rf_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ee4b504",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "pd.options.display.max_rows = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b8e22914",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "\n",
    "db_name = 'db_name'\n",
    "db_password = 'db_password'\n",
    "db_string = f\"postgresql://postgres:{db_password}@127.0.0.1:5432/{db_name}\"\n",
    "engine = create_engine(db_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7fc946",
   "metadata": {},
   "source": [
    "# Random Forest Classifier Model\n",
    "\n",
    "**Ultimatley we want our model to train with aggregated season data and a season average of the Massey Ordinal Rankings (https://masseyratings.com/faq.php)-**\n",
    "\n",
    "**Question: Will Team1 win or lose Team2**\n",
    "\n",
    "**Can we train our model w featured data to predict outcomes**\n",
    "\n",
    "**This random forest model is in a format we went over in the course;\n",
    "It can provide a sufficient model; while we evaluate other methods**\n",
    "\n",
    "Classification in random forests employs an ensemble methodology to attain the outcome. The training data is fed to train various decision trees. This dataset consists of observations and features that will be selected randomly during the splitting of nodes.Random Forest is based on the bagging algorithm and is Ensemble Learning. It creates as many trees on the subset of the data and combines the output of all the trees.\n",
    "\n",
    "\n",
    "**Pros: Reduces overfitting, reduces the variance and therefore improves the accuracy**\n",
    "\n",
    "**Cons: By default, it creates 100 trees in Python sklearn library. To do so, this algorithm requires much more computational power and resources**\n",
    "\n",
    "\n",
    "We have a base model we can weigh features differently and incoporate more data sources;\n",
    "\n",
    "\n",
    "Ideas we want to try hopefully;\n",
    "\n",
    "Incorporating; Stength of Schedule\n",
    "\n",
    "or Stength of Conference/\n",
    "\n",
    "DayNum/ Rankings?,\n",
    "\n",
    "DataRange Filter By Season(Does more season data improve/ less?,\n",
    "\n",
    "SeedingData,\n",
    "\n",
    "Last14DayForm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4370f66",
   "metadata": {},
   "source": [
    "# **Aggregate_Season Stats / Sets of Features for our Model//**\n",
    "\n",
    "## Basic Offensive/Defensive Statistical Data - Split into winning and losing team \n",
    "\n",
    "Wins, Losses, \n",
    "\n",
    "Win/ Loss Location,\n",
    "\n",
    "Winning/Losing Field Goals Made,\n",
    "\n",
    "Winning/Losing Field Goal Attempts,\n",
    "\n",
    "Winning/Losing Field Goal 3pters Made,\n",
    "\n",
    "Winning/Losing Field Goal 3pter Attemtpts,\n",
    "\n",
    "Winning/Losing Free Throws Made,\n",
    "\n",
    "Winning/Losing Free Throws Attempted,\n",
    "\n",
    "Winning/Losing Offensive Rebounds,\n",
    "\n",
    "Winning/Losing Defensive Rebounds,\n",
    "\n",
    "Winning/Losing Assists,\n",
    "\n",
    "Winning/Losing Stls,\n",
    "\n",
    "Winning/Losing Blocks,\n",
    "\n",
    "Winning/Losing Personal Fouls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11f86306",
   "metadata": {},
   "outputs": [
    {
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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>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fgm3</th>\n",
       "      <th>fga3</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>or</th>\n",
       "      <th>dr</th>\n",
       "      <th>ast</th>\n",
       "      <th>to</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>pf</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1102</td>\n",
       "      <td>21.105114</td>\n",
       "      <td>47.056818</td>\n",
       "      <td>6.332386</td>\n",
       "      <td>18.954545</td>\n",
       "      <td>12.744318</td>\n",
       "      <td>18.522727</td>\n",
       "      <td>5.931818</td>\n",
       "      <td>22.704545</td>\n",
       "      <td>13.653409</td>\n",
       "      <td>13.028409</td>\n",
       "      <td>6.417614</td>\n",
       "      <td>2.968750</td>\n",
       "      <td>16.701705</td>\n",
       "      <td>20121102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1103</td>\n",
       "      <td>24.487013</td>\n",
       "      <td>54.287879</td>\n",
       "      <td>5.841991</td>\n",
       "      <td>15.878788</td>\n",
       "      <td>15.004329</td>\n",
       "      <td>21.606061</td>\n",
       "      <td>11.127706</td>\n",
       "      <td>23.138528</td>\n",
       "      <td>13.439394</td>\n",
       "      <td>14.781385</td>\n",
       "      <td>7.346320</td>\n",
       "      <td>4.151515</td>\n",
       "      <td>18.887446</td>\n",
       "      <td>20121103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1104</td>\n",
       "      <td>23.281385</td>\n",
       "      <td>52.194805</td>\n",
       "      <td>3.980519</td>\n",
       "      <td>14.357143</td>\n",
       "      <td>12.677489</td>\n",
       "      <td>17.967532</td>\n",
       "      <td>10.575758</td>\n",
       "      <td>23.939394</td>\n",
       "      <td>12.214286</td>\n",
       "      <td>12.887446</td>\n",
       "      <td>6.913420</td>\n",
       "      <td>3.924242</td>\n",
       "      <td>18.634199</td>\n",
       "      <td>20121104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1105</td>\n",
       "      <td>22.342857</td>\n",
       "      <td>52.633333</td>\n",
       "      <td>5.395238</td>\n",
       "      <td>15.414286</td>\n",
       "      <td>14.266667</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>11.409524</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>11.142857</td>\n",
       "      <td>16.014286</td>\n",
       "      <td>6.780952</td>\n",
       "      <td>4.380952</td>\n",
       "      <td>21.066667</td>\n",
       "      <td>20121105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>1106</td>\n",
       "      <td>21.078947</td>\n",
       "      <td>54.852632</td>\n",
       "      <td>6.047368</td>\n",
       "      <td>19.497368</td>\n",
       "      <td>11.052632</td>\n",
       "      <td>19.121053</td>\n",
       "      <td>12.813158</td>\n",
       "      <td>22.555263</td>\n",
       "      <td>11.484211</td>\n",
       "      <td>14.878947</td>\n",
       "      <td>7.652632</td>\n",
       "      <td>3.192105</td>\n",
       "      <td>19.444737</td>\n",
       "      <td>20121106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  team_id        fgm        fga      fgm3       fga3        ftm  \\\n",
       "0    2012     1102  21.105114  47.056818  6.332386  18.954545  12.744318   \n",
       "1    2012     1103  24.487013  54.287879  5.841991  15.878788  15.004329   \n",
       "2    2012     1104  23.281385  52.194805  3.980519  14.357143  12.677489   \n",
       "3    2012     1105  22.342857  52.633333  5.395238  15.414286  14.266667   \n",
       "4    2012     1106  21.078947  54.852632  6.047368  19.497368  11.052632   \n",
       "\n",
       "         fta         or         dr        ast         to       stl       blk  \\\n",
       "0  18.522727   5.931818  22.704545  13.653409  13.028409  6.417614  2.968750   \n",
       "1  21.606061  11.127706  23.138528  13.439394  14.781385  7.346320  4.151515   \n",
       "2  17.967532  10.575758  23.939394  12.214286  12.887446  6.913420  3.924242   \n",
       "3  20.900000  11.409524  23.400000  11.142857  16.014286  6.780952  4.380952   \n",
       "4  19.121053  12.813158  22.555263  11.484211  14.878947  7.652632  3.192105   \n",
       "\n",
       "          pf        id  \n",
       "0  16.701705  20121102  \n",
       "1  18.887446  20121103  \n",
       "2  18.634199  20121104  \n",
       "3  21.066667  20121105  \n",
       "4  19.444737  20121106  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_results = pd.read_sql(\"select * from mregular_season_detailed_results\", engine)\n",
    "season_results = season_results.copy()\n",
    "season_results = season_results[season_results['season']>=2012].reset_index(drop=True)\n",
    "\n",
    "winning_cols = ['season', 'wteam_id', 'wloc', 'wfgm', 'wfga', 'wfgm3', 'wfga3', 'wftm', 'wfta', 'wor', 'wdr', 'wast', 'wto', 'wstl', 'wblk', 'wpf']\n",
