      "weighted avg       0.95      0.95      0.95      5852\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=RandomForestClassifier(n_estimators=428,criterion='entropy',max_depth=37,\n",
    "                             max_features='sqrt',min_samples_leaf=1,min_samples_split=2,random_state=10).fit(X_train_SMOTE,y_train_SMOTE)\n",
    "predictions=model.predict(X_test_scaled)\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(accuracy_score(y_test,predictions))\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1101e622",
   "metadata": {},
   "source": [
    "**We conclude that that the accuracy after doing Randomized Search Cv, the model accuracy is almost same, Even without doing hyperparameter tuning our model is giving us better accurcay but the precision & recall is a bit better than our previous model after Randomized Search Cv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "45ddf71d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=37, max_features='sqrt',\n",
       "                       n_estimators=428)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding best values estimators which will go in  RandomForestClassifie()\n",
    "best_random_grid=rf_randomcv.best_estimator_\n",
    "best_random_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24eede8c",
   "metadata": {},
   "source": [
    "**This is accuracy with Randomized Search Cv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "69ce78a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5163   73]\n",
      " [ 226  390]]\n",
      "Accuracy Score= 0.9489063568010937\n",
      "Classification report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97      5236\n",
      "           1       0.84      0.63      0.72       616\n",
      "\n",
      "    accuracy                           0.95      5852\n",
      "   macro avg       0.90      0.81      0.85      5852\n",
      "weighted avg       0.95      0.95      0.95      5852\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#predicting the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred=best_random_grid.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(\"Accuracy Score= {}\".format(accuracy_score(y_test,y_pred)))\n",
    "print(\"Classification report: {}\".format(classification_report(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2055315",
   "metadata": {},
   "source": [
    "## Statistic Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "492106bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#adding constant term\n",
    "x_train=sm.add_constant(X_train)\n",
    "x_test=sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3927f1ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Return</td>      <th>  R-squared:         </th> <td>   0.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   297.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Oct 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:22:10</td>     <th>  Log-Likelihood:    </th> <td>  823.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 13652</td>      <th>  AIC:               </th> <td>  -1570.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 13614</td>      <th>  BIC:               </th> <td>  -1285.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    37</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>               <td>    0.2216</td> <td>    0.014</td> <td>   16.235</td> <td> 0.000</td> <td>    0.195</td> <td>    0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rate</th>                <td>    0.0002</td> <td> 8.86e-06</td> <td>   25.178</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Amount</th>              <td> -6.77e-05</td> <td> 2.67e-06</td> <td>  -25.388</td> <td> 0.000</td> <td>-7.29e-05</td> <td>-6.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delivery_chrgs</th>      <td>   -0.0001</td> <td> 3.07e-05</td> <td>   -3.622</td> <td> 0.000</td> <td>   -0.000</td> <td> -5.1e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income</th>              <td> 4.807e-07</td> <td> 5.25e-08</td> <td>    9.162</td> <td> 0.000</td> <td> 3.78e-07</td> <td> 5.84e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marital_status</th>      <td>    0.0050</td> <td>    0.004</td> <td>    1.272</td> <td> 0.203</td> <td>   -0.003</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fiction</th>             <td>   -0.0010</td> <td>    0.014</td> <td>   -0.072</td> <td> 0.942</td> <td>   -0.028</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Furnishing</th>          <td>    0.0366</td> <td>    0.013</td> <td>    2.818</td> <td> 0.005</td> <td>    0.011</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kids</th>                <td>   -0.0055</td> <td>    0.009</td> <td>   -0.633</td> <td> 0.527</td> <td>   -0.023</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kitchen</th>             <td>    0.0315</td> <td>    0.013</td> <td>    2.445</td> <td> 0.015</td> <td>    0.006</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Non-Fiction</th>         <td>   -0.0141</td> <td>    0.013</td> <td>   -1.049</td> <td> 0.294</td> <td>   -0.041</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Personal Appliances</th> <td>   -0.0051</td> <td>    0.017</td> <td>   -0.294</td> <td> 0.769</td> <td>   -0.039</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tools</th>               <td>   -0.0117</td> <td>    0.013</td> <td>   -0.890</td> <td> 0.373</td> <td>   -0.037</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women</th>               <td>    0.0701</td> <td>    0.005</td> <td>   12.939</td> <td> 0.000</td> <td>    0.059</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>                   <td>    0.0507</td> <td>    0.007</td> <td>    7.666</td> <td> 0.000</td> <td>    0.038</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>                   <td>    0.1067</td> <td>    0.007</td> <td>   14.408</td> <td> 0.000</td> <td>    0.092</td> <td>    0.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>                   <td>    0.2310</td> <td>    0.008</td> <td>   27.586</td> <td> 0.000</td> <td>    0.215</td> <td>    0.