Regression – How to program the Best Fit Slope
This covers building up a Linear Regression model in Python based on the standard equation:-
Slope of the best fit line being equal to Mean of the X values times the Mean of the Y values, minus the Mean of the Xs times the Ys. Divided by the Mean of Xs to the power of 2, minus the of all the Xs to the power of 2 (I know confusing right lol).
The code was a fairly straight forward application of math. Comments and learnings are in the code:-
# Import Libs from statistics import mean import numpy as np import matplotlib.pyplot as plt # To set charts to save as images we need to change the default behaviour from matplotlib import style # inport style to change default behaviour of plot style.use('ggplot') # use ggplot # Define values xs = np.array([1,2,3,4,5,6], dtype=np.float64) # dtype lets you set the data type. Not needed for this example but useful in future ys = np.array([5,4,6,5,6,7], dtype=np.float64) def best_fit_slope(xs, ys): # defining function to calculate slope (m) - passing values of xs and ys m = ( ((mean(xs)*mean(ys)) - mean(xs * ys)) / # bracket space at the start and space slash at the end allows for a carridge return in the code ((mean(xs)**2)-mean(xs**2))) ## **2 raises to the power of 2 return m m = best_fit_slope(xs,ys) print(m) #plt.scatter(xs,ys) #plt.savefig('ML_Tutorial8.png', bbox_inches='tight') #Sets the output to save an image #plt.show() # exports the image