import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('test.csv')

X = dataset.iloc[:, :-1].values #Indepand variable
y = dataset.iloc[:, 1].values #Depand variable

# Calculate Mean
Sum_X = sum(X)
N_X = len(X)
Mean_X = Sum_X / N_X

Sum_y = sum(y)
N_y = len(y)
Mean_y = Sum_y / N_y

# Calcuate Deviation
Devi_X = []

for Row_X in X:
    Devi_X.append(Row_X - Mean_X)

Devi_y = []

for Row_y in y:
    Devi_y.append(Row_y - Mean_y)

# Multiply Deviation X and y

counter = 0
MD_Xy = []
while counter < len(Devi_X):
    MD_Xy_Value = Devi_X[counter] * Devi_y[counter]
    MD_Xy.append(MD_Xy_Value)
    counter += 1

# Sum of Multiply Deviation X and y
SMD_Xy = sum(MD_Xy)

# Squares of Calcuate Deviation
Sq_Devi_X = []

for DX in Devi_X:
    Sq_Devi_X.append(DX * DX)

Sq_Devi_y = []

for Dy in Devi_y:
    Sq_Devi_y.append(Dy * Dy)

# Sum of Squares of Calcuate Deviation
SSX = sum(Sq_Devi_X)

SSy = sum(Sq_Devi_y)

# Calculate Regression paramator
betaOne = SMD_Xy / SSX
betaZero = Mean_y - betaOne * Mean_X

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