In this chapter, we learned about predictive analytics and some concepts related to supervised machine learning, and gained essential knowledge about how to perform machine learning with Python. We also took a look at various practical applications of predictive analytics using scikit-learn. We took a closer look at how to train a classification model and then we used it to make predictions. We also built a regression model and used it to make predictions.
Become a Python Data Analyst
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Become a Python Data Analyst
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Overview of this book
Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.
Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.
In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.
By the end of this book, you will have hands-on experience performing data analysis with Python.
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Current Title:
Become a Python Data Analyst
Table of Contents (8 chapters)
Preface
Free Chapter
The Anaconda Distribution and Jupyter Notebook
Vectorizing Operations with NumPy
Pandas - Everyone's Favorite Data Analysis Library
Visualization and Exploratory Data Analysis
Statistical Computing with Python
Introduction to Predictive Analytics Models
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