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

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders

Synthetic Minority Over-sampling Technique

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

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.

Implementation of the Geometric SMOTE over-sampling algorithm.

Build and evaluate several machine learning algorithms to predict credit risk.

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

Data Mining of Caravan Insurance Data Set Using R

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

📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python

Udacity capstone project | Credit card fraud prediction | Supervised Learning | Ensemble model | Data Sampling

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

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

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.

A general interface for clustering based over-sampling algorithms.

🎲 Iterable dataset resampling in PyTorch

Identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. (Python, Logistic Regression Classifier, Unbalanced dataset).