Learning Predictive Analytics with Python: Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python - Tapa blanda

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Key Features

• A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices
• Get to grips with the basics of Predictive Analytics with Python
• Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering

Book Description

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

About the Author

Ashish Kumar has a B.Tech from IIT Madras and is a Young India Fellow from the batch of 2012-13. He is a data science enthusiast with extensive work experience in the field. As a part of his work experience, he has worked with tools, such as Python, R, and SAS. He has also implemented predictive algorithms to glean actionable insights for clients from transport and logistics, online payment, and healthcare industries. Apart from the data sciences, he is enthused by and adept at financial modelling and operational research. He is a prolific writer and has authored several online articles and short stories apart from running his own analytics blog. He also works pro-bono for a couple of social enterprises and freelances his data science skills.

He can be contacted on LinkedIn at https://goo.gl/yqrfo4, and on Twitter at https://twitter.com/asis64.

Table of Contents

1. Getting Started with Predictive Modelling
2. Data Cleaning
3. Data Wrangling
4. Statistical Concepts for Predictive Modelling
5. Linear Regression with Python
6. Logistic Regression with Python
7. Clustering with Python
8. Trees and Random Forests with Python
9. Best Practices for Predictive Modelling
10. A List of Links

Biografía del autor

Ashish Kumar has a B.Tech from IIT Madras and is a Young India Fellow from the batch of 2012-13. He is a data science enthusiast with extensive work experience in the field. As a part of his work experience, he has worked with tools, such as Python, R, and SAS. He has also implemented predictive algorithms to glean actionable insights for clients from transport and logistics, online payment, and healthcare industries. Apart from the data sciences, he is enthused by and adept at financial modelling and operational research. He is a prolific writer and has authored several online articles and short stories apart from running his own analytics blog. He also works pro-bono for a couple of social enterprises and freelances his data science skills. He can be contacted on LinkedIn at https://goo.gl/yqrfo4, and on Twitter at https://twitter.com/asis64.