Box-Cox transformation in Julia

This project analyzes and preprocesses the Online Retail dataset to uncover insights into customer purchasing behaviors, sales trends, and product performance. It includes data cleaning, exploration, and visualization, with the goal of enhancing understanding of online retail dynamics.

Statistical analysis of the Los Angeles real estate market in form of assessed values and rental prices

Projects for a biological data analysis course taught by Dr. Diane Thomson at the Keck Science Institute during Fall 2021.

Learn about Simple Linear Regression for Data Science

A Python-based utility for testing and visualizing data transformations. Includes normality tests (Shapiro-Wilk and Lilliefors), a variety of transformation methods, and visual analysis tools for histogram and Q-Q plots.

Analysis of Skewness and Kurtosis in Stock Return data and their Transformations

The R package ClusROC

socio-economic segmentation analysis to identify global development patterns. We analyze key indicators: child mortality, income, and GDP per capita. Our analysis employs data cleaning, exploratory analysis, Box-Cox transformation for normalization, and KMeans clustering to segment countries into developmental categories

Regression analysis of socioeconomic predictors of county-level voting behavior — model selection, cross-validation, and logistic regression in R

Applied Statistics I, 2021, UNC at Chapel Hill-linear regression

Statistical techniques for handling missing values, encoding, scaling, normalizing, detecting and treating outliers, and transforming data distributions

A Python-based utility for testing and visualizing data transformations. Includes normality tests (Shapiro-Wilk and Lilliefors), a variety of transformation methods, and visual analysis tools for histogram and Q-Q plots.