In this project, I’m trying to build a predictive model to find out whether a person makes more than $50,000 a year based on their demographic information. The dataset I’m using is census_train.csv, which contains 35,000 rows and 15 different features about people in the U.S. Let’s get started by loading everything in.

This project uses three admissions datasets spanning Fall 2022, Fall 2023, and Fall 2024 enrollment cycles. Each dataset contains thousands of student records, with approximately 45–50 features detailing the admissions journey: from initial inquiry and application status to admission offers, deposits, academic records (e.g., GPA, ACT/SAT scores), demographics (e.g., sex, region), and student interests (e.g., sports, academic majors).

During the peak of the COVID-19 pandemic, the financial world, like every other industry, experienced shockwaves. As a data science enthusiast and market observer, I wanted to answer a fundamental question: Can we predict the stock prices of FAANG companies during the COVID-19 pandemic using historical trends and pandemic-related economic data?