Oura Ring vs. Fitbit Charge 4 Project Type: Applied Data Science / Machine LearningFocus Areas: Wearables analytics, feature importance, model interpretabilityTools & Methods: Python, Linear Regression, Permutation Feature Importance, SHAP Project Overview Consumer wearables promise actionable insights into sleep quality and daily readiness, yet their scoring algorithms remain proprietary and opaque. This project reverse-engineers and
Building the Handbook Chatbot: Turning a Static PDF Into an AI-Powered Assistant For a long time, I wanted to build something practical—something that wasn’t just another tutorial notebook or toy demo. I wanted a project that pulled together everything I’d been learning about retrieval-augmented generation (RAG), embeddings, LLMs, and full-stack app development. So I picked
Gym Visit Analytics — 2024 Attendance InsightsIn this self-directed project, I leveraged attendance data from my local gym to quantify my workout consistency and membership cost efficiency for 2024. I collected visit logs from the gym’s web portal and asked key questions: Was I consistent in making it to the gym? How many visits did
Pulling data from an API to use for data analysis may not be the first lesson you learn in dealing with data, but it is a fundamental skill in data analysis. Data Visualization tools now make accessing API data much easier, but those applications may come with some limitations. ETL tools such as Informatica or
This visual is the output of work to identify factors that may impact the age of marriage. The original hypothesis of this data was that cost of living played an important role in when people choose to get married. However, after finding minimal correlations between the cost of living and population to the age at
https://www.kaggle.com/pmrich/obesitydataset-eda-data-prep-ml-hypertuning This data comes from the UCI Machine Learning Repository. This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. This notebook explores several popular machine learning classification models to predict the weight classification of patients. This notebook provides a walk-thru of
