ANL252 Slay-of-Path Evaluation Instruction to College students This Slay-of-Path Evaluation paper comprises 7 pages (along with the conceal page). You’re to contain the next particulars on your submission: Path Code, Title of the ECA, SUSS PI No., Your Title, and Submission Date. Unhurried submission would possibly be subjected to the marks deduction diagram. Please focus on over with the Pupil Manual for info. Crucial Veil ECA Submission Closing date: Friday, 03 April 2026 12:00 pm Part A (100 marks) Reply all questions in this portion. The dataset dilapidated in this paper comprises recordsdata about customer churn, and its recordsdata dictionary is equipped in Appendix. Please focus on over with Canvas for info of this dataset. Notes on project writing: Your writing can also honest composed be succinct but no longer at the expense of excluding associated info. The issues in the key characterize can also honest composed be presented in the expose per the sequence of the projects/questions listed in the project; that is, in the expose of Search recordsdata from 1, Search recordsdata from 2, …, and so on. To preserve a long way from excessive Turnitin ranking, raise out no longer reproduction the project questions into the characterize. Some questions can also honest no longer contain fully honest or snide solutions. For such questions, you would possibly additionally honest have the liberty to remark your views relating to the sphere. You’re moreover licensed to have interaction in just research to expose bigger-expose pondering skills when answering the questions. You’re advised to contain much less associated info on your Appendix, if any. Search recordsdata from 1 Imply and conduct no no longer as a lot as three (3) recordsdata pre-processing projects to neat and put together the given dataset on customer churn using Python. Present associated explanations. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)] (30 marks) Search recordsdata from 2 Train Python to construct three (3) figures per the processed customer churn dataset got from Search recordsdata from 1 . Discuss the insights for every figure accordingly. Every figure and its corresponding Python codes and insights collectively raise 10 marks. The figures and Python codes are to be offered as section of the answer in the key characterize. [No more than 450 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)] (30 marks) Search recordsdata from 3 Train Python to extra analyse or model the processed dataset got from Search recordsdata from 1 using a resolution tree, the establish the dependent variable is ‘Churn_Flag’. Demonstrate the associated steps occupied with constrcucting the resolution tree model. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list). You do not need to plot the decision tree in this question.] (20 marks) Search recordsdata from 4 Advise the resolution tree model got from Search recordsdata from 3 with Python. Discuss the associated insights per the tree build. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)] (10 marks) Search recordsdata from 5 Discuss varied recordsdata analytics ideas or fashions that would also honest be dilapidated to toughen the insights won from the resolution tree model above. Assumptions can also honest even be made to increase the discussion. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding reference list)] (10 marks) Appendix DATA DICTIONARY Variable Description Buyer ID Queer identifier of every and every customer StockCode Queer product/merchandise code for the product/merchandise purchased by the consumer Quantity Quite quite quite a bit of of items purchased in the transaction Rate Unit label of the product Nation Buyer’s country Customer_Age Buyer’s age Gender Buyer’s gender Customer_Segment Category of consumer Marketing_Channel Source of acquisition Category Product category Subcategory Extra remark product classification Discount_Applied Whether or no longer a decrease label changed into applied Payment_Method Mode of cost Delivery_Time_Days Supply lead time in days Churn_Flag Buyer is churned or active —– END OF ECA PAPER —– Write My Assignment Submit Your ANL252 Python for Recordsdata Analytics ECA with Self belief Native Singapore Writers Crew 100% Plagiarism-Free Essay Most practical seemingly Pride Rate Free Revision On-Time Supply
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