Last Updated on January 28, 2020 Before you can select and prepare your data for modeling, you need to understand what you’ve got to start with. If you’re a using the Python stack for machine learning, a library that you can use to better understand your data is Pandas. In this post you will discover some quick and dirty recipes for Pandas to improve the understanding of your data in terms of it’s structure, distribution and relationships. Kick-start your project […]

Last Updated on December 13, 2019 Before you can build machine learning models, you need to load your data into memory. In this post you will discover how to load data for machine learning in Python using scikit-learn. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples. Let’s get started. Update March/2018: Added alternate link to download the dataset as the original appears to have […]

Last Updated on June 4, 2020 Not all data attributes are created equal. More is not always better when it comes to attributes or columns in your dataset. In this post you will discover how to select attributes in your data before creating a machine learning model using the scikit-learn library. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples. Let’s get started. Update: For […]

Last Updated on August 21, 2019 Machine learning models are parameterized so that their behavior can be tuned for a given problem. Models can have many parameters and finding the best combination of parameters can be treated as a search problem. In this post, you will discover how to tune the parameters of machine learning algorithms in Python using the scikit-learn library. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python […]