The Hotel Booking Chatbot is a Natural Language Processing project that combines Fallback handler and Entity extractor, built with WebSocket chat UI. The project follows a clean, modular pipeline where text loading, processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical NLP techniques while producing a working, demo-ready application.
Traditional manual methods for this task are slow, inconsistent, and unable to scale as text data grows. Without an NLP-based approach built on Entity extractor and WebSocket chat UI, users cannot process, classify, or extract meaning from large amounts of text quickly, and there is no reliable way to evaluate the quality of the results.
This project applies NLP techniques through Fallback handler, orchestrated with WebSocket chat UI and Entity extractor. The pipeline is designed for quality and control, with preprocessing, feature extraction, and clear evaluation. It produces consistent, reusable results and can be adapted to related text tasks with minimal changes.
WebSocket chat UI
Python 3.11+
Pandas and NumPy
NLTK / spaCy
Evaluation and logging
Rasa / ChatterBot
Flask
Modular text pipeline around Fallback handler and Entity extractor
Configurable preprocessing and model settings
Clear logging, metrics, and error handling
Clean interface for viewing results
Reusable components for related NLP tasks
Evaluation of output quality
The project is layered: the input layer loads and preprocesses text through Fallback handler; the processing layer applies NLP models with WebSocket chat UI and Entity extractor; and the output layer formats and presents results via Dialogue manager. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the Python environment, project structure, and configuration files.
Build the input and preprocessing layer with Fallback handler.
Implement the core NLP pipeline using WebSocket chat UI and Entity extractor.
Add the output and presentation layer via Dialogue manager.
Wire up end-to-end flows and add error handling and logging.
Evaluate results, tune parameters, and refine the pipeline.
Package the project, document it, and prepare the demo and viva report.
Build production-style NLP applications
Apply Handling edge-case user inputs and Building intent classification
Preprocess and analyze real text data
Work with popular NLP libraries
Present and defend a complete NLP project in viva
Expose the pipeline as a REST API for other apps
Add deep learning models for higher accuracy
Add multilingual support
Deploy with caching for faster responses
The Hotel Booking Chatbot delivers a complete Natural Language Processing workflow — from text loading and preprocessing to analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand NLP skills.
