Imputation & EDA Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Imputation & EDA Project**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries \n",
    "\n",
    "import pandas as pd                 # Pandas for data analysis\n",
    "import numpy as np                  # Numpy for Mathematical Analysis\n",
    "import seaborn as sns               # Seaborn for visualization of plots/graphs\n",
    "import matplotlib.pyplot as plt     # PYplot from Matplotlib for visualization of plots/graphs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Dataset \n",
    "\n",
    "df = pd.read_csv(\"online.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>9</th>\n",
       "      <th>#@%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>13.0262</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Employee</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560045</td>\n",
       "      <td>28.0</td>\n",
       "      <td>77.6200</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swiggyscount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>School</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*7435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 order_status   Monthly Income  latitude Marital Status  Gender  \\\n",
       "0           0          Yes   25001 to 50000   13.0262        Married    Male   \n",
       "1           1          Yes        No Income   12.9770         Single    Male   \n",
       "2           2          Yes        No Income   12.9770         Single  Female   \n",
       "3           3          Yes        No Income   13.0019         Single  Female   \n",
       "4           4          Yes  More than 50000   13.0626        Married    Male   \n",
       "\n",
       "  employment_status  Family size   Reviews  Pin code   Age  longitude  \\\n",
       "0          Employee          5.0  Positive    560045  28.0    77.6200   \n",
       "1           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "2           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "3           Student          NaN  Positive    560003   NaN    77.5713   \n",
       "4    Self Employeed          NaN  Positive    560015   NaN    77.5284   \n",
       "\n",
       "  Educational Qualifications Unnamed: 13   9           #@%  \n",
       "0              Post Graduate         Yes NaN  swiggyscount  \n",
       "1              Post Graduate         Yes NaN         *7435  \n",
       "2              Post Graduate         Yes NaN         *7435  \n",
       "3              Post Graduate         Yes NaN         *7435  \n",
       "4                     School         Yes NaN         *7435  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View over top rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droppinf unwanted columns\n",
    "df.drop(columns = (['Unnamed: 0', 'Unnamed: 13','9','#@%']), axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
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       "      <th>Educational Qualifications</th>\n",
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       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
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       "      <td>560045</td>\n",
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       "      <td>Post Graduate</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status   Monthly Income  latitude Marital Status  Gender  \\\n",
       "0          Yes   25001 to 50000   13.0262        Married    Male   \n",
       "1          Yes        No Income   12.9770         Single    Male   \n",
       "2          Yes        No Income   12.9770         Single  Female   \n",
       "3          Yes        No Income   13.0019         Single  Female   \n",
       "4          Yes  More than 50000   13.0626        Married    Male   \n",
       "\n",
       "  employment_status  Family size   Reviews  Pin code   Age  longitude  \\\n",
       "0          Employee          5.0  Positive    560045  28.0    77.6200   \n",
       "1           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "2           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "3           Student          NaN  Positive    560003   NaN    77.5713   \n",
       "4    Self Employeed          NaN  Positive    560015   NaN    77.5284   \n",
       "\n",
       "  Educational Qualifications  \n",
       "0              Post Graduate  \n",
       "1              Post Graduate  \n",
       "2              Post Graduate  \n",
       "3              Post Graduate  \n",
       "4                     School  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verfying whether column dropped or not \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(388, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of dataset = (rows, columns) of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 388 entries, 0 to 387\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   order_status                388 non-null    object \n",
      " 1   Monthly Income              388 non-null    object \n",
      " 2   latitude                    388 non-null    float64\n",
      " 3   Marital Status              388 non-null    object \n",
      " 4   Gender                      388 non-null    object \n",
      " 5   employment_status           388 non-null    object \n",
      " 6   Family size                 369 non-null    float64\n",
      " 7   Reviews                     388 non-null    object \n",
      " 8   Pin code                    388 non-null    int64  \n",
      " 9   Age                         369 non-null    float64\n",
      " 10  longitude                   388 non-null    float64\n",
      " 11  Educational Qualifications  388 non-null    object \n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 36.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information on data columns and its datatype with null values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>388.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>388.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.972058</td>\n",
       "      <td>3.298103</td>\n",
       "      <td>560040.113402</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>77.600160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.044489</td>\n",
       "      <td>1.354550</td>\n",
       "      <td>31.399609</td>\n",
       "      <td>2.968128</td>\n",
