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We need to define a label function with the new example dataset before we start training. Here is the full dataset as a pandas dataframe. We can select a column and call it "label" and the next step will be to create a function that converts the labels to a string. Kaggle is the largest community of data scientists, developers, researchers and entrepreneurs working together to solve massive real-world challenges using the latest scientific and analytical techniques. So much more than an online coding competition, the Kaggle community is a powerful network of partners that helps you discover, develop, and implement data-driven solutions. View More... We have seen how we can model the data in order to predict whether a heart attack will occur within the next 10 days. Let’s take a look at how we can extend this analysis in order to create predictions about whether a heart attack will occur within 30 days. And this is exactly the next step in data analysis. It’s called Feature engineering. It’s the process of "extracting" features from the data. Most machine learning algorithms require a set of features that represent the data. So it’s an important step in the process to figure out what the data is telling us about our problem. A feature, which is also referred to as a dimension, is simply a property or attribute of your data. Many times, features are a combination of numerical and categorical data. For example, we could represent our data as follows: A feature engineering algorithm can take all of the information you have collected about your problem and produce a mathematical representation. Let's see what the Kaggle hearts-attack-prediction-model does. To start off, we will start off with some pre-defined features. And since we have only the age of the patient and the number of heart attacks they have experienced, we need to create new features to represent the data. The feature engineering algorithm will not only calculate these features, but it will also show the resulting algorithm and the features it has been created based on. If you look closely at the output, you’ll notice that we have six new features based on the age and number of heart attacks. These new features will now be ready for the algorithm to work with. But the feature engineering algorithm can go a bit further and try to predict if the heart attack will occur within the next 10 or 30 days. Let’s see how
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