Last Updated on August 22, 2019 When you are getting started in Weka, you may feel overwhelmed. There are so many datasets, so many filters and so many algorithms to choose from. There is too much choice. There are too many things you could be doing. Too much ChoicePhoto by emilio labrador, some rights reserved. Structured process is key. I have talked about process and the need for tasks like spot checking algorithms to overcome the overwhelm and start learning […]

Last Updated on August 22, 2019 When I first got started in machine learning I implemented algorithms by hand. It was really slow going. I was a terrible programmer at the time. I was trying to figure out the algorithms from books, how to use them on problems and how to write code – all at the same time. This was the biggest mistake I made when getting started. It made everything 3-times harder and killed my motivation. A friend […]

Last Updated on August 16, 2020 Working on a problem, you are always looking to get the most out of the data that you have available. You want the best accuracy you can get. Typically, the biggest wins are in better understanding the problem you are solving. This is why I stress you spend so much time up front defining your problem, analyzing the data, and preparing datasets for your models. A key part of data preparation is creating transforms […]

Last Updated on August 22, 2019 The promise of Data Mining was that algorithms would crunch data and find interesting patterns that you could exploit in your business. The exemplar of this promise is market basket analysis (Wikipedia calls it affinity analysis). Given a pile of transactional records, discover interesting purchasing patterns that could be exploited in the store, such as offers and product layout. In this post you will work through a market basket analysis tutorial using association rule learning […]

Last Updated on August 22, 2019 This is a guest post by Igor Shvartser, a clever young student I have been coaching. This post is part 1 in a 3 part series on modeling the famous Pima Indians Diabetes dataset that will introduce the problem and the data. Part 2 will investigate feature selection and spot checking algorithms and Part 3 in the series will investigate improvements to the classification accuracy and final presentation of results. Kick-start your project with my […]