During my experience, I have had the opportunity to work on modeling stacking in different blockchains. Specifically, I have:_Contributed to the documentation and research of Cosmos and Osmosis blockchains, gaining a deep understanding of their architecture and consensus mechanisms._Assisted the team in gathering and analyzing data to model accurately staking and validation.Additionally, I have also had the opportunity to work on an agent-based simulation for modeling a governance token, although due to confidentiality reasons I am unable to provide further details. To be part of this project, I took a Rad-Cad Masterclass course and was able to apply my knowledge to conduct a simple tokenomics exercise of an agent-based simulation to better understand the concept and effectively communicate it to the team.

Detect fraud in new insurance claims based on historical data:- Understand the business objective to meet the needs- Clean the data and replace missing values with the most frequent value of the column (Pandas, Numpy)- Visualize data through interactive plots to better understand relationships between variables (Matplotlib,Plotly)- Train classifiers and interpret results (EasyEnsemble, Baggin, RandomForest, Sklearn, Imblearn, Shap, Eli5)