Skip to content

GabrielMazzotta/PySpark---Demand-Forecasting-Model

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 

Repository files navigation

PySpark - Demand Forecasting Model

Shopping trolley in front of a laptop

Uncertainty plays a big role in how the supply chains plan and organize e-commerce operations to ensure that the products are delivered on time. These uncertainties can lead to challenges such as stockouts, delayed deliveries, and increased operational costs.

In this work I'm assisting in planning for the upcoming end-of-the-year sales. This insights can be used for promotional opportunities and inventory management.

The Data

Column Description
'InvoiceNo' A 6-digit number uniquely assigned to each transaction
'StockCode' A 5-digit number uniquely assigned to each distinct product
'Description' The product name
'Quantity' The quantity of each product (item) per transaction
'UnitPrice' Product price per unit
'CustomerID' A 5-digit number uniquely assigned to each customer
'Country' The name of the country where each customer resides
'InvoiceDate' The day and time when each transaction was generated "MM/DD/YYYY"
'Year' The year when each transaction was generated
'Month' The month when each transaction was generated
'Week' The week when each transaction was generated (1-52)
'Day' The day of the month when each transaction was generated (1-31)
'DayOfWeek' The day of the weeke when each transaction was generated
(0 = Monday, 6 = Sunday)