Jun 30, 2020 · “Data Wrangling with Python” The book “Data Wrangling with Python: Tips and Tools to Make Your Life Easier” was written by Jacqueline Kazil and Katharine Jarmul and was published in 2016. The focus of this book are the tools and methods to help you get raw data into a form ready for modeling.

Nov 23, 2023 · Learn how to analyze and visualize financial data, implement trading strategies, and backtest their performance. Understand the nuances of financial modeling, from regression analysis to...

This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges. Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas.

Feature engineering is invaluable for developing and enriching your machine learning models. In this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code. This book covers the following exciting features:

Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.

Dec 19, 2024 · This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices.

Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.

Oct 31, 2022 · With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.

The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you'll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning ...