目录

1. 下载数据集

2. 数据预处理

3. 模型训练与选择

4. 预测

1. 下载数据集

下载后数据如下：

FIFA World Cup | Kaggle

2. 数据预处理

reprocess_dataset() 方法是数据进行预处理。预处理过的数据如下：

save_dataset() 方法是对预处理过的数据，进行向量化。

完整代码如下：

import pandas as pdimport numpy as npfrom sklearn.feature_extraction import DictVectorizerimport joblibroot_path = "models"def reprocess_dataset():    #load data    results = pd.read_csv('datasets/WorldCupMatches.csv', encoding='gbk')    #Adding goal difference and establishing who is the winner    winner = []    for i in range (len(results['Home Team Name'])):        if results ['Home Team Goals'][i] > results['Away Team Goals'][i]:            winner.append(results['Home Team Name'][i])        elif results['Home Team Goals'][i] < results ['Away Team Goals'][i]:            winner.append(results['Away Team Name'][i])        else:            winner.append('Draw')    results['winning_team'] = winner    #adding goal difference column    results['goal_difference'] = np.absolute(results['Home Team Goals'] - results['Away Team Goals'])    # narrowing to team patcipating in the world cup, totally there are 32 football teams in 2022    worldcup_teams = ['Qatar','Germany','Denmark', 'Brazil','France','Belgium', 'Serbia',                      'Spain','Croatia', 'Switzerland', 'England','Netherlands', 'Argentina',' Iran',                      'Korea Republic','Saudi Arabia', 'Japan', 'Uruguay','Ecuador','Canada',                      'Senegal', 'Poland', 'Portugal','Tunisia',  'Morocco','Cameroon','USA',                      'Mexico','Wales','Australia','Costa Rica', 'Ghana']    df_teams_home = results[results['Home Team Name'].isin(worldcup_teams)]    df_teams_away = results[results['Away Team Name'].isin(worldcup_teams)]    df_teams = pd.concat((df_teams_home, df_teams_away))    df_teams.drop_duplicates()    df_teams.count()    #dropping columns that wll not affect matchoutcomes    df_teams_new =df_teams[[ 'Home Team Name','Away Team Name','winning_team']]    print(df_teams_new.head()  )                   #Building the model    #the prediction label: The winning_team column will show "2" if the home team has won, "1" if it was a tie, and "0" if the away team has won.    df_teams_new = df_teams_new.reset_index(drop=True)    df_teams_new.loc[df_teams_new.winning_team == df_teams_new['Home Team Name'],'winning_team']=2    df_teams_new.loc[df_teams_new.winning_team == 'Draw', 'winning_team']=1    df_teams_new.loc[df_teams_new.winning_team == df_teams_new['Away Team Name'], 'winning_team']=0    print(df_teams_new.count()   )    df_teams_new.to_csv('datasets/raw_train_data.csv', encoding='gbk', index =False)def save_dataset():    df_teams_new = pd.read_csv('datasets/raw_train_data.csv', encoding='gbk')    feature = df_teams_new[[ 'Home Team Name','Away Team Name']]    vec = DictVectorizer(sparse=False)    print(feature.to_dict(orient='records'))    X =vec.fit_transform(feature.to_dict(orient='records'))    X = X.astype('int')    print("===")    print(vec.get_feature_names())    print(vec.feature_names_)    y = df_teams_new[[ 'winning_team']]    y =y.astype('int')    print(X.shape)    print(y.shape)    joblib.dump(vec, root_path+"/vec.joblib")    np.savez('datasets/train_data', x= X, y = y)if __name__ == '__main__':    reprocess_dataset()    save_dataset();

