Linear Regression does not yeild correct values data with multiple inputs

gitlab link : Script file and csv file are at https://gitlab.com/pbmbjoshi/lineargradient

I am writing a python script to solve housing prediction example.

Housing prices are calculated as 4000 Rs per square feet (say base price). Additional 1% on the additional bed rooms ( more than one ), additional 3% on each floor rise ( above first floor ), discount of 2% on age (in years) of the building and 6% discount on distance ( in kilo meters ) from the nearest railway station

When I apply linear gradient on the data, I get ONLY the first parameter value (base price) near to correct one, but other parameters are totally incorrect. Can any one help me.

def linear_regression_with_multiple_variables():
    print("linear_regression_with_multiple_variables")
    filePath = r'LinearGradient_Example.csv'
    data = pd.read_csv(filePath)

    x1 = np.asarray(data['Total Area'])
    x2 = np.asarray(data['# of bed rooms'])
    x3 = np.asarray(data['Floor Count'])
    x4 = np.asarray(data['Age Of The Building'])
    x5 = np.asarray(data['Distance from nearest railway station'])
    y = np.asarray(data['Final Price'])

    x1_max = max(x1);
    x2_max = max(x2);
    x3_max = max(x3);
    x4_max = max(x4);
    x5_max = max(x5);

    x1 = x1 / x1_max;
    x2 = x2 / x2_max;
    x3 = x3 / x3_max;
    x4 = x4 / x4_max;
    x5 = x5 / x5_max;

    theta_1 = 0
    theta_2 = 0
    theta_3 = 0
    theta_4 = 0
    theta_5 = 0

    number_of_training_sets = len(x1)
    max_number_of_iterations = 1000000
    learning_rate = 0.001
    iteration_counter = 1;
    precision = 0.001

    while iteration_counter <= max_number_of_iterations:
        old_theta_1 = theta_1
        old_theta_2 = theta_2
        old_theta_3 = theta_3
        old_theta_4 = theta_4
        old_theta_5 = theta_5

        der_x1 = 0;
        der_x2 = 0;
        der_x3 = 0;
        der_x4 = 0;
        der_x5 = 0;

        for i in range(0, number_of_training_sets):
            diff_val = theta_1 * x1[i] + theta_2 * x2[i] + theta_3 * x3[i] + theta_4 * x4[i] + theta_5 * x5[i] - y[i]
            der_x1 = der_x1 + diff_val * x1[i]
            der_x2 = der_x2 + diff_val * x2[i]
            der_x3 = der_x3 + diff_val * x3[i]
            der_x4 = der_x4 + diff_val * x4[i]
            der_x5 = der_x5 + diff_val * x5[i]

        avg_der_x1 = der_x1 / number_of_training_sets
        avg_der_x2 = der_x2 / number_of_training_sets
        avg_der_x3 = der_x3 / number_of_training_sets
        avg_der_x4 = der_x4 / number_of_training_sets
        avg_der_x5 = der_x5 / number_of_training_sets

        theta_1 = theta_1 - learning_rate * avg_der_x1
        theta_2 = theta_2 - learning_rate * avg_der_x2
        theta_3 = theta_3 - learning_rate * avg_der_x3
        theta_4 = theta_4 - learning_rate * avg_der_x4
        theta_5 = theta_5 - learning_rate * avg_der_x5

        print(iteration_counter, theta_1, theta_2, theta_3, theta_4, theta_5)
        iteration_counter = iteration_counter + 1

        if (abs(theta_1 - old_theta_1) <= precision and
                abs(theta_2 - old_theta_2) <= precision and
                abs(theta_3 - old_theta_3) <= precision and
                abs(theta_4 - old_theta_4) <= precision and
                abs(theta_5 - old_theta_5) <= precision):
            break

    theta_1 = theta_1 / x1_max
    theta_2 = theta_2 / x2_max
    theta_3 = theta_3 / x3_max
    theta_4 = theta_4 / x4_max
    theta_5 = theta_5 / x5_max

    print('Final values : ', theta_1, theta_2, theta_3, theta_4, theta_5)```