       "      <td>0.051354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.865200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>560001.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>77.484200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.936900</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>560010.750000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>77.565275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.977000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>560033.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>77.592100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.997025</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>560068.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>77.630900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.102000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>560109.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>77.758200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         latitude  Family size       Pin code         Age   longitude\n",
       "count  388.000000   369.000000     388.000000  369.000000  388.000000\n",
       "mean    12.972058     3.298103  560040.113402   24.666667   77.600160\n",
       "std      0.044489     1.354550      31.399609    2.968128    0.051354\n",
       "min     12.865200     1.000000  560001.000000   18.000000   77.484200\n",
       "25%     12.936900     2.000000  560010.750000   23.000000   77.565275\n",
       "50%     12.977000     3.000000  560033.500000   24.000000   77.592100\n",
       "75%     12.997025     4.000000  560068.000000   26.000000   77.630900\n",
       "max     13.102000     6.000000  560109.000000   33.000000   77.758200"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Info of dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_status', 'Monthly Income', 'latitude', 'Marital Status',\n",
       "       'Gender', 'employment_status', 'Family size', 'Reviews', 'Pin code',\n",
       "       'Age', 'longitude', 'Educational Qualifications'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkign the names of columns present in dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                   19\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           19\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkign null valeus in dataset \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9369</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560095</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.6407</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Yes</td>\n",
       "      <td>10001 to 25000</td>\n",
       "      <td>12.9337</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Employee</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560011</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5900</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Yes</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>12.9706</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Employee</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>77.6529</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9854</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560048</td>\n",
       "      <td>23.0</td>\n",
       "      <td>77.7081</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9854</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560048</td>\n",
       "      <td>23.0</td>\n",
       "      <td>77.7081</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>12.9783</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560038</td>\n",
       "      <td>25.0</td>\n",
       "      <td>77.6408</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>No</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>12.9783</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>Employee</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Negative</td>\n",
       "      <td>560038</td>\n",
       "      <td>29.0</td>\n",
       "      <td>77.6408</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>No</td>\n",
       "      <td>10001 to 25000</td>\n",
       "      <td>12.9706</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Negative</td>\n",
       "      <td>560075</td>\n",
       "      <td>26.0</td>\n",
       "      <td>77.6529</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0223</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560049</td>\n",
       "      <td>23.0</td>\n",
       "      <td>77.7132</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>25.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_status   Monthly Income  latitude Marital Status  Gender  \\\n",
       "29           Yes        No Income   12.9369         Single  Female   \n",
       "59           Yes   10001 to 25000   12.9337         Single  Female   \n",
       "71           Yes   25001 to 50000   12.9706        Married    Male   \n",
       "82           Yes        No Income   12.9854         Single  Female   \n",
       "86           Yes        No Income   12.9854         Single  Female   \n",
       "..           ...              ...       ...            ...     ...   \n",
       "376          Yes  More than 50000   12.9783         Single    Male   \n",
       "377           No   25001 to 50000   12.9783        Married  Female   \n",
       "378           No   10001 to 25000   12.9706        Married  Female   \n",
       "384          Yes        No Income   13.0223         Single    Male   \n",
       "387          Yes        No Income   12.9770         Single    Male   \n",
       "\n",
       "    employment_status  Family size    Reviews  Pin code   Age  longitude  \\\n",
       "29            Student          2.0   Positive    560095  22.0    77.6407   \n",
       "59           Employee          4.0   Positive    560011  24.0    77.5900   \n",
       "71           Employee          3.0   Positive    560075  32.0    77.6529   \n",
       "82            Student          4.0   Positive    560048  23.0    77.7081   \n",
       "86            Student          4.0   Positive    560048  23.0    77.7081   \n",
       "..                ...          ...        ...       ...   ...        ...   \n",
       "376    Self Employeed          2.0   Positive    560038  25.0    77.6408   \n",
       "377          Employee          4.0  Negative     560038  29.0    77.6408   \n",
       "378    Self Employeed          5.0  Negative     560075  26.0    77.6529   \n",
       "384           Student          3.0   Positive    560049  23.0    77.7132   \n",
