Board Game Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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",
       "        text-align: left;\n",
       "    }\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>id</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>yearpublished</th>\n",
       "      <th>minplayers</th>\n",
       "      <th>maxplayers</th>\n",
       "      <th>playingtime</th>\n",
       "      <th>minplaytime</th>\n",
       "      <th>maxplaytime</th>\n",
       "      <th>minage</th>\n",
       "      <th>users_rated</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>bayes_average_rating</th>\n",
       "      <th>total_owners</th>\n",
       "      <th>total_traders</th>\n",
       "      <th>total_wanters</th>\n",
       "      <th>total_wishers</th>\n",
       "      <th>total_comments</th>\n",
       "      <th>total_weights</th>\n",
       "      <th>average_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12333</td>\n",
       "      <td>boardgame</td>\n",
       "      <td>Twilight Struggle</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20113</td>\n",
       "      <td>8.33774</td>\n",
       "      <td>8.22186</td>\n",
       "      <td>26647</td>\n",
       "      <td>372</td>\n",
       "      <td>1219</td>\n",
       "      <td>5865</td>\n",
       "      <td>5347</td>\n",
       "      <td>2562</td>\n",
       "      <td>3.4785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120677</td>\n",
       "      <td>boardgame</td>\n",
       "      <td>Terra Mystica</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14383</td>\n",
       "      <td>8.28798</td>\n",
       "      <td>8.14232</td>\n",
       "      <td>16519</td>\n",
       "      <td>132</td>\n",
       "      <td>1586</td>\n",
       "      <td>6277</td>\n",
       "      <td>2526</td>\n",
       "      <td>1423</td>\n",
       "      <td>3.8939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102794</td>\n",
       "      <td>boardgame</td>\n",
       "      <td>Caverna: The Cave Farmers</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9262</td>\n",
       "      <td>8.28994</td>\n",
       "      <td>8.06886</td>\n",
       "      <td>12230</td>\n",
       "      <td>99</td>\n",
       "      <td>1476</td>\n",
       "      <td>5600</td>\n",
       "      <td>1700</td>\n",
       "      <td>777</td>\n",
       "      <td>3.7761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25613</td>\n",
       "      <td>boardgame</td>\n",
       "      <td>Through the Ages: A Story of Civilization</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13294</td>\n",
       "      <td>8.20407</td>\n",
       "      <td>8.05804</td>\n",
       "      <td>14343</td>\n",
       "      <td>362</td>\n",
       "      <td>1084</td>\n",
       "      <td>5075</td>\n",
       "      <td>3378</td>\n",
       "      <td>1642</td>\n",
       "      <td>4.1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3076</td>\n",
       "      <td>boardgame</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>39883</td>\n",
       "      <td>8.14261</td>\n",
       "      <td>8.04524</td>\n",
       "      <td>44362</td>\n",
       "      <td>795</td>\n",
       "      <td>861</td>\n",
       "      <td>5414</td>\n",
       "      <td>9173</td>\n",
       "      <td>5213</td>\n",
       "      <td>3.2943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id       type                                       name  \\\n",
       "0   12333  boardgame                          Twilight Struggle   \n",
       "1  120677  boardgame                              Terra Mystica   \n",
       "2  102794  boardgame                  Caverna: The Cave Farmers   \n",
       "3   25613  boardgame  Through the Ages: A Story of Civilization   \n",
       "4    3076  boardgame                                Puerto Rico   \n",
       "\n",
       "   yearpublished  minplayers  maxplayers  playingtime  minplaytime  \\\n",
       "0         2005.0         2.0         2.0        180.0        180.0   \n",
       "1         2012.0         2.0         5.0        150.0         60.0   \n",
       "2         2013.0         1.0         7.0        210.0         30.0   \n",
       "3         2006.0         2.0         4.0        240.0        240.0   \n",
       "4         2002.0         2.0         5.0        150.0         90.0   \n",
       "\n",
       "   maxplaytime  minage  users_rated  average_rating  bayes_average_rating  \\\n",
       "0        180.0    13.0        20113         8.33774               8.22186   \n",
       "1        150.0    12.0        14383         8.28798               8.14232   \n",
       "2        210.0    12.0         9262         8.28994               8.06886   \n",
       "3        240.0    12.0        13294         8.20407               8.05804   \n",
       "4        150.0    12.0        39883         8.14261               8.04524   \n",
       "\n",
       "   total_owners  total_traders  total_wanters  total_wishers  total_comments  \\\n",
       "0         26647            372           1219           5865            5347   \n",
       "1         16519            132           1586           6277            2526   \n",
       "2         12230             99           1476           5600            1700   \n",
       "3         14343            362           1084           5075            3378   \n",
       "4         44362            795            861           5414            9173   \n",
       "\n",
       "   total_weights  average_weight  \n",
       "0           2562          3.4785  \n",
       "1           1423          3.8939  \n",
       "2            777          3.7761  \n",
       "3           1642          4.1590  \n",
       "4           5213          3.2943  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games = pd.read_csv('games.csv')\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'type', 'name', 'yearpublished', 'minplayers', 'maxplayers',\n",
       "       'playingtime', 'minplaytime', 'maxplaytime', 'minage', 'users_rated',\n",
       "       'average_rating', 'bayes_average_rating', 'total_owners',\n",
       "       'total_traders', 'total_wanters', 'total_wishers', 'total_comments',\n",
       "       'total_weights', 'average_weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81312, 20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb116da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(games[\"average_rating\"])\n",
    "plt.xlabel(\"Ratings of games\")\n",
    "plt.ylabel(\"Frequency of ratings\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The histogram looks skewed and is not normally distributed too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             318\n",
