ML_Project01_DataPreprocessing .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72a4ee2f",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b742076",
   "metadata": {},
   "source": [
    "# Project-01 Data Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ac6587",
   "metadata": {},
   "source": [
    "## Objective:\n",
    "The main objective of this project is to design and implement a robust data\n",
    "preprocessing system that addresses common challenges such as missing values,\n",
    "outliers, inconsistent formatting, and noise. By performing effective data preprocessing,\n",
    "the project aims to enhance the quality, reliability, and usefulness of the data for\n",
    "machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9922b1",
   "metadata": {},
   "source": [
    "## 1. Acquire the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8564b121",
   "metadata": {},
   "source": [
    "Dataset : Employee.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33eb2398",
   "metadata": {},
   "source": [
    "## 2. import the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 938,
   "id": "f07e80a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d8f3c8",
   "metadata": {},
   "source": [
    "## 3. import the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "id": "630ca66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 6)"
      ]
     },
     "execution_count": 939,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Employee.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 940,
   "id": "e8d40191",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8787.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Country  Gender\n",
       "0        TCS  20.0     NaN   Chennai   India       0\n",
       "1    Infosys  30.0     NaN    Mumbai   India       0\n",
       "2        TCS  35.0  2300.0  Calcutta   India       0\n",
       "3    Infosys  40.0  3000.0     Delhi   India       0\n",
       "4        TCS  23.0  4000.0    Mumbai   India       0\n",
       "..       ...   ...     ...       ...     ...     ...\n",
       "143      TCS  33.0  9024.0  Calcutta   India       1\n",
       "144  Infosys  22.0  8787.0  Calcutta   India       1\n",
       "145  Infosys  44.0  4034.0     Delhi   India       1\n",
       "146      TCS  33.0  5034.0    Mumbai   India       1\n",
       "147  Infosys  22.0  8202.0    Cochin   India       0\n",
       "\n",
       "[148 rows x 6 columns]"
      ]
     },
     "execution_count": 940,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79f400c",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 941,
   "id": "63ab8b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 942,
   "id": "df13b2ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 942,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 943,
   "id": "1e739965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 6)"
      ]
     },
     "execution_count": 943,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.shape\n",
    "# 4 duplicates removed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 944,
   "id": "b72fb60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
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       "      <th>Gender</th>\n",
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       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Infosys Pvt Lmt</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Company   Age  Salary     Place Country  Gender\n",
       "0                TCS  20.0     NaN   Chennai   India       0\n",
       "1            Infosys  30.0     NaN    Mumbai   India       0\n",
       "2                TCS  35.0  2300.0  Calcutta   India       0\n",
       "3            Infosys  40.0  3000.0     Delhi   India       0\n",
       "4                TCS  23.0  4000.0    Mumbai   India       0\n",
       "..               ...   ...     ...       ...     ...     ...\n",
       "139  Infosys Pvt Lmt  22.0  8202.0    Mumbai   India       0\n",
       "140              TCS  33.0  9024.0  Calcutta   India       1\n",
       "141          Infosys  44.0  4034.0     Delhi   India       1\n",
       "142              TCS  33.0  5034.0    Mumbai   India       1\n",
       "143          Infosys  22.0  8202.0    Cochin   India       0\n",
       "\n",
       "[144 rows x 6 columns]"
      ]
     },
     "execution_count": 944,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df.drop('index',axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 945,
   "id": "96bf96ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company   Age  Salary     Place Country  Gender\n",
       "0      TCS  20.0     NaN   Chennai   India       0\n",
       "1  Infosys  30.0     NaN    Mumbai   India       0\n",
       "2      TCS  35.0  2300.0  Calcutta   India       0\n",
       "3  Infosys  40.0  3000.0     Delhi   India       0\n",
       "4      TCS  23.0  4000.0    Mumbai   India       0"
      ]
     },
     "execution_count": 945,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 946,
   "id": "e45aaa57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 144 entries, 0 to 143\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Company  136 non-null    object \n",
      " 1   Age      127 non-null    float64\n",
      " 2   Salary   121 non-null    float64\n",
      " 3   Place    130 non-null    object \n",
      " 4   Country  144 non-null    object \n",
      " 5   Gender   144 non-null    int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 947,
   "id": "14390244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     8\n",
       "Age        17\n",
       "Salary     23\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 947,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if there is any null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 948,
   "id": "7d1ce99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company', 'Age', 'Salary', 'Place', 'Country', 'Gender'], dtype='object')"
      ]
     },
     "execution_count": 948,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 949,
   "id": "2a2fcc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unique value in each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 950,
   "id": "4f2a2f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     6\n",
       "Age        29\n",
       "Salary     40\n",
       "Place      11\n",
       "Country     1\n",
       "Gender      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 950,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique=df.nunique()\n",
    "unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 951,
   "id": "18660473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TCS', 'Infosys', 'CTS', nan, 'Tata Consultancy Services',\n",
       "       'Congnizant', 'Infosys Pvt Lmt'], dtype=object)"
      ]
     },
     "execution_count": 951,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 952,
   "id": "3b7b353c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# observations: here 'Infosys' & 'Infosys Pvt Lmt','TCS' & 'Tata Consultancy Services','CTS' & 'Congnizant'('Cognizant') \n",
    "# are same.\n",
    "# Treat them appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 953,
   "id": "fcd472cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TCS', 'Infosys', 'Cognizant', nan], dtype=object)"
      ]
     },
     "execution_count": 953,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].replace('Infosys Pvt Lmt','Infosys',inplace=True)\n",
    "df['Company'].replace('Tata Consultancy Services','TCS',inplace=True)\n",
    "df['Company'].replace('CTS','Congnizant',inplace=True)\n",
    "df['Company'].replace('Congnizant','Cognizant',inplace=True)\n",
    "df['Company'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 954,
   "id": "4a548230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20., 30., 35., 40., 23., nan, 34., 45., 18., 22., 32., 37., 50.,\n",
       "       21., 46., 36., 26., 41., 24., 25., 43., 19., 38., 51., 31., 44.,\n",
       "       33., 17.,  0., 54.])"
      ]
     },
     "execution_count": 954,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 955,
   "id": "1eca59a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 2300., 3000., 4000., 5000., 6000., 7000., 8000., 9000.,\n",
       "       1089., 1234., 3030., 3045., 3184., 4824., 5835., 7084., 8943.,\n",
       "       8345., 9284., 9876., 2034., 7654., 2934., 4034., 5034., 8202.,\n",
       "       9024., 4345., 6544., 6543., 3234., 4324., 5435., 5555., 8787.,\n",
       "       3454., 5654., 5009., 5098., 3033.])"
