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

Import Libraries: Import pandas, numpy, matplotlib.pyplot, and LinearRegression from scikit-learn.
Read Data: Read 'student_scores.csv' into a DataFrame (df) using pd.read_csv().
Data Preparation: Extract 'Hours' (x) and 'Scores' (y). Split data using train_test_split().
Model Training: Create regressor instance. Fit model with regressor.fit(x_train, y_train).
Prediction: Predict scores (y_pred) using regressor.predict(x_test).
Model Evaluation & Visualization: Calculate errors. Plot training and testing data. Print errors.

Program:

#Program to implement the simple linear regression model for predicting
#the marks scored.
#Developed by: Sanjay Ragavendar M K
#RegisterNumber:  212222100045

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error,mean_squared_error

df=pd.read_csv("/content/student_scores.csv")
print(df.head())

print(df.tail())

x = df.iloc[:,:-1].values
print(x)

y = df.iloc[:,1].values
print(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
regressor = LinearRegression()
regressor.fit(x_train,y_train)
y_pred = regressor.predict(x_test)
print(y_pred)
print(y_test)

#Graph plot for training data
plt.scatter(x_train,y_train,color='orange')
plt.plot(x_train,regressor.predict(x_train),color='red')
plt.title("Hours vs Scores(Training set)")
plt.xlabel("Hours")
plt.ylabel("Scores")
plt.show()

#Graph plot for test data
plt.scatter(x_test,y_test,color='purple')
plt.plot(x_train,regressor.predict(x_train),color='yellow')
plt.title("Hours vs Scores(Testing set)")
plt.xlabel("Hours")
plt.ylabel("Scores")
plt.show()

mse=mean_absolute_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)