Szczegóły ebooka

Data Science Projects with Python. A case study approach to successful data science projects using Python, pandas, and scikit-learn

Data Science Projects with Python. A case study approach to successful data science projects using Python, pandas, and scikit-learn

Stephen Klosterman

Ebook
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.

By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.
  • 1. Data Exploration and Cleaning
  • 2. Introduction to Scikit-Learn and Model Evaluation
  • 3. Details of Logistic Regression and Feature Exploration
  • 4. The Bias-Variance Trade-off
  • 5. Decision Trees and Random Forests
  • 6. Imputation of Missing Data, Financial Analysis, and Delivery to Client
  • Tytuł: Data Science Projects with Python. A case study approach to successful data science projects using Python, pandas, and scikit-learn
  • Autor: Stephen Klosterman
  • Tytuł oryginału: Data Science Projects with Python. A case study approach to successful data science projects using Python, pandas, and scikit-learn
  • ISBN: 9781838552602, 9781838552602
  • Data wydania: 2019-04-30
  • Format: Ebook
  • Identyfikator pozycji: e_2azy
  • Wydawca: Packt Publishing