SALARY_PREDICTION SHIJALIKHARE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ce30945",
   "metadata": {},
   "source": [
    "## Project:- Salary Prediction Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8b71b8",
   "metadata": {},
   "source": [
    "## SHIJALI KHARE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf83d8e8",
   "metadata": {},
   "source": [
    "## Concept Used:- Supervised Machine Learning using LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24c6dc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required in this task\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7164b5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data from dataset\n",
    "#df=pd.read_csv(\"C:\\Users\\shija\\Downloads\\Jupternotebook\\Salary_Data.csv\")\n",
    "df= pd.read_csv(r'C:\\Users\\shija\\Downloads\\Jupternotebook\\Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b9c903e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e51b4f46",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1   39343\n",
       "1              1.3   46205\n",
       "2              1.5   37731\n",
       "3              2.0   43525\n",
       "4              2.2   39891"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0729e18",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09d32491",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2b48277a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "    YearsExperience  Salary\n",
       "0             False   False\n",
       "1             False   False\n",
       "2             False   False\n",
       "3             False   False\n",
       "4             False   False\n",
       "5             False   False\n",
       "6             False   False\n",
       "7             False   False\n",
       "8             False   False\n",
       "9             False   False\n",
       "10            False   False\n",
       "11            False   False\n",
       "12            False   False\n",
       "13            False   False\n",
       "14            False   False\n",
       "15            False   False\n",
       "16            False   False\n",
       "17            False   False\n",
       "18            False   False\n",
       "19            False   False\n",
       "20            False   False\n",
       "21            False   False\n",
       "22            False   False\n",
       "23            False   False\n",
       "24            False   False\n",
       "25            False   False\n",
       "26            False   False\n",
       "27            False   False\n",
       "28            False   False\n",
       "29            False   False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking Null values of Dataset\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "542b90cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    False\n",
