Board Game Review Prediction (Jupyter Notebook).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Board Game Review Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing Libraries and Loading the Data\n",
    "\n",
    "After the .csv file 'games.csv' has been copied to the current directory, we can import the data as a Pandas DataFrame. As a DataFrame, we will be able to easily explore the type, amount, and distribution of data.  Furthermore, using a correlation matrix, we can explore the relationships between parameters.  This is an important step in determining the type of machine learning algorithm to utilize. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'id', u'type', u'name', u'yearpublished', u'minplayers', u'maxplayers',\n",
      "       u'playingtime', u'minplaytime', u'maxplaytime', u'minage',\n",
      "       u'users_rated', u'average_rating', u'bayes_average_rating',\n",
      "       u'total_owners', u'total_traders', u'total_wanters', u'total_wishers',\n",
      "       u'total_comments', u'total_weights', u'average_weight'],\n",
      "      dtype='object')\n",
      "(81312, 20)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120186a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Read in the data.\n",
    "games = pandas.read_csv(\"games.csv\")\n",
    "# Print the names of the columns in games.\n",
    "print(games.columns)\n",
    "print(games.shape)\n",
    "\n",
    "# Make a histogram of all the ratings in the average_rating column.\n",
    "plt.hist(games[\"average_rating\"])\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "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\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117bf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# games[games[\"average_rating\"] == 0]\n",
    "\n",
    "# Print the first row of all the games with zero scores.\n",
    "# The .iloc method on dataframes allows us to index by position.\n",
    "print(games[games[\"average_rating\"] == 0].iloc[0])\n",
    "# Print the first row of all the games with scores greater than 0.\n",
    "print(games[games[\"average_rating\"] > 0].iloc[0])\n",
    "\n",
    "# Remove any rows without user reviews.\n",
    "games = games[games[\"users_rated\"] > 0]\n",
    "# Remove any rows with missing values.\n",
    "games = games.dropna(axis=0)\n",
    "\n",
    "# Make a histogram of all the ratings in the average_rating column.\n",
    "plt.hist(games[\"average_rating\"])\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a80G2tb2BqvoNnYT9jCTXN+PacW37kSRJ6nWp6s0S9nhLcixwf1V9Zh2v3wRY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\n1q/645O4pEkRsaWkKcBW+XTelIgYXSBWY6GDjglekm6PiG2rjpX3fSZ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4colYETFZ0t/I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\nfCy/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x175bab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = games.corr()\n",
    "fig = plt.figure(figsize = (12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get all the columns from the dataframe.\n",
    "columns = games.columns.tolist()\n",
    "# Filter the columns to remove ones we don't want.\n",
    "columns = [c for c in columns if c not in [\"bayes_average_rating\", \"average_rating\", \"type\", \"name\", \"id\"]]\n",
    "\n",
    "# Store the variable we'll be predicting on.\n",
    "target = \"average_rating\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Linear Regression\n",
    "\n",
    "In the following cells, we will deploy a simple linear regression model to predict the average review of each board game.  We will use the mean squared error as a performance metric.  Furthermore, we will compare and contrast these results with the performance of an ensemble method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45515, 20)\n",
      "(11379, 20)\n"
     ]
    }
   ],
   "source": [
    "# Import a convenience function to split the sets.\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# Generate the training set.  Set random_state to be able to replicate results.\n",
    "train = games.sample(frac=0.8, random_state=1)\n",
    "# Select anything not in the training set and put it in the testing set.\n",
    "test = games.loc[~games.index.isin(train.index)]\n",
    "# Print the shapes of both sets.\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.078819032629326"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the linear regression model.\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Initialize the model class.\n",
    "model = LinearRegression()\n",
    "# Fit the model to the training data.\n",
    "model.fit(train[columns], train[target])\n",
    "\n",
    "# Import the scikit-learn function to compute error.\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Generate our predictions for the test set.\n",
    "predictions = model.predict(test[columns])\n",
    "\n",
    "# Compute error between our test predictions and the actual values.\n",
    "mean_squared_error(predictions, test[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4458564605687358"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the random forest model.\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Initialize the model with some parameters.\n",
    "model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10, random_state=1)\n",
    "# Fit the model to the data.\n",
    "model.fit(train[columns], train[target])\n",
    "# Make predictions.\n",
    "predictions = model.predict(test[columns])\n",
    "# Compute the error.\n",
    "mean_squared_error(predictions, test[target])"
   ]
  }
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