LINEAR REGRESSION USING PYTHON

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas.io.parsers import TextFileReader
data = pd.read_csv("C:\\Users\\ochin\\OneDrive\\Desktop\\ML Prog\\headbrain.csv") # type: Union[Union[TextFileReader, DataFrame], Any]
print(data.shape)
print(data.head())
X=data['Head Size(cm^3)'].values
Y=data['Brain Weight(grams)'].values
### Comments by #, y=mx+c or y= slope*x+intercept
mean_x=np.mean(X)
mean_y=np.mean(Y)
#total no of items
m=len(X)
#use the formula to calculate intercept and b1
num=0
denom=0
for i in range (m):
num +=(X[i]-mean_x) * (Y [i] - mean_y)
denom+= (X[i]-mean_x) **2
slope=num/denom
intercept= mean_y-(slope*mean_x)

#print coefficient
print("coefficient slope = ",slope,"Coefficient intercept = ", intercept)
# we get slope=0.263 and intercept=325.573
# plot graphically
max_x=np.max(X)+100
min_x=np.min(X)-100
#Calculating Line values x and y
x=np.linspace(min_x, max_x,1000)
y=intercept+slope*x
#ploting
plt.plot(x,y,color='#58b970', label='Regression Line')
#ploting scatter points
plt.scatter(X,Y,c='#ef5423', label='Scatter Plot')
plt.xlabel ('Head Size in cm3')
plt.ylabel ('Brain Weight in grams')
plt.legend()
# Calculate R square value
ss_t=0 #total sum of square
ss_r=0 #total sum of residual
for i in range(m):
y_pred=intercept+slope* X[i]
ss_t+=(Y[i]-mean_y) **2
ss_r+=(Y[i]-y_pred) **2
R_Sq=1-(ss_r/ss_t)
print("The value")
print("R square value = ",R_Sq)
plt.show()