Predictions for 2017 Bracket.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from __future__ import division\n",
    "import collections\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import linear_model\n",
    "from sklearn import tree\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D, MaxPooling2D, Convolution1D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from keras.utils import np_utils\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import sys\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import math\n",
    "import csv\n",
    "%matplotlib inline\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import urllib\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xTrain = np.load('PrecomputedMatrices/xTrain.npy')\n",
    "yTrain = np.load('PrecomputedMatrices/yTrain.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(115113, 17)\n"
     ]
    }
   ],
   "source": [
    "print xTrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Wins</th>\n",
       "      <th>PPG</th>\n",
       "      <th>PPGA</th>\n",
       "      <th>PowerConf</th>\n",
       "      <th>3PG</th>\n",
       "      <th>APG</th>\n",
       "      <th>TOP</th>\n",
       "      <th>Conference Champ</th>\n",
       "      <th>Tourney Conference Champ</th>\n",
       "      <th>Seed</th>\n",
       "      <th>SOS</th>\n",
       "      <th>SRS</th>\n",
       "      <th>RPG</th>\n",
       "      <th>SPG</th>\n",
       "      <th>Tourney Appearances</th>\n",
       "      <th>National Championships</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>15.266129</td>\n",
       "      <td>11.495392</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.003456</td>\n",
       "      <td>6.231567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>17.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.672811</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.501152</td>\n",
       "      <td>9.720046</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.109447</td>\n",
       "      <td>-6.464286</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-15.49</td>\n",
       "      <td>-29.52</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.896313</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>14.339719</td>\n",
       "      <td>0.476373</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.120051</td>\n",
       "      <td>6.784163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>5.76</td>\n",
       "      <td>18.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.199234</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.200893</td>\n",
       "      <td>1.906250</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.223214</td>\n",
       "      <td>-3.303571</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-14.70</td>\n",
       "      <td>-20.11</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.151786</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.855556</td>\n",
       "      <td>0.596296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.718519</td>\n",
       "      <td>-0.159259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.944444</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wins        PPG       PPGA  PowerConf       3PG       APG  TOP  \\\n",
       "0   7.0  15.266129  11.495392        1.0 -0.003456  6.231567  0.0   \n",
       "1 -12.0  -4.501152   9.720046       -1.0  0.109447 -6.464286 -0.0   \n",
       "2   9.0  14.339719   0.476373        1.0 -0.120051  6.784163  0.0   \n",
       "3  -5.0  -3.200893   1.906250       -1.0 -2.223214 -3.303571 -0.0   \n",
       "4   1.0  -0.855556   0.596296        0.0  0.718519 -0.159259  0.0   \n",
       "\n",
       "   Conference Champ  Tourney Conference Champ  Seed    SOS    SRS  RPG  \\\n",
       "0               0.0                       0.0 -22.0  13.75  17.34  0.0   \n",
       "1               0.0                       0.0  24.0 -15.49 -29.52 -0.0   \n",
       "2               0.0                       0.0 -17.0   5.76  18.88  0.0   \n",
       "3               0.0                       0.0  11.0 -14.70 -20.11 -0.0   \n",
       "4               0.0                       0.0   0.0   2.37   0.92  0.0   \n",
       "\n",
       "        SPG  Tourney Appearances  National Championships  Location  \n",