       "Salary             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a55ade84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bd7f24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iC3A7MLGszCjg/rS9MHd8FPBgRDRHxEvACmCCslkBjwbmpHKzgJPS9sS0Tzr+AZXNIliuSBfKKcD7IuK2iLgE+FTuJmZmvUpHulAkTZG0NJem5C41FHg6t7825eUtByal7ZOBwZL2TvkTJO2SVq4/Ctgf2BvYmFvgOH/NrfdLxzel8u0qMgrlpLL9R9OS92ZmvU5HRqFExAxgRhduNxW4TtLZZKvSryN7+fG+NA3JQ8CzwMNASxfu06Z2W+CSvp/bLu+L+UmtK2JmVgs1HIWyjqzVXDIs5W27V8T6iJgUEQcBl6a8jenn5RExJiKOAQQ8CTwP7CmpfxvX3Hq/dHyPVL5dlbpQRua2jyk79oZKFzUz6yk17ANfAoxMo0YGApOB7ZaTlDREUimOXkL2vBBJ/VJXCpJGk80ddV9EBFlf+anpnLPIBoiQrn1W2j4VuD+Vb1elLpRKJ/ahl1XNrJ4UGR5Y7DrRLOlCYB7QD5gZEaskTQOWRsRcYDwwXVKQdaFckE4fACxOzyBfBD6e6/f+PHC7pK8CvyKbMJD08xZJTcALZF8YFVUK4LtIOoislT4obSulQUX+AMzMdrRazoUSEXcDd5flXZbbnsO2ESX5Mn8lG4nS1jXXkI1waeucj3SkfpUC+AbgP9L2H3PbpX0zs16nQNdIw2g3gHsOFDOrR60N8Ip8UUVe5PmIpNJqPF+Q9MPUnWJm1uvU8CFmr1dkOtkvRsSdaSWeDwJXAd8hez20qkH7HdGF6lmjemX94uqFzDqhVg8x60GRNzFLg8+PB2ZExE+Bgd1XJTOzzutLLfAiAXxdWpHnNLJ5UHYqeJ6Z2Q4XHUj1rkgXykeBCcDVEbFR0r7Av3RvtczMOqelte+0LysG8DSd4mMR8fZSXkRsIBtiaGbW6/ShRekrd4VERAuwWtKbd1B9zMy6JFDhVO+KdKG8Hlgl6VHgpVJmRJzYbbUyM+uk1kbo3C6o0DDCbq+FmVmNtDZAy7qoIvOBP7AjKmJmVguN0DVSVJE3Md8taYmkzZK2SGqR9OKOqJyZWUe1oMKp3hXpQrmObFrDO4FDyNbGfFt3VsrMrLM8CqVMRDQB/SKiJSJuIhsXbmbW67R2INW7Ii3wl9NqFMskXUk2BrzvjJQ3s7riPvDtnZHKXUg2jHB/spXqzcx6nVYVT9VImiBptaQmSRe3cXy4pAWSVkhaJGlY7tiVklZJekLStcoMlrQsl56T9I1U/mxJz+aOnVutfkVGofxe0iBg34j4t+of2cys59RqGGF6E/16sjWB1wJLJM2NiF/nil0NzI6IWZKOBqYDZ0g6HHgv2VqYAD8HjoyIRcCY3D1+Cfwwd707IuLConUsMgrlw8Ay4N60P0bS3IonmZn1kJYOpCrGAU0RsSYitgC3AxPLyowC7k/bC3PHA9iZbObWncjWyHwmf6KktwFvBDo9t3KRLpQvk32QjQARsQwY0dkbmpl1p1apcJI0RdLSXJqSu9RQ4Onc/tqUl7ccmJS2TwYGS9o7Ih4mC+iluaPmRcQTZedOJmtx598dPSV1x8yRtH+1z1okgL8WEZvK8vrQy6pmVk86Mp1sRMyIiENyaUYHbzcVOFLSr4AjgXVAi6S3Au8AhpEF/aMlla9uMxm4Lbd/F3BARIwG5gOzqt283QAu6W5JI8jmQfkY0E/SSEnfBB4q/PHMzHagGg4jXEc2aKNkWMrbKiLWR8SkiDgIuDTlbSRrjT8SEZsjYjNwD/Ce0nmSDgT6R8Qvc9d6PiJeTbs3AgdXq2ClFvhNwDzgd8A/AK8C3wM2Af9c7cJmZj2hhqNQlgAjJY1IQ6knA9s9/5M0RFIpjl4CzEzbfyBrmfeXNICsdZ7vQjmd7VvfpLUWSk4sK9+mdgN4RNwJjAV2I1tO7Q6yTvw/AxdUu7CZWU+o1av0EdFMNnx6Hlkw/X5ErJI0TVJpNtbxZFNuPwnsA1ye8ucA/ws8TtZPvjwi7spd/qOUBXDgojTscDlwEXB2tc9abRjhFrKx3zuRBXL3fZtZr1ZkfHdREXE3cHdZ3mW57Tlkwbr8vBbg/ArXfUsbeZeQteILazeAS5oA/AfZPxnGRsTLHbmwmVlPaIRX5Iuq1AK/FPhIRKzaUZUxM+uqvtRN0G4Aj4jyIS9mZr1eLbtQersik1mZmdUNd6GYmdWpFrfAzczqk1vgZmZ1ygHczKxOeRSKmVmd8igUM7M65S4UM7M6VWChhobhAG5mDcVdKGZmdcpdKGZmdcqjUMzM6lRrHwrhDuBm1lD60kPMIosam5nVjRquiYmkCZJWS2qSdHEbx4dLWpBWkl8kaVju2JVphZ0nJF0rSSl/UbrmspTemPJ3knRHutcvJB1QrX4O4GbWUGq1JqakfsD1wLHAKOB0SaPKil0NzE4ryU8DpqdzDwfeC4wmW1P4ULJ1MUv+KSLGpPSnlPdJ4M8R8VbgGuCKap/VAdzMGkorUThVMQ5oiog1EbGFbE3giWVlRgH3p+2FueMB7AwMJFuScgDwTJX7TQRmpe05wAdKrfb2OICbWUOJDiRJUyQtzaUpuUsNBZ7O7a9NeXnLgUlp+2RgsKS9I+JhsoC+IaV5EZFfZf6m1H3yxVyQ3nq/tKDyJmDvSp/VDzHNrKF0ZBx4RMwAZnThdlOB6ySdDTwIrANaJL0VeAdQ6hOfL+mIiFhM1n2yTtJg4AfAGcDsztzcLXAzaygtROFUxTpg/9z+sJS3VUSsj4hJEXEQ2TrCRMRGstb4IxGxOSI2A/cA70nH16WffwG+R9ZVs939JPUH9gCer1RBB3Azayg1HIWyBBgpaYSkgcBkYG6+gKQhkkpx9BJgZtr+A3CkpP6SBpA9wHwi7Q9J5w4ATgBWpnPmAmel7VOB+yOi4reMu1DMrKHU6kWeiGiWdCEwD+gHzIyIVZKmAUsjYi4wHpguKci6UC5Ip88BjgYeJ+tuvzci7pK0KzAvBe9+wM+AG9I5/wXcIqkJeIHsC6MiVQnwXdZ/4NC+81qUFfbK+sU9XQXrhQYMeUuXp6L6PwdMLhxzrvnd7XU99ZVb4GbWUDyZlZlZnSrwcLJhOICbWUPxZFZmZnWq74RvB3AzazBugZuZ1Sk/xCwjqV9E9KVpds2sTkUfaoEXfRPzKUlXtTGVYpvyE8S0tr7UheqZmXVMDV+l7/WKBvADgSeBGyU9kgL07u0VjogZEXFIRBzyutftWpOKmpkVUcsFHXq7QgE8Iv4SETdExOHA54EvARskzUqzbpmZ9QqtEYVTvSvcBw4cD5wDHAB8HbgVOAK4G3hbN9XPzKxD6j8sF1d0FMpTZJOTXxURD+Xy50h6f+2rZWbWOR5GmJNa3zdHxLS2jkfERTWvlZlZJ3kUSk4aPnjCDqiLmVmXNROFU70r2oXyP5KuA+4Ato4LjIjHuqVWZmad1Jda4EUD+Jj0M9+NEmQTlpuZ9RqNMDywqKLDCI9qIzl4m1mvExGFUzWSJkhaLalJ0sVtHB8uaYGkFZIWSRqWO3alpFWSnpB0rTK7SPqppN+kY1/LlT9b0rNptfplks6tVr/Cc6FIOh54J7BzKa+9B5tmZj2lVqNQ0gCO64FjgLXAEklzI+LXuWJXA7MjYpako4HpwBmSDgfeC4xO5X5Oti7mo8DVEbEwrbO5QNKxEXFPKndHRFxYtI6FWuCSvgOcBnwWEPARYHjRm5iZ7Sg1fJV+HNAUEWsiYgtwOzCxrMwo4P60vTB3PMgauwOBnYABwDMR8XJELARI13yMbLX7Tin6Kv3hEXEm8OeI+DfgPfjlHTPrhVqJwik/b1NKU3KXGgo8ndtfm/LylgOT0vbJwGBJe0fEw2QBfUNK8yLiifyJkvYEPgwsyGWfkrpj5kjav9pnLRrAX0k/X5a0H/AasG/Bc83MdpiO9IHn521KaUYHbzcVOFLSr8i6SNYBLWmKkXeQta6HAkdLOqJ0kqT+wG3AtRGxJmXfBRwQEaOB+cCsajcvGsB/kr4triJr8v8u3dzMrFep4WRW64B8K3hYytsqItZHxKSIOAi4NOVtJGuNPxIRmyNiM3APWc9FyQzgqYj4Ru5az0fEq2n3RuDgahUsOgrlKxGxMSJ+QNb3/faI+GKRc83MdqTowH9VLAFGShqRHjhOBubmC0gaIqkURy8BZqbtP5C1zPtLGkDWOn8infNVYA/gc2XXyvdqnFgqX0nFUSiSJlU4RkT8sNoNzMx2pFqNQomIZkkXAvOAfsDMiFglaRqwNCLmAuOB6ZICeBC4IJ0+h+w9mcfJHmjeGxF3pWGGlwK/AR6TBHBdRNwIXCTpRKAZeAE4u1odVWkspKSbKn+++ES1G/QfOLTvvBZlhb2yfnFPV8F6oQFD3qKuXuOoYccUjjkL187v8v16UsUWeEScs6MqYmZWC36Vvg1+kcfM6kEjLNRQVNEFHb4D7AIcRfZ09FSyN4rMzHqVvhO+/SKPmTWYjrzIU++KdqGUv8jzAn6Rx8x6oUYIzEUVDeClF3muBH6Z8m7slhqZmXVBS/SdCWWrjQM/FHg6Ir6S9ncjG9f4G+Ca7q+emVnH9KVRKNX6wL8LbAFIixd/LeVtInsV1MysV6nlfOC9XbUulH4R8ULaPg2YkV6n/4GkZd1aMzOzTuhLfeDVWuD90qxZAB9g27y30IEx5GZmO4pb4NvcBjwg6TmykSiLAdJUiZu6uW5mZh3W0odWxaz2Kv3lkhaQDRm8L7Z9Zb2ObHUeM7NexW9i5kTEI23kPdk91TEz65q+NArF/dhm1lDcAjczq1NugZuZ1am+1AIvOpmVmVldaInWwqkaSRMkrZbUJOniNo4Pl7QgrSS/KK24Uzp2paRVkp6QdK3S8juSDpb0eLpmPn8vSfMlPZV+vr5a/RzAzayh1GpNTEn9gOuBY4FRwOmSRpUVuxqYnVaSnwZMT+ceDrwXGA38A3Ao2bqYAN8GzgNGpjQh5V8MLIiIkcCCtF+RA7iZNZSI1sKpinFAU0SsiYgtwO3AxLIyo9j2guPC3PEgW/xmILATMAB4Ji1cvHtEPJKGZc8GTkrnTARmpe1Zufx2OYCbWUPpyHzgkqZIWppLU3KXGgo8ndtfm/LylgOlxd9PBgZL2jsiHiYL6BtSmhcRT6Tz17ZzzX0iYkPa/iOwT7XP6oeYZtZQOvKKfETMoGsT800FrpN0Ntmq9OuAlvS2+juAUp/4fElHsG1thWr1irTSfUUO4GbWUGo4mdU6YP/c/rCUt1VErCe1wNN026dExEZJ5wGPRMTmdOwespXMbmFbUC+/5jOS9o2IDamr5U/VKuguFDNrKC2trYVTFUuAkZJGSBoITAbm5gtIGiKpFEcvAWam7T8AR0rqL2kA2QPMJ1IXyYuS3p1Gn5wJ/DidMxc4K22flctvlwO4mTWUWo1CiYhm4EJgHvAE8P2IWCVpmqQTU7HxwGpJT5L1WV+e8ucA/0u2AM5yYHlE3JWOfYZsRbOmVOaelP814BhJTwEfTPsVqbunVOw/cGjfGVVvhb2yfnFPV8F6oQFD3qKuXmOfPd5eOOY8s+k3Xb5fT3IfuJk1lL60oIMDuJk1lEZYqKEoB3AzaygFHk42DAdwM2so7kIxM6tT7kIxM6tTfWk6WQdwM2soXtDBzKxOuQVuZlanWgss1NAoHMDNrKH4IaaZWZ1yADczq1N9J3zvgMmsbBtJU9IE8mZb+ffCOsvTye5YU6oXsT7IvxfWKQ7gZmZ1ygHczKxOOYDvWO7ntLb498I6xQ8xzczqlFvgZmZ1ygHczKxONUwAV+bnko7N5X1E0r3dcK9FklZLWpbSnFrfo+x++3X3PazrJF0qaZWkFen34rAKZW+WdOqOrJ81noZ5EzMiQtKngDslLST7bP8OTOjM9ST1j4jmCkX+KSKWdubanajHesD/s/dikt4DnACMjYhXJQ0BBtbw+tV+H60PapgWOEBErATuAj4PXAb8N3CppEcl/UrSRABJB0haLOmxlA5P+eNT/lzg15J2lfRTScslrZR0WqX7S/qxpDPT9vmSbk3biyT9Z2qVrZQ0LuXvKmlmG/U7W9JcSfcDC1J9V6Zj/SRdJWlJaumdn6v7IklzJP1G0q2SlI4dKumh9DkelTS4vetYp+0LPBcRrwJExHMRsV7SZenPeKWkGaW/k7z2yqS/z29IWkr2e/xbSQPSsd3z+9ZHRURDJWBXYDXwODAd+HjK3xN4Mh3fBdg55Y8Elqbt8cBLwIi0fwpwQ+7ae6Sfi9I9lqV0VcrfB2gCjkj32itX/oa0/X5gZdr+93bqdzawNnf+AblzpgBfSNs7AUuBEanum4BhZF/MDwPvI2sFrgEOTefsTvavkzav09N/f/WagN3S78KTwLeAI1P+XrkytwAfTts3A6dWKbMI+Fbu2E3ASbnfg6/39Od26tnUMF0oJRHxkqQ7gM3AR4EPS5qaDu8MvBlYD1wnaQzQArwtd4lHI+K3aftx4OuSrgB+EhGLc+X+pgslIp6RdBmwEDg5Il7IHb4tlXkwtZ72BD4EnNhG/QDml51f8iFgdK7/dA+yL6Etqe5rASQtIwv8m4ANEbEk3f/FdLy965Q+u3VARGyWdDDZl/dRwB2SLgb+IulfyRoNewGryP6VmHdUhTJ35MrdCPwr8CPgHOC87vk0Vi8aLoAnrSkJOCUiVucPSvoy8AxwIFlr9a+5wy+VNiLiSUljgeOAr0paEBHTqtz7XcDzwH5l+eUD7qNC/Q7L16OMgM9GxLyyc8YDr+ayWqj899vmdazzIqKFrNW8SNLjwPnAaOCQiHg6/d7tnD9H0s5kLfb2yuR/H/8ndaeNB/pF1mVofVhD9YG3YR7w2Vyf4kEpfw+yVmkrcAbQr62TJe0HvBwR/w1cBYytdLPUt30scBAwVdKI3OHTUpn3AZsiYlOF+lX7TJ/O9YW+TdKuFcqvBvaVdGgqP1hS/05cxyqQ9PeSRuayxpD92QM8J2k32n4QvXOBMnmzge+RdadYH9eoLfCSrwDfAFZIeh1Z98AJZC2eH6QHjvfSfmv3XcBVklqB14BP547dKumVtP0ccDxwA3BOZA+v/i8wU9LRqcxfJf0KGAB8okr9KrmRrGvksRT4nwVOaq9wRGxJD1+/KWkQ8ArwwY5ex6rajezPeE+gmexZyBRgI7AS+COwpPykiNgo6YZKZcrcCnyV1CVnfZtfpd8BJC0Cppb3mZt1VHpmMTEizujpuljPa/QWuFnDkPRNsi6643q6LtY7uAVuZlanGv0hpplZw3IANzOrUw7gZmZ1ygHczKxOOYCbmdWp/w8zi7JAIfRvvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f3bf08",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a6bde7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2c468ea2490>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='YearsExperience',y='Salary',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2edbda",
   "metadata": {},
   "source": [
    "From the graph above, we can clearly see that there is a positive linear relation between the Salary and the no. of years of Experience."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83176439",
   "metadata": {},
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ac3890",
   "metadata": {},
   "source": [
    "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2423e305",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()\n",
    "x=df.iloc[:,:1].values\n",
    "y=df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a7e4bf03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1a7d97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff101cd",
   "metadata": {},
   "source": [
    "## Training the Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c7c6d1",
   "metadata": {},
   "source": [
    "We have split our data into training and testing sets, and now is finally the time to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42024e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regre=LinearRegression()\n",
    "regre.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d203d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting the regression line\n",
    "line =regre.coef_*x+regre.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd22b686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting for test data\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line,color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc79e11",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9535175e",
   "metadata": {},
   "source": [
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "80834dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=regre.predict(x_test)\n",
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84105356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.5]\n",