      ]
     },
     "execution_count": 955,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salary'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 956,
   "id": "0435dc23",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chennai', 'Mumbai', 'Calcutta', 'Delhi', 'Podicherry', 'Cochin',\n",
       "       nan, 'Noida', 'Hyderabad', 'Bhopal', 'Nagpur', 'Pune'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 956,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Place'].unique()\n",
    "# spelling correction for 'Podicherry'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 957,
   "id": "b2e535eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chennai', 'Mumbai', 'Calcutta', 'Delhi', 'Pondicherry', 'Cochin',\n",
       "       nan, 'Noida', 'Hyderabad', 'Bhopal', 'Nagpur', 'Pune'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 957,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Place'].replace('Podicherry','Pondicherry',inplace=True)\n",
    "df['Place'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 958,
   "id": "bfaa935b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['India'], dtype=object)"
      ]
     },
     "execution_count": 958,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 959,
   "id": "418b401b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 959,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 960,
   "id": "0032cbea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "id": "b2c537f6",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.527559</td>\n",
       "      <td>5283.471074</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.114717</td>\n",
       "      <td>2585.373600</td>\n",
       "      <td>0.417191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>9876.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Salary      Gender\n",
       "count  127.000000   121.000000  144.000000\n",
       "mean    30.527559  5283.471074    0.222222\n",
       "std     11.114717  2585.373600    0.417191\n",
       "min      0.000000  1089.000000    0.000000\n",
       "25%     22.000000  3030.000000    0.000000\n",
       "50%     33.000000  5000.000000    0.000000\n",
       "75%     37.500000  8000.000000    0.000000\n",
       "max     54.000000  9876.000000    1.000000"
      ]
     },
     "execution_count": 961,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 962,
   "id": "e3c86a69",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company   Age  Salary     Place Country  Gender\n",
       "0      TCS  20.0     NaN   Chennai   India       0\n",
       "1  Infosys  30.0     NaN    Mumbai   India       0\n",
       "2      TCS  35.0  2300.0  Calcutta   India       0\n",
       "3  Infosys  40.0  3000.0     Delhi   India       0\n",
       "4      TCS  23.0  4000.0    Mumbai   India       0"
      ]
     },
     "execution_count": 962,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a6afd8b",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 963,
   "id": "fd23bc9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Outlier detection\n",
    "#using IQR\n",
    "#Chances of getting outliers are only in the 'Age' and 'Salary' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 964,
   "id": "8f04ad98",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv('Employee.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 965,
   "id": "88fb7dac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.484615</td>\n",
       "      <td>5312.467742</td>\n",
       "      <td>0.222973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.096640</td>\n",
       "      <td>2573.764683</td>\n",
       "      <td>0.417654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.500000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.750000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>9876.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Salary      Gender\n",
       "count  130.000000   124.000000  148.000000\n",
       "mean    30.484615  5312.467742    0.222973\n",
       "std     11.096640  2573.764683    0.417654\n",
       "min      0.000000  1089.000000    0.000000\n",
       "25%     22.000000  3030.000000    0.000000\n",
       "50%     32.500000  5000.000000    0.000000\n",
       "75%     37.750000  8000.000000    0.000000\n",
       "max     54.000000  9876.000000    1.000000"
      ]
     },
     "execution_count": 965,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "id": "b6b23e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Outlier detection in 'Age' column=IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 967,
   "id": "be96c7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 967,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1=df['Age'].quantile(0.25)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 968,
   "id": "5a454079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.5"
      ]
     },
     "execution_count": 968,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3=df['Age'].quantile(0.75)\n",
    "q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 969,
   "id": "3c475c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.5"
      ]
     },
     "execution_count": 969,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr=q3-q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "id": "4983daf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.25"
      ]
     },
     "execution_count": 970,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower=q1-1.5*iqr\n",
    "lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "id": "d58fa4fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.75"
      ]
     },
     "execution_count": 971,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper=q3+1.5*iqr\n",
    "upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 972,
   "id": "ff027f36",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Company, Age, Salary, Place, Country, Gender]\n",
       "Index: []"
      ]
     },
     "execution_count": 972,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#outlier rows\n",
    "df[(df['Age']<lower) & (df['Age']>upper)]\n",
    "#no outliers detected using IQR. But Age=0 is an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 973,
   "id": "f8cd5de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Outlier detection in 'Salary' column=IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 974,
   "id": "3c88d81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3030.0"
      ]
     },
     "execution_count": 974,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1=df['Salary'].quantile(0.25)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 975,
   "id": "1646b475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000.0"
      ]
     },
     "execution_count": 975,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3=df['Salary'].quantile(0.75)\n",
    "q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 976,
   "id": "a1cefeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4970.0"
      ]
     },
     "execution_count": 976,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr=q3-q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 977,
   "id": "254b0480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4425.0"
      ]
     },
     "execution_count": 977,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower=q1-1.5*iqr\n",
    "lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 978,
   "id": "ae42c924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15455.0"
      ]
     },
     "execution_count": 978,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper=q3+1.5*iqr\n",
    "upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 979,
   "id": "87ecee4f",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Company, Age, Salary, Place, Country, Gender]\n",
       "Index: []"
      ]
     },
     "execution_count": 979,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#outlier rows\n",
    "df[(df['Salary']<lower) & (df['Salary']>upper)]\n",
    "#no outliers detected using IQR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5cea3bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 980,
   "id": "8e38bb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Treat missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "id": "532808d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     8\n",
       "Age        17\n",
