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:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

1.Import the standard Libraries.

2.Set variables for assigning dataset values.

3.Import linear regression from sklearn.

4.Assign the points for representing in the graph.

5.Predict the regression for marks by using the representation of the graph.

6.Compare the graphs and hence we obtained the linear regression for the given datas.

Program:

Program to implement the simple linear regression model for predicting the marks scored.
Developed by: SANJITH.R
RegisterNumber: 212223230191 

import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
df=pd.read_csv('/Users/home/...................../EX02/student_scores.csv')
df.head()
df.shape
df.info()
df.isnull().sum()
x=df.drop(columns='Scores')
y=df[['Scores']]
print(type(x))
print(type(y))
print(y)
print(x)
X_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.2,random_state=42)
X_train.shape
model=LinearRegression()
model.fit(X_train,Y_train)
y_pred=model.predict(X_test)
plt.scatter(x,y,label='Actual Data',color='black')
plt.plot(x,model.predict(x),color='purple')
plt.show()
mse=mean_squared_error(Y_test,y_pred)
print(f"Mean Squarred Error is :  {mse}")
mae=mean_absolute_error(Y_test,y_pred)
print(f"Mean Abslute Error is :  {mae}")
rmse=np.sqrt(mse)
print(f"Root Mean Squarred Error is :  {rmse}")
a=np.array([[13]])
yp=model.predict(a)
print(yp)           # 124-132 marks estimated

Output:

DATA

X AND Y:

train_test_split:

LinearRegression Model:

Visualization:

Error Analysis:

Predictive System:

Result:

Thus the program to implement the simple linear regression model for predicting the marks scored is written and verified using python programming.