Board Game Prediction Review.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "games=pd.read_csv('games.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>id</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",
       "      <td>count</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81309.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "      <td>81312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>72278.150138</td>\n",
       "      <td>1806.630668</td>\n",
       "      <td>1.992018</td>\n",
       "      <td>5.637703</td>\n",
       "      <td>51.634788</td>\n",
       "      <td>49.276833</td>\n",
       "      <td>51.634788</td>\n",
       "      <td>6.983975</td>\n",
       "      <td>161.886585</td>\n",
       "      <td>4.212144</td>\n",
       "      <td>1.157632</td>\n",
       "      <td>262.502509</td>\n",
       "      <td>9.236423</td>\n",
       "      <td>12.688890</td>\n",
       "      <td>42.719144</td>\n",
       "      <td>49.290031</td>\n",
       "      <td>16.488009</td>\n",
       "      <td>0.908083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>58818.237742</td>\n",
       "      <td>588.517834</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>56.076890</td>\n",
       "      <td>345.699969</td>\n",
       "      <td>334.483934</td>\n",
       "      <td>345.699969</td>\n",
       "      <td>5.035138</td>\n",
       "      <td>1145.978126</td>\n",
       "      <td>3.056551</td>\n",
       "      <td>2.340033</td>\n",
       "      <td>1504.536693</td>\n",
       "      <td>39.757408</td>\n",
       "      <td>60.764207</td>\n",
       "      <td>239.292628</td>\n",
       "      <td>284.862853</td>\n",
       "      <td>115.980285</td>\n",
       "      <td>1.176002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>21339.750000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>43258.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.265620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>128836.500000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.718777</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.916700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>184451.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>11299.000000</td>\n",
       "      <td>60120.000000</td>\n",
       "      <td>60120.000000</td>\n",
       "      <td>60120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>53680.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.221860</td>\n",
       "      <td>73188.000000</td>\n",
       "      <td>1395.000000</td>\n",
       "      <td>1586.000000</td>\n",
       "      <td>6402.000000</td>\n",
       "      <td>11798.000000</td>\n",
       "      <td>5996.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  yearpublished    minplayers    maxplayers   playingtime  \\\n",
       "count   81312.000000   81309.000000  81309.000000  81309.000000  81309.000000   \n",
       "mean    72278.150138    1806.630668      1.992018      5.637703     51.634788   \n",
       "std     58818.237742     588.517834      0.931034     56.076890    345.699969   \n",
       "min         1.000000   -3500.000000      0.000000      0.000000      0.000000   \n",
       "25%     21339.750000    1984.000000      2.000000      2.000000      8.000000   \n",
       "50%     43258.000000    2003.000000      2.000000      4.000000     30.000000   \n",
       "75%    128836.500000    2010.000000      2.000000      6.000000     60.000000   \n",
       "max    184451.000000    2018.000000     99.000000  11299.000000  60120.000000   \n",
       "\n",
       "        minplaytime   maxplaytime        minage   users_rated  average_rating  \\\n",
       "count  81309.000000  81309.000000  81309.000000  81312.000000    81312.000000   \n",
       "mean      49.276833     51.634788      6.983975    161.886585        4.212144   \n",
       "std      334.483934    345.699969      5.035138   1145.978126        3.056551   \n",
       "min        0.000000      0.000000      0.000000      0.000000        0.000000   \n",
       "25%       10.000000      8.000000      0.000000      0.000000        0.000000   \n",
       "50%       30.000000     30.000000      8.000000      2.000000        5.265620   \n",
       "75%       60.000000     60.000000     12.000000     16.000000        6.718777   \n",
       "max    60120.000000  60120.000000    120.000000  53680.000000       10.000000   \n",
       "\n",
       "       bayes_average_rating  total_owners  total_traders  total_wanters  \\\n",
       "count          81312.000000  81312.000000   81312.000000   81312.000000   \n",
       "mean               1.157632    262.502509       9.236423      12.688890   \n",
       "std                2.340033   1504.536693      39.757408      60.764207   \n",
       "min                0.000000      0.000000       0.000000       0.000000   \n",
       "25%                0.000000      1.000000       0.000000       0.000000   \n",
       "50%                0.000000      7.000000       0.000000       0.000000   \n",
       "75%                0.000000     51.000000       2.000000       3.000000   \n",
       "max                8.221860  73188.000000    1395.000000    1586.000000   \n",
       "\n",
       "       total_wishers  total_comments  total_weights  average_weight  \n",
       "count   81312.000000    81312.000000   81312.000000    81312.000000  \n",
       "mean       42.719144       49.290031      16.488009        0.908083  \n",
       "std       239.292628      284.862853     115.980285        1.176002  \n",
       "min         0.000000        0.000000       0.000000        0.000000  \n",
       "25%         0.000000        0.000000       0.000000        0.000000  \n",
       "50%         1.000000        1.000000       0.000000        0.000000  \n",
       "75%         7.000000        9.000000       2.000000        1.916700  \n",
       "max      6402.000000    11798.000000    5996.000000        5.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0\n",
       "type                     0\n",
       "name                    41\n",
       "yearpublished            3\n",
       "minplayers               3\n",
       "maxplayers               3\n",
       "playingtime              3\n",
       "minplaytime              3\n",
       "maxplaytime              3\n",
       "minage                   3\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            0\n",
       "total_comments           0\n",
       "total_weights            0\n",
       "average_weight           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadcc10dc8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "games['average_rating'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games[games['average_rating']==0].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "games=games[games['users_rated']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "games=games.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56894, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadf1fa308>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=games.corr()\n",
    "sns.heatmap(corrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=games.drop(['average_rating','type','name','id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56894, 16)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=games['average_rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56894,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45515, 16)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11379, 16)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45515,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11379,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0879581369945774"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(Y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "RFR=RandomForestRegressor(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionRFR=RFR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4429040666718635"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(Y_test,predictionRFR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratingLR=predictions[0]\n",
    "ratingRFR=predictionRFR[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.167160675423725"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratingLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.094065224999998"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratingRFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_Value=Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.06652"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_Value.iloc[0]"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}