    "losing_cols = ['season', 'lteam_id', 'lfgm', 'lfga', 'lfgm3', 'lfga3', 'lftm', 'lfta', 'lor', 'ldr', 'last', 'lto', 'lstl', 'lblk', 'lpf']\n",
    "columns = ['season', 'team_id', 'fgm', 'fga', 'fgm3', 'fga3', 'ftm', 'fta', 'or', 'dr', 'ast', 'to', 'stl', 'blk', 'pf']\n",
    "\n",
    "winning_stats = season_results[winning_cols].groupby(['season', 'wteam_id']).mean().reset_index()\n",
    "winning_stats.columns = columns\n",
    "\n",
    "losing_stats = season_results[losing_cols].groupby(['season', 'lteam_id']).mean().reset_index()\n",
    "losing_stats.columns = columns\n",
    "\n",
    "reg_season_stats = pd.concat([winning_stats, losing_stats]).groupby(['season', 'team_id']).mean().reset_index()\n",
    "reg_season_stats['id'] = reg_season_stats.season.astype(str) + reg_season_stats.team_id.astype(str)\n",
    "\n",
    "\n",
    "reg_season_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b52190",
   "metadata": {},
   "source": [
    "## Add Massey Ranks and aggregate the Ordinal Rank into Mean, Min, Max \n",
    "\n",
    "Massey Ordinal Rank gives us a team rank for every DayNum in the College Basketball Season.\n",
    "\n",
    "**In addition to using reg_season_stats/ statistics for each team- We will use Massey's Ordinal Rank to provide a strength indicator for our model;**\n",
    "\n",
    "The \"Massey Ordinal Rank \" This file lists out rankings (e.g. #1, #2, #3, ..., #N) of teams going back to the 2002-2003 season, under a large number of different ranking system methodologies. The information was gathered by Kenneth Massey and provided on his College Basketball Ranking Composite page. https://www.masseyratings.com/cb/compare.htm\n",
    "\n",
    " \n",
    "**Pros: Instead of training from DayNum and ranking by day throughout the year; take min, max, avg. by 'Season' & 'TeamID' and train model on massey ordinal rankings by ex: Season(2010) and TeamID. --Is a quick efficient way to snapshot a teams rank by yr**\n",
    " \n",
    " Kaggle also admits their rankings are submitted at different days of the week sometimes behind games etc\n",
    " \n",
    " Another reason to take a teams average rank(Ordinal Rank)\n",
    " https://www.kaggle.com/competitions/mens-march-mania-2022/data\n",
    " \n",
    " **Cons: \n",
    " High Seeded Teams/ Cinderella teams where rankings are low/ weighing them down ?,**\n",
    " \n",
    " **Taking a min, max, avg ranking is again weighing teams outside of top 25 heavily down, we would assume**\n",
    " \n",
    "**Also this is an order rank, if we had a rating rank that could differentiate teams better by strength, that would improve the model we think**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82c788d7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team_id</th>\n",
       "      <th>ordinal_rank_mean</th>\n",
       "      <th>ordinal_rank_min</th>\n",
       "      <th>ordinal_rank_max</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1102</td>\n",
       "      <td>158.029289</td>\n",
       "      <td>74</td>\n",
       "      <td>290</td>\n",
       "      <td>20121102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1103</td>\n",
       "      <td>84.768480</td>\n",
       "      <td>20</td>\n",
       "      <td>234</td>\n",
       "      <td>20121103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1104</td>\n",
       "      <td>29.412807</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>20121104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1105</td>\n",
       "      <td>321.891213</td>\n",
       "      <td>62</td>\n",
       "      <td>345</td>\n",
       "      <td>20121105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>1106</td>\n",
       "      <td>308.788006</td>\n",
       "      <td>228</td>\n",
       "      <td>344</td>\n",
       "      <td>20121106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>2022</td>\n",
       "      <td>1468</td>\n",
       "      <td>206.520604</td>\n",
       "      <td>113</td>\n",
       "      <td>332</td>\n",
       "      <td>20221468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>2022</td>\n",
       "      <td>1469</td>\n",
       "      <td>271.579670</td>\n",
       "      <td>175</td>\n",
       "      <td>342</td>\n",
       "      <td>20221469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3865</th>\n",
       "      <td>2022</td>\n",
       "      <td>1470</td>\n",
       "      <td>220.846154</td>\n",
       "      <td>122</td>\n",
       "      <td>345</td>\n",
       "      <td>20221470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3866</th>\n",
       "      <td>2022</td>\n",
       "      <td>1471</td>\n",
       "      <td>238.221154</td>\n",
       "      <td>34</td>\n",
       "      <td>313</td>\n",
       "      <td>20221471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>2022</td>\n",
       "      <td>1472</td>\n",
       "      <td>285.106443</td>\n",
       "      <td>114</td>\n",
       "      <td>358</td>\n",
       "      <td>20221472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3868 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season  team_id  ordinal_rank_mean  ordinal_rank_min  ordinal_rank_max  \\\n",
       "0       2012     1102         158.029289                74               290   \n",
       "1       2012     1103          84.768480                20               234   \n",
       "2       2012     1104          29.412807                 2                66   \n",
       "3       2012     1105         321.891213                62               345   \n",
       "4       2012     1106         308.788006               228               344   \n",
       "...      ...      ...                ...               ...               ...   \n",
       "3863    2022     1468         206.520604               113               332   \n",
       "3864    2022     1469         271.579670               175               342   \n",
       "3865    2022     1470         220.846154               122               345   \n",
       "3866    2022     1471         238.221154                34               313   \n",
       "3867    2022     1472         285.106443               114               358   \n",
       "\n",
       "            id  \n",
       "0     20121102  \n",
       "1     20121103  \n",
       "2     20121104  \n",
       "3     20121105  \n",
       "4     20121106  \n",
       "...        ...  \n",
       "3863  20221468  \n",
       "3864  20221469  \n",
       "3865  20221470  \n",
       "3866  20221471  \n",
       "3867  20221472  \n",
       "\n",
       "[3868 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks = pd.read_sql(\"select * from mmassey_ordinals_thruday128\", engine)\n",
    "\n",
    "ranks = ranks[ranks['season']>=2012].reset_index(drop=True)\n",
    "\n",
    "aggregate_ranks = ranks.groupby(['season', 'team_id']).agg({'ordinal_rank': ['mean', 'min', 'max']})\n",
    "aggregate_ranks.columns = ['_'.join(col) for col in aggregate_ranks.columns]\n",
    "\n",
    "massey_ranks = aggregate_ranks.reset_index()\n",
    "massey_ranks['id'] = massey_ranks.season.astype(str) + massey_ranks.team_id.astype(str)\n",
    "massey_ranks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c0e118",
   "metadata": {},
   "source": [
    "# Combine Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "527912ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(602, 33)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "is_win                     602\n",
       "ordinal_rank_mean_teama    602\n",
       "ordinal_rank_min_teama     602\n",
       "ordinal_rank_max_teama     602\n",
       "ordinal_rank_mean_teamb    602\n",
       "ordinal_rank_min_teamb     602\n",
       "ordinal_rank_max_teamb     602\n",
       "fgm_teama                  602\n",
       "fga_teama                  602\n",
       "fgm3_teama                 602\n",
       "fga3_teama                 602\n",
       "ftm_teama                  602\n",
       "fta_teama                  602\n",
       "or_teama                   602\n",
       "dr_teama                   602\n",
       "ast_teama                  602\n",
       "to_teama                   602\n",
       "stl_teama                  602\n",
