implementation-of-simple-linear-regression-model-for-predicting-the-marks-scored's Introduction

Implementation-of-Simple-Linear-Regression-Model-for-Predicting-the-Marks-Scored

Aim:

To write a program to predict the marks scored by a student using the simple linear regression model.

Equipments Required:

Hardware – PCs
Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

Step1:

Import the needed packages.

Step2:

Assigning hours To X and Scores to Y.

Step3:

Plot the scatter plot.

Step4:

Use mse,rmse,mae formula to find.

Program:

/*
Program to implement the simple linear regression model for predicting the marks scored.
Developed by: MS.JEEVA
RegisterNumber: 212221230040
*/

import numpy as np
import pandas as pd
df=pd.read_csv('student_scores.csv')

X=df.iloc[:,:-1].values
Y=df.iloc[:,1].values
print("X=",X)
print("Y=",Y)

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)

from sklearn.linear_model import LinearRegression
reg=LinearRegression()
reg.fit(X_train,Y_train)

Y_pred=reg.predict(X_test)
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error,mean_squared_error

plt.scatter(X_train,Y_train,color='red')
plt.plot(X_train,reg.predict(X_train),color='yellow')
plt.title('Training set(H vs S)',color='blue')
plt.xlabel('Hours',color='green')
plt.ylabel('Scores',color='green')

plt.scatter(X_test,Y_test,color='black')
plt.plot(X_test,reg.predict(X_test),color='blue')
plt.title('Test set(H vs S)',color='green')
plt.xlabel('Hours',color='brown')
plt.ylabel('Scores',color='brown')

mse=mean_squared_error(Y_test,Y_pred)
print('MSE = ',mse)

mae=mean_absolute_error(Y_test,Y_pred)
print('MAE = ',mae)

rmse=np.sqrt(mse)
print('RMSE = ',rmse)