       "387           Student          1.0   Positive    560009  25.0    77.5773   \n",
       "\n",
       "    Educational Qualifications  \n",
       "29                    Graduate  \n",
       "59               Post Graduate  \n",
       "71                    Graduate  \n",
       "82               Post Graduate  \n",
       "86               Post Graduate  \n",
       "..                         ...  \n",
       "376                   Graduate  \n",
       "377                   Graduate  \n",
       "378                   Graduate  \n",
       "384                   Graduate  \n",
       "387                   Graduate  \n",
       "\n",
       "[97 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Duplicated values in columnms\n",
    "\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total there are 97 duplicated rows, further we remove those as we need only unique values in dataset which reduce ambiguity in analyzing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Duplicate data from dataset \n",
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(291, 12)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital Status', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting to understand about \"Marital Status\" Column\n",
    "sns.countplot(x = df['Marital Status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here get insights on population whether they're are married, Single or Prefer not to declare their marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting to understand about \"Gender\" Column\n",
    "sns.countplot(x = df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>13.0262</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Employee</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560045</td>\n",
       "      <td>28.0</td>\n",
       "      <td>77.6200</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status   Monthly Income  latitude Marital Status  Gender  \\\n",
       "0          Yes   25001 to 50000   13.0262        Married    Male   \n",
       "1          Yes        No Income   12.9770         Single    Male   \n",
       "2          Yes        No Income   12.9770         Single  Female   \n",
       "3          Yes        No Income   13.0019         Single  Female   \n",
       "4          Yes  More than 50000   13.0626        Married    Male   \n",
       "\n",
       "  employment_status  Family size   Reviews  Pin code   Age  longitude  \\\n",
       "0          Employee          5.0  Positive    560045  28.0    77.6200   \n",
       "1           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "2           Student          NaN  Positive    560009   NaN    77.5773   \n",
       "3           Student          NaN  Positive    560003   NaN    77.5713   \n",
       "4    Self Employeed          NaN  Positive    560015   NaN    77.5284   \n",
       "\n",
       "  Educational Qualifications  \n",
       "0              Post Graduate  \n",
       "1              Post Graduate  \n",
       "2              Post Graduate  \n",
       "3              Post Graduate  \n",
       "4                     School  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Age'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting to understand about \"Gender\" and \"Marital Status\" Column for detecting any outlier and its range.\n",
    "# Age (Numerical) and gender (Categorical)\n",
    "\n",
    "sns.boxplot(x = df['Gender'], y = df['Age'], hue=df['Marital Status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above plot we can understand demographics about Gender with their Marital Status and their ranges in each respective reagion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                   18\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    83\n",
       "2.0    72\n",
       "4.0    43\n",
       "5.0    35\n",
       "6.0    23\n",
       "1.0    17\n",
       "Name: Family size, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding unique counts in 'Family Size' column\n",
    "df['Family size'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code reflects that there are major number of families having 3 members in dataset followed by 2 members and so on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the data type of family size to category/int as there could not be any family as float data type\n",
    "\n",
    "df['Family size'] = df['Family size'].astype('category')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 291 entries, 0 to 386\n",
      "Series name: Family size\n",
      "Non-Null Count  Dtype   \n",
      "--------------  -----   \n",
      "273 non-null    category\n",
      "dtypes: category(1)\n",
      "memory usage: 2.8 KB\n"
     ]
    }
   ],
   "source": [
    "df['Family size'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Imputation Methodologies**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Filling missing values using Mean, Median and Mode (Univariate Analysis)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Craeting 3 more copies of dataset for performing imputation on those and then copying back to original dataset \n",
    "df_mean = df.copy()\n",
    "df_median = df.copy()\n",
    "df_mode = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **1. Filling missing values using ***Mean*** methodology in df_mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>13.0262</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Employee</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560045</td>\n",
       "      <td>28.0</td>\n",
       "      <td>77.6200</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>Self Employeed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status   Monthly Income  latitude Marital Status  Gender  \\\n",
       "0          Yes   25001 to 50000   13.0262        Married    Male   \n",
       "1          Yes        No Income   12.9770         Single    Male   \n",
       "2          Yes        No Income   12.9770         Single  Female   \n",
       "3          Yes        No Income   13.0019         Single  Female   \n",
       "4          Yes  More than 50000   13.0626        Married    Male   \n",
       "\n",
       "  employment_status Family size   Reviews  Pin code   Age  longitude  \\\n",
       "0          Employee         5.0  Positive    560045  28.0    77.6200   \n",
       "1           Student         NaN  Positive    560009   NaN    77.5773   \n",