       "blk_teama                  602\n",
       "pf_teama                   602\n",
       "fgm_teamb                  602\n",
       "fga_teamb                  602\n",
       "fgm3_teamb                 602\n",
       "fga3_teamb                 602\n",
       "ftm_teamb                  602\n",
       "fta_teamb                  602\n",
       "or_teamb                   602\n",
       "dr_teamb                   602\n",
       "ast_teamb                  602\n",
       "to_teamb                   602\n",
       "stl_teamb                  602\n",
       "blk_teamb                  602\n",
       "pf_teamb                   602\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_winning(wteam, lteam):\n",
    "    if wteam < lteam:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "    \n",
    "march_tourney = pd.read_sql('select * from mncaa_tourney_compact_results', engine)\n",
    "march_tourney = march_tourney[march_tourney['season'] >= 2012].reset_index(drop=True) \n",
    "\n",
    "train = march_tourney\n",
    "\n",
    "train['is_win'] = train.apply(lambda x: is_winning(x['wteam_id'], x['lteam_id']), axis=1)\n",
    "train['team_a'] = train.season.astype(str) + train.wteam_id.astype(str)\n",
    "train['team_b'] = train.season.astype(str) + train.lteam_id.astype(str)\n",
    "\n",
    "train = train.drop(['wscore', 'lscore'], axis=1)\n",
    "train = pd.merge(train, massey_ranks, left_on='team_a', right_on='id').merge(massey_ranks, left_on='team_b', right_on='id', suffixes=('_teama', '_teamb'))\n",
    "\n",
    "train = train.drop(['season_x', 'season_y', 'id_teama', 'id_teamb', 'team_id_teama', 'team_id_teamb'], axis=1)\n",
    "train = pd.merge(train, reg_season_stats, left_on='team_a', right_on='id').merge(reg_season_stats, left_on='team_b', right_on='id', suffixes=('_teama', '_teamb'))\n",
    "\n",
    "train = train.drop(['season_x', 'season_y', 'team_a', 'team_b', 'team_id_teama', 'team_id_teamb', 'id_teama', 'id_teamb'], axis=1)\n",
    "train = train.drop(['daynum', 'wteam_id', 'lteam_id', 'num_ot', 'season', 'wloc'], axis=1)\n",
    "\n",
    "print(train.shape)\n",
    "train.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9456e287",
   "metadata": {},
   "source": [
    "**Model forming** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11b6ffaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "X = train.drop(['is_win'], axis=1)\n",
    "y = train.is_win\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df5fb5d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.67      0.62        57\n",
      "           1       0.66      0.58      0.62        64\n",
      "\n",
      "    accuracy                           0.62       121\n",
      "   macro avg       0.62      0.62      0.62       121\n",
      "weighted avg       0.62      0.62      0.62       121\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "#print(f\" Random forest predictive accuracy: {classification_report(y_test,y_pred):.3f})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a67bb92",
   "metadata": {},
   "source": [
    "# Heatmap the Classification Report w Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb12dd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "true = np.random.randint(0, 10, size=100)\n",
    "pred = np.random.randint(0, 10, size=100)\n",
    "labels = np.arange(9)\n",
    "target_names = list(\"ABCDEFGHI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c71f8165",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_report = classification_report(true,\n",
    "                                   pred,\n",
    "                                   labels=labels,\n",
    "                                   target_names=target_names,\n",
    "                                   output_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00a54289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .iloc[:-1, :] to exclude support\n",
    "sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31089eb3",
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fgm_teamb</td>\n",
       "      <td>0.040422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>stl_teama</td>\n",
       "      <td>0.040183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>to_teamb</td>\n",
       "      <td>0.039949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>to_teama</td>\n",
       "      <td>0.038769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>or_teama</td>\n",
       "      <td>0.038166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>stl_teamb</td>\n",
       "      <td>0.037045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>blk_teama</td>\n",
       "      <td>0.035694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fta_teama</td>\n",
       "      <td>0.035234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ordinal_rank_mean_teama</td>\n",
       "      <td>0.034098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pf_teamb</td>\n",
       "      <td>0.033546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature  importance\n",
       "0                fgm_teamb    0.040422\n",
       "1                stl_teama    0.040183\n",
       "2                 to_teamb    0.039949\n",
       "3                 to_teama    0.038769\n",
       "4                 or_teama    0.038166\n",
       "5                stl_teamb    0.037045\n",
       "6                blk_teama    0.035694\n",
       "7                fta_teama    0.035234\n",
       "8  ordinal_rank_mean_teama    0.034098\n",
       "9                 pf_teamb    0.033546"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create feature importances from model\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# get column names\n",
    "columns = X.columns\n",
    "\n",
    "# create a dataframe\n",
    "feature_importances_df = pd.DataFrame({\n",
    "    'feature': columns,\n",
    "    'importance': importances\n",
    "}) \n",
    "\n",
    "\n",
    "feature_importance = feature_importances_df.sort_values(by=['importance'], ascending=False, inplace=True)\n",
    "feature_importance = feature_importances_df.reset_index(drop=True, inplace=True)\n",
    "feature_importance = feature_importances_df.head(10)\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec42d04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top Ten Feature importances')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the top ten feature-importances_\n",
    "\n",
    "feature_importance.plot.barh(x=\"feature\", y=\"importance\")\n",
    "plt.title(\"Top Ten Feature importances\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "46306a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_value(data, data_type=None):\n",
    "    data = data.split('_')\n",
    "    if data_type == 'Team A':\n",
    "        return str(data[0]) + str(data[1])\n",
    "    else:\n",
    "        return str(data[0]) + str(data[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26123981",
   "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>ID</th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022_1103_1104</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022_1103_1112</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022_1103_1116</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022_1103_1120</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022_1103_1124</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>2022_1458_1461</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>2022_1458_1463</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>2022_1460_1461</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>2022_1460_1463</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>2022_1461_1463</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2278 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Pred\n",
       "0     2022_1103_1104   0.5\n",
       "1     2022_1103_1112   0.5\n",
       "2     2022_1103_1116   0.5\n",
       "3     2022_1103_1120   0.5\n",
       "4     2022_1103_1124   0.5\n",
       "...              ...   ...\n",
       "2273  2022_1458_1461   0.5\n",
       "2274  2022_1458_1463   0.5\n",
       "2275  2022_1460_1461   0.5\n",
       "2276  2022_1460_1463   0.5\n",
       "2277  2022_1461_1463   0.5\n",
       "\n",
