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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
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13
Index
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Index

A

aggregated functions

features derived, calculating from 248-252

Audio Video Interleave (AVI) 267

B

baseline model

building 64-66, 223-227, 257-262

Basic Linear Algebra Subprograms (BLAS) 246, 247

bee subspecies, classification 174

baseline model, building 177-182

data augmentation 176, 177

data, splitting 175, 176

model, refining iteratively 182-186

bee subspecies, data exploration 154

data, grouping in single dataset by locations 159-161

date and time 162-164

image data, exploring 156-159

images, with bees carrying pollen and not carrying pollen 172

quality checks 154, 155

subspecies 164-169

Black, Indigenous, and People of Color (BIPOC) 13

borrower demographic 125-128

C

CascadeObjectDetector class 271

Central Processing Unit (CPU) 24

classic Short-Term Average/Long-Term Average (STA/LTA) 244-246

code generation

with Kaggle Models 311, 312

comma-delimited format...

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