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资源说明：Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python
Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age.
This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy.
You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
What You Will Learn
Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries
Analyze the result parameters arising from the implementation of Predictive Analytics algorithms
Write Python modules/functions from scratch to execute segments or the whole of these algorithms
Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms
Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy
Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries
Understand the best practices while handling datasets in Python and creating predictive models out of them
Table of Contents
Chapter 1. Getting Started with Predictive Modelling
Chapter 2. Data Cleaning
Chapter 3. Data Wrangling
Chapter 4. Statistical Concepts for Predictive Modelling
Chapter 5. Linear Regression with Python
Chapter 6. Logistic Regression with Python
Chapter 7. Clustering with Python
Chapter 8. Trees and Random Forests with Python
Chapter 9. Best Practices for Predictive Modelling
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