Recopilatorio de diversos proyectos basados en algoritmos de machine learning.

Chart.js Graph-like Charts (tree, force directed)

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

Classification Model of Potential Credit Card Customers

Agglomerative Clustering from scratch without using built-in library with different hyper-parameters using Python and evaluated the cluster quality using intrinsic and extrinsic scores

Explore a comprehensive analysis of Netflix's extensive collection of movies and TV shows, clustering them into distinct categories. This GitHub repository contains all the details, code, and insights into how we've organized and grouped the vast content library into meaningful clusters.

Mall Customer Segmentation Data

This project aims to practice the steps of Crisp Data Mining ( CRISP-DM ). The repository includes 3 phases, data understanding, supervised learning, and unsupervised learning.

This is a R repository of studies that I made on some data sets. There are linear models, predicition models (boosting - bagging - RandomFlorest), clustering and dendograms.

Data Science - PCA (Principal Component Analysis)

Data Science - Clustering Work

Utilized hierarchical clustering to identify the most similar cryptocurrency clusters and determine which currencies had the most significant impact on each other. Constructed a portfolio based on these findings.

Hierarchical clustering analysis on Credit Card customers dataset.

Data prepration and preprocessing for predictive modeling with SAS and Python

This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN

This clustering analysis aims to provide valuable insights into the viability of introducing an original language cinema in Milan, Italy.

This repo explores KMeans and Agglomerative Clustering effectiveness in simplifying large datasets for ML. Goals include dataset download, finding optimal clusters via Elbow and Silhouette methods, comparing clustering techniques, validating optimal clusters, tuning hyperparameters. Detailed explanations and analysis are provided.

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

NETFLIX MOVIES AND TV SHOWS CLUSTERING is a project that aims to cluster the available movies and TV shows on Netflix based on their attributes such as genre, release year, and country of production.