Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

Joseph Babcock

ISBN 10: 1788992369 / ISBN 13: 9781788992367

Published by Packt Publishing Limited, 2017

About this Item

2017. Paperback. . . . . .

Seller Inventory # V9781788992367

Bibliographic Details

• Title: Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
• Publisher: Packt Publishing Limited
• Publication Date: 2017
• Language: English
• ISBN 10: 1788992369
• ISBN 13: 9781788992367
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Learn the art of regression analysis with Python

Key Features

• Become competent at implementing regression analysis in Python
• Solve some of the complex data science problems related to predicting outcomes
• Get to grips with various types of regression for effective data analysis

Book Description

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

What You Will Learn

• Format a dataset for regression and evaluate its performance
• Apply multiple linear regression to real-world problems
• Learn to classify training points
• Create an observation matrix, using different techniques of data analysis and cleaning
• Apply several techniques to decrease (and eventually fix) any overfitting problem
• Learn to scale linear models to a big dataset and deal with incremental data

Who This Book Is For

The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science./p>""

About the Authors

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.

Ashish Kumar is seasoned data science professional and a published author with a social bent of mind. An IIT Madras grad and a Young India Fellow, he has 6+ years of experience in implementing and deploying Data Science & ML solutions for challenging industry problems in both hands-on and managerial roles. NLP, IoT Analytics, R Shiny development, Customer & Product Analytics are some of his core expertise and is fluent in Python and R. When not crunching data, he sneaks to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling startups.