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You're reading from  Developing Kaggle Notebooks

Product typeBook
Published inDec 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781805128519
Edition1st Edition
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Author (1)
Gabriel Preda
Gabriel Preda
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Gabriel Preda

Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.
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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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Developing Kaggle Notebooks
Published in: Dec 2023Publisher: PacktISBN-13: 9781805128519

Author (1)

author image
Gabriel Preda

Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.
Read more about Gabriel Preda