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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Introduction


Weather prediction, Sensex prediction, sales prediction, and so on, are some of the common problems that are of interest to most who look up to Statistics or Machine Learning methods. The purpose is, of course, to get the prediction for the next few periods using models with reasonable accuracy. Weather prediction helps in planning for the vocational trip, Sensex prediction helps in investment planning, and sales prediction in an optimum inventory planning. The common structure among each of the three problems is that the observations are available, in general, at equally spaced time/epoch. The observations may have been obtained on a daily, weekly, or monthly basis and we will refer to such data as time series data. The observations are collected over a long time in the past and we believe that we have captured enough characteristics/traits of the series so that analytical models built on such historic data will have predictable power and we can obtain fairly accurate forecasts...

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