💳 Detect fraudulent credit card transactions through data analysis, helping financial institutions minimize risks and protect customer trust.

💳 Detect fraudulent financial transactions using XGBoost. Optimize performance with advanced techniques for imbalanced datasets and complex features.

XGB model trainer to classify an observation as an exoplanet, candidate or false positive

churnxgb 📉🚀😀 : Customer Churn Predictions # BQML # XGBoost Classifier

NIRF Rank Predictor: Web scraping, OCR, Model training using XGBoost, feature extraction using SHAP and permutation analysis.

Predicting future sales for Walmart stores using historical data, calendar features, and advanced regression models. This project covers data preprocessing, feature engineering (lag values & time-based), model training using Linear Regression and XGBoost, and visual analysis with rolling averages and seasonal decomposition.

Predicting online news popularity using classical and ensemble ML models on Spark

🥇 1st Place & Best Project Overall Stats Under the Stars 8 Hackathon (#SUS8) Organized by

PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition

comparison of different machine learning models such as GB, XGB and NN to see which performs better at real time SYN flood detection

daky's X11 Hotkey Daemon

This is the Classification supervised learning model used for predicting employee will get promotion or not

building a model to predict whether a food establishment passed inspection or not

Kaggle Bosch Production Line Performance, NO.74/top 6% (post competition analysis) 生產線分析、前 6 % ( 賽後分析 )

End to End Accident Prediction App with Hotspot Analysis

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.

Utilizing AutoXGB for Credit Card Financial Fraud Detection

This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.