Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f627a79b",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8aa076e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95421d7b",
   "metadata": {},
   "source": [
    "# Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e139d6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"salary_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9445ae",
   "metadata": {},
   "source": [
    "# Visualizing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "62276c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x = \"YearsExperience\", y = \"Salary\", style = 'o')\n",
    "plt.xlabel(\"Experince\")\n",
    "plt.ylabel(\"Salaries\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33f36d4",
   "metadata": {},
   "source": [
    "# Extracting Feature Matrix and Target Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dce3474",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(df['YearsExperience'])\n",
    "y = pd.DataFrame(df['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a388fa",
   "metadata": {},
   "source": [
    "# Spliting the data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1740c04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=1/3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "770153b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 1)\n",
      "(10, 1)\n",
      "(20, 1)\n",
      "(10, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe636aea",
   "metadata": {},
   "source": [
    "# Initializing the ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25a7173a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a904dd0",
   "metadata": {},
   "source": [
    "# Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76b6a583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3895a9",
   "metadata": {},
   "source": [
    "# Getting the intercept and Slope of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "faeedaae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[26137.2400142]\n"
     ]
    }
   ],
   "source": [
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4622468f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9158.13919873]]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "801916a1",
   "metadata": {},
   "source": [
    "# Predicting the Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16ffcf8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5048f5c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 74675.37776747],\n",
       "       [ 91160.02832519],\n",
       "       [ 61853.98288925],\n",
       "       [ 81086.07520659],\n",
       "       [ 67348.86640849],\n",
       "       [ 88412.58656557],\n",
       "       [113139.56240215],\n",
       "       [ 44453.51841166],\n",
       "       [105813.05104316],\n",
       "       [ 98486.53968418]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6461c467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>83088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>98273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>63218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>93940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>91738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>116969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>109431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salary\n",
       "17   83088\n",
       "21   98273\n",
       "10   63218\n",
       "19   93940\n",
       "14   61111\n",
       "20   91738\n",
       "26  116969\n",
       "3    43525\n",
       "24  109431\n",
       "22  101302"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbb1011",
   "metadata": {},
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a59475a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error :  37496296.6187984\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Error : \", metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49e4aa41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error :  5049.818093659744\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error : \", metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "58a77774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error :  6123.421969683161\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Error : \", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  }
 ],
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