This notebook uses a random forest classifier to predict a player's gender based on their trivia question performance.

predict-gender-from-trivia-performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Import the trivia data into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anon_id</th>\n",
       "      <th>category</th>\n",
       "      <th>correct</th>\n",
       "      <th>total</th>\n",
       "      <th>ratio</th>\n",
       "      <th>ratio_relative_to_overall</th>\n",
       "      <th>gender</th>\n",
       "      <th>overall_correct_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>amer_hist</td>\n",
       "      <td>128</td>\n",
       "      <td>148</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.100654</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.764211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>art</td>\n",
       "      <td>91</td>\n",
       "      <td>145</td>\n",
       "      <td>0.627586</td>\n",
       "      <td>-0.136624</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.764211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>bus_econ</td>\n",
       "      <td>100</td>\n",
       "      <td>113</td>\n",
       "      <td>0.884956</td>\n",
       "      <td>0.120745</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.764211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>class_music</td>\n",
       "      <td>56</td>\n",
       "      <td>102</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>-0.215191</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.764211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>curr_events</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>0.739583</td>\n",
       "      <td>-0.024627</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.764211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anon_id     category  correct  total     ratio  ratio_relative_to_overall  \\\n",
       "0       39    amer_hist      128    148  0.864865                   0.100654   \n",
       "1       39          art       91    145  0.627586                  -0.136624   \n",
       "2       39     bus_econ      100    113  0.884956                   0.120745   \n",
       "3       39  class_music       56    102  0.549020                  -0.215191   \n",
       "4       39  curr_events       71     96  0.739583                  -0.024627   \n",
       "\n",
       "  gender  overall_correct_pct  \n",
       "0   Male             0.764211  \n",
       "1   Male             0.764211  \n",
       "2   Male             0.764211  \n",
       "3   Male             0.764211  \n",
       "4   Male             0.764211  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "trivia_data = pd.read_csv('https://raw.githubusercontent.com/toddwschneider/learnedleague-analysis/master/learnedleague_category_stats.csv')\n",
    "trivia_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Wrangle the data into a format sklearn can work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "\n",
    "for anon_id, group_id in trivia_data.groupby('anon_id'):\n",
    "    group_id = group_id.copy()\n",
    "    group_id.sort('category', inplace=True)\n",
    "    X.append(list(group_id.ratio))\n",
    "    id_class = 1 if group_id.gender.values[0] == 'Male' else 0\n",
    "    y.append(id_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Train a random forest classifier on all of the data\n",
    "\n",
    "This achieves 100% accuracy - the random forest classifier can accurately determine whether the player is male or female based off of their trivia category performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X, y)\n",
    "rfc.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Perform cross-validation with the random forest classifier\n",
    "\n",
    "This shows the model's ability to generalize. It achieves about 86% on the testing set when we perform cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86627043090638933"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)\n",
    "rfc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}