March_Madness_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                             PROJECT 2: MARCH MADNESS FIRST ROUND PREDICTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import altair as alt\n",
    "alt.renderers.enable('notebook')\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.preprocessing import LabelBinarizer, StandardScaler\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import recall_score, make_scorer, f1_score\n",
    "from sklearn.feature_selection import f_classif, f_regression, chi2, SelectPercentile\n",
    "from sklearn.feature_selection import RFE, SelectKBest\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_season = pd.read_csv('data/regular_season.csv')\n",
    "march_madness = pd.read_csv('data/march_madness.csv')\n",
    "march_madness_seeds = pd.read_csv('data/march_madness_seeds.csv')\n",
    "teams_info = pd.read_csv('data/teams.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = teams_info.drop(columns='conference_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_name</th>\n",
       "      <th>conference_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125636</th>\n",
       "      <td>2014</td>\n",
       "      <td>1464</td>\n",
       "      <td>Youngstown St</td>\n",
       "      <td>Horizon League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125637</th>\n",
       "      <td>2015</td>\n",
       "      <td>1464</td>\n",
       "      <td>Youngstown St</td>\n",
       "      <td>Horizon League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125638</th>\n",
       "      <td>2016</td>\n",
       "      <td>1464</td>\n",
       "      <td>Youngstown St</td>\n",
       "      <td>Horizon League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125639</th>\n",
       "      <td>2017</td>\n",
       "      <td>1464</td>\n",
       "      <td>Youngstown St</td>\n",
       "      <td>Horizon League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125640</th>\n",
       "      <td>2018</td>\n",
       "      <td>1464</td>\n",
       "      <td>Youngstown St</td>\n",
       "      <td>Horizon League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        season  team_id      team_name conference_name\n",
       "125636    2014     1464  Youngstown St  Horizon League\n",
       "125637    2015     1464  Youngstown St  Horizon League\n",
       "125638    2016     1464  Youngstown St  Horizon League\n",
       "125639    2017     1464  Youngstown St  Horizon League\n",
       "125640    2018     1464  Youngstown St  Horizon League"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>seed</th>\n",
       "      <th>team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>W01</td>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>W02</td>\n",
       "      <td>1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>W03</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>W04</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>W05</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   season seed  team_id\n",
       "0    2003  W01     1328\n",
       "1    2003  W02     1448\n",
       "2    2003  W03     1393\n",
       "3    2003  W04     1257\n",
       "4    2003  W05     1280"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "march_madness_seeds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "og_march_madness = march_madness.iloc[:1136, :]\n",
    "march_madness = march_madness.iloc[1136:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "floor = march_madness['day_in_season'] == 136\n",
    "ceiling = march_madness['day_in_season'] == 137\n",
    "\n",
    "march_madness_round1 = march_madness[floor | ceiling]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = march_madness_round1.drop(columns=['winning_team_score', 'losing_team_id', 'losing_team_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    32\n",
       "2016    32\n",
       "2015    32\n",
       "2014    32\n",
       "2013    32\n",
       "2012    32\n",
       "2011    32\n",
       "2010    32\n",
       "2009    32\n",
       "2008    32\n",
       "2007    32\n",
       "2006    32\n",
       "2005    32\n",
       "2004    32\n",
       "2003    32\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "01    60\n",
       "02    56\n",
       "03    52\n",
       "04    49\n",
       "07    38\n",
       "05    36\n",
       "06    34\n",
       "08    34\n",
       "09    26\n",
       "11    26\n",
       "12    24\n",
       "10    22\n",
       "13    11\n",
       "14     8\n",
       "15     4\n",
       "Name: seed, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "df2 = pd.merge(df1, march_madness_seeds, left_on=['season', 'winning_team_id'], right_on=['season', 'team_id'])\n",
    "df2['seed'] = df2.seed.apply(lambda x: x.strip('ZWXYb'))\n",
    "#stripping the region from the seed. also stripping b from the one team that was 11b because that b just denotes they\n",
    "#had to play in the first four to gain their seed\n",
    "df2['seed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_of_wins = df2['seed'].value_counts()\n",
    "num_of_wins = pd.DataFrame(num_of_wins)\n",
    "num_of_wins = num_of_wins.sort_index()\n",
    "num_of_wins = num_of_wins.rename(columns={'seed': 'number_of_wins'})\n",
    "num_of_wins['seed'] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
    "num_of_wins = num_of_wins.append({'number_of_wins': 0, 'seed': 16}, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>seed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
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      "text/plain": [
       "    number_of_wins  seed\n",
       "0               60     1\n",
       "1               56     2\n",
       "2               52     3\n",
       "3               49     4\n",
       "4               36     5\n",
       "5               34     6\n",
       "6               38     7\n",
       "7               34     8\n",
       "8               26     9\n",
       "9               22    10\n",
       "10              26    11\n",
       "11              24    12\n",
       "12              11    13\n",
       "13               8    14\n",
       "14               4    15\n",
       "15               0    16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "num_of_wins"
   ]
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"
     },
     "metadata": {
      "jupyter-vega": "#e7f5a71b-1419-4a0b-a60b-a8beb3aaf5b1"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = alt.Chart(num_of_wins).mark_bar().encode(\n",
    "    x='seed:O',\n",
    "    y=\"number_of_wins:Q\"\n",
    ")\n",
    "\n",
    "line = alt.Chart(num_of_wins).mark_rule(color='red').encode(\n",
    "    y='median(number_of_wins)'\n",
    "    \n",
    ")\n",
    "    \n",
    "(bar + line).properties(width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This bar chart shows that when predicting the first round of March Madness, there's a very high probability that those colleges seated 1, 2, 3, and 4 will win, and their opponents, seats 13, 14, 15, 16, will lose. Teams seeded between 5 and 12, are more evenly matched, and as you can see, those 8 seeds have numbers closer to 30 (30 wins indicating that a seed won half of the games they played).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>season</th>\n",
       "      <th>day_in_season</th>\n",
       "      <th>winning_team_id</th>\n",
       "      <th>winning_team_score</th>\n",
       "      <th>losing_team_id</th>\n",
       "      <th>losing_team_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1112</td>\n",
       "      <td>80</td>\n",
       "      <td>1436</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1113</td>\n",
       "      <td>84</td>\n",
       "      <td>1272</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1141</td>\n",
       "      <td>79</td>\n",
       "      <td>1166</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1143</td>\n",
       "      <td>76</td>\n",
       "      <td>1301</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1163</td>\n",
       "      <td>58</td>\n",
       "      <td>1140</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  day_in_season  winning_team_id  winning_team_score  \\\n",
       "1137    2003            136             1112                  80   \n",
       "1138    2003            136             1113                  84   \n",
       "1139    2003            136             1141                  79   \n",
       "1140    2003            136             1143                  76   \n",
       "1141    2003            136             1163                  58   \n",
       "\n",
