Kodey | Data Science | Data Engineering | Machine Learning

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 […]