End-to-End ML Project: Student Performance Prediction

Overview

This project focuses on predicting student performance using machine learning techniques. By constructing an end-to-end ML pipeline, the project automates the entire workflow from data ingestion, transformation, and model training to real-time predictions using a Flask-based API. The model is deployed on various cloud platforms for scalability and accessibility.

Key Features

• End-to-End ML Pipeline: Covers the complete machine learning workflow, including data ingestion, preprocessing, model training, evaluation, and prediction.
• Exploratory Data Analysis (EDA): Insightful visualizations and statistical analysis to explore and understand data patterns.
• Feature Engineering: Creation of new features and optimization to improve model accuracy.
• Hyperparameter Tuning: Systematic approach to find the best hyperparameters for optimal model performance.
• Flask-Based API: Developed a real-time prediction pipeline using Flask for easy deployment and integration.
• Cloud Deployment: Model deployed on cloud platforms for real-world accessibility and scalability.

Project Workflow

1. Data Ingestion: Loading and cleaning the dataset.
2. EDA and Visualization: Conducting in-depth analysis of the dataset to gain insights into student performance.
3. Feature Engineering: Creating meaningful features from raw data to improve model performance.
4. Model Training and Evaluation:
   • Implementing various machine learning algorithms (e.g., Random Forest, XGBoost).
   • Evaluating models using metrics like accuracy, precision, recall, and F1-score.
5. Hyperparameter Tuning: Fine-tuning the models to enhance performance using grid search or random search.
6. Model Deployment:
   • Created a REST API using Flask for real-time predictions.
   • Deployed the trained model on cloud platforms like AWS, Azure etc.

Technologies Used

• Programming Languages: Python
• Libraries & Tools:
  • Pandas, NumPy: Data manipulation and analysis
  • Matplotlib, Seaborn: Data visualization
  • Scikit-learn, XGBoost: Machine learning algorithms and evaluation
  • Flask: API development for predictions
  • Cloud Platforms: AWS, Heroku (or other cloud services)

Installation & Setup

1. Clone the repository:

   git clone https://github.com/Ayush-ak87/ML-Project.git

2. Navigate to the project directory:

   cd ML-Project

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Flask API:

   python app.py

Usage

Once the Flask API is up and running, you can make a POST request to the endpoint to predict student performance based on the input features.

Example request:

curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{
    "feature1": value,
    "feature2": value,
    ...
}'

The API will return a prediction based on the trained model.

Model Evaluation

• Performance of the model is evaluated using accuracy, F1-score, and other classification metrics.
• Hyperparameter tuning is used to ensure optimal performance of the selected model.

Future Enhancements

• Add more sophisticated models such as neural networks.
• Integrate additional cloud platforms for wider accessibility.
• Enhance API functionality with authentication and logging.

Contributing

Contributions are welcome! Please feel free to submit a pull request or raise an issue.

License

This project is licensed under the MIT License.

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Feel free to modify this as per your specific requirements!