EE461 Project Breakdown

 

EE461 Project Blog: Glucose Estimation Model Development

Welcome to the official blog for our EE461 project!
This space will serve as a central hub for logging our progress, sharing insights, backing up key developments, and tracking milestones throughout the project. Posts will be updated fortnightly, ensuring consistent documentation of our journey as we work towards building a robust glucose estimation model.

Project Overview

The primary goal of this project is to develop a reliable machine learning model capable of estimating glucose levels based on available physiological or sensor data. This task involves building the model itself and ensuring the quality and relevance of the input data, engineering meaningful features, and validating model performance through sound evaluation metrics.

Project Objectives

To achieve our goals, the project will be guided by the following key objectives:

  1. Data Preprocessing
    Cleaning and preparing the raw dataset is a crucial first step. This includes handling missing values, normalizing data, and ensuring consistency in the dataset.

  2. Dimensionality Reduction
    Reducing the number of features while preserving the most relevant information will help improve model performance and interpretability. Techniques such as PCA (Principal Component Analysis) and CCA (Curvilinear Component Analysis) may be explored.

  3. Feature Engineering & Selection
    Creating new features from existing data and selecting the most informative ones is essential for improving the model’s predictive accuracy. We will experiment with various techniques, including statistical analysis.

  4. Model Testing & Evaluation
    Several machine learning models will be trained and evaluated using metrics such as RMSE, MAE, or R². Cross-validation and hyperparameter tuning will also be applied to ensure robustness.

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