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.

Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.

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.

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

Feature Engineering, Regression, Classification, Model Explanation. My 2 biggest projects exploring the link between economic indicators and U.S. presidential election results.

This repository contains implementations of regression models on the Starbucks stock market. The goal is to provide a comprehensive understanding of the performance of these models. Also, implement metrics without relying on external machine learning libraries. ☕️📈

🔍✨ A machine learning project that predicts income based on various demographic factors using Random Forest and Gradient Boosting algorithms. Includes data preprocessing, hyperparameter tuning, and model evaluation with detailed performance metrics. 📊🤖

This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.

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 🦀

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

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.

MlFlow Project creating pipelines and using Grid-Search Cross Validation to find optimal parameters for Old School Runescape Machine Learning datasets.

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