An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background

Sample programs and data for the book for chatbot

NewOaks AI is an AI chatbot builder, which allows you to engage with customers, nurture leads and book conversational appointments for your website, text SMS and files by Custom ChatGPT and Claude. The open source code is the chat bubble embedded on websites to engage with customers.

Code for Joe Mayo's book: Programming the Microsoft Bot Framework: A Multiplatform Approach to Building Chatbots

Chatbot for The Carbon Almanac book or a climate change related topic

CurAi is an innovative Smart Medical Appointment Booking System designed to simplify and enhance the process of scheduling medical appointments. This mobile application, built with Flutter, offers a seamless experience for users to search for doctors, book appointments, and receive personalized recommendations through an AI-powered chatbot.

The Mental Health Chatbot is a MERN stack web app that provides users with mental health support through an AI-powered chatbot. Users can sign up, log in, and book therapy sessions at specific time slots. Developed for the university's wellness center, it offers potential for data analytics to enhance mental health care and user insights.

EldCare is a revolutionary application designed to empower elderly individuals by providing them with a seamless platform to connect with healthcare professionals and book appointments effortlessly. The application utilizes a user-friendly chatbot interface for a smooth user experience and offers both web and mobile app versions for accessibility.

Purpose : Major Project Team Size : 4 Duration : 10 Months [ Oct. 1, 2021 - June 31, 2022 ] Key Skills : Rasa AI , Python , NLP , Flask , HTML , CSS , JavaScript It is a Web-based Chatbot to automate healthcare management with audio assistance. Users can get immediate medication for their symptoms and book appointments via an audio feature. In addition to text assistance, this chatbot has an audio assistance feature. This feature eliminates the restrictions that visually impaired patients face with currently available text-enabled healthcare chatbots. This voice-enabled chatbot was designed and developed using the Rasa interface for the backend, the Web Speech API, and the Talkify API for voice input and output, respectively. Title: ' TaBiB: Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence '. The project was presented and was approved at the 6th National Conference of Science and Engineering (NCSEM), 2022. Tools used : Rasa AI, Python, NLP, Flask, Web Speech API, Talkify API, HTML, CSS in Atom Editor.

This the code for the Laracon EU chatbot from my book "Build Chatbots with PHP"

Aurleaf is a medical platform that helps users find nearby Ayurvedic clinics, doctors, and hospitals. It offers AI-generated diet plans, exercise routines, and doctor-approved medical products like blood sugar monitors. Users can also access an AI chatbot for queries and book doctor appointments or lab tests using a pincode-based system.

A virtual assistant is an independent contractor who provides administrative services to clients while operating outside of the client's office. A virtual assistant typically operates from a home office but can access the necessary planning documents, such as shared calendars, remotely. People employed as virtual assistants often have several years of experience as an administrative assistant or office manager. New opportunities are opening up for virtual assistants who are skilled in social media, content management, blog post writing, graphic design, and Internet marketing. As working from home has become more accepted for both workers and employers, the demand for skilled virtual assistants is expected to grow. In today’s era almost all tasks are digitalized. We have Smartphone in hands and it is nothing less than having world at your fingertips. These days we aren’t even using fingers. We just speak of the task and it is done. There exist systems where we can say Text Friend, “I will Call you Letter.” And the text is sent or any one may make call by saying “Call To Mr. Roy” That is the task of a Virtual Assistant. It also supports specialized task such as booking a flight, or finding cheapest book online from various e-commerce sites and then providing an interface to book an order are helping automate search, discovery and online order operations. An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term "chatbot" is sometimes used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands. A similar concept, however with differences, lays under the dialogue systems. The project aims to develop a personal-assistant for Linux-based / Windows systems using Python, Jupitar Notebook, PyCharm. Voice Assistant using Python draws its inspiration from virtual assistants like Cortana for Windows, and Siri for iOS. It has been designed to provide a user-friendly interface for carrying out a variety of tasks by employing certain well-defined commands. Users can interact with the assistant either through voice commands or using sound or Voice input input. As a personal assistant, this system assists the end-user with day-to-day activities like general human conversation, searching queries in google, Bing or yahoo, searching for videos, retrieving images, live weather conditions, word meanings, searching for medicine details, health recommendations based on symptoms and reminding the user about the scheduled events and tasks. The user statements/commands are analyzed with the help of machine learning to give an optimal solution.

The project builds a smart public library system with a web platform, chatbot (RAG-based), and real-time book recommendations (REINFORCE). It includes a data warehouse for evaluation, with all components containerized and orchestrated using Apache Airflow.

