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

This project focuses on performing customer segmentation analysis using the K-means clustering algorithm

Advanced Customer Segmentation Analysis using the Online Retail II dataset. This project implements and compares multiple clustering algorithms (K-Means, DBSCAN, GMM, Hierarchical, Mean Shift) to identify distinct customer behaviors for targeted marketing strategies

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

Customer segmentation analysis using unsupervised learning with k-means clustering algorithm

This project focuses on customer segmentation analysis using the K-Means clustering algorithm.

Customer segmentation project using clustering algorithms. Includes RFM analysis, K-means clustering, and visualization of customer groups.

This project aims to perform customer segmentation using RFM analysis and K-means clustering algorithm.

Customer Segmentation of Credit Card Users Using Clustering Method with K-Means Algorithm for Consumer Behavior Analysis

Airline Customer was segmented based on RFM analysis. The segmentation was using K-Means as Clustering algorithm. #clustering #kmeans #unsupervisedlearing #airlinecustomersegmentation #segmentation

Segment customers by behavior and demographics using clustering algorithms. Retain long-time loyal customers for greater impact on company success. Data from Kaggle's "Customer Personality Analysis." Utilizes K-Means Clustering, DBScan, and Agglomerative Algorithm for segmentation.`

Using Python data libraries (Pandas, Matplotlib, Plotly) and K-Means clustering algorithm to wrangle retail data, perform exploratory data analysis(EDA) and undertake a customer segmentation analysis using RFM analysis to understand and unravel the intricacies of customer behavior.

Credit Card Customer Segmentation using Unsupervised Learning. This project applies the K-Means clustering algorithm on a credit card dataset to group customers based on spending patterns, balance, and transaction behavior. It helps identify different customer segments for better analysis and decision-making.

This project applies unsupervised learning to segment credit card customers based on their spending and payment behavior. Using clustering algorithms like K-Means and Agglomerative Clustering, the goal is to identify groups of customers with similar financial activity patterns to help with customer segmentation, marketing, and risk analysis.

Complete customer purchasing behaviour analysis was done on a retail dataset. Initially data cleaning and EDA was done. Each customer was grouped to a cohort to analyze their retention rate. RFM model was created based on their receny, frequency and monetary values. RFM scores were given to each customer and each customer was grouped to 4 clusters as high value, medium value based on their RFM scores. Finally customer segmentation was done using K means clustering algorithm. 5 clusters were selected using elbow method and customers were grouped to one of the clusters. Additionally charts were made in tableau to visualize our results better. numpy, pandas, , matplotlib, seaborn, sklearn libraries were used in the project.

Customer Segmentation Analysis Using K-Means Clustering Algorithm

Customer Segmentation Analysis using Clustering Algorithms (K-Means)

This project leverages KMeans Clustering to transform retail through data-driven customer segmentation, enabling targeted marketing and driving strategic decision-making.

Customer segmentation analysis using K-means clustering algorithm in Python

Customer Personality Analysis and Segmentation using K-Means Clustering Algorithm

Customer Segmentation Analysis using K-means Clustering Algorithm in R

CUSTOMER SEGMENTATION USING CLUSTERING ALGORITHMS - CLUSTER ANALYSIS WITH K-MEANS CLUSTERING AND AGGLOMERATIVE CLUSTERING

Analysis on customer dataset segmentations using unsupervised machine learning algorithm k-means clustering.

Customer segmentation analysis using a k-means clustering algorithm on bank marketing data. The

Cohort Analysis to determine customer's retention rate and Customer Segmentation using K-means Clustering Algorithm

This project demonstrates a simple clustering analysis on the Mall Customer Segmentation Data using the K-Means algorithm.

This project implements a comprehensive customer segmentation analysis for a mall using two popular clustering algorithms: K-means and Hierarchical clustering.

"Customer segmentation analysis using the K-Means clustering algorithm to group mall customers based on their annual income and spending patterns."

An interactive Streamlit dashboard for e-commerce customer segmentation using RFM analysis and clustering algorithms (K-Means, GMM, DBSCAN).

Customer segmentation using RFM analysis and K-means/Hierarchical clustering, combined with purchase prediction using SVM and Random Forest algorithms.