Popular GitHub Repositories for Smote

Synthetic Minority Over-Sampling Technique for Regression

Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Bank customers churn dashboard with predictions from several machine learning models.

spark tutorial for big data mining。包括app流量运营分析、als推荐、smote样本采样、RFM客户价值分群、AHP层次分析客户价值得分、手机定位数据商圈挖掘、马尔可夫智能邮件预测、时序预测、关联规则、推荐电影好友等。

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

Apply 7 common Machine Learning Algorithms to detect fraud, while dealing with imbalanced dataset

A redeveloped hackathon bank customer churn prediction Core ML project.

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.

AI MLOps Project – A production-grade MLOps pipeline for scalable, reproducible, and cloud-ready machine learning with Spark, scikit-learn, MLflow, and LLM-powered analytics.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

Cyber Bully Detection System is an AI-powered system that uses NLP to detect and prevent cyberbullying in online spaces. By combining synthetic training data with real-world testing, it aims to create safer, more respectful digital interactions.

Accurate solar flare prediction is crucial for mitigating risks to astronauts, space equipment, and satellite communication. Our study enhances prediction accuracy using advanced preprocessing and a novel deep learning-based classifier called ContReg on the SWAN-SF dataset, outperforming previous methods.

AIcrowd Contest: ADDI Alzheimer's Detection Challenge

I built Sentiment Analysis models leveraging a deep learning approach utilizing the customer reviews of Amazon products. Since Long Short Term Memory Network (LSTM) is very effective in dealing with long sequence data and learning long-term dependencies, I used it for automatic sentiment classification of future product reviews.