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.

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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.

 

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