Student Performance Predictor

A machine learning application that predicts student academic performance using Linear Regression based on study hours and attendance percentage.

Status: Active | Language: Python 3.11

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Overview

This project implements a predictive model to forecast student marks based on key learning indicators. It enables educators and students to identify performance trends and make data-driven improvement decisions.

Table of Contents

• Features
• Technology Stack
• Installation
• Usage
• Model Details
• Dataset
• Results

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Features

• Predictive Modeling: Linear Regression for mark prediction
• Real-Time Predictions: Get instant predictions based on user input
• Simple Interface: User-friendly command-line application
• Extensible Design: Ready for additional features and advanced models
• CSV Data Management: Lightweight data storage and handling

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Technology Stack

• Python 3.11 - Core language
• scikit-learn - Machine learning library
• pandas - Data processing and manipulation
• NumPy - Numerical computing

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Installation

Prerequisites

• Python 3.11 or higher
• pip (Python Package Manager)

Quick Start

# Clone repository
git clone https://github.com/BhagyasriPasupuleti/Student-Performance-Predictor
cd student-performance-predictor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install pandas scikit-learn numpy

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Usage

Running the Application

python app.py

Example

Enter Study Hours: 5
Enter Attendance Percentage: 85

Predicted Marks: 72.45

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Model Details

Algorithm: Linear Regression

The model establishes a linear relationship between input features and student marks:

Input Features:
  - Study Hours (continuous)
  - Attendance % (continuous)
        ↓
  [Linear Regression]
        ↓
  Output: Predicted Marks (0-100)

Training Code

from sklearn.linear_model import LinearRegression

# Load data
X = data[["StudyHours", "Attendance"]]
y = data["Marks"]

# Train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
prediction = model.predict([[5, 85]])

Why Linear Regression?

• Simple and interpretable results
• Fast training and prediction
• Clear understanding of feature importance
• Effective for initial performance analysis

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Dataset

Features

Feature	Type	Range
StudyHours	Float	0-15 hours
Attendance	Float	0-100%
Marks	Float	0-100 (target)

Sample Data

StudyHours,Attendance,Marks
2,65,35
3,70,42
4,75,48
5,80,55
6,85,62
7,90,70
8,95,80

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Results

Sample Predictions

Study Hours	Attendance (%)	Predicted Marks
2	60	28.45
4	75	54.30
6	85	72.15
8	95	89.90

Key Insights

• Strong positive correlation between study hours and marks
• Attendance is a key indicator of academic performance
• Model captures the relationship between effort and outcomes

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Project Structure

student-performance-predictor/
├── README.md                 # Documentation
├── app.py                    # Main application
├── student_data.csv          # Dataset
└── .gitignore               # Git configuration

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Author

Bhagyasri

• B.Tech Student | AI & ML Enthusiast
• GitHub: https://github.com/BhagyasriPasupuleti

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