Use data science for store segmentation: data preprocessing, EDA, clustering, and segment profiling in retail

What you'll learn

An approach for applying the data science lifecycle to a real-world retail segmentation problem
Preprocess and transformation of retail data for analysis
Performing exploratory data analysis
Interpretation of PCA components in a clustering context
How to build and evaluate stable store clusters using machine learning
Profiling segments in cluster analysis using Decision Trees
Describe and present store segments in a way that supports decision-making

Requirements

Python Programming
BigQuery
Machine Learning (PCA, Decision Tree, K-Means)
Scikit-Learn
Webscraping Selenium

Description

This course guides you through applying machine learning and data science techniques to build a store segmentation from raw data in order to generate actionable, easy-to-understand segments for stakeholders. Based on a real-world project implemented in a retail company (with synthetic data due to confidentiality), the course follows key steps in the data science lifecycle.We begin by defining the business problem and identifying relevant variables, including customer demographics, shopping behavior, section-level contributions, operational performance, store size, city-level economic indicators, and weather data. You'll then explore common data sources and extraction methods (ranging from data warehouses like BigQuery to APIs, web scraping, and Google Sheets).Next, we dive into data cleaning, preprocessing, and feature engineering, followed by exploratory analysis using correlation matrices, distribution plots, and boxplots. We apply data transformations such as winsorization, Yeo-Johnson, and standardization before running a PCA to explore latent structure and guide the segmentation process.For modeling, we focus on finding the most stable clustering solution, using Jaccard similarity to evaluate consistency across random states. We evaluate the optimal number of clusters with the Elbow method and assess quality of the clustering using Silhouette score.To describe the resulting segments, we adapt a profiling technique inspired by SAS Miner. We use decision trees to identify the most distinguishing features per segment, then visualize distributions to compare each segment against the overall population. This allows us to craft simple, stakeholder-friendly

Description

s based on key deviations.Finally, we wrap everything up with a presentation of results, ready to support data-driven decision-making in a retail context.

Who this course is for

Machine learning Practitioners with an Interest in Retail Segmentation
Data Scientists in Retail Companies
Data Analysts in Retail Companies
Data Scientists / Analysts in General
Homepage:

https://www.udemy.com/course/store-segmentation/