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Developing Kaggle Notebooks

Developing Kaggle Notebooks

By : Gabriel Preda
5 (29)
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Developing Kaggle Notebooks

Developing Kaggle Notebooks

5 (29)
By: Gabriel Preda

Overview of this book

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques. For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable. Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.
Table of Contents (14 chapters)
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1
Introducing Kaggle and Its Basic Functions
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4
Take a Break and Have a Beer or Coffee in London
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6
Can You Predict Bee Subspecies?
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12
Other Books You May Enjoy
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13
Index
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Introducing the LANL Earthquake Prediction competition

The LANL Earthquake Prediction competition centers on utilizing seismic signals to determine the precise timing of a laboratory-induced earthquake. Currently, predicting natural earthquakes remains beyond the reach of our scientific knowledge and technological capabilities. The ideal scenario for scientists is to predict the timing, location, and magnitude of such an event.

Simulated earthquakes, however, created in highly controlled artificial environments, mimic real-world seismic activities. These simulations enable attempts to forecast lab-generated quakes using the same types of signals observed in natural settings. In this competition, participants use an acoustic data input signal to estimate the time until the next artificial earthquake occurs, as detailed in Reference 3. The challenge is to predict the timing of the earthquake, addressing one of the three critical unknowns in earthquake forecasting: when it will happen...

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