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
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
This is a Jupyter notebook I created and published on Kaggle to demonstrate how clustering can be used for customer segmentation. https://www.kaggle.com/pmrich/clustering-approaches-k-mean-birch-agg Using clustering and credit card data, I can group the credit card users into three (or more) distinct groups that could be used for various business applications such as marketing and promotional campaigns
Web scraping, data science, javascript, Python, Flask, HTML, and CSS are most of the technologies used to build this project. It is not fully featured, but I was able to learn what I needed to build it and deploy it on Heroku. With the data, I built the recommendation engine using a content-based filtering approach.
