CustomerScope is a machine learning project for customer segmentation using clustering techniques. It helps analyze customer behavior and visualize meaningful customer groups.

A machine learning clustering model for customer segmentation to define marketing strategy.

Developed and deployed a scalable machine learning model for real-time customer segmentation using FastAPI, Docker, Kubernetes, and GitHub Actions, with an end-to-end CI/CD pipeline on Azure Kubernetes Service, enhancing targeted marketing strategies through robust and seamless integration and deployment

LEO CDP is an open-source, AI-first Customer Data Platform for building customizable, self-hosted, privacy-friendly CDP infrastructure. It unifies data collection, enables real-time analytics, audience segmentation, and personalized marketing — powered by big data and machine learning.

Unsupervised learning techniques applied on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

Business Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all

This project demonstrates how to perform customer segmentation for a shopping mall using machine learning algorithms. This is an unsupervised clustering problem.

Inspired by the infamous RFM (Recency, Frequency, Monetary) segmentation framework in Marketing, in this repository I decide to deploy an end to end machine learning model used for customer segmentation. Disclaimer: for security reasons, only a small subset of the dataset will be uploaded and this model is not the final version ready for deployment

I used the online retail dataset from the UCI Machine Learning Repository for exploratory data analysis and customer segmentation using RFM Analysis K-Means Clustering.

A collection of applied AI use cases for the telecom retail industry. Includes ready-to-use demos for customer churn prediction, referral-based growth engines, customer segmentation, and more, designed to help telecom operators retain customers and drive acquisition using machine learning and predictive analytics.

This repo contains all my work for Project 2 of Udacity's Machine Learning Basic Nano-degree Program. In this project I applied unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. I first explored the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I preprocessed the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I applied PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I compared the segmentation found with an additional labelling and consider ways this information could assist the wholesale distributor with future service changes.

Machine Learning for Online Retail Analytics & Customer Segmentation

Final project for the Udacity Machine Learning Engineer Nanodegree. Customer Segmentation, based on regional demographic and customer data. Supervised Learning Techniques applied for customer acquisition..

📊 Streamlit + Plotly dashboard for customer segmentation, RFM analysis, and churn prediction using machine learning.

This repo is developed for the well-known solution for customer segmentation using Machine Learning algorithms with Python programming.

This is my attempt at Coursera Project 'Unsupervised Machine Learning for Customer Segmentation' which I completed during COVID Lockdown phase.

Customer Segmentation and Targeting: Develop machine learning algorithms to segment customers based on demographics, behavior, and preferences, enabling marketers to tailor marketing campaigns and advertisements for different audience segments.

End-to-end e-commerce analytics pipeline using MySQL, Power BI, and Machine Learning (K-Means) for customer segmentation and business intelligence.

A machine learning project for customer segmentation using the DBSCAN density-based clustering algorithm on the Wholesale Customers dataset. The project identifies purchasing patterns, detects outliers, analyzes customer behavior, and visualizes cluster differences across spending categories.

This project implements customer segmentation using machine learning techniques, including K-Means clustering, to group customers based on their purchasing behavior and demographic characteristics. The analysis helps businesses understand different customer segments for targeted marketing strategies.

This project analyzes a Customer Segmentation Dataset (commonly known as the Mall Customers dataset) to derive business insights through exploratory data analysis and clustering techniques. A practical application of Data Analytics and Machine Learning for customer behavior understanding and targeted marketing.

End-to-End Machine Learning project for Customer Churn Prediction using EDA, Feature Engineering, Model Development, Model Evaluation, Threshold Tuning, SHAP Explainability, and Churn Risk Segmentation.

This project applies machine learning clustering algorithms (K-Means, Hierarchical, DBSCAN) to sales data for customer segmentation. By analyzing RFM metrics, it uncovers patterns for personalized marketing, customer retention, and revenue growth, enabling data-driven business decisions.

Production ready AI-powered platform for advanced customer analytics and engagement. Leverages machine learning for sentiment analysis, segmentation, and personalized recommendations. Features include real-time interaction tracking, feedback analysis, and predictive model. Empowers businesses to enhance customer experiences and drive growth.

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

This project applies machine learning to predict customer spending scores based on demographic features like age, gender, and annual income. By analyzing these patterns, we aim to enhance customer segmentation, optimize marketing strategies, and support decision-making for targeted campaigns.

Sales Prediction Using Python: Predicting future sales is crucial for businesses to optimize their marketing strategies, allocate resources efficiently, and maximize revenue potential. This project leverages machine learning techniques to forecast sales based on factors like advertising expenses, customer segmentation, and marketing platforms.

Segmentation in marketing is a technique used to divide customers or other entities into groups based on attributes such as behaviour or demographics. It is useful to identify segments of customers who may respond in a similar way to specific marketing techniques such as email subject lines or display advertisements. As it gives businesses the ability to tailor marketing messages and timing to generate better response rates and provide improved consumer experiences.In the following post, I will be using a dataset containing a number of behavioural attributes for credit card customers. The dataset can be downloaded from the Kaggle website. I will be using the scikit-learn python machine learning library to apply an unsupervised machine learning technique known as clustering to identify segments that may not immediately be apparent to human cognition. The dataset consists of 18 features about the behaviour of credit card customers. These include variables such as the balance currently on the card, the number of purchases that have been made on the account, the credit limit, and many others. A complete data dictionary can be found in the repository.

Machine Learning Project for segmentation of mall customers

Unsupervised Machine Learning Algorithm for Mall Customer Segmentation Prediction