Book Image

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials - Third Edition

By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
Table of Contents (11 chapters)

Feature selection

With respect to the machine learning algorithm that you are going to use, irrelevant and redundant features may play a role in the lack of interpretability of the resulting model, long training times and, most importantly, overfitting and poor generalization.

Overfitting is related to the ratio of the number of observations and the variables available in your dataset. When the variables are many compared to the observations, your learning algorithm will have more chance of ending up with some local optimization or the fitting of some spurious noise due to the correlation between variables.

Apart from dimensionality reduction, which requires you to transform data, feature selection can be the solution to the aforementioned problems. It simplifies high-dimensional structures by choosing the most predictive set of variables; that is, it picks the features that work...