Python Tutorial: Feature selection vs feature extraction

Want to learn more? Take the full course at https://learn.datacamp.com/courses/dimensionality-reduction-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Reducing the number of dimensions in your dataset has multiple benefits. Your dataset will become simpler and thus easier to work with, require less disk space to store and computations will run faster. In addition, models are less likely to overfit on a dataset with fewer dimensions. The simplest way to reduce dimensionality is to only select the features or columns that are important to you from a larger dataset. The hard part here is to decide on which features are important. If you're an expert on what the data is about, you may know this by heart. You would, for example, know that a person's favorite color is irrelevant if you want to predict whether they'll default on a loan. And with the pandas dataframe .drop() method, you could remove that feature easily. Just make sure to pass the axis argument '1', to specify we're dropping a column instead of a row. If you're new to a dataset, you'll have to do some exploring before you can take a decision on which features can be dropped. Seaborn's pairplot() is excellent to visually explore small to medium sized datasets. It provides a one by one comparison of each numeric feature in the dataset in the form of a scatterplot plus, diagonally, a view of the distribution of each feature. The example shown here, visualizes a sample of the US army body measurement dataset called ANSUR. And we've set the kind of plot to show on the diagonal to 'hist' for histogram. We can spot that the weight in pounds is perfectly correlated to the weight in kilograms since all points fall on a diagonal line. Since both features hold the same information it makes perfect sense to drop one of them. If there would have been a numeric feature without any variance in the dataset such as the constant that ha