DAY 21 - Employee Salary Prediction - Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "literary-morocco",
   "metadata": {},
   "source": [
    "# Regression\n",
    "- It is one of the most important statistical and machine learning tools.\n",
    "- It is defined as the parametric technique that allows us to take decisions based on data.\n",
    "- It allows us to make predictions based upon data by learning the relationship between input and output variables.\n",
    "- The output variable dependent on the input variables are continuous valued real numbers.\n",
    "- Regression help us to understand how the value of output variable changes with respect to the changes in the input variable.\n",
    "- Regression techniques are used for the prediction of prices, economics and variations.\n",
    "\n",
    "\n",
    "# Simple Linear Regression\n",
    "- It is the simplest form of linear regression used when there is one single input variable(input) for the output variable(target).\n",
    "- The input variable helps in predicting the value of the output variable.\n",
    "- It is referred to as X.\n",
    "- The output or target variable is the variable that we want to predict(y)\n",
    "<br><br>\n",
    "y(target) = (B0+B1)(coefficients) X(input) + E(random error)\n",
    "<br><br>\n",
    "- B0, called the intercept, shows the point where the estimated regression line crosses the y-axis.\n",
    "- B1 determines the slope of the estimated regression line.\n",
    "- Random error describes the random component of the linear component between independent and dependent variable.\n",
    "- The true regression model is usually never known.\n",
    "- The value of the random error term corresponding to observed data points remains unknown.\n",
    "- Regression model can be estimated by calculating the parameters of the model for an observed dataset.\n",
    "- The main aim of regression is to estimate the parameters B0 and B1 from the sample.\n",
    "- Once we find the optimum values for these two parameters, a line of best fit can be used to find the vales of y given the values of X.\n",
    "- Fit a line to find the relationship between input and output variables.\n",
    "- The line is used to predict the output of unseen inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plain-japan",
   "metadata": {},
   "source": [
    "# Employee Salary Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "anticipated-spirit",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('salary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "greater-soldier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "favorite-theology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "another-swift",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "hawaiian-aruba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    30\n",
       "Salary             30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "prepared-summit",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,:-1].values\n",
    "y = data.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "prerequisite-impossible",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1],\n",
       "       [ 1.3],\n",
       "       [ 1.5],\n",
       "       [ 2. ],\n",
       "       [ 2.2],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.2],\n",
       "       [ 3.2],\n",
       "       [ 3.7],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.5],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.3],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.8],\n",
       "       [ 7.1],\n",
       "       [ 7.9],\n",
       "       [ 8.2],\n",
       "       [ 8.7],\n",
       "       [ 9. ],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [10.3],\n",
       "       [10.5]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# independent variable\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "steady-dryer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
       "       122391., 121872.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dependent variable\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "chicken-phoenix",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1da76edafc8>"
      ]
     },
     "execution_count": 9,
     "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": [
    "# scatter plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x, y, color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "smooth-tamil",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting dataset into training set and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "olive-denmark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.9],\n",
       "       [ 5.1],\n",
       "       [ 3.2],\n",
       "       [ 4.5],\n",
       "       [ 8.2],\n",
       "       [ 6.8],\n",
       "       [ 1.3],\n",
       "       [10.5],\n",
       "       [ 3. ],\n",
       "       [ 2.2],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 3.7],\n",
       "       [ 3.2],\n",
       "       [ 9. ],\n",
       "       [ 2. ],\n",
       "       [ 1.1],\n",
       "       [ 7.1],\n",
       "       [ 4.9],\n",
       "       [ 4. ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying x_train\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "identified-kennedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56642.,  66029.,  64445.,  61111., 113812.,  91738.,  46205.,\n",
       "       121872.,  60150.,  39891.,  81363.,  93940.,  57189.,  54445.,\n",
       "       105582.,  43525.,  39343.,  98273.,  67938.,  56957.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying y_train\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "expired-contact",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying y_test\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "elect-termination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting simple linear regression to the training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "thermal-enzyme",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the test set results\n",
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "alive-aquatic",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying y_pred (Predicted salary)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cognitive-binding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying y_test (Real salary)\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "plastic-greeting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3104.10590871,   688.39940819,  8053.55626083,    47.36777221,\n",
       "       -1366.35454631, -1305.1085008 ,  3902.23969801,  8405.96201652,\n",
       "       -6738.31280742,  -652.8624553 ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating error/ residue\n",
    "residue = y_pred - y_test\n",
    "residue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "decreased-music",
   "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": [
    "# visualizing the training set results\n",
    "plt.scatter(x_train, y_train, color=\"red\")\n",
    "plt.plot(x_train, regressor.predict(x_train), color=\"yellow\")\n",
    "plt.title(\"Salary VS Experience(Training Set)\")\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "alive-principal",
   "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": [
    "# visualizing the testing set results\n",
    "plt.scatter(x_test, y_test, color=\"red\")\n",
    "plt.plot(x_train, regressor.predict(x_train), color=\"yellow\")\n",
    "plt.title(\"Salary VS Experience(Testing Set)\")\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "registered-gasoline",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9345.94244312]\n",
      "26816.192244031183\n"
     ]
    }
   ],
   "source": [
    "# calculating intercept and coefficient\n",
    "print(regressor.coef_)\n",
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "national-charge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "important-singer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "lasting-damages",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 4585.4157204675885\n",
      "R2 Score = 0.9749154407708353\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"RMSE =\", rmse)\n",
    "print(\"R2 Score =\", r2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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