Banks often receive a multitude of customer complaints. Due to the sheer volume, bank customer service teams frequently struggle to categorize these complaints accurately. Consequently, the complaint resolution process slows down, leading to customer dissatisfaction.
o address this issue, we can employ Natural Language Processing (NLP) technology. With NLP, we can create a system that automatically recognizes the content of customer complaints and determines the appropriate product category. This will make the complaint handling process faster and more efficient.
The goal of using this NLP system is to speed up the response time of the customer service team and improve the accuracy in classifying complaints. The target is to create an NLP system with an accuracy rate of at least 80%, measured by metrics such as Accuracy, Precision, Recall, and F1-Score.
With this system, banks are expected to respond to customer complaints more quickly and accurately, thereby increasing customer satisfaction and the efficiency of the customer service team. I hope this explanation helps you convey your project more clearly to the readers.
- Title:
Model NLP Classification Bank Customer Complaint - Authors: FyrnDly
- Model: LSTM
- Deploy: Streamlit
| Dataset | Download |
|---|---|
| complaints.csv | download |
- Environment App
pip install -r requirements.txt - Running Streamlit
streamlit run app.py - Training Model
Open file /models/model_train.ipnb on python notebook to training model
| Description | Model | Vectorizer |
|---|---|---|
| Model Undersampling Training | model | vectorizer |
| Model Oversampling Training | model | vectorizer |