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@@ -0,0 +1,799 @@
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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>id</th>\n",
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+ " <th>type</th>\n",
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+ " <th>name</th>\n",
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+ " <th>yearpublished</th>\n",
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+ " <th>minplayers</th>\n",
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+ " <th>maxplayers</th>\n",
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+ " <th>playingtime</th>\n",
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+ " <th>minplaytime</th>\n",
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+ " <th>maxplaytime</th>\n",
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+ " <th>minage</th>\n",
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+ " <th>users_rated</th>\n",
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+ " <th>average_rating</th>\n",
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+ " <th>bayes_average_rating</th>\n",
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+ " <th>total_owners</th>\n",
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+ " <th>total_traders</th>\n",
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+ " <th>total_wanters</th>\n",
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+ " <th>total_wishers</th>\n",
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+ " <th>total_comments</th>\n",
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+ " <th>total_weights</th>\n",
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+ " <th>average_weight</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>12333</td>\n",
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+ " <td>boardgame</td>\n",
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+ " <td>Twilight Struggle</td>\n",
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+ " <td>2005</td>\n",
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+ " <td>2</td>\n",
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+ " <td>2</td>\n",
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+ " <td>180</td>\n",
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+ " <td>180</td>\n",
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+ " <td>180</td>\n",
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+ " <td>13</td>\n",
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+ " <td>20113</td>\n",
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+ " <td>8.33774</td>\n",
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+ " <td>8.22186</td>\n",
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+ " <td>26647</td>\n",
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+ " <td>372</td>\n",
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+ " <td>1219</td>\n",
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+ " <td>5347</td>\n",
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+ " <td>3.4785</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>120677</td>\n",
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+ " <td>boardgame</td>\n",
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+ " <td>Terra Mystica</td>\n",
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+ " <td>2012</td>\n",
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+ " <td>2</td>\n",
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+ " <td>5</td>\n",
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+ " <td>150</td>\n",
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+ " <td>60</td>\n",
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+ " <td>150</td>\n",
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+ " <td>12</td>\n",
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+ " <td>3.8939</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>102794</td>\n",
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+ " <td>boardgame</td>\n",
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+ " <td>Caverna: The Cave Farmers</td>\n",
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+ " <td>2013</td>\n",
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+ " <td>1</td>\n",
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+ " <td>7</td>\n",
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+ " <td>210</td>\n",
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+ " <td>30</td>\n",
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+ " <td>210</td>\n",
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+ " <td>12</td>\n",
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+ " <td>12230</td>\n",
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+ " <td>99</td>\n",
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+ " <td>1476</td>\n",
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+ " <td>5600</td>\n",
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+ " <td>1700</td>\n",
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+ " <td>777</td>\n",
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+ " <td>3.7761</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>25613</td>\n",
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+ " <td>boardgame</td>\n",
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+ " <td>Through the Ages: A Story of Civilization</td>\n",
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+ " <td>2006</td>\n",
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+ " <td>2</td>\n",
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+ " <td>14343</td>\n",
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+ " <td>1084</td>\n",
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+ " <td>3378</td>\n",
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+ " <td>1642</td>\n",
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+ " <td>4.1590</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>3076</td>\n",
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+ " <td>boardgame</td>\n",
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+ " <td>Puerto Rico</td>\n",
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+ " <td>2002</td>\n",
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+ " <td>2</td>\n",
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+ " <td>5</td>\n",
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+ " <td>150</td>\n",
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+ " <td>8.04524</td>\n",
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+ " <td>44362</td>\n",
