/*
Program to implement the linear regression using gradient descent.
Developed by: 212221230071
RegisterNumber:  NITHISHWAR S
*/


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
dataset = pd.read_csv('/content/student_scores - student_scores.csv')
dataset.head()
dataset.tail()
x  = dataset.iloc[:,:-1].values 
y  = dataset.iloc[:,1].values
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 1/3,random_state=0)
regressor = LinearRegression()
regressor.fit(x_train,y_train)
y_pred=regressor.predict(x_test)
plt.scatter(x_train,y_train,color = "green")
plt.plot(x_train,regressor.predict(x_train),color= "purple")
plt.title("hours Vs scores(train)")
plt.xlabel("hours")
plt.ylabel("scores")
plt.show()
plt.scatter(x_test,y_test,color = "blue")
plt.plot(x_test,regressor.predict(x_test),color= "black")
plt.title("hours Vs scores(train)")
plt.xlabel("hours")
plt.ylabel("scores")
plt.show()