This project involves building a machine learning model to detect fraudulent credit card transactions. The classification is based on decision trees, leveraging transaction features to predict fraudulent activity with high accuracy.
- Algorithm: Decision Tree Classifier
- Accuracy: 99.98% on test data
- Dataset: A dataset of credit card transactions, labeled as fraudulent or non-fraudulent.
- Data Preparation: Preprocessing the data and splitting it into training and test sets.
- Model Training: Using a Decision Tree to classify transactions.
- Model Evaluation: Testing the model on unseen data to check its accuracy.