This repository contains a collection of research projects covering machine learning, data analysis, regression modeling, and statistical inference across various domains.
- Car Color and Resale Value Analysis – Analyzing how car color impacts resale value using Craigslist car listings and Bayesian statistics.
- Case Studies – Various write-ups on different topics regarding the ethics, privacy, security, and policies of data science.
- Customer Purchase Prediction Model – Developing a machine learning model to predict whether customers will make a purchase based on product pricing and discounts.
- Salary Prediction with LARS and LM Models – Comparing Least Angle Regression (LARS) and Linear Regression (LM) to predict baseball player salaries using performance metrics.
- Climate Disaster Trends Analysis Using NOAA Data – Investigating long-term trends in climate-related disasters such as droughts, floods, storms, and wildfires using NOAA and GISS data.
- Regression Analysis of Climate Disasters and Temperature Trends – Using logistic and linear regression to model the relationship between temperature changes and the frequency of wildfires, droughts, and extreme weather events.
- False Discovery Rate (FDR) Analysis in Cancer Genes – Identifying statistically significant genes related to melanoma, CNS, and leukemia using FDR correction on the NCI60 dataset.
- Multiway ANOVA on Crop Yield Data – Using multi-factor ANOVA to assess the impact of fertilizer, density, and block effects on crop yield and their statistical significance.
- The Environmental and Economic Impact of Cryptocurrency – A comprehensive research project analyzing the detrimental effects of cryptocurrency on the environment, featuring source evaluations, an annotated bibliography, and a final research analysis.
- K-Means Clustering & Data Preprocessing – Implementing K-Means clustering and data preprocessing techniques to analyze numerical datasets and uncover hidden patterns.
- Data Preprocessing and Machine Learning Pipeline – A comprehensive workflow that involves data cleaning, normalization, and preparation for machine learning models. This project covers essential preprocessing techniques and model training steps to ensure data quality and improve predictive performance.