Skip to content
#

adasyn-sampling

Here are 10 public repositories matching this topic...

Language: All
Filter by language

In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.

  • Updated Nov 19, 2023
  • Jupyter Notebook

Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.

  • Updated Jan 30, 2024
  • Jupyter Notebook

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

  • Updated May 26, 2024
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the adasyn-sampling topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the adasyn-sampling topic, visit your repo's landing page and select "manage topics."

Learn more