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.

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Spis treści

Learning Predictive Analytics with Python. Click here to enter text eBook -- spis treści

• 1. Getting started with Predictive Modelling
• 2. Data Cleaning
• 3. Data Wrangling
• 4. Statistics Concepts for Predictive Modelling
• 5. Linear Regression with Python
• 6. Logistic Regression with Python
• 7. Clustering with Python
• 8. Decision Tree & Random Forests with Python
• 9. Exploring Best Practices
• 10. Appendix - List of links