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This repository contains a collection of Jupyter notebooks developed for building and deploying a customer support chatbot tailored for e-commerce platforms like Amazon. The chatbot is designed to handle various customer queries, leveraging state-of-the-art natural language processing (NLP) techniques, including intent classification, named entity recognition, and heuristic keyword exploration.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience
The journey begins with clustering intents and generating document embeddings using unsupervised learning techniques. This process helps in organizing the customer queries into meaningful clusters, which serve as the basis for further classification and response generation.
To understand the true intents hidden within the customer data, heuristic clustering is applied using keyword-based approaches. This method acts as a strong foundation for distinguishing between various customer intents, ensuring minimal overlap and enhancing the chatbot's accuracy.
The notebooks include a strategy for generating training data by leveraging pre-existing intent buckets. This step is crucial for training the chatbot on a diverse set of examples, enabling it to handle a wide range of customer interactions.
Moving from unsupervised to supervised learning, the project transitions to using PyTorch for intent classification. The labeled data generated in previous steps is utilized to train a robust model capable of accurately classifying new, unseen user queries.
The chatbot not only classifies intents but also identifies and labels named entities within user utterances. These entities are stored in the dialogue management system, contributing to more accurate and context-aware responses.
The final step involves deploying the trained chatbot model. Although the notebook lacks a markdown description, this phase typically includes setting up the model on a server, integrating it with a user interface, and ensuring it can handle real-time customer queries effectively.
Various visualizations and analysis were conducted to effectively communicate the insights and progress throughout the project:
- Intent Clustering Visuals: Depicting how customer queries are grouped based on document embeddings.
- Keyword Distribution Graphs: Illustrating the distribution of keywords used to explore and classify intents.
- Training Data Analysis: Visualizations showing the diversity and coverage of the generated training data.
- Classification Metrics: Bar charts and confusion matrices representing the performance of the intent classification model.
- Entity Recognition Examples: Output samples demonstrating how entities are extracted and labeled from customer utterances.
Key outcomes from this project include:
- Effective Intent Clustering: The chatbot can efficiently categorize customer queries into relevant intents, leading to faster and more accurate responses.
- Comprehensive Training Data: By generating diverse training data, the chatbot is equipped to handle a wide range of customer interactions.
- Accurate Intent Classification: The PyTorch-based model delivers high accuracy in classifying customer intents, ensuring reliable performance in real-world scenarios.
- Enhanced Entity Recognition: The named entity recognition system significantly improves the chatbot's ability to understand and respond to user queries in a context-aware manner.
- Data Quality and Preprocessing: High-quality data and rigorous preprocessing are critical for achieving accurate intent classification and entity recognition.
- Iterative Development: The development process benefits from an iterative approach, where insights from one phase inform and improve subsequent phases.
- Importance of Visualization: Visual tools are indispensable for understanding and refining the model's performance throughout the project.
- Handling Large Datasets: Efficiently managing and processing large datasets posed significant challenges, especially during the training and deployment phases.
- Clustering Complexity: Selecting the right clustering algorithms and tuning their parameters required extensive experimentation.
- Data Integration: Combining data from multiple sources presented challenges in terms of format consistency and preprocessing requirements.
This project demonstrates the successful development of a customer support chatbot capable of understanding and responding to a wide range of queries in an e-commerce context. The use of both heuristic and supervised learning techniques, combined with robust data preprocessing and model training, results in a powerful tool that can significantly enhance customer service operations.
This work was conducted as part of a project aimed at improving customer service automation.