A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
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Updated
Jan 3, 2024 - Jupyter Notebook
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
Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances
Synthetic Minority Over-sampling Technique
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).
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
Data Mining of Caravan Insurance Data Set Using R
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.
🎲 Iterable dataset resampling in PyTorch
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras
Identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. (Python, Logistic Regression Classifier, Unbalanced dataset).
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