xboost

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

Machine Learning model to predict the health of the car engines using various algorithms like KNN, SVM, Random Forest, Xboost Classifier.

End-to-end project to predict basketball player points. (regression problem)

how to predict score credit to home credit indonesia with machine learning modeling, find more to Home Credit Indonesia

An interactive ML decision engine and Streamlit dashboard for real-time credit risk modeling, Probability of Default (PD) scoring, and portfolio expected loss analytics.

AI-powered project management assistant using ML to predict task delays, classify project risk, and optimize resource allocation. Features an NLP query interface, automated reporting, and a Streamlit dashboard. Built with FastAPI, scikit-learn, and XGBoost — runs locally, no cloud dependency required.

Augmentation based on position applied to CRF and XGBOOST algorithms applied to african languages for POS task

Strona w HTML przewidująca popyt na rowery w Seoulu, porównująca wyniki modelu XBoost i regresji liniowej

Credit risk prediction using machine learning with Logistic Regression, SHAP explainability, threshold optimization and XGBoost comparison.

Implemented time series forecasting to predict future orders for Glovo, utilizing models such as ARIMA/SARIMA, LASSO, XGBoost, and Linear Regression.

AI-powered real-time traffic intelligence for India

Comparison of multiple machine learning classifiers for corporate bankruptcy prediction using financial ratios, class imbalance handling, and cross‑validation.

Sustainability is our shared responsibility. This interface helps visualize environmental tracking, making complex carbon data clear and actionable.

ML and data processing

XGBoost vasitəsilə neftin gələcək qiymətinin proqnozlaşdırılması layihəsi

Machine learning pipeline for football match outcome prediction using feature engineering, Random Forest and XGBoost models.

Proyecto de predicción de ventas para las proximas 6 semanas de todas las tiendas , con representación de datos, usando Python con librerías distintas como Pandas, Numpy,Matlab, Seaborn para análisis de datos, recopilación e interpretación de los datos, además de modelos predictivos.

Benchmarking Tree-Based Ensemble Methods for Multi-Year Daily Precipitation Forecasting Across the Contiguous United States (2000-2023)

AI-Powered Quick Commerce Delivery Intelligence System — End-to-end analytics & ML prediction dashboard for Blinkit, Zepto, Swiggy Instamart | 9.4L orders | Random Forest R²=0.9658 | Streamlit + Plotly + XGBoost | Live Deployed