Evaluation of post-lockdown policies using social contacts and risk of professional exposure.

I built and evaluated several machine-learning models to predict credit risk using free data from LendingClub. I employed different techniques for training and evaluating models with imbalanced classes and used the imbalanced-learn and Scikit-learn libraries to build and evaluate models.

To identify customers who are more likely to default loan repayment

Biblioteca para mercado financeiro

Vigilante Vixen has learned that there were many security vulnerabilities from their technical, behavioral, law, and human resources aspects. Despite us not being directly involved in offshore financial services or the legal profession, technology roles have a considerable amount of opportunity to review this case and implement security regulations

Cyberus is a tool to check the generic and sentimental legitimacy of the message, and it gives an approximate idea of the risk, based on the dataset, on which it has trained, and some machine learning models for predicting the risk quantitatively.

Insurance Risk Assessment. Predicting insurance policy claims. Using machine learning and customer data to predict whether insurance policy holders will initiate an auto insurance claim within the next year.

A Whale off the Port(folio)

Develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan.

In this project, we'll analyse data from italian stock market for bank stocks.

In this project, we aim to optimize the performance of retail chain stores by establishing control stores based on their performance compared to selected trial stores. By leveraging data analytics and strategic insights, we seek to enhance sales revenue and drive growth within the retail chain.

Data/QE analyses.

MADS Capstone - Power Grid Protagonists

A custom created application with a GUI utilizing Python and libraries PyPDF2 to scrape, scan and evaluate a person's funding capacity based on their PDF credit report.

Supervised ML - Classification Using Python this project demonstrates the effectiveness of machine learning techniques in predicting cardiovascular risk using the Framingham Heart Study dataset. The developed machine learning model can be used by healthcare professionals to identify individuals at high risk of cardiovascular disease .

Project as part of the course Introduction to risk modeling and management at the ETH Risk Center at ETH Zurich, held in spring 2024

Anonymized report from one of Safety AI's consulting projects

Project source code and data for ML-enhanced risk analysis framework for combustion instability prediction