       "      losing_team_id  losing_team_score  \n",
       "1137            1436                 51  \n",
       "1138            1272                 71  \n",
       "1139            1166                 73  \n",
       "1140            1301                 74  \n",
       "1141            1140                 53  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_madness_round1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>season</th>\n",
       "      <th>seed</th>\n",
       "      <th>team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>01</td>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>02</td>\n",
       "      <td>1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>03</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>04</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>05</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season seed  team_id\n",
       "0    2003   01     1328\n",
       "1    2003   02     1448\n",
       "2    2003   03     1393\n",
       "3    2003   04     1257\n",
       "4    2003   05     1280"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_madness_seeds['seed'] = march_madness_seeds.seed.apply(lambda x: x.strip('WXYZba')) #strips the regions (WXYZ) and a and b from play-in teams\n",
    "df3_march_madness_seeds = march_madness_seeds\n",
    "df3_march_madness_seeds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_march_madness_seeds['seed'] = pd.to_numeric(df3_march_madness_seeds['seed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>season</th>\n",
       "      <th>winning_team_id</th>\n",
       "      <th>losing_team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>2003</td>\n",
       "      <td>1112</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>2003</td>\n",
       "      <td>1113</td>\n",
       "      <td>1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>2003</td>\n",
       "      <td>1141</td>\n",
       "      <td>1166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2003</td>\n",
       "      <td>1143</td>\n",
       "      <td>1301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2003</td>\n",
       "      <td>1163</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  winning_team_id  losing_team_id\n",
       "1137    2003             1112            1436\n",
       "1138    2003             1113            1272\n",
       "1139    2003             1141            1166\n",
       "1140    2003             1143            1301\n",
       "1141    2003             1163            1140"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_march_madness_round1 = march_madness_round1.drop(columns=['day_in_season', 'winning_team_score', 'losing_team_score'])\n",
    "df3_march_madness_round1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_march_madness_round1.columns = ['season', 'team_id', 'losing_team_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.merge(df3_march_madness_round1, march_madness_seeds, left_on=['season', 'team_id'], right_on=['season', 'team_id'])\n",
    "df3 = df3[['season', 'team_id', 'seed', 'losing_team_id']]\n",
    "df3.columns = ['season', 'winning_team_id', 'w_seed', 'team_id']\n",
    "df3 = pd.merge(df3, march_madness_seeds, left_on=['season', 'team_id'], right_on=['season', 'team_id'])\n",
    "df3.columns =['season', 'winning_team_id', 'w_seed', 'losing_team_id', 'l_seed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>season</th>\n",
       "      <th>winning_team_id</th>\n",
       "      <th>w_seed</th>\n",
       "      <th>losing_team_id</th>\n",
       "      <th>l_seed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>1112</td>\n",
       "      <td>1</td>\n",
       "      <td>1436</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>1113</td>\n",
       "      <td>10</td>\n",
       "      <td>1272</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>1141</td>\n",
       "      <td>11</td>\n",
       "      <td>1166</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>1143</td>\n",
       "      <td>8</td>\n",
       "      <td>1301</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>1163</td>\n",
       "      <td>5</td>\n",
       "      <td>1140</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  winning_team_id  w_seed  losing_team_id  l_seed\n",
       "0    2003             1112       1            1436      16\n",
       "1    2003             1113      10            1272       7\n",
       "2    2003             1141      11            1166       6\n",
       "3    2003             1143       8            1301       9\n",
       "4    2003             1163       5            1140      12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_march_madness_seeds\n",
    "\n",
    "dfA = df3.iloc[:, :3]\n",
    "dfB = df3.iloc[:, 3:]\n",
    "\n",
    "dfB['season'] = dfA.season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>losing_team_id</th>\n",
       "      <th>l_seed</th>\n",
       "      <th>season</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1436</td>\n",
       "      <td>16</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1272</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1166</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1301</td>\n",
       "      <td>9</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1140</td>\n",
       "      <td>12</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   losing_team_id  l_seed  season  outcome\n",
       "0            1436      16    2003        0\n",
       "1            1272       7    2003        0\n",
       "2            1166       6    2003        0\n",
       "3            1301       9    2003        0\n",
       "4            1140      12    2003        0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfA['outcome'] = 1\n",
    "dfB['outcome'] = 0\n",
    "dfB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfA.columns = ['season', 'team_id', 'seed', 'outcome']\n",
    "dfB.columns = ['team_id', 'seed', 'season', 'outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    }
   ],
   "source": [
    "dfC = dfA.append(dfB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>outcome</th>\n",
       "      <th>season</th>\n",
       "      <th>seed</th>\n",
       "      <th>team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>10</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>11</td>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>1163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   outcome  season  seed  team_id\n",
       "0        1    2003     1     1112\n",
       "1        1    2003    10     1113\n",
       "2        1    2003    11     1141\n",
       "3        1    2003     8     1143\n",
       "4        1    2003     5     1163"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "round_one_results = pd.merge(march_madness_seeds, dfC, left_on=['season', 'team_id', 'seed'], right_on=['season', 'team_id', 'seed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(round_one_results[['seed']], round_one_results[['outcome']], test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.74609375"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver='lbfgs')\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7552083333333334"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "This naive model shows exacly what one would expect. Probabilities for teams seeded in the top 4 are closer to 100% winning, and those in the bottom 4, approaching 0%. Combined with the 8 middle seeds being more evenly matched and probabilites in closer proximity to 50%, we would expect a model based solely on seeding to be around 75%.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conference Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Next, I am going to introduce information based on conferences. The significance of this feature is that some conferences are more competitive than others. For example, The Atlantic Coast Conference has University of North Carolina, Duke, and Virginia, among other competitive schools. The Big Ten has Indiana, Illinois, and Purdue. Members of these conferences may not have win records as strong as teams in less competitive conferences, who do not face as high of a calibre opponents during the regular season. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = teams.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = teams.drop(columns=['team_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_one_results = pd.merge(round_one_results, teams, left_on=['season', 'team_id'], right_on=['season','team_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>season</th>\n",
       "      <th>seed</th>\n",
