random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3d%</th>\n",
       "      <th>3d%_allowed</th>\n",
       "      <th>TOP</th>\n",
       "      <th>date</th>\n",
       "      <th>first_downs</th>\n",
       "      <th>first_downs_allowed</th>\n",
       "      <th>ha</th>\n",
       "      <th>margin</th>\n",
       "      <th>opp</th>\n",
       "      <th>pass_yards</th>\n",
       "      <th>...</th>\n",
       "      <th>rush_yards</th>\n",
       "      <th>rush_yards_allowed</th>\n",
       "      <th>sacked</th>\n",
       "      <th>sacks</th>\n",
       "      <th>takeaways</th>\n",
       "      <th>team</th>\n",
       "      <th>total_points</th>\n",
       "      <th>total_yards</th>\n",
       "      <th>total_yards_allowed</th>\n",
       "      <th>turnovers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>28.350000</td>\n",
       "      <td>9/10/2009</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>away</td>\n",
       "      <td>-3</td>\n",
       "      <td>PIT</td>\n",
       "      <td>234</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>TEN</td>\n",
       "      <td>3</td>\n",
       "      <td>320</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>36.183333</td>\n",
       "      <td>9/10/2009</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>home</td>\n",
       "      <td>3</td>\n",
       "      <td>TEN</td>\n",
       "      <td>321</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>PIT</td>\n",
       "      <td>3</td>\n",
       "      <td>357</td>\n",
       "      <td>320</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>29.116667</td>\n",
       "      <td>9/13/2009</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>away</td>\n",
       "      <td>-12</td>\n",
       "      <td>ATL</td>\n",
       "      <td>163</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>MIA</td>\n",
       "      <td>26</td>\n",
       "      <td>259</td>\n",
       "      <td>281</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>30.883333</td>\n",
       "      <td>9/13/2009</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>home</td>\n",
       "      <td>12</td>\n",
       "      <td>MIA</td>\n",
       "      <td>213</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>ATL</td>\n",
       "      <td>26</td>\n",
       "      <td>281</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>26.550000</td>\n",
       "      <td>9/13/2009</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>away</td>\n",
       "      <td>5</td>\n",
       "      <td>CIN</td>\n",
       "      <td>227</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>DEN</td>\n",
       "      <td>19</td>\n",
       "      <td>302</td>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        3d%  3d%_allowed        TOP       date  first_downs  \\\n",
       "0  0.307692     0.285714  28.350000  9/10/2009           18   \n",
       "1  0.285714     0.307692  36.183333  9/10/2009           19   \n",
       "2  0.363636     0.400000  29.116667  9/13/2009           16   \n",
       "3  0.400000     0.363636  30.883333  9/13/2009           19   \n",
       "4  0.250000     0.333333  26.550000  9/13/2009           10   \n",
       "\n",
       "   first_downs_allowed    ha  margin  opp  pass_yards    ...      rush_yards  \\\n",
       "0                   19  away      -3  PIT         234    ...              86   \n",
       "1                   18  home       3  TEN         321    ...              36   \n",
       "2                   19  away     -12  ATL         163    ...              96   \n",
       "3                   16  home      12  MIA         213    ...              68   \n",
       "4                   16  away       5  CIN         227    ...              75   \n",
       "\n",
       "   rush_yards_allowed  sacked  sacks  takeaways team  total_points  \\\n",
       "0                  36       1      4          3  TEN             3   \n",
       "1                  86       4      1          2  PIT             3   \n",
       "2                  68       4      2          0  MIA            26   \n",
       "3                  96       2      4          4  ATL            26   \n",
       "4                  86       3      3          2  DEN            19   \n",
       "\n",
       "   total_yards  total_yards_allowed  turnovers  \n",
       "0          320                  357          2  \n",
       "1          357                  320          3  \n",
