A go-to resource for preparing your data for data science. Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation.
Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation. How you prepare one set of data will most likely be different from how you prepare another set of data. Therefore this guide aims to provide an overarching guide that you can refer to when preparing any particular set of data.
Before we get into the guide, I should probably go over what Data Preparation is…
Data preparation is the step after data collection in the machine learning life cycle and it’s the process of cleaning and transforming the raw data you collected. By doing so, you’ll have a much easier time when it comes to analyzing and modeling your data.
There are three main parts to data preparation that I’ll go over in this article:
Exploratory data analysis, or EDA for short, is exactly what it sounds like, exploring your data. In this step, you’re simply getting an understanding of the data that you’re working with. In the real world, datasets are not as clean or intuitive as Kaggle datasets.
The more you explore and understand the data you’re working with, the easier it’ll be when it comes to data preprocessing.
Below is a list of things that you should consider in this step:
Determine what the feature (input) variables are and what the target variable is. Don’t worry about determining what the final input variables are, but make sure you can identify both types of variables.
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The dataset also includes information on time and distance of flights which might also have an effect on delays. These columns can be analyzed with similar methods.
This is because AI and analytics tools are very picky: The data has to be in just the right format, and anything unexpected throws a wrench into the system.