11. Challange.md

#Function to print best hyperparamaters: 
def print_best_params(gd_model):
    param_dict = gd_model.best_estimator_.get_params()
    model_str = str(gd_model.estimator).split('(')[0]
    print("\n*** {} Best Parameters ***".format(model_str))
    for k in param_dict:
        print("{}: {}".format(k, param_dict[k]))
    print()

#test train split
X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size = 0.33, random_state = 0 )

#standard scaler (fit transform on train, fit only on test)
sc = StandardScaler()
X_train = sc.fit_transform(X_train.astype(np.float))
X_test= sc.transform(X_test.astype(np.float))

###Challenge 1: SVR parameter grid###
param_grid_svr = dict(kernel=[ 'linear', 'poly'],
                     degree=[2],
                     C=[600, 700, 800, 900],
                     epsilon=[0.0001, 0.00001, 0.000001])
svr = GridSearchCV(SVR(), param_grid=param_grid_svr, cv=5, verbose=3)


#fit model
svr = svr.fit(X_train,y_train.values.ravel())

#print score
print('\n\nsvr train score %.3f, svr test score: %.3f' % (
svr.score(X_train,y_train),
svr.score(X_test, y_test)))
#print(svr.best_estimator_.get_params())

print_best_params(svr)




###Challenge 2:Decision Tree parameter grid###
param_grid_dt = dict(min_samples_leaf=np.arange(9, 13, 1, int), 
                  max_depth = np.arange(4,7,1, int),
                  min_impurity_decrease = [0, 1, 2],
                 )

dt = GridSearchCV(DecisionTreeRegressor(random_state=0), param_grid=param_grid_dt, cv=5,  verbose=3)



#fit model
dt = dt.fit(X_train,y_train.values.ravel())


#print score
print('\n\ndt train score %.3f, dt test score: %.3f' % (
dt.score(X_train,y_train),
dt.score(X_test, y_test)))
print_best_params(dt)



###Challenge 3:Random Forest parameter grid###
param_grid_rf = dict(n_estimators=[20],
                     max_depth=np.arange(1, 13, 2),
                     min_samples_split=[2],
                     min_samples_leaf= np.arange(1, 15, 2, int),
                     bootstrap=[True, False],
                     oob_score=[False, ])


forest = GridSearchCV(RandomForestRegressor(random_state=0), param_grid=param_grid_rf, cv=5, verbose=3)

#fit model
forest.fit(X_train,y_train.values.ravel())


#print score
print('\n\nforest train score %.3f, forest test score: %.3f' % (
forest.score(X_train,y_train),
forest.score(X_test, y_test)))

print_best_params(forest)