Linear_regression_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c6baf15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the libraries\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de1de654",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the dataset\n",
    "\n",
    "df = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "481b1838",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,:-1].values\n",
    "y = df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f929e312",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the data into the Training and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d6e06ded",
   "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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1825a575",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the Simple Linear Regression Model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "702336f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lrs = LinearRegression()\n",
    "lrs.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed8dc62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the Test Set results\n",
    "y_pred = lrs.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bb884a45",
   "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": [
    "#Visualize the Training Set\n",
    "\n",
    "plt.scatter(x_train,y_train,color='blue')\n",
    "plt.plot(x_train,lrs.predict(x_train), color = 'pink')\n",
    "plt.title('Salary Acc. to Experiance')\n",
    "plt.xlabel('Experiance - years')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b97acc5b",
   "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": [
    "#Visualize the test set\n",
    "\n",
    "plt.scatter(x_test, y_test, color='blue')\n",
    "plt.plot(x_train,lrs.predict(x_train), color = 'pink')\n",
    "plt.title('Salary Acc. to Experiance Test')\n",
    "plt.xlabel('Experiance - years')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4541e3a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([138531.00067138])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrs.predict([[12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7b3cc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9312.57512673])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrs.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "629c9ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26780.099150628186"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrs.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "050d7fd9",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}