WCSSkills plugin for CS2

Assignment-08-PCA-Data-Mining-Wine data. Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we h…

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it …

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…

Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage histo…

Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include informati…

👨 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.

k-Means Clustering

Content: Unsupervised ML, Clustering, Customer Segmentation, WCSS, elbow method

This project performs customer segmentation using the K-Means clustering algorithm on mall customer data. By analyzing features like annual income and spending score, it groups customers into distinct clusters to help businesses understand customer behavior and enable data-driven marketing strategies.

ML model using K Means for Mall Customer Segmentation

Clustring unsupervised credit card dataset

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…

Customer Segmentation Model using KMeans Clustering

Used libraries and functions as follows:

Project segregates the customers on the basis of their spending score and annual income using K-Means Clustering that is a part of unsupervised learning

The projects are a part of the internship by The Sparks Foundation

A project I did for FYP where we looked at 5 different formulations to determine which one was the best to move to upscale operations

Clustering users on the basis of ages, incomes, ets