Preface

More than six years ago, before I first discovered Kaggle, I was searching for a new path in my professional career. A few years later, I was firmly entrenched in a new job, which Kaggle helped me find. Before discovering this marvelous site, I was looking around on different sites, reading articles, downloading and analyzing datasets, trying out pieces of code from GitHub or other sites, doing online trainings, and reading books. With Kaggle, I found more than a source of information; I found a community sharing the same interest in machine learning, and, more generally, in data science, looking to learn, share knowledge, and solve difficult challenges. I also discovered that in this community, if you want, you can experience an accelerated learning curve, because you can learn from the best, sometimes competing against them, and other times collaborating with them. You can also learn from the less experienced; after all these years on the platform, I am still learning from both crowds.

This mix of continuous challenges and fruitful collaboration makes Kaggle a unique platform, where new and old contributors can feel equally welcome and find things to learn or share. In my first months on the platform, I mostly learned from the vast collections of datasets and notebooks, analyzing competition data and offering solutions for active or past competitions and on the discussion threads. I soon started to contribute, mostly to notebooks, and discovered how rewarding it is to share your own findings and get feedback from other people on the platform. This book is about sharing this joy and what I learned while sharing my findings, ideas, and solutions with the community.

This book is intended to introduce you to the wide world of data analysis, with a focus on how you can use Kaggle Notebooks resources to help you achieve mastery in this field. We will cover simple concepts to more advanced ones. The book is also a personal journey and will take you down a similar path to the one I took while experimenting and learning about analyzing datasets and preparing for competitions.

Who this book is for

The book is intended for a wide audience with a keen interest in data science and machine learning and who would like to use Kaggle Notebooks to improve their skills as well as raise their Kaggle Notebooks ranks. To be precise, this book caters to:

• Absolute beginners on their Kaggle journey
• Experienced contributors who would like to develop various data ingestion, preparation, exploration, and visualization skills
• Experts who want to learn from one of the early Kaggle Notebooks Grandmasters how to rise in the upper Kaggle rankings
• Professionals who already use Kaggle for learning and competing and would like to learn more about data analytics

What this book covers

Chapter 1, Introducing Kaggle and Its Basic Functions, is a quick introduction to Kaggle and its main features, including competitions, datasets, code (formerly known as kernels or notebooks), discussions and additional resources, models, and learning.

Chapter 2, Getting Ready for Your Kaggle Environment, contains more details about the code features on Kaggle, with information about computing environments, how to use the online editor, how to fork and modify an existing example, and how to use the source control facilities on Kaggle to either save or run a new notebook.

Chapter 3, Starting Our Travel – Surviving the Titanic Disaster, introduces a simple dataset that will help you to build a foundation for the skills that we will further develop in the book. Most Kagglers will start their journey on the platform with this competition. We introduce some tools for data analysis in Python (pandas and NumPy), data visualization (Matplotlib, Seaborn, and Plotly), and suggestions on how to create the visual identity of your notebook. We will perform univariate and bivariate analysis of the features, analyze missing data, and generate new features with various techniques. You will also receive your first look into deep diving into data and using analysis combined with model baselining and iterative improvement to go from exploration to preparation when building a model. 

Chapter 4, Take a Break and Have a Beer or Coffee in London, combines multiple tabular and map datasets to explore geographical data. We start with two datasets: the first dataset contains the spatial distribution of pubs in the United Kingdom (Every Pub in England), and the second contains the distribution of Starbucks coffee shops across the world (Starbucks Locations Worldwide).

We start by analyzing them separately, investigating missing data and understanding how we can fill in missing data by using alternative data sources. Then we analyze the datasets together and focus on one small region, i.e., London, where we superpose the data. We will also discuss aligning data with different spatial resolutions. More insights into style, presentation organization, and storytelling will be provided.

Chapter 5, Get Back to Work and Optimize Microloans for Developing Countries, goes one step further and starts analyzing data from a Kaggle analytics competition, Data Science for Good: Kiva Crowdfunding. Here, we combine multiple loan history, demographics, country development, and map datasets to create a story about how to improve the allocation of microloans in developing countries. One of the focuses of this chapter will be on creating a unified and personal presentation style, including a color scheme, section decorations, and graphics style. Another focus will be on creating a coherent story about and based on the data that supports the thesis of the notebook. We end the chapter with a quick investigation into an alternative data analytics competition dataset, Meta Kaggle, where we disprove a hypothesis about a perceived trend in the community.

Chapter 6, Can You Predict Bee Subspecies?, teaches you how to explore a dataset of images. The dataset used for this analysis is The BeeImage Dataset: Annotated Honeybee Images. We combine techniques for image analysis with techniques for the analysis and visualization of tabular data to create a rich and insightful analysis and prepare for building a machine learning pipeline for multiclass image classification. You will learn how to input and display sets of images, how to analyze the images, metadata, how to perform image augmentation, and how to work with different resizing options. We will also show how to start with a baseline model and then, based on the training and validation error analysis, iteratively refine the model.

