Michael Nordmo​​​​‌‍​‍​‍‌‍‌​‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍​‍​‍​‍‍​‍​‍‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‍‍‌‌‍​‍​‍​‍​​‍​‍‌‍‍​‌​‍‌‍‌‌‌‍‌‍​‍​‍​‍‍​‍​‍‌‍‍​‌‌​‌‌​‌​​‌​​‍‍​‍​‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‍‌‌‍‍‌‌​‌‍‌‌‌‍‍‌‌​​‍‌‍‌‌‌‍‌​‌‍‍‌‌‌​​‍‌‍‌‌‍‌‍‌​‌‍‌‌​‌‌​​‌​‍‌‍‌‌‌​‌‍‌‌‌‍‍‌‌​‌‍​‌‌‌​‌‍‍‌‌‍‌‍‍​‍‌‍‍‌‌‍‌​​‌​​​​​‌​‌‌​​‌‍‌‌‌‍‌‍​​‌‌‍​‍​‍‌‌‍​‍​​‍‌‍‌‌​​​​‍‌​‌​​‍​‌‍​‍​‌‌​‍‌​‍​‌‍‌​​‍​‌‍‌​​‍‌‌‍​‍‌‍‌‍​‌‌​​‍​‌‌‌‍​‍​​​​‌​‌‍​‌‌​‌‍​‍‌​‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌‍​‌‌‌‌‌‌​‌‍‍​‌‍‌​‍​‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌‍‌‌‌‌‍​‌‍​‌​‍‌‍​‌‌‍‌‌‍‌‌​‌‍​‍‌‍​‌‌​‌‍‌‌‌‌‌‌‌​‍‌‍​​‌‌‍‍​‌‌​‌‌​‌​​‌​​‍‌‌​​‌​​‌​‍‌‌​​‍‌​‌‍​‍‌‌​​‍‌​‌‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‌‍‍‌‌‍‌​​‌​​​​​‌​‌‌​​‌‍‌‌‌‍‌‍​​‌‌‍​‍​‍‌‌‍​‍​​‍‌‍‌‌​​​​‍‌​‌​​‍​‌‍​‍​‌‌​‍‌​‍​‌‍‌​​‍​‌‍‌​​‍‌‌‍​‍‌‍‌‍​‌‌​​‍​‌‌‌‍​‍​​​​‌​‌‍​‌‌​‌‍​‍‌​‍‌‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌‍​‌‌‌‌‌‌​‌‍‍​‌‍‌​‍​‍‌‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌‍‌‌‌‌‍​‌‍​‌​‍‌‍​‌‌‍‌‌‍‌‌​‍​‍‌‌

The most important part of any data science project is the data. It does not matter how fancy your algorithm is if your data has low quality or does not capture the relationships you are interested in. In the end it all comes down to the simple truth: “garbage in, garbage out”. Data cleaning refers to a variety of methods for improving data quality. It can be a time consuming and challenging process, but the reward for properly cleaning your data can be great.​​​​‌‍​‍​‍‌‍‌​‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍​‍​‍​‍‍​‍​‍‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‍‍‌‌‍​‍​‍​‍​​‍​‍‌‍‍​‌​‍‌‍‌‌‌‍‌‍​‍​‍​‍‍​‍​‍‌‍‍​‌‌​‌‌​‌​​‌​​‍‍​‍​‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‍‌‌‍‍‌‌​‌‍‌‌‌‍‍‌‌​​‍‌‍‌‌‌‍‌​‌‍‍‌‌‌​​‍‌‍‌‌‍‌‍‌​‌‍‌‌​‌‌​​‌​‍‌‍‌‌‌​‌‍‌‌‌‍‍‌‌​‌‍​‌‌‌​‌‍‍‌‌‍‌‍‍​‍‌‍‍‌‌‍‌​​‌‌‍​‍‌‍​​‌‌​‌​‌‍‌‌‌‍‌‍​‌​​​​‍‌​‍‌​‌‍​​​‌‍‌‍​‍‌​‌​‌‍‌‍​​​​‌​‍‌‌‍​‍​‌​​‌‌‌‍​​‍‌​​‍​​​‌‍​‍‌‍​‌​‌​​‌‍​‍​‌‍​‍​‌‌‌‍‌‌​‍​​​‌​‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‌‍​‍‌‍​‌‌​‌‍‌‌‌‌‌‌‌​‍‌‍​​‌‌‍‍​‌‌​‌‌​‌​​‌​​‍‌‌​​‌​​‌​‍‌‌​​‍‌​‌‍​‍‌‌​​‍‌​‌‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‌‍‍‌‌‍‌​​‌‌‍​‍‌‍​​‌‌​‌​‌‍‌‌‌‍‌‍​‌​​​​‍‌​‍‌​‌‍​​​‌‍‌‍​‍‌​‌​‌‍‌‍​​​​‌​‍‌‌‍​‍​‌​​‌‌‌‍​​‍‌​​‍​​​‌‍​‍‌‍​‌​‌​​‌‍​‍​‌‍​‍​‌‌‌‍‌‌​‍​​​‌​‍‌‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‍​‍‌‌

