Python Data Mining Quick Start Guide: A beginner's guide to extracting valuable insights from your data
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Teadmiskirjandus
Explore the different data mining techniques using the libraries and packages offered by Python
Key Features
• Grasp the basics of data loading, cleaning, analysis, and visualization
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• Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining
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• Your one-stop guide to build efficient data mining pipelines without going into too much theory
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Book Description
Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining.
This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques.
By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
What you will learn
• Explore the methods for summarizing datasets and visualizing/plotting data
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• Collect and format data for analytical work
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• Assign data points into groups and visualize clustering patterns
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• Learn how to predict continuous and categorical outputs for data
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• Clean, filter noise from, and reduce the dimensions of data
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• Serialize a data processing model using scikit-learn's pipeline feature
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• Deploy the data processing model using Python's pickle module
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Who this book is for
Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
© 2019 Packt Publishing (E-raamat): 9781789806403
Väljaandmise kuupäev
E-raamat: 25. aprill 2019
- Sõja lapsed Martin Walker
- Allüürnik Freida McFadden
- Kui muutuda, siis jäädavalt Rangan Chatterjee
- Petlik tõde Colleen Hoover
- Tööpäev Tõnu Õnnepalu
- Threshing Day (Wing and Claw Collection) Rebecca Yarros
- Karulõks Berit Sootak
- Kaneelisaiakese raamatupood Laurie Gilmore
- Mõrvad Rue Morgue'il Edgar Allan Poe
- Koole maha, kaunitar Helena Marchmont
- Misterioso Arne Dahl
- Mõrv Langley metsas Betty Rowlands
- Märgistatud Max Seeck
- Kui meri rahuneb Jørn Lier Horst
- Variatsioon Rebecca Yarros
- Allüürnik Freida McFadden
- Prouad tõsielusarjas ja armastuse otsinguil Sirje Salu
- Petlik tõde Colleen Hoover
- Cragside’i häärber L. J. Ross
- Märgistatud Max Seeck
- Ära kasutatud, vägistatud ja müüdud Jessika Devert, Paulina Bengtsson
- Lenda minuga Katariina Tammert
- Külm nagu kivi Alex Smith
- Koole maha, kaunitar Helena Marchmont
- Safranisügis Maija Kajanto
- Matilda Leelo Kassikäpp
- Karulõks Berit Sootak
- Kaneelisaiakese raamatupood Laurie Gilmore
- Ja ei jäänud teda ka Agatha Christie
- Mees. Otse ja ausalt Jesper Parve
- Petlik tõde Colleen Hoover
- Märgistatud Max Seeck
- Ära kasutatud, vägistatud ja müüdud Jessika Devert, Paulina Bengtsson
- Cragside’i häärber L. J. Ross
- Safranisügis Maija Kajanto
- Rehepapp Andrus Kivirähk
- Kaneelisaiakese raamatupood Laurie Gilmore
- Mälestused temast Colleen Hoover
- Võib-olla ühel päeval Ketlin Priilinn
- Seitse õde. Maia lugu Lucinda Riley
- Armastuse hüpotees Ali Hazelwood
- Kõrvitsavürtsi kohvik Laurie Gilmore
- Kaneelirullisuvi Maija Kajanto
- Jääprintsess Camilla Läckberg
- Keelutsoon M. W. Craven
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