ada_boost.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 AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   Unnamed: 0  country_id  league_id  season  stage        date  match_api_id  \\\n",
       "0           0        1729       1729    2008      1  2008-08-17        489042   \n",
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       "\n",
       "   home_team_api_id  away_team_api_id  home_team_goal    ...     \\\n",
       "0             10260             10261               1    ...      \n",
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       "\n",
       "   away_chanceCreationShootingClass  away_chanceCreationPositioningClass  \\\n",
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       "\n",
       "   away_defenceAggressionClass  away_defenceTeamWidth  \\\n",
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       "4                       Double                     45   \n",
       "\n",
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       "\n",
       "[5 rows x 159 columns]"
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     },
     "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": [
    {
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       "   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",
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       "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",
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       "\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": 4,
   "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": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=100, random_state=123456)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a random forest classifier \n",
    "cls = AdaBoostClassifier(n_estimators=100, random_state=123456)\n",
    "cls.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy score: 0.796\n"
     ]
    }
   ],
   "source": [
    "# predict and get metrics\n",
    "predicted = cls.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, predicted)\n",
    "\n",
    "print(f'Mean accuracy score: {accuracy:.3}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e714c45710>"
      ]
     },
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score: 0.7956521739130434\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
}