When I decided to write a blog post called "How to talk like a machine", I discovered that the question of how to write a simple blog post had a lot of deep implications. The follow-up question was "How can I make my blog post read like a machine-made blog post?". It turns out that there is a difference between writing a single paragraph of prose and a long essay in a natural language. There are a few things you need to be aware of when you write a machine-made blog post.​​​​‌‍​‍​‍‌‍‌​‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍​‍​‍​‍‍​‍​‍‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‍‍‌‌‍​‍​‍​‍​​‍​‍‌‍‍​‌​‍‌‍‌‌‌‍‌‍​‍​‍​‍‍​‍​‍‌‍‍​‌‌​‌‌​‌​​‌​​‍‍​‍​‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‍‌‌‍‍‌‌​‌‍‌‌‌‍‍‌‌​​‍‌‍‌‌‌‍‌​‌‍‍‌‌‌​​‍‌‍‌‌‍‌‍‌​‌‍‌‌​‌‌​​‌​‍‌‍‌‌‌​‌‍‌‌‌‍‍‌‌​‌‍​‌‌‌​‌‍‍‌‌‍‌‍‍​‍‌‍‍‌‌‍‌​​‌‌‍‌​​‌‌‌‍​‍​‌‌​​‍​​​​‌‌​‌​‍‌​​‍‌‍​​‌​‌​‍‌​‌​​‍​​​‌‍​‌​‍‌​‍​​​‍​‌​​‌​​‍‌​‌‌‍‌‍‌‍‌​​‌‌​‍‌​​‌​‍​​‌​​‍‌‌‍​‍​‌‌‌‍‌​​‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‌‍​‍‌‍​‌‌​‌‍‌‌‌‌‌‌‌​‍‌‍​​‌‌‍‍​‌‌​‌‌​‌​​‌​​‍‌‌​​‌​​‌​‍‌‌​​‍‌​‌‍​‍‌‌​​‍‌​‌‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‌‍‍‌‌‍‌​​‌‌‍‌​​‌‌‌‍​‍​‌‌​​‍​​​​‌‌​‌​‍‌​​‍‌‍​​‌​‌​‍‌​‌​​‍​​​‌‍​‌​‍‌​‍​​​‍​‌​​‌​​‍‌​‌‌‍‌‍‌‍‌​​‌‌​‍‌​​‌​‍​​‌​​‍‌‌‍​‍​‌‌‌‍‌​​‍‌‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‍​‍‌‌

There are many different types of machine learning models. Linear models are some of the simplest, but also some of the most widely used. In this post we’ll explain what a linear model is and why linear models are so popular.​​​​‌‍​‍​‍‌‍‌​‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍​‍​‍​‍‍​‍​‍‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‍‍‌‌‍​‍​‍​‍​​‍​‍‌‍‍​‌​‍‌‍‌‌‌‍‌‍​‍​‍​‍‍​‍​‍‌‍‍​‌‌​‌‌​‌​​‌​​‍‍​‍​‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‍‌‌‍‍‌‌​‌‍‌‌‌‍‍‌‌​​‍‌‍‌‌‌‍‌​‌‍‍‌‌‌​​‍‌‍‌‌‍‌‍‌​‌‍‌‌​‌‌​​‌​‍‌‍‌‌‌​‌‍‌‌‌‍‍‌‌​‌‍​‌‌‌​‌‍‍‌‌‍‌‍‍​‍‌‍‍‌‌‍‌​​‌‌‍​‌​‌‍​​‍‌‍‌‌‌‍‌‍​‌​‌‌​​‌​‍‌​‌‌‍​‌‌‍​‌‍‌​​‍‌​‌​​‌‌​​​​​‍​‍‌​‍​​​‍​​‌​‌​‍‌‌‍​​‍‌​​‌‌‍‌​​‌​‌‌​‌​‌‍​‌​‍‌​​‌‍‌‌‌‍‌​​‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‌‍​‍‌‍​‌‌​‌‍‌‌‌‌‌‌‌​‍‌‍​​‌‌‍‍​‌‌​‌‌​‌​​‌​​‍‌‌​​‌​​‌​‍‌‌​​‍‌​‌‍​‍‌‌​​‍‌​‌‍‌‍​‍‌‍‌‌‌‍‍‌‍‍​‍‌‌‍​‍‌‍​‌‍‌‍‌‌‍‌​‍‌‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‌‍‌‍‌‌‌​‍‌‍​‌‍‌‌‌‍​​‍‍‌‍​‌‌​​‌​​​‍‌‍‌‍‍‌‌‍‌​​‌‌‍​‌​‌‍​​‍‌‍‌‌‌‍‌‍​‌​‌‌​​‌​‍‌​‌‌‍​‌‌‍​‌‍‌​​‍‌​‌​​‌‌​​​​​‍​‍‌​‍​​​‍​​‌​‌​‍‌‌‍​​‍‌​​‌‌‍‌​​‌​‌‌​‌​‌‍​‌​‍‌​​‌‍‌‌‌‍‌​​‍‌‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌​​‌‍‌​‌‌​​‍‌‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌​‌‍‌‌‌​‌‍​‌​‍‌‍‍‌‌​​‌‌​‌‍‍‌‌‍‌‍‍​‍​‍‌‌