       "      <th>team_id</th>\n",
       "      <th>outcome</th>\n",
       "      <th>conference_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>1328</td>\n",
       "      <td>1</td>\n",
       "      <td>Big 12 Conference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>1448</td>\n",
       "      <td>1</td>\n",
       "      <td>Atlantic Coast Conference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>1393</td>\n",
       "      <td>1</td>\n",
       "      <td>Big East Conference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  seed  team_id  outcome            conference_name\n",
       "0    2003     1     1328        1          Big 12 Conference\n",
       "1    2003     2     1448        1  Atlantic Coast Conference\n",
       "2    2003     3     1393        1        Big East Conference"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = round_one_results\n",
    "combined_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer() #using label binarizer to one hot encode conference columns\n",
    "lb.fit_transform(combined_data['conference_name'])\n",
    "\n",
    "combined_data = combined_data.join(\n",
    "    pd.DataFrame(lb.fit_transform(combined_data['conference_name']),\n",
    "    columns=lb.classes_, \n",
    "    index=combined_data.index)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = combined_data.drop(columns=['conference_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [columns for columns in combined_data if columns != 'outcome']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(combined_data[features], combined_data['outcome'], \n",
    "                                                    test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7434895833333334"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver='lbfgs')\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7604166666666666"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Adding the conferences information in to the data actually did not make much impact. The original model scored a .746 on the training data and a .755 on the test data.\n",
    "The updated dataset with the conference in addition to the seeds scored .743 on the training data, and a .76 on the test data.\n",
    "I think that this is indicative of the fact that the NCAA does a great job at factoring season performance based on conferences into seeding. A team like Louisville or Syracuse (Atlantic Coast Conference) may not have a winning record that is comparable to those teams similiarly seeded, but they play in a conference with the teams that are consistently ranked 1 and 2. \n",
    "\n",
    "\n",
    "I assume that conference information would become more relelvant when looking at predictions for later rounds.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INCORPORATING REGULAR SEASON STATS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In the data_prep notebook, I organized and cleaned regular season data for the seasons associated with the March Madness data used above. The information that is being imported is separted by team and year and includes:\n",
    "                \n",
    "                -Wins (number of wins per season)\n",
    "                -Plus Minus (accumulation of plus and minus over the entire season)\n",
    "                -Three Point Average (Average of 3 point percentage per game)\n",
    "                -Free Throw Attempts (Average of free throw attempts per game)\n",
    "                -Offensive Rebounds (Average of offensive rebounds per game)\n",
    "                -Turnovers per Game (Average of turnovers per game)\n",
    "                -Field Goals Average (Average of field goals percentage per game)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_stats = pd.read_csv('data/season_stats_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5130, 10)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_stats = season_stats.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5130, 9)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(combined_data, season_stats, left_on=['season', 'team_id'], right_on=['season', 'team_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>season</th>\n",
       "      <th>seed</th>\n",
       "      <th>team_id</th>\n",
       "      <th>outcome</th>\n",
       "      <th>America East Conference</th>\n",
       "      <th>American Athletic Conference</th>\n",
       "      <th>Atlantic 10 Conference</th>\n",
       "      <th>Atlantic Coast Conference</th>\n",
       "      <th>Atlantic Sun Conference</th>\n",
       "      <th>Big 12 Conference</th>\n",
       "      <th>Big East Conference</th>\n",
       "      <th>Big Sky Conference</th>\n",
       "      <th>Big South Conference</th>\n",
       "      <th>Big Ten Conference</th>\n",
       "      <th>Big West Conference</th>\n",
       "      <th>Colonial Athletic Association</th>\n",
       "      <th>Conference USA</th>\n",
       "      <th>Horizon League</th>\n",
       "      <th>Ivy League</th>\n",
       "      <th>Metro Atlantic Athletic Conference</th>\n",
       "      <th>Mid-American Conference</th>\n",
       "      <th>Mid-Continent Conference</th>\n",
       "      <th>Mid-Eastern Athletic Conference</th>\n",
       "      <th>Missouri Valley Conference</th>\n",
       "      <th>Mountain West Conference</th>\n",
       "      <th>Northeast Conference</th>\n",
       "      <th>Ohio Valley Conference</th>\n",
       "      <th>Pacific-10 Conference</th>\n",
       "      <th>Pacific-12 Conference</th>\n",
       "      <th>Patriot League</th>\n",
       "      <th>Southeastern Conference</th>\n",
       "      <th>Southern Conference</th>\n",
       "      <th>Southland Conference</th>\n",
       "      <th>Southwest Athletic Conference</th>\n",
       "      <th>Summit League</th>\n",
       "      <th>Sun Belt Conference</th>\n",
       "      <th>West Coast Conference</th>\n",
       "      <th>Western Athletic Conference</th>\n",
       "      <th>wins</th>\n",
       "      <th>plus_minus</th>\n",
       "      <th>three_point_avg</th>\n",
       "      <th>ft_attempts_pergame</th>\n",
       "      <th>off_rebounds_pergame</th>\n",
       "      <th>turnovers_pergame</th>\n",
       "      <th>field_goals_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>1328</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>330</td>\n",
       "      <td>0.391225</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>12.133333</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.446221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>1448</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>313</td>\n",
       "      <td>0.341486</td>\n",
       "      <td>26.620690</td>\n",
       "      <td>14.758621</td>\n",
       "      <td>15.103448</td>\n",
       "      <td>0.457965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>1393</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>24</td>\n",
       "      <td>296</td>\n",
       "      <td>0.339134</td>\n",
       "      <td>23.620690</td>\n",
       "      <td>14.310345</td>\n",
       "      <td>13.620690</td>\n",
       "      <td>0.472113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>1257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>401</td>\n",
       "      <td>0.359227</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.366667</td>\n",
       "      <td>0.459027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>300</td>\n",
       "      <td>0.345604</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>15.633333</td>\n",
       "      <td>0.471577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  seed  team_id  outcome  America East Conference  \\\n",
       "0    2003     1     1328        1                        0   \n",
       "1    2003     2     1448        1                        0   \n",
       "2    2003     3     1393        1                        0   \n",
       "3    2003     4     1257        1                        0   \n",
       "4    2003     5     1280        0                        0   \n",
       "\n",
       "   American Athletic Conference  Atlantic 10 Conference  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   Atlantic Coast Conference  Atlantic Sun Conference  Big 12 Conference  \\\n",
       "0                          0                        0                  1   \n",
       "1                          1                        0                  0   \n",
       "2                          0                        0                  0   \n",
       "3                          0                        0                  0   \n",
