Educational Chatbot (Rasa NLU-Rasa Core) Test phase, srs, design phase and source code final deliverable

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Educational Chatbot (Rasa NLU-Rasa Core) Test phase, srs, design phase and source code final deliverable

Project Domain / Category

Web Application, Artificial Intelligence, Machine Learning

Abstract / Introduction

In online educational system, students need answers related to their queries about programming courses urgently. So it is required to have a chatbot in each course that could answer queries related to course material from its intelligence. For such type of chatbot, there must be an automated system behind it, that works like a brain of teacher and give correct answers. Python is a high level programming language, that is taught as introductory courses of programming at undergraduate level of Computer Science and Information Technology universities. Students of this projects are required to build this chatbot as a web application that could answers students queries related to their course theoretically. Coding examples are not required at this level.

Functional Requirements:

1. Chatbot Interface:

Students will build a GUI( Graphical User Interface) which shows interface like given in screen shot here.

Answers to queries should be correct according to the fundamentals and its accuracy must be mentioned.

Rasa framework is to use Rasa NLU (Natural language understanding), Rasa Core and other features of Rasa for classification, training and testing model.

Visit this website for complete documentation and designing and logic’s for Rasa models..

https://rasa.com/docs/rasa/

2. User Role

User will write his/her query in chatbot about C++ concepts theoretically and will get reply from chatbot in this chatbot.

3. Chatbot Role

Chatbot will get the message from input box and builds Rasa NLU and Rasa Core models and pretrain these models to pick correct reply for this input and then perform test to know accuracy of models. Then answer the query of user by getting correct answer from back-end Rasa models.

4. Building of Chatbot
   1. Training

Take 70% of data for training.

Write complete code in python from scratch.

Write all the methods used for training like tokenizer, entity extractor, classifier etc and train the model.Also include required libraries to use those methods.

Check accuracy of the model.

1. Testing

Take 30% of remaining data for testing of models. Check accuracy of the model.

1. Execution

Build front end of chatbot using any tool of GUI and attach it with back-end.           User will ask questions and chatbot will reply answers using this front end. Supporting material:

1. Take data for training and testing only from mentioned chapters of given book (download from given link)

Chapter No. Title…………………………………..……………………Page No

Chapter 1. Introduction to Programming……………………………………. 69

Chapter 1. Introduction to Programming……………………………………. 69

Chapter 2. Primitive Types and Variables …………………………………. 111

Chapter 3. Operators and Expressions ……………………………………… 139

Chapter 4. Console Input and Output ………………………………………. 165

Chapter 5. Conditional Statements ………………………………………….. 195

Chapter 6. Loops ………………………………………………………………….. 211

Chapter 7. Arrays …………………………………………………………………. 235

Chapter 8. Numeral Systems ………………………………………………….. 265

Chapter 9. Methods ………………………………………………………………. 293 Chapter 11. Creating and Using Objects …………………………………… 385

Chapter 12. Exception Handling ……………………………………………… 415

Chapter 13. Strings and Text Processing ………………………………….. 457

Chapter 14. Defining Classes ………………………………………………….. 499

Chapter 16. Linear Data Structures …………………………………………. 641

Chapter 20. Object-Oriented Programming Principles ………………… 807

Chapter 21. High-Quality Programming Code ……………………………. 853

2. Build Chatbot Using Rasa in Python https://www.youtube.com/watch?v=PmETWC1fkhY

Watch above given video to understand how Rasa chatbot is configured, work and execute.Which algorithms are used for tokenization , featurization, entity extraction,

classification and further processing.

3. Helping Book to understand Python, Chatbot, Machine Learning, Artificial Intelligence concepts.

Tools:

Rasa Frame work(Rasa NLU, Rasa Core)

Tensor flow library

Python language,

HMTL, CSS and any styling tools for GUI

Supervisor:

Name: Neelam Alam