Tkinter Chatbot Application using NLP

The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.

Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Live Session
Deliverables? : Project Files, Report and Presentation
Ask For Price

Description

Description:

The process starts with a user’s request using a Chatbot app or an app using text input The user request is recorded by a so-called Natural Language Parser and is translated into the programming language of the conversation engine. the conservation engine analyses the question and redirects it to the back-end The back-end is connected to one or several databases (DB) or information systems (IS), which give the request to the corresponding query.

Objective:

  • The main aim of this project is to design a chatbot that takes in input from the user and gives the relevant response in accordance with the user’s question.
  • The main objective of the project entire activity is to automate the ticket booking process of day to day activities of the system Library like:
  • 1.      Ticket activities.
  • 2.      Creation of a Customer id.
  • 3.      Assign bus Tickets according to customer’s demand.
  • 5.     Bus Ticket Cancellation.
  • 6. Feedback.
  • 7.    AI Chatbot
  • Various NLP techniques are employed in order to accomplish this process.

ABSTRACT:

                    Chatbots as new information, communication, and transaction channel enable businesses to reach their target audience through messenger apps like Facebook, WhatsApp, or WeChat. Compared to traditional chats, chatbots are not handled by human persons, but the software is leading through conversations. Latest chatbots developments in customer services and sales are remarkable. However, in the field of public transport, little research has been published on chatbots so far. With chatbots, passengers find out timetables, buy tickets, and have a personal, digital travel advisor providing real-time and context-relevant information about trips. Chatbots collect and provide different data about users and their journey in public transportation systems. They include travel, product, service and content preferences, 

usage patterns, demographic and location-based data. Chatbots have many advantages for both companies and mobile users. They enable new user touchpoints, improve convenience, reduce service,  sales, and support costs, one-to-one marketing, new data collections, and deep learning. Using chatbots,  smartphone users can reach a company anytime and anywhere. The questioned users of an investigated prototype are remarkably open to new mobile services and they quickly adapt to this technology. 


INTRODUCTION:

                     The Bus ticket reservation system is currently maintaining Transport Company’s process manually which is a very time-consuming process. It deals with the transport industry’s ticket booking and transport maintenance, so it becomes a very tedious job for the ticket booking transporter to look after these particulars to complete the task at right time. The bus ticket booking system not only deals with transporters’ owned vehicles but also takes into consideration the other types Like clarifying the doubts on the spot by AI Chatbot. The Rise of Chatbots  The number and variety of chatbots strongly increased in the last couple of years. In April  2017, more than 100’000 chatbots are available in Facebook messenger only and the potential global annual revenue generated by chatbot transactions is estimated up to 32 billion US Dollars However, chatbots as a personal, interactive, and disruptive information, communication, and transaction channel not only generate high revenues, they also reduce costs: The potential annual salary savings created by chatbots is estimated to 12 billion US Dollars in insurance sale, 15 billion for financial services and sales representatives and 23 billion for common customer service personnel in the US . The uses of chatbots. The word “chatbot” consists of the terms “chat” and “robot”. Originally, the term chatbot was used for a computer program, which simulates human language with the aid of a text-based dialogue system. Chatbots contain a text input and output mask, which allows mobile users to  communicate with the software behind them, giving them the feeling of chatting with a Real Person


EXISTING SYSTEM:

                           Currently, the type of system being used at the counter is an internal system that is manually used in selling bus tickets. The problems facing the company are that customers have to go to the counter to buy a bus ticket or ask for the bus schedule, customers will also have to queue up for a long time in order to secure a bus ticket and will also need to pay cash when they buy the bus ticket.


Disadvantages:

  • Early QASs were developed for restricted domains and have limited capabilities. 
  • Current QASs focus on types of questions generally asked by users, but difficult to analyze the correct result.
  • The time period to analyze is very difficult.

PROPOSED SYSTEM :

                    It is recommended that despite the present functionality of the Question and answer software, additional functionality such as the use of Link sharing and notifications is implemented into the system. Moreover, less time-consuming. HTML language was used for the front-end of the software while the back end was designed using MySQL. The software achieved is capable of improving the customer hand and relationship management in chatbot operations. The main objective of the project entire activity is to automate the ticket booking process of day to day activities of the system Library like:

  1. Ticket activities.
  2. Creation of a Customer id.
  3. Assign a bus Ticket according to customer’s demand.
  4.    Bus Ticket Cancellation.
  5. Feedbacks.
  6.   AI Chatbot

Advantages:

  • This very simple approach shows the important role of semantic processing that has characterized Question Answering from its beginning, exploiting information other than facts available in database systems, and distinguishing it from information retrieval.
  • QA systems cover a wide scope of different techniques, such as question type ontology.

