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Developing Kaggle Notebooks

You're reading from   Developing Kaggle Notebooks Pave your way to becoming a Kaggle Notebooks Grandmaster

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781805128519
Length 370 pages
Edition 1st Edition
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Author (1):
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Gabriel Preda Gabriel Preda
Author Profile Icon Gabriel Preda
Gabriel Preda
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Table of Contents (14) Chapters Close

Preface 1. Introducing Kaggle and Its Basic Functions FREE CHAPTER 2. Getting Ready for Your Kaggle Environment 3. Starting Our Travel – Surviving the Titanic Disaster 4. Take a Break and Have a Beer or Coffee in London 5. Get Back to Work and Optimize Microloans for Developing Countries 6. Can You Predict Bee Subspecies? 7. Text Analysis Is All You Need 8. Analyzing Acoustic Signals to Predict the Next Simulated Earthquake 9. Can You Find Out Which Movie Is a Deepfake? 10. Unleash the Power of Generative AI with Kaggle Models 11. Closing Our Journey: How to Stay Relevant and on Top 12. Other Books You May Enjoy
13. Index

Exploring our competition data

The LANL Earthquake Prediction dataset consists of the following data:

  • A train.csv file, with two columns only:
    • acoustic_data: This is the amplitude of the acoustic signal.
    • time_to_failure: This is the time to failure corresponding to the current data segment.
  • A test folder with 2,624 files with small segments of acoustic data.
  • A sample_submission.csv file; for each test file, those competing will need to give an estimate for time to failure.

The training data (9.56 GB) contains 692 million rows. The actual time constant for the samples in the training data results from the continuous variation of time_to_failure values. The acoustic data is integer values, from -5,515 to 5,444, with an average of 4.52 and a standard deviation of 10.7 (values oscillating around 0). The time_to_failure values are real numbers, ranging from 0 to 16, with a mean of 5.68 and a standard deviation of 3.67...

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