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"

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

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

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

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

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

Approximation Bayesian Computation: Population Monte Carlo in MATLAB and Python

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.

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

time series analysis in R use cases

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)

https://andrewnana.github.io/R-Smoking-and-head-and-neck-cancer/. In this project, using data collected from the medical records of 215 patients treated definitively for HNSCC at the University of Texas MD Anderson Cancer Center, we propose to evaluate the impact of smoking history on head and neck cancer patient’s survival.

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

Working through the book and exercises Pandas for Everyone by Daniel Chen

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

Detailed implementation of various time series analysis models and concepts on real datasets.

Model diagnostics based on cumulative residuals (R package) https://kkholst.github.io/gof/

Model Diagnostics App for Logistic Regression