Machine learning techniques have revolutionized data analysis across various industries, including actuarial science. This project aims to explore and apply machine learning paradigms to actuarial problems using Python and pre-built packages. By leveraging these tools, students will gain practical experience in employing advanced computational methods to solve real-world actuarial challenges.

Students will begin with comprehensive tutorials and workshops on Python-based machine learning libraries. Each topic will span approximately 3-4 weeks, consisting of theoretical study, practical implementation, and project-specific applications.

Policyholder Classification (Decision Trees, k-means)
Objective: Develop models to predict which individuals are likely to purchase term life
insurance and their expected coverage amounts.
Example Dataset: Utilize TermLife.csv to build predictive models based on demographic and
financial data.
Optical Character Recognition (OCR, Tesseract)
Objective: Convert documents into readable and searchable text.
Example Application: Extracting data from claims documents to facilitate easier analysis and processing of insurance claims.
Sentiment Analysis (TextBlob)
Objective: Analyze text to determine attitudes or sentiments.
Example Application: Assessing the sentiment of ESG (Environmental, Social, and Governance) investments or analyzing the sentiment within claim documents.
Image Classification (Convolutional Neural Networks, planet.org)
Objective: Apply edge detection techniques on satellite images to monitor changes in coastlines.
Example Application: Supporting flood insurance and catastrophe (CAT) bonds by identifying coastal erosion trends.

This project offers students a unique opportunity to bridge theoretical knowledge with practical application in actuarial science using advanced machine learning techniques. Students will gain an appreciation for machine learning and how it can be used to solve problems old and new.

Supervisor: Eric Icaza