       "0  4.672811                  4.0                     0.0       1.0  \n",
       "1  2.896313                -12.0                    -1.0       1.0  \n",
       "2  5.199234                 14.0                     0.0       1.0  \n",
       "3  0.151786                 -5.0                     0.0       1.0  \n",
       "4 -1.944444                 10.0                     0.0      -1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories=['Wins','PPG','PPGA','PowerConf','3PG', 'APG','TOP','Conference Champ','Tourney Conference Champ',\n",
    "           'Seed','SOS','SRS', 'RPG', 'SPG', 'Tourney Appearances','National Championships','Location']\n",
    "df = pd.DataFrame(xTrain, columns=categories)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newCategories=['Wins','PPG','PPGA','PowerConf','3PG', 'APG','TOP','Conference Champ','Tourney Conference Champ',\n",
    "           'SOS','SRS', 'RPG', 'SPG', 'Tourney Appearances','National Championships','Location']\n",
    "newXtrain = df[newCategories]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Wins</th>\n",
       "      <th>PPG</th>\n",
       "      <th>PPGA</th>\n",
       "      <th>PowerConf</th>\n",
       "      <th>3PG</th>\n",
       "      <th>APG</th>\n",
       "      <th>TOP</th>\n",
       "      <th>Conference Champ</th>\n",
       "      <th>Tourney Conference Champ</th>\n",
       "      <th>SOS</th>\n",
       "      <th>SRS</th>\n",
       "      <th>RPG</th>\n",
       "      <th>SPG</th>\n",
       "      <th>Tourney Appearances</th>\n",
       "      <th>National Championships</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>15.266129</td>\n",
       "      <td>11.495392</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.003456</td>\n",
       "      <td>6.231567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>17.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.672811</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-12.0</td>\n",
       "      <td>-4.501152</td>\n",
       "      <td>9.720046</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.109447</td>\n",
       "      <td>-6.464286</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.49</td>\n",
       "      <td>-29.52</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2.896313</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>14.339719</td>\n",
       "      <td>0.476373</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.120051</td>\n",
       "      <td>6.784163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.76</td>\n",
       "      <td>18.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.199234</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-3.200893</td>\n",
       "      <td>1.906250</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.223214</td>\n",
       "      <td>-3.303571</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-14.70</td>\n",
       "      <td>-20.11</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.151786</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.855556</td>\n",
       "      <td>0.596296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.718519</td>\n",
       "      <td>-0.159259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.944444</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wins        PPG       PPGA  PowerConf       3PG       APG  TOP  \\\n",
       "0   7.0  15.266129  11.495392        1.0 -0.003456  6.231567  0.0   \n",
       "1 -12.0  -4.501152   9.720046       -1.0  0.109447 -6.464286 -0.0   \n",
       "2   9.0  14.339719   0.476373        1.0 -0.120051  6.784163  0.0   \n",
       "3  -5.0  -3.200893   1.906250       -1.0 -2.223214 -3.303571 -0.0   \n",
       "4   1.0  -0.855556   0.596296        0.0  0.718519 -0.159259  0.0   \n",
       "\n",
       "   Conference Champ  Tourney Conference Champ    SOS    SRS  RPG       SPG  \\\n",
       "0               0.0                       0.0  13.75  17.34  0.0  4.672811   \n",
       "1               0.0                       0.0 -15.49 -29.52 -0.0  2.896313   \n",
       "2               0.0                       0.0   5.76  18.88  0.0  5.199234   \n",
       "3               0.0                       0.0 -14.70 -20.11 -0.0  0.151786   \n",
       "4               0.0                       0.0   2.37   0.92  0.0 -1.944444   \n",
       "\n",