       "2           Student         NaN  Positive    560009   NaN    77.5773   \n",
       "3           Student         NaN  Positive    560003   NaN    77.5713   \n",
       "4    Self Employeed         NaN  Positive    560015   NaN    77.5284   \n",
       "\n",
       "  Educational Qualifications  \n",
       "0              Post Graduate  \n",
       "1              Post Graduate  \n",
       "2              Post Graduate  \n",
       "3              Post Graduate  \n",
       "4                     School  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                   18\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here with help of mean imputation we'll fill mean values of age in 'Age' column which consist of 18 null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.681318681318682"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Pandas to find mean value of Age column\n",
    "df_mean['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rounding the mean value to next integer, as age can't be in decimals\n",
    "mean_values =round(df_mean['Age'].mean())\n",
    "mean_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Age' Column before filling mean values\n",
    "df_mean['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling mean values in null values using 'fillna' from pandas\n",
    "df_mean['Age'] = df_mean['Age'].fillna(mean_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in dataset after filling mean values in 'Age' Column\n",
    "df_mean['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RKC\\AppData\\Local\\Temp\\ipykernel_3616\\1658161300.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x = df['Age'], ax=ax[0])\n",
      "C:\\Users\\RKC\\AppData\\Local\\Temp\\ipykernel_3616\\1658161300.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x = df_mean['Age'], ax=ax[1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting distribution plot with mean values imputed in 2nd plot \n",
    "\n",
    "fig, ax  = plt.subplots(nrows = 1, ncols=2, figsize=(13,5))\n",
    "\n",
    "sns.distplot(x = df['Age'], ax=ax[0])\n",
    "plt.title(label=\"Normal Dataset Distribution in Age column\")\n",
    "\n",
    "sns.distplot(x = df_mean['Age'], ax=ax[1])\n",
    "plt.title(label=\"Imputed Dataset Distribution with Mean values in Age column\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Filling missing values using ***Median*** methodology in df_mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 291 entries, 0 to 386\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype   \n",
      "---  ------                      --------------  -----   \n",
      " 0   order_status                291 non-null    object  \n",
      " 1   Monthly Income              291 non-null    object  \n",
      " 2   latitude                    291 non-null    float64 \n",
      " 3   Marital Status              291 non-null    object  \n",
      " 4   Gender                      291 non-null    object  \n",
      " 5   employment_status           291 non-null    object  \n",
      " 6   Family size                 273 non-null    category\n",
      " 7   Reviews                     291 non-null    object  \n",
      " 8   Pin code                    291 non-null    int64   \n",
      " 9   Age                         273 non-null    float64 \n",
      " 10  longitude                   291 non-null    float64 \n",
      " 11  Educational Qualifications  291 non-null    object  \n",
      "dtypes: category(1), float64(3), int64(1), object(7)\n",
      "memory usage: 27.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_median.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values before filling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_values = int(df_median['Age'].median())\n",
    "median_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_median['Age'] = df_median['Age'].fillna(median_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values after filling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RKC\\AppData\\Local\\Temp\\ipykernel_3616\\1999908238.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x = df['Age'], ax=ax[0] )\n",
      "C:\\Users\\RKC\\AppData\\Local\\Temp\\ipykernel_3616\\1999908238.py:5: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x = df_median['Age'], ax=ax[1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots(nrows = 1, ncols=2, figsize=(13,5))\n",
    "\n",
    "sns.distplot(x = df['Age'], ax=ax[0] )\n",
    "\n",
    "sns.distplot(x = df_median['Age'], ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Filling missing values using ***Mode*** methodology in df_mode**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                   18\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mode.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_values = int(df_mode['Family size'].mode())\n",
    "mode_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mode['Family size'] = df_mode['Family size'].fillna(int(df_mode['Family size'].mode()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mode['Family size'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                    0\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mode.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Filling main dataset 'df' with Mode values in place of null values for 'Family Size' Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                   18\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_values = int(df['Family size'].mode()[0])\n",
    "mode_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Family size'] = df['Family size'].fillna(int(df['Family size'].mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Family size'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                   0\n",
       "Monthly Income                 0\n",
       "latitude                       0\n",
       "Marital Status                 0\n",
       "Gender                         0\n",
       "employment_status              0\n",
       "Family size                    0\n",
       "Reviews                        0\n",
       "Pin code                       0\n",
       "Age                           18\n",
       "longitude                      0\n",
       "Educational Qualifications     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Handling Outliers**\n",
    "\n",
    "Methods for detecting Outliers :- \n",
    "- Z-Score \n",
    "- IQR \n",
