Share your photos, anyone can like them. Only you can delete them.

Evaluación académica del Sprint 2 del bootcamp TripleTen. Limpieza y análisis básico de datos de usuarios/as usando condicionales, bucles, listas y validación de errores

TripleTen Data Science Professional Training Program projects.

This is a demo created for online session practice for Tripleten cohort

Make.com automation that classifies customer feedback sentiment with Google Gemini AI and logs structured results to Google Sheets.

Foundational Python projects covering data cleaning, data processing, control flow, and introductory data analysis.

Aplicación Full Stack interactiva desarrollada como proyecto final en Tripleten. Implementa lógica avanzada con TypeScript, arquitectura modular (Client/Server) y despliegue con Docker.

The library: a web project to solidify CSS styling using the BEM framework.

Interactive dashboard analyzing car listing data using SQL and visualization tools.

A project finished 04-02-2026 as part of the TripleTen Data Science program using real-world data and mimicking real-world project requirements. Task was to train a machine learning model for use on the Rusty Bargain app to estimate a car's market value on demand. Lowest RMSE was 1710.25, and that model delivered predictions in 207 milliseconds.

Statistical data analysis using descriptive statistics, probability theory, hypothesis testing, and business decision-making with Python.

Intermediate Python exercises and projects completed during the TripleTen Data Analytics Bootcamp, covering dictionaries, functions, Pandas, and data preprocessing.

The final project finished 05-27-2026 as part of the TripleTen Data Science program using real-world data and mimicking real-world project requirements. Task was to train a machine learning model to predict which Interconnect customers are soon to terminate their contracts to offer special deals and reduce churn rate. Best AUC-ROC was 0.885.

Sitio web de biblioteca desarrollado con HTML y CSS, enfocado en estructura semántica, diseño limpio y maquetación responsiva.

A RAG-powered knowledge assistant built with ChromaDB and Sentence Transformers that answers Machine Learning questions with confidence-based response handling and source attribution.

Mira el resultado de las practicas con React Router quedo muy linda la página y sobre todo las rutas dinámicas que hemos hecho siguiendo los pasos 👇🤖

Projeto do Sprint 5 - Implementar um Dashboard de aplicativo Web no Render

Software development tools, Git, GitHub, version control, and web application deployment using Render.