       "   Tourney Appearances  National Championships  Location  \n",
       "0                  4.0                     0.0       1.0  \n",
       "1                -12.0                    -1.0       1.0  \n",
       "2                 14.0                     0.0       1.0  \n",
       "3                 -5.0                     0.0       1.0  \n",
       "4                 10.0                     0.0      -1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newXtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115113, 16)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newXtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\n",
       "       fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\n",
       "       normalize=False, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.BayesianRidge()\n",
    "model.fit(newXtrain, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.761701240488\n"
     ]
    }
   ],
   "source": [
    "model = linear_model.BayesianRidge()\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(newXtrain, yTrain)\n",
    "results = model.fit(X_train, Y_train)\n",
    "preds = model.predict(X_test)\n",
    "\n",
    "preds[preds < .5] = 0\n",
    "preds[preds >= .5] = 1\n",
    "print (np.mean(preds == Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def handleDifferentCSV(df):\n",
    "    # The stats CSV is a lit different in terms of naming so below is just some data cleaning\n",
    "    df['School'] = df['School'].replace('(State)', 'St', regex=True) \n",
    "    df['School'] = df['School'].replace('Albany (NY)', 'Albany NY') \n",
    "    df['School'] = df['School'].replace('Boston University', 'Boston Univ')\n",
    "    df['School'] = df['School'].replace('Central Michigan', 'C Michigan')\n",
    "    df['School'] = df['School'].replace('(Eastern)', 'E', regex=True)\n",
    "    df['School'] = df['School'].replace('Louisiana St', 'LSU')\n",
    "    df['School'] = df['School'].replace('North Carolina St', 'NC State')\n",
    "    df['School'] = df['School'].replace('Southern California', 'USC')\n",
    "    df['School'] = df['School'].replace('University of California', 'California', regex=True) \n",
    "    df['School'] = df['School'].replace('American', 'American Univ')\n",
    "    df['School'] = df['School'].replace('Arkansas-Little Rock', 'Ark Little Rock')\n",
    "    df['School'] = df['School'].replace('Arkansas-Pine Bluff', 'Ark Pine Bluff')\n",
    "    df['School'] = df['School'].replace('Bowling Green St', 'Bowling Green')\n",
    "    df['School'] = df['School'].replace('Brigham Young', 'BYU')\n",
    "    df['School'] = df['School'].replace('Cal Poly', 'Cal Poly SLO')\n",
    "    df['School'] = df['School'].replace('Centenary (LA)', 'Centenary')\n",
    "    df['School'] = df['School'].replace('Central Connecticut St', 'Central Conn')\n",
    "    df['School'] = df['School'].replace('Charleston Southern', 'Charleston So')\n",
    "    df['School'] = df['School'].replace('Coastal Carolina', 'Coastal Car')\n",
    "    df['School'] = df['School'].replace('College of Charleston', 'Col Charleston')\n",
    "    df['School'] = df['School'].replace('Cal St Fullerton', 'CS Fullerton')\n",
    "    df['School'] = df['School'].replace('Cal St Sacramento', 'CS Sacramento')\n",
    "    df['School'] = df['School'].replace('Cal St Bakersfield', 'CS Bakersfield')\n",
    "    df['School'] = df['School'].replace('Cal St Northridge', 'CS Northridge')\n",
    "    df['School'] = df['School'].replace('East Tennessee St', 'ETSU')\n",
    "    df['School'] = df['School'].replace('Detroit Mercy', 'Detroit')\n",
    "    df['School'] = df['School'].replace('Fairleigh Dickinson', 'F Dickinson')\n",
    "    df['School'] = df['School'].replace('Florida Atlantic', 'FL Atlantic')\n",
    "    df['School'] = df['School'].replace('Florida Gulf Coast', 'FL Gulf Coast')\n",
    "    df['School'] = df['School'].replace('Florida International', 'Florida Intl')\n",
    "    df['School'] = df['School'].replace('George Washington', 'G Washington')\n",
    "    df['School'] = df['School'].replace('Georgia Southern', 'Ga Southern')\n",
    "    df['School'] = df['School'].replace('Gardner-Webb', 'Gardner Webb')\n",
    "    df['School'] = df['School'].replace('Illinois-Chicago', 'IL Chicago')\n",
    "    df['School'] = df['School'].replace('Kent St', 'Kent')\n",
