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  3. Preprocessing for Machine Learning in Python
  • 1

    Introduction to Data Preprocessing

    Free

    In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data.

  • 2

    Standardizing Data

    This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance.

  • 3

    Feature Engineering

    In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features.

  • 4

    Selecting Features for Modeling

    This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA).

  • 5

    Putting It All Together

    Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings.

Initializing

Exercise

Extracting datetime components

There are several columns in the volunteer dataset comprised of datetimes. Let's take a look at the start_date_date column and extract just the month to use as a feature for modeling.

Instructions

100 XP
  • Convert the start_date_date column into a pandas datetime column and store it in a new column called start_date_converted.
  • Retrieve the month component of start_date_converted and store it in a new column called start_date_month.
  • Print the .head() of just the start_date_converted and start_date_month columns.