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Comet for Data Science

You're reading from   Comet for Data Science Enhance your ability to manage and optimize the life cycle of your data science project

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801814430
Length 402 pages
Edition 1st Edition
Tools
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Author (1):
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Angelica Lo Duca Angelica Lo Duca
Author Profile Icon Angelica Lo Duca
Angelica Lo Duca
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1 – Getting Started with Comet
2. Chapter 1: An Overview of Comet FREE CHAPTER 3. Chapter 2: Exploratory Data Analysis in Comet 4. Chapter 3: Model Evaluation in Comet 5. Section 2 – A Deep Dive into Comet
6. Chapter 4: Workspaces, Projects, Experiments, and Models 7. Chapter 5: Building a Narrative in Comet 8. Chapter 6: Integrating Comet into DevOps 9. Chapter 7: Extending the GitLab DevOps Platform with Comet 10. Section 3 – Examples and Use Cases
11. Chapter 8: Comet for Machine Learning 12. Chapter 9: Comet for Natural Language Processing 13. Chapter 10: Comet for Deep Learning 14. Chapter 11: Comet for Time Series Analysis 15. Other Books You May Enjoy

Exploring the Prophet package

Prophet is an algorithm for time series analysis, released by Facebook’s Core Data Science team. Other algorithms exist for time series analysis, including Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). You can refer to the books in the Further reading section if you are interested in them.

In this section, you will investigate Prophet, with a focus on the following aspects:

  • Introducing the Prophet package
  • Integrating Prophet with Comet

Let’s start with the first point, introducing the Prophet package.

Introducing the Prophet package

To build a model using Prophet, you can proceed as follows:

  1. Firstly, you import the Prophet library:
    from prophet import Prophet
  2. Then, you build a Prophet() object:
    model = Prophet()
  3. You train the model:
    model.fit(df)
  4. You build a dataset with future dates:
    future = model.make_future_dataframe(periods...
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