    "    df['School'] = df['School'].replace('Long Island University', 'Long Island')\n",
    "    df['School'] = df['School'].replace('Loyola Marymount', 'Loy Marymount')\n",
    "    df['School'] = df['School'].replace('Loyola (MD)', 'Loyola MD')\n",
    "    df['School'] = df['School'].replace('Loyola (IL)', 'Loyola-Chicago')\n",
    "    df['School'] = df['School'].replace('Massachusetts', 'MA Lowell')\n",
    "    df['School'] = df['School'].replace('Maryland-Eastern Shore', 'MD E Shore')\n",
    "    df['School'] = df['School'].replace('Miami (FL)', 'Miami FL')\n",
    "    df['School'] = df['School'].replace('Miami (OH)', 'Miami OH')\n",
    "    df['School'] = df['School'].replace('Missouri-Kansas City', 'Missouri KC')\n",
    "    df['School'] = df['School'].replace('Monmouth', 'Monmouth NJ')\n",
    "    df['School'] = df['School'].replace('Mississippi Valley St', 'MS Valley St')\n",
    "    df['School'] = df['School'].replace('Montana St', 'MTSU')\n",
    "    df['School'] = df['School'].replace('Northern Colorado', 'N Colorado')\n",
    "    df['School'] = df['School'].replace('North Dakota St', 'N Dakota St')\n",
    "    df['School'] = df['School'].replace('Northern Illinois', 'N Illinois')\n",
    "    df['School'] = df['School'].replace('Northern Kentucky', 'N Kentucky')\n",
    "    df['School'] = df['School'].replace('North Carolina A&T', 'NC A&T')\n",
    "    df['School'] = df['School'].replace('North Carolina Central', 'NC Central')\n",
    "    df['School'] = df['School'].replace('Pennsylvania', 'Penn')\n",
    "    df['School'] = df['School'].replace('South Carolina St', 'S Carolina St')\n",
    "    df['School'] = df['School'].replace('Southern Illinois', 'S Illinois')\n",
    "    df['School'] = df['School'].replace('UC-Santa Barbara', 'Santa Barbara')\n",
    "    df['School'] = df['School'].replace('Southeastern Louisiana', 'SE Louisiana')\n",
    "    df['School'] = df['School'].replace('Southeast Missouri St', 'SE Missouri St')\n",
    "    df['School'] = df['School'].replace('Stephen F. Austin', 'SF Austin')\n",
    "    df['School'] = df['School'].replace('Southern Methodist', 'SMU')\n",
    "    df['School'] = df['School'].replace('Southern Mississippi', 'Southern Miss')\n",
    "    df['School'] = df['School'].replace('Southern', 'Southern Univ')\n",
    "    df['School'] = df['School'].replace('St. Bonaventure', 'St Bonaventure')\n",
    "    df['School'] = df['School'].replace('St. Francis (NY)', 'St Francis NY')\n",
    "    df['School'] = df['School'].replace('Saint Francis (PA)', 'St Francis PA')\n",
    "    df['School'] = df['School'].replace('St. John\\'s (NY)', 'St John\\'s')\n",
    "    df['School'] = df['School'].replace('Saint Joseph\\'s', 'St Joseph\\'s PA')\n",
    "    df['School'] = df['School'].replace('Saint Louis', 'St Louis')\n",
    "    df['School'] = df['School'].replace('Saint Mary\\'s (CA)', 'St Mary\\'s CA')\n",
    "    df['School'] = df['School'].replace('Mount Saint Mary\\'s', 'Mt St Mary\\'s')\n",
    "    df['School'] = df['School'].replace('Mount St. Mary\\'s', 'Mt St Mary\\'s')\n",
    "    df['School'] = df['School'].replace('Saint Peter\\'s', 'St Peter\\'s')\n",
    "    df['School'] = df['School'].replace('Texas A&M-Corpus Christian', 'TAM C. Christian')\n",
    "    df['School'] = df['School'].replace('Texas Christian', 'TCU')\n",
    "    df['School'] = df['School'].replace('Tennessee-Martin', 'TN Martin')\n",
    "    df['School'] = df['School'].replace('Texas-Rio Grande Valley', 'UTRGV')\n",
    "    df['School'] = df['School'].replace('Texas Southern', 'TX Southern')\n",
    "    df['School'] = df['School'].replace('Alabama-Birmingham', 'UAB')\n",
    "    df['School'] = df['School'].replace('UC-Davis', 'UC Davis')\n",
    "    df['School'] = df['School'].replace('UC-Irvine', 'UC Irvine')\n",
    "    df['School'] = df['School'].replace('UC-Riverside', 'UC Riverside')\n",
    "    df['School'] = df['School'].replace('Central Florida', 'UCF')\n",
    "    df['School'] = df['School'].replace('Louisiana-Lafayette', 'ULL')\n",
    "    df['School'] = df['School'].replace('Louisiana-Monroe', 'ULM')\n",
    "    df['School'] = df['School'].replace('Maryland-Baltimore County', 'UMBC')\n",
    "    df['School'] = df['School'].replace('North Carolina-Asheville', 'UNC Asheville')\n",
