Data
Clustering from scratch — K-means and hierarchical clustering
By Davy AGONMA — Freelance web developer & AI integrator
Problem
Calling `fit()` on a library is no guarantee you understand what a clustering algorithm does, nor that you can pick the right number of groups on a real dataset.
Solution
Manual reimplementation of K-means and hierarchical clustering, without any machine learning library, exposed through a Streamlit interface: CSV import, automatic K suggestion via the elbow method, dendrogram, intra- and inter-cluster analysis, and cluster prediction for an unseen point.
Outcome
An interactive teaching tool delivered for the Big Data module of the software engineering master's at IFRI.
Similar projects
Data science & machine learning: what this project covers
The data projects in this portfolio come from my transition into data science: supervised regression, clustering, recommendation systems and constraint programming. They're built in Python, covering the full cycle — exploration, cleaning, modelling, evaluation.
They show what I can do with data today, alongside web development and automation: prepare a dataset, pick a model that fits the problem, and make the result usable inside an interface.
Written by Davy AGONMA, freelance web developer & ai integrator.