This is the code repository for the book Developing Kaggle Notebooks: Paving your way to becoming a Kaggle Notebooks Grandmaster, published by Packt.
Developing Kaggle Notebooks is here to introduce you to the wide world of data analysis, with a focus on using Kaggle Notebooks resources to help you achieve mastery in this field as well as rising to the top in the Kaggle Notebooks tier. The book is structured as a seven-step trip into the world of analysis, exploring the features available in Kaggle Notebooks alongside various data analysis techniques and different kinds of datasets.
- Approach a new dataset or competition to perform a data analysis via a Notebook and get noticed
- Start exploring a new source of data, from tools to use for ingestion to treating various issues with ingested data
- Structure your code using reusable components
- Perform a deep dive for both small and large datasets of various types
- Differentiate yourself from the crowd with the content of your analysis
- Improve the style of your Notebook: color scheme, content organization, visual effects, and theme
- Use storytelling techniques to captivate your audience, improve the clarity of the presentation, and raise its impact
This book is suitable for a wide audience with a keen interest in data science and machine learning and those who want to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. Beginners on Kaggle from any background will benefit Seasoned contributors who want to improve various skills like ingestion, preparation, exploration, and visualization Expert contributors who would like to learn from the Grandmasters to rise into the upper Kaggle rankings Professionals who already use Kaggle for learning and competing
The following are links for the notebooks associated with each chapter. The first column in the table gives the chapter. The 2nd column gives the link to the resource (notebook or utility script) in the book repository. The 3rd column gives the link to the resource on Kaggle. By following the link for a notebook on Kaggle, you can directly fork the notebook and start using it directly on the platform.
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Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.