Project Overview

1. Objective

The primary objective of this internship project is to develop and implement a Gaussian Process model for predicting key properties of polymers, leveraging historical datasets and domain knowledge in material science.

2. Key Contributions

• Data Analysis and Preprocessing: Cleaning and transforming polymer property datasets for model input.
• Gaussian Process Implementation: Developing a robust machine learning pipeline using Gaussian Processes for accurate property predictions.
• Collaborative Approach: Collaborating with cross-functional teams to integrate domain expertise into the modeling framework.

Current Status

This project is ongoing. Current efforts are focused on optimizing the Gaussian Process model, validating predictions with real-world data, and refining the feature engineering process to improve model accuracy and reliability.

Final Remarks

This project underscores the application of advanced statistical and machine learning techniques to solve practical challenges in material science. The ongoing work highlights the importance of combining technical expertise with domain knowledge for impactful outcomes.