       "type                     boardgame\n",
       "name                    Looney Leo\n",
       "yearpublished                    0\n",
       "minplayers                       0\n",
       "maxplayers                       0\n",
       "playingtime                      0\n",
       "minplaytime                      0\n",
       "maxplaytime                      0\n",
       "minage                           0\n",
       "users_rated                      0\n",
       "average_rating                   0\n",
       "bayes_average_rating             0\n",
       "total_owners                     0\n",
       "total_traders                    0\n",
       "total_wanters                    0\n",
       "total_wishers                    1\n",
       "total_comments                   0\n",
       "total_weights                    0\n",
       "average_weight                   0\n",
       "Name: 13048, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print first row of zero ratings\n",
    "games[games[\"average_rating\"] == 0].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we can observe, this game had only 1 wisher. So my initial guess is that it was never published. Hence, it has no reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  12333\n",
       "type                            boardgame\n",
       "name                    Twilight Struggle\n",
       "yearpublished                        2005\n",
       "minplayers                              2\n",
       "maxplayers                              2\n",
       "playingtime                           180\n",
       "minplaytime                           180\n",
       "maxplaytime                           180\n",
       "minage                                 13\n",
       "users_rated                         20113\n",
       "average_rating                    8.33774\n",
       "bayes_average_rating              8.22186\n",
       "total_owners                        26647\n",
       "total_traders                         372\n",
       "total_wanters                        1219\n",
       "total_wishers                        5865\n",
       "total_comments                       5347\n",
       "total_weights                        2562\n",
       "average_weight                     3.4785\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print first row of ratings above zero\n",
    "games[games[\"average_rating\"] > 0].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, let's remove games which had no user ratings, as a part of data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1a2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "games = games[games[\"users_rated\"] > 0]\n",
    "\n",
    "games = games.dropna(axis=0)\n",
    "\n",
    "plt.hist(games[\"average_rating\"])\n",
    "plt.xlabel(\"Ratings for games\")\n",
    "plt.ylabel(\"Frequency of average ratings\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAJwCAYAAACd2TcUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYcEsSZIkbcKCWZIkSdrE/wJJZIsP+OdYbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x469fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = games.corr()\n",
    "\n",
    "figure = plt.figure(figsize=(12,9))\n",
    "sns.heatmap(corrmat , vmax= 0.3 , square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we can observe, game type and ID attributes are won't much worth but they are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = games.iloc[: , 3:18].values\n",
    "y = games.iloc[: , 19:20].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Splitting datasets now for model input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Viraj Pawar\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "x_train , x_test , y_train , y_test = train_test_split(X,y,test_size = 0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's build linear regression model now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.58014257],\n",
       "       [ 2.12569128],\n",
       "       [ 0.92907874],\n",
       "       ..., \n",
       "       [ 0.87767713],\n",
       "       [ 1.88000205],\n",
       "       [ 1.89639667]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.79900795017101"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_test,y_test) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Let's experiment backward elimination of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = np.append(values=X , arr = np.ones((len(X),1)).astype(int) , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_opt = X [: , 3:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_opt = X[: , [10,11,12,15]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "regressor_OLS = sm.OLS(y,X_opt).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.444</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.444</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.135e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:25:51</td>     <th>  Log-Likelihood:    </th> <td> -96095.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 56894</td>      <th>  AIC:               </th> <td>1.922e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 56890</td>      <th>  BIC:               </th> <td>1.922e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    0.3722</td> <td>    0.002</td> <td>  180.811</td> <td> 0.000</td> <td>    0.368</td> <td>    0.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> 1.639e-05</td> <td> 1.03e-05</td> <td>    1.592</td> <td> 0.111</td> <td>-3.79e-06</td> <td> 3.66e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>    0.0015</td> <td>    0.000</td> <td>    6.246</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>   -0.0003</td> <td> 5.88e-05</td> <td>   -4.948</td> <td> 0.000</td> <td>   -0.000</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>8604.338</td> <th>  Durbin-Watson:     </th> <td>   1.199</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>13254.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.085</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 3.937</td>  <th>  Cond. No.          </th> <td>    696.</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.444\n",
       "Model:                            OLS   Adj. R-squared:                  0.444\n",
       "Method:                 Least Squares   F-statistic:                 1.135e+04\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        17:25:51   Log-Likelihood:                -96095.\n",
       "No. Observations:               56894   AIC:                         1.922e+05\n",
