Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition

English | 2021 | ISBN: 978-1800564480 | 420 Pages | PDF, EPUB, MOBI | 79 MB

Gain hands-on experience in Python programming with industry-standard machine learning tools using pandas, scikit-learn, and XGBoost

Key Features

• Think critically about data by exploring and cleaning it
• Choose an appropriate machine learning model and train it on your data
• Communicate data-driven insights with confidence and clarity

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.

In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.

You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.

Now in its second edition, this book will take you through the process of exploring data and delivering machine learning models. Updated to the latest version of Python, this new edition for 2021 includes brand new content on XGBoost, SHAP values, and how to evaluate and monitor machine learning models.

By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.

What You Will Learn

• Load, explore, and process data using the pandas Python package
• Use Matplotlib to create compelling data visualizations
• Implement predictive machine learning models with scikit-learn
• Use lasso and ridge regression to reduce model overfitting
• Evaluate random forest and logistic regression model performance
• Create state-of-the-art models with XGBoost
• Learn to use SHAP values to explain model predictions
• Deliver business insights by presenting clear, convincing conclusions