Machine LearningCompleted
End-To-End ML Project for Student Performance Prediction
Jan 2024 - Apr 2024
Developing an end-to-end ML pipeline to predict student performance, including data preprocessing, feature engineering, model building, and deployment planning.
About This Project
Developed a comprehensive end-to-end machine learning pipeline to predict student performance based on various demographic and academic factors. The project involves extensive data cleaning and preprocessing, followed by sophisticated feature engineering to enhance predictive power. Multiple machine learning algorithms including regression models were implemented, trained, and evaluated. The modular programming approach ensures code reusability and maintainability. Plans include deploying the model as a web service for real-time predictions.
Technologies Used
PythonRegressionModular ProgrammingSupervised LearningScikit-learnPandas
Key Highlights
- Focus on end-to-end ML lifecycle from data collection to deployment
- Application of feature engineering techniques for improved predictive power
- Comparative analysis of multiple regression algorithms
- Modular code architecture for easy maintenance and extension