Where can you get good datasets to practice machine learning? Datasets that are real-world so that they are interesting and relevant, although small enough for you to review in Excel and work through on your desktop. In this post you will discover a database of high-quality, real-world, and well understood machine learning datasets that you can use to practice applied machine learning. This database is called the UCI machine learning repository and you can use it to structure a self-study program and build a solid foundation in machine learning. Practice Practice Practice Photo by , some rights reserved. Why Do We Need Practice Datasets? If you are interested in practicing applied machine learning, you need datasets on which to practice. This problem can stop you dead. I teach a top-down approach to machine learning where I encourage you to learn a process for working a problem end-to-end, map that process onto a tool and practice the process on data in a targeted way. So How Do You Practice In A Targeted Way? I teach that the best way to get started is to practice on datasets that have specific traits. You can create a program of traits to study and learn about and the algorithm you need to address them, by designing a program of test problem datasets to work through. This will keep them interesting and introduce the challenges that come with real data. This is important when you are getting started and learning because you need quick feedback as to how well you are performing close to state-of-the-art or something is broken. For beginners, you can get everything you need and more in terms of datasets to practice on from the UCI Machine Learning Repository. What is the UCI Machine Learning Repository? The is a database of machine learning problems that you can access for free. It is hosted and maintained by the at the University of California, Irvine. It was originally created by as a graduate student at UC Irvine. For more than 25 years it has been the go-to place for machine learning researchers and machine learning practitioners that need a dataset. UCI Machine Learning Repository Each dataset gets its own webpage that lists all the details known about it including any relevant publications that investigate it. The datasets themselves can be downloaded as ASCII files, often the useful CSV format. For example, here is the webpage for the that requires the prediction of the age of abalone from their physical measurements. This can provide a useful baseline for comparison. Browse the 300+ datasets using that supports sorting and searching. This is limiting for those interested in natural language, computer vision, recommender and other data. Take a look at the as it shows featured datasets, the newest datasets as well as which datasets are currently the most popular. A Self-Study Program So, how can you make the best use of the UCI machine learning repository? I would advise you to think about the traits in problem datasets that you would like to learn about. These may be traits that you would like to model like regression , or algorithms that model these traits that you would like to get more skillful at using like random forest for multi-class classification. I have listed one dataset for each trait, but you could pick 2-3 different datasets and complete a few small projects to improve your understanding and put in more practice. The write-up is a key part. It allows you to build up a portfolio of projects that you refer back to as a reference on future projects and get a jump-start, as well as use as a public resume or your growing skills and capabilities in applied machine learning. Pick a tool or platform like Weka, R or scikit-learn and use this process to learn a tool. Cover off both practicing machine learning and getting good at your tool at the same time. It has a graphical user interface and no programming is required. I would recommend this to beginners regardless of whether they can program or not because the process of working machine learning problems maps so well onto the platform. With a strong systematic process and a good tool that covers the whole process, I think that you could work through a problem in one-or-two hours. This means you could complete one project in an evening or over two evenings. You choose the level of detail to investigate and it is a good idea to keep it light and simple when just starting out. The dataset pages provide some background on the dataset. Often you can dive deeper by looking at publications or the information files accompanying the main dataset. I have little to no experience working through machine learning problems. Now is your time to start. Pick a , pick a simple dataset and a tool like and work through your first problem. Place that first stone in your machine learning foundation. I have no experience at data analysis. No experience in data analysis is required. The datasets are simple, easy to understand and well explained. You simply need to read up on them using the data sets home page and by looking at the data files themselves. Action Step Select a dataset and get started. If you are serious about your self-study, consider designing a modest list of traits and corresponding datasets to investigate. You will learn a lot and build a valuable foundation for diving into more complex and interesting problems. Did you find this post useful? Leave a comment and let me know. The webpage requires… Or the dataset requires? I should try to draw a plot for each feature? Could you give some advice what steps should be taken? Regards, Adam Hey Jason, this is really nicely broken down into steps. I always felt that I get too involved into the problems that I miss the big picture but I think keeping a process and working through it is a good way to approach learning. And I am definitely looking forward to practising like you suggest. How do you handle the datasets not seeming to have any benchmarks for what a poor, fair, or good accuracy is for prediction? The answer is to use ZeroR or similar to baseline the problem and determine the point from which all other results can be compared. From there, interpretation of results is problem specific. Hi LinboLee, good questions. You can evaluate the performance of your models by estimating their performance on unseen data. You can do this with resampling methods like k-fold cross validation. You can then compare the skill of multiple algorithms on the problem. You can compare to previously published results by re-creating their test setup.
