Developed a Windows tool using PyQt5, integrating K-means clustering for data analysis. The application recommends optimal cluster numbers, identifies cluster members, and allows exporting results to Excel.
Pytorch implementation of standard metrics for clustering
An upper bound of the Average Silhouette Width.
A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.
It's the HAC algorithm that Im using to sort newspaper articles by news. You can adapt it to pretty much any type of text.
Clustering K-Means with Streamlit App Deployment
OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.
This code demonstrates how to integrate Apache Beam with scikit-learn datasets and perform simple data transformations. It loads the Linnerud dataset from scikit-learn, converts it into a Pandas DataFrame for easier manipulation.
A web-based clustering application developed for my undergraduate thesis, utilizing K-Means and K-Medoids algorithms with Silhouette Coefficient optimization. Features include CSV input, exploratory data analysis (EDA), automated preprocessing, and result visualizations.
Implemented an auto-clustering tool with seed and number of clusters finder. Optimizing algorithms: Silhouette, Elbow. Clustering algorithms: k-Means, Bisecting k-Means, Gaussian Mixture. Module includes micro-macro pivoting, and dashboards displaying radius, centroids, and inertia of clusters. Used: Python, Pyspark, Matplotlib, Spark MLlib.
Selection of the best centroid based clustering version with k-medoids and k-means
Brief Customer Segmentation with Kmeans
A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.
This repository contains all program files and datasets used in implementation of Masters Thesis Research Work for the topic - "Efficient Clustering via Kernel Principal Component Analysis and Optimal One Dimensional Clustering".
This project implements customer segmentation using Principal Component Analysis (PCA) for dimensionality reduction and Gaussian Mixture Models (GMM) for clustering. It analyzes customer data and visualizes the clusters in 2D and 3D. The optimal number of clusters is determined using the Bayesian Information Criterion (BIC).
A Python implementation of the K-Means clustering algorithm with silhouette analysis to evaluate cluster quality.
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