    "- Percentile \n",
    "\n",
    "Methods for Treating outliers :- \n",
    "1. Trimming \n",
    "2. Capping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding outliers in 'Age' column\n",
    "df['Age'].plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copying a old dataset named to df_outliers for performing operations on outliers in dataset.\n",
    "df_outliers = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Treating Outliers**\n",
    "\n",
    "- Trimming - Remove all the outliers \n",
    "- Capping - Capping outliers with Lower limit and upper limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    273.000000\n",
       "mean      24.681319\n",
       "std        3.023970\n",
       "min       18.000000\n",
       "25%       23.000000\n",
       "50%       24.000000\n",
       "75%       26.000000\n",
       "max       33.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statstical values of \"Age\" column \n",
    "df_outliers.describe()['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IQR=\n",
    "\n",
    "        IQR = Q3 - Q1 \n",
    "\n",
    "\n",
    "Lower Limit = \n",
    "        \n",
    "        Q1 - 1.5 * IQR\n",
    "\n",
    "Upper Limit = \n",
    "        \n",
    "        Q3 + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 =  23.0\n",
      "Q3 =  26.0\n"
     ]
    }
   ],
   "source": [
    "# Finding Q1 and Q3 for finding outlier using IQR method\n",
    "Q1 = df_outliers.describe()['Age']['25%']\n",
    "\n",
    "print(\"Q1 = \", Q1)\n",
    "\n",
    "Q3 = df_outliers.describe()['Age']['75%']\n",
    "\n",
    "print(\"Q3 = \", Q3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    IQR = Q3 - Q1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR =  3.0\n"
     ]
    }
   ],
   "source": [
    "# Finding IQR\n",
    "IQR = Q3 - Q1\n",
    "print(\"IQR = \", IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper lIMIT = 18.5\n",
      "Upper lIMIT = 30.5\n"
     ]
    }
   ],
   "source": [
    "LL = Q1 - 1.5*IQR\n",
    "\n",
    "print(\"Upper lIMIT =\", LL)\n",
    "\n",
    "UL = Q3 + 1.5*IQR\n",
    "\n",
    "print(\"Upper lIMIT =\", UL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Using clip() function to cap all the UL and LL values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_outliers['Age'] = df_outliers['Age'].clip(LL, UL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Capped BoxPlot'}>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# COmparing Outliers using Capping method for treating outliers with normal outliers detected using Boxplot\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (13,5))\n",
    "\n",
    "df['Age'].plot(kind = 'box', ax =ax[0], title=\"Normal BoxPlot\")\n",
    "df_outliers['Age'].plot(kind = 'box', ax=ax[1], title=\"Capped BoxPlot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copying Capped values to main dataset named 'df'\n",
    "df['Age'] = df['Age'].clip(LL, UL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling null values with median of the \"Age\" column\n",
    "df['Age'] = df['Age'].fillna(df['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                  0\n",
       "Monthly Income                0\n",
       "latitude                      0\n",
       "Marital Status                0\n",
       "Gender                        0\n",
       "employment_status             0\n",
       "Family size                   0\n",
       "Reviews                       0\n",
       "Pin code                      0\n",
       "Age                           0\n",
       "longitude                     0\n",
       "Educational Qualifications    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verfying removal of null values in age column \n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Handling Categorical Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_status Yes    222\n",
      "No      69\n",
      "Name: order_status, dtype: int64\n",
      "--------------------------------------------\n",
      "Monthly Income No Income          135\n",
      "25001 to 50000      52\n",
      "More than 50000     47\n",
      "10001 to 25000      38\n",
      "Below Rs.10000      19\n",
      "Name: Monthly Income, dtype: int64\n",
      "--------------------------------------------\n",
      "Marital Status Single               195\n",
      "Married               87\n",
      "Prefer not to say      9\n",
      "Name: Marital Status, dtype: int64\n",
      "--------------------------------------------\n",
      "Gender Male      167\n",
      "Female    124\n",
      "Name: Gender, dtype: int64\n",
      "--------------------------------------------\n",
      "employment_status Student           149\n",
      "Employee           95\n",
      "Self Employeed     38\n",
      "House wife          9\n",
      "Name: employment_status, dtype: int64\n",
      "--------------------------------------------\n",
      "Reviews Positive     236\n",
      "Negative      55\n",
      "Name: Reviews, dtype: int64\n",
      "--------------------------------------------\n",
      "Educational Qualifications Graduate         130\n",
      "Post Graduate    127\n",
      "Ph.D              21\n",
      "School            11\n",
      "Uneducated         2\n",
      "Name: Educational Qualifications, dtype: int64\n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# fetching out columns with 'object' as datatype\n",
    "\n",
    "for i in df:\n",
    "    if df[i].dtype == 'object':\n",
    "        print(i, df[i].value_counts())\n",
    "        print(\"--------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Segregating columns\n",
    "\n",
    "##### Ordinal : Education, Qualifications, Reviews, Monthly_Income\n",
    "\n",
    "##### Nominal : Employement_Status, Gender, Marital Status, Order_Status\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Conversion of categorical (Nominal) data to Numerical Values**\n",
    "##### **Nominal : Employement_Status, Gender, Marital Status, Order_Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                  0\n",
       "Monthly Income                0\n",
       "latitude                      0\n",
       "Marital Status                0\n",
       "Gender                        0\n",
       "employment_status             0\n",
       "Family size                   0\n",
       "Reviews                       0\n",
       "Pin code                      0\n",