       "4                          0                        0                  0   \n",
       "\n",
       "   Big East Conference  Big Sky Conference  Big South Conference  \\\n",
       "0                    0                   0                     0   \n",
       "1                    0                   0                     0   \n",
       "2                    1                   0                     0   \n",
       "3                    0                   0                     0   \n",
       "4                    0                   0                     0   \n",
       "\n",
       "   Big Ten Conference  Big West Conference  Colonial Athletic Association  \\\n",
       "0                   0                    0                              0   \n",
       "1                   0                    0                              0   \n",
       "2                   0                    0                              0   \n",
       "3                   0                    0                              0   \n",
       "4                   0                    0                              0   \n",
       "\n",
       "   Conference USA  Horizon League  Ivy League  \\\n",
       "0               0               0           0   \n",
       "1               0               0           0   \n",
       "2               0               0           0   \n",
       "3               1               0           0   \n",
       "4               0               0           0   \n",
       "\n",
       "   Metro Atlantic Athletic Conference  Mid-American Conference  \\\n",
       "0                                   0                        0   \n",
       "1                                   0                        0   \n",
       "2                                   0                        0   \n",
       "3                                   0                        0   \n",
       "4                                   0                        0   \n",
       "\n",
       "   Mid-Continent Conference  Mid-Eastern Athletic Conference  \\\n",
       "0                         0                                0   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   Missouri Valley Conference  Mountain West Conference  Northeast Conference  \\\n",
       "0                           0                         0                     0   \n",
       "1                           0                         0                     0   \n",
       "2                           0                         0                     0   \n",
       "3                           0                         0                     0   \n",
       "4                           0                         0                     0   \n",
       "\n",
       "   Ohio Valley Conference  Pacific-10 Conference  Pacific-12 Conference  \\\n",
       "0                       0                      0                      0   \n",
       "1                       0                      0                      0   \n",
       "2                       0                      0                      0   \n",
       "3                       0                      0                      0   \n",
       "4                       0                      0                      0   \n",
       "\n",
       "   Patriot League  Southeastern Conference  Southern Conference  \\\n",
       "0               0                        0                    0   \n",
       "1               0                        0                    0   \n",
       "2               0                        0                    0   \n",
       "3               0                        0                    0   \n",
       "4               0                        1                    0   \n",
       "\n",
       "   Southland Conference  Southwest Athletic Conference  Summit League  \\\n",
       "0                     0                              0              0   \n",
       "1                     0                              0              0   \n",
       "2                     0                              0              0   \n",
       "3                     0                              0              0   \n",
       "4                     0                              0              0   \n",
       "\n",
       "   Sun Belt Conference  West Coast Conference  Western Athletic Conference  \\\n",
       "0                    0                      0                            0   \n",
       "1                    0                      0                            0   \n",
       "2                    0                      0                            0   \n",
       "3                    0                      0                            0   \n",
       "4                    0                      0                            0   \n",
       "\n",
       "   wins  plus_minus  three_point_avg  ft_attempts_pergame  \\\n",
       "0    24         330         0.391225            18.600000   \n",
       "1    24         313         0.341486            26.620690   \n",
       "2    24         296         0.339134            23.620690   \n",
       "3    24         401         0.359227            25.100000   \n",
       "4    21         300         0.345604            17.633333   \n",
       "\n",
       "   off_rebounds_pergame  turnovers_pergame  field_goals_avg  \n",
       "0             12.133333          11.800000         0.446221  \n",
       "1             14.758621          15.103448         0.457965  \n",
       "2             14.310345          13.620690         0.472113  \n",
       "3             13.200000          13.366667         0.459027  \n",
       "4             12.666667          15.633333         0.471577  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I've made a decision to leave conference information out of my final model because I think it was shown above that in terms of first round predictions, it does not hold much weight.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data[['seed', 'outcome', 'wins', 'plus_minus', 'three_point_avg', 'ft_attempts_pergame', \n",
    "             'off_rebounds_pergame', 'turnovers_pergame', 'field_goals_avg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114edea20>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 351,
       "width": 491
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data2.corr(), annot=True, cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['season', 'team_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>seed</th>\n",
       "      <th>outcome</th>\n",
       "      <th>America East Conference</th>\n",
       "      <th>American Athletic Conference</th>\n",
       "      <th>Atlantic 10 Conference</th>\n",
       "      <th>Atlantic Coast Conference</th>\n",
       "      <th>Atlantic Sun Conference</th>\n",
       "      <th>Big 12 Conference</th>\n",
       "      <th>Big East Conference</th>\n",
       "      <th>Big Sky Conference</th>\n",
       "      <th>Big South Conference</th>\n",
       "      <th>Big Ten Conference</th>\n",
       "      <th>Big West Conference</th>\n",
       "      <th>Colonial Athletic Association</th>\n",
       "      <th>Conference USA</th>\n",
       "      <th>Horizon League</th>\n",
       "      <th>Ivy League</th>\n",
       "      <th>Metro Atlantic Athletic Conference</th>\n",
       "      <th>Mid-American Conference</th>\n",
       "      <th>Mid-Continent Conference</th>\n",
       "      <th>Mid-Eastern Athletic Conference</th>\n",
       "      <th>Missouri Valley Conference</th>\n",
       "      <th>Mountain West Conference</th>\n",
       "      <th>Northeast Conference</th>\n",
       "      <th>Ohio Valley Conference</th>\n",
       "      <th>Pacific-10 Conference</th>\n",
       "      <th>Pacific-12 Conference</th>\n",
       "      <th>Patriot League</th>\n",
       "      <th>Southeastern Conference</th>\n",
       "      <th>Southern Conference</th>\n",
       "      <th>Southland Conference</th>\n",
       "      <th>Southwest Athletic Conference</th>\n",
       "      <th>Summit League</th>\n",
       "      <th>Sun Belt Conference</th>\n",
       "      <th>West Coast Conference</th>\n",
       "      <th>Western Athletic Conference</th>\n",
       "      <th>wins</th>\n",
       "      <th>plus_minus</th>\n",
       "      <th>three_point_avg</th>\n",
       "      <th>ft_attempts_pergame</th>\n",
       "      <th>off_rebounds_pergame</th>\n",
       "      <th>turnovers_pergame</th>\n",
       "      <th>field_goals_avg</th>\n",
       "      <th>wins * plus_minus</th>\n",
       "      <th>ft_attempts * off_rebounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>330</td>\n",
