Machine learning & Predictive Modeling

MACHINE LEARNING & PREDICTIVE MODELING

Building Models That Learn Patterns and Generate Predictions

Predictive models grow like branching structures—learning patterns, forming connections, and adapting to improve performance.

The neural-tree visual symbolizes the complexity and depth of machine learning algorithms, from decision trees to neural networks.

In this section, I walk through the development, evaluation, and optimization of models, focusing on how different techniques extract patterns, improve accuracy, and generalize to real-world data. Each project highlights the process behind selecting algorithms, tuning hyperparameters, validating performance, and interpreting results to deliver actionable insights.

Diabetes Prediction (Machine Learning Classification)

This project builds and evaluates machine learning models to predict the likelihood of diabetes using clinical and demographic data. It covers the full workflow—from exploratory analysis and preprocessing to model training, evaluation, and interpretation—highlighting how predictive models can support medically meaningful insights.