Low-Code AI by Gwendolyn Stripling

Author:Gwendolyn Stripling
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2023-09-13T00:00:00+00:00

Exploring the Dataset Using Pandas, Matplotlib, and Seaborn

Before you begin learning about scikit-learn and Keras, you should follow the workflow discussed in earlier chapters around understanding and preparing data for ML. Though you have used Google Colab briefly in earlier chapters to load the data from BigQuery into a DataFrame and do some basic visualization, you have not gone through the data preparation and model training process completely in the Jupyter Notebook environment.

This section revisits how to load data into a Google Colab notebook using Pandas. Once the data is loaded into a DataFrame, you will explore, clean, and transform the data before creating the datasets that you will use to train your ML model. As you have seen in previous chapters, much of the work goes not into training the model but into understanding and preparing the training data.

All of the code in this section, including some additional examples, is included in a Jupyter notebook in the low-code-ai repo on GitHub.

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