Applied Multivariate Analysis
Exploring wine attributes through EDA, MANOVA, and classification models reveals significant chemical differences and predictors of wine type and quality.
A curated collection of statistical analyses and data-driven insights.
I’ve completed various projects applying advanced statistical and machine learning techniques to solve practical problems. Notably, I leveraged MANOVA and classification methods to uncover significant predictors of wine type and quality, analyzed U.S. healthcare spending trends from 1980-2014 to identify factors behind rising costs, and developed a Random Forest model achieving high accuracy to classify patient symptom severity. Additionally, I’ve created interactive dashboards—including a Quarto dashboard visualizing 100m sprint results and Shiny apps analyzing UK accident data and Yelp spatial reviews—and delivered machine learning models with measurable outcomes, such as a 56% performance increase in forecasting customer returns and accurate classification of wine origins and income brackets exceeding $50K.
Exploring wine attributes through EDA, MANOVA, and classification models reveals significant chemical differences and predictors of wine type and quality.
This report examines the surging healthcare costs in the U.S. from 1980 to 2014, revealing the factors behind its status as one of the world's most expensive countries for healthcare.
Using symptom analysis, this study employs a Random Forest approach to predict severity levels in patients, aiming to enhance healthcare decision-making.
The 100m Dashboard! Dash your way to victory and look beyond! Take your gold medal and observe that this dashboard was built using just quarto.
This accident time series app allows you to explore and analyze data related accidents in UK. Feel free to download the data and explore other methods of analysis.
An interactive Shiny dashboard for exploring Yelp business reviews, spatial patterns, and advanced spatial modeling.
Predict customer product returns, leveraging detailed product characteristics and customer demographics.
Our project sets a new bar in wine origin identification, transforming how industry professionals use critic data.
Expertly predicting $50K+ incomes through ML, our project highlights the synergy of data science skills and team collaboration.
---
title: "Statistical & Data Science Portfolio"
subtitle: "A curated collection of statistical analyses and data-driven insights."
listing:
template: ../../assets/templates/listings/projects.ejs # The EJS template file
contents:
- "**/*.qmd" # Grabs all project .qmd files from their subdirectory folders
ai-summary:
banner-title: "Yapper Labs | AI Summary"
model-title: "Model: ChatGPT 4.5"
model-img: "/assets/images/OpenAI-white-monoblossom.svg"
summary: "I've completed various projects applying advanced statistical and machine learning techniques to solve practical problems. Notably, I leveraged MANOVA and classification methods to uncover significant predictors of wine type and quality, analyzed U.S. healthcare spending trends from 1980-2014 to identify factors behind rising costs, and developed a Random Forest model achieving high accuracy to classify patient symptom severity. Additionally, I've created interactive dashboards—including a Quarto dashboard visualizing 100m sprint results and Shiny apps analyzing UK accident data and Yelp spatial reviews—and delivered machine learning models with measurable outcomes, such as a 56% performance increase in forecasting customer returns and accurate classification of wine origins and income brackets exceeding $50K."
format:
html:
code-tools: true
css: ../../assets/themes/projectsCustom.css
page-layout: full
include-in-header:
- text: |
<script type="text/javascript" src="/assets/scripts/projects/new-banner.js"></script>
---