       "[2278 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"./MDataFiles_Stage2_/MSampleSubmissionStage2.csv\")\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc55a852",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aheri\\anaconda3\\envs\\mlenv\\lib\\site-packages\\pandas\\core\\frame.py:9203: FutureWarning: Passing 'suffixes' which cause duplicate columns {'team_id_teama', 'season_teama', 'id_teama'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  validate=validate,\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pred</th>\n",
       "      <th>ordinal_rank_mean_teama</th>\n",
       "      <th>ordinal_rank_min_teama</th>\n",
       "      <th>ordinal_rank_max_teama</th>\n",
       "      <th>ordinal_rank_mean_teamb</th>\n",
       "      <th>ordinal_rank_min_teamb</th>\n",
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       "      <th>fgm_teama</th>\n",
       "      <th>fga_teama</th>\n",
       "      <th>...</th>\n",
       "      <th>fga3_teamb</th>\n",
       "      <th>ftm_teamb</th>\n",
       "      <th>fta_teamb</th>\n",
       "      <th>or_teamb</th>\n",
       "      <th>dr_teamb</th>\n",
       "      <th>ast_teamb</th>\n",
       "      <th>to_teamb</th>\n",
       "      <th>stl_teamb</th>\n",
       "      <th>blk_teamb</th>\n",
       "      <th>pf_teamb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>17.380764</td>\n",
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       "      <td>51</td>\n",
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       "      <td>...</td>\n",
       "      <td>30.309717</td>\n",
       "      <td>15.255061</td>\n",
       "      <td>20.943320</td>\n",
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       "      <td>0.5</td>\n",
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       "      <td>10.693548</td>\n",
       "      <td>26.989247</td>\n",
       "      <td>17.091398</td>\n",
       "      <td>14.172043</td>\n",
       "      <td>5.634409</td>\n",
       "      <td>4.784946</td>\n",
       "      <td>17.462366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022_1103_1116</td>\n",
       "      <td>0.5</td>\n",
       "      <td>142.390110</td>\n",
       "      <td>51</td>\n",
       "      <td>327</td>\n",
       "      <td>33.667105</td>\n",
       "      <td>3</td>\n",
       "      <td>147</td>\n",
       "      <td>22.851010</td>\n",
       "      <td>52.699495</td>\n",
       "      <td>...</td>\n",
       "      <td>21.775000</td>\n",
       "      <td>16.817500</td>\n",
       "      <td>22.427500</td>\n",
       "      <td>9.630000</td>\n",
       "      <td>24.002500</td>\n",
       "      <td>13.847500</td>\n",
       "      <td>13.035000</td>\n",
       "      <td>7.480000</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>17.792500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022_1104_1116</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17.380764</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>33.667105</td>\n",
       "      <td>3</td>\n",
       "      <td>147</td>\n",
       "      <td>27.370445</td>\n",
       "      <td>62.850202</td>\n",
       "      <td>...</td>\n",
       "      <td>21.775000</td>\n",
       "      <td>16.817500</td>\n",
       "      <td>22.427500</td>\n",
       "      <td>9.630000</td>\n",
       "      <td>24.002500</td>\n",
       "      <td>13.847500</td>\n",
       "      <td>13.035000</td>\n",
       "      <td>7.480000</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>17.792500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>2022_1437_1463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.607742</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>156.880495</td>\n",
       "      <td>59</td>\n",
       "      <td>335</td>\n",
       "      <td>23.601648</td>\n",
       "      <td>56.975275</td>\n",
       "      <td>...</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>13.573232</td>\n",
       "      <td>18.616162</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>25.161616</td>\n",
       "      <td>11.631313</td>\n",
       "      <td>12.828283</td>\n",
       "      <td>5.840909</td>\n",
       "      <td>3.191919</td>\n",
       "      <td>18.108586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>2022_1439_1463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>51.533516</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>156.880495</td>\n",
       "      <td>59</td>\n",
       "      <td>335</td>\n",
       "      <td>25.222826</td>\n",
       "      <td>55.215580</td>\n",
       "      <td>...</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>13.573232</td>\n",
       "      <td>18.616162</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>25.161616</td>\n",
       "      <td>11.631313</td>\n",
       "      <td>12.828283</td>\n",
       "      <td>5.840909</td>\n",
       "      <td>3.191919</td>\n",
       "      <td>18.108586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>2022_1458_1463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>23.186198</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>156.880495</td>\n",
       "      <td>59</td>\n",
       "      <td>335</td>\n",
       "      <td>24.544643</td>\n",
       "      <td>59.693452</td>\n",
       "      <td>...</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>13.573232</td>\n",
       "      <td>18.616162</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>25.161616</td>\n",
       "      <td>11.631313</td>\n",
       "      <td>12.828283</td>\n",
       "      <td>5.840909</td>\n",
       "      <td>3.191919</td>\n",
       "      <td>18.108586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>2022_1460_1463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>211.928571</td>\n",
       "      <td>79</td>\n",
       "      <td>350</td>\n",
       "      <td>156.880495</td>\n",
       "      <td>59</td>\n",
       "      <td>335</td>\n",
       "      <td>26.825000</td>\n",
       "      <td>59.080769</td>\n",
       "      <td>...</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>13.573232</td>\n",
       "      <td>18.616162</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>25.161616</td>\n",
       "      <td>11.631313</td>\n",
       "      <td>12.828283</td>\n",
       "      <td>5.840909</td>\n",
       "      <td>3.191919</td>\n",
       "      <td>18.108586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>2022_1461_1463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>55.416438</td>\n",
       "      <td>4</td>\n",
       "      <td>211</td>\n",
       "      <td>156.880495</td>\n",
       "      <td>59</td>\n",
       "      <td>335</td>\n",
       "      <td>24.020833</td>\n",
       "      <td>55.104167</td>\n",
       "      <td>...</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>13.573232</td>\n",
       "      <td>18.616162</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>25.161616</td>\n",
       "      <td>11.631313</td>\n",
       "      <td>12.828283</td>\n",
       "      <td>5.840909</td>\n",
       "      <td>3.191919</td>\n",
       "      <td>18.108586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2278 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Pred  ordinal_rank_mean_teama  ordinal_rank_min_teama  \\\n",
       "0     2022_1103_1104   0.5               142.390110                      51   \n",
       "1     2022_1103_1112   0.5               142.390110                      51   \n",
       "2     2022_1104_1112   0.5                17.380764                       1   \n",
       "3     2022_1103_1116   0.5               142.390110                      51   \n",
       "4     2022_1104_1116   0.5                17.380764                       1   \n",
       "...              ...   ...                      ...                     ...   \n",
       "2273  2022_1437_1463   0.5                12.607742                       1   \n",
       "2274  2022_1439_1463   0.5                51.533516                       2   \n",
       "2275  2022_1458_1463   0.5                23.186198                       2   \n",
       "2276  2022_1460_1463   0.5               211.928571                      79   \n",
       "2277  2022_1461_1463   0.5                55.416438                       4   \n",
