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Key: descriptive statistics and exploratory data analysis, forecasting (linear regressions, ARMIA, Prophet), and a Tableau dashboard that delivers customer insights such as RFM analysis.

zhangkelly014/Online-Superstore-Insight-Analysis-Challenge

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Online-Superstore-Insight-Analysis

Background

This simple dataset gives insights on online orders of a US superstore from 2015-2018.

Data Description

There are total 21 variables in our dataset:

  • 'Row ID' - This is nothing but Serial No.
  • 'Order ID' - ID created when a product order is placed.
  • 'Order Date' - Date on which a customer places his/her order.
  • 'Ship Date' - Date on which the order is shipped.
  • 'Ship Mode' - Mode of shipment of each order.
  • 'Customer ID' - ID assigned to each customer who places an order.
  • 'Customer Name' - Name of Customer.
  • 'Segment' - Section from where the order is placed.
  • 'Country' - Country details of this data set. We are looking only for US store data.
  • 'City' - Cities of US are listed here.
  • 'State' - States of US are listed here.
  • 'Postal Code' - pin code
  • 'Region' - grouped into region wise
  • 'Product ID' - Product ID of each product
  • 'Category' - Category to which each product belongs to.
  • 'Sub-Category' - Sub-Category of each Category
  • 'Product Name' - Name of products.
  • 'Sales' - total selling Price of each product order.
  • 'Quantity' - number of quantity available for a particular product order.
  • 'Discount' - Discount available on each product order.
  • 'Profit' - Profit gained on each product order.

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Key: descriptive statistics and exploratory data analysis, forecasting (linear regressions, ARMIA, Prophet), and a Tableau dashboard that delivers customer insights such as RFM analysis.

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