Obtaining a dataset

As you can imagine, one of the most important aspects of the model building process is obtaining a high-quality dataset. A dataset is used to train the model on what the output should be in the case of the aforementioned case of supervised learning. In the case of unsupervised learning, labeling is required for the dataset. A common misconception when creating a dataset is that bigger is better. This is far from the truth in a lot of cases. Continuing the preceding example, what if all of the poll results answered the same way for every single question? At that point, your dataset is composed of all the same data points and your model will not be able to properly predict any of the other candidates. This outcome is called overfitting. A diverse but representative dataset is required for machine learning algorithms to properly build a production-ready model. 

In Chapter 11, Training and Building Production Models, we will deep dive into the methodology of obtaining quality datasets, looking at helpful resources, ways to manage your datasets, and transforming data, commonly referred to as data wrangling.