One question that is often asked by those who know Machine Learning to me is how do I build a Bayesian Logistic Regression model? If you know how to build a logistic regression model in sklearn or a standard machine learning library it’s quite easy to learn how to do the Bayesian version. In this screencast… Continue reading New Screencast: How do I build a Logistic Regression model the Bayesian way?

# Tag: Python

## How to build a bayesian model in 30 minutes?

You have a problem that you think might need some Bayesian modelling A common question I’m asked is how do you start? In this tutorial I take you from a fresh data set, the data set is an educational dataset. I don’t know anything about the data, and I have no specific domain knowledge. I… Continue reading How to build a bayesian model in 30 minutes?

## How do I visualise the results of a Bayesian Model: Rugby models in Arviz

I’ve been recently playing around with ‘arviz’. For those of you who don’t know Arviz is a library for exploratory analysis Bayesian Models. I’ve got a Bayesian model built – or someone has built one for me, how do I explore it? How do I plot it? This is the fundamental question that Arviz answers.… Continue reading How do I visualise the results of a Bayesian Model: Rugby models in Arviz

## What happens when you import modules in Python

I’ve been using Python for a number of years now – but like most things I didn’t really understand this until I investigated it. Firstly let’s introduce what a module is, this is one of Python’s main abstraction layers, and probably the most natural one. Abstraction layers allow a programmer to separate code into parts… Continue reading What happens when you import modules in Python

## A map of the PyData Stack

One question you have when you use Python is what do I do with my data. How do I process it and analyze it. The aim of this flow chart is to simply provide a simple to use ‘map’ of the PyData stack. At PyData Amsterdam I’ll present this and explain it in more detail… Continue reading A map of the PyData Stack

## What I’ve been working on

This is just a little wrapper post to include some of the things I’ve worked on lately. I wrote up a short piece on Exploring new numpy features including the new matrix operator I wrote up some PyMC3 examples on my Github – this includes some Bayesian Logistic Regression and some classical examples of conversion modelling. I… Continue reading What I’ve been working on

## Exploring the new NumPy features: Rewrite Python for Data Analysis

The new version of NumPy 1.10 contains the new Python @ operator. This is for matrix multiplication and greatly simplifies some code. This also appeals to me as a Math geek because it makes it really easy to write code down based on what you read in a paper. This makes implementing a linear algebra… Continue reading Exploring the new NumPy features: Rewrite Python for Data Analysis

## Marketing data with PyMC3

My friend Erik put up an example of conversion analysis with PyMC2 recently. I decided to reproduce this with PyMC3. We want a good model with uncertainty estimates of various marketing channels. I’ll restate his assumptions for the model and then show the gist. Let’s make some assumptions about the model: The cost per transaction… Continue reading Marketing data with PyMC3

## Interview with a Data Scientist: Nathalie Hockham

(Linkedin picture) I was very happy to interview Natalie about her data science stuff – as she gave a really cool Machine Learning focused talk at PyData in London this year, which was full of insights into the challenges of doing Machine Learning with Imbalanced data sets. Natalie leads the data team at GoCardless, a… Continue reading Interview with a Data Scientist: Nathalie Hockham