A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Applied Data Science Project

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

A collection of boosting algorithms written in Rust 🦀

Fit interpretable models. Explain blackbox machine learning.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

Master the essentials of data science and machine learning by building a used car price prediction model from scratch, turning raw data into accurate pricing insights

A game theoretic approach to explain the output of any machine learning model.

This project implements a stock price prediction model using various technical indicators and an ensemble of machine learning algorithms. The model predicts the direction of price movements and provides price predictions with uncertainty bounds for the next 8 hours.

Deciphering how customer's purchasing habits are influenced by wholesale pricing and examining its impact on final retail cost.

Train Gradient Boosting models that are both high-performance *and* Fair!

In my code portfolio, I generally try new techniques and methods in machine learning. I don't like only copying and pasting.

Apply ensemble technique of model stacking to predict patient's readmission

AIML Projects

Applied Data Science Project

Boosted Hybrids of ensemble gradient algorithm for the long-term time series forecasting (LTSF)

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

Analyze the impact of COVID-19 on Airbnb bookings in Chicago and Boston, focusing on changes in traveler preferences, occupancy rates, and revenue