A multi-label approach of the SMOTE algorithm
-
Updated
Aug 6, 2024 - Python
A multi-label approach of the SMOTE algorithm
Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)
This research advances credit card fraud detection by integrating machine learning and deep learning techniques. Key findings include improved model adaptability through hyperparameter tuning.
A collection of my practices using the following algorithms: DBSCAN, KNN, Decision Tree Classifier, K-means, Apriori, SMOTE, SVM
Data analysis, visualization and prediction to predict whether a patient has benign or malignant breast cancer based on properties of the cancer
Supervised Machine Learning and Credit Risk
Supervised Machine Learning and Credit Risk
Predicting toxicity of molecules. Project on course "Data Mining 2"
Add a description, image, and links to the smote-algorithm topic page so that developers can more easily learn about it.
To associate your repository with the smote-algorithm topic, visit your repo's landing page and select "manage topics."