       "Salary     23\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 981,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "id": "ba2eba3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the 'Company' column we have rows with 'nan' values. It should be replacedby mode()-most repeated value, \"TCS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "id": "afab0a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TCS'"
      ]
     },
     "execution_count": 983,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "id": "0342cac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Company'].fillna(df['Company'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "id": "05837236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     0\n",
       "Age        17\n",
       "Salary     23\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 985,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "id": "a1a1bb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#treat 'Age' column\n",
    "# replace the value'0' in Age as 'NaN'\n",
    "# For the 'Age' column, fillna by mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "id": "695c8fd0",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>TCS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3045.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Cognizant</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>Pondicherry</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>TCS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3234.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Cognizant</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1234.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company  Age  Salary        Place Country  Gender\n",
       "87     Infosys  0.0  3030.0     Calcutta   India       0\n",
       "91         TCS  0.0  3045.0        Delhi   India       0\n",
       "100  Cognizant  0.0  2034.0  Pondicherry   India       0\n",
       "106        TCS  0.0  9024.0      Chennai   India       1\n",
       "110    Infosys  0.0  3234.0       Mumbai   India       0\n",
       "120  Cognizant  0.0  1234.0     Calcutta   India       0"
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "id": "612945e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].replace(0,np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "id": "29f05871",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     0\n",
       "Age        23\n",
       "Salary     23\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "id": "2ff87022",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.041322</td>\n",
       "      <td>5283.471074</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.989250</td>\n",
       "      <td>2585.373600</td>\n",
       "      <td>0.417191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>3030.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>9876.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Salary      Gender\n",
       "count  121.000000   121.000000  144.000000\n",
       "mean    32.041322  5283.471074    0.222222\n",
       "std      8.989250  2585.373600    0.417191\n",
       "min     17.000000  1089.000000    0.000000\n",
       "25%     23.000000  3030.000000    0.000000\n",
       "50%     33.000000  5000.000000    0.000000\n",
       "75%     40.000000  8000.000000    0.000000\n",
       "max     54.000000  9876.000000    1.000000"
      ]
     },
     "execution_count": 990,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#now the minimum value for 'Age' is 17 and max is 54 (no outliers). so replace the null values using mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "id": "f884641f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 991,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounded_mean=round(df['Age'].mean(),0)\n",
    "rounded_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "id": "5b31a9a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(rounded_mean,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "id": "2e7dc28d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     0\n",
       "Age         0\n",
       "Salary     23\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 993,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "id": "381d7008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treat 'Salary' column\n",
    "# replace the nulls by mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "id": "abcb8844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5283.0"
      ]
     },
     "execution_count": 995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounded_salary=round(df['Salary'].mean(),0)\n",
    "rounded_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "id": "0e59e234",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Salary'].fillna(rounded_salary,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "id": "11cf35dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company     0\n",
       "Age         0\n",
       "Salary      0\n",
       "Place      14\n",
       "Country     0\n",
       "Gender      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "id": "44071344",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treat 'Place' column\n",
    "# replace the nulls by mode value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "id": "c7ab8943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Mumbai\n",
       "Name: Place, dtype: object"
      ]
     },
     "execution_count": 999,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Place'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "id": "dc1d44e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Place'].fillna(df['Place'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "id": "c323855e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company    0\n",
       "Age        0\n",
       "Salary     0\n",
       "Place      0\n",
       "Country    0\n",
       "Gender     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1001,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "id": "6fcc0eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now the dataset df is cleaned "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "id": "7d7bb74c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Country  Gender\n",
       "0        TCS  20.0  5283.0   Chennai   India       0\n",
       "1    Infosys  30.0  5283.0    Mumbai   India       0\n",
       "2        TCS  35.0  2300.0  Calcutta   India       0\n",
       "3    Infosys  40.0  3000.0     Delhi   India       0\n",
       "4        TCS  23.0  4000.0    Mumbai   India       0\n",
       "..       ...   ...     ...       ...     ...     ...\n",
       "139  Infosys  22.0  8202.0    Mumbai   India       0\n",
       "140      TCS  33.0  9024.0  Calcutta   India       1\n",
       "141  Infosys  44.0  4034.0     Delhi   India       1\n",
       "142      TCS  33.0  5034.0    Mumbai   India       1\n",
       "143  Infosys  22.0  8202.0    Cochin   India       0\n",
       "\n",
       "[144 rows x 6 columns]"
      ]
     },
     "execution_count": 1003,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7355ef5",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1004,
   "id": "17473c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter the data with age >40 and salary<5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "id": "a544bed5",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3234.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Cognizant</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3033.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company   Age  Salary      Place Country  Gender\n",
       "21     Infosys  50.0  3184.0      Delhi   India       0\n",
       "32     Infosys  45.0  4034.0   Calcutta   India       0\n",
       "39     Infosys  41.0  3000.0     Mumbai   India       0\n",
       "50     Infosys  41.0  3000.0    Chennai   India       0\n",
       "57     Infosys  51.0  3184.0  Hyderabad   India       0\n",
       "68     Infosys  43.0  4034.0     Mumbai   India       0\n",
       "75     Infosys  44.0  3000.0     Cochin   India       0\n",
       "85     Infosys  41.0  3000.0      Delhi   India       0\n",
       "92     Infosys  54.0  3184.0     Mumbai   India       0\n",
       "103    Infosys  44.0  4034.0      Delhi   India       0\n",
       "121    Infosys  44.0  3234.0     Mumbai   India       0\n",
       "128    Infosys  50.0  3184.0   Calcutta   India       0\n",
       "135  Cognizant  44.0  3033.0     Cochin   India       0\n",
       "137    Infosys  44.0  4034.0  Hyderabad   India       0\n",
       "141    Infosys  44.0  4034.0      Delhi   India       1"