       "      <td>0.391225</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>12.133333</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.446221</td>\n",
       "      <td>7920</td>\n",
       "      <td>225.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>313</td>\n",
       "      <td>0.341486</td>\n",
       "      <td>26.620690</td>\n",
       "      <td>14.758621</td>\n",
       "      <td>15.103448</td>\n",
       "      <td>0.457965</td>\n",
       "      <td>7512</td>\n",
       "      <td>392.884661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>296</td>\n",
       "      <td>0.339134</td>\n",
       "      <td>23.620690</td>\n",
       "      <td>14.310345</td>\n",
       "      <td>13.620690</td>\n",
       "      <td>0.472113</td>\n",
       "      <td>7104</td>\n",
       "      <td>338.020214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>401</td>\n",
       "      <td>0.359227</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.366667</td>\n",
       "      <td>0.459027</td>\n",
       "      <td>9624</td>\n",
       "      <td>331.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>300</td>\n",
       "      <td>0.345604</td>\n",
       "      <td>17.633333</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>15.633333</td>\n",
       "      <td>0.471577</td>\n",
       "      <td>6300</td>\n",
       "      <td>223.355556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seed  outcome  America East Conference  American Athletic Conference  \\\n",
       "0     1        1                        0                             0   \n",
       "1     2        1                        0                             0   \n",
       "2     3        1                        0                             0   \n",
       "3     4        1                        0                             0   \n",
       "4     5        0                        0                             0   \n",
       "\n",
       "   Atlantic 10 Conference  Atlantic Coast Conference  Atlantic Sun Conference  \\\n",
       "0                       0                          0                        0   \n",
       "1                       0                          1                        0   \n",
       "2                       0                          0                        0   \n",
       "3                       0                          0                        0   \n",
       "4                       0                          0                        0   \n",
       "\n",
       "   Big 12 Conference  Big East Conference  Big Sky Conference  \\\n",
       "0                  1                    0                   0   \n",
       "1                  0                    0                   0   \n",
       "2                  0                    1                   0   \n",
       "3                  0                    0                   0   \n",
       "4                  0                    0                   0   \n",
       "\n",
       "   Big South Conference  Big Ten Conference  Big West Conference  \\\n",
       "0                     0                   0                    0   \n",
       "1                     0                   0                    0   \n",
       "2                     0                   0                    0   \n",
       "3                     0                   0                    0   \n",
       "4                     0                   0                    0   \n",
       "\n",
       "   Colonial Athletic Association  Conference USA  Horizon League  Ivy League  \\\n",
       "0                              0               0               0           0   \n",
       "1                              0               0               0           0   \n",
       "2                              0               0               0           0   \n",
       "3                              0               1               0           0   \n",
       "4                              0               0               0           0   \n",
       "\n",
       "   Metro Atlantic Athletic Conference  Mid-American Conference  \\\n",
       "0                                   0                        0   \n",
       "1                                   0                        0   \n",
       "2                                   0                        0   \n",
       "3                                   0                        0   \n",
       "4                                   0                        0   \n",
       "\n",
       "   Mid-Continent Conference  Mid-Eastern Athletic Conference  \\\n",
       "0                         0                                0   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   Missouri Valley Conference  Mountain West Conference  Northeast Conference  \\\n",
       "0                           0                         0                     0   \n",
       "1                           0                         0                     0   \n",
       "2                           0                         0                     0   \n",
       "3                           0                         0                     0   \n",
       "4                           0                         0                     0   \n",
       "\n",
       "   Ohio Valley Conference  Pacific-10 Conference  Pacific-12 Conference  \\\n",
       "0                       0                      0                      0   \n",
       "1                       0                      0                      0   \n",
       "2                       0                      0                      0   \n",
       "3                       0                      0                      0   \n",
       "4                       0                      0                      0   \n",
       "\n",
       "   Patriot League  Southeastern Conference  Southern Conference  \\\n",
       "0               0                        0                    0   \n",
       "1               0                        0                    0   \n",
       "2               0                        0                    0   \n",
       "3               0                        0                    0   \n",
       "4               0                        1                    0   \n",
       "\n",
       "   Southland Conference  Southwest Athletic Conference  Summit League  \\\n",
       "0                     0                              0              0   \n",
       "1                     0                              0              0   \n",
       "2                     0                              0              0   \n",
       "3                     0                              0              0   \n",
       "4                     0                              0              0   \n",
       "\n",
       "   Sun Belt Conference  West Coast Conference  Western Athletic Conference  \\\n",
       "0                    0                      0                            0   \n",
       "1                    0                      0                            0   \n",
       "2                    0                      0                            0   \n",
       "3                    0                      0                            0   \n",
       "4                    0                      0                            0   \n",
       "\n",
       "   wins  plus_minus  three_point_avg  ft_attempts_pergame  \\\n",
       "0    24         330         0.391225            18.600000   \n",
       "1    24         313         0.341486            26.620690   \n",
       "2    24         296         0.339134            23.620690   \n",
       "3    24         401         0.359227            25.100000   \n",
       "4    21         300         0.345604            17.633333   \n",
       "\n",
       "   off_rebounds_pergame  turnovers_pergame  field_goals_avg  \\\n",
       "0             12.133333          11.800000         0.446221   \n",
       "1             14.758621          15.103448         0.457965   \n",
       "2             14.310345          13.620690         0.472113   \n",
       "3             13.200000          13.366667         0.459027   \n",
       "4             12.666667          15.633333         0.471577   \n",
       "\n",
       "   wins * plus_minus  ft_attempts * off_rebounds  \n",
       "0               7920                  225.680000  \n",
       "1               7512                  392.884661  \n",
       "2               7104                  338.020214  \n",
       "3               9624                  331.320000  \n",
