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c++ question to solve apu, Assignments of Computer Communication Systems

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2022/2023

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CT077-3-2-DSTR Lab Work #1 + Solution Work Page 1 of 5
Level 2 Asia Pacific University of Technology and Innovation 2025
ANALYSING CUSTOMER BEHAVIOUR IN E-COMMERCE
USING SEARCH AND SORT ALGORITHMS
(Working Duration: Monday of Week 5 to Monday of Week 10 30 Marks)
Background:
E-commerce has revolutionized the retail industry, enabling customers to purchase products
online with ease. With millions of transactions happening daily, understanding customer
behavior is essential for businesses to improve user experience, predict sales trends, and detect
fraudulent activities.
One of the key challenges in e-commerce is efficiently managing and processing vast
amounts of customer data, including purchase histories, product reviews, and browsing
behavior. The ability to search for specific transactions and sort large datasets efficiently
is crucial for optimizing business operations.
Data structures play a vital role in these processes. Arrays and linked lists are fundamental
data structures that can be used to store and manage e-commerce data, while different
searching and sorting algorithms determine the efficiency of retrieving and analyzing
information.
In this assignment, your goal is to evaluate how different search and sorting algorithms perform
when processing e-commerce customer data and explore how the choice of data structure, such
as array or linked list, affects performance in terms of time and memory efficiency.
Question:
In this assignment, you are provided with two datasets:
transactions.csv Contains customer transaction details (Customer ID, Product, Price,
Date, Category, Payment Method).
reviews.csv Contains customer reviews for various products (Product ID, Customer
ID, Rating, Review Text).
You are required to apply various search and sort algorithms to analyse these datasets and
answer the following:
1. How can you efficiently sort customer transactions by date and display the total number of
transactions in both datasets?
2. What percentage of purchases in the “Electronics” category were made using Credit Card
payments?
(Hint: You will need to search and filter transactions based on category and payment method.)
3. Which words are most frequently used in product reviews rated 1-star?
(Hint: Extract the most common words, sort them by frequency, and present the results.)
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ANALYSING CUSTOMER BEHAVIOUR IN E-COMMERCE

USING SEARCH AND SORT ALGORITHMS

(Working Duration: Monday of Week 5 to Monday of Week 10 – 30 Marks) Background: E-commerce has revolutionized the retail industry, enabling customers to purchase products online with ease. With millions of transactions happening daily, understanding customer behavior is essential for businesses to improve user experience, predict sales trends, and detect fraudulent activities. One of the key challenges in e-commerce is efficiently managing and processing vast amounts of customer data , including purchase histories, product reviews, and browsing behavior. The ability to search for specific transactions and sort large datasets efficiently is crucial for optimizing business operations. Data structures play a vital role in these processes. Arrays and linked lists are fundamental data structures that can be used to store and manage e-commerce data, while different searching and sorting algorithms determine the efficiency of retrieving and analyzing information. In this assignment, your goal is to evaluate how different search and sorting algorithms perform when processing e-commerce customer data and explore how the choice of data structure, such as array or linked list, affects performance in terms of time and memory efficiency. Question: In this assignment, you are provided with two datasets :

  • transactions.csv – Contains customer transaction details (Customer ID, Product, Price, Date, Category, Payment Method).
  • reviews.csv – Contains customer reviews for various products (Product ID, Customer ID, Rating, Review Text). You are required to apply various search and sort algorithms to analyse these datasets and answer the following:
  1. How can you efficiently sort customer transactions by date and display the total number of transactions in both datasets?
  2. What percentage of purchases in the “Electronics” category were made using Credit Card payments? (Hint: You will need to search and filter transactions based on category and payment method. )
  3. Which words are most frequently used in product reviews rated 1-star? (Hint: Extract the most common words, sort them by frequency, and present the results. )

Minimum Requirements for Lab Work #

  1. Each group may have up to FOUR (4) members.
  2. This assignment requires the development of two separate programs : a) Array Implementation a) Implement an array-based data structure to store and process the customer data. b) Perform operations like sorting, searching, and filtering transactions based on certain attributes (e.g. data, category). b) Linked List Implementation a) Implement a linked list-based data structure to store the same data. b) Similar to the array-based approach, allow efficient operations like sorting and searching. 3. Sorting Requirement:
    • Sort customer transactions by date using suitable sorting algorithm for both data structures.
  3. Search Requirement:
    • Implement search algorithms to filter and retrieve relevant transactions based on specific conditions (e.g. category, payment method).
  4. Review Analysis:
    • Process text-based customer reviews and identify the most frequently used words in negative reviews (1-star ratings).
    • Sort and display the top frequent words.
  5. Performance Comparison:
    • Compare the time and memory efficiency of search and sorting algorithms for Arrays vs Linked Lists. 7. Error Handling:
    • Implement robust error handling and data validation to address unexpected formats or missing data.

Submission Guidelines #2: Documentation Submission – Solution Work ( 15 Marks) What to include in your documentation:

  1. Cover Page
  2. Workload Matrix Table with Signature Note that this table will impact each member's personal final mark in Lab Evaluation Work # based on their stated contribution percentage.
  3. Theoretical Explanation
    • Explain data manipulations in your chosen data structures.
  4. Input-Output Screenshots
    • Include system input and output screenshots.
  5. Summary Discussions
    • Analyze system efficiency (execution time, time efficiency, space efficiency, etc.)
    • Summarize and discuss observations made during development.
    • Critically evaluate strengths and weaknesses of the code.
  6. Conclusion and Reflection
    • Summarize key findings.
    • Discuss potential improvements and system weaknesses.
    • Share personal thoughts on the assignment.
  7. References (if applicable)
    • Properly cite external sources (APA format). Failure to reference code will be treated as plagiarism.
  8. Appendix (if applicable) Submission & Formatting:
    • File Naming Format: .docx
    • For example, G1_TP012345_TP014556_TP067554_TP034325.docx Deadline: Monday of Week 10 , before 5:00PM Max Pages: 30 pages Max Words: 4500 words

MARKING CRITERIA

(Lab Evaluation Work #1 - 15 MARKS) This Lab Evaluation Work #1 will be evaluated according to the following performance criteria: Assessment Components Inclusive 15 Marks CLO3: Lab Evaluation Task #1 – 30 - Minute Video Recording ( Assessment will be based on individual performance ) Practical Skills: Use of Data Structures & Algorithms + Personal Understanding Utilization of data structures Technical Proficiency Implementation of relevant algorithms Technical Proficiency Demonstrates understanding of data structures/algorithms used Comprehensive Understanding Justifies choices of structures/algorithms Insightful Justification

MARKING CRITERIA

(Solution Work - 15 MARKS) This solution work will be evaluated according to the following performance criteria: Assessment Components Inclusive 15 Marks CLO 2 : Solution Work - Documentation ( Assessment will be based on group component ) Theoretical Explanation (e.g., Data Structures, Algorithms) Clear explanation Input Output Screenshots Adequate Screenshots Summary Discussions (Inclusive Time and Space Complexity) Clear and insightful analysis Conclusion & Reflection (Other Relevant / Importance Discussions) Clearly highlighted and insightful Content Organization Well-structured and logical flow Approximation of Total Pages for the documentation: 3 0 (max). Approximation of Words for the documentation: 45 00 words (max)