      ]
     },
     "execution_count": 1005,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Age']>40) & (df['Salary']<5000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "id": "3d066f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the chart with age and salary\n",
    "#correlation between salary and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "id": "b276445b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Age'],df['Salary'],color='g')\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.title(\"Age v/s Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f181da94",
   "metadata": {},
   "source": [
    "### Observations from scatter plot: \n",
    "1. There is no visible correlation between the salary and age of an employee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "id": "84165150",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count the number of people from each place and represent it visually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "id": "138d0891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai         48\n",
       "Calcutta       32\n",
       "Chennai        14\n",
       "Delhi          14\n",
       "Cochin         13\n",
       "Noida           8\n",
       "Hyderabad       8\n",
       "Pondicherry     3\n",
       "Pune            2\n",
       "Bhopal          1\n",
       "Nagpur          1\n",
       "Name: Place, dtype: int64"
      ]
     },
     "execution_count": 1009,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Place'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "id": "e3516051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mumbai', 'Calcutta', 'Chennai', 'Delhi', 'Cochin', 'Noida',\n",
       "       'Hyderabad', 'Pondicherry', 'Pune', 'Bhopal', 'Nagpur'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1010,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df['Place'].value_counts().index\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "id": "ca4e88cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([48, 32, 14, 14, 13,  8,  8,  3,  2,  1,  1], dtype=int64)"
      ]
     },
     "execution_count": 1011,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['Place'].value_counts().values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "id": "b3df3865",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y,color='m')\n",
    "plt.xlabel(\"Cities\")\n",
    "plt.ylabel(\"No: of Employees\")\n",
    "plt.title(\"Employees from Each Cities\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f590a825",
   "metadata": {},
   "source": [
    "### Observations from bar chart: \n",
    "1. Most of the employees are from Mumbai.\n",
    "2. Nagpur consisting of the least number of employee"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac9882f",
   "metadata": {},
   "source": [
    "# Data Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "id": "1aa9c90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical data encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "id": "42801c3f",
   "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>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   Age  Salary    Place Country  Gender\n",
       "0     TCS  20.0  5283.0  Chennai   India       0"
      ]
     },
     "execution_count": 1014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "id": "638ba636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the 'Gender' column is a 'Nominal categorical' column. so it couldn't be ranked like this.\n",
    "# Assume M=0 & F=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "id": "286a3e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'].replace(0,'M',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "id": "0446a7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'].replace(1,'F',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "id": "0be0a7cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>India</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Company   Age  Salary     Place Country Gender\n",
       "0      TCS  20.0  5283.0   Chennai   India      M\n",
       "1  Infosys  30.0  5283.0    Mumbai   India      M\n",
       "2      TCS  35.0  2300.0  Calcutta   India      M\n",
       "3  Infosys  40.0  3000.0     Delhi   India      M\n",
       "4      TCS  23.0  4000.0    Mumbai   India      M"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "id": "89f26627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    112\n",
       "F     32\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "id": "fbe548d4",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.034722</td>\n",
       "      <td>5283.395833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.234681</td>\n",
       "      <td>2368.350171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.750000</td>\n",
       "      <td>3045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>5283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>7084.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>9876.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Salary\n",
       "count  144.000000   144.000000\n",
       "mean    32.034722  5283.395833\n",
       "std      8.234681  2368.350171\n",
       "min     17.000000  1089.000000\n",
       "25%     23.750000  3045.000000\n",
       "50%     32.000000  5283.000000\n",
       "75%     36.000000  7084.000000\n",
       "max     54.000000  9876.000000"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "id": "e94e7d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 144 entries, 0 to 143\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Company  144 non-null    object \n",
      " 1   Age      144 non-null    float64\n",
      " 2   Salary   144 non-null    float64\n",
      " 3   Place    144 non-null    object \n",
      " 4   Country  144 non-null    object \n",
      " 5   Gender   144 non-null    object \n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "id": "fd6a764b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we have 4 categorical columns, \"Company\", \"Place\", \"Country\" & \"Gender\".\n",
    "# Four of them are Nominal Categorical columns.\n",
    "# since only one country's data is available,so we can skip the 'Country' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "id": "9d673108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Gender\n",
       "0        TCS  20.0  5283.0   Chennai      M\n",
       "1    Infosys  30.0  5283.0    Mumbai      M\n",
       "2        TCS  35.0  2300.0  Calcutta      M\n",
       "3    Infosys  40.0  3000.0     Delhi      M\n",
       "4        TCS  23.0  4000.0    Mumbai      M\n",
       "..       ...   ...     ...       ...    ...\n",
       "139  Infosys  22.0  8202.0    Mumbai      M\n",
       "140      TCS  33.0  9024.0  Calcutta      F\n",
       "141  Infosys  44.0  4034.0     Delhi      F\n",
       "142      TCS  33.0  5034.0    Mumbai      F\n",
       "143  Infosys  22.0  8202.0    Cochin      M\n",
       "\n",
       "[144 rows x 5 columns]"
      ]
     },
     "execution_count": 1023,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Country',axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19dc8e4",
   "metadata": {},
   "source": [
    "## OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "id": "26fba0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding Categorical datas using OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "id": "3f798234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TCS', 'Infosys', 'Cognizant'], dtype=object)"
      ]
     },
     "execution_count": 1025,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "id": "fca65ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chennai', 'Mumbai', 'Calcutta', 'Delhi', 'Pondicherry', 'Cochin',\n",
       "       'Noida', 'Hyderabad', 'Bhopal', 'Nagpur', 'Pune'], dtype=object)"
      ]
     },
     "execution_count": 1026,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Place'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "id": "b0cf5d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F'], dtype=object)"
      ]
     },
     "execution_count": 1027,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "id": "2d903038",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use 'OneHotEncoder' to create dummy variables for each of the nominal categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "id": "f309b4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries \n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "id": "4e63e13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe=OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "id": "d36c3534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder()"