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>                   <td>    0.3352</td> <td>    0.009</td> <td>   36.512</td> <td> 0.000</td> <td>    0.317</td> <td>    0.353</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>August</th>              <td>    0.0007</td> <td>    0.010</td> <td>    0.065</td> <td> 0.948</td> <td>   -0.019</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>December</th>            <td>    0.0770</td> <td>    0.010</td> <td>    7.979</td> <td> 0.000</td> <td>    0.058</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>February</th>            <td>   -0.0139</td> <td>    0.010</td> <td>   -1.413</td> <td> 0.158</td> <td>   -0.033</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>January</th>             <td>    0.0695</td> <td>    0.009</td> <td>    7.347</td> <td> 0.000</td> <td>    0.051</td> <td>    0.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>July</th>                <td>   -0.0038</td> <td>    0.010</td> <td>   -0.375</td> <td> 0.708</td> <td>   -0.023</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>June</th>                <td>   -0.0026</td> <td>    0.010</td> <td>   -0.257</td> <td> 0.798</td> <td>   -0.023</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>March</th>               <td>   -0.0022</td> <td>    0.010</td> <td>   -0.220</td> <td> 0.826</td> <td>   -0.022</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>                 <td>    0.0704</td> <td>    0.010</td> <td>    7.210</td> <td> 0.000</td> <td>    0.051</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>November</th>            <td>    0.0758</td> <td>    0.010</td> <td>    7.839</td> <td> 0.000</td> <td>    0.057</td> <td>    0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>October</th>             <td>    0.0741</td> <td>    0.010</td> <td>    7.762</td> <td> 0.000</td> <td>    0.055</td> <td>    0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>September</th>           <td>   -0.0114</td> <td>    0.010</td> <td>   -1.144</td> <td> 0.253</td> <td>   -0.031</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Books</th>               <td>   -0.0197</td> <td>    0.010</td> <td>   -2.008</td> <td> 0.045</td> <td>   -0.039</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Clothing</th>            <td>   -0.0286</td> <td>    0.007</td> <td>   -4.257</td> <td> 0.000</td> <td>   -0.042</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electronics</th>         <td>   -0.0065</td> <td>    0.010</td> <td>   -0.671</td> <td> 0.502</td> <td>   -0.026</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Footwear</th>            <td>   -0.0260</td> <td>    0.007</td> <td>   -3.676</td> <td> 0.000</td> <td>   -0.040</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Home and kitchen</th>    <td>    0.0055</td> <td>    0.011</td> <td>    0.497</td> <td> 0.619</td> <td>   -0.016</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2.0</th>                 <td>   -0.2272</td> <td>    0.009</td> <td>  -25.307</td> <td> 0.000</td> <td>   -0.245</td> <td>   -0.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3.0</th>                 <td>   -0.3768</td> <td>    0.009</td> <td>  -39.880</td> <td> 0.000</td> <td>   -0.395</td> <td>   -0.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4.0</th>                 <td>   -0.4199</td> <td>    0.007</td> <td>  -60.360</td> <td> 0.000</td> <td>   -0.434</td> <td>   -0.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5.0</th>                 <td>   -0.4344</td> <td>    0.006</td> <td>  -68.224</td> <td> 0.000</td> <td>   -0.447</td> <td>   -0.422</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3217.110</td> <th>  Durbin-Watson:     </th> <td>   2.016</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>11759.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.150</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.922</td>  <th>  Cond. No.          </th> <td>1.06e+06</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.06e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Return   R-squared:                       0.447\n",
       "Model:                            OLS   Adj. R-squared:                  0.445\n",
       "Method:                 Least Squares   F-statistic:                     297.3\n",
       "Date:                Sun, 03 Oct 2021   Prob (F-statistic):               0.00\n",
       "Time:                        12:22:10   Log-Likelihood:                 823.16\n",
       "No. Observations:               13652   AIC:                            -1570.\n",
       "Df Residuals:                   13614   BIC:                            -1285.\n",
       "Df Model:                          37                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const                   0.2216      0.014     16.235      0.000       0.195       0.248\n",
       "Rate                    0.0002   8.86e-06     25.178      0.000       0.000       0.000\n",
       "Amount               -6.77e-05   2.67e-06    -25.388      0.000   -7.29e-05   -6.25e-05\n",
       "Delivery_chrgs         -0.0001   3.07e-05     -3.622      0.000      -0.000    -5.1e-05\n",
       "Income               4.807e-07   5.25e-08      9.162      0.000    3.78e-07    5.84e-07\n",
       "Marital_status          0.0050      0.004      1.272      0.203      -0.003       0.013\n",
       "Fiction                -0.0010      0.014     -0.072      0.942      -0.028       0.026\n",
       "Furnishing              0.0366      0.013      2.818      0.005       0.011       0.062\n",
       "Kids                   -0.0055      0.009     -0.633      0.527      -0.023       0.012\n",
       "Kitchen                 0.0315      0.013      2.445      0.015       0.006       0.057\n",
       "Non-Fiction            -0.0141      0.013     -1.049      0.294      -0.041       0.012\n",
       "Personal Appliances    -0.0051      0.017     -0.294      0.769      -0.039       0.029\n",
       "Tools                  -0.0117      0.013     -0.890      0.373      -0.037       0.014\n",
       "Women                   0.0701      0.005     12.939      0.000       0.059       0.081\n",
       "2                       0.0507      0.007      7.666      0.000       0.038       0.064\n",
       "3                       0.1067      0.007     14.408      0.000       0.092       0.121\n",
