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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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12
Other Books You May Enjoy
13
Index

Summary

In this chapter, we delved into handling signal data, focusing particularly on audio signals. We explored various storage formats for such data and examined libraries for loading, transforming, and visualizing this data type. To develop potent features, we applied a range of signal-processing techniques. Our feature engineering efforts transformed time-series data from each training segment and aggregated features for each test set.

We consolidated all feature engineering processes into a single function, applicable to all training segments and test sets. The transformed features underwent scaling. We then used this prepared data to train a baseline model utilizing the LGBMRegressor algorithm. This model employed cross-validation, and we generated predictions for the test set using the model trained in each fold. Subsequently, we aggregated these predictions to create the submission file. Additionally, we captured and visualized the feature importance for each fold.

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