EmployeeSalaryPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45af78c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6caa108b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e1cbb82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "998f0f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97aefdb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9cb49f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Amit Raj\\Desktop\\Salary_Data.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1293576c",
   "metadata": {},
   "source": [
    "Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c67ad68",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc895290",
   "metadata": {},
   "source": [
    "Here X is the independent variable which is the \"Years of Experience\"\n",
    "and y is the dependent variable which is the \"Salary\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76580052",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1].values\n",
    "y=df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0ab157",
   "metadata": {},
   "source": [
    "For X, we specify dataset.iloc[:, :-1].values\n",
    "which simply means take all rows and all columns except last one.\n",
    "And for y, we specify dataset.iloc[:, 1].values\n",
    "which simply means take all rows and only columns with index 1 - In python indexes begin at 0 - so index 1 here is the second column which is Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05eb3a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=1/3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13c7448",
   "metadata": {},
   "source": [
    "#splitting the data using this module and setting the test size as 1/3 . Rest 2/3 is used for training the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f850911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d092c0",
   "metadata": {},
   "source": [
    "Fit Simple Linear Regression model to training set. We will be using the LinearRegression class from the library sklearn.linear_model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "185b126d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59950f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1aeffde7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83380376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
       "        76349.68719258, 100649.1375447 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b8b765",
   "metadata": {},
   "source": [
    "Make Prediction. Now we have the y_pred which are the predicted values from our Model and y_test which are the actual values. \n",
    "Let us compare and see how well our model did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17d11437",
   "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": [
    "# plot the actual data points of training set\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "# plot the regression line\n",
    "plt.plot(X_train, regressor.predict(X_train), color='blue')\n",
    "plt.title('Salary vs Experience (Training set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary of an employee')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f8b1b00",
   "metadata": {},
   "source": [
    "Visualize training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8475c84f",
   "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": [
    "# plot the actual data points of training set\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "# plot the regression line\n",
    "plt.plot(X_train, regressor.predict(X_train), color='blue')\n",
