CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins

Customer Segmentation Using Elbow Method and K-Means Algorithm

Go K-Means Image Color Separation Dominant Color Finder written in Go

This dataset from "ShufersalML" captures customer order history, aiming to predict future purchases using Python. It involves interconnected files that detail customer orders over time. The goal is to build a predictive model leveraging past order patterns to anticipate which products a user is likely to include in their next order.

Language: R. Study, Exploratory Data Analytics and Data Visualizations about stationarity in data scientists roles applying the following techniques: PCA, Factor Analysis, Clustering, KMeans and Hierarchical Clustering.

Modelling road to victory in Pokémon Unite 🏆 with statistical learning methods in R 🧪

InvertedIndex using MapReduce

Project recommendation system for DonorsChoose.org in R based on RFM analysis and K-Means Clustering

This is a repository with exercises extracted from the book "Introduction to machine learning with R" from Scott V. Burger. It will help you gain a solid foundation in machine learning principles. Using the R programming and then move into more advanced topics such as neural networks and tree-based methods.

This repository contains code and analysis for performing RFM (Recency, Frequency, Monetary) analysis on retail store customer data. The analysis is followed by customer segmentation using the KMeans clustering algorithm to gain insights into customer behavior and enable data-driven marketing strategies.

The K-Means Visualizer is an interactive web application designed to help users understand and visualize the K-Means clustering algorithm. Through an intuitive interface, users can experiment with different numbers of data points and clusters, and observe how the algorithm iteratively updates centroids and assigns data points to clusters.

The End of Science: The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. Welcome to the Petabyte Age.

A Jupyter notebook that run PCA and KMeans on population demographic data.

Etude de marché dans le but d'un développement à l'international

Python implementation of K-Means Clustering algorithm for unsupervised learning. Efficiently groups data points into clusters based on similarity. Simple yet powerful tool for data exploration, segmentation, and pattern recognition tasks in various fields.

Seasonality and text analysis of Boston Airbnb data

Categorization of world countries using socio-economic and health factors

An unsupervised machine learning project

Contains a collection of my experimentations, explorations, and data analysis of random datasets