Visualization of many Clustering Algorithms, via Notebook or GUI

Demonstration and tutorial notebooks for the Higra library

This repository is made following the course by Sir Jose Portilla, and focuses on unsupervised Machine Learning algorithms. I studied all these concepts in January 2024

Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks

clusters similar images and searches disoriented images and matches it with original image.

This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN

A library of implementations in the 'iads' directory, plus Jupyter notebooks for testing

I created this notebook to training my datascience skills, ssing different automatic learning models

This repository contains a Jupyter Notebook that explores various clustering techniques applied to the Fashion MNIST dataset like K-Means, Hierarchical,etc.

All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.

This notebook is about creating a 2D dataset and using unsupervised machine learning algorithms like kmeans, kmeans++, and Agglomerative Hierarchical clustering methods to classify data points, and finally comparing the results.

This repository contains the Lab practices of Machine Learning performed in Jupyter Notebook using python language. This repo consists of Clustering algorithms like K-Means and Hierarchical to perform unsupervised method on the given dataset.

Notebook desenvolvido como avaliação da disciplina Introdução à Inteligência Artificial. O mesmo contempla partes de pré-processamento das bases de dados e o desenvolvimento dos algoritmos Multilayer Perceptron (MLP), Random Forest, K-means e Agrupamento hierárquico

In this notebook, i have tried to appy KMeans, Hierarchical and DBSCAN clustering along PCA. The dataset used is Mall_Customers. In DBSCAN, certain type of Heatmaps are used to find the Epsilon and min_samples value which have performed quite well in identifying the correct number of clusters.

Jupyter Notebooks exploring Machine Learning techniques -- regression, classification (K-nearest neighbour (KNN), Decision Trees, Logistic regression vs Linear regression, Support Vector Machine), clustering (k-means, Hierarchical Clustering, DBSCAN), sci-kit learn and SciPy -- and where it applies to the real world, including cancer detection, …