# Program to implement the linear regression using gradient descent.
# Developed by: Syed Abdul Wasih H
# RegisterNumber:  212221240057
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv("student_scores.csv")
data.head()
data.isnull().sum()
x=data.Hours
y=data.Scores
y.head()
n=len(x)
m=0
c=0
L=0.001
loss=[]
for i in range(10000):
    ypred=m*x+c
    MSE=(1/n)*sum((ypred-y)*2)
    dm=(2/n)*sum(x*(ypred-y))
    dc=(2-n)*sum(ypred-y)
    c=c-L*dc
    m=m-L*dm
    loss.append(MSE)
    #print(m)
print(m,c)
y_pred=m*x+c
plt.scatter(x,y,color="black")
plt.plot(x,y_pred,color="red")
plt.xlabel("Study hours")
plt.ylabel("Scores")
plt.title("Study hours vs Scores")
plt.plot(loss)
plt.xlabel("iteration")
plt.ylabel("loss")