      ]
     },
     "execution_count": 1031,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe\n",
    "#created an object in the class()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "id": "5e6bd011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 1.],\n",
       "       [0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 1., ..., 0., 1., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dummies into an array\n",
    "df_array=ohe.fit_transform(df[['Company','Place','Gender']]).toarray()\n",
    "df_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "id": "c779e231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Cognizant', 'Infosys', 'TCS'], dtype=object),\n",
       " array(['Bhopal', 'Calcutta', 'Chennai', 'Cochin', 'Delhi', 'Hyderabad',\n",
       "        'Mumbai', 'Nagpur', 'Noida', 'Pondicherry', 'Pune'], dtype=object),\n",
       " array(['F', 'M'], dtype=object)]"
      ]
     },
     "execution_count": 1033,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "id": "85201ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cognizant',\n",
       " 'Infosys',\n",
       " 'TCS',\n",
       " 'Bhopal',\n",
       " 'Calcutta',\n",
       " 'Chennai',\n",
       " 'Cochin',\n",
       " 'Delhi',\n",
       " 'Hyderabad',\n",
       " 'Mumbai',\n",
       " 'Nagpur',\n",
       " 'Noida',\n",
       " 'Pondicherry',\n",
       " 'Pune',\n",
       " 'F',\n",
       " 'M']"
      ]
     },
     "execution_count": 1034,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories=[np.array(['Cognizant','Infosys','TCS']),\n",
    "            np.array(['Bhopal','Calcutta','Chennai','Cochin','Delhi','Hyderabad','Mumbai','Nagpur','Noida','Pondicherry','Pune']),\n",
    "            np.array(['F','M'])]\n",
    "\n",
    "\n",
    "single_list=[innerlistone for main_array in categories for innerlistone in main_array]\n",
    "single_list\n",
    "#now it's in one list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "id": "564dcc94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cognizant</th>\n",
       "      <th>Infosys</th>\n",
       "      <th>TCS</th>\n",
       "      <th>Bhopal</th>\n",
       "      <th>Calcutta</th>\n",
       "      <th>Chennai</th>\n",
       "      <th>Cochin</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Mumbai</th>\n",
       "      <th>Nagpur</th>\n",
       "      <th>Noida</th>\n",
       "      <th>Pondicherry</th>\n",
       "      <th>Pune</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cognizant  Infosys  TCS  Bhopal  Calcutta  Chennai  Cochin  Delhi  \\\n",
       "0          0.0      0.0  1.0     0.0       0.0      1.0     0.0    0.0   \n",
       "1          0.0      1.0  0.0     0.0       0.0      0.0     0.0    0.0   \n",
       "2          0.0      0.0  1.0     0.0       1.0      0.0     0.0    0.0   \n",
       "3          0.0      1.0  0.0     0.0       0.0      0.0     0.0    1.0   \n",
       "4          0.0      0.0  1.0     0.0       0.0      0.0     0.0    0.0   \n",
       "..         ...      ...  ...     ...       ...      ...     ...    ...   \n",
       "139        0.0      1.0  0.0     0.0       0.0      0.0     0.0    0.0   \n",
       "140        0.0      0.0  1.0     0.0       1.0      0.0     0.0    0.0   \n",
       "141        0.0      1.0  0.0     0.0       0.0      0.0     0.0    1.0   \n",
       "142        0.0      0.0  1.0     0.0       0.0      0.0     0.0    0.0   \n",
       "143        0.0      1.0  0.0     0.0       0.0      0.0     1.0    0.0   \n",
       "\n",
       "     Hyderabad  Mumbai  Nagpur  Noida  Pondicherry  Pune    F    M  \n",
       "0          0.0     0.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "1          0.0     1.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "2          0.0     0.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "3          0.0     0.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "4          0.0     1.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "..         ...     ...     ...    ...          ...   ...  ...  ...  \n",
       "139        0.0     1.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "140        0.0     0.0     0.0    0.0          0.0   0.0  1.0  0.0  \n",
       "141        0.0     0.0     0.0    0.0          0.0   0.0  1.0  0.0  \n",
       "142        0.0     1.0     0.0    0.0          0.0   0.0  1.0  0.0  \n",
       "143        0.0     0.0     0.0    0.0          0.0   0.0  0.0  1.0  \n",
       "\n",
       "[144 rows x 16 columns]"
      ]
     },
     "execution_count": 1035,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataset using the dummies array,'df_array' created using onehotencoder,\n",
    "# use 'single_list' for column labelling.\n",
    "df_new=pd.DataFrame(df_array,columns=single_list)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "id": "97f259c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#concat our dataset 'df' with 'df_new'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "id": "80c24027",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
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       "      <th>Cognizant</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Infosys</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>F</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Gender  Cognizant  Infosys  TCS  Bhopal  \\\n",
       "0        TCS  20.0  5283.0   Chennai      M        0.0      0.0  1.0     0.0   \n",
       "1    Infosys  30.0  5283.0    Mumbai      M        0.0      1.0  0.0     0.0   \n",
       "2        TCS  35.0  2300.0  Calcutta      M        0.0      0.0  1.0     0.0   \n",
       "3    Infosys  40.0  3000.0     Delhi      M        0.0      1.0  0.0     0.0   \n",
       "4        TCS  23.0  4000.0    Mumbai      M        0.0      0.0  1.0     0.0   \n",
       "..       ...   ...     ...       ...    ...        ...      ...  ...     ...   \n",
       "139  Infosys  22.0  8202.0    Mumbai      M        0.0      1.0  0.0     0.0   \n",
       "140      TCS  33.0  9024.0  Calcutta      F        0.0      0.0  1.0     0.0   \n",
       "141  Infosys  44.0  4034.0     Delhi      F        0.0      1.0  0.0     0.0   \n",
       "142      TCS  33.0  5034.0    Mumbai      F        0.0      0.0  1.0     0.0   \n",
       "143  Infosys  22.0  8202.0    Cochin      M        0.0      1.0  0.0     0.0   \n",
       "\n",
       "     Calcutta  ...  Cochin  Delhi  Hyderabad  Mumbai  Nagpur  Noida  \\\n",
       "0         0.0  ...     0.0    0.0        0.0     0.0     0.0    0.0   \n",
       "1         0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "2         1.0  ...     0.0    0.0        0.0     0.0     0.0    0.0   \n",
       "3         0.0  ...     0.0    1.0        0.0     0.0     0.0    0.0   \n",
       "4         0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "..        ...  ...     ...    ...        ...     ...     ...    ...   \n",
       "139       0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "140       1.0  ...     0.0    0.0        0.0     0.0     0.0    0.0   \n",
       "141       0.0  ...     0.0    1.0        0.0     0.0     0.0    0.0   \n",
       "142       0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "143       0.0  ...     1.0    0.0        0.0     0.0     0.0    0.0   \n",
       "\n",
       "     Pondicherry  Pune    F    M  \n",
       "0            0.0   0.0  0.0  1.0  \n",
       "1            0.0   0.0  0.0  1.0  \n",
       "2            0.0   0.0  0.0  1.0  \n",
       "3            0.0   0.0  0.0  1.0  \n",
       "4            0.0   0.0  0.0  1.0  \n",
       "..           ...   ...  ...  ...  \n",
       "139          0.0   0.0  0.0  1.0  \n",
       "140          0.0   0.0  1.0  0.0  \n",
       "141          0.0   0.0  1.0  0.0  \n",
       "142          0.0   0.0  1.0  0.0  \n",
       "143          0.0   0.0  0.0  1.0  \n",
       "\n",
       "[144 rows x 21 columns]"
      ]
     },
     "execution_count": 1037,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml=pd.concat([df,df_new],axis=1)\n",
    "df_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "id": "d9e0ed4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete the categorical column from dataset 'df_ml'\n",
