🎓 Student Performance Predictor (ML Web App)

📌 Overview

The Student Performance Predictor is an end-to-end machine learning web application that predicts a student’s academic performance based on demographic and academic input features.

This project demonstrates:

• Full ML pipeline development
• Model training & evaluation
• Web deployment using Flask

🚀 Features

• Data preprocessing & feature engineering
• Multiple regression models evaluated
• Final model: Linear Regression with hyperparameter tuning
• Interactive web interface (Flask)
• Real-time predictions
• Deployed on AWS Elastic Beanstalk
• Automated deployment via CI/CD pipeline

🧠 Machine Learning Workflow

1. Data Collection

Dataset includes student attributes such as gender, race/ethnicity, parental education, lunch type, test preparation course, reading & writing scores.

2. Exploratory Data Analysis (EDA)

• Identified feature relationships
• Checked distributions and correlations

3. Data Preprocessing

• Encoding categorical variables
• Feature scaling
• Pipeline creation

4. Model Training

Models evaluated:

• Linear Regression
• Ridge
• Lasso
• Decision Tree
• Random Forest
• CatBoost

5. Model Selection

Best model: Linear Regression with hyperparameter tuning

6. Model Saving

Artifacts stored in: artifacts/

• model.pkl
• preprocessor.pkl

🗂️ Project Structure

PROJECT/

• artifacts/
• notebook/
• src/
• templates/
• static/
• application.py
• requirements.txt
• setup.py

⚙️ Installation & Setup

git clone cd student-performance-predictor python -m venv venv venv\Scripts\activate pip install -r requirements.txt

▶️ Run the Application

python application.py Open: http://localhost:5000

☁️ Deployment

Deployed using AWS Elastic Beanstalk with CI/CD pipeline.

📊 Input Features

• Gender
• Race/Ethnicity
• Parental education
• Lunch
• Test preparation
• Reading score
• Writing score

📈 Output

Predicted Math Score

🛠️ Technologies Used

• Python
• Pandas, NumPy
• Scikit-learn
• CatBoost
• Flask
• HTML, CSS
• AWS

🔮 Future Enhancements

• Advanced models
• Better UI
• Docker deployment