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.

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

A lightweight self-organizing notebook for your thoughts, powered by embeddings.

This is part of my speed run series. Notebook completed in roughly 4 hours. Cluster global news based on social media engagement and attempt classification

A Jupyter notebook to identify collective cell signalling in raw microscopy images

The notebook with the experiments to replicate and enhance the stock clustering proposed by Han(2022) for alogtrading, with KMeans Optimization

A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.

Here I have provided some common algorithms used in big data, I've attached an HTML(Python), Python Notebook and R project for each demonstration

This notebook analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.

Data tools in Jupyter notebooks served from a container. Includes examples of cleaning, classification, clustering, graph drawing, and principal component analysis.

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

Repositório com notebooks de comandos básicos e projetos de Data Mining utilizando python.

Este repositorio contiene la colección de Jupyter Notebooks generados para implementar la metodología descrita en el documento de Tesis "Detección de genomas anómalos de sars-cov-2 por medio de algoritmos de aprendizaje no supervisados".

Data Science, ML & DL Notebooks

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow

Code and example notebook demostrating usage of Tangent Space Least Adaptive Clustering algorithm for molecular dynamics simulations with known collective variables

Iconographic Visualization Inside Computational Notebooks

Materials for all my UWaterloo Data Science workshops including summaries, notebooks, slides, and video. Topics include deep learning, clustering, recommender systems, and regression analysis.

Jupyter Notebook: documentation of the implementation of the ToMATo algorithm to the GUDHI library (Topological Data Analysis), using real datasets.