    "    df['School'] = df['School'].replace('North Carolina-Greensboro', 'UNC Greensboro')\n",
    "    df['School'] = df['School'].replace('North Carolina-Wilmington', 'UNC Wilmington')\n",
    "    df['School'] = df['School'].replace('Nevada-Las Vegas', 'UNLV')\n",
    "    df['School'] = df['School'].replace('Texas-Arlington', 'UT Arlington')\n",
    "    df['School'] = df['School'].replace('Texas-San Antonio', 'UT San Antonio')\n",
    "    df['School'] = df['School'].replace('Texas-El Paso', 'UTEP')\n",
    "    df['School'] = df['School'].replace('Virginia Commonwealth', 'VA Commonwealth')\n",
    "    df['School'] = df['School'].replace('Western Carolina', 'W Carolina')\n",
    "    df['School'] = df['School'].replace('Western Illinois', 'W Illinois')\n",
    "    df['School'] = df['School'].replace('Western Kentucky', 'WKU')\n",
    "    df['School'] = df['School'].replace('Western Michigan', 'W Michigan')\n",
    "    df['School'] = df['School'].replace('Abilene Christian', 'Abilene Chr')\n",
    "    df['School'] = df['School'].replace('Montana State', 'Montana St')\n",
    "    df['School'] = df['School'].replace('Central Arkansas', 'Cent Arkansas')\n",
    "    df['School'] = df['School'].replace('Houston Baptist', 'Houston Bap')\n",
    "    df['School'] = df['School'].replace('South Dakota St', 'S Dakota St')\n",
    "    df['School'] = df['School'].replace('Maryland-Eastern Shore', 'MD E Shore')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function for handling the annoying cases of Florida and FL, as well as State and St\n",
    "def handleCases(arr):\n",
    "    indices = []\n",
    "    listLen = len(arr)\n",
    "    for i in range(listLen):\n",
    "        if (arr[i] == 'St' or arr[i] == 'FL'):\n",
    "            indices.append(i)\n",
    "    for p in indices:\n",
    "        arr[p-1] = arr[p-1] + ' ' + arr[p]\n",
    "    for i in range(len(indices)): \n",
    "        arr.remove(arr[indices[i] - i])\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "listACCteams = ['North Carolina','Virginia','Florida St','Louisville','Notre Dame','Syracuse','Duke','Virginia Tech','Georgia Tech','Miami','Wake Forest','Clemson','NC State','Boston College','Pittsburgh']\n",
    "listPac12teams = ['Arizona','Oregon','UCLA','California','USC','Utah','Washington St','Stanford','Arizona St','Colorado','Washington','Oregon St']\n",
    "listSECteams = ['Kentucky','South Carolina','Florida','Arkansas','Alabama','Tennessee','Mississippi St','Georgia','Ole Miss','Vanderbilt','Auburn','Texas A&M','LSU','Missouri']\n",
    "listBig10teams = ['Maryland','Wisconsin','Purdue','Northwestern','Michigan St','Indiana','Iowa','Michigan','Penn St','Nebraska','Minnesota','Illinois','Ohio St','Rutgers']\n",
    "listBig12teams = ['Kansas','Baylor','West Virginia','Iowa St','TCU','Kansas St','Texas Tech','Oklahoma St','Texas','Oklahoma']\n",
    "listBigEastteams = ['Butler','Creighton','DePaul','Georgetown','Marquette','Providence','Seton Hall','St John\\'s','Villanova','Xavier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def checkPower6Conference(team_id):\n",
    "    teamName = teams_pd.values[team_id-1101][1]\n",
    "    if (teamName in listACCteams or teamName in listBig10teams or teamName in listBig12teams\n",
    "       or teamName in listSECteams or teamName in listPac12teams or teamName in listBigEastteams):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conference_pd = pd.read_csv('Data/Conference.csv')\n",
    "tourney_seeds_pd = pd.read_csv('Data/TourneySeeds.csv')\n",
    "tourney_results_pd = pd.read_csv('Data/TourneyResults.csv')\n",
    "NCAAChampionsList = tourney_results_pd['NCAA Champion'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def checkConferenceChamp(team_id, year):\n",
    "    name = getTeamName(team_id)\n",
    "    champs =['SMU','North Carolina','Vermont','Dayton','FL Gulf Coast','Villanova','North Dakota',\n",
    "            'Winthrop','Purdue','Kansas','UC Irvine','UNC Wilmington','Middle Tennessee St','Oakland',\n",
    "            'Princeton','Monmouth','Akron','NC Central','Wichita St','Nevada','Mt St. Mary\\'s',\n",
    "            'Arizona','Bucknell','Kentucky','East Tennessee St','New Orleans','South Dakota','Texas Southern',\n",
    "            'Gonzaga']\n",
