Built a fraud detection system for transactions using the BERT LLM to analyze transaction data and user behavior.
- Data Loading: Acquire the credit card transactions dataset and preprocess it according to requirements.
- Text Tokenization: Utilize the BERT tokenizer to tokenize the transaction descriptions efficiently.
- Data Splitting: Partition the dataset into training and testing subsets to facilitate model training and assessment.
- Model Training: Employ a BERT-based model for sequence classification to train on the designated training data.
- Model Evaluation: Assess the performance of the trained model on the test set by analyzing evaluation metrics like the classification report.
Note: I leveraged a Kaggle dataset for both training and testing the model.