A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features

Synthetic Minority Over-Sampling Technique for Regression

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

Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

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

A fraud detection project that processes user or credit card data using machine learning and deep learning algorithms.

A repository of resources for understanding the concepts of machine learning/deep learning. 

Synthetic Minority Over-sampling Technique

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。

Dealing with class imbalance problem in machine learning. Synthetic oversampling(SMOTE, ADASYN).

Address imbalance classes in machine learning projects.

Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.

Implementation of novel oversampling algorithms.

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

No description provided.

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

This repository presents the code for digital modulation detection in Communication networks

Approx-SMOTE: fast SMOTE for Big Data on Apache Spark

Credit Card Fraud Detection Project

Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.

A python library for repurposing traditional classification-based resampling techniques for regression tasks

🚨 Fraud Detection with Deep Neural Networks (PoC) 🤖 A hands-on personal project to predict fraudulent financial transactions using deep learning. Covers the full pipeline: from exploratory data analysis (EDA) and preprocessing to model training and evaluation. An experimental approach to tackling real-world financial fraud. 📊🔍

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.

Sampling-based methods for correcting for class imbalance in two-category classification problems

ICSE'18: Tuning Smote

Data mining with Go.

HR Analytics Dataset