Over 60 practical recipes on data exploration and analysis About
This Book * Clean dirty data, extract accurate information, and
explore the relationships between variables * Forecast the output
of an electric plant and the water flow of American rivers using
pandas, NumPy, Statsmodels, and scikit-learn * Find and extract the
most important features from your dataset using the most efficient
Python libraries Who This Book Is For If you are a beginner or
intermediate-level professional who is looking to solve your
day-to-day, analytical problems with Python, this book is for you.
Even with no prior programming and data analytics experience, you
will be able to finish each recipe and learn while doing so. What
You Will Learn * Read, clean, transform, and store your data usng
Pandas and OpenRefine * Understand your data and explore the
relationships between variables using Pandas and D3.js * Explore a
variety of techniques to classify and cluster outbound marketing
campaign calls data of a bank using Pandas, mlpy, NumPy, and
Statsmodels * Reduce the dimensionality of your dataset and extract
the most important features with pandas, NumPy, and mlpy * Predict
the output of a power plant with regression models and forecast
water flow of American rivers with time series methods using
pandas, NumPy, Statsmodels, and scikit-learn * Explore social
interactions and identify fraudulent activities with graph theory
concepts using NetworkX and Gephi * Scrape Internet web pages using
urlib and BeautifulSoup and get to know natural language processing
techniques to classify movies ratings using NLTK * Study simulation
techniques in an example of a gas station with agent-based modeling
In Detail Data analysis is the process of systematically applying
statistical and logical techniques to describe and illustrate,
condense and recap, and evaluate data. Its importance has been most
visible in the sector of information and communication
technologies. It is an employee asset in almost all economy
sectors. This book provides a rich set of independent recipes that
dive into the world of data analytics and modeling using a variety
of approaches, tools, and algorithms. You will learn the basics of
data handling and modeling, and will build your skills gradually
toward more advanced topics such as simulations, raw text
processing, social interactions analysis, and more. First, you will
learn some easy-to-follow practical techniques on how to read,
write, clean, reformat, explore, and understand your data-arguably
the most time-consuming (and the most important) tasks for any data
scientist. In the second section, different independent recipes
delve into intermediate topics such as classification, clustering,
predicting, and more. With the help of these easy-to-follow
recipes, you will also learn techniques that can easily be expanded
to solve other real-life problems such as building recommendation
engines or predictive models. In the third section, you will
explore more advanced topics: from the field of graph theory
through natural language processing, discrete choice modeling to
simulations. You will also get to expand your knowledge on
identifying fraud origin with the help of a graph, scrape Internet
websites, and classify movies based on their reviews. By the end of
this book, you will be able to efficiently use the vast array of
tools that the Python environment has to offer. Style and approach
This hands-on recipe guide is divided into three sections that
tackle and overcome real-world data modeling problems faced by data
analysts/scientist in their everyday work. Each independent recipe
is written in an easy-to-follow and step-by-step fashion.
General
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