This repository contains applied data science and analytics projects developed between 2019 and 2023, prior to building large-scale production-grade distributed AI platforms.
It documents my technical evolution toward complex AI systems, including the Tradu platform.
This repository includes real-world analytical projects focused on real estate and financial markets, combining data engineering, statistical analysis, visualization, and application development.
Key components include:
- Data preprocessing and feature engineering
- Geospatial and financial data analysis
- Interactive dashboards
- Web-based analytical applications
- Predictive modeling
Applied machine learning and geospatial analysis project focused on predicting residential property prices.
- 👉 Prediccion precios propiedades.ipynb — 2023
- 👉 applied-financial-analysis.ipynb (Financial data pipelines)
📜 Original commit: 2023 (preserved for technical reference)
Highlights:
- GeoPandas-based spatial analysis
- Socio-economic feature integration
- Exploratory and predictive modeling
End-to-end analytical project focused on stock market indicators, financial visualization, and dashboard-driven decision support.
Includes:
- Technical indicator computation
- Interactive data visualization
- Web-based analytical interface
- Google Colab deployment for rapid prototyping
👉 See: Tablero_analitico.ipynb (includes Colab deployment link)
This project demonstrates early experience in transforming data pipelines into usable analytical products.
- Python
- Pandas
- GeoPandas
- NumPy
- yfinance
- pandas-datareader
- Matplotlib / Seaborn / Plotly
- Jupyter Notebook
- Google Colab
These projects represent foundational work in applied data science, financial analytics, and data-driven application development.
Since then, my professional focus has evolved toward distributed systems, observability, and production-grade AI platforms, including the Tradu ecosystem.
This repository documents that progression.