This repository contains machine learning algorithms implemented from scratch and using scikit-learn, covering classification, regression, and clustering. Each algorithm is well-documented, with clear code and explanations. To use K-Medoids, install sklearn_extra via pip install scikit-learn-extra. Contributions are welcome!

Bank Marketing Dataset (BMD), Machine Learning Repository (UCI), classification, clustering, R

Explore insightful projects on data analysis with MATLAB: k-means, k-medoid, and LDA. Polished PDF reports generated using LaTeX showcase valuable insights from diverse datasets. Discover the power of numerical methods in extracting knowledge from data!

UNI S6: K medoids, Gaussian naive bayes & dbscan on SORLIE dataset

BPNN, K-means, K-medoids

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.

Customer Segmentation

Implemented K-Means and K-Medoids on custom dataset. ML ASSIGNMENT 3 => Q3

A small repository implementing clustering algorithm from scratch

Data analysis of marketing campaigns

Algorithms for K-Medoids Clustering

[ECML 2022] SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting

The Partitioning Around Medoids (PAM) implementation of the K-Medoids algorithm in Python [Unmaintained]

Machine Learning Algorithm Implementation from Scratch using Pyhon

Unsupervised machine learning methods built from scratch, KMeans, KMedoids

TCC do curso de pós graduação em Ciência de Dados da PUC-MG (oferta 2021)

Prototype based clustering on seeds dataset

Using the credit card customer base dataset, identify different segments in the existing customer base, taking into account their spending patterns as well as past interactions with the bank.

Unsupervised machine learning on type of glass dataset