       "\n",
       "      ordinal_rank_max_teama  ordinal_rank_mean_teamb  ordinal_rank_min_teamb  \\\n",
       "0                        327                17.380764                       1   \n",
       "1                        327                 4.730323                       1   \n",
       "2                         51                 4.730323                       1   \n",
       "3                        327                33.667105                       3   \n",
       "4                         51                33.667105                       3   \n",
       "...                      ...                      ...                     ...   \n",
       "2273                      88               156.880495                      59   \n",
       "2274                     132               156.880495                      59   \n",
       "2275                     144               156.880495                      59   \n",
       "2276                     350               156.880495                      59   \n",
       "2277                     211               156.880495                      59   \n",
       "\n",
       "      ordinal_rank_max_teamb  fgm_teama  fga_teama  ...  fga3_teamb  \\\n",
       "0                         51  22.851010  52.699495  ...   30.309717   \n",
       "1                         51  22.851010  52.699495  ...   23.005376   \n",
       "2                         51  27.370445  62.850202  ...   23.005376   \n",
       "3                        147  22.851010  52.699495  ...   21.775000   \n",
       "4                        147  27.370445  62.850202  ...   21.775000   \n",
       "...                      ...        ...        ...  ...         ...   \n",
       "2273                     335  23.601648  56.975275  ...   21.750000   \n",
       "2274                     335  25.222826  55.215580  ...   21.750000   \n",
       "2275                     335  24.544643  59.693452  ...   21.750000   \n",
       "2276                     335  26.825000  59.080769  ...   21.750000   \n",
       "2277                     335  24.020833  55.104167  ...   21.750000   \n",
       "\n",
       "      ftm_teamb  fta_teamb   or_teamb   dr_teamb  ast_teamb   to_teamb  \\\n",
       "0     15.255061  20.943320  11.684211  24.374494  14.514170  14.115385   \n",
       "1     14.145161  19.500000  10.693548  26.989247  17.091398  14.172043   \n",
       "2     14.145161  19.500000  10.693548  26.989247  17.091398  14.172043   \n",
       "3     16.817500  22.427500   9.630000  24.002500  13.847500  13.035000   \n",
       "4     16.817500  22.427500   9.630000  24.002500  13.847500  13.035000   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "2273  13.573232  18.616162   7.416667  25.161616  11.631313  12.828283   \n",
       "2274  13.573232  18.616162   7.416667  25.161616  11.631313  12.828283   \n",
       "2275  13.573232  18.616162   7.416667  25.161616  11.631313  12.828283   \n",
       "2276  13.573232  18.616162   7.416667  25.161616  11.631313  12.828283   \n",
       "2277  13.573232  18.616162   7.416667  25.161616  11.631313  12.828283   \n",
       "\n",
       "      stl_teamb  blk_teamb   pf_teamb  \n",
       "0      6.987854   4.605263  18.840081  \n",
       "1      5.634409   4.784946  17.462366  \n",
       "2      5.634409   4.784946  17.462366  \n",
       "3      7.480000   3.950000  17.792500  \n",
       "4      7.480000   3.950000  17.792500  \n",
       "...         ...        ...        ...  \n",
       "2273   5.840909   3.191919  18.108586  \n",
       "2274   5.840909   3.191919  18.108586  \n",
       "2275   5.840909   3.191919  18.108586  \n",
       "2276   5.840909   3.191919  18.108586  \n",
       "2277   5.840909   3.191919  18.108586  \n",
       "\n",
       "[2278 rows x 34 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['team_a'] = test.apply(lambda x: return_value(x.ID, 'Team A'), axis=1)\n",
    "test['team_b'] = test.apply(lambda x: return_value(x.ID), axis=1)\n",
    "test = pd.merge(test, massey_ranks, left_on='team_a', right_on='id').merge(massey_ranks, left_on='team_b', right_on='id', suffixes=('_teama', '_teamb'))\n",
    "test = pd.merge(test, reg_season_stats, left_on='team_a', right_on='id').merge(reg_season_stats, left_on='team_b', right_on='id', suffixes=('_teama', '_teamb'))\n",
    "test = test.drop(['season_teama', 'season_teamb', 'team_a', 'team_b', 'team_id_teama', 'team_id_teamb', 'id_teama', 'id_teamb'], axis=1)\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25c518b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022_1103_1104</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022_1103_1112</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022_1104_1112</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022_1103_1116</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022_1104_1116</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>2022_1437_1463</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>2022_1439_1463</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>2022_1458_1463</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>2022_1460_1463</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>2022_1461_1463</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2278 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Pred\n",
       "0     2022_1103_1104  0.51\n",
       "1     2022_1103_1112  0.46\n",
       "2     2022_1104_1112  0.65\n",
       "3     2022_1103_1116  0.51\n",
       "4     2022_1104_1116  0.67\n",
       "...              ...   ...\n",
       "2273  2022_1437_1463  0.42\n",
       "2274  2022_1439_1463  0.51\n",
       "2275  2022_1458_1463  0.45\n",
       "2276  2022_1460_1463  0.39\n",
       "2277  2022_1461_1463  0.52\n",
       "\n",
       "[2278 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = test.drop(['ID', 'Pred'], axis=1)\n",
    "\n",
    "test['Pred'] = model.predict_proba(X)[:, 1]\n",
    "test = test[['ID', 'Pred']]\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c2cd64",
   "metadata": {},
   "source": [
    "## Formatting dataframe for dashboard table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5cef7ebb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aheri\\anaconda3\\envs\\mlenv\\lib\\site-packages\\pandas\\core\\frame.py:3641: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Pred</th>\n",
       "      <th>Season</th>\n",
       "      <th>Team 1</th>\n",
       "      <th>Team 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022_1103_1104</td>\n",
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       "      <td>1103</td>\n",
       "      <td>1104</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022_1103_1112</td>\n",
       "      <td>0.46</td>\n",
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       "      <td>1103</td>\n",
       "      <td>1112</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022_1104_1112</td>\n",
       "      <td>0.65</td>\n",
       "      <td>2022</td>\n",
       "      <td>1104</td>\n",
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       "      <th>3</th>\n",
       "      <td>2022_1103_1116</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2022</td>\n",
       "      <td>1103</td>\n",
       "      <td>1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022_1104_1116</td>\n",
       "      <td>0.67</td>\n",
       "      <td>2022</td>\n",
       "      <td>1104</td>\n",
       "      <td>1116</td>\n",
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       "    <tr>\n",
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       "      <th>2273</th>\n",
       "      <td>2022_1437_1463</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2022</td>\n",
       "      <td>1437</td>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>2022_1439_1463</td>\n",
       "      <td>0.51</td>\n",
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       "      <td>1439</td>\n",
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       "      <td>1458</td>\n",
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       "    <tr>\n",
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       "      <td>2022_1460_1463</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2022</td>\n",
