Don’t delete your missing data - learn how to deal with it. All the data we have We learn on clean fabricated datasets but when it comes to real life problems machine learning becomes something completely different. Very often we must deal with missing values. A common approach is to simply delete observations that have them but by doing so we can lose a lot of important information. That is the main reason why we should be “data imputation ninjas” as it can save us a lot of time when trying to find a model fitting our data well. Many cases - many solutions Missing values can be very different for each dataset.

Posting is really easy. Blog posts are simple Markdown files. Files The content/post folder is where blog posts are stored. To create a post, add your markdown file to content/post directory with the following format: yyyy-mm-dd-title.md. Where yyyy is a year, mm is a month, and dd is a day, and md is the file extension representing the format used in the file. For example: 2020-05-18-how-to-add-post.md. File content An example of a post file you can find here. A blog post file begins with a front matter which is used to set metadata. For example: --- date: "2020-05-18" title: How to add a post authors: ["Alicja Gosiewska"] tags: - tutorial --- For more authors, add elements to the list, for example ["Alicja Gosiewska", "Second Author"].