linear-regression.py

from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use('fivethirtyeight')

xs = np.array([1,2,3,4,5,6], dtype=np.float64)
ys = np.array([5,4,6,5,6,7], dtype=np.float64)


def best_fit_slope_and_intercept(xs,ys):
	m = ((mean(xs) * mean(ys)) - mean(xs*ys)) / ((mean(xs)**2) -  (mean(xs**2)))

	b = (mean(ys) - (m*mean(xs)))
	
	return m, b

def squared_error(ys_origin, ys_line):
	return sum((ys_line-ys_origin)**2)
	
def coefficient_of_determination(ys_origin, ys_line):
	y_mean_line = [mean(ys_origin) for y in ys_origin]
	squared_error_regr = squared_error(ys_origin, ys_line)
	squared_error_y_mean = squared_error(ys_origin, y_mean_line)
	return 1 - (squared_error_regr / squared_error_y_mean)


m, b = best_fit_slope_and_intercept(xs,ys)
regression_line = [(m*x + b) for x in xs]

predict_x = 7
predict_y = m*predict_x + b

r_squared = coefficient_of_determination(ys, regression_line)
print(r_squared)

plt.scatter(xs,ys)
plt.scatter(predict_x, predict_y)
plt.plot(xs, regression_line)
plt.show()