       "2          259                  281          4  \n",
       "3          281                  259          0  \n",
       "4          302                  307          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/nfl.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['3d%', '3d%_allowed', 'TOP', 'date', 'first_downs',\n",
       "       'first_downs_allowed', 'ha', 'margin', 'opp', 'pass_yards',\n",
       "       'pass_yards_allowed', 'penalty_yards', 'plays', 'points',\n",
       "       'points_allowed', 'result', 'rush_yards', 'rush_yards_allowed',\n",
       "       'sacked', 'sacks', 'takeaways', 'team', 'total_points', 'total_yards',\n",
       "       'total_yards_allowed', 'turnovers'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df[\"result\"]\n",
    "target_names = [\"win\", \"loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3d%</th>\n",
       "      <th>3d%_allowed</th>\n",
       "      <th>TOP</th>\n",
       "      <th>first_downs</th>\n",
       "      <th>first_downs_allowed</th>\n",
       "      <th>pass_yards</th>\n",
       "      <th>pass_yards_allowed</th>\n",
       "      <th>penalty_yards</th>\n",
       "      <th>plays</th>\n",
       "      <th>rush_yards</th>\n",
       "      <th>...</th>\n",
       "      <th>team_NYG</th>\n",
       "      <th>team_NYJ</th>\n",
       "      <th>team_OAK</th>\n",
       "      <th>team_PHI</th>\n",
       "      <th>team_PIT</th>\n",
       "      <th>team_SEA</th>\n",
       "      <th>team_SFO</th>\n",
       "      <th>team_TBB</th>\n",
       "      <th>team_TEN</th>\n",
       "      <th>team_WAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>28.350000</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>234</td>\n",
       "      <td>321</td>\n",
       "      <td>28</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>36.183333</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>321</td>\n",
       "      <td>234</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>36</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>29.116667</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>163</td>\n",
       "      <td>213</td>\n",
       "      <td>27</td>\n",
       "      <td>56</td>\n",
       "      <td>96</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>30.883333</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>213</td>\n",
       "      <td>163</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>26.550000</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>227</td>\n",
       "      <td>221</td>\n",
       "      <td>39</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        3d%  3d%_allowed        TOP  first_downs  first_downs_allowed  \\\n",
       "0  0.307692     0.285714  28.350000           18                   19   \n",
       "1  0.285714     0.307692  36.183333           19                   18   \n",
       "2  0.363636     0.400000  29.116667           16                   19   \n",
       "3  0.400000     0.363636  30.883333           19                   16   \n",
       "4  0.250000     0.333333  26.550000           10                   16   \n",
       "\n",
       "   pass_yards  pass_yards_allowed  penalty_yards  plays  rush_yards    ...     \\\n",
       "0         234                 321             28     61          86    ...      \n",
       "1         321                 234             60     70          36    ...      \n",
       "2         163                 213             27     56          96    ...      \n",
       "3         213                 163             35     65          68    ...      \n",
       "4         227                 221             39     51          75    ...      \n",
       "\n",
       "   team_NYG  team_NYJ  team_OAK  team_PHI  team_PIT  team_SEA  team_SFO  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         1         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   team_TBB  team_TEN  team_WAS  \n",
       "0         0         1         0  \n",
       "1         0         0         0  \n",
       "2         0         0         0  \n",
       "3         0         0         0  \n",
       "4         0         0         0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.drop([\"result\", \"date\", \"margin\", \"points\", \"points_allowed\"], axis=1)\n",
    "data = pd.get_dummies(data)\n",
    "feature_names = data.columns\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72046589018302831"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82612312811980038"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=200)\n",
