问为什么我的logistic回归模型获得了100%的准确性？

EN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn 
from sklearn import preprocessing
import seaborn as sns
%matplotlib inline

 df =pd.read_csv('./EngineeredData_2.csv')
    df =df.dropna()

X= df.drop (['Week','Div', 'Date', 'HomeTeam', 'AwayTeam','HTHG', 'HTAG','HTR', 
            'FTAG', 'FTHG','HGKPP', 'AGKPP', 'FTR'], axis =1)

 L = preprocessing.LabelEncoder ()
    matchresults = L.fit_transform (list (df['FTR']))
    y =list(matchresults)

from sklearn.model_selection import train_test_split
X_tng,X_tst, y_tng, y_tst =train_test_split (X, y, test_size = 50, shuffle=False)
X_tng.head()

from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression ()

 logreg.fit (X_tng, y_tng)

    acc = logreg. score (X_tst, y_tst)
    print (acc)