linear regression

dataframe = datasets.load_diabetes()
print(dataframe)

X = dataframe.data[:,:]
Y = dataframe.target

X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.30, random_state=42)

train_size = X_train.shape[0]
test_size  = X_test.shape[0]
dt = dataframe.data.shape[1]
#print(X_train)

w= np.zeros((1,dt))
print(w)
b=0

alpha=0.000001


for i in range (500):
    yht = np.dot(X_train, w.T)
    yht += b
    diff = yht - Y_train.reshape((yht.shape))
    b = b - alpha*((1/X_train.shape[0])*(np.sum(diff)))
    m = diff.shape[0]
    #print(diff.shape)

    for j in range(10):

        w[0,j]=w[0,j]-(alpha*((1/m)*(np.dot(X_train[:, j].reshape(1,m), diff))))
        #print(w)
        df = yht - Y_train.reshape(m,1)
        sum=0

        for i in range (df.shape[0]):
            sum+=df[i, 0]**2

       print(sum/2*m)


#print(w.shape)