# Load the Diabetes Housing dataset
columns = “age sex bmi map tc ldl hdl tch ltg glu”.split() # Declare the columns names
diabetes = datasets.load_diabetes() # Call the diabetes dataset from sklearn
df = pd.DataFrame(diabetes.data, columns=columns) # load the dataset as a pandas data frame
y = diabetes.target # define the target variable (dependent variable) as y
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.2)
# fit a model
lm = linear_model.LinearRegression()
model = lm.fit(X_train, y_train)
predictions = lm.predict(X_test)
accuracy_score(y_test, predictions)

from sklearn.model_selection import cross_val_score
    accuracies = cross_val_score(estimator = model, X = X_train, y = y_train, cv = 7, n_jobs = 1)
print accuracy_score(y_test, predictions)

cr=0
for i in range(0,7):
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
    lm = linear_model.LinearRegression()
    model = lm.fit(X_train, y_train)
    predictions = lm.predict(X_test)
    #from sklearn.metrics import accuracy_score
    print accuracy_score(y_test, predictions)
    cr=cr+accuracy_score(y_test, predictions)
result=cr/10