About This Event
Valuable data that could be used for business insights and decision-making is often only available in free form text fields. The presentation will review one method of converting text to more usable information by using Latent Dirichlet Allocation (LDA) topic modeling.
Capability Model:
C:MM:L2: Model design and selection to replicate a real-world process, evaluate model inputs, and interpret results.
S:CT:L2: Evaluates problems by identifying stakeholder requirements, proposing criteria for decision making, evaluating options against proposed criteria, presenting a solution, and managing an iterative process based on feedback.
S:LT:L2: Uses latest technology (e.g., cloud-based platforms, Python/R, version control, containers, Continuous Integration /Continuous Development, machine learning techniques for tabular/structured datasets, data formats such as JSON and Parquet) to solve standard business problems within the actuary's area of practice.
T:AN:L2: Designs analytic steps to validate signals vs noises and provide justification for insights.
