Customer-base segmentation over e-commerce sales data

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

An approach to Customer Segmentation Using ML

A data science project demonstrating customer segmentation using K-Means clustering. It features RFM-based feature engineering, data cleaning, outlier handling, and provides actionable insights.

In this project, I showcased my skills in data cleaning, data visualization, SQL, A/B testing, and ML model building (precisely customer segmentation model).

Customer Segmentation using KMeans clustering with a full ML pipeline including data preprocessing, model training, API serving via FastAPI, visualization with Streamlit dashboard, logging, error handling, and Docker deployment.

Customer Segmentation for Marketing Optimization K-Means clustering on credit card data.

This project segments customers into clusters based on purchasing behavior using unsupervised machine learning techniques.

Customer Segmentation using KMeans clustering with a full ML pipeline including data preprocessing, model training, API serving via FastAPI, visualization with Streamlit dashboard, logging, error handling, and Docker deployment.

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Customer segmentation model developed using Spark ML

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Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Problem Statement You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.

Airline Customer Segmentation using Unsupervised ML: Implementing K-Means Clustering to categorize airline customers based on their behavior and preferences. This project aims to assist airline companies in tailoring their loyalty programs effectively by identifying distinct customer segments.

A Smart Loan Recovery System-- An end-to-end ML system that predicts loan defaults and creates personalized recovery strategies for borrowers. Tools & Techniques used: -Customer Segmentation and clustering -Explainable AI (XAI) for transparent decisions

This repository analyzes e-commerce customer behavior and product categorization using clustering, keyword extraction, and ML models (SVM, k-NN, Random Forest). It includes data cleaning, segmentation, and visual insights to aid in targeted marketing and decision-making.

A complete Retail analytics project using SQL and Python (Machine Learning) to analyze customer behavior and profitability, visualized in an interactive Power BI dashboard. End-to-end retail analysis: from SQL querying and Python ML models (segmentation, forecasting) to a final business intelligence dashboard in Power BI.

🌟 Data Science & Machine Learning Internship Projects 📊 Explore a curated collection of DS & ML notebooks covering topics like regression models, clustering, NLP, and deep learning. Dive into real-world projects such as price prediction, sentiment analysis, and customer segmentation. This repository reflects modern data-driven industry solutions

Marketing Data Science is a first of a series of courses on Business Data Science. The course was constructed to be a meeting point between Marketing and Data Science. A marketing framework analysis is proposed composed of four blocs: Profiling, Segmentation, Targeting and Recommendation. For each of these blocs a Data Science analysis is applied. The pivoting question is: How to better understand your customer. Throughout the course we will use a single database to apply the different concepts and Data Science techniques. Three tools will be presented and used: SQL, Power BI and Microsoft Azure ML If you are a marketer who want to be introduced to the Data-Driven analysis field, then this course is for you. If you have a technical background (IT professional, Developer) and you inspire to become a Data Scientist, then you can take advantage of the Marketing Framework Analysis to introduce you to the business skills a has to know.

Context A real online retail transaction data set of two years. Content 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. Column Descriptors InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated. UnitPrice: Unit price. Numeric, Product price per unit in sterling. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal, the name of the country where each customer resides. Acknowledgements Here you can find references about data set: http://archive.ics.uci.edu/ml/datasets/Online+Retail and Relevant Papers: The evolution of direct, data and digital marketing, Richard Webber, Journal of Direct, Data and Digital Marketing Practice (2013) 14, 291–309. Clustering Experiments on Big Transaction Data for Market Segmentation, Ashishkumar Singh, Grace Rumantir, Annie South, Blair Bethwaite, Proceedings of the 2014 International Conference on Big Data Science and Computing. A decision-making framework for precision marketing, Zhen You, Yain-Whar Si, Defu Zhang, XiangXiang Zeng, Stephen C.H. Leung c, Tao Li, Expert Systems with Applications, 42 (2015) 3357–3367. Citation Request: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197–208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17). Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

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Categorical clustering techniques, KModes and KPrototype, to predict customer segmentation.

In this Data Science R Project series, we will perform one of the most essential applications of machine learning – Customer Segmentation. In this project, we will implement customer segmentation in R. Whenever you need to find your best customer, customer segmentation is the ideal methodology. In this machine learning project, DataFlair will provide you the background of customer segmentation. Then we will explore the data upon which we will be building our segmentation model. Also, in this data science project, we will see the descriptive analysis of our data and then implement several versions of the K-means algorithm.

This is a machine learning project, Where we analyse and cluster a customers data based on their spending habits and their income.

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Customer segmentation using K-Means clustering on mall customer data. ML analysis for customer behavioral patterns.

End-to-end ML project building an intelligent e-commerce recommendation engine with EDA, customer segmentation, collaborative filtering, and business dashboard.