代做BIG DATA ANALYTICS 2024帮做Python编程

MSC LOGISTICS AND SUPPLY CHAIN MANAGEMENT

MSC PROCUREMENT AND SUPPLY CHAIN MANAGEMENT

BIG DATA ANALYTICS

Submission deadline: 10/04/2024

ASSIGNMENT

Introduction 

In this Big Data Analytics Assignment, you will be assessed in two areas: theory and practice. In the theory area, we expect you to critique recent publications in the big data analytics

domain. In the practice area, we present you with datasets on which we expect you to perform appropriate analysis and draw managerial conclusions. Each of these areas will comprise 45% of your marks. As is the case in many other assignments, 10% of the marks are attributable to style. and presentation.

The word limit for this assignment is 1,500. It is an upper limit, not a target. Please report the number of words you use and do not exceed this limit (this includes references). Although theory and practice questions are equally weighted, we anticipate you will use more words in the theory part, roughly 2/3 of your upper limit.

Please use the discussion board for any questions you might have about this assignment. We will answer all questions that are asked on or before 01/04/2024 to minimise the last-minute stress and encourage timely attention to the assignment.

It has been an absolute pleasure to teach you big data analytics. We hope you enjoy this assignment and use the techniques in the future.

Q1 Theory [45 marks]

Identify three recent academic papers- two on predictive analytics and one on prescriptive analytics within logistics and supply chain context.

Critically discuss the application of predictive and prescriptive analytical techniques for improving supply chain performance.

Using your favourite research database (e.g. Scopus), search and select three papers that explains the application of different predictive and prescriptive analytical techniques to solve logistics/supply chain problem. Please do not use a literature review paper in your selection. We are looking for applied research papers and should cover analytical technique. Don’t forget to include selected three papers in the list of references. Please highlight them separately to other references that you may use.

Q2 Practice [45 marks]

For the practical part of the assignment, we have prepared a dataset named “Supply Chain Data.csv” for you to replicate the analyses we have done in the module. The supply chain dataset records information of customer orders placed on the company SC Global from 2015 to 2018. Each customer order has several attributes, including type of transaction made, days for shipping (actual), days for shipment (scheduled), and profit per order, etc. A description of these attributes  is given  in the second dataset “Description  Supply Chain Data.csv”.   In machine learning, these attributes are called features.

Task 1 (20 marks): Perform EDA (Exploratory Data Analysis) on the Supply Chain Data.csv dataset and answer following questions:

1.   Split the features of the dataset into numerical and non-numerical features (4 marks)

2.  What type of transaction is most likely to be Fraud? (4 marks)

3.  Which top 3 categories of products have high risks of Fraud? (4 marks)

4.  Which shipping mode has the highest risk of late delivery? (4 marks)

5.  Which customer segment has the highest risk of late delivery? (4 marks)

Task 2 (25 marks): Please detect fraudulent activities in the supply chain using two types of classifiers,  i.e.,  SVM  SVC(kernel=’rbf’, set other parameters  as  default  values)   and DecisionTreeClassifier (max_depth=4, set other parameters as default values). Evaluate the performances of two classifiers and answer the following questions:

6.  Which model performs better for Fraud detection and why? Please make a comparison using precision rate, recall rate and F1-score in the Classification Report. (20 marks)

7.   Calculate feature importance for the best performing classifier and identify the top 3 most importance features.  (5 marks)

Please add your Python coding as an appendix with appropriate comments.

Style. and Presentation [10 marks]

You will receive marks for the style. and presentation of your assignment. Please pay attention to

1.  Writing grammatically correctly and concisely.

2.   Providing captions for your tables and figures and citing them in the text.

3.   Using appropriate level of accuracy (no need for 5+ figures after the decimal point. We recommend 3).

4.   Presenting your assignment in a report structure so we can map your answers to the questions we have asked.

5.   Citing the references you used in preparing your answers.