    "    if (name in champs):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getTourneyAppearances(team_id):\n",
    "    return len(tourney_seeds_pd[tourney_seeds_pd['Team'] == team_id].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def checkConferenceTourneyChamp(team_id, year):\n",
    "    name = getTeamName(team_id)\n",
    "    champs =['SMU','Duke','Vermont','Rhode Island','FL Gulf Coast','Villanova','North Dakota',\n",
    "            'Winthrop','Michigan','Iowa St','UC Davis','UNC Wilmington','Middle Tennessee St','Northern Kentucky',\n",
    "            'Princeton','Iona','Kent St','NC Central','Wichita St','Nevada','Mt St. Mary\\'s','Jacksonville St',\n",
    "            'Arizona','Bucknell','Kentucky','East Tennessee St','New Orleans','S Dakota St','Troy','Texas Southern',\n",
    "            'New Mexico St','Gonzaga']\n",
    "    if (name in champs):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getTeamName(team_id):\n",
    "    return teams_pd[teams_pd['Team_Id'] == team_id].values[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getNumChampionships(team_id):\n",
    "    name = getTeamName(team_id)\n",
    "    return NCAAChampionsList.count(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predictGame(team_1_vector, team_2_vector, home):\n",
    "    diff = [a - b for a, b in zip(team_1_vector, team_2_vector)]\n",
    "    diff.append(home)\n",
    "    #return model.predict_proba([diff])\n",
    "    return model.predict([diff])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Team_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1282.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.221988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1191.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1282.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1373.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1464.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Team_Id\n",
       "count   364.000000\n",
       "mean   1282.500000\n",
       "std     105.221988\n",
       "min    1101.000000\n",
       "25%    1191.750000\n",
       "50%    1282.500000\n",
       "75%    1373.250000\n",
       "max    1464.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_pd = pd.read_csv('Data/Teams.csv')\n",
    "teams_pd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getSeasonData(team_id, year):    \n",
    "    stats_SOS_pd = pd.read_csv('Data/MMStats/MMStats_'+str(year)+'.csv')\n",
    "    stats_SOS_pd = handleDifferentCSV(stats_SOS_pd)\n",
    "    ratings_pd = pd.read_csv('Data/RatingStats/RatingStats_'+str(year)+'.csv')\n",
    "    ratings_pd = handleDifferentCSV(ratings_pd)\n",
    "    \n",
    "    name = getTeamName(team_id)\n",
    "    team = stats_SOS_pd[stats_SOS_pd['School'] == name]\n",
    "    team_rating = ratings_pd[ratings_pd['School'] == name]\n",
    "    if (len(team.index) == 0 or len(team_rating.index) == 0): #Can't find the team\n",
    "        total3sMade = 0\n",
    "        totalTurnovers = 0\n",
    "        totalAssists = 0\n",
    "        sos = 0\n",
    "        totalRebounds = 0\n",
    "        srs = 0\n",
    "        totalSteals = 0\n",
    "        totalPointsScored = 0\n",
    "        totalPointsAllowed = 0\n",
    "        numGames = 0\n",
    "        \n",
    "    else:\n",
    "        avgPointsScored = team_rating['Pts'].values[0]\n",
    "        avgPointsAllowed = team_rating['Opp'].values[0]\n",
    "        numWins = team_rating['W'].values[0]\n",
    "        numLosses = team_rating['L'].values[0]\n",
    "        numGames = numWins + numLosses\n",
    "        total3sMade = team['X3P'].values[0]\n",
    "        totalTurnovers = team['TOV'].values[0]\n",
    "        if (math.isnan(totalTurnovers)):\n",
    "            totalTurnovers = 0\n",
    "        totalAssists = team['AST'].values[0]\n",
    "        if (math.isnan(totalAssists)):\n",
    "            totalAssists = 0\n",
    "        sos = team['SOS'].values[0]\n",
    "        srs = team['SRS'].values[0]\n",
    "        totalRebounds = team['TRB'].values[0]\n",
    "        if (math.isnan(totalRebounds)):\n",
    "            totalRebounds = 0\n",
    "        totalSteals = team['STL'].values[0]\n",
    "        if (math.isnan(totalSteals)):\n",
    "            totalSteals = 0\n",
    "    \n",
