Multivariate LR using Sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2104</td>\n",
       "      <td>3</td>\n",
       "      <td>399900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>369000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1416</td>\n",
       "      <td>2</td>\n",
       "      <td>232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>539900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Size  Bedrooms   Price\n",
       "0  2104         3  399900\n",
       "1  1600         3  329900\n",
       "2  2400         3  369000\n",
       "3  1416         2  232000\n",
       "4  3000         4  539900"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=os.getcwd() +'/data/ex1data2.txt'\n",
    "data=pd.read_csv(path,header=None,names=['Size', 'Bedrooms', 'Price'])\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2000.680851</td>\n",
       "      <td>3.170213</td>\n",
       "      <td>340412.659574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>794.702354</td>\n",
       "      <td>0.760982</td>\n",
       "      <td>125039.899586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>852.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>169900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1432.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>249900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1888.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>299900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2269.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>384450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4478.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>699900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Size   Bedrooms          Price\n",
       "count    47.000000  47.000000      47.000000\n",
       "mean   2000.680851   3.170213  340412.659574\n",
       "std     794.702354   0.760982  125039.899586\n",
       "min     852.000000   1.000000  169900.000000\n",
       "25%    1432.000000   3.000000  249900.000000\n",
       "50%    1888.000000   3.000000  299900.000000\n",
       "75%    2269.000000   4.000000  384450.000000\n",
       "max    4478.000000   5.000000  699900.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = data[['Size','Bedrooms']]\n",
    "y = data['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_coef = pd.DataFrame(reg.coef_,index =x.columns,columns = ['Coeff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>139.281414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedrooms</th>\n",
       "      <td>159.885347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Coeff\n",
       "Size      139.281414\n",
       "Bedrooms  159.885347"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>199900</td>\n",
       "      <td>236007.627137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>549000</td>\n",
       "      <td>651086.845384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>579900</td>\n",
       "      <td>500781.595530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>329900</td>\n",
       "      <td>286706.061905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>229900</td>\n",
       "      <td>236425.471379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual      predicted\n",
       "17  199900  236007.627137\n",
       "38  549000  651086.845384\n",
       "33  579900  500781.595530\n",
       "1   329900  286706.061905\n",
       "36  229900  236425.471379"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict({'Actual':y_test,'predicted':y_pred})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fbfb2b8e438>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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8GgsW/BuCAPz2txfg/PNH5dxHLBYT0z5aQQVI9r4IhYBFi6x48UUr9u834brr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\n8sMyZPD4f8TgyDiy97qHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfb2806f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(x_train, y_train,  color='black')\n",
    "#plt.plot(x_test,y_pred, color='blue', linewidth=3)\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "x2=np.linspace(data.Bedrooms.min(),data.Bedrooms.max(),47)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(x_test.Size,x_test.Bedrooms,zs = np.array(y_pred).ravel())\n",
    "ax.scatter(x_train.Size,x_train.Bedrooms,y_train)\n"
   ]
  },
  {
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}