Academic Specialization in SAS Programming and Machine Learning

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

Milestone Project

A. Objective of the Project

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.

B. Base SAS Programming Using SAS Studio 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.

C. SAS Visual Data Mining and Machine Learning

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.

D. Instructions

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

E. Datasets Description

• 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.