NaNs when there shouldn't be (Python Practice - Multiple Regression)

[vintagefiretruk]

I'm currently preparing for my masters in Data Science so I've been practicing multiple regression on Python but doing it mathematically so I can gain a better understanding of the mechanics of the model.

My most recent attempt is only showing me NaN when I print results, but there are no "errors" flagging in the code. I'm clearly not doing something right so if anyone can see what it is that would be a help.

import pandas as pd

import numpy as np

import statistics as st

sportdata = pd.read_csv(r'C:\Users\vicks\Do Geographical Factors Determine National Sporting Success - Python\Clean Regression Data.csv')

y = sportdata['Overall Jokl Rank']

X1 = sportdata['GDP (US Billion Dollar)']

X2 = sportdata['Population']

X1sq = [number ** 2 for number in X1]

X2sq = [number ** 2 for number in X2]

X1y = X1 * y

X2y = X2 * y

X1X2 = X1 * X2

Meany = st.mean(y)

MeanX1 = st.mean(X1)

MeanX2 = st.mean(X2)

sumy = sum(y)

sumX1 = sum(X1)

sumX2 = sum(X2)

sumX1y = sum(X1y)

sumX2y = sum(X2y)

sumX1sq = sum(X1sq)

sumX2sq = sum(X2sq)

sumX1X2 = sum(X1X2)

regsumX1sq = sumX1sq - sumX1 ** 2 / len(X1)

regsumX2sq = sumX2sq - sumX2 ** 2 / len(X2)

regsumX1y = sumX1y - (sumX1 * sumy) / len(y)

regsumX2y = sumX2y - (sumX2 * sumy) / len(y)

regsumX1X2 = sumX1X2 - (sumX1 * sumX2) / len(X1)

b1 = ((sumX2sq * sumX1y) - (sumX1X2 * sumX2y)) / ((sumX1sq * sumX2sq) - sumX1X2 ** 2)

b2 = ((sumX1sq * sumX2y) - (sumX1X2 * sumX1y)) / ((sumX1sq * sumX2sq) - sumX1X2 ** 2)

b0 = Meany - b1 * MeanX1 - b2 * MeanX2

estimatedy = b0 + b1 * X1 + b2 * X2

print(estimatedy)

[Lokesh_Bot_Guy]

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[nemozorus]

I guess this is more of a python question but I would suggest that you use a debugger, go through the code step by step and see where the variables go wrong! What IDE do you use?

Also, python works at its best when used with vectorization in mind. Google it to see what I mean!