    "rf = rf.fit(X_train, y_train)\n",
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.083166510742781322, 'takeaways'),\n",
       " (0.082277738384514035, 'turnovers'),\n",
       " (0.080001225090098202, 'TOP'),\n",
       " (0.066723181403600174, 'rush_yards'),\n",
       " (0.064860418611624865, 'rush_yards_allowed'),\n",
       " (0.052304854722960668, 'total_yards'),\n",
       " (0.049525396430826929, 'total_yards_allowed'),\n",
       " (0.044854593998618075, '3d%'),\n",
       " (0.043655722411562278, '3d%_allowed'),\n",
       " (0.039354514597200255, 'sacks'),\n",
       " (0.037189483632583446, 'sacked'),\n",
       " (0.034064496206968213, 'plays'),\n",
       " (0.032118456294455894, 'pass_yards'),\n",
       " (0.031789416942641105, 'penalty_yards'),\n",
       " (0.031621275401023601, 'first_downs_allowed'),\n",
       " (0.031250894155760441, 'pass_yards_allowed'),\n",
       " (0.030820639925983814, 'first_downs'),\n",
       " (0.029888036346468083, 'total_points'),\n",
       " (0.0070321109396787942, 'ha_home'),\n",
       " (0.0061741659995368002, 'ha_away'),\n",
       " (0.0047965570967533231, 'team_NEP'),\n",
       " (0.0038374474088605729, 'opp_NEP'),\n",
       " (0.0025041175849208563, 'opp_CLE'),\n",
       " (0.0024721864306157543, 'team_WAS'),\n",
       " (0.0024313493880237643, 'opp_BAL'),\n",
       " (0.0023048417460422212, 'team_TBB'),\n",
       " (0.0022858111874414573, 'opp_JAX'),\n",
       " (0.0022732239732716018, 'team_DET'),\n",
       " (0.0022492216730992933, 'team_CLE'),\n",
       " (0.0022457065467115455, 'opp_DET'),\n",
       " (0.0022279167143727241, 'opp_MIA'),\n",
       " (0.0022273065573098745, 'team_BAL'),\n",
       " (0.0022079014011855084, 'team_CIN'),\n",
       " (0.0021990681691174806, 'opp_WAS'),\n",
       " (0.0021423602851534666, 'team_IND'),\n",
       " (0.0021253347255979702, 'opp_KCC'),\n",
       " (0.0021085299752286136, 'opp_GBP'),\n",
       " (0.0020744536283640435, 'team_JAX'),\n",
       " (0.002010802187472223, 'team_NYJ'),\n",
       " (0.0020057499423195542, 'team_PIT'),\n",
       " (0.0019627891853953739, 'team_MIA'),\n",
       " (0.0019557246852628233, 'opp_NYJ'),\n",
       " (0.0019507464330740477, 'team_LAR'),\n",
       " (0.0019422037974059045, 'opp_TBB'),\n",
       " (0.0019376193734660413, 'opp_IND'),\n",
       " (0.0019282167346829477, 'team_KCC'),\n",
       " (0.0019134022461606806, 'team_PHI'),\n",
       " (0.0018787269037034704, 'opp_DAL'),\n",
       " (0.0018737299958889305, 'team_GBP'),\n",
       " (0.0018387045284034011, 'opp_PIT'),\n",
       " (0.0018182144248988072, 'team_DAL'),\n",
       " (0.0018098043124094598, 'team_MIN'),\n",
       " (0.0017442100028647251, 'team_LAC'),\n",
       " (0.0017422335689999027, 'team_ARI'),\n",
       " (0.001741071108872695, 'opp_LAR'),\n",
       " (0.0017401242976676518, 'opp_CHI'),\n",
       " (0.0017370750404931336, 'team_TEN'),\n",
       " (0.0017285016087213679, 'opp_DEN'),\n",
       " (0.0017116748212853234, 'opp_CAR'),\n",
       " (0.001706620906472426, 'team_CHI'),\n",
       " (0.0016988426270579493, 'team_SEA'),\n",
       " (0.0016876945998721238, 'opp_SEA'),\n",
       " (0.0016661225305308358, 'opp_MIN'),\n",
       " (0.0016576631424089622, 'opp_ARI'),\n",
       " (0.0016274489376670437, 'team_NYG'),\n",
       " (0.0016241258402620667, 'opp_OAK'),\n",
       " (0.0016096211470210673, 'team_OAK'),\n",
       " (0.0015829121222910656, 'opp_NOS'),\n",
       " (0.001564343600863926, 'opp_CIN'),\n",
       " (0.001542976383388619, 'team_ATL'),\n",
       " (0.0015260819579120926, 'opp_ATL'),\n",
       " (0.0015155713491363755, 'team_CAR'),\n",
       " (0.0014967542127010355, 'opp_NYG'),\n",
       " (0.001491864117686634, 'team_NOS'),\n",
       " (0.0014889031374092435, 'opp_TEN'),\n",
       " (0.0014705569516345793, 'team_HOU'),\n",
       " (0.0014165386750334087, 'opp_PHI'),\n",
       " (0.0014038161497735601, 'opp_LAC'),\n",
       " (0.0013959222910231482, 'team_BUF'),\n",
       " (0.0013876407866174087, 'opp_HOU'),\n",
       " (0.0013837288896867822, 'team_DEN'),\n",
       " (0.0013805922740695556, 'opp_BUF'),\n",
       " (0.0012071126903787535, 'team_SFO'),\n",
       " (0.0011087527486959106, 'opp_SFO')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(rf.feature_importances_, feature_names), reverse=True)"
   ]
  }
 ],
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