Machine Learning Engineering Projects in Talagang

Machine Learning Engineering Projects in TalagangMachine Learning Engineering Projects in Talagang

Cover image for Overview
This project provides an interactive

Overview This project provides an interactive platform for financial data analysis and predictive modeling. Built with Python and Streamlit, it enables users to visualize financial trends and explore machine learning-driven insights through an intuitive, web-based interface. Key Features Data Pipeline: Automated ingestion and cleaning of financial datasets. Feature Engineering: Implementation of financial indicators and technical features to capture market dynamics. Predictive Modeling: A streamlined training and evaluation workflow using robust machine learning algorithms. Interactive Visualization: Real-time dashboards allowing users to experiment with different parameters and model configurations. Technical Workflow Data Loading: Robust ingestion of historical financial data. Preprocessing: Handling missing values, noise reduction, and data normalization. Feature Engineering: Extraction of meaningful market features (e.g., technical indicators, volatility metrics). Model Training & Evaluation: A modular approach to training, testing, and validating model performance using industry-standard metrics. Experience It Live Explore the application and interact with the model here: 👉 Financial ML Dashboard (https://vtt4xouy7ifekdm7s5c4zj.streamlit.app/)

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Cover image for Project Title: Privacy-Preserving Sepsis Prediction

Project Title: Privacy-Preserving Sepsis Prediction Model Role: AI Research Intern The Challenge: Integrating complex Recurrent Neural Network (RNN) architectures within a privacy-preserving Multi-Party Computation (MPC) environment. Addressing the "fixed-point arithmetic" limitations inherent in cryptographic frameworks when handling 48-hour sequential medical data. My Approach: Mentored by a Yale University researcher to bridge the gap between AI and secure cryptographic protocols. Independently engineered custom normalization and clipping techniques to resolve data overflow issues caused by sequential computation loops. Successfully optimized the model for the MIMIC-III dataset, ensuring high-quality, functional code performance. The Outcome: Successfully contributed the finalized research implementation to the official open-source repository. Demonstrated technical maturity in handling complex engineering bottlenecks while adhering to formal research methodologies. Here is my repo link: https://github.com/sum710/sequre

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