       "4                       0.2310      0.008     27.586      0.000       0.215       0.247\n",
       "5                       0.3352      0.009     36.512      0.000       0.317       0.353\n",
       "August                  0.0007      0.010      0.065      0.948      -0.019       0.020\n",
       "December                0.0770      0.010      7.979      0.000       0.058       0.096\n",
       "February               -0.0139      0.010     -1.413      0.158      -0.033       0.005\n",
       "January                 0.0695      0.009      7.347      0.000       0.051       0.088\n",
       "July                   -0.0038      0.010     -0.375      0.708      -0.023       0.016\n",
       "June                   -0.0026      0.010     -0.257      0.798      -0.023       0.017\n",
       "March                  -0.0022      0.010     -0.220      0.826      -0.022       0.017\n",
       "May                     0.0704      0.010      7.210      0.000       0.051       0.089\n",
       "November                0.0758      0.010      7.839      0.000       0.057       0.095\n",
       "October                 0.0741      0.010      7.762      0.000       0.055       0.093\n",
       "September              -0.0114      0.010     -1.144      0.253      -0.031       0.008\n",
       "Books                  -0.0197      0.010     -2.008      0.045      -0.039      -0.000\n",
       "Clothing               -0.0286      0.007     -4.257      0.000      -0.042      -0.015\n",
       "Electronics            -0.0065      0.010     -0.671      0.502      -0.026       0.013\n",
       "Footwear               -0.0260      0.007     -3.676      0.000      -0.040      -0.012\n",
       "Home and kitchen        0.0055      0.011      0.497      0.619      -0.016       0.027\n",
       "2.0                    -0.2272      0.009    -25.307      0.000      -0.245      -0.210\n",
       "3.0                    -0.3768      0.009    -39.880      0.000      -0.395      -0.358\n",
       "4.0                    -0.4199      0.007    -60.360      0.000      -0.434      -0.406\n",
       "5.0                    -0.4344      0.006    -68.224      0.000      -0.447      -0.422\n",
       "==============================================================================\n",
       "Omnibus:                     3217.110   Durbin-Watson:                   2.016\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11759.855\n",
       "Skew:                           1.150   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.922   Cond. No.                     1.06e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.06e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train=sm.OLS(y_train,x_train.astype(float)).fit()\n",
    "model_train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d651a99e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding constant term\n",
    "x_train=sm.add_constant(X_train)\n",
    "x_test=sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a69a41c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Return</td>      <th>  R-squared (uncentered):</th>      <td>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   516.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Oct 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:22:10</td>     <th>  Log-Likelihood:    </th>          <td>  978.50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19504</td>      <th>  AIC:               </th>          <td>  -1883.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19467</td>      <th>  BIC:               </th>          <td>  -1592.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    37</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rate</th>                <td>    0.0003</td> <td> 6.81e-06</td> <td>   42.580</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Amount</th>              <td>-8.394e-05</td> <td> 2.15e-06</td> <td>  -39.106</td> <td> 0.000</td> <td>-8.81e-05</td> <td>-7.97e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delivery_chrgs</th>      <td>-9.467e-05</td> <td> 2.57e-05</td> <td>   -3.681</td> <td> 0.000</td> <td>   -0.000</td> <td>-4.43e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income</th>              <td> 6.694e-07</td> <td> 4.24e-08</td> <td>   15.771</td> <td> 0.000</td> <td> 5.86e-07</td> <td> 7.53e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marital_status</th>      <td>    0.0148</td> <td>    0.003</td> <td>    4.536</td> <td> 0.000</td> <td>    0.008</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fiction</th>             <td>   -0.0070</td> <td>    0.012</td> <td>   -0.598</td> <td> 0.550</td> <td>   -0.030</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Furnishing</th>          <td>    0.0408</td> <td>    0.011</td> <td>    3.754</td> <td> 0.000</td> <td>    0.020</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kids</th>                <td>    0.0021</td> <td>    0.007</td> <td>    0.285</td> <td> 0.775</td> <td>   -0.012</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kitchen</th>             <td>    0.0388</td> <td>    0.011</td> <td>    3.531</td> <td> 0.000</td> <td>    0.017</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Non-Fiction</th>         <td>   -0.0054</td> <td>    0.011</td> <td>   -0.467</td> <td> 0.640</td> <td>   -0.028</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Personal Appliances</th> <td>   -0.0068</td> <td>    0.015</td> <td>   -0.458</td> <td> 0.647</td> <td>   -0.036</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tools</th>               <td>   -0.0062</td> <td>    0.011</td> <td>   -0.558</td> <td> 0.577</td> <td>   -0.028</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women</th>               <td>    0.0919</td> <td>    0.004</td> <td>   20.657</td> <td> 0.000</td> <td>    0.083</td> <td>    0.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>                   <td>    0.0882</td> <td>    0.005</td> <td>   16.588</td> <td> 0.000</td> <td>    0.078</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>                   <td>    0.1546</td> <td>    0.006</td> <td>   26.462</td> <td> 0.000</td> <td>    0.143</td> <td>    0.