regression model

knn=KNeighborsRegressor()
svr=SVR()
lr=LinearRegression()
dt=DecisionTreeRegressor()
gbm=GradientBoostingRegressor()
ada=AdaBoostRegressor()
rfr=RandomForestRegressor()
xgb=XGBRegressor()
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models=[]
models.append(('KNeighborsRegressor',knn))
models.append(('SVR',svr))
models.append(('LinearRegression',lr))
models.append(('DecisionTreeRegressor',dt))
models.append(('GradientBoostingRegressor',gbm))
models.append(('AdaBoostRegressor',ada))
models.append(('RandomForestRegressor',rfr))
models.append(('XGBRegressor',xgb))
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from sklearn.metrics import r2_score,mean_squared_error
from sklearn.model_selection import train_test_split,cross_val_score
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=42)
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Model=[]
r2score=[]
rmse=[]
cv=[]

for name,model in models:
    print('*****************',name,'*******************')
    print('\n')
    Model.append(name)
    model.fit(x_train,y_train)
    print(model)
    pre=model.predict(x_test)
    print('\n')
    score=r2_score(y_test,pre)
    print('R2score  -',score)
    r2score.append(score*100)
    print('\n')
    sc=cross_val_score(model,x,y,cv=5,scoring='r2').mean()
    print('cross_val_score  -',sc)
    cv.append(sc*100)
    print('\n')
    rmsescore=np.sqrt(mean_squared_error(y_test,pre))
    print('rmse_score  -',rmsescore)
    rmse.append(rmsescore)
    print('\n')
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result=pd.DataFrame({'Model':Model,'R2_score':r2score,'RMSEscore':rmse,'Cross_val_score':cv})
result