Popular GitHub Repositories for Smote

Synthetic Minority Over-Sampling Technique for Regression

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

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

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.

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.

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.

AIcrowd Contest: ADDI Alzheimer's Detection Challenge

End-to-end comments classification task focusing on Semantic Representation of text. Comparison different approaches: TF-IDF, Word2Vec and Doc2Vec.

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.

Solution for SemEval-2025 Task 9: The Food Hazard Detection Challenge. Utilized machine learning, fine-tuned LLMs (GPT-2, LLaMA, Flan-T5-XL), and ensemble learning to classify food hazards and detect specific products and hazards from unstructured text.

my graduation project in Epsilon AI Institute (kaggle competition)

Classification task for machine laerning models to predict customer churn for a telecom company. Includes EDA, work plan, and model training/evaluation/comparison.

Streamlining the process to generate synthetic data. Just focus on the data, not the code!