Making Your Dataset Smaller

[MUSIC] 0:00 It may seem like more data is always better, but we want to make sure 0:04 we're only looking at information that's relevant to our problem. 0:08 Let's talk about some reasons you may actually want to make your data set 0:12 smaller by removing some of the features. 0:15 When looking at data, 0:19 we're usually looking to find relationships between our features. 0:20 Often, there's one feature we're particularly interested in, 0:24 sometimes called the target, or dependant variable. 0:27 This is the information we want to predict, or learn more about. 0:30 The other features are treated as independent variables. 0:34 We want to know what influence these features have on our target variable. 0:37 Including irrelevant data can waste computing resources. 0:42 Storing data takes up space on a computer hard drive. 0:45 So keeping data we don't need wastes storage space. 0:49 It also takes longer to run an analysis on a larger data set. 0:52 If some of that data isn't needed, it makes your computer take extra time 0:56 to process data that isn't helping your analysis. 1:00 It can also mislead an analysis, 1:04 causing it to try to find trends in data that don't actually exist. 1:06 Large amounts of data are difficult to visualize in a meaningful way. 1:10 The more features we have, 1:14 the more difficult it is to make a clear visualization of that data. 1:15 With two features, 1:20 we can plot the relationship as a simple scatter plot or line. 1:21 As the number of features increases, 1:25 it becomes more difficult to visualize the data. 1:27 We start to need 3D graphs or complex pairings of 2D graphs. 1:30 These make it more difficult to look at and 1:34 understand the relationships within our data set. 1:36 Getting rid of the unnecessary features helps to focus on 1:39 only the important information. 1:42 And allows us to make a clearer graph. 1:44 To address this, we want to choose the most relevant data to explore or 1:47 analyze further. 1:51 We don't want to get rid of any important data, but 1:52 keeping only useful data can improve our analysis. 1:55 We can make our data sets smaller by removing data we don't want or 1:58 by grouping similar data together into a single feature. 2:02 In the next few videos, 2:06 we'll talk about ways we can make sure we're keeping only the best data. 2:07