regression model

1knn=KNeighborsRegressor()
2svr=SVR()
3lr=LinearRegression()
4dt=DecisionTreeRegressor()
5gbm=GradientBoostingRegressor()
6ada=AdaBoostRegressor()
7rfr=RandomForestRegressor()
8xgb=XGBRegressor()
9------------------------------------------------------------------------
10models=[]
11models.append(('KNeighborsRegressor',knn))
12models.append(('SVR',svr))
13models.append(('LinearRegression',lr))
14models.append(('DecisionTreeRegressor',dt))
15models.append(('GradientBoostingRegressor',gbm))
16models.append(('AdaBoostRegressor',ada))
17models.append(('RandomForestRegressor',rfr))
18models.append(('XGBRegressor',xgb))
19-------------------------------------------------------------------------
20from sklearn.metrics import r2_score,mean_squared_error
21from sklearn.model_selection import train_test_split,cross_val_score
22x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=42)
23-------------------------------------------------------------------------
24Model=[]
25r2score=[]
26rmse=[]
27cv=[]
28
29for name,model in models:
30    print('*****************',name,'*******************')
31    print('\n')
32    Model.append(name)
33    model.fit(x_train,y_train)
34    print(model)
35    pre=model.predict(x_test)
36    print('\n')
37    score=r2_score(y_test,pre)
38    print('R2score  -',score)
39    r2score.append(score*100)
40    print('\n')
41    sc=cross_val_score(model,x,y,cv=5,scoring='r2').mean()
42    print('cross_val_score  -',sc)
43    cv.append(sc*100)
44    print('\n')
45    rmsescore=np.sqrt(mean_squared_error(y_test,pre))
46    print('rmse_score  -',rmsescore)
47    rmse.append(rmsescore)
48    print('\n')
49 ------------------------------------------------------------------------
50result=pd.DataFrame({'Model':Model,'R2_score':r2score,'RMSEscore':rmse,'Cross_val_score':cv})
51result