logo Use CA10RAM to get 10%* Discount.
Order Nowlogo

For the practical example, you are given a data set of products in which the company wishes to determine which products it should continue to sell, and which products to remove from their inventory.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

For the practical example, you are given a data set of products in which the company wishes to determine which products it should continue to sell, and which products to remove from their inventory. The file contains historical sales data and active inventory, which can be discerned with the column titled "File Type". 

 If you apply big data analytics methods on this dataset (eg decision tree, logistic regression, or some other machine learning model) you can help the company generate a value (i.e., probability score) for each product, that can be used as the main determinant when evaluating the inventory. Each row in the file represents one product. There are many products in this dataset and few of them tend to sell (only about 10% sell each year) and many of the products are sold only once in a year.  

The file contains historical sales data (identified with the column titled File_Type) along with current active inventory that should be evaluated (i.e., File Type = "Active"). The historical data shows sales for the past 6 months. The binary target (1 = sale, 0 = no sale in past six months) is likely the primary target that should drive your analysis. Other columns contain numeric and categorical attributes that are considered relevant to sales.  

When you analyse this data, you will observe that some of the SKUs with historical sales are also included in the active inventory. The company keeps a record of the following attributes, but not all of them would be relevant to your analysis.  

  1. Order: A sequential counter. No further information captured in this.  
  2. File_type: If historical, the information applies to past six months, if active, it is currently in the inventory waiting to be sold.  
  3. SKU_number: A unique identifier for each product. 
  4. SoldFlag: Equals to 1 if the SKU is sold in past 6 mos, 0 otherwise.  
  5. SoldCount: Number of units sold for the product. 
  6. MarketingType: Two categories of how the product is marketed. This can probably be ignored, or, each type can be considered independently.  
  7. ReleaseNumber: The counter for the number of releases the product had, 0 for new product launch.  
  8. New_Release_Flag: Any product that has had a future release (i.e., Release Number > 1) 
  9. StrengthFactor: An estimate of the market size of the product, reliability of this estimate is questionable. 
  10. PriceReg: Regular price  
  11. ReleaseYear: The year in which the product was released.  
  12. ItemCount: Number of items in stock  
  13. DiscountedPrice: Price when the product goes on discount.  
  14. PromotionPrice: Price when there is a promotion on the product (bundle, rather than discount, though there is no data on with what it was bundled).  

Please develop a model that will provide this company with a probability estimate of a sale for each of their SKU. Please provide an evaluation of the accuracy of your selected model.

 

Related Questions

. Introgramming & Unix Fall 2018, CRN 44882, Oakland University Homework Assignment 6 - Using Arrays and Functions in C

DescriptionIn this final assignment, the students will demonstrate their ability to apply two ma

. The standard path finding involves finding the (shortest) path from an origin to a destination, typically on a map. This is an

Path finding involves finding a path from A to B. Typically we want the path to have certain properties,such as being the shortest or to avoid going t

. Develop a program to emulate a purchase transaction at a retail store. This program will have two classes, a LineItem class and a Transaction class. The LineItem class will represent an individual

Develop a program to emulate a purchase transaction at a retail store. Thisprogram will have two classes, a LineItem class and a Transaction class. Th

. SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of Sea Ports. Here are the classes and their instance variables we wish to define:

1 Project 1 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of

. Project 2 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of Sea Ports. Here are the classes and their instance variables we wish to define:

1 Project 2 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of

Ask This Question To Be Solved By Our ExpertsGet A+ Grade Solution Guaranteed

expert
Atharva PatilComputer science

916 Answers

Hire Me
expert
Chrisantus MakokhaComputer science

615 Answers

Hire Me
expert
AyooluwaEducation

655 Answers

Hire Me
expert
RIZWANAMathematics

881 Answers

Hire Me

Get Free Quote!

323 Experts Online