Determination of Drivers of Stock-Out Performance of Retail Stores using Data Mining Techniques

This project utilizes data mining techniques to determine the drivers of stock-out performance. Best performing and worst performing clusters of stores were identified using data clustering techniques. Logistic regression and multiple ordinary-least-squares regression were then used to gain further insights and quantify the drivers of stock-outs.

Author: Khalid Usman
Advisor: Chris Caplice