ML

In my last article, I spoke about auto-sklearn. I said that, the library would train several models and then use them in conjunction with one another to make a final prediction. This is what we call ensemble modelling (or meta algorithms). It’s the process of including multiple models into the prediction process with the goal of […]

An automl workflow should be able to preprocess data; select the right model to use; tune the hyper parameters and provide us with the best possible model as a result. One such automl library is auto-sklearn. This library automatically finds the right algorithm for the dataset you have provided and automatically tunes the hyper parameters […]

You work for a fitness centre. Let’s say you’ve recently deployed a machine learning model to predict whether a customer will churn at the end of their current contract. Your input features to the model are: Average times visited per week over the last month Average times visited per week over the last 6 months […]

Recently, I’ve been working to deploy a new machine learning model into a production environment. This is the first time I’ve had to deploy a model that runs across such huge datasets. The requirement is to make 30,000,000 predictions each time the model runs. In terms of the pipeline, it’s three distinct phases. The first, […]

In this article, we are going to go through some of the key steps to implementing a random forest machine learning model. The data we will be using is from Kaggle – it’s a dataset describing whether patients have diabetes or not. Luckily, this dataset is nice and clean, so we don’t need to worry […]

Timeseries Decomposition is a mathematical procedure which allows us to transform our single timeseries into multiple series. These help us to extract seasonality information and trend easily. Doing this in Python is quite a simple task, I have outlined it below. However, before we get into that, we need to understand the difference between additive […]

Timeseries forecasting is quite a big topic to cover. I’ve spoken about key terminology and exponential smoothing in this article and I’ve spoken about how we might remove timeseries outliers here. In this post, I am going to discuss the different components of the ARIMA model (AR and MA), in addition to the ARIMA model […]

An early view of my new book ‘Data Badass’ is available to view using this link. It’s not yet been through thorough editing and will be added to over time but I am keen to gather some feedback. It’s a book that covers the data basics; data platforms (including Hadoop, Kafka, Flume, Hive, Spark) and […]