Description
EDA and Data Visualization
Dec 2026 Examination
Q1 A retail company has customer data containing categorical variables such as city, payment method, and product category, and numerical variables such as purchase amount and customer age. It also has an ordinal variable, customer satisfaction level (Low, Medium, High). The company wants to use this data for machine learning to predict high-value customers. Question: a) Using your knowledge of Label Encoding and One-Hot Encoding: b) Recommend the appropriate encoding method for city, payment method, product category, and customer satisfaction level. Justify why you selected each encoding method. Explain the possible c) advantages and disadvantages (trade-offs) of your choices for machine learning models. (10 Marks)
Ans 1.
Introduction
Machine learning models need numbers, not raw text labels, so every categorical and ordinal variable in this customer dataset must first be converted into a numeric form. The choice of encoding method is not a minor technical step. A poor choice can mislead the model into reading false relationships between categories or can make the dataset needlessly wide and sparse. City, payment method, and product category are nominal variables with no natural order, while customer satisfaction level is ordinal, with a genuine low to high ranking. Choosing between Label Encoding and One-Hot Encoding for each variable, with a clear justification, is the starting point
Its Half solved only
Buy Complete assignment from us
Price – 190/ assignment
NMIMS Online University Complete SolvedAssignments session DEC 2026
Last date 28 Oct 2026
buy cheap assignment help online from us easily
we are here to help you with the best and cheap help
Contact No – 8791514139 (WhatsApp)
OR
Mail us- [email protected]
Our website – www.assignmentsupport.in
Q2 (A) A financial company uses real-time data for fraud detection and regulatory reporting. Currently, data quality is checked once every three months, but recent problems have occurred because some data is outdated, inconsistent, or incomplete. The company is considering moving to continuous data quality assessment, but management is concerned about cost and resources. Question: Evaluate the benefits and challenges of moving from periodic to continuous data quality assessment in a financial organization. Justify whether the company should adopt continuous assessment, considering cost, technology requirements, regulatory risks, and improvements in business performance. (5 Marks)
Ans 2(A).
Introduction
Checking data quality only once every three months leaves a financial company exposed, since fraud detection and regulatory reporting both depend on data that is accurate right now, not three months ago. Moving to continuous data quality assessment addresses this gap directly, but it brings its own cost, technology, and resourcing questions that management must weigh carefully
Q2 (B) A company wants to create an interactive sales dashboard showing monthly sales for different regions and product categories. The current dashboard has too many filters, 3D charts, and complicated graphs, making it difficult for senior users to understand. Analyze the problem of too much interactivity and complexity in the dashboard. How can the dashboard be redesigned to reduce cognitive overload while still allowing users to explore detailed sales data? (5 Marks)
Ans 2(B).
Introduction
A sales dashboard loaded with too many filters, 3D charts, and complicated graphs defeats its own purpose. Senior users need quick, confident answers, not a puzzle to solve before every decision. Redesigning this dashboard means stripping away visual complexity without losing the