       "4               6300                  223.355556  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['wins * plus_minus'] = data['wins'] * data['plus_minus']\n",
    "data['ft_attempts * off_rebounds'] = data['ft_attempts_pergame'] * data['off_rebounds_pergame']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [columns for columns in data if columns != 'outcome']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data[features], data['outcome'], test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seed</th>\n",
       "      <th>America East Conference</th>\n",
       "      <th>American Athletic Conference</th>\n",
       "      <th>Atlantic 10 Conference</th>\n",
       "      <th>Atlantic Coast Conference</th>\n",
       "      <th>Atlantic Sun Conference</th>\n",
       "      <th>Big 12 Conference</th>\n",
       "      <th>Big East Conference</th>\n",
       "      <th>Big Sky Conference</th>\n",
       "      <th>Big South Conference</th>\n",
       "      <th>Big Ten Conference</th>\n",
       "      <th>Big West Conference</th>\n",
       "      <th>Colonial Athletic Association</th>\n",
       "      <th>Conference USA</th>\n",
       "      <th>Horizon League</th>\n",
       "      <th>Ivy League</th>\n",
       "      <th>Metro Atlantic Athletic Conference</th>\n",
       "      <th>Mid-American Conference</th>\n",
       "      <th>Mid-Continent Conference</th>\n",
       "      <th>Mid-Eastern Athletic Conference</th>\n",
       "      <th>Missouri Valley Conference</th>\n",
       "      <th>Mountain West Conference</th>\n",
       "      <th>Northeast Conference</th>\n",
       "      <th>Ohio Valley Conference</th>\n",
       "      <th>Pacific-10 Conference</th>\n",
       "      <th>Pacific-12 Conference</th>\n",
       "      <th>Patriot League</th>\n",
       "      <th>Southeastern Conference</th>\n",
       "      <th>Southern Conference</th>\n",
       "      <th>Southland Conference</th>\n",
       "      <th>Southwest Athletic Conference</th>\n",
       "      <th>Summit League</th>\n",
       "      <th>Sun Belt Conference</th>\n",
       "      <th>West Coast Conference</th>\n",
       "      <th>Western Athletic Conference</th>\n",
       "      <th>wins</th>\n",
       "      <th>plus_minus</th>\n",
       "      <th>three_point_avg</th>\n",
       "      <th>ft_attempts_pergame</th>\n",
       "      <th>off_rebounds_pergame</th>\n",
       "      <th>turnovers_pergame</th>\n",
       "      <th>field_goals_avg</th>\n",
       "      <th>wins * plus_minus</th>\n",
       "      <th>ft_attempts * off_rebounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>15</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>261</td>\n",
       "      <td>0.400969</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>16.366667</td>\n",
       "      <td>0.525545</td>\n",
       "      <td>6264</td>\n",
       "      <td>282.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>27</td>\n",
       "      <td>326</td>\n",
       "      <td>0.313890</td>\n",
       "      <td>22.156250</td>\n",
       "      <td>10.406250</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>0.448231</td>\n",
       "      <td>8802</td>\n",
       "      <td>230.563477</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>268</td>\n",
       "      <td>0.354477</td>\n",
       "      <td>19.878788</td>\n",
       "      <td>12.060606</td>\n",
       "      <td>11.424242</td>\n",
       "      <td>0.448859</td>\n",
       "      <td>7236</td>\n",
       "      <td>239.750230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>204</td>\n",
       "      <td>0.373894</td>\n",
       "      <td>24.419355</td>\n",
       "      <td>14.645161</td>\n",
       "      <td>14.967742</td>\n",
       "      <td>0.433790</td>\n",
       "      <td>4284</td>\n",
       "      <td>357.625390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.381405</td>\n",
       "      <td>26.129032</td>\n",
       "      <td>13.419355</td>\n",
       "      <td>13.806452</td>\n",
       "      <td>0.465872</td>\n",
       "      <td>7200</td>\n",
       "      <td>350.634755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     seed  America East Conference  American Athletic Conference  \\\n",
       "302    15                        0                             0   \n",
       "467     4                        0                             0   \n",
       "294     7                        0                             0   \n",
       "548     5                        0                             0   \n",
       "465     2                        0                             0   \n",
       "\n",
       "     Atlantic 10 Conference  Atlantic Coast Conference  \\\n",
       "302                       0                          0   \n",
       "467                       0                          0   \n",
       "294                       0                          0   \n",
       "548                       0                          0   \n",
       "465                       0                          0   \n",
       "\n",
       "     Atlantic Sun Conference  Big 12 Conference  Big East Conference  \\\n",
       "302                        0                  0                    0   \n",
       "467                        0                  0                    0   \n",
       "294                        0                  0                    0   \n",
       "548                        0                  1                    0   \n",
       "465                        0                  0                    1   \n",
       "\n",
       "     Big Sky Conference  Big South Conference  Big Ten Conference  \\\n",
       "302                   0                     0                   0   \n",
       "467                   0                     0                   1   \n",
       "294                   0                     0                   0   \n",
       "548                   0                     0                   0   \n",
       "465                   0                     0                   0   \n",
       "\n",
       "     Big West Conference  Colonial Athletic Association  Conference USA  \\\n",
       "302                    0                              0               0   \n",
       "467                    0                              0               0   \n",
       "294                    0                              0               0   \n",
       "548                    0                              0               0   \n",
       "465                    0                              0               0   \n",
       "\n",
       "     Horizon League  Ivy League  Metro Atlantic Athletic Conference  \\\n",
       "302               0           0                                   0   \n",
       "467               0           0                                   0   \n",
       "294               0           0                                   0   \n",
       "548               0           0                                   0   \n",
       "465               0           0                                   0   \n",
       "\n",
       "     Mid-American Conference  Mid-Continent Conference  \\\n",
       "302                        0                         0   \n",
       "467                        0                         0   \n",
       "294                        0                         0   \n",
       "548                        0                         0   \n",
       "465                        0                         0   \n",
       "\n",
       "     Mid-Eastern Athletic Conference  Missouri Valley Conference  \\\n",
       "302                                0                           0   \n",
       "467                                0                           0   \n",
       "294                                0                           0   \n",
       "548                                0                           0   \n",
       "465                                0                           0   \n",
       "\n",
       "     Mountain West Conference  Northeast Conference  Ohio Valley Conference  \\\n",
       "302                         0                     0                       0   \n",
       "467                         0                     0                       0   \n",
       "294                         1                     0                       0   \n",
       "548                         0                     0                       0   \n",
       "465                         0                     0                       0   \n",
       "\n",
