Discover how to use advanced techniques in Python, including fitting regression and classification machine learning models, data cleaning, feature engineering, preliminary visualisations, and reporting to investigate lapse rates in this end to end walkthrough.
If you’re looking to learn how to utilise advanced applications of Python in an insurance context, this course is ideal for you. An individual subscription gives you 3 months’ online access to:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
You can also request to access to a coding project to practice the skills you learn in this course.
This course serves as an end-to-end walk-through of an investigation into lapse rate analysis through applying machine learning techniques using the Python programming language.
The aim of using machine learning techniques is to better understand our data, the key drivers behind lapse rates, and how various models can be fitted and their performance compared. We guide the student through importing, cleaning, investigating, modelling, visualising, and interpreting data using Notebooks.
This course is structured as a case study to help you, the student, better understand machine learning techniques and how they may be applied to a business context. The goal is not to provide a model that can be copied and applied to any situation, but rather to teach you how to apply machine learning techniques.
We will demonstrate, with the use of a practical case study, how the full cycle of actuarial analysis is evolving, from data collection and data enhancement, feature engineering, modelling, verification, and ultimately application & communication.
The case study showcases classification modelling and introduces regression modelling using standard techniques and more advanced machine learning with application to insurance.
If you're not already familiar with Python, we recommend that you start with our Foundations in Python course.
Chapter 1 of the course serves as an Introduction to the course, discussing the implications of lapses on insurance companies and the dataset that will be used.
Chapter 2 is on Problem Specification, covering the business context and the techniques available. It also offers an exploratory overview of the data and discusses external libraries and packages that we will be using.
Chapter 3 covers Data Management, which includes a preliminary analysis and feature engineering to reduce dimensionality of the data and make the data more suitable for model fitting.
Chapter 4 is Model Building, in which we fit various classification models and a regression model to determine whether or not an individual will lapse.
Chapter 5 is the Reporting chapter which reports on the findings of the model fitting stage by analysing and comparing the results of the various models fitted and the merits of each model, and in which we discuss the key factors driving lapses.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
