This project focuses on predicting student academic performance using a machine learning pipeline.
The system analyzes historical student data and predicts performance outcomes based on multiple academic and behavioral factors.
The project is designed with a modular Python architecture, making it easy to maintain, extend, and deploy.
Educational institutions often struggle to identify students who may underperform academically.
👉 Goal:
Build a machine learning model that predicts student performance early so that timely academic support can be provided.
performance_prediction/
│
├── data_generation.py # Data loading & preparation
├── data_preprocessing.py # Cleaning & feature engineering
├── model_training.py # Model training & evaluation
├── main.py # Main entry point
├── student_performance.csv # Dataset
├── requirements.txt
├── README.md
└── .gitignore
---
## 🔄 Workflow
1️⃣ Load and analyze student dataset
2️⃣ Preprocess data (cleaning & feature engineering)
3️⃣ Train machine learning model
4️⃣ Evaluate performance metrics
5️⃣ Display predictions and results
---
## ▶️ How to Run the Project
```bash
# Activate environment
conda activate ml_env
# Install dependencies
pip install -r requirements.txt
# Run the project
python main.py
---
📊 Output
1. Model training results
2. Performance metrics (accuracy, evaluation scores)
3. Console-based prediction output
---
🌟 Key Highlights
✔ Modular and scalable code structure
✔ Clear separation of data, preprocessing, and training logic
✔ Beginner-friendly yet industry-aligned design
✔ Easily extendable to a web app (Streamlit)
---
🔮 Future Enhancements
1. Streamlit-based web interface
2. Model optimization & hyperparameter tuning
3. Advanced visualization dashboards
---
👤 Author
Piyush Kumar