Udit Thakkar (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fc534dd",
   "metadata": {},
   "source": [
    "# Salary Prediction Model\n",
    "## Udit Thakkar\n",
    "## Concept- Supervised Machine Learning and Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ca01bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8f7c292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/CSCult/salary-prediction/main/Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ef8f9ba",
   "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>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>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6f3ab9c",
   "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>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>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing our dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50c1c215",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking statistical information about the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fea372c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba365bea",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e745cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "672b33de",
   "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": [
    "# Analyzing the data by plotting the distribution\n",
    "df.plot(x=\"YearsExperience\",y=\"Salary\",style=\"o\")\n",
    "plt.title(\"Year of experience vs Salary\")\n",
    "plt.xlabel(\"Year of experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7acf799b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the data\n",
    "x = df.iloc[:,:1].values\n",
    "y = df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2392c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividing the data into train and test\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33ce564b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Implementing the linear regression algorithm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d706fe5e",
   "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 the regression line\n",
    "line =reg.coef_*x+reg.intercept_\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a646ffe",
   "metadata": {},
   "source": [
    "### Testing our algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56bd75f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100978.09000057,  71622.40090904, 116634.45751606,  36395.5739992 ,\n",
       "       111741.84266747,  54987.51042384])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64c1b852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.9],\n",
       "       [4.9],\n",
       "       [9.5],\n",
       "       [1.3],\n",
       "       [9. ],\n",
       "       [3.2]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "007e3c23",
   "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>101302.0</td>\n",
       "      <td>100978.090001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67938.0</td>\n",
       "      <td>71622.400909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>116634.457516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46205.0</td>\n",
       "      <td>36395.573999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105582.0</td>\n",
       "      <td>111741.842667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64445.0</td>\n",
       "      <td>54987.510424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual      predicted\n",
       "0  101302.0  100978.090001\n",
       "1   67938.0   71622.400909\n",
       "2  116969.0  116634.457516\n",
       "3   46205.0   36395.573999\n",
       "4  105582.0  111741.842667\n",
       "5   64445.0   54987.510424"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_values=pd.DataFrame({\"Actual\":y_test,\"predicted\":y_pred})\n",
    "compare_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3188904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.958086359922431\n",
      "Test Score: 0.9394460269118792\n"
     ]
    }
   ],
   "source": [
    "# Estimating training training and test score\n",
    "print(\"Training Score:\", reg.score(x_train, y_train))\n",
    "print(\"Test Score:\", reg.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c4e9a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103913.65890972699"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting score for 8.2 years of experience\n",
    "years = [[8.2]]\n",
    "prediction = reg.predict(years)\n",
    "prediction[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be782d8a",
   "metadata": {},
   "source": [
    "## The predicted salary if an employee had worked for 8.2 years is 103913.65890972699"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}