    "plt.title('Salary vs Years of Experience (Test Data)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary of an employee')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a8714e",
   "metadata": {},
   "source": [
    " Visualize test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0768c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['YearsExperience'],kde=False,bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52021b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Salary', ylabel='Count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAP/UlEQVR4nO3dfYxldX3H8ffHHXF5EAFZCS67XUwJqTWND2OVpfEBfMCHSG1IgYii1S6J0frQaCEktSaNqdU0tFiVKfWZ4ipiVdqKVoWm1SC7SHlwoVBBdl10hxofqom6+u0f96w7Tndn7+rcc2bm934lN/fc3z13zvd+N/PZM79z7zmpKiRJ7XjQ0AVIkvpl8EtSYwx+SWqMwS9JjTH4JakxU0MXMI5jjz22NmzYMHQZkrSsbN269YGqWjN/fFkE/4YNG9iyZcvQZUjSspLk6/sad6pHkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNWZiwZ/kPUl2JbltztgxST6b5K7u/uhJbV+StG+T3ON/H3DGvLELgc9V1UnA57rHkqQeTSz4q+rfgG/PGz4TeH+3/H7gdye1fUnSvvU9x39cVd0P0N0/Yn8rJtmUZEuSLbOzs70VuJStXbeeJCvitnbd+qHbKTVryZ6yoapmgBmA6elpLxMG7NyxnbMv++LQZSyKzRdsHLoEqVl97/F/K8nxAN39rp63L0nN6zv4Pwmc3y2fD3yi5+1LUvMm+XHOK4EvAScn2ZHk5cBfAM9MchfwzO6xJKlHE5vjr6pz9/PU6ZPapiTpwPzmriQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxgwR/ktcluT3JbUmuTLJ6iDokqUW9B3+StcAfAdNV9RhgFXBO33VIUquGmuqZAg5NMgUcBuwcqA5Jak7vwV9V3wDeDtwH3A98t6o+M3+9JJuSbEmyZXZ2tu8yJWnFGmKq52jgTOBE4JHA4UnOm79eVc1U1XRVTa9Zs6bvMiVpxRpiqucZwD1VNVtVPwGuBjYOUIckNWmI4L8PeHKSw5IEOB3YNkAdktSkIeb4bwCuAm4Cbu1qmOm7Dklq1dQQG62qNwFvGmLbktQ6v7krSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaswgwZ/kqCRXJbkjybYkpwxRhyS1aGqg7f418OmqOivJIcBhA9UhSc3pPfiTHAk8BXgpQFX9GPhx33VIUquGmOp5FDALvDfJV5JcnuTwAeqQpCYNEfxTwOOBd1XV44AfABfOXynJpiRbkmyZnZ3tu0ZJy9jadetJsiJua9etX/T+DDHHvwPYUVU3dI+vYh/BX1UzwAzA9PR09VeepOVu547tnH3ZF4cuY1FsvmDjov/M3vf4q+qbwPYkJ3dDpwNf7bsOSWrVWMGf5NRxxg7Cq4ErktwCPBZ4y6/wsyRJB2HcqZ5LGc3LH2hsLFV1MzD9y7xWkvSrWTD4uy9WbQTWJHn9nKeOBFZNsjBJ0mQcaI//EOCIbr2Hzhn/HnDWpIqSJE3OgsFfVdcD1yd5X1V9vaeaJEkTNO4c/0OSzAAb5r6mqk6bRFGSpMkZN/g/CrwbuBz46eTKkSRN2rjBv7uq3jXRSiRJvRj3C1yfSvLKJMcnOWbPbaKVSZImYtw9/vO7+zfMGStGJ1yTJC0jYwV/VZ046UIkSf0YK/iTvGRf41X1gcUtR5I0aeNO9TxxzvJqRidWuwkw+CVpmRl3qufVcx8neRjwwYlUJEmaqF/2tMw/BE5azEIkSf0Yd47/U4w+xQOjk7P9BvCRSRUlSZqccef43z5neTfw9araMYF6JEkTNu4c//VJjmPvQd67JlfS4lq7bj07d2wfugxJWjLGner5feBtwHVAgEuTvKGqrppgbYvCa29K0i8ad6rnYuCJVbULIMka4F8ZXShdkrSMjPupngftCf3O/xzEayVJS8i4e/yfTnItcGX3+GzgnydTkiRpkg50zd1fB46rqjck+T3gdxjN8X8JuKKH+iRJi+xA0zWXAN8HqKqrq+r1VfU6Rnv7l0y2NEnSJBwo+DdU1S3zB6tqC6PLMEqSlpkDBf/qBZ47dDELkST140DBf2OSP5w/mOTlwNbJlCRJmqQDfarntcDHk7yIvUE/DRwCvHCCdUmSJmTB4K+qbwEbkzwdeEw3/E9V9fmJVyZJmohxz9XzBeALE65FktQDv30rSY0x+CWpMQa/JDXG4Jekxhj8ktSYwYI/yaokX0lyzVA1SFKLhtzjfw2wbcDtS1KTBgn+JCcAzwMuH2L7ktSyofb4LwHeCPxsfysk2ZRkS5Its7OzvRUmHYy169aTZEXc1q5bP3Q71ZNxr8C1aJI8H9hVVVuTPG1/61XVDDADMD09Xf1UJx2cnTu2c/ZlXxy6jEWx+YKNQ5egngyxx38q8IIk9wIfBk5L8qEB6pCkJvUe/FV1UVWdUFUbgHOAz1fVeX3XIUmt8nP8ktSY3uf456qq64DrhqxBklrjHr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNGfR8/JKWkAdNkWToKtQDg1/SyM92e+H4RjjVI0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmN6D/4k65J8Icm2JLcneU3fNUhSy4a4EMtu4I+r6qYkDwW2JvlsVX11gFokqTm97/FX1f1VdVO3/H1gG7C27zokqVWDXnoxyQbgccAN+3huE7AJYP369f0Wpsnz+q7SYAYL/iRHAB8DXltV35v/fFXNADMA09PT1XN5mrQVcn1Xr+2q5WiQT/UkeTCj0L+iqq4eogZJatUQn+oJ8PfAtqr6q763L0mtG2KP/1TgxcBpSW7ubs8doA5JalLvc/xV9e+AR/UkaSB+c1eSGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhozSPAnOSPJnUnuTnLhEDVIUqt6D/4kq4C/BZ4DPBo4N8mj+65Dklo1xB7/bwN3V9XXqurHwIeBMweoQ5KalKrqd4PJWcAZVfWK7vGLgSdV1avmrbcJ2NQ9PBm4c0IlHQs8MKGfvRLYn4XZn4XZn4VNuj+/VlVr5g9OTXCD+5N9jP2//32qagaYmXgxyZaqmp70dpYr+7Mw+7Mw+7OwofozxFTPDmDdnMcnADsHqEOSmjRE8N8InJTkxCSHAOcAnxygDklqUu9TPVW1O8mrgGuBVcB7qur2vuuYY+LTScuc/VmY/VmY/VnYIP3p/eCuJGlYfnNXkhpj8EtSY1ZU8CdZleQrSa7pHh+T5LNJ7uruj56z7kXdKSPuTPLsOeNPSHJr99zfJEk3/pAkm7vxG5Js6P0N/gqSHJXkqiR3JNmW5BT7s1eS1yW5PcltSa5Msrrl/iR5T5JdSW6bM9ZLP5Kc323jriTn9/SWD8p++vO27vfrliQfT3LUnOeWVn+qasXcgNcD/wBc0z3+S+DCbvlC4K3d8qOB/wQeApwI/Dewqnvuy8ApjL5v8C/Ac7rxVwLv7pbPATYP/X4PsjfvB17RLR8CHGV/ft6btcA9wKHd448AL225P8BTgMcDt80Zm3g/gGOAr3X3R3fLRw/djzH78yxgqlt+61Luz+ANXMR/iBOAzwGnsTf47wSO75aPB+7sli8CLprz2mu75h8P3DFn/FzgsrnrdMtTjL5tl6Hf95i9OZJRsGXeuP2pnwf/9u6XaQq4pvslbro/wIZ5wTbxfsxdp3vuMuDcoXsxTn/mPfdC4Iql2p+VNNVzCfBG4Gdzxo6rqvsBuvtHdON7ftH32NGNre2W54//wmuqajfwXeDhi/oOJudRwCzw3m4q7PIkh2N/AKiqbwBvB+4D7ge+W1Wfwf7M10c/9vezlps/YLQHD0uwPysi+JM8H9hVVVvHfck+xmqB8YVesxxMMfqz9F1V9TjgB4z+VN+fpvrTzVWfyejP8EcChyc5b6GX7GNsxfZnDIvZj2XfpyQXA7uBK/YM7WO1QfuzIoIfOBV4QZJ7GZ3t87QkHwK+leR4gO5+V7f+/k4bsaNbnj/+C69JMgU8DPj2JN7MBOwAdlTVDd3jqxj9R2B/Rp4B3FNVs1X1E+BqYCP2Z74++rGsT+nSHWx9PvCi6uZiWIL9WRHBX1UXVdUJVbWB0YGQz1fVeYxOBbHnqPf5wCe65U8C53RHzk8ETgK+3P35+v0kT+6Orr9k3mv2/Kyzum0siz2RqvomsD3Jyd3Q6cBXsT973Ac8Oclh3fs6HdiG/Zmvj35cCzwrydHdX2LP6saWvCRnAH8CvKCqfjjnqaXXn6EPkEzggMvT2Htw9+GMDvje1d0fM2e9ixkdXb+T7kh6Nz4N3NY99w72frt5NfBR4G5GR+IfNfR7Pci+PBbYAtwC/COjTwTYn73v683AHd17+yCjT2A02x/gSkbHO37CaC/z5X31g9H8+N3d7WVD9+Ig+nM3o/n3m7vbu5dqfzxlgyQ1ZkVM9UiSxmfwS1JjDH5JaozBL0mNMfglqTEGv8To25YZnZ3zliQ3J3nSAuu+L8lZfdYnLabeL70oLTVJTmH0bcvHV9WPkhzL6Aymi/Xzp2p0vhVpSXCPXxqdJfGBqvoRQFU9UFU7k/xpkhszOkf/zJ5zpc+1v3WSXJfkLUmuBy5Ock+SB3fPHZnk3j2Ppb4Z/BJ8BliX5L+SvDPJU7vxd1TVE6vqMcChjP4qmG+hdY6qqqdW1ZuB64DndePnAB+r0XmBpN4Z/GpeVf0v8ARgE6PTV29O8lLg6d3Vj25ldJ2H39zHyxdaZ/Oc5cuBl3XLLwPeu7jvQhqfc/wSUFU/ZbRXfl0X4hcAvwVMV9X2JH/G6PwpP5dkNfDOBdb5wZyf/x9JNnR/TayqqtuQBuIev5qX5OQkJ80Zeiyjk2kBPJDkCEZnSJxv9RjrzPUBRif3cm9fg3KPX4IjgEszujj2bkZnPdwEfAe4FbgXuHH+i6rqO0n+bqF15rkC+HNG4S8NxrNzSj3pPvt/ZlW9eOha1Db3+KUeJLkUeA7w3KFrkdzjl6TGeHBXkhpj8EtSYwx+SWqMwS9JjTH4Jakx/wfvrel5Td1EkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d62be57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='YearsExperience'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='YearsExperience',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b24411c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='YearsExperience',y='Salary',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cc7fa4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "24f60967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error of the Model: 3426.4269374307123\n",
      "Mean Squared Error of the Model:  21026037.329511296\n",
      "Root Mean Squared Error of the Model:  58.53568943329114\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Mean Absolute Error of the Model:',metrics.mean_absolute_error(y_test,y_pred))\n",
    "print('Mean Squared Error of the Model: ',metrics.mean_squared_error(y_test,y_pred))\n",
    "print('Root Mean Squared Error of the Model: ',np.sqrt(metrics.mean_absolute_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "090710a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe72ab4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9749154407708353"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67de2d78",
   "metadata": {},
   "source": [
    "#This shows that our model is completely accurate\n",
    "#R value lies between 0 to 1. Value of 1 represents it is completely accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9fe43c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}