Machine Learning For Dummies. John Paul Mueller

FIGURE 4-8: The files that you want to add to the repository appear as part of an upload list.

      Understanding the datasets used in this book

      Apart from the datasets offered by Scikit-learn (https://scikit-learn.org/stable/datasets/), this book uses a number of datasets that you can access at https://github.com/lmassaron/datasets. These datasets demonstrate various ways in which you can interact with data, and you use them in the examples to perform a variety of tasks. The following list provides a quick overview of the function used to import each of the datasets into your Python code:

      The technique for loading each of these datasets can vary according to the source. The following example shows how to load the Air Passengers dataset. You can find the code in the ML4D2E; 04; Dataset Load.ipynb notebook. The downloadable datasets are archived in the Apache Arrow-based Feather File Format (https://arrow.apache.org/docs/python/feather.html). To make this file format accessible in Notebook, you open an Anaconda prompt and type the following command:

       conda install feather-format -c conda-forge

      The command takes a while to complete as it collects the package information and solves the environment (determines what to do to perform the installation). At some point, you'll need to type y and press Enter to complete the installation. To verify that you have a good installation, use this command:

       conda list feather-format

      After a few moments, you see output similar to this:

       # packages in environment at C:\Users\John\anaconda3:## Name Version Build Channelfeather-format 0.4.1 pyh9f0ad1d_0 conda-forge

      Now that you have the required library to use, you can load a dataset from those supplied on the book’s dataset site. To start, download the air_passengers.feather file from https://github.com/lmassaron/datasets and place it in folder you created for this book. (In later chapters, you see how to download the .feather files directly from the book's dataset site, but performing the download now keeps things simple.) Here is an example of the code you use to load Air Passengers dataset as a dataframe.

       import pyarrow.feather as featherread_df = feather.read_feather('air_passengers.feather')print(read_df)

The result is a 144-row dataframe containing the number of passengers per month. Figure 4-9 shows typical output.