random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\akhireld\\documents\\projects\\football-data-analysis\\venv\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>...</th>\n",
       "      <th>away_chanceCreationShootingClass</th>\n",
       "      <th>away_chanceCreationPositioningClass</th>\n",
       "      <th>away_defencePressure</th>\n",
       "      <th>away_defencePressureClass</th>\n",
       "      <th>away_defenceAggression</th>\n",
       "      <th>away_defenceAggressionClass</th>\n",
       "      <th>away_defenceTeamWidth</th>\n",
       "      <th>away_defenceTeamWidthClass</th>\n",
       "      <th>away_defenceDefenderLineClass</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-17</td>\n",
       "      <td>489042</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>45</td>\n",
       "      <td>Medium</td>\n",
       "      <td>65</td>\n",
       "      <td>Press</td>\n",
       "      <td>45</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "      <td>75811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16</td>\n",
       "      <td>489043</td>\n",
       "      <td>9825</td>\n",
       "      <td>8659</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>70</td>\n",
       "      <td>High</td>\n",
       "      <td>70</td>\n",
       "      <td>Double</td>\n",
       "      <td>70</td>\n",
       "      <td>Wide</td>\n",
       "      <td>Cover</td>\n",
       "      <td>60361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16</td>\n",
       "      <td>489044</td>\n",
       "      <td>8472</td>\n",
       "      <td>8650</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Lots</td>\n",
       "      <td>Organised</td>\n",
       "      <td>40</td>\n",
       "      <td>Medium</td>\n",
       "      <td>60</td>\n",
       "      <td>Press</td>\n",
       "      <td>40</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "      <td>48707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16</td>\n",
       "      <td>489045</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>35</td>\n",
       "      <td>Medium</td>\n",
       "      <td>70</td>\n",
       "      <td>Double</td>\n",
       "      <td>35</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "      <td>35303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-08-16</td>\n",
       "      <td>489047</td>\n",
       "      <td>8668</td>\n",
       "      <td>8655</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Organised</td>\n",
       "      <td>45</td>\n",
       "      <td>Medium</td>\n",
       "      <td>70</td>\n",
       "      <td>Double</td>\n",
       "      <td>45</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Cover</td>\n",
       "      <td>40157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 159 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  country_id  league_id  season  stage        date  match_api_id  \\\n",
       "0           0        1729       1729    2008      1  2008-08-17        489042   \n",
       "1           1        1729       1729    2008      1  2008-08-16        489043   \n",
       "2           2        1729       1729    2008      1  2008-08-16        489044   \n",
       "3           3        1729       1729    2008      1  2008-08-16        489045   \n",
       "4           4        1729       1729    2008      1  2008-08-16        489047   \n",
       "\n",
       "   home_team_api_id  away_team_api_id  home_team_goal    ...     \\\n",
       "0             10260             10261               1    ...      \n",
       "1              9825              8659               1    ...      \n",
       "2              8472              8650               0    ...      \n",
       "3              8654              8528               2    ...      \n",
       "4              8668              8655               2    ...      \n",
       "\n",
       "   away_chanceCreationShootingClass  away_chanceCreationPositioningClass  \\\n",
       "0                            Normal                            Organised   \n",
       "1                            Normal                            Organised   \n",
       "2                              Lots                            Organised   \n",
       "3                            Normal                            Organised   \n",
       "4                            Normal                            Organised   \n",
       "\n",
       "   away_defencePressure  away_defencePressureClass  away_defenceAggression  \\\n",
       "0                    45                     Medium                      65   \n",
       "1                    70                       High                      70   \n",
       "2                    40                     Medium                      60   \n",
       "3                    35                     Medium                      70   \n",
       "4                    45                     Medium                      70   \n",
       "\n",
       "   away_defenceAggressionClass  away_defenceTeamWidth  \\\n",
       "0                        Press                     45   \n",
       "1                       Double                     70   \n",
       "2                        Press                     40   \n",
       "3                       Double                     35   \n",
       "4                       Double                     45   \n",
       "\n",
       "   away_defenceTeamWidthClass  away_defenceDefenderLineClass  capacity  \n",
       "0                      Normal                          Cover     75811  \n",
       "1                        Wide                          Cover     60361  \n",
       "2                      Normal                          Cover     48707  \n",
       "3                      Normal                          Cover     35303  \n",
       "4                      Normal                          Cover     40157  \n",
       "\n",
       "[5 rows x 159 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load training dataset\n",
    "df = pd.read_csv('result.csv')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>country_id</th>\n",
       "      <th>league_id</th>\n",
       "      <th>season</th>\n",
       "      <th>stage</th>\n",
       "      <th>match_api_id</th>\n",
       "      <th>home_team_api_id</th>\n",
       "      <th>away_team_api_id</th>\n",
       "      <th>home_team_goal</th>\n",
       "      <th>away_team_goal</th>\n",
       "      <th>...</th>\n",
       "      <th>away_chanceCreationPositioningClass_Organised</th>\n",
       "      <th>away_defencePressureClass_Deep</th>\n",
       "      <th>away_defencePressureClass_High</th>\n",
       "      <th>away_defencePressureClass_Medium</th>\n",
       "      <th>away_defenceAggressionClass_Double</th>\n",
       "      <th>away_defenceAggressionClass_Press</th>\n",
       "      <th>away_defenceTeamWidthClass_Narrow</th>\n",
       "      <th>away_defenceTeamWidthClass_Normal</th>\n",
