This research discussion explores how recommender systems can reinforce or mitigate human bias, drawing on Evan Estola’s When Recommendation Systems Go Bad and related literature. The assignment reflects on the ethical responsibilities of data scientists in designing fair recommendation algorithms. It includes a simulation in R that illustrates how biased historical data can lead to unequal recommendations, along with technical strategies to address these issues.

Matrix Factorization with SVD

The goal of this project is to experiment with different recommendation algorithms on a user-item ratings dataset.

How do state-level energy production patterns reflect regional strengths and vulnerabilities, and what are the implications for U.S. energy security?

Are U.S. children suffering from food insecurity, malnutrition, and starvation, and what are the long-term implications?

This file has additional section where I combined both highlighted tables -2022-2023 and 2023-2024- for easy and accessible comparison

This is the final project for DATA 605. The project is about "You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. " kaggle.com

CoinToss Programs

Animation of my initials

Data 607 discussion 11: analyze an existing recommender system that you find interesting.

The goal of this assignment is to extend on one of my classmates' Tidyverse CREATE assignment

Data 607

Data 607

The goal of this assignment is to choose one of the New York Times APIs, construct an interface in R to read in the JSON data, and transform it into an R DataFrame.

Project#2 Data 607 Fall 2023

Lab 2 DATA606 Spring 2023

Exploration of data (teaching ratings)

Factorial, sequence and quadratic function.