🎓 Student Performance Prediction

A Machine Learning project that predicts student academic performance based on demographic, academic, and lifestyle factors. The project includes data preprocessing, feature engineering, model training, evaluation, visualization, and an interactive Streamlit web application for real-time predictions.

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📌 Features

• Data preprocessing and cleaning
• Feature engineering
• Exploratory Data Analysis (EDA)
• Data visualization
• Multiple machine learning models
• Model evaluation using standard metrics
• Real-time student performance prediction
• Interactive Streamlit dashboard
• Modular and production-ready project structure

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📂 Project Structure

Student-Performance-Prediction/
│
├── app/
│   └── streamlit_app.py
│
├── data/
│   ├── raw/
│   │   └── student_performance.csv
│   └── processed/
│
├── models/
│
├── notebooks/
│
├── reports/
│   └── figures/
│
├── src/
│   ├── preprocessing.py
│   ├── feature_engineering.py
│   ├── train_model.py
│   ├── evaluate_model.py
│   ├── predict.py
│   ├── visualization.py
│   └── utils.py
│
├── requirements.txt
├── .gitignore
└── README.md

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Joblib
• Streamlit

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📊 Workflow

1. Collect student performance dataset.
2. Clean and preprocess the data.
3. Perform feature engineering.
4. Train machine learning models.
5. Evaluate model performance.
6. Save the best model.
7. Launch the Streamlit application.
8. Predict student performance from user input.

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⚙️ Installation

Clone the repository

git clone https://github.com/erchandrain-ui/Student-Performance-Prediction.git
cd Student-Performance-Prediction

Create Virtual Environment

Windows

python -m venv venv
venv\Scripts\activate

Linux / macOS

python3 -m venv venv
source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

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🚀 Run the Project

Train the Model

python src/train_model.py

Evaluate the Model

python src/evaluate_model.py

Launch Streamlit App

streamlit run app/streamlit_app.py

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📈 Machine Learning Pipeline

• Data Loading
• Data Cleaning
• Feature Encoding
• Feature Scaling
• Train-Test Split
• Model Training
• Model Evaluation
• Prediction
• Visualization

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📊 Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix
• Classification Report

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📷 Dashboard

The Streamlit dashboard allows users to:

• Enter student information
• Predict academic performance
• View prediction results
• Explore dataset insights
• Visualize important features

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📦 Requirements

• Python 3.10+
• Streamlit
• Scikit-learn
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Joblib

Install using:

pip install -r requirements.txt

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🤝 Contributing

Contributions are welcome.

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Push the branch
5. Open a Pull Request

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📄 License

This project is released under the MIT License.

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👨‍💻 Author

Chand Rain

B.Tech CSE Student

Machine Learning & Data Science Enthusiast

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⭐ If you found this project helpful, consider giving it a Star on GitHub.