vistaarak near Final -micro project Copy.pptx

micro project

1. SMART INDIA HACKATHON 2024 Group No. - 6 Problem Statement Title - Online Chatbot based ticketing system PS Category- Software Team Name - VISTAARAK

2. Develop a chatbot that automates ticket booking for various museum services using NLP and API integration. Our solution is an AI-powered chatbot designed to automate ticket bookings for multiple services, for museum tickets. It integrates with real-time booking APIs, enabling users to book tickets instantly and seamlessly across different platforms. This chatbot eliminates the need for multiple booking platforms by providing a single, unified solution for ticket reservations. It improves user experience by offering quick responses and personalized recommendations, reducing the time and effort users spend on booking. The chatbot is unique in its ability to integrate multiple service APIs (travel, museums, etc.) under one umbrella. It uses advanced Natural Language Processing (NLP) to understand and process complex user queries, providing accurate and relevant results. This combination of multi-service integration and intelligent query handling sets it apart from other ticketing solutions. 2 @SIH Idea submission- Template IDEA TITLE VISTAARAK

3. TECHNICAL APPROACH Programming Language: Python (for backend and chatbot training. highly recommended because of its extensive support for AI and machine learning libraries) Frameworks: Rasa And Dialogflow (for chatbot) Front End: Figma and XML APIs: Custom booking APIs for museum 3 @SIH Idea submission- Template VISTAARAK

4. TECHNICAL APPROACH 4 @SIH Idea submission- Template VISTAARAK

5. FEASIBILITY AND VIABILITY Challenges: Integrating with Multiple APIs Connecting the chatbot with various APIs for booking services (for museums) can be complex. Each API has its own requirements and data formats, which can make integration challenging. Ensuring Accurate Intent Detection The chatbot needs to accurately understand user requests and intentions. Misinterpreting user inputs can lead to incorrect responses and a poor user experience. Solutions: Modular Architecture Implementing a modular system helps manage and integrate multiple APIs more effectively. Each module handles a specific API, making it easier to update or replace individual components without affecting the entire system. Advanced Training with Rasa Use advanced NLP tools like Rasa to train the chatbot. By focusing on various user queries and intents during training, the chatbot's ability to understand and respond accurately is improved. 5 @SIH Idea submission- Template VISTAARAK

6. IMPACT AND BENEFITS Impact 1. Simplifies Ticket Booking The chatbot streamlines the booking process by guiding users through a step-by-step interface. This makes it easier for users to find and book tickets or accommodations quickly, without navigating complex systems. 2. Improves Customer Experience With instant responses and 24/7 availability, the chatbot enhances the overall user experience. It provides timely assistance, personalized recommendations, and answers to queries, leading to higher customer satisfaction. 3. Reduces Operational Overhead Automating the booking process with a chatbot reduces the need for manual intervention, cutting down on administrative tasks and operational costs. This allows staff to focus on more strategic activities and improves overall efficiency. 6 @SIH Idea submission- Template VISTAARAK

7. IMPACT AND BENEFITS Benefits Social: Easier Access to Booking The chatbot offers users a convenient way to make bookings at any time, from anywhere. This ease of access is especially valuable for users who prefer quick, digital solutions over traditional booking methods. Economic: Reduces Dependency on Customer Service Agents By handling routine booking tasks automatically, the chatbot decreases the reliance on customer service agents. This leads to cost savings and operational efficiency, as fewer resources are needed for manual booking processes. 7 @SIH Idea submission- Template VISTAARAK

8. RESEARCH AND REFERENCES Research: Chatbot Frameworks : Look into frameworks like Rasa and Dialogflow , which provide tools and libraries for building and training chatbots, including natural language understanding and dialogue management. API Documentation : Refer to documentation from APIs like Amadeus and Cleartrip for detailed guidelines on integrating booking services, including endpoints, authentication, and data formats. References: Rasa : Provides open-source tools for building conversational AI. Dialogflow : Google’s platform for developing natural language interfaces. Amadeus : Offers APIs for booking data. Bookingkit : Provides API documentation for museum bookings. 8 @SIH Idea submission- Template VISTAARAK