       "      <th>away_defenceTeamWidthClass_Wide</th>\n",
       "      <th>away_defenceDefenderLineClass_Cover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>489042</td>\n",
       "      <td>10260</td>\n",
       "      <td>10261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>489043</td>\n",
       "      <td>9825</td>\n",
       "      <td>8659</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>489044</td>\n",
       "      <td>8472</td>\n",
       "      <td>8650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>489045</td>\n",
       "      <td>8654</td>\n",
       "      <td>8528</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>489047</td>\n",
       "      <td>8668</td>\n",
       "      <td>8655</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 514 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  country_id  league_id  season  stage  match_api_id  \\\n",
       "0           0        1729       1729    2008      1        489042   \n",
       "1           1        1729       1729    2008      1        489043   \n",
       "2           2        1729       1729    2008      1        489044   \n",
       "3           3        1729       1729    2008      1        489045   \n",
       "4           4        1729       1729    2008      1        489047   \n",
       "\n",
       "   home_team_api_id  away_team_api_id  home_team_goal  away_team_goal  \\\n",
       "0             10260             10261               1               1   \n",
       "1              9825              8659               1               0   \n",
       "2              8472              8650               0               1   \n",
       "3              8654              8528               2               1   \n",
       "4              8668              8655               2               3   \n",
       "\n",
       "                  ...                   \\\n",
       "0                 ...                    \n",
       "1                 ...                    \n",
       "2                 ...                    \n",
       "3                 ...                    \n",
       "4                 ...                    \n",
       "\n",
       "   away_chanceCreationPositioningClass_Organised  \\\n",
       "0                                              1   \n",
       "1                                              1   \n",
       "2                                              1   \n",
       "3                                              1   \n",
       "4                                              1   \n",
       "\n",
       "   away_defencePressureClass_Deep  away_defencePressureClass_High  \\\n",
       "0                               0                               0   \n",
       "1                               0                               1   \n",
       "2                               0                               0   \n",
       "3                               0                               0   \n",
       "4                               0                               0   \n",
       "\n",
       "   away_defencePressureClass_Medium  away_defenceAggressionClass_Double  \\\n",
       "0                                 1                                   0   \n",
       "1                                 0                                   1   \n",
       "2                                 1                                   0   \n",
       "3                                 1                                   1   \n",
       "4                                 1                                   1   \n",
       "\n",
       "   away_defenceAggressionClass_Press  away_defenceTeamWidthClass_Narrow  \\\n",
       "0                                  1                                  0   \n",
       "1                                  0                                  0   \n",
       "2                                  1                                  0   \n",
       "3                                  0                                  0   \n",
       "4                                  0                                  0   \n",
       "\n",
       "   away_defenceTeamWidthClass_Normal  away_defenceTeamWidthClass_Wide  \\\n",
       "0                                  1                                0   \n",
       "1                                  0                                1   \n",
       "2                                  1                                0   \n",
       "3                                  1                                0   \n",
       "4                                  1                                0   \n",
       "\n",
       "   away_defenceDefenderLineClass_Cover  \n",
       "0                                    1  \n",
       "1                                    1  \n",
       "2                                    1  \n",
       "3                                    1  \n",
       "4                                    1  \n",
       "\n",
       "[5 rows x 514 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pre process data\n",
    "# 1. One hot encoding\n",
    "df = pd.get_dummies(df)\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and testing dataset\n",
    "\n",
    "feature_names = df.columns[1:].tolist()\n",
    "feature_names.remove('match_result')\n",
    "\n",
    "# Removing player ids\n",
    "for col in [f'home_player_{i}' for i in range(1, 12)]:\n",
    "    feature_names.remove(col)\n",
    "for col in [f'away_player_{i}' for i in range(1, 12)]:\n",
    "    feature_names.remove(col)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[feature_names], df['match_result'], test_size=0.2, random_state=123456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=True, random_state=123456, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a random forest classifier \n",
    "cls = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=123456)\n",
    "cls.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-bag score estimate: 0.649\n",
      "Mean accuracy score: 0.63\n"
     ]
    }
   ],
   "source": [
    "# predict and get metrics\n",
    "predicted = cls.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predicted)\n",
    "\n",
    "print(f'Out-of-bag score estimate: {cls.oob_score_:.3}')\n",
    "print(f'Mean accuracy score: {accuracy:.3}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d0b0f25080>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate confusion matrix\n",
    "import seaborn as sns\n",
    "\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, predicted), columns=['home_win', 'home_lose', 'draw'])\n",
    "sns.heatmap(cm, annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score: 0.6304347826086957\n"
     ]
    }
   ],
   "source": [
    "# Calculate f1 score\n",
    "from sklearn.metrics import f1_score\n",
    "f1 = f1_score(y_test, predicted, average='micro')  \n",
    "\n",
    "print(f'f1 score: {f1}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}