Simple_Linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation 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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([112635.,  55794.,  83088., 101302.,  56642.,  66029.,  64445.,\n",
       "        61111., 113812.,  91738.,  46205., 121872.,  60150.,  39891.,\n",
       "        81363.,  93940.,  57189.,  54445., 105582.,  43525.,  39343.,\n",
       "        98273.,  67938.,  56957.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit simple linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regressor = LinearRegression()\n",
    "linear_regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting test result\n",
    "y_pred = linear_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize test set result\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, linear_regressor.predict(X_train), color = 'blue')\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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_test, y_pred, color = 'blue')\n",
    "plt.title('Salary vs Experience (Test set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "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",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}