Projeto de people analytics, utilizando machine learning na clusterização de dados de funcionários que poderam solicitar demissão de seu trabalho.

This project aims to redefine content discovery by delivering personalized article recommendations tailored to individual user preferences. We use advanced machine learning techniques like PCA and K-means clustering to analyze user behavior and article characteristics to provide highly accurate recommendations.

An overview of all clustering techniques with examples of data.

Our Topological Hyperparameter Evaluation Mapping Algorithm.

Implementation of k-means algorithm in C (standard C89/C90, K&R code style)

Customer segmentation is essential for enhancing marketing efficiency and satisfaction. By categorizing customers based on demographics, interests, and purchasing behavior, companies tailor messages to engage each segment effectively. Our app utilizes advanced clustering algos like KMeans, DBSCAN, and AGNES to extract insights from data

Applying statistical data science methods into loan default prediction task

Analysis of bond characteristic in high drug-likeness score compound

This Flask app lets users upload images and convert them into cartoonized versions using edge detection and color quantization. The process involves reading the image, detecting edges, reducing colors with k-means clustering, and blending for a cartoon effect. Try it out by running the app and uploading an image!

Developed and deployed a scalable machine learning model for real-time customer segmentation using FastAPI, Docker, Kubernetes, and GitHub Actions, with an end-to-end CI/CD pipeline on Azure Kubernetes Service, enhancing targeted marketing strategies through robust and seamless integration and deployment

Raku package for Machine Learning (ML) clustering algorithms

This repository houses a diverse collection of projects developed using Jupyter Notebooks, focusing on testing various machine learning pipelines, neural network models, and statistical machine learning approaches. Through exploration of different datasets, the projects delve into predictive modeling, classification tasks, and in-depth analyses.

PICAFlow: a complete R workflow dedicated to flow/mass cytometry data, from data pre-processing to deep and comprehensive analysis.

Python Customer Segmentation & Clustering

Multi-level network clustering based on the Map Equation

Repo for the "Identifying Weather Patterns using Azure ML and Clustering" hands-on lab.

Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.

Efficient implementation of object condensation losses for use in various projects

Clustering with Agglomerative and DBSCAN algorithm Machine Learning

Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles.