Credit card fraud is a significant concern for both financial institutions and cardholders. Early detection of fraudulent transactions helps minimize financial losses and protects customers' identities. This project aims to develop a machine learning model that can effectively identify fraudulent credit card transactions.
- Imbalanced Data: Credit card transactions are overwhelmingly legitimate (around 99.8% in this dataset). This imbalance can hinder the model's ability to detect fraudulent transactions (the minority class).
- High-Dimensionality: The dataset may contain a large number of features. High dimensionality can increase computational costs and potentially lead to overfitting.
- Data Privacy: Financial institutions cannot share sensitive customer information, making it difficult to train models with the most relevant features.
- Adapting Fraud Techniques: Fraudsters constantly develop new methods, requiring models to adapt and evolve.
- Data Balancing Techniques: Techniques like SMOTE (Synthetic Minority Oversampling Technique) or undersampling the majority class can be applied to create a more balanced dataset.
- Feature Engineering: Feature selection or dimensionality reduction techniques can be used to identify the most relevant features and reduce computational complexity.
- Cost-Sensitive Learning: Assigning higher weights to misclassify fraudulent transactions can incentivize the model to prioritize detecting fraud even with imbalanced data.
- Model Monitoring and Updating: Continuously monitor model performance and retrain it with new data to adapt to evolving fraud patterns.
Use this link to down the data:
https://drive.google.com/file/d/1J_DWfdimrMUHKwSH_g0ABZ_O93ypwfmV/view?usp=sharing
Make sure your data should be in the root folder