This is a project created for Coursera "Reproducible Research". It uses NOAA Storm database to access how severe weather events affect human health and local economies.

Analyzing smart device data to unlock global growth for Bellabeat. This case study reveals that standard "step-counting" goals fail to engage 72% of users. Using SQL and R, I identify a massive "Low Activity" market opportunity and propose the "Active Companion" strategy: a pivot from passive tracking to AI-driven, personalized motivation that prioritizes efficiency over volume.

AM vs LSMEANS

This is extracting the top genes from the previous data project in data frame build of the samples of multiple sclerosis cDNA data from NCBI's GSE293036, details in last project. The machine learning model isn't built yet but should be relatively straight forward from other projects. This is part 2 after exploratory data analysis. This goes directly to finding the cDNA fragments of 20 base pairs long that had the most fold change or increase or decrease compared to healthy control samples. The top 50 genes in enhancer fold change and bottom 50 genes in silencer fold change selected from sample repeats of the 1st patient, then 2nd patient, and then a comparative commercial patient using mean values of samples vs control mean values. This found 41 top genes in common to all samples. Next will be the machine learning after data transformation to see how well these gene variants can predict the class of the sample as healthy or MS.

Introduction to R for Geospatial analysis for IA 210 at Lafayette College