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>                   <td>    0.2922</td> <td>    0.006</td> <td>   45.249</td> <td> 0.000</td> <td>    0.280</td> <td>    0.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>                   <td>    0.4050</td> <td>    0.007</td> <td>   57.595</td> <td> 0.000</td> <td>    0.391</td> <td>    0.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>August</th>              <td>    0.0572</td> <td>    0.008</td> <td>    7.257</td> <td> 0.000</td> <td>    0.042</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>December</th>            <td>    0.1409</td> <td>    0.008</td> <td>   18.765</td> <td> 0.000</td> <td>    0.126</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>February</th>            <td>    0.0508</td> <td>    0.008</td> <td>    6.612</td> <td> 0.000</td> <td>    0.036</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>January</th>             <td>    0.1367</td> <td>    0.007</td> <td>   18.632</td> <td> 0.000</td> <td>    0.122</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>July</th>                <td>    0.0573</td> <td>    0.008</td> <td>    7.259</td> <td> 0.000</td> <td>    0.042</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>June</th>                <td>    0.0640</td> <td>    0.008</td> <td>    7.989</td> <td> 0.000</td> <td>    0.048</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>March</th>               <td>    0.0611</td> <td>    0.008</td> <td>    7.838</td> <td> 0.000</td> <td>    0.046</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>                 <td>    0.1401</td> <td>    0.008</td> <td>   18.639</td> <td> 0.000</td> <td>    0.125</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>November</th>            <td>    0.1393</td> <td>    0.007</td> <td>   18.596</td> <td> 0.000</td> <td>    0.125</td> <td>    0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>October</th>             <td>    0.1352</td> <td>    0.007</td> <td>   18.124</td> <td> 0.000</td> <td>    0.121</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>September</th>           <td>    0.0526</td> <td>    0.008</td> <td>    6.725</td> <td> 0.000</td> <td>    0.037</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Books</th>               <td>    0.0237</td> <td>    0.008</td> <td>    2.979</td> <td> 0.003</td> <td>    0.008</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Clothing</th>            <td>    0.0037</td> <td>    0.005</td> <td>    0.687</td> <td> 0.492</td> <td>   -0.007</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electronics</th>         <td>    0.0397</td> <td>    0.008</td> <td>    5.031</td> <td> 0.000</td> <td>    0.024</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Footwear</th>            <td>    0.0080</td> <td>    0.006</td> <td>    1.411</td> <td> 0.158</td> <td>   -0.003</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Home and kitchen</th>    <td>    0.0372</td> <td>    0.009</td> <td>    4.095</td> <td> 0.000</td> <td>    0.019</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2.0</th>                 <td>   -0.1830</td> <td>    0.007</td> <td>  -25.403</td> <td> 0.000</td> <td>   -0.197</td> <td>   -0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3.0</th>                 <td>   -0.3337</td> <td>    0.008</td> <td>  -43.580</td> <td> 0.000</td> <td>   -0.349</td> <td>   -0.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4.0</th>                 <td>   -0.3734</td> <td>    0.005</td> <td>  -70.041</td> <td> 0.000</td> <td>   -0.384</td> <td>   -0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5.0</th>                 <td>   -0.3865</td> <td>    0.005</td> <td>  -81.049</td> <td> 0.000</td> <td>   -0.396</td> <td>   -0.377</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4895.497</td> <th>  Durbin-Watson:     </th> <td>   1.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>16211.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.263</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.684</td>  <th>  Cond. No.          </th> <td>9.10e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 9.1e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                 Return   R-squared (uncentered):                   0.495\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.494\n",
       "Method:                 Least Squares   F-statistic:                              516.5\n",
       "Date:                Sun, 03 Oct 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        12:22:10   Log-Likelihood:                          978.50\n",
       "No. Observations:               19504   AIC:                                     -1883.\n",
       "Df Residuals:                   19467   BIC:                                     -1592.\n",
       "Df Model:                          37                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Rate                    0.0003   6.81e-06     42.580      0.000       0.000       0.000\n",
       "Amount              -8.394e-05   2.15e-06    -39.106      0.000   -8.81e-05   -7.97e-05\n",
       "Delivery_chrgs      -9.467e-05   2.57e-05     -3.681      0.000      -0.000   -4.43e-05\n",
       "Income               6.694e-07   4.24e-08     15.771      0.000    5.86e-07    7.53e-07\n",
       "Marital_status          0.0148      0.003      4.536      0.000       0.008       0.021\n",
       "Fiction                -0.0070      0.012     -0.598      0.550      -0.030       0.016\n",
       "Furnishing              0.0408      0.011      3.754      0.000       0.020       0.062\n",
       "Kids                    0.0021      0.007      0.285      0.775      -0.012       0.016\n",
       "Kitchen                 0.0388      0.011      3.531      0.000       0.017       0.060\n",
       "Non-Fiction            -0.0054      0.011     -0.467      0.640      -0.028       0.017\n",
       "Personal Appliances    -0.0068      0.015     -0.458      0.647      -0.036       0.022\n",
       "Tools                  -0.0062      0.011     -0.558      0.577      -0.028       0.016\n",
       "Women                   0.0919      0.004     20.657      0.000       0.083       0.101\n",
       "2                       0.0882      0.005     16.588      0.000       0.078       0.099\n",
