PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
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Updated
Mar 30, 2025 - Jupyter Notebook
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
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)
Tool for evaluating atmospheric carbon dioxide concentrations as simulated by Earth system models
This repository contains some of the time series analysis, diagnostics and forecasting projects I have done.
Approximation Bayesian Computation: Population Monte Carlo in MATLAB and Python
Global challenge to create Species Distribution Model to predict occurrence of frog species, Litoria fallax, in Australia.
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.
Using linear regression models to assess the most important aspects of winning baseball
Working through the book and exercises Pandas for Everyone by Daniel Chen
Extended discrete choice modeling notebook with Fair's affair data & STAR98 education analysis. Covers Logit, Probit, GLM, diagnostics, model comparison, marginal effects, and advanced topics like censored regression and count models. Complete with visualizations and validation techniques.
A comprehensive explanation of Generalized Linear Models (GLMs) with Python examples, covering Binomial, Gamma, and Gaussian families, model diagnostics, formula interface, and alternative estimation approaches using statsmodels.
Lending Club's loan data analysis using data cleaning/wrangling to predictive modeling
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