    "    #Finding tournament seed for that year\n",
    "\n",
    "    # There are some teams who may have dropped to Division 2, so they won't have games \n",
    "    # a certain year. In this case, we don't want to divide by 0, so we'll just set the\n",
    "    # averages to 0 instead\n",
    "    if numGames == 0:\n",
    "        avgPointsScored = 0\n",
    "        avgPointsAllowed = 0\n",
    "        avg3sMade = 0\n",
    "        avgTurnovers = 0\n",
    "        avgAssists = 0\n",
    "        avgRebounds = 0\n",
    "        avgSteals = 0\n",
    "    else:\n",
    "        avg3sMade = total3sMade/numGames\n",
    "        avgTurnovers = totalTurnovers/numGames\n",
    "        avgAssists = totalAssists/numGames\n",
    "        avgRebounds = totalRebounds/numGames\n",
    "        avgSteals = totalSteals/numGames\n",
    "    return [numWins, avgPointsScored, avgPointsAllowed, checkPower6Conference(team_id), avg3sMade, avgAssists, avgTurnovers,\n",
    "           checkConferenceChamp(team_id, year), checkConferenceTourneyChamp(team_id, year), \n",
    "            sos, srs, avgRebounds, avgSteals, getTourneyAppearances(team_id), getNumChampionships(team_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arizona Wins\n",
      "[ 0.97256063]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "for i in range(1):\n",
    "    team1name = 'Arizona'\n",
    "    team2name = 'Yale'\n",
    "    team1_vector = getSeasonData(teams_pd[teams_pd['Team_Name'] == team1name].values[0][0], 2017)\n",
    "    team2_vector = getSeasonData(teams_pd[teams_pd['Team_Name'] == team2name].values[0][0], 2017)\n",
    "    team_1_win = predictGame(team1_vector, team2_vector, 0)\n",
    "    if (team_1_win > random.random()):\n",
    "        print team1name, 'Wins'\n",
    "        print team_1_win\n",
    "    else:\n",
    "        print team2name, 'Wins'\n",
    "        print team_1_win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def showFeatureImportance(my_categories):\n",
    "    fx_imp = pd.Series(model.feature_importances_, index=my_categories)\n",
    "    fx_imp /= fx_imp.max()\n",
    "    fx_imp.sort()\n",
    "    fx_imp.plot(kind='barh')\n",
    "showFeatureImportance(newCategories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Id</th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017_1112_1116</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017_1112_1124</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017_1112_1137</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017_1112_1139</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017_1112_1153</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  Pred\n",
       "0  2017_1112_1116   0.5\n",
       "1  2017_1112_1124   0.5\n",
       "2  2017_1112_1137   0.5\n",
       "3  2017_1112_1139   0.5\n",
       "4  2017_1112_1153   0.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_sub_pd = pd.read_csv('Data/samplesubmission2017.csv')\n",
    "(sample_sub_pd.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def createPrediction():\n",
    "    results = [[0 for x in range(2)] for x in range(len(sample_sub_pd.index))]\n",
    "    for index, row in sample_sub_pd.iterrows():\n",
    "        matchup_id = row['Id']\n",
    "        year = matchup_id[0:4]\n",
    "        team1_id = matchup_id[5:9]\n",
    "        team2_id = matchup_id[10:14]\n",
    "        team1_vector = getSeasonData(int(team1_id), int(year))\n",
    "        team2_vector = getSeasonData(int(team2_id), int(year))\n",
    "        pred = predictGame(team1_vector, team2_vector, 0)\n",
    "        results[index][0] = matchup_id\n",
    "        results[index][1] = pred[0]\n",
    "        #results[index][1] = pred[0][1]\n",
    "    results = pd.np.array(results)\n",
    "    firstRow = [[0 for x in range(2)] for x in range(1)]\n",
    "    firstRow[0][0] = 'id'\n",
    "    firstRow[0][1] = 'pred'\n",
    "    with open(\"predictions2017.csv\", \"wb\") as f:\n",
    "        writer = csv.writer(f)\n",
    "        writer.writerows(firstRow)\n",
    "        writer.writerows(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = linear_model.BayesianRidge()\n",
    "model.fit(newXtrain, yTrain)\n",
    "createPrediction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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