      " [10.3]\n",
      " [ 4.1]\n",
      " [ 3.9]\n",
      " [ 9.5]\n",
      " [ 8.7]]\n"
     ]
    }
   ],
   "source": [
    "# Testing Data\n",
    "print(x_test)\n",
    "y_pred= regre.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d446da01",
   "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>Actual</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731</td>\n",
       "      <td>40748.961841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391</td>\n",
       "      <td>122699.622956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081</td>\n",
       "      <td>64961.657170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218</td>\n",
       "      <td>63099.142145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969</td>\n",
       "      <td>115249.562855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431</td>\n",
       "      <td>107799.502753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual      predicted\n",
       "0   37731   40748.961841\n",
       "1  122391  122699.622956\n",
       "2   57081   64961.657170\n",
       "3   63218   63099.142145\n",
       "4  116969  115249.562855\n",
       "5  109431  107799.502753"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "compare_values=pd.DataFrame({\"Actual\":y_test,\"predicted\":y_pred})\n",
    "compare_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9a5ce6",
   "metadata": {},
   "source": [
    "## EVALUATING MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e43ff3",
   "metadata": {},
   "source": [
    "The final step is to evaluate the performance of algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31138baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 2446.1723690465055\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "75b11d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9411949620562126\n",
      "Test Score: 0.988169515729126\n"
     ]
    }
   ],
   "source": [
    "# Estimating training training and test score\n",
    "print(\"Training Score:\", regre.score(x_train, y_train))\n",
    "print(\"Test Score:\", regre.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1433cc4",
   "metadata": {},
   "source": [
    "## Testing our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "12608b9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Salary for the year you entered: {} [91968.12503773]\n"
     ]
    }
   ],
   "source": [
    "# Predicting score for 8.0 YearsExperience\n",
    "years= [[7.0]]\n",
    "own_predict= regre.predict(years)\n",
    "print(\"Predicted Salary for the year you entered: {}\",own_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d425ebdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Salary for the year you entered: {} [119905.85041792]\n"
     ]
    }
   ],
   "source": [
    "years= [[10.0]]\n",
    "own_predict= regre.predict(years)\n",
    "print(\"Predicted Salary for the year you entered: {}\",own_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f94abd9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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