sklearn cross_val_score() returns NaN values

i'm trying to predict next customer purchase to my job. I followed a guide, but when i tried to use cross_val_score() function, it returns NaN values.Google Colab notebook screenshot

Variables:

• X_train is a dataframe
• X_test is a dataframe
• y_train is a list
• y_test is a list

Code:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)
X_train = X_train.reset_index(drop=True)
X_train
X_test = X_test.reset_index(drop=True)

y_train = y_train.astype('float')
y_test = y_test.astype('float')

models = []
models.append(("LR",LogisticRegression()))
models.append(("NB",GaussianNB()))
models.append(("RF",RandomForestClassifier()))
models.append(("SVC",SVC()))
models.append(("Dtree",DecisionTreeClassifier()))
models.append(("XGB",xgb.XGBClassifier()))
models.append(("KNN",KNeighborsClassifier()))´

for name,model in models:
   kfold = KFold(n_splits=2, random_state=22)
   cv_result = cross_val_score(model,X_train,y_train, cv = kfold,scoring = "accuracy")
   print(name, cv_result)
>>
LR [nan nan]
NB [nan nan]
RF [nan nan]
SVC [nan nan]
Dtree [nan nan]
XGB [nan nan]
KNN [nan nan]

help me please!

Well thanks everyone for your answers. The answer of Anna helped me a lot!, but i don't used X_train.values, instead i assigned an unique ID to the Customers, then dropped Customers column and it works!

Now the models has this output :)

LR [0.73958333 0.74736842]
NB [0.60416667 0.71578947]
RF [0.80208333 0.82105263]
SVC [0.79166667 0.77894737]
Dtree [0.82291667 0.83157895]
XGB [0.85416667 0.85263158]
KNN [0.79166667 0.75789474]