Feature Engineering for Machine Learning and Predictive Analytics
Imagine you are placing an online order and see a recommended product that perfectly complements the item you are buying. You place the item into your cart, feel content with your online experience and how the brand was able to predict related items that were "just what you need" based on your web behavior. Or what about the heartbreaking feeling of completing your favorite Netflix series, only to immediately be presented with great new recommendations for shows you might enjoy next, based on the show you just finished and previously viewed genres on the platform? Both examples demonstrate the power of predictive analytics, where businesses can analyze current and historical customer data to make predictions about future outcomes. What might be less obvious is that these examples also demonstrate the power of having clean, carefully chosen data underlying your analytics. Is there is a way to make a predictive model even more powerful? Yes - with feature engineering.
Feature engineering is not actually a new concept, though it has recently resurfaced as a "hot topic" in the world of data analytics, as it is a critical process that supports successful machine learning and predictive analytics. As you read more about feature engineering, you might also recognize it as fundamental data processes known as data manipulation, pre-processing or normalization.
In this blog post, we will dive into the basics and significance of feature engineering and we'll highlight how you can successfully implement some of the most common feature engineering techniques for your organization in Minitab Statistical Software.
What is Feature Engineering?
To get the most out of your data and to define the best fitting predictive model, feature engineering is the crucial first step. Feature engineering is the task of using the knowledge about a process and its resulting data to extract properties, or features, that make predictive models work. A feature typically comes in the form of structured columns, or attributes, and can be engineered by splitting features, combining features or creating new features (recoding). To get the best possible results from your predictive model, clean, quality data is key to proper feature engineering process and model performance accuracy.
Why is Feature Engineering Important?
Feature engineering is an important step when exploring and preparing data.
Benefits of feature engineering:
1. Helps to accurately structure data and ensure the dataset is compatible with the machine learning algorithm.
2. Improves machine learning model performance and accuracy.
3. Provides deeper understanding of your data, resulting in additional insights.