Data Science Projects with Python: A case study approach to successful data science projects using Python,pandas,and scikit-learn
Authors: Stephen Klosterman
ISBN-10: 1838551026
ISBN-13: 9781838551025
Released: 2019-04-30
Print Length 页数: 374 pages
Book Description
By finelybook
Gain hands-on experience with industry-standard data analysis and machine learning tools in Python
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python,with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs,and extract the insights you seek to derive.
You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms,such as regularized logistic regression and random forest,using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and,unseen data. As you delve into later chapters,you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.
By the end of this book,you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.
What you will learn
Install the required packages to set up a data science coding environment
Load data into a Jupyter Notebook running Python
Use Matplotlib to create data visualizations
Fit a model using scikit-learn
Use lasso and ridge regression to reduce overfitting
Fit and tune a random forest model and compare performance with logistic regression
Create visuals using the output of the Jupyter Notebook
contents
1 Data Exploration and Cleaning
2 Introduction toScikit-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
