Milestone Project for obtaining the SAS Academic Specialization in SAS Programming and Machine Learning from SAS and AUEB's MSc in Business Analytics

This Milestone Project is a crucial step toward obtaining the SAS Academic Specialization in SAS Programming and Machine Learning. The project aims to apply techniques for accessing, processing, managing, and mining real-world data to provide solutions to contemporary business problems using Base SAS Programming, SAS Visual Analytics, and SAS Visual Data Mining and Machine Learning on SAS Viya.

1. Data Pre-processing:
   • Calculate the number of SKUs per invoice and total value of SKUs per invoice.
   • Divide invoice observations into sales and returns transactions.
   • Calculate customer age and categorize them into age ranges.
2. Customer Profiling:
   • Analyze demographic characteristics such as age, gender, and region.
   • Segment customers by age range and analyze behavioral characteristics.
3. Exploration and Understanding of Sales:
   • Analyze sales and returns levels.
   • Investigate average basket size and top products per product line.
   • Analyze the contribution of each region to the company's revenues.
4. Promotional Activities:
   • Analyze the percentage of products sold with and without promotions.
   • Investigate the distribution of sales per day of the week.
5. Supplier Analysis:
   • Determine the percentage and actual revenues of products sold by each supplier.
6. Customer Segmentation:
   • Profile customers based on Recency, Frequency, and Monetary parameters.

7. Customer Clustering:
   • Analyze RFM data set using SAS Visual Data Mining and Machine Learning.
8. Association Analysis:
   • Identify associations among product categories in the whole data set and within identified customer clusters.

• Address answers to business people in an understandable manner.
• Include charts, tables, and screenshots documenting the results.
• Include SAS code in the appendix.

• Customer Table: Contains customer details such as name, address, gender, and birthdate.
• Invoice Table: Contains data about issued invoices including date, customer ID, and payment method.
• Basket Table: Contains details about products sold in each invoice.
• Products Table: Includes product details such as type, price, and origin.
• Promotions Table: Contains information about promotions and discounts.
• Product Origin Table: Provides details about the origin country of each product.
• Suppliers Table: Includes information about product suppliers.