This project aims to enhance customer support efficiency and accuracy in banking services by utilizing a Long Short-Term Memory (LSTM) model to classify user queries into predefined categories. These categories include user information, debit/credit card issues, and transaction problems. By automating the intent detection process, banks can respond more quickly and accurately to customer inquiries.
Customer support is a critical component of the banking industry, where timely and accurate responses to customer inquiries can significantly improve user satisfaction. This project leverages machine learning, specifically an LSTM model, to classify user queries into specific categories, thereby streamlining the customer support process.
The dataset used in this project comprises user queries collected from banking customer support interactions. The queries are labeled into three primary categories:
User Information: Inquiries about account details, personal information updates, etc. Debit/Credit Card Issues: Problems related to card transactions, lost/stolen cards, etc. Transaction Problems: Issues with bank transfers, deposits, withdrawals, etc.
Text Cleaning: Removal of special characters, stop words, and tokenization. Word Embedding: Conversion of text data into numerical vectors using techniques like Word2Vec or GloVe. Padding: Ensuring all sequences are of the same length for uniformity in LSTM input.
The LSTM model architecture consists of the following layers:
Embedding Layer: Converts input text into dense vectors of fixed size. LSTM Layer: Captures the temporal dependencies in the query sequences. Dense Layers: Fully connected layers to map the LSTM outputs to the desired number of categories. Output Layer: Uses a softmax activation function to classify the queries into one of the predefined categories.
The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that the LSTM model effectively classifies user queries with high accuracy, significantly improving the efficiency and accuracy of customer support responses.
Expand Dataset: Incorporate more diverse and extensive data to improve model generalization. Multi-Language Support: Extend the model to support multiple languages for global applicability. Integration with Live Systems: Implement the model in real-time customer support systems for live query handling.
Contributions are welcome! Please fork this repository and submit pull requests with your improvements.
This project is licensed under the MIT License. See the LICENSE file for details.
Special thanks to the open-source community for providing valuable resources and tools. Gratitude to the contributors and reviewers who helped enhance this project.