Chatbots in Tourism Hospitality Industry: The future of chatbot is here; this technology has recently witnessed rapid diffusion in many sectors. Basic versions of chatbots are currently utilized, which usually start conversations with easy automated options for patrons and offer basic services like ordering or booking. However, fully functional chatbots that will be ready to replace customer service personnel will likely become more widespread by 2020, with AI bots powering 85% of all customer service interactions. Chatbots have the potential to assist the tourism industry in many ways – Chatbots in Tourism Hospitality Industry For any industry, accessibility to the company’s offerings is vital to the customer in both the pre-sale and therefore the post-sale process. Now, as more and more people are using instant messaging services like Facebook Messenger and WhatsApp, this simple use is often further enhanced by a company’s offering all of its services where consumers are afore chatting with their friends. Performing common administrative and menial tasks through chatbots, like scheduling appointments, setting reminders, booking tickets, and sharing traffic or weather updates, is very valued. Although there are some potential pitfalls, discussed later, the potential of chatbots in diverse sectors of the tourism industry is gigantic. Hotels, restaurants, hire car services, travel agencies, and tourist information centers can all enjoy this technology. The hotel industry can particularly enjoy the direct application of chatbots. Increasing the share of online bookings impacts sales growth, confirming the value of the hotel chatbot. Expedia took advantage of Facebook’s technology to launch a basic bot to assist travelers book hotels. Marriott Hotels also introduced a chatbot service to supply basic services like booking an area over chat, utilizing the Facebook chatbot interface. Chatbots are often particularly helpful in enriching the prearrival experience, allowing users to book rooms and other amenities, like: Spa Treatments Airport transfers Dinner Reservations Chatbots in the Hotel Industry A bot that interacts with guests in the least stages of the customer journey can gather valuable data, which algorithms and hotel staff alike can then use to supply personalized services. The direct application of chatbots within the restaurant business is often very impactful also. Restaurants and nutriment giants like Burger King, Pizza Hut, and Dominos have followed suit with their proprietary chatbots. Soon placing delivery orders over the phone is going to be obsolete; customers will do that through Facebook, WhatsApp, or other social networking sites. Chatbots will eventually accept payments as well; MasterCard already provides such services through its Masterpass app. Chatbots in the Restaurants Positioning chatbots can decrease costs for both customers and firms. Customers don’t get to call, which reduces their communication expenditures, and corporations will not get to hire customer service representatives or outsource answering services to a call center facility. The advantages aren’t limited to the ordering and delivery processes. Other possible chatbot benefits highlights include allowing customers to perform subsequent tasks without having to download mobile apps: Observe and survey restaurant reviews, menus, prices, and available tables Control restaurant reservations on the go, change, cancel, or re-book tables Search and find restaurants consistent with party size, date, time, preferred cuisine, price, or distance. Chatbots in the Airline Industry – Chatbots in Tourism Hospitality Industry Customer service within the airline industry is one of the primary areas that would enjoy chatbots as a result of the high volume of customer contact through inquiries and bookings. an honest customer service bot could economize by automating tasks and unclogging call centers. It might help consumers find suitable flight options by meeting information like time, date, and other preferences. It could help on the wing booking, saving customers the difficulty of visiting the airline’s website and entering page after page of data. It could give status updates about flights, like information about delays or cancellations. It could also provide digital boarding passes, a service Turkish Airlines has begun to provide; offer baggage information; and gather feedback. it’s reported that its introduction has recorded an enormous surge in online booking. Chatbot Challenges Although AI and chatbots have created excitement within the tourism and hospitality industry, many concerns and problems can affect their adoption. The media’s portrayal of AI as being capable of handling much of the tasks within the tourism and hospitality industry is sometimes overrated. the push toward chatbots is partly thanks to the recognition of several new messaging services. The testing with chatbot adoption involves technical issues, cost, culture, and organization size. one among the foremost significant technical issues in language processing. Chatbots still commonly struggle with lexical and semantic ambiguity. We have study the role of chatbots in several areas of the tourism and hospitality industry. This is often the age of chatbots. As an information-intensive industry, firms that lead in its early adoption are set to experience first-mover advantage, that is, the benefit gained by being the primary to launch a service. The interlinked nature of the tourism industry will subject industry laggards into undue pressures, which can not be favorable to their strategic directions at that point. So, the time to plan is now!

In today's frantic environment, traditional ways of seeing the doctor and protracted processes of making an appointment might be very exhausting. We devised an efficient solution while keeping costs and time in mind. Users may use our solution to make doctor appointments, video chat with physicians and patients, save pharmaceutical prescriptions for later use, and book ambulances using GMap. We also used Dialogflow to incorporate a mental health chatbot for patients' healthcare.

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This project is written in NodeJS and it's product is a WhatsApp Chatbot which is used for Dental Appointment Bookings and these appointments are saved in google calendar

A GPT based web chatbot for user-uploaded .epub book

Hoki is a Hotel Booking Chatbot, it makes it easy for users to book their choice of hotels in a easy way!

This project is a smart Arabic-speaking chatbot developed using Rasa for a travel agency. It enables users to book flights and hotels, change options, and confirm reservations through natural language conversations. The chatbot understands and extracts key information such as cities, dates, hotel categories, and more using custom intents and entiti

An interactive chatbot application built with Streamlit, utilizing Google’s Generative AI and Langchain for document-based Q&A. The app allows users to upload text, PDF, or Word documents, interact with the content using AI, and book appointments through a conversational interface with input validation.

The website is one stop solution for all medical facilities. It can predict diseases and allow you to book appointment as per your convenience. It also provides home remedies for different kind of non-fatal diseases. It has facility of chatbot through which you can chat with service provider. It also recommends exercises according to your age.

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simple rasa chat bot for flight booking

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A chatbot which is trained on a text of the book and gives relevant answers when questioned related to the book. It uses Ada-002 embedding model to generate embeddings and for question answering it uses GPT 3.5 Turbo.

This repository contains the source code and configuration files for a chatbot that helps users book flight tickets. The chatbot is built using the Rasa framework and uses machine learning to understand natural language inputs from users and provide relevant responses.

The Online Chatbot Ticket Booking System is an AI-powered web platform developed for the Smart India Hackathon 2024 under the Ministry of Culture. Built with Django and Rasa, it enables museum visitors to book tickets via text or voice, supporting multilingual chat, QR-based tickets, and secure Razorpay payments.

Build a textbook project using spec kit plus, docusaurus, implemented RAG chatbot for live Q&A inside the book content & Claude code - A Hackathon project assigned by GIAIC