This post explores fod affordability inequalities among women-headed households in California using data-driven insights from machine learning models. The primary goal is to predict affordability ratios using features related to annual income, family demographics, and regional data. Additionally, the post includes anomaly detection to identify unique cases and SHAP analysis for feature interpretability.
The data used in this post looks at numerous medical and lifestyle records for 2,149 unique patients with and without diagnosed Alzheimer's disease. This analysis will first determine the distribution of Mini-Mental State Exam data for patients with and without a family history of Alzheimer's. Afterwards, a Mann-Whitney test will show that there is no significant difference between the two groups. Finally, conclusions and future study recommendations will be made based on the results of this post's study.
Case study competition blog post in progress ...
Finding a job can be tough regardless of location ... so why not use our skills as data scientists to make things a little easier? This blog post will describe how I was able to pull data from one of the most popular online job boards, and prepare it for future data exploration. This will all be done in hopes of gaining a better picture of what to expect in the LA job market.
A brief tutorial post to get other stat 386 students familiar with using Seaborn as a data visualization tool.