bayesian bootstrapping in python

**curve_fit_utils** is a Python module containing useful tools for curve fitting

Generate error bars and perform binning analysis using jackknife or bootstrap resampling. Calculate average and error in quantum Monte Carlo data (or other data) and on functions of averages (such as fluctuations, skew, and kurtosis).

These projects were part of the course Data Analysis (ECE Department, AUTH, 7th semester).

Jackknife & bootstrap resampling in Fortran with python bindings

MATLAB code for quality control of peak frequency estimates via bootstrapping.

Implementation of LOWESS (Locally Weighted Scatterplot Smoothing) algorithm with bootstrap confidence intervals for nonparametric regression and data smoothing in Python.

Statistical analysis of salary differences based on job mode (remote vs. in-person) and education levels using Welch’s t-test, ANOVA, and bootstrapping.

Jackknife with R to estimate the bias of a statistic

Browser-based and Python tool for robust SWOT–AHP prioritization with bootstrap uncertainty, scenario sensitivity, TOWS strategy translation, and SPI ranking. No installation required.

Empirical simulation study comparing K-Means vs. GMM under geometric stress tests (anisotropy, heteroscedasticity, overlap). Demonstrates the bias-variance trade-off in unsupervised learning using Monte Carlo and Bootstrap resampling.

XGBoost churn classifier on Kaggle e-commerce data. Part 1 of 2 — see Part 2 for MLOps implementation: https://github.com/anozk/Ecommerce_Churn_Prediction_Part2_MLOps_AWS_Deployment

Tools for julia programming of statistical analysis

End-to-end Python research pipeline replicating "Beyond Benchmark Rankings": treats LLM selection as portfolio construction rather than leaderboard ranking. Estimates marginal utility, informational novelty, and correlated-failure risk per model, then runs budget-feasible greedy optimization to select LLM ensembles.