Actuaries' Analytical Cookbook

Introduction to Python

About Python
Setting up Your Python Environment
An Introductory Example
Learn More
Useful Python packages for Data Science

Introduction to R

About R
Setting Up R
Introduction to R
Next Steps With R
Useful Packages
R: data.table for actuaries

Workflow Management

Version control

Regression, Classification and Technical Price

Py: Customer Churn Classification
Py/R: Multitasking Risk Pricing Using Deep Learning
Py: Explainable Models with SHAP

Life Insurance

R: Life Modelling Recipes
R: Life Industry Stats in Tableau and R
R: Bayesian Applications
R: Decision Tree Applications

General Insurance

R: Reserving with GLMs
R: Reserving with LASSO
R: Machine Learning Triangles
Py: Machine Learning Triangles

Unsupervised Learning

Py: Socio-Economic Index Construction
Py: Clustering Credit Card Fraud
Py: K-means clustering of COVID dataset
Py: Hierarchical clustering on COVID dataset

Natural Language Processing

R: Word Cloud Case Study
Py: Text Classification
Py: Decision Tree Classification
Py: Neural Net Classification
Py: Classifying review sentiment
Py: Customer Sentiment Analysis

Business Optimization

Py: Linear Programming

Image Recognition

Py: Image Recognition

Data Ethics

Automated Decision-Making Systems

Reference

Bibliography

Contributing

Contributing

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By YDAWG, DAPC, YAC and other contributors.

The Actuaries' Analytical Cookbook is a series of data and analytics recipes to help actuaries quickly get started with a new project. This site is intended to be a resource to actuaries in both data science and traditional fields. Opinions expressed in this publication are the opinions of contributors and do not necessarily represent those of either the Institute of Actuaries of Australia (the ‘Institute’), its members, directors, officers, employees, agents, or that of the employers of the contributors.
© Institute of Actuaries of Australia and Contributors 2021. All rights reserved.