Spatiotemporal resampling methods for mlr3

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

Resampling tools for openPMD PIC data

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

A MATLAB package for multivariate permutation testing and effect size measurement

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

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 Tibshira…

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.

Resampling procedure for weakly dependent stationary observations.

Resampling procedure for weakly dependent stationary observations.

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

CLI Image Processing and Editing Suite

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

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

To Detect Early Sepsis Disease

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

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

Supervisor : Dr. Bhaswati Ganguly