       "Age                           0\n",
       "longitude                     0\n",
       "Educational Qualifications    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['employment_status', 'Gender','Marital Status'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "      <th>employment_status_House wife</th>\n",
       "      <th>employment_status_Self Employeed</th>\n",
       "      <th>employment_status_Student</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Marital Status_Prefer not to say</th>\n",
       "      <th>Marital Status_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>25001 to 50000</td>\n",
       "      <td>13.0262</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560045</td>\n",
       "      <td>28.0</td>\n",
       "      <td>77.6200</td>\n",
       "      <td>Post Graduate</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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No Income</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560003</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>More than 50000</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560015</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>School</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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": [
       "  order_status   Monthly Income  latitude Family size   Reviews  Pin code  \\\n",
       "0          Yes   25001 to 50000   13.0262         5.0  Positive    560045   \n",
       "1          Yes        No Income   12.9770         3.0  Positive    560009   \n",
       "2          Yes        No Income   12.9770         3.0  Positive    560009   \n",
       "3          Yes        No Income   13.0019         3.0  Positive    560003   \n",
       "4          Yes  More than 50000   13.0626         3.0  Positive    560015   \n",
       "\n",
       "    Age  longitude Educational Qualifications  employment_status_House wife  \\\n",
       "0  28.0    77.6200              Post Graduate                             0   \n",
       "1  24.0    77.5773              Post Graduate                             0   \n",
       "2  24.0    77.5773              Post Graduate                             0   \n",
       "3  24.0    77.5713              Post Graduate                             0   \n",
       "4  24.0    77.5284                     School                             0   \n",
       "\n",
       "   employment_status_Self Employeed  employment_status_Student  Gender_Male  \\\n",
       "0                                 0                          0            1   \n",
       "1                                 0                          1            1   \n",
       "2                                 0                          1            0   \n",
       "3                                 0                          1            0   \n",
       "4                                 1                          0            1   \n",
       "\n",
       "   Marital Status_Prefer not to say  Marital Status_Single  \n",
       "0                                 0                      0  \n",
       "1                                 0                      1  \n",
       "2                                 0                      1  \n",
       "3                                 0                      1  \n",
       "4                                 0                      0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Conversion of categorical (Ordinal) data to Numerical Values**\n",
    "#### **Ordinal : Educational_Qualifications, Reviews, Monthly_Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Income          135\n",
       "25001 to 50000      52\n",
       "More than 50000     47\n",
       "10001 to 25000      38\n",
       "Below Rs.10000      19\n",
       "Name: Monthly Income, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_median['Monthly Income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing above Monthly Income column values to belwo listed ones.\n",
    "d = {\"No Income\":'Poor',\"Below Rs.10000\":'Low Income' ,\"10001 to 25000\":'Moderate Income', '25001 to 50000':'High Income', 'More than 50000':'Very High Income'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Map function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the column values with map function \n",
    "df['Monthly Income'] = df['Monthly Income'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Poor                135\n",
       "High Income          52\n",
       "Very High Income     47\n",
       "Moderate Income      38\n",
       "Low Income           19\n",
       "Name: Monthly Income, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verfying the above code\n",
    "df['Monthly Income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "      <th>employment_status_House wife</th>\n",
       "      <th>employment_status_Self Employeed</th>\n",
       "      <th>employment_status_Student</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Marital Status_Prefer not to say</th>\n",
       "      <th>Marital Status_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Poor</td>\n",
       "      <td>13.0078</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560055</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5577</td>\n",
       "      <td>Graduate</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>357</th>\n",
       "      <td>No</td>\n",
       "      <td>High Income</td>\n",
       "      <td>12.9757</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Negative</td>\n",
       "      <td>560023</td>\n",
       "      <td>26.0</td>\n",
       "      <td>77.5586</td>\n",
       "      <td>Post Graduate</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>184</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Very High Income</td>\n",
       "      <td>12.9306</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560085</td>\n",
       "      <td>21.0</td>\n",
       "      <td>77.5434</td>\n",
       "      <td>Ph.D</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",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>No</td>\n",
       "      <td>High Income</td>\n",
       "      <td>12.9551</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Negative</td>\n",
       "      <td>560017</td>\n",
       "      <td>25.0</td>\n",
       "      <td>77.6593</td>\n",