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+ " <td>795</td>\n",
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+ " <td>861</td>\n",
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+ " <td>5414</td>\n",
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+ " <td>9173</td>\n",
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+ " <td>5213</td>\n",
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+ " <td>3.2943</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " id type name \\\n",
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+ "0 12333 boardgame Twilight Struggle \n",
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+ "1 120677 boardgame Terra Mystica \n",
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+ "2 102794 boardgame Caverna: The Cave Farmers \n",
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+ "3 25613 boardgame Through the Ages: A Story of Civilization \n",
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+ "4 3076 boardgame Puerto Rico \n",
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+ "\n",
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+ " yearpublished minplayers maxplayers playingtime minplaytime \\\n",
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+ "0 2005 2 2 180 180 \n",
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+ "1 2012 2 5 150 60 \n",
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+ "2 2013 1 7 210 30 \n",
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+ "3 2006 2 4 240 240 \n",
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+ "4 2002 2 5 150 90 \n",
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+ "\n",
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+ " maxplaytime minage users_rated average_rating bayes_average_rating \\\n",
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+ "0 180 13 20113 8.33774 8.22186 \n",
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+ "1 150 12 14383 8.28798 8.14232 \n",
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+ "2 210 12 9262 8.28994 8.06886 \n",
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+ "3 240 12 13294 8.20407 8.05804 \n",
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+ "4 150 12 39883 8.14261 8.04524 \n",
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+ "\n",
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+ " total_owners total_traders total_wanters total_wishers total_comments \\\n",
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+ "0 26647 372 1219 5865 5347 \n",
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+ "1 16519 132 1586 6277 2526 \n",
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+ "2 12230 99 1476 5600 1700 \n",
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+ "3 14343 362 1084 5075 3378 \n",
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+ "4 44362 795 861 5414 9173 \n",
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+ "\n",
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+ " total_weights average_weight \n",
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+ "0 2562 3.4785 \n",
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+ "1 1423 3.8939 \n",
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+ "2 777 3.7761 \n",
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+ "3 1642 4.1590 \n",
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+ "4 5213 3.2943 "
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+ ]
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+ },
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+ "execution_count": 3,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "import pandas\n",
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+ "\n",
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+ "board_games = pandas.read_csv(\"board_games.csv\")\n",
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+ "board_games = board_games.dropna(axis=0)\n",
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+ "board_games = board_games[board_games[\"users_rated\"] > 0]\n",
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+ "\n",
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+ "board_games.head()"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "(array([ 602., 1231., 2824., 5206., 8223., 13593., 13849.,\n",
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+ " 8470., 2224., 672.]),\n",
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+ " array([ 1. , 1.9, 2.8, 3.7, 4.6, 5.5, 6.4, 7.3, 8.2,\n",
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+ " 9.1, 10. ]),\n",
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+ " <a list of 10 Patch objects>)"
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+ ]
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+ },
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+ "execution_count": 4,
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+ "metadata": {},
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+ "gSBJagwESRIA/x9ipXt3iOuOXAAAAABJRU5ErkJggg==\n"
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+ ],
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+ "text/plain": [
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+ "<matplotlib.figure.Figure at 0x1039faf98>"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
|
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|
+ ],
|
|
|
+ "source": [
|
|
|
+ "%matplotlib inline\n",
|
|
|
+ "import matplotlib.pyplot as plt\n",
|
|
|
+ "\n",
|
|
|
+ "plt.hist(board_games[\"average_rating\"])"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 5,
|
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|
+ "metadata": {
|
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|
+ "collapsed": false
|
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|
+ },
|
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+ "outputs": [
|
|
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
|
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+ "1.57882993483\n",
|
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+ "6.01611284933\n"
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|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "print(board_games[\"average_rating\"].std())\n",
|
|
|
+ "print(board_games[\"average_rating\"].mean())\n"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Error metric\n",
|
|
|
+ "\n",
|
|
|
+ "In this data set, using mean squared error as an error metric makes sense. This is because the data is continuous, and follows a somewhat normal distribution. We'll be able to compare our error to the standard deviation to see how good the model is at predictions."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 39,
