1. 背景

2. 输入特征选择

3. 数据集-特征获取

4. 数据预处理

5. 模型训练与选择

6. 预测

7. 2018后新的数据

8. 个人总结

1. 背景

在这四年一次的足球世界杯即将来临之际，公司将要举办一个模型预测与人工预测的比赛活动，虽然本人不是足球迷也不是伪球迷，但是绷着对机器学习的热爱，决定凭借自己匮乏的足球知识建个模型参加这个比赛。无论成败与否，享受其中即可。

2. 输入特征选择

特征的选择通俗的讲就是选择什么特征最能影响足球比赛。能我让这个不懂足球的人想到的是下面这些特征：

1. 明星队员：比如梅西，C罗，姆巴佩，内马尔等等。但是这些球员的实力怎么衡量呢？还有就是不断有新的球星出现，是不是这个特征有点多？再有这个数据是不是好得到呢？

2. 主客场：听说篮球这个特征很重要，但是世界杯就只有主办方一家是主场，其它是客场。

3. 裁判：有些比赛裁判可能偏向一个国家队，这就操成该罚球的不罚，但是裁判这个特征随机性太强，每届都在变，而且你也不知道他偏向哪个队。

4. 教练：感觉也是特征，而且比较稳定。但是怎么衡量呢

5.球队状态：影响是有，但感觉无从下手的样子。

6. 观众：历史数据应该还有办法，但是新比赛拿不到。

最后就是球队了，如果球队的整体实力比较高，哪怕是没有明星队员，结果也不差，而且球队的整体实力其实暗含教练，球员和明星球员的这几个特征的。综合这些特征个方面的因素和提取的难易程度，最后选择了球队名字作为输入特征。

3. 数据集-特征获取

由于比赛来的太突然，就几天就要开始比赛当时，所以带着侥幸的心理在网上搜了一下是不是有相关的数据集，果然有很多。最终选择Kaggle里下载已经整理好的数据而且还是免费的。

下载数据集

下载后数据如下：

FIFA World Cup | Kaggle

百度网盘也可以下载

链接: https://pan.baidu.com/s/1ky-73f9YTe2o1YI1TyMt9A 提取码: 1pt6

4. 数据预处理

虽然Kaggle这里提供的数据有很多特征，但是我们只需要三个特征，模型的输入是两个队的名字，模型的输出是结果（2:Home Team队胜，1:平，0:Away Team 队胜）。

数据预处理大致的步骤是

从Excel里加载数据；
根据每队的得分生成新队列Winning_Team；
去掉和本次世界杯无关的国家队；
去掉无关的特征；
winning_team数值化；
保持新的Excel；

代码如下：

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
import joblib
root_path = "models"#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 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']=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']=0print(df_teams_new.count()   )
df_teams_new.to_csv('datasets/raw_train_data.csv', encoding='gbk', index =False)

数据预处理后会在datasets文件下生成raw_train_data.csv，里面的数据如下：

保持数据集大致步骤如下：

加载数据集；
利用DictVectorizer对两个队伍的名字进行数字化；
保持数据集方便后面训练模型用。

代码如下：

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)

以上代码会在datasets文件下生成train_data.npz文件

数据预处理的完整代码如下：

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();

reprocess_dataset() 方法是数据进行预处理。

save_dataset() 方法是对预处理过的数据，进行向量化。

由于这个数据集里没有2018年后的数据，所以我自己又手动收集了一下2018后新的数据。可以把这个新的数据集和上面预处理后数据集合在一起。

2018年和2022年的数据可以下面百度网盘下载
链接: https://pan.baidu.com/s/1fe_z6kRXB8T69wx1HBxO8g 提取码: o55d

5. 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

Model	Training Accuracy	Test Accuracy
Logistic Regression（逻辑回归）	67.40%	61.60%
SVM（支持向量机）	67.30%	62.70%
Naive Bayes（朴素贝叶斯）	65.50%	63.80%
Random Forest（随机森林）	90.80%	65.50%
XGB（X (Extreme) GBoosted）	75.30%	62.00%

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"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)

执行上面代码会生成下面文件：

6. 预测

预测逻辑大致如下：

输入两个队伍名字
对队伍名字进行验证
加载模型
利用模型进行预测并输出每个的概率

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

预测的完整代码如下：

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"
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)