       "      <td>1460</td>\n",
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       "      <td>1461</td>\n",
       "      <td>1463</td>\n",
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       "<p>2278 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Pred Season Team 1 Team 2\n",
       "0     2022_1103_1104  0.51   2022   1103   1104\n",
       "1     2022_1103_1112  0.46   2022   1103   1112\n",
       "2     2022_1104_1112  0.65   2022   1104   1112\n",
       "3     2022_1103_1116  0.51   2022   1103   1116\n",
       "4     2022_1104_1116  0.67   2022   1104   1116\n",
       "...              ...   ...    ...    ...    ...\n",
       "2273  2022_1437_1463  0.42   2022   1437   1463\n",
       "2274  2022_1439_1463  0.51   2022   1439   1463\n",
       "2275  2022_1458_1463  0.45   2022   1458   1463\n",
       "2276  2022_1460_1463  0.39   2022   1460   1463\n",
       "2277  2022_1461_1463  0.52   2022   1461   1463\n",
       "\n",
       "[2278 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['Season', 'Team 1', 'Team 2']] = test['ID'].str.split('_', expand=True)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1e6958b",
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   "outputs": [
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       "<p>2278 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                  ID  Pred Season Team 1 Team 2\n",
       "0     2022_1103_1104  0.51   2022   1103   1104\n",
       "1     2022_1103_1112  0.46   2022   1103   1112\n",
       "2     2022_1104_1112  0.65   2022   1104   1112\n",
       "3     2022_1103_1116  0.51   2022   1103   1116\n",
       "4     2022_1104_1116  0.67   2022   1104   1116\n",
       "...              ...   ...    ...    ...    ...\n",
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       "2276  2022_1460_1463  0.39   2022   1460   1463\n",
       "2277  2022_1461_1463  0.52   2022   1461   1463\n",
       "\n",
       "[2278 rows x 5 columns]"
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     "execution_count": 18,
     "metadata": {},
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   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b8cb682",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      Pred Team 1 Team 2\n",
       "0     0.51   1103   1104\n",
       "1     0.46   1103   1112\n",
       "2     0.65   1104   1112\n",
       "3     0.51   1103   1116\n",
       "4     0.67   1104   1116\n",
       "...    ...    ...    ...\n",
       "2273  0.42   1437   1463\n",
       "2274  0.51   1439   1463\n",
       "2275  0.45   1458   1463\n",
       "2276  0.39   1460   1463\n",
       "2277  0.52   1461   1463\n",
       "\n",
       "[2278 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "test = test.drop(['ID', 'Season'], axis=1)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "069f4bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Team 1 Team 2  Pred\n",
       "0      1103   1104  0.51\n",
       "1      1103   1112  0.46\n",
       "2      1104   1112  0.65\n",
       "3      1103   1116  0.51\n",
       "4      1104   1116  0.67\n",
       "...     ...    ...   ...\n",
       "2273   1437   1463  0.42\n",
       "2274   1439   1463  0.51\n",
       "2275   1458   1463  0.45\n",
       "2276   1460   1463  0.39\n",
       "2277   1461   1463  0.52\n",
       "\n",
       "[2278 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = test[['Team 1', 'Team 2', 'Pred']]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6921efe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Team 1 Team 2  Probability\n",
       "0      1103   1104         0.51\n",
       "1      1103   1112         0.46\n",
       "2      1104   1112         0.65\n",
       "3      1103   1116         0.51\n",
       "4      1104   1116         0.67\n",
       "...     ...    ...          ...\n",
       "2273   1437   1463         0.42\n",
       "2274   1439   1463         0.51\n",
       "2275   1458   1463         0.45\n",
       "2276   1460   1463         0.39\n",
       "2277   1461   1463         0.52\n",
       "\n",
       "[2278 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.rename(columns = {'Pred':'Probability'}, inplace = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "750ee402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1101: 'Abilene Chr',\n",
       " 1102: 'Air Force',\n",
       " 1103: 'Akron',\n",
       " 1104: 'Alabama',\n",
       " 1105: 'Alabama A&M',\n",
       " 1106: 'Alabama St',\n",
       " 1107: 'SUNY Albany',\n",
       " 1108: 'Alcorn St',\n",
       " 1109: 'Alliant Intl',\n",
       " 1110: 'American Univ',\n",
       " 1111: 'Appalachian St',\n",
       " 1112: 'Arizona',\n",
       " 1113: 'Arizona St',\n",
       " 1114: 'Ark Little Rock',\n",
       " 1115: 'Ark Pine Bluff',\n",
       " 1116: 'Arkansas',\n",
       " 1117: 'Arkansas St',\n",
       " 1118: 'Armstrong St',\n",
       " 1119: 'Army',\n",
       " 1120: 'Auburn',\n",
       " 1121: 'Augusta',\n",
       " 1122: 'Austin Peay',\n",
       " 1123: 'Ball St',\n",
       " 1124: 'Baylor',\n",
       " 1125: 'Belmont',\n",
       " 1126: 'Bethune-Cookman',\n",
       " 1127: 'Binghamton',\n",
       " 1128: 'Birmingham So',\n",
       " 1129: 'Boise St',\n",
       " 1130: 'Boston College',\n",
       " 1131: 'Boston Univ',\n",
       " 1132: 'Bowling Green',\n",
       " 1133: 'Bradley',\n",
       " 1134: 'Brooklyn',\n",
       " 1135: 'Brown',\n",
       " 1136: 'Bryant',\n",
       " 1137: 'Bucknell',\n",
       " 1138: 'Buffalo',\n",
       " 1139: 'Butler',\n",
       " 1140: 'BYU',\n",
       " 1141: 'C Michigan',\n",
       " 1142: 'Cal Poly',\n",
       " 1143: 'California',\n",
       " 1144: 'Campbell',\n",
       " 1145: 'Canisius',\n",
       " 1146: 'Cent Arkansas',\n",
       " 1147: 'Centenary',\n",
       " 1148: 'Central Conn',\n",
       " 1149: 'Charleston So',\n",
       " 1150: 'Charlotte',\n",
       " 1151: 'Chattanooga',\n",
       " 1152: 'Chicago St',\n",
       " 1153: 'Cincinnati',\n",
       " 1154: 'Citadel',\n",
       " 1155: 'Clemson',\n",
       " 1156: 'Cleveland St',\n",
       " 1157: 'Coastal Car',\n",
       " 1158: 'Col Charleston',\n",
       " 1159: 'Colgate',\n",
       " 1160: 'Colorado',\n",
       " 1161: 'Colorado St',\n",
       " 1162: 'Columbia',\n",
       " 1163: 'Connecticut',\n",
       " 1164: 'Coppin St',\n",
       " 1165: 'Cornell',\n",
       " 1166: 'Creighton',\n",
       " 1167: 'CS Bakersfield',\n",
       " 1168: 'CS Fullerton',\n",
       " 1169: 'CS Northridge',\n",
       " 1170: 'CS Sacramento',\n",
       " 1171: 'Dartmouth',\n",
       " 1172: 'Davidson',\n",
       " 1173: 'Dayton',\n",
       " 1174: 'Delaware',\n",
       " 1175: 'Delaware St',\n",
       " 1176: 'Denver',\n",
       " 1177: 'DePaul',\n",
       " 1178: 'Detroit',\n",
       " 1179: 'Drake',\n",
       " 1180: 'Drexel',\n",
       " 1181: 'Duke',\n",
       " 1182: 'Duquesne',\n",
       " 1183: 'E Illinois',\n",
       " 1184: 'E Kentucky',\n",
       " 1185: 'E Michigan',\n",
       " 1186: 'E Washington',\n",
       " 1187: 'East Carolina',\n",
       " 1188: 'SIUE',\n",
       " 1189: 'Elon',\n",
       " 1190: 'ETSU',\n",
       " 1191: 'Evansville',\n",
       " 1192: 'F Dickinson',\n",
       " 1193: 'Fairfield',\n",
       " 1194: 'FL Atlantic',\n",
       " 1195: 'FL Gulf Coast',\n",
       " 1196: 'Florida',\n",
       " 1197: 'Florida A&M',\n",
       " 1198: 'Florida Intl',\n",
       " 1199: 'Florida St',\n",
       " 1200: 'Fordham',\n",
       " 1201: 'Fresno St',\n",
       " 1202: 'Furman',\n",
       " 1203: 'G Washington',\n",
       " 1204: 'Ga Southern',\n",
       " 1205: 'Gardner Webb',\n",
       " 1206: 'George Mason',\n",
       " 1207: 'Georgetown',\n",
       " 1208: 'Georgia',\n",
       " 1209: 'Georgia St',\n",
       " 1210: 'Georgia Tech',\n",
       " 1211: 'Gonzaga',\n",
       " 1212: 'Grambling',\n",
       " 1213: 'Grand Canyon',\n",
       " 1214: 'Hampton',\n",
       " 1215: 'Hardin-Simmons',\n",
       " 1216: 'Hartford',\n",
       " 1217: 'Harvard',\n",
       " 1218: 'Hawaii',\n",
       " 1219: 'High Point',\n",
       " 1220: 'Hofstra',\n",
       " 1221: 'Holy Cross',\n",
       " 1222: 'Houston',\n",
       " 1223: 'Houston Bap',\n",
