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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.

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

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

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

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.

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

Análise de dados de clientes com Python — TripleTen Sprint 01

Análise estatística de planos de telecomunicações com Python — TripleTen Sprint 04

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

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

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.

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 👇🤖

Portfolio of TripleTen Data Science projects covering Python, SQL, visualization, and machine learning.

A project finished 04-09-2026 as part of the TripleTen Data Science program using real-world data and mimicking real-world project requirements. Task was to analyze time series data and use machine learning to predict future hourly taxi order rates for Sweet Lift Taxi. Lowest RMSE was 41.16 using a SARIMA model.