Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the dataset\n",
    "dataset=pd.read_csv('S:\\My ML course\\C2 Regression\\Simple Linear Regression\\Salary_Data.csv')\n",
    "X=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the test results\n",
    "y_pred=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40817.78327049, 123188.08258899,  65154.46261459,  63282.41035735,\n",
       "       115699.87356004, 108211.66453108, 116635.89968866,  64218.43648597,\n",
       "        76386.77615802])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training set results')"
      ]
     },
     "execution_count": 27,
     "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": [
    "#Visualizing the results\n",
    "plt.scatter(X_train,y_train,color='purple')\n",
    "plt.plot(X_train,regressor.predict(X_train),color='green')\n",
    "plt.xlabel('No of years of experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.title('Training set results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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