       "      <td>Graduate</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>177</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Poor</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Positive</td>\n",
       "      <td>560009</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_status    Monthly Income  latitude Family size    Reviews  Pin code  \\\n",
       "201          Yes              Poor   13.0078         3.0   Positive    560055   \n",
       "357           No       High Income   12.9757         2.0  Negative     560023   \n",
       "184          Yes  Very High Income   12.9306         3.0   Positive    560085   \n",
       "44            No       High Income   12.9551         4.0  Negative     560017   \n",
       "177          Yes              Poor   12.9770         3.0   Positive    560009   \n",
       "\n",
       "      Age  longitude Educational Qualifications  employment_status_House wife  \\\n",
       "201  24.0    77.5577                   Graduate                             1   \n",
       "357  26.0    77.5586              Post Graduate                             0   \n",
       "184  21.0    77.5434                       Ph.D                             0   \n",
       "44   25.0    77.6593                   Graduate                             0   \n",
       "177  24.0    77.5773              Post Graduate                             0   \n",
       "\n",
       "     employment_status_Self Employeed  employment_status_Student  Gender_Male  \\\n",
       "201                                 0                          0            0   \n",
       "357                                 0                          0            0   \n",
       "184                                 0                          0            1   \n",
       "44                                  0                          0            0   \n",
       "177                                 0                          1            1   \n",
       "\n",
       "     Marital Status_Prefer not to say  Marital Status_Single  \n",
       "201                                 0                      0  \n",
       "357                                 0                      0  \n",
       "184                                 0                      0  \n",
       "44                                  0                      0  \n",
       "177                                 0                      1  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here 'Negative' has space at the end like \"Negative \" hence previously we were getting error of 55 values in reviews columns, after recognising the error, we mitigated it with the same by entering the space at end of word and we got the error cleared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all Ordinal columns to \"object\" Dtype\n",
    "df['Reviews'] = df['Reviews'].astype('object')\n",
    "df['Educational Qualifications'] = df['Educational Qualifications'].astype('object')\n",
    "df['Monthly Income'] = df['Monthly Income'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding\n",
    "mappings = {\n",
    "    \"Reviews\":{\"Negative \":0, \"Positive\":1},\n",
    "    \"Educational Qualifications\": {\"Uneducated\":0, \"School\":1, \"Graduate\":2, \"Post Graduate\":3, \"Ph.D\":4},\n",
    "    \"Monthly Income\" : {\"Poor\":0, \"Low Income\":1, \"Moderate Income\":2, \"High Income\":3, \"Very High Income\":4}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df:\n",
    "    if i in mappings:\n",
    "        df[i]= df[i].map(mappings[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>Monthly Income</th>\n",
       "      <th>latitude</th>\n",
       "      <th>Family size</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Pin code</th>\n",
       "      <th>Age</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Educational Qualifications</th>\n",
       "      <th>employment_status_House wife</th>\n",
       "      <th>employment_status_Self Employeed</th>\n",
       "      <th>employment_status_Student</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Marital Status_Prefer not to say</th>\n",
       "      <th>Marital Status_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0262</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560045</td>\n",
       "      <td>28.0</td>\n",
       "      <td>77.6200</td>\n",
       "      <td>3</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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560009</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>12.9770</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560009</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5773</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560003</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5713</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0626</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560015</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5284</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0503</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560013</td>\n",
       "      <td>27.0</td>\n",
       "      <td>77.5529</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>12.9337</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>560011</td>\n",
       "      <td>26.0</td>\n",
       "      <td>77.5900</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>12.9883</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560051</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.5987</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>12.9850</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560010</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.5533</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>12.9698</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>560066</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.7500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_status  Monthly Income  latitude Family size  Reviews  Pin code  \\\n",
       "0            Yes               3   13.0262         5.0        1    560045   \n",
       "1            Yes               0   12.9770         3.0        1    560009   \n",
       "2            Yes               0   12.9770         3.0        1    560009   \n",
       "3            Yes               0   13.0019         3.0        1    560003   \n",
       "4            Yes               4   13.0626         3.0        1    560015   \n",
       "..           ...             ...       ...         ...      ...       ...   \n",
