[Anonymous]

import matplotlib.pyplot as plt
import numpy as np

# Linear regression
def mean(x):
s = 0
le = len(x)
for A in range(le):
s += x[A]
return s / le

X = (8, 2, 11, 6, 5, 2, 12, 9, 6, 1,)
Y = (4, 12, 2, 5, 8, 12, 1, 3, 5, 17)
# trying to find best slope from thr data we have using m= (x-xmn)(y-ymn)/(x-xmn)
mnX = mean(X)
mnY = mean(Y)

N = 0
D = 0
for a in range(len(X)):
xm = X[a] – mnX
D += xm ** 2
N += xm * (Y[a] – mnY)
m = N / D
m = round(m, 3)
# now we have slope
# we’ll now find Y intercept
c = mnY – m * mnX
# print(“y=” + str(m) + “x +” + str(c))

#ERROR FUNCTION / COST FUNCTION / LOSS FUNCTION
#USING RMS

x1 = np.linspace(min(X), max(X), 100)
y1 = m * x1 + c
plt.plot(x1, y1) # plotting line

plt.scatter(X, Y, color=”r”) # plotting dataset
plt.title(“LINE THAT BEST FITS [Linear Regression]”)
plt.show()

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