Regression/Simple Linear Regrassion/simple linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "# %matplotlib qt to open a new window for plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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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       "    }\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",
       "    <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39343.],\n",
       "       [ 46205.],\n",
       "       [ 37731.],\n",
       "       [ 43525.],\n",
       "       [ 39891.],\n",
       "       [ 56642.],\n",
       "       [ 60150.],\n",
       "       [ 54445.],\n",
       "       [ 64445.],\n",
       "       [ 57189.],\n",
       "       [ 63218.],\n",
       "       [ 55794.],\n",
       "       [ 56957.],\n",
       "       [ 57081.],\n",
       "       [ 61111.],\n",
       "       [ 67938.],\n",
       "       [ 66029.],\n",
       "       [ 83088.],\n",
       "       [ 81363.],\n",
       "       [ 93940.],\n",
       "       [ 91738.],\n",
       "       [ 98273.],\n",
       "       [101302.],\n",
       "       [113812.],\n",
       "       [109431.],\n",
       "       [105582.],\n",
       "       [116969.],\n",
       "       [112635.],\n",
       "       [122391.],\n",
       "       [121872.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data into training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = tts(x,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.7],\n",
       "       [ 2. ],\n",
       "       [ 4. ],\n",
       "       [ 3.2],\n",
       "       [ 9.5],\n",
       "       [ 1.1],\n",
       "       [ 3.7],\n",
       "       [ 2.9],\n",
       "       [ 3.2],\n",
       "       [ 9.6],\n",
       "       [ 5.9],\n",
       "       [ 3. ],\n",
       "       [10.3],\n",
       "       [ 4.5],\n",
       "       [ 8.2],\n",
       "       [ 9. ],\n",
       "       [ 4.1],\n",
       "       [ 7.9],\n",
       "       [ 5.3],\n",
       "       [ 1.5],\n",
       "       [ 3.9],\n",
       "       [ 6.8]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.2],\n",
       "       [ 4.9],\n",
       "       [ 1.3],\n",
       "       [10.5],\n",
       "       [ 6. ],\n",
       "       [ 7.1],\n",
       "       [ 5.1],\n",
       "       [ 4. ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[109431.],\n",
       "       [ 43525.],\n",
       "       [ 55794.],\n",
       "       [ 64445.],\n",
       "       [116969.],\n",
       "       [ 39343.],\n",
       "       [ 57189.],\n",
       "       [ 56642.],\n",
       "       [ 54445.],\n",
       "       [112635.],\n",
       "       [ 81363.],\n",
       "       [ 60150.],\n",
       "       [122391.],\n",
       "       [ 61111.],\n",
       "       [113812.],\n",
       "       [105582.],\n",
       "       [ 57081.],\n",
       "       [101302.],\n",
       "       [ 83088.],\n",
       "       [ 37731.],\n",
       "       [ 63218.],\n",
       "       [ 91738.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39891.],\n",
       "       [ 67938.],\n",
       "       [ 46205.],\n",
       "       [121872.],\n",
       "       [ 93940.],\n",
       "       [ 98273.],\n",
       "       [ 66029.],\n",
       "       [ 56957.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting simple linear regression to Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor  = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 46778.63815399],\n",
       "       [ 72266.59800596],\n",
       "       [ 38282.65153666],\n",
       "       [125130.51473599],\n",
       "       [ 82650.58164936],\n",
       "       [ 93034.56529276],\n",
       "       [ 74154.59503204],\n",
       "       [ 63770.61138864]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 39891.],\n",
       "       [ 67938.],\n",
       "       [ 46205.],\n",
       "       [121872.],\n",
       "       [ 93940.],\n",
       "       [ 98273.],\n",
       "       [ 66029.],\n",
       "       [ 56957.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the training set result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "plt.scatter(x_train,y_train,color=\"red\")\n",
    "plt.plot(x_train,regressor.predict(x_train))\n",
    "plt.title(\"Salary VS Experience (Training Set)\")\n",
    "plt.xlabel(\"Years of Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Test set result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "plt.scatter(x_test,y_test,color=\"red\")\n",
    "plt.plot(x_train,regressor.predict(x_train))\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": [
    "    "
   ]
  }
 ],
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