       "381           No               4   13.0503         4.0        1    560013   \n",
       "382           No               4   12.9337         3.0        0    560011   \n",
       "383          Yes               0   12.9883         5.0        1    560051   \n",
       "385          Yes               2   12.9850         5.0        1    560010   \n",
       "386          Yes               2   12.9698         3.0        1    560066   \n",
       "\n",
       "      Age  longitude  Educational Qualifications  \\\n",
       "0    28.0    77.6200                           3   \n",
       "1    24.0    77.5773                           3   \n",
       "2    24.0    77.5773                           3   \n",
       "3    24.0    77.5713                           3   \n",
       "4    24.0    77.5284                           1   \n",
       "..    ...        ...                         ...   \n",
       "381  27.0    77.5529                           4   \n",
       "382  26.0    77.5900                           3   \n",
       "383  24.0    77.5987                           3   \n",
       "385  22.0    77.5533                           3   \n",
       "386  22.0    77.7500                           2   \n",
       "\n",
       "     employment_status_House wife  employment_status_Self Employeed  \\\n",
       "0                               0                                 0   \n",
       "1                               0                                 0   \n",
       "2                               0                                 0   \n",
       "3                               0                                 0   \n",
       "4                               0                                 1   \n",
       "..                            ...                               ...   \n",
       "381                             0                                 0   \n",
       "382                             0                                 0   \n",
       "383                             0                                 0   \n",
       "385                             0                                 0   \n",
       "386                             0                                 0   \n",
       "\n",
       "     employment_status_Student  Gender_Male  Marital Status_Prefer not to say  \\\n",
       "0                            0            1                                 0   \n",
       "1                            1            1                                 0   \n",
       "2                            1            0                                 0   \n",
       "3                            1            0                                 0   \n",
       "4                            0            1                                 0   \n",
       "..                         ...          ...                               ...   \n",
       "381                          0            1                                 0   \n",
       "382                          0            1                                 0   \n",
       "383                          1            0                                 0   \n",
       "385                          1            0                                 0   \n",
       "386                          0            0                                 0   \n",
       "\n",
       "     Marital Status_Single  \n",
       "0                        0  \n",
       "1                        1  \n",
       "2                        1  \n",
       "3                        1  \n",
       "4                        0  \n",
       "..                     ...  \n",
       "381                      1  \n",
       "382                      1  \n",
       "383                      1  \n",
       "385                      1  \n",
       "386                      1  \n",
       "\n",
       "[291 rows x 15 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status                        0\n",
       "Monthly Income                      0\n",
       "latitude                            0\n",
       "Family size                         0\n",
       "Reviews                             0\n",
       "Pin code                            0\n",
       "Age                                 0\n",
       "longitude                           0\n",
       "Educational Qualifications          0\n",
       "employment_status_House wife        0\n",
       "employment_status_Self Employeed    0\n",
       "employment_status_Student           0\n",
       "Gender_Male                         0\n",
       "Marital Status_Prefer not to say    0\n",
       "Marital Status_Single               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 291 entries, 0 to 386\n",
      "Data columns (total 15 columns):\n",
      " #   Column                            Non-Null Count  Dtype   \n",
      "---  ------                            --------------  -----   \n",
      " 0   order_status                      291 non-null    object  \n",
      " 1   Monthly Income                    291 non-null    int64   \n",
      " 2   latitude                          291 non-null    float64 \n",
      " 3   Family size                       291 non-null    category\n",
      " 4   Reviews                           291 non-null    int64   \n",
      " 5   Pin code                          291 non-null    int64   \n",
      " 6   Age                               291 non-null    float64 \n",
      " 7   longitude                         291 non-null    float64 \n",
      " 8   Educational Qualifications        291 non-null    int64   \n",
      " 9   employment_status_House wife      291 non-null    uint8   \n",
      " 10  employment_status_Self Employeed  291 non-null    uint8   \n",
      " 11  employment_status_Student         291 non-null    uint8   \n",
      " 12  Gender_Male                       291 non-null    uint8   \n",
      " 13  Marital Status_Prefer not to say  291 non-null    uint8   \n",
      " 14  Marital Status_Single             291 non-null    uint8   \n",
      "dtypes: category(1), float64(3), int64(4), object(1), uint8(6)\n",
      "memory usage: 22.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the prepared data to .csv file for Machine Learning Algortihms \n",
    "df.to_csv(\"onlineclean.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **END**"
   ]
  }
 ],
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