|
|
|
+ "metadata": {
|
|
|
+ "collapsed": false
|
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|
+ },
|
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|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=5, n_init=10,\n",
|
|
|
+ " n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
|
|
|
+ " verbose=0)"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 39,
|
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+ "metadata": {},
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+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "from sklearn.cluster import KMeans\n",
|
|
|
+ "\n",
|
|
|
+ "clus = KMeans(n_clusters=5)\n",
|
|
|
+ "cols = list(board_games.columns)\n",
|
|
|
+ "cols.remove(\"name\")\n",
|
|
|
+ "cols.remove(\"id\")\n",
|
|
|
+ "cols.remove(\"type\")\n",
|
|
|
+ "numeric = board_games[cols]\n",
|
|
|
+ "\n",
|
|
|
+ "clus.fit(numeric)"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
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|
|
+ "cell_type": "code",
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+ "execution_count": 26,
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|
+ "metadata": {
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|
+ "collapsed": false
|
|
|
+ },
|
|
|
+ "outputs": [],
|
|
|
+ "source": [
|
|
|
+ "import numpy\n",
|
|
|
+ "game_mean = numeric.apply(numpy.mean, axis=1)\n",
|
|
|
+ "game_std = numeric.apply(numpy.std, axis=1)"
|
|
|
+ ]
|
|
|
+ },
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|
+ {
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|
+ "cell_type": "code",
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+ "execution_count": 27,
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+ "metadata": {
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+ "collapsed": false
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "<matplotlib.collections.PathCollection at 0x10b5516d8>"
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+ ]
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+ "execution_count": 27,
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+ "output_type": "execute_result"
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+ {
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+ "data": {
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+ "image/png": [
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+ "hdVp4gf2BF4EXgUeIOhY7xTxAxcTJLx5BHeTxUs5duBOgv6aFoK2/i+3Zbztfd7ZQvxnUuTzpj9I\n",
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+ "6JxzriAl04TlnHOuc/EE4pxzriCeQJxzzhXEE4hzzrmCeAJxzjlXEE8gzjnnCuIJxDnnXEE8gTjn\n",
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+ "nCvI/wPHlKEPLEczaAAAAABJRU5ErkJggg==\n"
|
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+ ],
|
|
|
+ "text/plain": [
|
|
|
+ "<matplotlib.figure.Figure at 0x10b4b26d8>"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "display_data"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "labels = clus.labels_\n",
|
|
|
+ "\n",
|
|
|
+ "plt.scatter(x=game_mean, y=game_std, c=labels)"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Game clusters\n",
|
|
|
+ "\n",
|
|
|
+ "It looks like most of the games are similar, but as the game attributes tend to increase in value (such as number of users who rated), there are fewer high quality games. So most games don't get played much, but a few get a lot of players."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 29,
|
|
|
+ "metadata": {
|
|
|
+ "collapsed": false
|
|
|
+ },
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "yearpublished 0.108461\n",
|
|
|
+ "minplayers -0.032701\n",
|
|
|
+ "maxplayers -0.008335\n",
|
|
|
+ "playingtime 0.048994\n",
|
|
|
+ "minplaytime 0.043985\n",
|
|
|
+ "maxplaytime 0.048994\n",
|
|
|
+ "minage 0.210049\n",
|
|
|
+ "users_rated 0.112564\n",
|
|
|
+ "average_rating 1.000000\n",
|
|
|
+ "bayes_average_rating 0.231563\n",
|
|
|
+ "total_owners 0.137478\n",
|
|
|
+ "total_traders 0.119452\n",
|
|
|
+ "total_wanters 0.196566\n",
|
|
|
+ "total_wishers 0.171375\n",
|
|
|
+ "total_comments 0.123714\n",
|
|
|
+ "total_weights 0.109691\n",
|
|
|
+ "average_weight 0.351081\n",
|
|
|
+ "Name: average_rating, dtype: float64"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 29,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "correlations = numeric.corr()\n",
|
|
|
+ "\n",
|
|
|
+ "correlations[\"average_rating\"]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Correlations\n",
|
|
|
+ "\n",
|
|
|
+ "The `yearpublished` column is surprisingly highly correlated with `average_rating`, showing that more recent games tend to be rated more highly. Games for older players (`minage` is high) tend to be more highly rated. The more \"weighty\" a game is (`average_weight` is high), the more highly it tends to be rated."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 40,
|
|
|
+ "metadata": {
|
|
|
+ "collapsed": false
|
|
|
+ },
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "2.0933969758339361"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 40,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "from sklearn.linear_model import LinearRegression\n",
|
|
|
+ "\n",
|
|
|
+ "reg = LinearRegression()\n",
|
|
|
+ "cols.remove(\"average_rating\")\n",
|
|
|
+ "cols.remove(\"bayes_average_rating\")\n",
|
|
|
+ "reg.fit(board_games[cols], board_games[\"average_rating\"])\n",
|
|
|
+ "predictions = reg.predict(board_games[cols])\n",
|
|
|
+ "\n",
|
|
|
+ "numpy.mean((predictions - board_games[\"average_rating\"]) ** 2)"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "markdown",
|
|
|
+ "metadata": {},
|
|
|
+ "source": [
|
|
|
+ "## Game clusters\n",
|
|
|
+ "\n",
|
|
|
+ "The error rate is close to the standard deviation of all board game ratings. This indicates that our model may not have high predictive power. We'll need to dig more into which games were scored well, and which ones weren't."
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "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",
|
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|
+ "name": "python",
|
|
|
+ "nbconvert_exporter": "python",
|
|
|
+ "pygments_lexer": "ipython3",
|
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|
+ "version": "3.4.2"
|
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+ }
|
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|
+ },
|
|
|
+ "nbformat": 4,
|
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|
+ "nbformat_minor": 0
|
|
|
+}
|
Vik Paruchuri