Description: This report proposes a methodology to isolate crop yield improvement due to technological change and favorable and unfavorable weather.

Description: The advent of machine learning algorithms has taken predictive analytics and risk scoring to a new level. From gradient boosting machines to k-nearest neighbors regression, the use of these techniques has gained significant traction in healthcare - including health systems like the Cleveland Clinic. This session will discuss the use of predictive analytics to go beyond just assessing the risk of a patient, and instead focus on prescribing a specific intervention of care. In practice, the recommendation of a care plan goes beyond just attributing a score to a patient. It provides physicians and care coordinators the most probabilistic options - e.g. referral to social worker, depression screening, weight-loss program - for positively impacting a patient's future healthcare outcome.

Description: Data science and predictive analytics are rapidly growing fields and the tools are evolving at a formidable pace. The actuary who wants to be most effective will most likely have to learn at least the basics of more than one programming language, such as R, Python, or Julia, depending on the problem at hand. The Jupyter Notebook App allows you to create interactive notebook documents that are relatively language agnostic, so you only need to learn a single integrated development environment. More importantly, Jupyter Notebooks allows you to easily combine input (code), output (including visualizations), and documentation in a single, shareable, document. Learn how to make better use of your time with these very useful notebooks. This demonstration-based talk will also cover JupyterLab, the next generation of notebooks.

Description: Highlights of two 2018 health events organized by the Colombian Association of Actuaries and sponsored by the Colombian Government. Seminar on Risk Adjustment and Predictive Modeling in Health. Seminar on Risk Management for the Health Insurers

Description: This article discusses how to handle semisupervised data. This type of data might have a large number of observations, yet a small number of labels.

Description: You’ve built or updated a predictive model. Is it any good? How can you interpret and understand its predictions? How does it compare to alternative models? This session will look at traditional and non-traditional ways of validating predictive models, with examples from case studies.

Description: In How Significant Is Statistical Significance? The author discusses the p-value that is often used to determine significance. Sometimes we place too much reliance upon canned metrics and lose the ability to notice when something is mathematically sound but not sensible. The article provides three simple examples that require no complicated mathematics to follow yet provide a basis for some deep thought about statistical significance versus substantive significance.

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk

Description: Read the Society of Actuaries' latest update about how health care executives view the future of predictive analytics in the health care industry.

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.