       "3                       0.1546      0.006     26.462      0.000       0.143       0.166\n",
       "4                       0.2922      0.006     45.249      0.000       0.280       0.305\n",
       "5                       0.4050      0.007     57.595      0.000       0.391       0.419\n",
       "August                  0.0572      0.008      7.257      0.000       0.042       0.073\n",
       "December                0.1409      0.008     18.765      0.000       0.126       0.156\n",
       "February                0.0508      0.008      6.612      0.000       0.036       0.066\n",
       "January                 0.1367      0.007     18.632      0.000       0.122       0.151\n",
       "July                    0.0573      0.008      7.259      0.000       0.042       0.073\n",
       "June                    0.0640      0.008      7.989      0.000       0.048       0.080\n",
       "March                   0.0611      0.008      7.838      0.000       0.046       0.076\n",
       "May                     0.1401      0.008     18.639      0.000       0.125       0.155\n",
       "November                0.1393      0.007     18.596      0.000       0.125       0.154\n",
       "October                 0.1352      0.007     18.124      0.000       0.121       0.150\n",
       "September               0.0526      0.008      6.725      0.000       0.037       0.068\n",
       "Books                   0.0237      0.008      2.979      0.003       0.008       0.039\n",
       "Clothing                0.0037      0.005      0.687      0.492      -0.007       0.014\n",
       "Electronics             0.0397      0.008      5.031      0.000       0.024       0.055\n",
       "Footwear                0.0080      0.006      1.411      0.158      -0.003       0.019\n",
       "Home and kitchen        0.0372      0.009      4.095      0.000       0.019       0.055\n",
       "2.0                    -0.1830      0.007    -25.403      0.000      -0.197      -0.169\n",
       "3.0                    -0.3337      0.008    -43.580      0.000      -0.349      -0.319\n",
       "4.0                    -0.3734      0.005    -70.041      0.000      -0.384      -0.363\n",
       "5.0                    -0.3865      0.005    -81.049      0.000      -0.396      -0.377\n",
       "==============================================================================\n",
       "Omnibus:                     4895.497   Durbin-Watson:                   1.907\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            16211.819\n",
       "Skew:                           1.263   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.684   Cond. No.                     9.10e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 9.1e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train=sm.OLS(y,X.astype(float)).fit()\n",
    "model_train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "49591aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['Fiction','Tools','Personal Appliances','Kids','Fiction'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b648a391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Return</td>      <th>  R-squared (uncentered):</th>      <td>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   579.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Oct 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:22:10</td>     <th>  Log-Likelihood:    </th>          <td>  978.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19504</td>      <th>  AIC:               </th>          <td>  -1890.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19471</td>      <th>  BIC:               </th>          <td>  -1630.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    33</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rate</th>             <td>    0.0003</td> <td> 6.81e-06</td> <td>   42.613</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Amount</th>           <td>-8.394e-05</td> <td> 2.15e-06</td> <td>  -39.127</td> <td> 0.000</td> <td>-8.81e-05</td> <td>-7.97e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delivery_chrgs</th>   <td>-9.447e-05</td> <td> 2.57e-05</td> <td>   -3.674</td> <td> 0.000</td> <td>   -0.000</td> <td>-4.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income</th>           <td> 6.698e-07</td> <td> 4.24e-08</td> <td>   15.791</td> <td> 0.000</td> <td> 5.87e-07</td> <td> 7.53e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marital_status</th>   <td>    0.0149</td> <td>    0.003</td> <td>    4.543</td> <td> 0.000</td> <td>    0.008</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Furnishing</th>       <td>    0.0439</td> <td>    0.009</td> <td>    4.701</td> <td> 0.000</td> <td>    0.026</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kitchen</th>          <td>    0.0419</td> <td>    0.009</td> <td>    4.423</td> <td> 0.000</td> <td>    0.023</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Non-Fiction</th>      <td>   -0.0032</td> <td>    0.011</td> <td>   -0.292</td> <td> 0.770</td> <td>   -0.025</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women</th>            <td>    0.0915</td> <td>    0.004</td> <td>   21.688</td> <td> 0.000</td> <td>    0.083</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>                <td>    0.0882</td> <td>    0.005</td> <td>   16.607</td> <td> 0.000</td> <td>    0.078</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>                <td>    0.1547</td> <td>    0.006</td> <td>   26.513</td> <td> 0.000</td> <td>    0.143</td> <td>    0.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>                <td>    0.2922</td> <td>    0.006</td> <td>   45.293</td> <td> 0.000</td> <td>    0.280</td> <td>    0.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>                <td>    0.4050</td> <td>    0.007</td> <td>   57.658</td> <td> 0.000</td> <td>    0.391</td> <td>    0.