Spatiotemporal resampling methods for mlr3

R package cross-validation, bootstrap, permutation, and rolling window resampling techniques for the tidyverse.

Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023

A MATLAB package for multivariate permutation testing and effect size measurement

Resampling tools for openPMD PIC data.

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Stanford Online course STATSX0001 "Statistical Learning" follows closely the sequence of chapters in the course text "An Introduction to Statistical Learning, with Applications in R" (James, Witten, Hastie, Tibshirani - Springer 2013). Trevor Hastie Professor of Statistics and of Biomedical Data Sciences, Stanford University, and Robert Tibshirani Professor of Biomedical Data Science and Statistics, Stanford University

Credit risk poses a classification problem that’s inherently imbalanced. Using a dataset of historical lending activity from a peer-to-peer lending services company, build a model that can identify the creditworthiness of borrowers.

Importance of HBA1c in predictive Modeling of probability of Hospital Re-admission (CAPSTONE PROJECT)

Resampling procedure for weakly dependent stationary observations.

Resampling procedure for weakly dependent stationary observations.

No description provided.

Constructs a continuous-time function from a list of real numbers.

In this project, we perform exploratory analysis on the data and try out different models to give the best results.

CLI Image Processing and Editing Suite

Mathematical and statistical applications for HP Prime.

No description provided.

A small C library for computation of U-statistics

To Detect Early Sepsis Disease

Modeling Agri-finance Credit Risk with Data & Machine Learning using Python (Jupyter Notebook)

Graphical user interface for Theta applications.

This repository contains two methods to address bias to missing pixels in methane plume detection CNNs. Our methods are transferable to other tasks.

Working through the labs of Introduction to Statistical learning with R by Hastie et al.

A collection of statistical resampling methods

This project explores data analysis, blending core Probability Theory and Descriptive Statistics with Statistical Inference and Bayesian Machine Learning (Regression/Classification). It concludes with a comparative study of Frequentist vs. Bayesian A/B Testing.

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we needed to employ different techniques to train and evaluate models with unbalanced classes. Jill asks us to use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling

Customer Churn Prediction using machine learning techniques with resampling, ensemble methods, and counterfactual explainability. This repository implements and validates the approach discussed in our published journal

For this project, I predicted credit risk with the supervised machine learning models I built and evaluated using Python.

Supervisor : Dr. Bhaswati Ganguly

Pattern Jitter is an algorithm for generating artificial spike trains that are maximally random while preserving the smoothed firing rates and the recent spike histories in a recorded spike train.