Implementation of the core concept/algorithm used Hierarchical clustering.

Implementation of the core concept/algorithm using K-means Clustering.

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.

Kmeans & Hierarchical Agglomerative Clustering algorithms implementation on Mall Customers dataset

This is a project that cluster the customers using the dataset named as Mall customers and predict by using KNN

Segment mall customers using a K-Means model trained on the Mall Customers dataset. The model takes Annual Income (k$) and Spending Score (1–100) as inputs and returns a segment (cluster) label, with outputs including a cluster profile and validation plots.

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This project aims to develop an unsupervised Machine Learning model to group customers who visit a shopping mall into clusters with similar characteristics, facilitating the creation of marketing campaigns for specific consumer groups.

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

Clustering model on Mall customers

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

Mall Customers Clustering Project Unleash the power of K-means clustering to decode mall customer behavior! From data exploration to 3D visualizations, we navigate through demographics and spending patterns. Join our journey into customer segmentation for strategic insights. Let's redefine retail analytics! 🛍️📈

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.

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

In K-means clustering, n_init is the number of times the algorithm runs independently. Each run uses different random centroids to choose the final model with the lowest within-cluster sum of squared errors (SSE). The default value for n_init is 10

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.

GUI Clustering on Walmart's Mall-Customers Dataset

Different Clustering Algorithms are applied on Mall Customer dataset

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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 repository contains end-to-end analyses for two datasets (Iris and Mall Customers), applying K-Means clustering to identify patterns and segment groups.

Project: Customer segmentation using KMeans algorithm on mall customers. Identify univariate, bivariate, and multivariate clusters. Perform summary statistics to find the best marketing group.

Clustering-Project To find the clusters or groups existing in the data set which is unlabelled so that the mall client can target their loyal customers in better way with their liked products. AIM: A mall has collected information about their client and given them spending score (1-100) on the basis of their spending, visit frequency, annual income, and age. The client has asked to categorize their customers into groups so that they can target them better. Also measure the accuracy achieved with applying K-Mean, Agglomarative Clustering and see which will perform better? Data Set: THe data set is called Mall_Customers.csv given by the client having 200 records, a very small data set which is to be used as POC project. The real data set we can't publish or talk about due to privacy concern. The data set has CustomerID, Genre, Age, Annual Income (k$), and Spending Score (1-100). Approach: It is a unlabelled data set and Clearly we don’t know how many clusters could be? It becomes a clustering problem. We used K-Mean clustering algorithm to find the number of the clusters. We used Elbow method to find visually how many clusters could be? Result: The are total of 5 clusters we are able to extract from the data set.The clusters were namely Target, Standard, Careless, Sensible, and Careful. Target: The annual income and spending score are higher. Standard: The annual income and spending score both are in mid range. Careless: Annual income are less but the spends more. Careful: Annual income on higher side but they spends less. Sensible: Both annual income and spending score are on lower side means their income are less so they donot spends more. The process based on the Elbow method and WCSS=Within cluster sum of square parameter to decide the number of clusters we can have.

Clustering the Mall customers to analyze the different market segments of the Mall

Example of BIRCH clustering algorithm applied to a Mall Customer Segmentation Dataset from Kaggle

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This is a Clustering analysis on mall customers

Mall data customer segmentation using K- means clustering.