Projects
Here are some of my academic and professional projects that showcase my skills in actuarial science, data analysis, and programming.
Knowledge graphs in Actuarial science
Development of a causal knowledge graph, automatically extracted from NTSB accident reports using an LLM and analyzed with a GNN, to test claims reserve estimation and the prediction of safety recommendations in insurance.
Python
GNN
LLM
Statistical Analysis
Predictive Modelling
Impact of Unemployment Rates on Inflation
Analyzed the relationship between unemployment rates and inflation using econometric models. Conducted time series analysis to identify patterns and correlations between these economic indicators.
R
Econometrics
Linear model
Data Visualization
Excel Data Quality Verification
Developed a comprehensive data quality verification framework for insurance datasets. Implemented automated checks and validation procedures to ensure data integrity for actuarial analysis.
Excel
VBA
Data Analysis
Statistics
New Actuarial Methods and Machine Learning
Explored innovative actuarial methods and machine learning applications in insurance. Compared traditional actuarial approaches with modern machine learning techniques for pricing and risk assessment.
XGBoost
Random Forest
GLM
Data Visualization
Data Analysis and Clustering on Automotive Datasets
Performed comprehensive data analysis using real-world automotive and customer datasets. Applied various supervised and unsupervised learning methods including ANOVA, PCA, LDA, and Hierarchical Clustering.
Python
R
ANOVA
PCA/LDA
Clustering
SAS Data Processing for Insurance
Developed SAS scripts for processing and analyzing insurance data. Created efficient data pipelines for actuarial modeling and reporting.
SAS
Data Processing
Statistical Analysis
Personal Project
Innovative Actuarial Modeling
This project demonstrates my commitment to innovative actuarial modeling and creative problem-solving in data science. It reflects my passion for leveraging analytical insights to address real-world challenges in the actuarial domain.
Key Features
- Advanced statistical modeling techniques for risk assessment
- Integration of machine learning with traditional actuarial methods
- Practical applications in insurance pricing and risk management
- Comprehensive documentation and reproducible research