Customer segmentation using k-means clustering in python

Analyzing and transforming a UK-based retail dataset (2010-2011) into a customer-centric format for customer segmentation using K-means clustering. Implementing a personalized recommendation system to enhance marketing strategies and boost sales.

Customer Segmentation Using K-Means Clustering

This code performs customer segmentation using RFM (Recency, Frequency, Monetary) analysis. It creates an RFM dataframe, determines optimal clusters with k-means, and orders clusters from worst to best. Customers are segmented into high, mid, and low value groups and visualized in a 3D plot for better marketing strategies.

A Repository Maintaining My Summer Internship Work At Datalogy As A Data Science Intern Working On Customer Segmentation Models Using Heirarchical Clustering, K-Means Clustering And Identifying Loyal Customers Based On Creation Of Recency, Frequence, Monetary (RFM) Matrix.

👨 K-Means Clustering Customer Segmentation is an interactive Streamlit app that uses machine learning to group customers by income and spending habits. It helps businesses target marketing, personalize offers, and gain insights with easy retraining, visualizations, and modular code.

I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predictive analysis). Used deep learning to enhance my model.

Repository for customer segmentation using KMeans clustering, utilizing techniques for data analysis and cluster identification. Includes dataset from Kaggle and open-source tools.

No description available

No description available

Customer segmentation using K-Means clustering based on age, income, frequency, and spending.

In this project, based on the personal and purchase behavior data of store customers, we aim to segment customers into different clusters using clustering algorithms like DBSCAN and K-Means. This segmentation helps the store understand and optimize customer relationships, sales strategies, and marketing campaigns.

Customer Segmentation for an online retail store based on Recency - Frequency - Monetary value (RFM) model, using unsupervised K-means clustering

This project involves segmenting customers using k-means 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.

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.

Customer Segmentation using K-Means, DBSCAN, and Hierarchical Clustering to classify customers based on purchasing behavior and provide actionable insights for targeted marketing and engagement strategies.

Data science project using the UCL Online Retail Dataset to perform customer segmentation based on spending behavior. Includes RFM analysis, K-Means clustering, customer profiling, and actionable business insights with visualizations.

Customer segmentation by using the RFM method and K-Means clustering

We cluster mall customers into different categories using Machine Learning's K-Means Clustering Model.

Customer segmentation is a powerful technique used by businesses to understand their customers better and tailor their marketing strategies accordingly. In this project, we aim to apply the K-means clustering algorithm to segment customers based on their similarities and differences in purchasing behavior, preferences, and characteristics.

No description available

Customer Segmentation based on RFM using K-Means & Hierarchical Clustering

In this project, I performed clustering analysis by using k- means algorithm on the Mall dataset or Mall Customers Segmentation.

Customer segmentation using K-Means clustering to group mall customers based on their annual income and spending score. Helps businesses target marketing strategies through data-driven insights.

Customer Segmentation - Using k-means, About: Customer Segmentation is a popular application of unsupervised learning. Using clustering, identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits.

Clustering is a popular unsupervised machine learning technique used to group similar data points based on specific criteria. It has many applications in various fields such as customer segmentation, image recognition, and anomaly detection. K-means clustering is a widely used clustering algorithm that partitions the data into k clusters, where eac

This project focuses on customer segmentation using machine learning techniques to analyze and group customers based on their behaviors, preferences, and demographics. It applies clustering algorithms like K-means to identify distinct customer segments, helping businesses target marketing efforts and improve customer experiences. Built with Python

No description available

This project deals with the segmentation and grouping of the bank credit card customers using UnSupervised K-Means Clustering Algorithm. The project involves below steps in the life-cycle and implementation. 1. Data Exploration, Analysis and Visualisations 2. Data Cleaning 3. Data Pre-Processing and Scaling 4. Model Fitting 5. Model Validation using Performance Quality Metrics namely WCSS, Elbow Method and Silhouette Coefficient/Score 6. Optimized Model Selection with appropriate number of clusters based on the various Performance Quality Metrics 7. Analysis Insights and Interpretations of 2 different business scenarios with various Visualisations

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.