       " 1224: 'Howard',\n",
       " 1225: 'Idaho',\n",
       " 1226: 'Idaho St',\n",
       " 1227: 'IL Chicago',\n",
       " 1228: 'Illinois',\n",
       " 1229: 'Illinois St',\n",
       " 1230: 'Incarnate Word',\n",
       " 1231: 'Indiana',\n",
       " 1232: 'Indiana St',\n",
       " 1233: 'Iona',\n",
       " 1234: 'Iowa',\n",
       " 1235: 'Iowa St',\n",
       " 1236: 'PFW',\n",
       " 1237: 'IUPUI',\n",
       " 1238: 'Jackson St',\n",
       " 1239: 'Jacksonville',\n",
       " 1240: 'Jacksonville St',\n",
       " 1241: 'James Madison',\n",
       " 1242: 'Kansas',\n",
       " 1243: 'Kansas St',\n",
       " 1244: 'Kennesaw',\n",
       " 1245: 'Kent',\n",
       " 1246: 'Kentucky',\n",
       " 1247: 'La Salle',\n",
       " 1248: 'Lafayette',\n",
       " 1249: 'Lamar',\n",
       " 1250: 'Lehigh',\n",
       " 1251: 'Liberty',\n",
       " 1252: 'Lipscomb',\n",
       " 1253: 'Long Beach St',\n",
       " 1254: 'LIU Brooklyn',\n",
       " 1255: 'Longwood',\n",
       " 1256: 'Louisiana Tech',\n",
       " 1257: 'Louisville',\n",
       " 1258: 'Loy Marymount',\n",
       " 1259: 'Loyola MD',\n",
       " 1260: 'Loyola-Chicago',\n",
       " 1261: 'LSU',\n",
       " 1262: 'MA Lowell',\n",
       " 1263: 'Maine',\n",
       " 1264: 'Manhattan',\n",
       " 1265: 'Marist',\n",
       " 1266: 'Marquette',\n",
       " 1267: 'Marshall',\n",
       " 1268: 'Maryland',\n",
       " 1269: 'Massachusetts',\n",
       " 1270: 'McNeese St',\n",
       " 1271: 'MD E Shore',\n",
       " 1272: 'Memphis',\n",
       " 1273: 'Mercer',\n",
       " 1274: 'Miami FL',\n",
       " 1275: 'Miami OH',\n",
       " 1276: 'Michigan',\n",
       " 1277: 'Michigan St',\n",
       " 1278: 'Minnesota',\n",
       " 1279: 'Mississippi',\n",
       " 1280: 'Mississippi St',\n",
       " 1281: 'Missouri',\n",
       " 1282: 'Missouri KC',\n",
       " 1283: 'Missouri St',\n",
       " 1284: 'Monmouth NJ',\n",
       " 1285: 'Montana',\n",
       " 1286: 'Montana St',\n",
       " 1287: 'Morehead St',\n",
       " 1288: 'Morgan St',\n",
       " 1289: 'Morris Brown',\n",
       " 1290: 'MS Valley St',\n",
       " 1291: \"Mt St Mary's\",\n",
       " 1292: 'MTSU',\n",
       " 1293: 'Murray St',\n",
       " 1294: 'N Colorado',\n",
       " 1295: 'N Dakota St',\n",
       " 1296: 'N Illinois',\n",
       " 1297: 'N Kentucky',\n",
       " 1298: 'Navy',\n",
       " 1299: 'NC A&T',\n",
       " 1300: 'NC Central',\n",
       " 1301: 'NC State',\n",
       " 1302: 'NE Illinois',\n",
       " 1303: 'NE Omaha',\n",
       " 1304: 'Nebraska',\n",
       " 1305: 'Nevada',\n",
       " 1306: 'New Hampshire',\n",
       " 1307: 'New Mexico',\n",
       " 1308: 'New Mexico St',\n",
       " 1309: 'New Orleans',\n",
       " 1310: 'Niagara',\n",
       " 1311: 'Nicholls St',\n",
       " 1312: 'NJIT',\n",
       " 1313: 'Norfolk St',\n",
       " 1314: 'North Carolina',\n",
       " 1315: 'North Dakota',\n",
       " 1316: 'North Florida',\n",
       " 1317: 'North Texas',\n",
       " 1318: 'Northeastern',\n",
       " 1319: 'Northern Arizona',\n",
       " 1320: 'Northern Iowa',\n",
       " 1321: 'Northwestern',\n",
       " 1322: 'Northwestern LA',\n",
       " 1323: 'Notre Dame',\n",
       " 1324: 'Oakland',\n",
       " 1325: 'Ohio',\n",
       " 1326: 'Ohio St',\n",
       " 1327: 'Okla City',\n",
       " 1328: 'Oklahoma',\n",
       " 1329: 'Oklahoma St',\n",
       " 1330: 'Old Dominion',\n",
       " 1331: 'Oral Roberts',\n",
       " 1332: 'Oregon',\n",
       " 1333: 'Oregon St',\n",
       " 1334: 'Pacific',\n",
       " 1335: 'Penn',\n",
       " 1336: 'Penn St',\n",
       " 1337: 'Pepperdine',\n",
       " 1338: 'Pittsburgh',\n",
       " 1339: 'Portland',\n",
       " 1340: 'Portland St',\n",
       " 1341: 'Prairie View',\n",
       " 1342: 'Presbyterian',\n",
       " 1343: 'Princeton',\n",
       " 1344: 'Providence',\n",
       " 1345: 'Purdue',\n",
       " 1346: 'Quinnipiac',\n",
       " 1347: 'Radford',\n",
       " 1348: 'Rhode Island',\n",
       " 1349: 'Rice',\n",
       " 1350: 'Richmond',\n",
       " 1351: 'Rider',\n",
       " 1352: 'Robert Morris',\n",
       " 1353: 'Rutgers',\n",
       " 1354: 'S Carolina St',\n",
       " 1355: 'S Dakota St',\n",
       " 1356: 'S Illinois',\n",
       " 1357: 'Sacred Heart',\n",
       " 1358: 'Sam Houston St',\n",
       " 1359: 'Samford',\n",
       " 1360: 'San Diego',\n",
       " 1361: 'San Diego St',\n",
       " 1362: 'San Francisco',\n",
       " 1363: 'San Jose St',\n",
       " 1364: 'UC Santa Barbara',\n",
       " 1365: 'Santa Clara',\n",
       " 1366: 'Savannah St',\n",
       " 1367: 'SC Upstate',\n",
       " 1368: 'SE Louisiana',\n",
       " 1369: 'SE Missouri St',\n",
       " 1370: 'Seattle',\n",
       " 1371: 'Seton Hall',\n",
       " 1372: 'SF Austin',\n",
       " 1373: 'Siena',\n",
       " 1374: 'SMU',\n",
       " 1375: 'South Alabama',\n",
       " 1376: 'South Carolina',\n",
       " 1377: 'South Dakota',\n",
       " 1378: 'South Florida',\n",
       " 1379: 'Southern Miss',\n",
       " 1380: 'Southern Univ',\n",
       " 1381: 'Southern Utah',\n",
       " 1382: 'St Bonaventure',\n",
       " 1383: 'St Francis NY',\n",
       " 1384: 'St Francis PA',\n",
       " 1385: \"St John's\",\n",
       " 1386: \"St Joseph's PA\",\n",
       " 1387: 'St Louis',\n",
       " 1388: \"St Mary's CA\",\n",
       " 1389: \"St Peter's\",\n",
       " 1390: 'Stanford',\n",
       " 1391: 'Stetson',\n",
       " 1392: 'Stony Brook',\n",
       " 1393: 'Syracuse',\n",
       " 1394: 'TAM C. Christi',\n",
       " 1395: 'TCU',\n",
       " 1396: 'Temple',\n",
       " 1397: 'Tennessee',\n",
       " 1398: 'Tennessee St',\n",
       " 1399: 'Tennessee Tech',\n",
       " 1400: 'Texas',\n",
       " 1401: 'Texas A&M',\n",
       " 1402: 'Texas St',\n",
       " 1403: 'Texas Tech',\n",
       " 1404: 'TN Martin',\n",
       " 1405: 'Toledo',\n",
       " 1406: 'Towson',\n",
       " 1407: 'Troy',\n",
       " 1408: 'Tulane',\n",
       " 1409: 'Tulsa',\n",
       " 1410: 'UTRGV',\n",
       " 1411: 'TX Southern',\n",
       " 1412: 'UAB',\n",
       " 1413: 'UC Davis',\n",
       " 1414: 'UC Irvine',\n",
       " 1415: 'UC Riverside',\n",
       " 1416: 'UCF',\n",
       " 1417: 'UCLA',\n",
       " 1418: 'Louisiana',\n",
       " 1419: 'ULM',\n",
       " 1420: 'UMBC',\n",
       " 1421: 'UNC Asheville',\n",
       " 1422: 'UNC Greensboro',\n",
       " 1423: 'UNC Wilmington',\n",
       " 1424: 'UNLV',\n",
       " 1425: 'USC',\n",
       " 1426: 'UT Arlington',\n",
       " 1427: 'UT San Antonio',\n",
       " 1428: 'Utah',\n",
       " 1429: 'Utah St',\n",
       " 1430: 'Utah Valley',\n",
       " 1431: 'UTEP',\n",
       " 1432: 'Utica',\n",
       " 1433: 'VCU',\n",
       " 1434: 'Valparaiso',\n",
       " 1435: 'Vanderbilt',\n",
       " 1436: 'Vermont',\n",
       " 1437: 'Villanova',\n",
       " 1438: 'Virginia',\n",
       " 1439: 'Virginia Tech',\n",
       " 1440: 'VMI',\n",
       " 1441: 'W Carolina',\n",
       " 1442: 'W Illinois',\n",
       " 1443: 'WKU',\n",
       " 1444: 'W Michigan',\n",
       " 1445: 'W Salem St',\n",
       " 1446: 'W Texas A&M',\n",
       " 1447: 'Wagner',\n",
       " 1448: 'Wake Forest',\n",
       " 1449: 'Washington',\n",
       " 1450: 'Washington St',\n",
       " 1451: 'Weber St',\n",
       " 1452: 'West Virginia',\n",
       " 1453: 'WI Green Bay',\n",
       " 1454: 'WI Milwaukee',\n",
       " 1455: 'Wichita St',\n",
       " 1456: 'William & Mary',\n",
       " 1457: 'Winthrop',\n",
       " 1458: 'Wisconsin',\n",
       " 1459: 'Wofford',\n",
       " 1460: 'Wright St',\n",
       " 1461: 'Wyoming',\n",
       " 1462: 'Xavier',\n",
       " 1463: 'Yale',\n",
       " 1464: 'Youngstown St',\n",
       " 1465: 'Cal Baptist',\n",
       " 1466: 'North Alabama',\n",
       " 1467: 'Merrimack',\n",
       " 1468: 'Bellarmine',\n",
       " 1469: 'Dixie St',\n",
       " 1470: 'Tarleton St',\n",
       " 1471: 'UC San Diego',\n",
       " 1472: 'St Thomas MN'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_names = pd.read_sql('select * from teams', engine)\n",
    "team_names = team_names.drop(['firstd1season', 'lastd1season'], axis=1)\n",
    "team_names = pd.Series(team_names.teamname.values,index=team_names.teamid).to_dict()\n",
    "\n",