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>August</th>           <td>    0.0573</td> <td>    0.008</td> <td>    7.278</td> <td> 0.000</td> <td>    0.042</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>December</th>         <td>    0.1411</td> <td>    0.008</td> <td>   18.798</td> <td> 0.000</td> <td>    0.126</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>February</th>         <td>    0.0509</td> <td>    0.008</td> <td>    6.641</td> <td> 0.000</td> <td>    0.036</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>January</th>          <td>    0.1368</td> <td>    0.007</td> <td>   18.655</td> <td> 0.000</td> <td>    0.122</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>July</th>             <td>    0.0574</td> <td>    0.008</td> <td>    7.285</td> <td> 0.000</td> <td>    0.042</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>June</th>             <td>    0.0641</td> <td>    0.008</td> <td>    8.012</td> <td> 0.000</td> <td>    0.048</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>March</th>            <td>    0.0611</td> <td>    0.008</td> <td>    7.851</td> <td> 0.000</td> <td>    0.046</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>              <td>    0.1402</td> <td>    0.008</td> <td>   18.663</td> <td> 0.000</td> <td>    0.125</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>November</th>         <td>    0.1394</td> <td>    0.007</td> <td>   18.627</td> <td> 0.000</td> <td>    0.125</td> <td>    0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>October</th>          <td>    0.1354</td> <td>    0.007</td> <td>   18.164</td> <td> 0.000</td> <td>    0.121</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>September</th>        <td>    0.0527</td> <td>    0.008</td> <td>    6.741</td> <td> 0.000</td> <td>    0.037</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Books</th>            <td>    0.0213</td> <td>    0.007</td> <td>    3.031</td> <td> 0.002</td> <td>    0.008</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Clothing</th>         <td>    0.0037</td> <td>    0.005</td> <td>    0.693</td> <td> 0.488</td> <td>   -0.007</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electronics</th>      <td>    0.0383</td> <td>    0.007</td> <td>    5.205</td> <td> 0.000</td> <td>    0.024</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Footwear</th>         <td>    0.0087</td> <td>    0.005</td> <td>    1.656</td> <td> 0.098</td> <td>   -0.002</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Home and kitchen</th> <td>    0.0339</td> <td>    0.007</td> <td>    4.735</td> <td> 0.000</td> <td>    0.020</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2.0</th>              <td>   -0.1831</td> <td>    0.007</td> <td>  -25.418</td> <td> 0.000</td> <td>   -0.197</td> <td>   -0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3.0</th>              <td>   -0.3337</td> <td>    0.008</td> <td>  -43.606</td> <td> 0.000</td> <td>   -0.349</td> <td>   -0.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4.0</th>              <td>   -0.3735</td> <td>    0.005</td> <td>  -70.079</td> <td> 0.000</td> <td>   -0.384</td> <td>   -0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5.0</th>              <td>   -0.3865</td> <td>    0.005</td> <td>  -81.085</td> <td> 0.000</td> <td>   -0.396</td> <td>   -0.377</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4895.749</td> <th>  Durbin-Watson:     </th> <td>   1.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>16211.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.263</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.683</td>  <th>  Cond. No.          </th> <td>9.04e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 9.04e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                 Return   R-squared (uncentered):                   0.495\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.495\n",
       "Method:                 Least Squares   F-statistic:                              579.2\n",
       "Date:                Sun, 03 Oct 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        12:22:10   Log-Likelihood:                          978.02\n",
       "No. Observations:               19504   AIC:                                     -1890.\n",
       "Df Residuals:                   19471   BIC:                                     -1630.\n",
       "Df Model:                          33                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Rate                 0.0003   6.81e-06     42.613      0.000       0.000       0.000\n",
       "Amount           -8.394e-05   2.15e-06    -39.127      0.000   -8.81e-05   -7.97e-05\n",
       "Delivery_chrgs   -9.447e-05   2.57e-05     -3.674      0.000      -0.000   -4.41e-05\n",
       "Income            6.698e-07   4.24e-08     15.791      0.000    5.87e-07    7.53e-07\n",
       "Marital_status       0.0149      0.003      4.543      0.000       0.008       0.021\n",
       "Furnishing           0.0439      0.009      4.701      0.000       0.026       0.062\n",
       "Kitchen              0.0419      0.009      4.423      0.000       0.023       0.060\n",
       "Non-Fiction         -0.0032      0.011     -0.292      0.770      -0.025       0.018\n",
       "Women                0.0915      0.004     21.688      0.000       0.083       0.100\n",
       "2                    0.0882      0.005     16.607      0.000       0.078       0.099\n",
       "3                    0.1547      0.006     26.513      0.000       0.143       0.166\n",
       "4                    0.2922      0.006     45.293      0.000       0.280       0.305\n",
       "5                    0.4050      0.007     57.658      0.000       0.391       0.419\n",
       "August               0.0573      0.008      7.278      0.000       0.042       0.073\n",
       "December             0.1411      0.008     18.798      0.000       0.126       0.156\n",
       "February             0.0509      0.008      6.641      0.000       0.036       0.066\n",
       "January              0.1368      0.007     18.655      0.000       0.122       0.151\n",
       "July                 0.0574      0.008      7.285      0.000       0.042       0.073\n",
       "June                 0.0641      0.008      8.012      0.000       0.048       0.080\n",
       "March                0.0611      0.008      7.851      0.000       0.046       0.076\n",
       "May                  0.1402      0.008     18.663      0.000       0.125       0.155\n",
       "November             0.1394      0.007     18.627      0.000       0.125       0.154\n",
