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.