    "team_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66b69b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['Team 1'] = results['Team 1'].astype(str)\n",
    "results ['Team 2'] = results['Team 2'].astype(str)\n",
    "team_names = {str(k):str(v) for k,v in team_names.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70de53e8",
   "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>Team 1</th>\n",
       "      <th>Team 2</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>Villanova</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>Wright St</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2278 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team 1    Team 2  Probability\n",
       "0             Akron   Alabama         0.51\n",
       "1             Akron   Arizona         0.46\n",
       "2           Alabama   Arizona         0.65\n",
       "3             Akron  Arkansas         0.51\n",
       "4           Alabama  Arkansas         0.67\n",
       "...             ...       ...          ...\n",
       "2273      Villanova      Yale         0.42\n",
       "2274  Virginia Tech      Yale         0.51\n",
       "2275      Wisconsin      Yale         0.45\n",
       "2276      Wright St      Yale         0.39\n",
       "2277        Wyoming      Yale         0.52\n",
       "\n",
       "[2278 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['Team 1'] = results['Team 1'].map(team_names)\n",
    "# results.replace({\"Team 1\": team_names, 'Team 2':team_names}, inplace=True)\n",
    "results['Team 2'] = results['Team 2'].map(team_names)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a40f743e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predicted_winner(results):\n",
    "    if results['Probability'] >= .5:\n",
    "        return results['Team 1']\n",
    "    elif results['Probability'] < .5:\n",
    "        return results ['Team 2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2c1cd24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team 1</th>\n",
       "      <th>Team 2</th>\n",
       "      <th>Probability</th>\n",
       "      <th>Predicted Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.51</td>\n",
       "      <td>Akron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.46</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.65</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akron</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.51</td>\n",
       "      <td>Akron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "  </tbody>\n",
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       "<p>2278 rows × 4 columns</p>\n",
       "</div>"
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       "             Team 1    Team 2  Probability Predicted Winner\n",
       "0             Akron   Alabama         0.51            Akron\n",
       "1             Akron   Arizona         0.46          Arizona\n",
       "2           Alabama   Arizona         0.65          Alabama\n",
       "3             Akron  Arkansas         0.51            Akron\n",
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       "...             ...       ...          ...              ...\n",
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       "2277        Wyoming      Yale         0.52          Wyoming\n",
       "\n",
       "[2278 rows x 4 columns]"
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     "execution_count": 26,
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   "source": [
    "results['Predicted Winner'] = results.apply(predicted_winner, axis=1)\n",
    "results"
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  {
   "cell_type": "code",
   "execution_count": 27,
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       "             Team 1    Team 2 Predicted Winner  Probability\n",
       "0             Akron   Alabama            Akron         0.51\n",
       "1             Akron   Arizona          Arizona         0.46\n",
       "2           Alabama   Arizona          Alabama         0.65\n",
       "3             Akron  Arkansas            Akron         0.51\n",
       "4           Alabama  Arkansas          Alabama         0.67\n",
       "...             ...       ...              ...          ...\n",
       "2273      Villanova      Yale             Yale         0.42\n",
       "2274  Virginia Tech      Yale    Virginia Tech         0.51\n",
       "2275      Wisconsin      Yale             Yale         0.45\n",
       "2276      Wright St      Yale             Yale         0.39\n",
       "2277        Wyoming      Yale          Wyoming         0.52\n",
       "\n",
       "[2278 rows x 4 columns]"
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    "results"
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   "id": "a2544988",
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       "             Team 1    Team 2  Probability Predicted Winner\n",
       "0             Akron   Alabama         0.51            Akron\n",
       "1             Akron   Arizona         0.46          Arizona\n",
       "2           Alabama   Arizona         0.65          Alabama\n",
       "3             Akron  Arkansas         0.51            Akron\n",
       "4           Alabama  Arkansas         0.67          Alabama\n",
       "...             ...       ...          ...              ...\n",
       "2273      Villanova      Yale         0.42             Yale\n",
       "2274  Virginia Tech      Yale         0.51    Virginia Tech\n",
       "2275      Wisconsin      Yale         0.45             Yale\n",
       "2276      Wright St      Yale         0.39             Yale\n",
       "2277        Wyoming      Yale         0.52          Wyoming\n",
       "\n",
       "[2278 rows x 4 columns]"
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   "execution_count": 32,
   "id": "1d75f9ba",
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     "text": [
      "C:\\Users\\aheri\\anaconda3\\envs\\mlenv\\lib\\site-packages\\pandas\\core\\frame.py:5047: SettingWithCopyWarning: \n",
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       "             Team 1    Team 2  P(T1 Win) Predicted Winner\n",
       "0             Akron   Alabama       0.51            Akron\n",
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       "2277        Wyoming      Yale       0.52          Wyoming\n",
       "\n",
       "[2278 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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    "results.rename(columns = {'Probability':'P(T1 Win)'}, inplace = True)\n",
    "results"
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  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eea1644c",
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   "outputs": [
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Yale</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2278 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team 1    Team 2  P(T1 Win) Predicted Winner  Actual Winner\n",
       "0             Akron   Alabama       0.51            Akron            NaN\n",
       "1             Akron   Arizona       0.46          Arizona            NaN\n",
       "2           Alabama   Arizona       0.65          Alabama            NaN\n",
       "3             Akron  Arkansas       0.51            Akron            NaN\n",
       "4           Alabama  Arkansas       0.67          Alabama            NaN\n",
       "...             ...       ...        ...              ...            ...\n",
       "2273      Villanova      Yale       0.42             Yale            NaN\n",
       "2274  Virginia Tech      Yale       0.51    Virginia Tech            NaN\n",
       "2275      Wisconsin      Yale       0.45             Yale            NaN\n",
       "2276      Wright St      Yale       0.39             Yale            NaN\n",
       "2277        Wyoming      Yale       0.52          Wyoming            NaN\n",
       "\n",
       "[2278 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['Actual Winner'] = np.nan\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4d7116e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('model_results.csv', encoding='utf-8', index=False)"
   ]
  }
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