A modern Fortran statistical library.

Significant Network Interval Mining

Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases

This repository is a fork of a repository originally created by Lucas Descause. It is the codebase used for my Master's dissertation "Reinforcement Learning with Function Approximation in Continuing Tasks: Discounted Return or Average Reward?" which was also an extension of Luca's work.

Analysis of 2.2 million Realtor.com listings using Python and machine learning to uncover U.S. real estate market patterns. The project identifies market segments, predicts property prices, and reveals regional trends, providing data-driven insights for real estate professionals and investors.

This repository will include Python | Jupyter-Notebook statistical testing | tests and analysis. Highly useful for in depth data analysis & model development.

Multivariate analysis (MVA) of high dimensional heterogeneous data

GUI app for doing sliding window Z-score transformations of quantitative proteomics data

A/B Testing Analysis on Marketing Campaigns

Statistical Modelling and Data Visualization of a Climate Change Dataset (January 1984 to December 2008 ) Sourced from Kaggle

MATLAB functions for Beta distribution test

Yeast TMT data - 3 different carbon sources (from Gygi lab) analyzed with PAW pipeline and MaxQuant

Analysis of proportions using Anscombe transform

This repository contains all about the proposed solutions to the assignments that I was required to complete as part of the Quantium Data Analytics Virtual Experience Program. 📊📈📉👨‍💻

🐟 Statistical analysis of fish dimensions and weights implemented into linear regression (Ordinary Least Squares) predictive model

Analysis of factors affecting learning performance based on a data set from Learning Management System software. Conducted with R language.

This repository focuses on Mean Squared Error (MSE), Regression Analysis, and Central Limit Theorem (CLT). It includes Python implementations for image reconstruction, regression modeling, and distribution analysis.

Analyzing biological networks using statistical testing to uncover significant differences in protein distributions based on functional relationships.