Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

VAPT Tool Web is a comprehensive online platform designed for vulnerability assessment and penetration testing. Our tool offers a range of features to help organizations identify, prioritize, and mitigate security vulnerabilities within their systems and networks.

This repository contains a dataset and analysis focused on the risk factors associated with Alzheimer's disease. The dataset includes comprehensive patient information, demographic details, lifestyle factors, medical history, clinical measurements, cognitive assessments, and symptomatology.

Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost

Time series forecasting with machine learning models

Distributed ML Training and Fine-Tuning on Kubernetes

A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.

Solutions to Water Pump Kaggle challenge exploring different machine learning models. Methods: Auto ML, H2o, XgBoost, Random Forest.

Let’s explore something interesting together. In this project, we developed a machine learning digital twin using Intel-optimized XGBoost and daal4py to simulate and optimize the Rate of Penetration (ROP) in geothermal drilling. We leveraged SHAP for Explainable AI (XAI) to interpret model predictions.

Tree Explainer interprets ensemble tree models by analyzing individual trees and their predictions, providing insights into the decision-making process.

This project uses machine learning, specifically an XGBoost regressor, to predict the price of Solana (SOL) based on historical data and engineered features.

Python for Data Science

Identify fraudulent transactions.Develop a machine learning model that can identify fraudulent transactions in credit card data or insurance claims.Show how your model can reduce financial losses by flagging suspicious activities.

Knowledge of various Time Series Forecasting topics: Long Short-Term Memory (LSTM), Exponential Smoothing, Autoregressive integrated moving average (ARIMA), TBATS, Multivariate Time Series Forecasting, XGboost, N_BEATS, and Prophet.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

📘 The experiment tracker for foundation model training

Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

AI Machine learning Deep learning Projects with code

Visualize decision trees in Python