3. 模型训练与选择

用不同的传统机器学习方法进行训练，训练后的模型比较

可以看到随机森林模型在测试集上准确率最高，所以我们可以用它来做预测。

下面是完整训练代码：

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsimport matplotlib.ticker as tickerimport matplotlib.ticker as pltickerfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionfrom sklearn import svmimport sklearn as sklearnfrom sklearn.feature_extraction import DictVectorizerfrom sklearn.naive_bayes import  MultinomialNBfrom sklearn.ensemble import RandomForestClassifierimport joblibfrom sklearn.metrics import classification_reportfrom xgboost import XGBClassifierfrom sklearn.metrics import confusion_matrixroot_path = "models_1"def get_dataset():    train_data = np.load('datasets/train_data.npz')    return train_datadef train_by_LogisticRegression(train_data):    X = train_data['x']    y = train_data['y']     # Separate train and test sets    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)    logreg = LogisticRegression()    logreg.fit(X_train, y_train)    joblib.dump(logreg, root_path+'/LogisticRegression_model.joblib')    score = logreg.score(X_train, y_train)    score2 = logreg.score(X_test, y_test)    print("LogisticRegression Training set accuracy: ", '%.3f'%(score))    print("LogisticRegression Test set accuracy: ", '%.3f'%(score2))def train_by_svm(train_data):    X = train_data['x']    y = train_data['y']    # Separate train and test sets    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)    model = svm.SVC(kernel='linear', verbose=True, probability=True)    model.fit(X_train, y_train)    joblib.dump(model, root_path+'/svm_model.joblib')    score = model.score(X_train, y_train)    score2 = model.score(X_test, y_test)    print("SVM Training set accuracy: ", '%.3f' % (score))    print("SVM Test set accuracy: ", '%.3f' % (score2))def train_by_naive_bayes(train_data):    X = train_data['x']    y = train_data['y']    # Separate train and test sets    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)    model = MultinomialNB()    model.fit(X_train, y_train)    joblib.dump(model, root_path+'/naive_bayes_model.joblib')    score = model.score(X_train, y_train)    score2 = model.score(X_test, y_test)    print("naive_bayes Training set accuracy: ", '%.3f' % (score))    print("naive_bayes Test set accuracy: ", '%.3f' % (score2))def train_by_random_forest(train_data):    X = train_data['x']    y = train_data['y']    # Separate train and test sets    X_train = X    y_train = y    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)    model = RandomForestClassifier(criterion='gini', max_features='sqrt')    model.fit(X_train, y_train)    joblib.dump(model, root_path+'/random_forest_model.joblib')    score = model.score(X_train, y_train)    score2 = model.score(X_test, y_test)    print("random forest Training set accuracy: ", '%.3f' % (score))    print("random forest Test set accuracy: ", '%.3f' % (score2))def train_by_xgb(train_data):    X = train_data['x']    y = train_data['y']    # Separate train and test sets    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)    model = XGBClassifier(use_label_encoder=False)    model.fit(X_train, y_train)    joblib.dump(model, root_path+'/xgb_model.joblib')    score = model.score(X_train, y_train)    score2 = model.score(X_test, y_test)    print("xgb Training set accuracy: ", '%.3f' % (score))    print("xgb Test set accuracy: ", '%.3f' % (score2))    y_pred = model.predict(X_test)    report = classification_report(y_test, y_pred, output_dict=True)    # show_confusion_matrix(y_test, y_pred)    print(report)def show_confusion_matrix(y_true, y_pred, pic_name = "confusion_matrix"):    confusion = confusion_matrix(y_true=y_true, y_pred=y_pred)    print(confusion)    sns.heatmap(confusion, annot=True, cmap= 'Blues', xticklabels=['0','1','2'], yticklabels=['0','1','2'], fmt = '.20g')    plt.xlabel('Predicted class')    plt.ylabel('Actual Class')    plt.title(pic_name)    # plt.savefig('pic/' + pic_name)    plt.show()if __name__ == '__main__':    train_data = get_dataset()    train_by_LogisticRegression(train_data)    train_by_svm(train_data)    train_by_naive_bayes(train_data)    train_by_random_forest(train_data)    train_by_xgb(train_data)

4. 预测

执行下面预测代码，结果是Ecuador胜于Qatar, 英国队胜于伊朗队。

[2][[0.05    0.22033333 0.72966667]]Probability of Ecuador winning: 0.730Probability of Draw: 0.220Probability of Qatar winning: 0.050[2][[0.02342857 0.21770455 0.75886688]]Probability of England winning: 0.759Probability of Draw: 0.218Probability of  Iran winning: 0.023

完整代码

import joblibworldcup_teams = ['Qatar','Germany','Denmark', 'Brazil','France','Belgium', 'Serbia',                  'Spain','Croatia', 'Switzerland', 'England','Netherlands', 'Argentina',' Iran',                  'Korea Republic','Saudi Arabia', 'Japan', 'Uruguay','Ecuador','Canada',                  'Senegal', 'Poland', 'Portugal','Tunisia',  'Morocco','Cameroon','USA',                  'Mexico','Wales','Australia','Costa Rica', 'Ghana']root_path = "models_1"def verify_team_name(team_name):    for worldcup_team in worldcup_teams:        if team_name==worldcup_team:            return True    return Falsedef predict(model_dir =root_path+'/LogisticRegression_model.joblib', team_a='France', team_b = 'Mexico'):    if not verify_team_name(team_a):        print(team_a, ' is not correct')        return    if not verify_team_name(team_b) :        print(team_b, ' is not correct')        return    logreg = joblib.load(model_dir)    input_x = [{'Home Team Name': team_a, 'Away Team Name': team_b}]    vec = joblib.load(root_path+"/vec.joblib")    input_x = vec.transform(input_x)    result = logreg.predict(input_x)    print(result)    result1 = logreg.predict_proba(input_x)    print(result1)    print('Probability of ',team_a , ' winning:', '%.3f'%result1[0][2])    print('Probability of Draw:', '%.3f' % result1[0][1])    print('Probability of ', team_b, ' winning:', '%.3f' % result1[0][0])if __name__ == '__main__':    team_a = 'Ecuador'    team_b = 'Qatar'    predict('models/random_forest_model.joblib', team_a, team_b)    team_a = 'England'    team_b = ' Iran'    predict('models/random_forest_model.joblib', team_a, team_b)