Random idea I had for a linear regression algorithm by using medians

lr.py

# This was put in a hurry will be bad but the key idea is
# - we calculate the slope between every 2 point and take the median out of all those slopes
# - now using the median slope we calculate the value of the b intercept and take the median out of all those b intercepts
# the line formed is y = median_slope * x + median_intercept
# This is probably not the best way. I am open for suggestions.

import matplotlib.pyplot as plt
import random

random_slope = random.randint(1, 10)
random_intercept = random.randint(0, 10)

def median(a):
    a.sort()

    median_index = (len(a) + 1) // 2
    median = a[median_index - 1]

    return median

def rise_over_run(x2, x1, y2, y1):
    return (y2 - y1) / (x2 - x1)

def intercept(x, m, y):
    return y - (m * x)

def line(x, m, b):
    return m * x + b

data_x = [i for i in range(random.randint(0, 10), 100)]
data_y = [line(x, random_slope + random.random() / 10, random_intercept + random.random() / 10) for x in data_x]

def main():
    data_x.sort()
    data_y.sort()

    slopes = [rise_over_run(data_x[i], data_x[i + 1], data_y[i], data_y[i + 1]) for i in range(len(data_y) - 1)]

    slope_median = median(slopes)
    print(f"slope median is {slope_median}")

    intercepts = [intercept(data_x[i], slope_median, data_y[i]) for i in range(len(data_y))]
    intercept_median = median(intercepts)
    print(f"intercept median is {intercept_median}")

    my_y = [line(x, slope_median, intercept_median) for x in data_x]

    print(f"line formed y = {slope_median}x + {intercept_median}")

    errors = [(((data_y[i] - my_y[i]) ** 2) ** (1 / 2)) for i in range(len(my_y))]

    print(f"Median error {median(errors)}")

    plt.plot(data_x, data_y)
    plt.plot(data_x, my_y)

    plt.show()

if __name__ == "__main__":
    main()