Project - Creating customer segments | Unsupervised learning | Python | PCA | Gaussian Mixture Model

1) Group your audience based on powerful filters such as demographics, channels, behaviors, traffic sources to see how different groups engage with your business. 2) Show the outcome in graphical format. 3) Create a sample data set to implement this task.

Customer segmentation using the machine learning model.

This project involves segmenting customers using BIRCH clustering in Jupyter Notebook. Customer segmentation is a powerful technique used in marketing and business analytics to divide customers into distinct groups based on their behaviors, preferences, or demographics.

This repository contains code archives for models that predict Customer Segmentation to Define Marketing strategy. Link deployment:

Class project for CRM Data Analysis

Project explores the transaction history of an online household goods store through detailed data analysis, visualizations, and statistical hypothesis testing, offering valuable insights into purchase trends, customer behavior, and strategic product decisions.

This Python report is designed for a business which is worried by high customer churn.

Improving customer clustering using the K-means algorithm by adding more features to the RFM model

Customer Profile & Shopping Behavior Analysis is an R-based project analyzing customer data from retail stores, focusing on segmentation, seasonal trends, and market behaviors.

This project utilizes unsupervised machine learning to segment bank customers for targeted marketing campaigns. It covers tasks like data exploration, determining optimal clusters, and applying k-means for segmentation. Ideal for marketing departments in banking and retail industries.

🎯 Targeted Marketing using Customer Segmentation | Retail & E-Commerce | Python, SQL, Azure, Power BI, ML Cluster customer data to generate personalized campaigns. Includes end-to-end pipeline: data cleaning, KMeans segmentation, campaign strategy, Power BI dashboards, and Azure integration.

Customer segmentation using k-means clustering.

A diverse collection of Python-based projects, including Machine Learning and Django applications, with more to come. Explore practical examples and innovative solutions across various domains

Consequently, the main purpose of this study is to develop a systematic implementation of customer segmentation for the business. To distinguish diverse customers, customers’ behavioral characteristics are obtained from the RFM model (Recency, Frequency, Monetary Value).

Customer Purchase Behaviour Analysis in Retail using Python, RFM Segmentation, Market Basket Analysis, and Power BI Dashboard.

A machine learning project for predicting customer activity decline in the "One Click" online store

RFM-based customer segmentation using R and Tableau. Includes data cleaning, scoring, and an interactive dashboard to uncover actionable insights.

Customer Segmentation using RFM and Pareto Analysis on E-commerce data, with actionable insights and an interactive Power BI dashboard.