CM2015 Programming with Data Midterm Coursework Assignment | University of London

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Category	Assignment	Subject	Computer Science
University	University of London	Module Title	CM2015 Programming with Data

CM2015 Midterm Coursework Assignment

This coursework is worth 50 marks.

Chatbot Project

Now that you have had a chance to explore some techniques and tools in Python, it is time to start integrating them into your own chatbot project. This is a chance for you to build a practical application using your knowledge of Python and Data Programming.

Expectations

Develop a functional and interactive chatbot without errors.
Demonstrate a strong use of core Python concepts, including:
o Data structures (dictionaries, lists, tuples) to managing intents, patterns, and responses. o Using Conditional logic and loops appropriately to drive chatbot interaction.
o Implementing functional and modular programs breaking logic into clear, reusable functions with well-defined inputs and outputs. o Organising code into multiple files (code + data) or modules to enhance maintainability and readability.
Adopt software engineering best practices by keeping code modular and reusable using functions, classes (optional), and configuration files (e.g., JSON).
Write test cases to verify and highlight the chatbot’s functionality and robustness.
Include clear and consistent documentation using comments.
Utilise Data Processing Techniques to clean and tokenise input data (e.g., user queries or pattern sets).
Uses libraries like NLTK, re (regex) for pattern matching, and tokenisation.
Implements basic NLP features, such as stop word removal, stemming/lemmatisation, or part-of-speech tagging.
(Optional) Incorporates sentiment analysis, keyword extraction, or named entity recognition for more intelligent responses.

Submission Requirements

For the midterm coursework, you will submit the following documents on the submission page:

A shareable link to the Jupyter notebook environment that has o A single Jupyter notebook file with chatbot demo (ipynb file) o Supplementary dataset (intents.json file). The dataset (intents.json) should not be more than 10MB in total size.
o A PDF report
Project ZIP file o ZIP file with all the files in the Jupyter notebook environment (ipynb, intents.json, PDF).
An exported HTML file o Export the Jupyter notebook code into a HTML file and submit it.
PDF report o A copy of the PDF report.

CM2015 Marking rubric

The marking rubric includes a description of expectations and deliverables. Sections and corresponding marks given below.

Sub part	Criteria	Marks awarded	Mark breakdown
1	Title/Domain of chatbot	1	Provide the title of chatbot in the Jupyter notebook
2	Main loop	3	Chatbot uses a main loop that takes in user input and terminates only when the user types “exit” or “quit”.
3	Data Structures	3	The data structures used to manage intents, patterns, and responses are compact and involve the use of Python dictionaries.
4	Code organisation	5	The different chatbot components are specified as functions that are called from the main loop or other associated functions. This includes: Function to load intents from JSON files Function to load and search using regex patterns Function to generate responses. Correct Interaction between main loop and functions (argument passing and return calls).
5	Pattern recognition	5	The chatbot must use pattern recognition incorporating regular expression-based matching. Students are expected to: Write flexible pattern matching statements to recognise multiple instances of the same intent type. Utilise basic regex constructs (e.g., \d, \w, ., *, +, ?) for robust pattern handling. Build regex patterns that account for variations in user input (e.g., case insensitivity, optional words). (Optionally) Implement advanced regex features like grouping, or lookaheads for more nuanced understanding.
6	Response generation	5	The chatbot must demonstrate diversity in response generation. The basic functionality involves retrieving one response for one input. Implement these additional techniques for more diverse responses: Choose responses randomly from a list of options. Applying dynamic strings substitutions with a memory (e.g., store user’s name or colour or some personal information in an object and substitute it in a response). Combination of techniques mentioned above.
7	File usage	5	To promote modularity, reusability, and scalability, all chatbot data including intents, patterns, and responses must be stored and loaded from external JSON files. Key expectations include: Each intent should contain a list of regexbased patterns stored in the JSON file. Responses should be written as template strings in the JSON file. These templates can include placeholders (e.g., {name}, {color}) that are dynamically filled at runtime using string substitution based on user input or stored memory. The chatbot should implement a function that loads the JSON file at runtime, extracts patterns and responses, and builds internal structures such as pattern2intent and intent2response dictionaries.
8	Preprocessing	5	Expectations regarding text preprocessing techniques include: Splitting user input into individual words or tokens to analyse structure and meaning more easily. Eliminating characters like commas, or periods that are not essential for intent detection. Reducing words to their root form to match patterns more broadly and accurately.
9	Other Advanced features	3	Any other advanced feature that you have added and features that you have added beyond lecture material.
10	Process reflection	5	Discuss the week-by-week iterative development of your chatbot. What was the feedback you received? How did you work on the feedback to improve chatbot? What new features did you add?
11	Report	5	Report should cover the main aspects of chatbot such as: Chatbot application (e.g., use-case, domain of operation). Describe 3 different test cases that clearly illustrate chatbot behaviour. Any other steps to organise data/code that you implemented. Describe advanced techniques that you used.
12	Code	5	Code should: Be reproducible in the current notebook format including making relevant data sources and libraries accessible and explicit. Use proper conventions e.g. relative path vs absolute. Be explained or described where libraries are used in relation to their utility/ability to solve a particular problem in an efficient manner. Notebooks should be structured with a logical set of processes/procedures including clear, logical headings. Not overly verbose e.g. including comments to describe print statements.

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