simple linear regression salary -checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "myData = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = myData.iloc[:,:-1].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  39343.,   46205.,   37731.,   43525.,   39891.,   56642.,\n",
       "         60150.,   54445.,   64445.,   57189.,   63218.,   55794.,\n",
       "         56957.,   57081.,   61111.,   67938.,   66029.,   83088.,\n",
       "         81363.,   93940.,   91738.,   98273.,  101302.,  113812.,\n",
       "        109431.,  105582.,  116969.,  112635.,  122391.,  121872.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = myData.iloc[:,1].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as myplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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m2A6475bAIpJicyuKyDKjtCBn0A2wtte28od1u9i0t/9FLo6uCBodZpTnU5ijOSnjQVSj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\nP9ENVDWeKBfGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x959d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(myData)\n",
    "myplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mcuaQHfyMSiLi7oiYioipY8eOdfvHDYVhGNrp1tRgSefXSSH8MHA4M18uz7fTCI33IuKa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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c31668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot.scatter(X,Y)\n",
    "myplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.97824162],\n",
       "       [ 0.97824162,  1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(myData.YearsExperience, myData.Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "myModel = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myModel.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = myModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kpsl9+cvlQfNP/5RVMw4aM6snVzZN6vnn+65dtnlz38U0zczqwZVNE/roR8uD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\nlrv/DxZ8tL8cb/K0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc43af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot.scatter(X_train,Y_train, color='r')\n",
    "myplot.plot(X_train,myModel.predict(X_train), color='b')\n",
    "myplot.title('Salary vs Exp')\n",
    "myplot.xlabel('Years of exp')\n",
    "myplot.ylabel('Salary')\n",
    "myplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pP5xpveXKxsy2adOmtkEzd66DxrrPlY2ZtcvVjPUlVzZmVuall9oGzcqVDhrrHVc2ZvYW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\nehYm45YDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4d3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot.scatter(X_test,Y_test, color='r')\n",
    "myplot.plot(X_train,myModel.predict(X_train), color='b')\n",
    "myplot.title('Salary vs Exp')\n",
    "myplot.xlabel('Years of exp')\n",
    "myplot.ylabel('Salary')\n",
    "myplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}