       "     Pacific-10 Conference  Pacific-12 Conference  Patriot League  \\\n",
       "302                      0                      0               0   \n",
       "467                      0                      0               0   \n",
       "294                      0                      0               0   \n",
       "548                      0                      0               0   \n",
       "465                      0                      0               0   \n",
       "\n",
       "     Southeastern Conference  Southern Conference  Southland Conference  \\\n",
       "302                        0                    0                     1   \n",
       "467                        0                    0                     0   \n",
       "294                        0                    0                     0   \n",
       "548                        0                    0                     0   \n",
       "465                        0                    0                     0   \n",
       "\n",
       "     Southwest Athletic Conference  Summit League  Sun Belt Conference  \\\n",
       "302                              0              0                    0   \n",
       "467                              0              0                    0   \n",
       "294                              0              0                    0   \n",
       "548                              0              0                    0   \n",
       "465                              0              0                    0   \n",
       "\n",
       "     West Coast Conference  Western Athletic Conference  wins  plus_minus  \\\n",
       "302                      0                            0    24         261   \n",
       "467                      0                            0    27         326   \n",
       "294                      0                            0    27         268   \n",
       "548                      0                            0    21         204   \n",
       "465                      0                            0    24         300   \n",
       "\n",
       "     three_point_avg  ft_attempts_pergame  off_rebounds_pergame  \\\n",
       "302         0.400969            25.200000             11.200000   \n",
       "467         0.313890            22.156250             10.406250   \n",
       "294         0.354477            19.878788             12.060606   \n",
       "548         0.373894            24.419355             14.645161   \n",
       "465         0.381405            26.129032             13.419355   \n",
       "\n",
       "     turnovers_pergame  field_goals_avg  wins * plus_minus  \\\n",
       "302          16.366667         0.525545               6264   \n",
       "467          10.875000         0.448231               8802   \n",
       "294          11.424242         0.448859               7236   \n",
       "548          14.967742         0.433790               4284   \n",
       "465          13.806452         0.465872               7200   \n",
       "\n",
       "     ft_attempts * off_rebounds  \n",
       "302                  282.240000  \n",
       "467                  230.563477  \n",
       "294                  239.750230  \n",
       "548                  357.625390  \n",
       "465                  350.634755  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "mapper = DataFrameMapper([\n",
    "    (['seed'], StandardScaler()),\n",
    "    (['wins'], StandardScaler()),\n",
    "    (['plus_minus'], StandardScaler()),\n",
    "    (['three_point_avg'], StandardScaler()),\n",
    "    (['ft_attempts_pergame'], StandardScaler()),\n",
    "    (['off_rebounds_pergame'], StandardScaler()),\n",
    "    (['turnovers_pergame'], StandardScaler()),\n",
    "    (['field_goals_avg'], StandardScaler()),\n",
    "    (['wins * plus_minus'], StandardScaler()),\n",
    "    (['ft_attempts * off_rebounds'], StandardScaler())\n",
    "    ], \n",
    "    df_out=True)\n",
    "\n",
    "mapper.fit(X_train)\n",
    "Z_train = mapper.transform(X_train)\n",
    "Z_test = mapper.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>seed</th>\n",
       "      <th>wins</th>\n",
       "      <th>plus_minus</th>\n",
       "      <th>three_point_avg</th>\n",
       "      <th>ft_attempts_pergame</th>\n",
       "      <th>off_rebounds_pergame</th>\n",
       "      <th>turnovers_pergame</th>\n",
       "      <th>field_goals_avg</th>\n",
       "      <th>wins * plus_minus</th>\n",
       "      <th>ft_attempts * off_rebounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>-0.752206</td>\n",
       "      <td>0.570497</td>\n",
       "      <td>1.288635</td>\n",
       "      <td>1.870318</td>\n",
       "      <td>-0.759658</td>\n",
       "      <td>0.251468</td>\n",
       "      <td>0.429086</td>\n",
       "      <td>1.894125</td>\n",
       "      <td>1.115074</td>\n",
       "      <td>-0.296514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>1.189261</td>\n",
       "      <td>-0.610478</td>\n",
       "      <td>-0.306300</td>\n",
       "      <td>0.400581</td>\n",
       "      <td>0.547645</td>\n",
       "      <td>0.253922</td>\n",
       "      <td>0.297396</td>\n",
       "      <td>0.435539</td>\n",
       "      <td>-0.441390</td>\n",
       "      <td>0.433750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>1.404980</td>\n",
       "      <td>-0.019990</td>\n",
       "      <td>-0.645962</td>\n",
       "      <td>0.846297</td>\n",
       "      <td>-0.662937</td>\n",
       "      <td>-0.155867</td>\n",
       "      <td>-1.872291</td>\n",
       "      <td>-0.094892</td>\n",
       "      <td>-0.599328</td>\n",
       "      <td>-0.483494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>1.189261</td>\n",
       "      <td>-0.610478</td>\n",
       "      <td>-1.214527</td>\n",
       "      <td>-0.490398</td>\n",
       "      <td>2.154258</td>\n",
       "      <td>0.700753</td>\n",
       "      <td>-0.476315</td>\n",
       "      <td>-0.622167</td>\n",
       "      <td>-1.084852</td>\n",
       "      <td>1.699754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>0.757824</td>\n",
       "      <td>-0.315234</td>\n",
       "      <td>-0.055245</td>\n",
       "      <td>0.686244</td>\n",
       "      <td>-2.662492</td>\n",
       "      <td>-1.905861</td>\n",
       "      <td>-1.747680</td>\n",
       "      <td>-0.480804</td>\n",
       "      <td>-0.202241</td>\n",
       "      <td>-2.269273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         seed      wins  plus_minus  three_point_avg  ft_attempts_pergame  \\\n",
       "836 -0.752206  0.570497    1.288635         1.870318            -0.759658   \n",
       "477  1.189261 -0.610478   -0.306300         0.400581             0.547645   \n",
       "350  1.404980 -0.019990   -0.645962         0.846297            -0.662937   \n",
       "893  1.189261 -0.610478   -1.214527        -0.490398             2.154258   \n",
       "923  0.757824 -0.315234   -0.055245         0.686244            -2.662492   \n",
       "\n",
       "     off_rebounds_pergame  turnovers_pergame  field_goals_avg  \\\n",
       "836              0.251468           0.429086         1.894125   \n",
       "477              0.253922           0.297396         0.435539   \n",
       "350             -0.155867          -1.872291        -0.094892   \n",
       "893              0.700753          -0.476315        -0.622167   \n",
       "923             -1.905861          -1.747680        -0.480804   \n",
       "\n",
       "     wins * plus_minus  ft_attempts * off_rebounds  \n",
       "836           1.115074                   -0.296514  \n",
       "477          -0.441390                    0.433750  \n",
       "350          -0.599328                   -0.483494  \n",
       "893          -1.084852                    1.699754  \n",
       "923          -0.202241                   -2.269273  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7578125"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(Z_train, y_train)\n",
    "logreg.score(Z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = logreg.predict(Z_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7552083333333334"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(Z_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Intercept: [-0.01475076]\n",
      "Logistic Regression Coefficient: [[-1.22714447 -0.21356653  0.46504265 -0.13873001 -0.18505592  0.01932104\n",
      "  -0.3301426   0.14150921  0.14132697  0.26430461]]\n"
     ]
    }
   ],
   "source": [
    "print(f'Logistic Regression Intercept: {logreg.intercept_}')\n",
