Clustering Analysis Performed on the Customers of a Mall based on some common attributes such as salary, buying habits, age and purchasing power etc, using Machine Learning Algorithms.

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

This repository contains mall customers clustering analysis. This repository also uses SAS Enterprise Miner to perform clustering and identify each cluster's characteristics. Full explanations about this repository can be seen on: https://medium.com/@caesarmario/mall-customers-clustering-analysis-da594bd2718b

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.

Conducted a comprehensive clustering analysis on customer data from a shopping mall to gain insights into consumer behavior and preferences. Leveraged unsupervised machine learning techniques, including k-means clustering and hierarchical clustering, to categorize customers based on their shopping patterns.

This is a Clustering analysis on mall customers

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

Customer Segmentation project using K-Means, Hierarchical Clustering, and DBSCAN on the Mall Customers dataset with comparative analysis and evaluation metrics.

Goal:Understand about customers coming up in mall. Performed steps of data profiling, data preprocessing and exploratory data analysis on the dataset. Used K-Means clustering and Hierarchical clustering for clusters formation.

It is important to know the needs and understand what the customer desires. Here is an analysis of the customers visiting a mall using K-Means algorithm for Clustering technique

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.

This data analysis project showcases my ability to perform customer segmentation using Python and Jupyter Notebook. Based on a tutorial from "Absent Data" YouTube channel, I used KMeans clustering algorithm to identify the best marketing group for a mall's customers.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Here we try to analyse the customers visiting the Shopping Mall

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. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. 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.

A machine learning project that segments mall customers into distinct groups based on demographics and purchasing behavior using clustering algorithms. Insights from this analysis support targeted marketing and strategic decision-making.

Customer segmentation analysis on mall customer data using exploratory data analysis and clustering techniques.

Customer segmentation analysis using K-Means clustering on Mall Customers data to identify distinct customer groups.

K-Means Mall Customers analysis with the calculation of number of clusters.

Customer segmentation using K-Means clustering on mall customer data with income and spending score analysis.

Mall Customer Segmentation Analysis using K - Means Clustering algorith in Unsuprvised machine learning

"Customer Segmentation Analysis for Mall Marketing Strategy: Uncovering Patterns and Insights using DBSCAN Clustering"

"Customer Segmentation Analysis for Mall Marketing Strategy: Uncovering Patterns and Insights Using K-means Clustering"

"Hierarchical Customer Segmentation Analysis for Mall Marketing Strategy: Unveiling Patterns and Insights through Hierarchical Clustering"

Analysis of a customer data of a mall using K-Means Clustering Algorithm in python

Comparative analysis of clustering methods: K-means vs Deep Embedded Clustering (DEC) on the Mall_Customers dataset using PyTorch.

Mall-Customer-Segmentation A data science project utilizing K-Means clustering to segment mall customers based on their demographics and spending behavior. This repository includes data preprocessing, exploratory data analysis, clustering analysis, and visualization to uncover valuable customer insights for targeted marketing strategies.

Mall Customer Segmentation Data (Market Basket Analysis) - K-means clustering algorithm to group customers of a retail store based on their purchase history.

Clustering and grouping with the analysis of the database containing customer data of the mall center (Python)

A clustering analysis of mall customers to identify distinct customer segments based on income and spending. The project uses K-Means clustering and visualization to provide actionable marketing insights.

This project applies K-Means clustering to segment mall customers based on annual income and spending score, facilitating insights into distinct customer groups for strategic analysis.