human
* As we can see here. we are improve the accuracy slightly *
* And 0.395765861155 is higher than baseline 0.07142857142857142 *
 <h3>3. Linear Regression</h3> 
* predict the overall of the player. ***
* define a new cross validation *"
Please write code following the instructions in jupyter notebook style.

Write me a code to * As we can see here?
assistant
#Here is code to * As we can see here.

def cross_Validation_reg(reg, X, y, k = 3):
    
    tMSE = list()

    for train_index, test_index in KFold(n_splits=k, random_state=None, shuffle=False).split(X):
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]
        regm = reg.fit(X_train, y_train)
        tMSE.append(np.mean((y_test - regm.predict(X_test)) ** 2))
    return np.mean(tMSE)

## set y overall
overall = np.array(df_fifa.iloc[:,0:1])[:,0]
Xb = csr_matrix(df_fifa.iloc[:, 1:-1])
Xb.toarray()

class baseline:
    
    def fit(self, X, y):
        return self
    
    def predict(self, X):
        n = X.shape[0]
        res = np.zeros(n)
        for i in range(n):
            res[i] = np.mean(X[i,:])
        return res

# set the baseline class for certain player
bl = baseline()

# test baseline for all
cross_Validation_reg(bl, Xb, overall, 5)