recaptcha07/Ml_Pipeline

End TO End Machine Learning Project

🎓 Student Performance Predictor

The Student Performance Predictor is a Machine Learning project designed to predict students' academic performance based on various input features. This system helps educators and institutions identify students who may need additional academic support and enables data-driven decision-making.

🚀 Features

✅ End-to-end Machine Learning pipeline ✅ Data preprocessing and model training ✅ Flask-based web application ✅ User-friendly interface for predictions ✅ Modular project structure for scalability ✅ Ready for deployment

🧠 Tech Stack

• —Python
• —Machine Learning (Scikit-learn, Pandas, NumPy)
• —Flask
• —HTML/CSS
• —Jupyter Notebook

📁 Project Structure

Student_performance_Predictor/
│
├── artifacts/          # Saved models and processed data
├── notebook/          # Jupyter notebooks for experimentation
├── src/               # Source code for pipeline and components
├── templates/         # HTML templates for Flask app
├── app.py             # Main Flask application
├── application.py     # Deployment entry point
├── requirements.txt   # Project dependencies
├── setup.py           # Package setup
└── README.md

⚙️ Installation Guide

Follow these steps to run the project locally.

1️⃣ Clone the Repository

git clone https://github.com/Prerna-Prahsar/Student_performance_Predictor.git
cd Student_performance_Predictor

2️⃣ Create a Virtual Environment (Recommended)

👉 For Windows:

python -m venv venv
venv\Scripts\activate

👉 For Mac/Linux:

python3 -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

▶️ How to Run the Project

Start the Flask application:

python app.py

🌐 Access the Application

After running the app, open your browser and go to:

http://127.0.0.1:5000

Enter the required student details and get the predicted performance instantly.

📊 Machine Learning Workflow

1. 1.Data Collection
2. 2.Data Cleaning & Preprocessing
3. 3.Feature Engineering
4. 4.Model Training
5. 5.Model Evaluation
6. 6.Deployment using Flask

🎯 Use Cases

• —Identify students at academic risk
• —Improve institutional performance tracking
• —Support teachers with predictive insights
• —Enable early intervention strategies

🤝 Contributing

Contributions are welcome!

If you'd like to improve this project:

1. 1.Fork the repository
2. 2.Create a new branch
3. 3.Make your changes
4. 4.Submit a Pull Request

👩‍💻 Author

Prerna Prashar