    "#df_ml.drop(['index','Company','Place','Gender'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "id": "04c9d4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
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       "      <th>...</th>\n",
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       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>F</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Gender  Cognizant  Infosys  TCS  Bhopal  \\\n",
       "0        TCS  20.0  5283.0   Chennai      M        0.0      0.0  1.0     0.0   \n",
       "1    Infosys  30.0  5283.0    Mumbai      M        0.0      1.0  0.0     0.0   \n",
       "2        TCS  35.0  2300.0  Calcutta      M        0.0      0.0  1.0     0.0   \n",
       "3    Infosys  40.0  3000.0     Delhi      M        0.0      1.0  0.0     0.0   \n",
       "4        TCS  23.0  4000.0    Mumbai      M        0.0      0.0  1.0     0.0   \n",
       "..       ...   ...     ...       ...    ...        ...      ...  ...     ...   \n",
       "139  Infosys  22.0  8202.0    Mumbai      M        0.0      1.0  0.0     0.0   \n",
       "140      TCS  33.0  9024.0  Calcutta      F        0.0      0.0  1.0     0.0   \n",
       "141  Infosys  44.0  4034.0     Delhi      F        0.0      1.0  0.0     0.0   \n",
       "142      TCS  33.0  5034.0    Mumbai      F        0.0      0.0  1.0     0.0   \n",
       "143  Infosys  22.0  8202.0    Cochin      M        0.0      1.0  0.0     0.0   \n",
       "\n",
       "     Calcutta  ...  Cochin  Delhi  Hyderabad  Mumbai  Nagpur  Noida  \\\n",
       "0         0.0  ...     0.0    0.0        0.0     0.0     0.0    0.0   \n",
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       "4         0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "..        ...  ...     ...    ...        ...     ...     ...    ...   \n",
       "139       0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "140       1.0  ...     0.0    0.0        0.0     0.0     0.0    0.0   \n",
       "141       0.0  ...     0.0    1.0        0.0     0.0     0.0    0.0   \n",
       "142       0.0  ...     0.0    0.0        0.0     1.0     0.0    0.0   \n",
       "143       0.0  ...     1.0    0.0        0.0     0.0     0.0    0.0   \n",
       "\n",
       "     Pondicherry  Pune    F    M  \n",
       "0            0.0   0.0  0.0  1.0  \n",
       "1            0.0   0.0  0.0  1.0  \n",
       "2            0.0   0.0  0.0  1.0  \n",
       "3            0.0   0.0  0.0  1.0  \n",
       "4            0.0   0.0  0.0  1.0  \n",
       "..           ...   ...  ...  ...  \n",
       "139          0.0   0.0  0.0  1.0  \n",
       "140          0.0   0.0  1.0  0.0  \n",
       "141          0.0   0.0  1.0  0.0  \n",
       "142          0.0   0.0  1.0  0.0  \n",
       "143          0.0   0.0  0.0  1.0  \n",
       "\n",
       "[144 rows x 21 columns]"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final encoded dataset for Machine Learning,\n",
    "df_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50522d44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c700425",
   "metadata": {},
   "source": [
    "## LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "id": "a990fd98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# example for encoding using LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "id": "4bb88b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the cleaned dataset 'df' in external\n",
    "df.to_csv('Employee_cleaned.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "id": "e47ca147",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import library\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "id": "fac50713",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LabelEncoder()\n"
     ]
    }
   ],
   "source": [
    "label=LabelEncoder()\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "id": "3e75005a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample=pd.read_csv('Employee_cleaned.csv')\n",
    "df_sample['Company']=label.fit_transform(df_sample['Company'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "id": "481c4f71",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
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       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>Delhi</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>4000.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Gender\n",
       "0          2  20.0  5283.0   Chennai      M\n",
       "1          1  30.0  5283.0    Mumbai      M\n",
       "2          2  35.0  2300.0  Calcutta      M\n",
       "3          1  40.0  3000.0     Delhi      M\n",
       "4          2  23.0  4000.0    Mumbai      M\n",
       "..       ...   ...     ...       ...    ...\n",
       "139        1  22.0  8202.0    Mumbai      M\n",
       "140        2  33.0  9024.0  Calcutta      F\n",
       "141        1  44.0  4034.0     Delhi      F\n",
       "142        2  33.0  5034.0    Mumbai      F\n",
       "143        1  22.0  8202.0    Cochin      M\n",
       "\n",
       "[144 rows x 5 columns]"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6625126",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "id": "abc98b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all numerical columns in the dataset to be standardized before ML model generation\n",
    "# here we have 'Age' and 'Salary' columns only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "id": "45fc0152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Place</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>Delhi</td>\n",
       "      <td>M</td>\n",
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       "      <td>TCS</td>\n",
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       "      <th>139</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>Calcutta</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TCS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Infosys</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   Age  Salary     Place Gender\n",
       "0        TCS  20.0  5283.0   Chennai      M\n",
       "1    Infosys  30.0  5283.0    Mumbai      M\n",
       "2        TCS  35.0  2300.0  Calcutta      M\n",
       "3    Infosys  40.0  3000.0     Delhi      M\n",
       "4        TCS  23.0  4000.0    Mumbai      M\n",
       "..       ...   ...     ...       ...    ...\n",
       "139  Infosys  22.0  8202.0    Mumbai      M\n",
       "140      TCS  33.0  9024.0  Calcutta      F\n",
       "141  Infosys  44.0  4034.0     Delhi      F\n",
       "142      TCS  33.0  5034.0    Mumbai      F\n",
       "143  Infosys  22.0  8202.0    Cochin      M\n",
       "\n",
       "[144 rows x 5 columns]"
      ]
     },
     "execution_count": 1063,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaned dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12d4375",
   "metadata": {},
   "source": [
    "## StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "id": "0d95119d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using standard scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "id": "18077af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Salary\n",
       "0    20.0  5283.0\n",
       "1    30.0  5283.0\n",
       "2    35.0  2300.0\n",
       "3    40.0  3000.0\n",
       "4    23.0  4000.0\n",
       "..    ...     ...\n",
       "139  22.0  8202.0\n",
       "140  33.0  9024.0\n",
       "141  44.0  4034.0\n",
       "142  33.0  5034.0\n",
       "143  22.0  8202.0\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 1072,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df.iloc[ : ,[1,2]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "id": "de8618dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "id": "ddc89a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StandardScaler()\n"
     ]
    }
   ],
   "source": [
    "scaler=StandardScaler().fit(data)\n",
    "print(scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "id": "20366d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardize the data\n",
    "data_scaled=scaler.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "id": "bb147b08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.08108108 0.47729601]\n",
      " [0.35135135 0.47729601]\n",
      " [0.48648649 0.13781723]\n",
      " [0.62162162 0.21748037]\n",
      " [0.16216216 0.33128485]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.16216216 0.6726983 ]\n",
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      " [0.16216216 0.47729601]\n",
