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 pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
import joblib
root_path = "models"def reprocess_dataset():#load dataresults = pd.read_csv('datasets/WorldCupMatches.csv', encoding='gbk')#Adding goal difference and establishing who is the winnerwinner = []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 columnresults['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 2022worldcup_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 matchoutcomesdf_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']=2df_teams_new.loc[df_teams_new.winning_team == 'Draw', 'winning_team']=1df_teams_new.loc[df_teams_new.winning_team == df_teams_new['Away Team Name'], 'winning_team']=0print(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 pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
import matplotlib.ticker as plticker
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import svm
import sklearn as sklearn
from sklearn.feature_extraction import DictVectorizer
from sklearn.naive_bayes import  MultinomialNB
from sklearn.ensemble import RandomForestClassifier
import joblib
from sklearn.metrics import classification_report
from xgboost import XGBClassifier
from 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 setsX_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 setsX_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 setsX_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 setsX_train = Xy_train = yX_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 setsX_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: 0.730
Probability of Draw: 0.220
Probability of  Qatar  winning: 0.050
[2]
[[0.02342857 0.21770455 0.75886688]]
Probability of  England  winning: 0.759
Probability of Draw: 0.218
Probability 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 Truereturn 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')returnif not verify_team_name(team_b) :print(team_b, ' is not correct')returnlogreg = 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)