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

Read the given dataset
Assign values for x and y and plot them
Split the dataset into train and test data
Import linear regression and train the data
find Y predict
Plot train and test data
Calculate mse,mae and rmse

Program:

/*
Program to implement the simple linear regression model for predicting the marks scored.
Developed by:S.SRIMATHI
RegisterNumber:212220040160 

import numpy as np

import pandas as pd

dataset=pd.read_csv('/content/student_scores.csv')

dataset.head()

dataset.tail()

#assingning hrs to x and scores to y

x=dataset.iloc[:,:-1].values

y=dataset.iloc[:,1].values

print(x)

print(y)

x=dataset.iloc[:,:-1].values

y=dataset.iloc[:,1].values

print(x)

print(y)

import matplotlib.pyplot as plt

from sklearn.metrics import mean_absolute_error, mean_squared_error

plt.scatter(x_train,y_train,color='grey')

plt.plot(x_train,reg.predict(x_train),color='magenta')

plt.title('Training set(H vs S)')

plt.xlabel('Hours')

plt.ylabel('Scores')

plt.show()

plt.scatter(x_test,y_test,color='magenta')

plt.plot(x_test,reg.predict(x_test),color='grey')

plt.title('Test set(H vs S)')

plt.xlabel('Hours')

plt.ylabel('Scores')

plt.show()

print(y_predict)

print(y_test)

mse=mean_squared_error(y_test,y_predict)

print('MSE = ',mse)

mae=mean_absolute_error(y_test,y_predict)

print('MAE = ',mae)

import numpy as np

np. sqrt(3)

rmse=np.sqrt(mse)

print('RMSE = ',rmse)

*/