Chapter 7, Text Analysis Is All You Need, uses Jigsaw Unintended Bias in Toxicity Classification, a dataset from a text classification competition. The data is from online postings and, before we use it to build a model, we will need to perform data quality assessment and data cleaning for text data. We will then explore the data, analyze the frequency of words and vocabulary peculiarities, get a few insights into syntactic and semantic analysis, perform sentiment analysis and topic modeling, and start the preparation for training a model. We will check the coverage of the vocabulary available with our tokenization or embedding solution for the corpus in our dataset and apply data processing to improve this vocabulary coverage.

Chapter 8, Analyzing Acoustic Signals to Predict the Next Simulated Earthquake, will look at how to work with time series, while analyzing the dataset for the LANL Earthquake EDA and Prediction competition.

After performing an analysis of the features, using various types of modality analysis to reveal the hidden patterns in the signals, we will learn how to generate features using the fast Fourier transform, Hilbert transform, and other transformations for this time-series model. Then we will learn how to generate several features using the various signal processing functions. Readers will learn the basics about analyzing signal data, as well as how to generate features using various signal processing transformations to build a model.

Chapter 9, Can You Find Out Which Movie Is a Deepfake?, discusses how to perform image and video analysis on Deepfake Detection Challenge, a large video dataset from a famous Kaggle competition. Analysis will start with training and data exploration, and readers will learn how to manipulate the .mp4 format, extract images from video, check video metadata information, perform pre-processing of extracted images, and find objects, including body, upper body, face, eyes, or mouth, in the images using either computer vision techniques or pre-trained models. Finally, we will prepare to build a model to come up with a solution for this deep fake detection competition.

Chapter 10, Unleash the Power of Generative AI with Kaggle Models, will provide unique and expert insights into how we can use Kaggle models to combine the semantic power of Large Language Models (LLMs) with LangChain and vector databases to unleash the power of Generative AI and prototype the latest breed of AI applications using the Kaggle platform.

Chapter 11, Closing Our Journey: How to Stay Relevant and on Top, provides insights on how to not only become one of the top Kaggle Notebooks contributors but also maintain that position, while creating quality notebooks, with a good structure and a great impact.

To get the most out of this book

You should have a basic understanding of Python and familiarity with Jupyter Notebooks. Ideally, you will also need some basic knowledge of libraries like pandas and NumPy.

The chapters contain both theory and code. If you want to run the code in the book, the easiest way is to follow the links on the README.md introduction page in the GitHub project for each notebook, fork the notebook, and run it on Kaggle. The Kaggle environment is pre-installed with all the needed Python libraries. Alternatively, you can download the notebooks from the GitHub project, upload them on Kaggle, attach the dataset resources mentioned in the book for each specific example, and run them. Another alternative is to download the datasets on Kaggle, install your own local environment, and run the notebooks there. In this case, however, you will need more advanced knowledge about how to set up a conda environment locally and install Python libraries using pip install or conda install.

Requirements for the chapter exercises	Version no.
Python	3.9 or higher

All exercises developed on the Kaggle platform use the current Python version, which is 3.10 at the time of writing this book.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Developing-Kaggle-Notebooks. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781805128519.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Run the info() function for each dataset.”

A block of code is set as follows:

for sentence in selected_text["comment_text"].head(5):
    print("\n")
    doc = nlp(sentence)
    for ent in doc.ents:
        print(ent.text, ent.start_char, ent.end_char, ent.label_)
    displacy.render(doc, style="ent",jupyter=True)

Any command-line input or output is written as follows:

!pip install kaggle

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “You will have to start a notebook and then choose the Set as Utility Script menu item from the File menu.”

Warnings or important notes appear like this.

Tips and tricks appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [e-mail] and mention the book’s title in the subject of your message. If you have questions about any aspect of this book, please email us at [e-mail].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you reported this to us. Please visit http://www.packtpub.com/submit-errata, click Submit Errata, and fill in the form.

Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [e-mail] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit http://authors.packtpub.com.

Download a free PDF copy of this book

Thanks for purchasing this book!

Do you like to read on the go but are unable to carry your print books everywhere?

Is your eBook purchase not compatible with the device of your choice?

Don’t worry, now with every Packt book you get a DRM-free PDF version of that book at no cost.

Read anywhere, any place, on any device. Search, copy, and paste code from your favorite technical books directly into your application.

The perks don’t stop there, you can get exclusive access to discounts, newsletters, and great free content in your inbox daily

Follow these simple steps to get the benefits:

1. Scan the QR code or visit the link below

https://packt.link/free-ebook/9781805128519

2. Submit your proof of purchase
3. That’s it! We’ll send your free PDF and other benefits to your email directly

Share your thoughts

Once you’ve read Developing Kaggle Notebooks, we’d love to hear your thoughts! Please click here to go straight to the Amazon review page for this book and share your feedback.

Your review is important to us and the tech community and will help us make sure we’re delivering excellent quality content.