       "October              0.1354      0.007     18.164      0.000       0.121       0.150\n",
       "September            0.0527      0.008      6.741      0.000       0.037       0.068\n",
       "Books                0.0213      0.007      3.031      0.002       0.008       0.035\n",
       "Clothing             0.0037      0.005      0.693      0.488      -0.007       0.014\n",
       "Electronics          0.0383      0.007      5.205      0.000       0.024       0.053\n",
       "Footwear             0.0087      0.005      1.656      0.098      -0.002       0.019\n",
       "Home and kitchen     0.0339      0.007      4.735      0.000       0.020       0.048\n",
       "2.0                 -0.1831      0.007    -25.418      0.000      -0.197      -0.169\n",
       "3.0                 -0.3337      0.008    -43.606      0.000      -0.349      -0.319\n",
       "4.0                 -0.3735      0.005    -70.079      0.000      -0.384      -0.363\n",
       "5.0                 -0.3865      0.005    -81.085      0.000      -0.396      -0.377\n",
       "==============================================================================\n",
       "Omnibus:                     4895.749   Durbin-Watson:                   1.907\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            16211.109\n",
       "Skew:                           1.263   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.683   Cond. No.                     9.04e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 9.04e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train=sm.OLS(y,X.astype(float)).fit()\n",
    "model_train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "89cda388",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['Clothing','Non-Fiction',],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "06e71df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Return</td>      <th>  R-squared (uncentered):</th>      <td>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   616.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 03 Oct 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:22:10</td>     <th>  Log-Likelihood:    </th>          <td>  977.74</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19504</td>      <th>  AIC:               </th>          <td>  -1893.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19473</td>      <th>  BIC:               </th>          <td>  -1649.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rate</th>             <td>    0.0003</td> <td> 6.74e-06</td> <td>   43.105</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Amount</th>           <td>-8.407e-05</td> <td> 2.14e-06</td> <td>  -39.339</td> <td> 0.000</td> <td>-8.83e-05</td> <td>-7.99e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delivery_chrgs</th>   <td>-9.455e-05</td> <td> 2.57e-05</td> <td>   -3.677</td> <td> 0.000</td> <td>   -0.000</td> <td>-4.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income</th>           <td> 6.723e-07</td> <td> 4.23e-08</td> <td>   15.903</td> <td> 0.000</td> <td> 5.89e-07</td> <td> 7.55e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marital_status</th>   <td>    0.0150</td> <td>    0.003</td> <td>    4.585</td> <td> 0.000</td> <td>    0.009</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Furnishing</th>       <td>    0.0440</td> <td>    0.009</td> <td>    4.703</td> <td> 0.000</td> <td>    0.026</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kitchen</th>          <td>    0.0419</td> <td>    0.009</td> <td>    4.425</td> <td> 0.000</td> <td>    0.023</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women</th>            <td>    0.0917</td> <td>    0.004</td> <td>   21.778</td> <td> 0.000</td> <td>    0.083</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>                <td>    0.0886</td> <td>    0.005</td> <td>   16.780</td> <td> 0.000</td> <td>    0.078</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>                <td>    0.1553</td> <td>    0.006</td> <td>   26.811</td> <td> 0.000</td> <td>    0.144</td> <td>    0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>                <td>    0.2929</td> <td>    0.006</td> <td>   45.819</td> <td> 0.000</td> <td>    0.280</td> <td>    0.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>                <td>    0.4057</td> <td>    0.007</td> <td>   58.327</td> <td> 0.000</td> <td>    0.392</td> <td>    0.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>August</th>           <td>    0.0580</td> <td>    0.008</td> <td>    7.423</td> <td> 0.000</td> <td>    0.043</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>December</th>         <td>    0.1417</td> <td>    0.007</td> <td>   19.040</td> <td> 0.000</td> <td>    0.127</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>February</th>         <td>    0.0516</td> <td>    0.008</td> <td>    6.774</td> <td> 0.000</td> <td>    0.037</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>January</th>          <td>    0.1375</td> <td>    0.007</td> <td>   18.919</td> <td> 0.000</td> <td>    0.123</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>July</th>             <td>    0.0581</td> <td>    0.008</td> <td>    7.442</td> <td> 0.000</td> <td>    0.043</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>June</th>             <td>    0.0648</td> <td>    0.008</td> <td>    8.163</td> <td> 0.000</td> <td>    0.049</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>March</th>            <td>    0.0618</td> <td>    0.008</td> <td>    8.005</td> <td> 0.000</td> <td>    0.047</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>              <td>    0.1409</td> <td>    0.007</td> <td>   18.956</td> <td> 0.000</td> <td>    0.126</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>November</th>         <td>    0.1401</td> <td>    0.007</td> <td>   18.882</td> <td> 0.000</td> <td>    0.126</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>October</th>          <td>    0.1361</td> <td>    0.007</td> <td>   18.417</td> <td> 0.000</td> <td>    0.122</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>September</th>        <td>    0.0534</td> <td>    0.008</td> <td>    6.880</td> <td> 0.000</td> <td>    0.038</td> <td>    0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Books</th>            <td>    0.0185</td> <td>    0.006</td> <td>    3.195</td> <td> 0.001</td> <td>    0.007</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electronics</th>      <td>    0.0362</td> <td>    0.007</td> <td>    5.390</td> <td> 0.000</td> <td>    0.023</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Footwear</th>         <td>    0.0066</td> <td>    0.004</td> <td>    1.541</td> <td> 0.123</td> <td>   -0.002</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Home and kitchen</th> <td>    0.0319</td> <td>    0.007</td> <td>    4.887</td> <td> 0.000</td> <td>    0.019</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2.0</th>              <td>   -0.1825</td> <td>    0.007</td> <td>  -25.512</td> <td> 0.000</td> <td>   -0.197</td> <td>   -0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3.0</th>              <td>   -0.3331</td> <td>    0.008</td> <td>  -43.842</td> <td> 0.000</td> <td>   -0.348</td> <td>   -0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4.0</th>              <td>   -0.3728</td> <td>    0.005</td> <td>  -71.100</td> <td> 0.000</td> <td>   -0.383</td> <td>   -0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5.0</th>              <td>   -0.3859</td> <td>    0.005</td> <td>  -82.461</td> <td> 0.000</td> <td>   -0.395</td> <td>   -0.377</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4898.125</td> <th>  Durbin-Watson:     </th> <td>   1.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>16197.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.264</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.680</td>  <th>  Cond. No.          </th> <td>8.90e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 8.9e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                 Return   R-squared (uncentered):                   0.495\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.495\n",
       "Method:                 Least Squares   F-statistic:                              616.6\n",
       "Date:                Sun, 03 Oct 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        12:22:10   Log-Likelihood:                          977.74\n",
       "No. Observations:               19504   AIC:                                     -1893.\n",
       "Df Residuals:                   19473   BIC:                                     -1649.\n",
       "Df Model:                          31                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Rate                 0.0003   6.74e-06     43.105      0.000       0.000       0.000\n",
       "Amount           -8.407e-05   2.14e-06    -39.339      0.000   -8.83e-05   -7.99e-05\n",
       "Delivery_chrgs   -9.455e-05   2.57e-05     -3.677      0.000      -0.000   -4.41e-05\n",
       "Income            6.723e-07   4.23e-08     15.903      0.000    5.89e-07    7.55e-07\n",
       "Marital_status       0.0150      0.003      4.585      0.000       0.009       0.021\n",
       "Furnishing           0.0440      0.009      4.703      0.000       0.026       0.062\n",
       "Kitchen              0.0419      0.009      4.425      0.000       0.023       0.060\n",
       "Women                0.0917      0.004     21.778      0.000       0.083       0.100\n",
       "2                    0.0886      0.005     16.780      0.000       0.078       0.099\n",
       "3                    0.1553      0.006     26.811      0.000       0.144       0.167\n",
       "4                    0.2929      0.006     45.819      0.000       0.280       0.305\n",
       "5                    0.4057      0.007     58.327      0.000       0.392       0.419\n",
       "August               0.0580      0.008      7.423      0.000       0.043       0.073\n",
       "December             0.1417      0.007     19.040      0.000       0.127       0.156\n",
       "February             0.0516      0.008      6.774      0.000       0.037       0.066\n",
       "January              0.1375      0.007     18.919      0.000       0.123       0.152\n",
       "July                 0.0581      0.008      7.442      0.000       0.043       0.073\n",
       "June                 0.0648      0.008      8.163      0.000       0.049       0.080\n",
       "March                0.0618      0.008      8.005      0.000       0.047       0.077\n",
       "May                  0.1409      0.007     18.956      0.000       0.126       0.156\n",
       "November             0.1401      0.007     18.882      0.000       0.126       0.155\n",
       "October              0.1361      0.007     18.417      0.000       0.122       0.151\n",
       "September            0.0534      0.008      6.880      0.000       0.038       0.069\n",
       "Books                0.0185      0.006      3.195      0.001       0.007       0.030\n",
       "Electronics          0.0362      0.007      5.390      0.000       0.023       0.049\n",
       "Footwear             0.0066      0.004      1.541      0.123      -0.002       0.015\n",
       "Home and kitchen     0.0319      0.007      4.887      0.000       0.019       0.045\n",
       "2.0                 -0.1825      0.007    -25.512      0.000      -0.197      -0.168\n",
       "3.0                 -0.3331      0.008    -43.842      0.000      -0.348      -0.318\n",
       "4.0                 -0.3728      0.005    -71.100      0.000      -0.383      -0.363\n",
       "5.0                 -0.3859      0.005    -82.461      0.000      -0.395      -0.377\n",
       "==============================================================================\n",
       "Omnibus:                     4898.125   Durbin-Watson:                   1.907\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            16197.352\n",
       "Skew:                           1.264   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.680   Cond. No.                     8.90e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 8.9e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train=sm.OLS(y,X.astype(float)).fit()\n",
    "model_train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de9d849",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6b2b3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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