From 763d5866957de73c98c3685cc0177694295afd7d Mon Sep 17 00:00:00 2001 From: Thomas George Thomas Date: Tue, 7 Nov 2023 16:00:33 -0500 Subject: [PATCH] Update README.md with basic information --- README.md | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 381b1a5..34a8b4f 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,25 @@ [![Pytest](https://github.com/Thomas-George-T/Ecommerce-Data-MLOps/actions/workflows/pytest.yml/badge.svg)](https://github.com/Thomas-George-T/Ecommerce-Data-MLOps/actions/workflows/pytest.yml) +# Ecommerce Customer Segmentation & MLOps +# Introduction +In today's data-driven world, businesses are constantly seeking ways to better understand their customers, anticipate their needs, and tailor their products and services accordingly. One powerful technique that has emerged as a cornerstone of customer-centric strategies is “Customer segmentation”: the process of dividing a diverse customer base into distinct groups based on shared characteristics, that allows organizations to effectively target their marketing efforts, personalize customer experiences, and optimize resource allocation. Clustering, being a fundamental method within the field of unsupervised machine learning, plays a pivotal role in the process of customer segmentation by leveraging the richness of customer data, including behaviors, preferences, purchase history, beyond the geographic demographics to recognize hidden patterns and subsequently group customers who exhibit similar traits or tendencies. As population demographics are proven to strongly follow the Gaussian distribution, a characteristic tendency in an individual could be possessed by other individuals in the relevant cluster, which then may serve as the foundation for tailored marketing campaigns, product recommendations, and service enhancements. By understanding the unique needs and behaviors of each segment, companies can deliver highly personalized experiences, ultimately fostering customer loyalty and driving revenue growth. +In this project of clustering for customer segmentation, we will delve into the essential exploratory data analysis techniques, unsupervised learning methods such as K-means clustering, followed by Cluster Analysis to create targeted profils for customers. The goals of this project comprise data pipeline preparation, ML model training, ML model update, exploring the extent of data and concept drifts (if any), and CI/CD Process demonstration. Thus, this project shall serve as a simulation for real-world application in the latest competitive business landscape. We aim to further apply these clustering algorithms to gain insights into customer behavior, and create a recommendation system as a future scope for lasting impact on customer satisfaction and business success. +# Dataset Information +This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. +## Data Card +- Size: 541909 rows × 8 columns +- Data Types: +Variable Name Role Type Description +InvoiceNo ID Categorical a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation +StockCode ID Categorical a 5-digit integral number uniquely assigned to each distinct product +Description Feature Categorical product name +Quantity Feature Integer the quantities of each product (item) per transaction +InvoiceDate Feature Date the day and time when each transaction was generated +UnitPrice Feature Continuous product price per unit +CustomerID Feature Categorical a 5-digit integral number uniquely assigned to each customer +Country Feature Categorical the name of the country where each customer resides - -# Ecommerce Customer Segmentation MLOps -Work in Progress +## Data Sources +URL: UCI repository # Changelog @@ -11,4 +28,4 @@ Work in Progress ``` pytest --pylint pytest - ``` \ No newline at end of file + ```