       "Df Residuals:                   56890   BIC:                         1.922e+05\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.3722      0.002    180.811      0.000       0.368       0.376\n",
       "x2          1.639e-05   1.03e-05      1.592      0.111   -3.79e-06    3.66e-05\n",
       "x3             0.0015      0.000      6.246      0.000       0.001       0.002\n",
       "x4            -0.0003   5.88e-05     -4.948      0.000      -0.000      -0.000\n",
       "==============================================================================\n",
       "Omnibus:                     8604.338   Durbin-Watson:                   1.199\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            13254.616\n",
       "Skew:                           1.085   Prob(JB):                         0.00\n",
       "Kurtosis:                       3.937   Cond. No.                         696.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.668</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.668</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.274e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 19 Jan 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:25:51</td>     <th>  Log-Likelihood:    </th> <td> -81381.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 56894</td>      <th>  AIC:               </th> <td>1.628e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 56885</td>      <th>  BIC:               </th> <td>1.629e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td> 8.389e-05</td> <td> 5.23e-06</td> <td>   16.037</td> <td> 0.000</td> <td> 7.36e-05</td> <td> 9.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td> 8.389e-05</td> <td> 5.23e-06</td> <td>   16.037</td> <td> 0.000</td> <td> 7.36e-05</td> <td> 9.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>    0.0211</td> <td>    0.001</td> <td>   23.593</td> <td> 0.000</td> <td>    0.019</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>   -0.0002</td> <td> 2.63e-05</td> <td>   -8.864</td> <td> 0.000</td> <td>   -0.000</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>    0.1338</td> <td>    0.001</td> <td>  103.472</td> <td> 0.000</td> <td>    0.131</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>    0.1721</td> <td>    0.002</td> <td>   84.564</td> <td> 0.000</td> <td>    0.168</td> <td>    0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td> 7.776e-05</td> <td>  1.3e-05</td> <td>    5.968</td> <td> 0.000</td> <td> 5.22e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>    0.0007</td> <td>    0.000</td> <td>    3.152</td> <td> 0.002</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>    0.0013</td> <td> 8.91e-05</td> <td>   14.970</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>    0.0003</td> <td> 7.94e-05</td> <td>    3.438</td> <td> 0.001</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>6417.139</td> <th>  Durbin-Watson:     </th> <td>   1.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9168.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.873</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 3.907</td>  <th>  Cond. No.          </th> <td>3.96e+16</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.668\n",
       "Model:                            OLS   Adj. R-squared:                  0.668\n",
       "Method:                 Least Squares   F-statistic:                 1.274e+04\n",
       "Date:                Sat, 19 Jan 2019   Prob (F-statistic):               0.00\n",
       "Time:                        17:25:51   Log-Likelihood:                -81381.\n",
       "No. Observations:               56894   AIC:                         1.628e+05\n",
       "Df Residuals:                   56885   BIC:                         1.629e+05\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          8.389e-05   5.23e-06     16.037      0.000    7.36e-05    9.41e-05\n",
       "x2          8.389e-05   5.23e-06     16.037      0.000    7.36e-05    9.41e-05\n",
       "x3             0.0211      0.001     23.593      0.000       0.019       0.023\n",
       "x4            -0.0002   2.63e-05     -8.864      0.000      -0.000      -0.000\n",
       "x5             0.1338      0.001    103.472      0.000       0.131       0.136\n",
       "x6             0.1721      0.002     84.564      0.000       0.168       0.176\n",
       "x7          7.776e-05    1.3e-05      5.968      0.000    5.22e-05       0.000\n",
       "x8             0.0007      0.000      3.152      0.002       0.000       0.001\n",
       "x9             0.0013   8.91e-05     14.970      0.000       0.001       0.002\n",
       "x10            0.0003   7.94e-05      3.438      0.001       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                     6417.139   Durbin-Watson:                   1.800\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9168.431\n",
       "Skew:                           0.873   Prob(JB):                         0.00\n",
       "Kurtosis:                       3.907   Cond. No.                     3.96e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.93e-22. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optimal set of features\n",
    "X_opt = X[: , [4,6,7,8,9,10,11,12,13,15] ]\n",
    "\n",
    "regressor_OLS = sm.OLS(y,X_opt).fit()\n",
    "\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train , X_Test , y_train , y_test = train_test_split(X_opt,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor1 = LinearRegression()\n",
    "regressor1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.1499432669\n"
     ]
    }
   ],
   "source": [
    "accuracy = regressor1.score(X_Test,y_test)\n",
    "print(accuracy * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Viraj Pawar\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\ipykernel\\__main__.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=140, n_jobs=-1,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=140,random_state=0,n_jobs=-1)\n",
    "\n",
    "\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.443405524230315"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_train,y_train) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.981008144027498"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_test,y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Viraj Pawar\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr1 = SVR(kernel='poly')\n",
    "\n",
    "svr1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "svr1.score(X_train,y_train) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "svr1.score(X_Test,y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "5:28 pm started!\n",
    "6:30 \n",
    "    \n",
    "9:23 pm started again!"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}