PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
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Updated
Jun 27, 2024 - Jupyter Notebook
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
Implementation of 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
Sub-package of spatstat containing functionality for parametric modelling and inference
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
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)
This repository contains some of the time series analysis, diagnostics and forecasting projects I have done.
Working through the book and exercises Pandas for Everyone by Daniel Chen
Detailed implementation of various time series analysis models and concepts on real datasets.
analysis of stackoverflow developer survey // logistic regression
Using linear regression models to assess the most important aspects of winning baseball
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.
Global challenge to create Species Distribution Model to predict occurrence of frog species, Litoria fallax, in Australia.
Model diagnostics based on cumulative residuals (R package) https://kkholst.github.io/gof/
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
time series analysis in R use cases
Model Diagnostics App for Logistic Regression
This work came from the Stats525 course in Regression Analysis in R during the Spring semester of 2021 with Professor Maryclare Griffin
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