Unsupervised ML project segmenting Palmer Penguins via Agglomerative Hierarchical Clustering. Features data preprocessing , Dendrogram analysis (Ward's method) for optimal cluster selection, and interactive Plotly visualizations. Demonstrates distinct species grouping without labeled training data.

Superimpose a set of protein structures and report a RSMD matrix, in CSV and Mega-compatible formats.

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

This report presents a segmentation analysis conducted on a UK bank's customer dataset using hierarchical and two-step clustering techniques. The objective was to identify homogeneous customer groups to support the development of targeted financial products and services.

Agglomerative HC step by step concept

This project focuses on segmenting customers based on their spending behavior, age, income, and preferences using clustering algorithms like K-Means and Hierarchical Clustering. The outcome is a system that helps businesses understand different groups of customers to better tailor their marketing strategies.

Forest Fires Prediction using Unsupervised Learning

I performed cluster analysis on a dataset of smart contracts in Python to identify similar risk profiles.

Binary classification of Brain Tumor

This project performs hierarchical clustering on a dataset containing network usage and performance metrics. It includes data preprocessing, encoding, normalization, and visualization of clustering results using dendrograms. The purpose is to analyze and group similar data points, offering insights into patterns and relationships within the dataset

Produire une étude de marché avec Python

Compilation of various projects based on machine learning algorithms.

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)