SYSTEM ARCHITECTURE:

Tkinter Chatbot Application using NLP
Tkinter Chatbot Application using NLP

HARDWARE AND SOFTWARE SPECIFICATION

Hardware:

 

  1. Windows 7,8,10 64 bit
  2. RAM 4GB

Software :

  1. Data Set
  2. Python
  3. Python

References:

Atwell, E., Bayan, A.-S. (2015). ALICE Chatbot: Trials & Outputs. In: vol. 19

Braun, A. (2003). Chatbots in der Kundenkommunikation. Berlin: Axel Springer Verlag. 

Business Insider (2017a). Here’s how chatbot metrics differ from traditional apps, BI Intelligence, 

Available: www.businessinsider.com/heres-how-chatbot-metrics-differ-from-traditional-apps-2016- 10, accessed 24th of June 2017. 

Business Insider (2017b). Facebook has opened up analytics and developer tools for Messenger bots, BI 

Intelligence Available: http://www.businessinsider.com/facebook-opens-analytics-developer-tools

for-messenger-bots-2016-11, accessed 24th of June 2017. 

Christensson, P. (2009), Avatar, Available: http://techterms.com/definition/avatar, accessed 24th of June 2017 

Coniam, D. (2008). Evaluating the language resources of chatbots for their potential in English as a 

second language. In: ReCall (20), pp. 98-116. 

Di Pietro, G., Gallo, L., Howlett, R., & Jain, L. (2016). Smart Innovation, Systems and Technologies. 

Switzerland: Springer International Publishing Switzerland. 

Dempt, F. (2016). Futuregram. Available: http://futuregram.trendone.com/index.html#einstieg, accessed 

24th of June 2017 

Gill, A., & Oberlander, J. (2002). Taking Care of the Linguistic Features of Extraversion, Available: 

http://hfac.gmu.edu/-cogscilfinaLind_files/gilLOberlander.pdf, accessed 24th of June 2017 

Hill, J., Ford, W., & Farreras, I. (2015). Real conversations with artificial intelligence: A comparison 

between human-human online conversations and human–chatbot conversations. In: Computers in 

Human Behavior (49), pp. 245-250. 

Jia, J. (2009). CSIEC: A computer-assisted English learning chatbot based on textual knowledge and 

reasoning. In: Knowledge-Based Systems, pp. 249-255. 

Laurel, B. (1997). Interface Agents: Metaphors with Character. Software Agents, pp. 67-77. 

McNeal, M., & Newyear, D. (2013). Chatbot Creation Options. Library Technology Reports (49). 

Mena, J. (2012). Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere 

Anytime (A5), Auerbach Publications, New York. 

Reeves, B., & Nass, C. (1996). The media equation: how people treat computers, television and new 

media like real people and places. Cambridge University Press, New York. 

Statista (2017a). Statista, VentureBeat, 11/2016. The number of chatbots for the Facebook Messanger

Available under: https://de.statista.com/statistik/daten/studie/662144/umfrage/anzahl-der

verfuegbaren-chatbots-fuer-den-facebook-messenger/, accessed 24th of June 2017. 

Statista (2017b). Statista, Bitkom, 11/2016. For which fields would you use chatbots? Available under: 

https://de.statista.com/statistik/daten/studie/660267/umfrage/umfrage-zu-einsatzmoeglichkeiten-von

chatbots-in-Deutschland, accessed 24th of June 2017. 

Trendone (2016). Futuregram. Available: http://futuregram.trendone.com, accessed 24th of June 2017. 

Van Lun, E. (2016). Chatbots. Available: https://www.chatbots.org/chatbot/, accessed 24th of June 2017. 

Wang, Y., & Petrina, S. (2013). Using Learning Analytics to Understand the Design of an Intelligent 

Language Tutor. In: International Journal of Advanced Computer Science & Applications (11), 

  1. 124-131. 

 

Customer Reviews

There are no reviews yet.

Be the first to review “Tkinter Chatbot Application using NLP”

Your email address will not be published. Required fields are marked *