PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

Implementation of NAACL 2024 Outstanding Paper "LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models"

🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights

This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.

Tool for evaluating atmospheric carbon dioxide concentrations as simulated by Earth system models

Sub-package of spatstat containing functionality for parametric modelling and inference

Polyhedron is a domain-driven Python optimization modeling framework that turns business entities into transparent MILP/MIQP models, with built-in diagnostics, scenario analysis, and robust solver interoperability.

This project uses the Reaction Time Survey dataset to develop a linear regression model for accurately predicting student reaction times based on various predictors. Tech: R (RStudio)

Approximation Bayesian Computation: Population Monte Carlo in MATLAB and Python

This repository contains some of the time series analysis, diagnostics and forecasting projects I have done.

Tool for evaluating atmospheric carbon dioxide concentrations as simulated by Earth system models

Low-cost plasticity diagnostics for reinforcement learning models.

Explain why your model fails — not just how accurate it is.

Global challenge to create Species Distribution Model to predict occurrence of frog species, Litoria fallax, in Australia.

Nine diagnostic tools for detecting and understanding overfitting in scikit-learn models — polynomial overfitting, learning curves, validation curves, bias-variance decomposition, regularisation sweeps, data leakage detection, and more. Companion code for the ML Diagnostics Mastery series.

time series analysis in R use cases

Objective of this project is to perform predictive assesment on the Gross Domestic Product of India through an inferential analysis of various socio-economic factors to find out which predictors contribute most to the GDP. Various models are compared and Stepwise Regression model is implemented which resulted in 5.7% Test MSE.

Predicting wage in the uswage dataset (Linear Regression). Model Selection, Model Diagnostics etc.

Lending Club's loan data analysis using data cleaning/wrangling to predictive modeling