      " [0.45945946 0.        ]\n",
      " [0.75675676 0.47729601]\n",
      " [0.02702703 0.01650165]\n",
      " [0.62162162 0.21748037]\n",
      " [0.16216216 0.21748037]\n",
      " [0.16216216 0.2208945 ]\n",
      " [0.45945946 0.44508934]\n",
      " [0.13513514 0.47729601]\n",
      " [0.40540541 0.47729601]\n",
      " [0.54054054 0.22260157]\n",
      " [0.89189189 0.23842039]\n",
      " [0.10810811 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.16216216 0.89382042]\n",
      " [0.45945946 0.82576534]\n",
      " [0.75675676 0.93262775]\n",
      " [0.16216216 1.        ]\n",
      " [0.48648649 0.10754524]\n",
      " [0.78378378 0.74712644]\n",
      " [0.08108108 0.20996927]\n",
      " [0.75675676 0.33515421]\n",
      " [0.51351351 0.44895869]\n",
      " [0.24324324 0.80949129]\n",
      " [0.48648649 0.90303858]\n",
      " [0.40540541 0.47729601]\n",
      " [0.48648649 0.47729601]\n",
      " [0.45945946 0.13781723]\n",
      " [0.64864865 0.21748037]\n",
      " [0.18918919 0.3705474 ]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.21621622 0.6726983 ]\n",
      " [0.48648649 0.78650279]\n",
      " [0.78378378 0.90030727]\n",
      " [0.18918919 0.47729601]\n",
      " [0.40540541 0.        ]\n",
      " [0.7027027  0.47729601]\n",
      " [0.05405405 0.01650165]\n",
      " [0.64864865 0.21748037]\n",
      " [0.18918919 0.21748037]\n",
      " [0.10810811 0.2208945 ]\n",
      " [0.48648649 0.44508934]\n",
      " [0.10810811 0.62080346]\n",
      " [0.40540541 0.74712644]\n",
      " [0.56756757 0.22260157]\n",
      " [0.91891892 0.23842039]\n",
      " [0.16216216 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.21621622 0.89382042]\n",
      " [0.51351351 0.82576534]\n",
      " [0.64864865 0.93262775]\n",
      " [0.21621622 0.62068966]\n",
      " [0.37837838 0.10754524]\n",
      " [0.64864865 0.47729601]\n",
      " [0.10810811 0.20996927]\n",
      " [0.7027027  0.33515421]\n",
      " [0.40540541 0.44895869]\n",
      " [0.10810811 0.80949129]\n",
      " [0.45945946 0.90303858]\n",
      " [0.18918919 0.47729601]\n",
      " [0.45945946 0.47729601]\n",
      " [0.54054054 0.13781723]\n",
      " [0.72972973 0.21748037]\n",
      " [0.40540541 0.33128485]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.24324324 0.6726983 ]\n",
      " [0.40540541 0.78650279]\n",
      " [0.7027027  0.90030727]\n",
      " [0.13513514 0.47729601]\n",
      " [0.43243243 0.        ]\n",
      " [0.         0.01650165]\n",
      " [0.64864865 0.21748037]\n",
      " [0.10810811 0.21748037]\n",
      " [0.40540541 0.2208945 ]\n",
      " [0.51351351 0.44508934]\n",
      " [0.10810811 0.47729601]\n",
      " [0.45945946 0.47729601]\n",
      " [0.40540541 0.22260157]\n",
      " [1.         0.23842039]\n",
      " [0.13513514 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.13513514 0.89382042]\n",
      " [0.43243243 0.82576534]\n",
      " [0.72972973 0.93262775]\n",
      " [0.13513514 0.47729601]\n",
      " [0.40540541 0.10754524]\n",
      " [0.72972973 0.47729601]\n",
      " [0.13513514 0.20996927]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]\n",
      " [0.40540541 0.90303858]\n",
      " [0.43243243 0.47729601]\n",
      " [0.43243243 0.47729601]\n",
      " [0.43243243 0.13781723]\n",
      " [0.40540541 0.24411062]\n",
      " [0.13513514 0.36815751]\n",
      " [0.40540541 0.49459429]\n",
      " [0.40540541 0.50825083]\n",
      " [0.13513514 0.87606692]\n",
      " [0.43243243 0.87606692]\n",
      " [0.72972973 1.        ]\n",
      " [0.13513514 0.47729601]\n",
      " [0.43243243 0.        ]\n",
      " [0.72972973 0.47729601]\n",
      " [0.40540541 0.01650165]\n",
      " [0.72972973 0.24411062]\n",
      " [0.40540541 0.2691476 ]\n",
      " [0.13513514 0.87606692]\n",
      " [0.43243243 0.51951747]\n",
      " [0.08108108 0.44611358]\n",
      " [0.35135135 0.45624218]\n",
      " [0.43243243 0.22260157]\n",
      " [0.89189189 0.23842039]\n",
      " [0.40540541 0.68225788]\n",
      " [0.13513514 0.89382042]\n",
      " [0.37837838 0.82576534]\n",
      " [0.62162162 0.93262775]\n",
      " [0.40540541 0.47729601]\n",
      " [0.37837838 0.10754524]\n",
      " [0.72972973 0.22123592]\n",
      " [0.13513514 0.20996927]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]\n",
      " [0.43243243 0.90303858]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]]\n"
     ]
    }
   ],
   "source": [
    "print(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1090,
   "id": "1deb6e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.17247504e-17 1.03312420e-16]\n"
     ]
    }
   ],
   "source": [
    "#check the mean, for a standarzide data it's = 0 or near,\n",
    "print(data_scaled.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "id": "095d47de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1.]\n"
     ]
    }
   ],
   "source": [
    "#check the std, for a standarzide data it's = 1,\n",
    "print(data_scaled.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "id": "5b1a5c74",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.466569</td>\n",
       "      <td>-0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.247954</td>\n",
       "      <td>-0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.361353</td>\n",
       "      <td>-1.264091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.970661</td>\n",
       "      <td>-0.967495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.100985</td>\n",
       "      <td>-0.543786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>-1.222846</td>\n",
       "      <td>1.236638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.117630</td>\n",
       "      <td>1.584926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.458106</td>\n",
       "      <td>-0.529380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.117630</td>\n",
       "      <td>-0.105671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>-1.222846</td>\n",
       "      <td>1.236638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age    Salary\n",
       "0   -1.466569 -0.000168\n",
       "1   -0.247954 -0.000168\n",
       "2    0.361353 -1.264091\n",
       "3    0.970661 -0.967495\n",
       "4   -1.100985 -0.543786\n",
       "..        ...       ...\n",
       "139 -1.222846  1.236638\n",
       "140  0.117630  1.584926\n",
       "141  1.458106 -0.529380\n",
       "142  0.117630 -0.105671\n",
       "143 -1.222846  1.236638\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 1094,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert it into dataframe-table format\n",
    "scaled_data_set=pd.DataFrame(data_scaled,columns=data.columns)\n",
    "scaled_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f51a1caf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "81319c82",
   "metadata": {},
   "source": [
    "## MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "id": "d825edf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using minmax scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "id": "9beb4c3c",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>5283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>33.0</td>\n",
       "      <td>9024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>33.0</td>\n",
       "      <td>5034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Salary\n",
       "0    20.0  5283.0\n",
       "1    30.0  5283.0\n",
       "2    35.0  2300.0\n",
       "3    40.0  3000.0\n",
       "4    23.0  4000.0\n",
       "..    ...     ...\n",
       "139  22.0  8202.0\n",
       "140  33.0  9024.0\n",
       "141  44.0  4034.0\n",
       "142  33.0  5034.0\n",
       "143  22.0  8202.0\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 1095,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df.iloc[ : ,[1,2]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "id": "b20ef921",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "id": "339b5111",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinMaxScaler()\n"
     ]
    }
   ],
   "source": [
    "scaler=MinMaxScaler().fit(data)\n",
    "print(scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "id": "e1e6c6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  17., 1089.])"
      ]
     },
     "execution_count": 1101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check min and max\n",
    "scaler.data_min_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "id": "a70a5e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  54., 9876.])"
