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  3. Feature Engineering for Machine Learning in Python
  • 1

    Creating Features

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    In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

  • 2

    Dealing with Messy Data

    This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

  • 3

    Conforming to Statistical Assumptions

    In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

  • 4

    Dealing with Text Data

    Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Initializing

Exercise

How sparse is my data?

Most datasets contain missing values, often represented as NaN (Not a Number). If you are working with Pandas you can easily check how many missing values exist in each column.

Let's find out how many of the developers taking the survey chose to enter their age (found in the Age column of so_survey_df) and their gender (Gender column of so_survey_df).

Instructions 1/2

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  • Subset the DataFrame to only include the 'Age' and 'Gender' columns.
  • Print the number of non-missing values in both columns.