In a previous post I looked at how to create an age/gender classifier using Keras. In this post we'll look at the final product - a web app that uses the classifier to generate a life insurance quote. To get from the python model to the finished product involves a few steps:

In the next two posts I'll cover how to use the Keras python library and the R Shiny package to create a web app capable of producing a life insurance quote from an image of a face. To do this, we'll need to have a model that can estimate a persons age and gender from our input image - for this we will use a convolutional neural network (CNN).

Following on from a previous post, this article discusses:

• Assessing how well the GLM generalises to new data with cross validation
• Automated GLM selection using grid search

Previously, we looked at using H2O's GLM function to set a rating plan for life insurance contracts. Importing and preparing the data, the use of offsets and fitting the GLM with h2o were all covered here more ...

Here I will show you how to use generalised linear models (GLM) with mortality claims data. We will fit a poisson GLM to model count data using the h2o package. This post will cover:

• Preparing data
• Basic h2o functions
• Use of offsets
• Fitting a basic GLM
• Extracting a rating plan from the model

This article will be split into two parts - part 1 will cover the above and part 2 will cover cross validation and 'tuning' a GLM using h2o's grid search functionality. Finally, in a later post we will look at how to build simple pricing apps using the models we develop.

In this post I'll show you how to simulate a large dataset of mortality claims. Sometimes it's useful to use simulated data. For example, if you wanted to experiment with new modelling techniques but were unable to find a useful dataset, it may be easiest just to create your own. Alternatively, you may simply wish to supplement existing data with simulated data. If you have some assumptions about the variables you want to create, it's possible to create entire datasets from scratch.