LR - ML1

import numpy as np

import matplotlib.pyplot as plt

def estimate_coefficients(x, y):

    # size of the dataset OR number of observations/points

    n = np.size(x)

 

    # mean of x and y

    # Since we are using numpy just calling mean on numpy is sufficient

    mean_x, mean_y = np.mean(x), np.mean(y)

 

    # calculating cross-deviation and deviation about x

  SS_xy=np.sum(y*x)-(n*mean_y*mean_x)
  SS_xx=np.sum(x*x)-(n*mean_x*mean_x)

    # calculating regression coefficients

    b_1 = SS_xy / SS_xx

    b_0 = mean_y - b_1*mean_x

 

    return(b_0, b_1)

    # x,y are the location of points on graph

    # color of the points change it to red blue orange play around

def plot_regression_line(x, y, b):

    # plotting the points as per dataset on a graph

    plt.scatter(x, y, color = "m",marker = "o", s = 30)

    # predicted response vector

    y_pred = b[0] + b[1]*x

 

    # plotting the regression line

    plt.plot(x, y_pred, color = "g")

 

    # putting labels for x and y axis

    plt.xlabel('Size')

    plt.ylabel('Cost')

 

# figimage only adds a background to the current figure. If you don't have an already existing figure, the command wont render anything.

    data = np.random.randn(500, 500)

    plt.figimage(data)

    # function to show plotted graph

    plt.show()

  

def main():

    # Datasets which we create

    x = np.array([ 1,   2,   3,   4,   5,   6,   7,   8,    9,   10])

    y = np.array([300, 350, 500, 700, 800, 850, 900, 900, 1000, 1200])

 

    # estimating coefficients

    b = estimate_coefficients(x, y)

    print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1]))

 

    # plotting regression line

    plot_regression_line(x, y, b)

   

if __name__ == "__main__":

    main()