      ]
     },
     "execution_count": 1102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.data_max_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1104,
   "id": "f540449d",
   "metadata": {
    "scrolled": false
   },
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.034722</td>\n",
       "      <td>5283.395833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.234681</td>\n",
       "      <td>2368.350171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.750000</td>\n",
       "      <td>3045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>5283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>7084.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>9876.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Salary\n",
       "count  144.000000   144.000000\n",
       "mean    32.034722  5283.395833\n",
       "std      8.234681  2368.350171\n",
       "min     17.000000  1089.000000\n",
       "25%     23.750000  3045.000000\n",
       "50%     32.000000  5283.000000\n",
       "75%     36.000000  7084.000000\n",
       "max     54.000000  9876.000000"
      ]
     },
     "execution_count": 1104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "id": "af77d35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 1105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check feature range\n",
    "scaler.feature_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "id": "f928926d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardize the data\n",
    "data_scaled_mm=scaler.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "id": "60f3bc93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.08108108 0.47729601]\n",
      " [0.35135135 0.47729601]\n",
      " [0.48648649 0.13781723]\n",
      " [0.62162162 0.21748037]\n",
      " [0.16216216 0.33128485]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.16216216 0.6726983 ]\n",
      " [0.45945946 0.78650279]\n",
      " [0.75675676 0.90030727]\n",
      " [0.16216216 0.47729601]\n",
      " [0.45945946 0.        ]\n",
      " [0.75675676 0.47729601]\n",
      " [0.02702703 0.01650165]\n",
      " [0.62162162 0.21748037]\n",
      " [0.16216216 0.21748037]\n",
      " [0.16216216 0.2208945 ]\n",
      " [0.45945946 0.44508934]\n",
      " [0.13513514 0.47729601]\n",
      " [0.40540541 0.47729601]\n",
      " [0.54054054 0.22260157]\n",
      " [0.89189189 0.23842039]\n",
      " [0.10810811 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.16216216 0.89382042]\n",
      " [0.45945946 0.82576534]\n",
      " [0.75675676 0.93262775]\n",
      " [0.16216216 1.        ]\n",
      " [0.48648649 0.10754524]\n",
      " [0.78378378 0.74712644]\n",
      " [0.08108108 0.20996927]\n",
      " [0.75675676 0.33515421]\n",
      " [0.51351351 0.44895869]\n",
      " [0.24324324 0.80949129]\n",
      " [0.48648649 0.90303858]\n",
      " [0.40540541 0.47729601]\n",
      " [0.48648649 0.47729601]\n",
      " [0.45945946 0.13781723]\n",
      " [0.64864865 0.21748037]\n",
      " [0.18918919 0.3705474 ]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.21621622 0.6726983 ]\n",
      " [0.48648649 0.78650279]\n",
      " [0.78378378 0.90030727]\n",
      " [0.18918919 0.47729601]\n",
      " [0.40540541 0.        ]\n",
      " [0.7027027  0.47729601]\n",
      " [0.05405405 0.01650165]\n",
      " [0.64864865 0.21748037]\n",
      " [0.18918919 0.21748037]\n",
      " [0.10810811 0.2208945 ]\n",
      " [0.48648649 0.44508934]\n",
      " [0.10810811 0.62080346]\n",
      " [0.40540541 0.74712644]\n",
      " [0.56756757 0.22260157]\n",
      " [0.91891892 0.23842039]\n",
      " [0.16216216 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.21621622 0.89382042]\n",
      " [0.51351351 0.82576534]\n",
      " [0.64864865 0.93262775]\n",
      " [0.21621622 0.62068966]\n",
      " [0.37837838 0.10754524]\n",
      " [0.64864865 0.47729601]\n",
      " [0.10810811 0.20996927]\n",
      " [0.7027027  0.33515421]\n",
      " [0.40540541 0.44895869]\n",
      " [0.10810811 0.80949129]\n",
      " [0.45945946 0.90303858]\n",
      " [0.18918919 0.47729601]\n",
      " [0.45945946 0.47729601]\n",
      " [0.54054054 0.13781723]\n",
      " [0.72972973 0.21748037]\n",
      " [0.40540541 0.33128485]\n",
      " [0.40540541 0.44508934]\n",
      " [0.40540541 0.55889382]\n",
      " [0.24324324 0.6726983 ]\n",
      " [0.40540541 0.78650279]\n",
      " [0.7027027  0.90030727]\n",
      " [0.13513514 0.47729601]\n",
      " [0.43243243 0.        ]\n",
      " [0.         0.01650165]\n",
      " [0.64864865 0.21748037]\n",
      " [0.10810811 0.21748037]\n",
      " [0.40540541 0.2208945 ]\n",
      " [0.51351351 0.44508934]\n",
      " [0.10810811 0.47729601]\n",
      " [0.45945946 0.47729601]\n",
      " [0.40540541 0.22260157]\n",
      " [1.         0.23842039]\n",
      " [0.13513514 0.42505975]\n",
      " [0.40540541 0.54011608]\n",
      " [0.40540541 0.68225788]\n",
      " [0.13513514 0.89382042]\n",
      " [0.43243243 0.82576534]\n",
      " [0.72972973 0.93262775]\n",
      " [0.13513514 0.47729601]\n",
      " [0.40540541 0.10754524]\n",
      " [0.72972973 0.47729601]\n",
      " [0.13513514 0.20996927]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]\n",
      " [0.40540541 0.90303858]\n",
      " [0.43243243 0.47729601]\n",
      " [0.43243243 0.47729601]\n",
      " [0.43243243 0.13781723]\n",
      " [0.40540541 0.24411062]\n",
      " [0.13513514 0.36815751]\n",
      " [0.40540541 0.49459429]\n",
      " [0.40540541 0.50825083]\n",
      " [0.13513514 0.87606692]\n",
      " [0.43243243 0.87606692]\n",
      " [0.72972973 1.        ]\n",
      " [0.13513514 0.47729601]\n",
      " [0.43243243 0.        ]\n",
      " [0.72972973 0.47729601]\n",
      " [0.40540541 0.01650165]\n",
      " [0.72972973 0.24411062]\n",
      " [0.40540541 0.2691476 ]\n",
      " [0.13513514 0.87606692]\n",
      " [0.43243243 0.51951747]\n",
      " [0.08108108 0.44611358]\n",
      " [0.35135135 0.45624218]\n",
      " [0.43243243 0.22260157]\n",
      " [0.89189189 0.23842039]\n",
      " [0.40540541 0.68225788]\n",
      " [0.13513514 0.89382042]\n",
      " [0.37837838 0.82576534]\n",
      " [0.62162162 0.93262775]\n",
      " [0.40540541 0.47729601]\n",
      " [0.37837838 0.10754524]\n",
      " [0.72972973 0.22123592]\n",
      " [0.13513514 0.20996927]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]\n",
      " [0.43243243 0.90303858]\n",
      " [0.72972973 0.33515421]\n",
      " [0.43243243 0.44895869]\n",
      " [0.13513514 0.80949129]]\n"
     ]
    }
   ],
   "source": [
    "print(data_scaled_mm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "id": "93935618",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.477296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.477296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.486486</td>\n",
       "      <td>0.137817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.217480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.162162</td>\n",
       "      <td>0.331285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.809491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.903039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.729730</td>\n",
       "      <td>0.335154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.432432</td>\n",
       "      <td>0.448959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.809491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age    Salary\n",
       "0    0.081081  0.477296\n",
       "1    0.351351  0.477296\n",
       "2    0.486486  0.137817\n",
       "3    0.621622  0.217480\n",
       "4    0.162162  0.331285\n",
       "..        ...       ...\n",
       "139  0.135135  0.809491\n",
       "140  0.432432  0.903039\n",
       "141  0.729730  0.335154\n",
       "142  0.432432  0.448959\n",
       "143  0.135135  0.809491\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 1106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert it into dataframe-table format\n",
    "mm_scaled_data_set=pd.DataFrame(data_scaled_mm,columns=data.columns)\n",
    "mm_scaled_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "id": "ab9dad10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset is now completely preprocessed for Machine Learning Modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78235ab5",
   "metadata": {},
   "source": [
    "*************************************************************************************************************************"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4183edf",
   "metadata": {},
   "source": [
    "*************************************************************************************************************************"
   ]
  }
 ],
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