    "print(f'Logistic Regression Coefficient: {logreg.coef_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[74, 21],\n",
       "       [26, 71]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = metrics.confusion_matrix(y_test, y_hat)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explorative. Looking at other models and tuning hyper parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6991681521093287"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knn, Z_train, y_train, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(Z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.671875"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(Z_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  60 out of  60 | elapsed:    3.9s finished\n"
     ]
    }
   ],
   "source": [
    "knn_params = {\n",
    "    'n_neighbors':[11,13,15,17,19,21],\n",
    "    'weights':['uniform','distance'],\n",
    "    'metric':['euclidean']\n",
    "}\n",
    "\n",
    "knn_gridsearch = GridSearchCV(KNeighborsClassifier(), knn_params, cv=5, verbose=1, n_jobs=2)\n",
    "\n",
    "knn_gridsearch = knn_gridsearch.fit(Z_train, y_train)\n",
    "\n",
    "##started with lower number, but kept choosing highest n_neighbor as param, so increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'euclidean', 'n_neighbors': 17, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.71484375"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_gridsearch.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6875"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_knn = knn_gridsearch.best_estimator_\n",
    "best_knn.score(Z_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 10)\n",
      "(768, 5)\n"
     ]
    }
   ],
   "source": [
    "select = SelectPercentile(percentile=50)\n",
    "select.fit(Z_train, y_train)\n",
    "\n",
    "\n",
    "Z_train_selected = select.transform(Z_train)\n",
    "\n",
    "print(Z_train.shape)\n",
    "print(Z_train_selected.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "F, p = f_classif(Z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1b079390>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 244,
       "width": 409
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(p, 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the P-values associated with each feature. The striking plot-point is number 4 which clearly has a much higher P-value than the other features selected. This indicates that free throw attempts is not an indicative informative feature for our target. \n",
    "\n",
    "*Important to note, however, that the poly feature of free throw attempts and offensive rebounds (number 9) has a drastically lower P-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True False False False  True False  True False]\n"
     ]
    }
   ],
   "source": [
    "mask = select.get_support()\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "skb = SelectKBest(k=7)\n",
    "skb.fit(Z_train, y_train)\n",
    "\n",
    "skb.transform(Z_train).shape\n",
    "\n",
    "X_train_selected = skb.transform(Z_train)\n",
    "X_test_selected = skb.transform(Z_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7578125"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(Z_train, y_train)\n",
    "lr.score(Z_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7552083333333334"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Z_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True False False False  True False  True False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a1ba469e8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 116,
       "width": 935
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = select.get_support()\n",
    "print(mask)\n",
    "# visualize the mask. black is True, white is False\n",
    "plt.matshow(mask.reshape(1, -1), cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a1bdddb00>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 116,
       "width": 935
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "select = RFE(RandomForestClassifier(n_estimators=100, random_state=42), n_features_to_select=3)\n",
    "\n",
    "select.fit(Z_train, y_train)\n",
    "# visualize the selected features:\n",
    "mask = select.get_support()\n",
    "plt.matshow(mask.reshape(1, -1), cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.765625"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_train_rfe = select.transform(Z_train)\n",
    "Z_test_rfe = select.transform(Z_test)\n",
    "\n",
    "LogisticRegression().fit(Z_train_rfe, y_train).score(Z_test_rfe, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Recursive Feature Elimination scored the best model with a test score at .7656\n",
    "\n",
    "        \n",
    "The RFE narrowed the best predictors to: \n",
    "        \n",
    "        -seed \n",
    "        -plus/minus \n",
    "        -wins and plus/minus (poly-feature)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(mapper, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['dataframemapper', 'logisticregression'])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.named_steps.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('dataframemapper', DataFrameMapper(default=False, df_out=True,\n",
       "        features=[(['seed'], StandardScaler(copy=True, with_mean=True, with_std=True)), (['wins'], StandardScaler(copy=True, with_mean=True, with_std=True)), (['plus_minus'], StandardScaler(copy=True, with_mean=True, with_std=Tru...enalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.22714955, -0.2134791 ,  0.46521748, -0.13872479, -0.18514114,\n",
       "         0.01923034, -0.33014298,  0.14150379,  0.14105549,  0.26444987]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.named_steps['logisticregression'].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('logisticregression',\n",
       " LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "           n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
       "           tol=0.0001, verbose=0, warm_start=False))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.steps[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The best model ended up being a logistic regresstion, using the Recursive Feature Elimination tool. With this method, the process of dropping features and model building was repeated until only 3 elements were left: 'seed', 'plus/minus',  and 'wins * plus/minus'. It can be seen from looking at most of the data and the modeling that the seeding remains the most important predictor. Our first naive model scored .746 on the training data and .755 on the test data. The second model, which incorporated conferences with seeds, the training data scored .743 and the test data .76. The final model using Recursive Feature Elimination only slightly outscored the second model. This is all to say, that as was said before, most of the teams that are seeded between 5-12 have similar season statistics so predicting who will win these games is extremely challenging. Every team that qualifies for the March Madness tournament has impecable records, so to qualify above 12, means that your team most likely only lost a handfull of times, and is extremely competitive.\n",
    "\n",
    "Although I failed to make a model that could make amazing predictions (and make you money!), I am not completely shocked by this. The NCAA seedings are highly reflective of regular season stats, and the conferences that they occurred in. It would be shocking to me if incorporating those statistics could out predict NCAA's own analysis of each team's worth by any LARGE margin (I was hopeful for an .8 though!).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Taking it further ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I continue working on this project, I would like to consider the following:\n",
    "    \n",
    "   -I would solely focus on teams seeded between 5 and 12. These are the interesting match ups that are hard to predict.\n",
    "   \n",
    "   -I would also like to incorporate the performance of the team during the previous tournament. If teams did well in the previous year and have returning players, this type of experience is significant.\n",
    "   \n",
    "   -I would look in to more information on non-regular season games, including which teams won their conferences, and which teams played eachother in early season tournaments. Teams from different conferences often play eachother in early season tournaments, and those outcomes could aid in predicting first round matchups, and in attempting to measure the strengths of different conferences."
   ]
  }
 ],
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