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Python Data Mining Quick Start Guide : a Beginner's Guide to Extracting Valuable Insights from Your Data. [electronic resource]

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자료유형E-Book
개인저자Greeneltch, Nathan.
서명/저자사항Python Data Mining Quick Start Guide :a Beginner's Guide to Extracting Valuable Insights from Your Data.[electronic resource]
발행사항Birmingham : Packt Publishing, Limited, 2019.
형태사항1 online resource (181 pages)
소장본 주기Added to collection customer.56279.3
ISBN1789806402
9781789806403
일반주기 Prediction nomenclature
내용주기Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Data Mining and Getting Started with Python Tools; Descriptive, predictive, and prescriptive analytics; What will and will not be covered in this book; Recommended readings for further explanation; Setting up Python environments for data mining; Installing the Anaconda distribution and Conda package manager; Installing on Linux; Installing on Windows; Installing on macOS; Launching the Spyder IDE; Launching a Jupyter Notebook; Installing high-performance Python distribution
Recommended libraries and how to installRecommended libraries; Summary; Chapter 2: Basic Terminology and Our End-to-End Example; Basic data terminology; Sample spaces; Variable types; Data types; Basic summary statistics; An end-to-end example of data mining in Python; Loading data into memory -- viewing and managing with ease using pandas; Plotting and exploring data -- harnessing the power of Seaborn; Transforming data -- PCA and LDA with scikit-learn; Quantifying separations -- k-means clustering and the silhouette score; Making decisions or predictions; Summary
Chapter 3: Collecting, Exploring, and Visualizing DataTypes of data sources and loading into pandas; Databases; Basic Structured Query Language (SQL) queries; Disks; Web sources; From URLs; From Scikit-learn and Seaborn-included sets; Access, search, and sanity checks with pandas; Basic plotting in Seaborn; Popular types of plots for visualizing data; Scatter plots; Histograms; Jointplots; Violin plots; Pairplots; Summary; Chapter 4: Cleaning and Readying Data for Analysis; The scikit-learn transformer API; Cleaning input data; Missing values; Finding and removing missing values
Imputing to replace the missing valuesFeature scaling; Normalization; Standardization; Handling categorical data; Ordinal encoding; One-hot encoding; Label encoding; High-dimensional data; Dimension reduction; Feature selection; Feature filtering; The variance threshold; The correlation coefficient; Wrapper methods; Sequential feature selection; Transformation; PCA; LDA; Summary; Chapter 5: Grouping and Clustering Data; Introducing clustering concepts; Location of the group; Euclidean space (centroids); Non-Euclidean space (medioids); Similarity; Euclidean space; The Euclidean distance
The Manhattan distanceMaximum distance; Non-Euclidean space; The cosine distance; The Jaccard distance; Termination condition; With known number of groupings; Without known number of groupings; Quality score and silhouette score; Clustering methods; Means separation; K-means; Finding k; K-means++; Mini batch K-means; Hierarchical clustering; Reuse the dendrogram to find number of clusters; Plot dendrogram; Density clustering; Spectral clustering; Summary; Chapter 6: Prediction with Regression and Classification; Scikit-learn Estimator API; Introducing prediction concepts
요약This book is an introduction to data mining and its practical demonstration of working with real-world data sets. With this book, you will be able to extract useful insights using common Python libraries. You will also learn key stages like data loading, cleaning, analysis, visualization to build an efficient data mining pipeline.
일반주제명Data mining.
Python (Computer program language)
Data mining.
Python (Computer program language)
언어영어
기타형태 저록Print version:Greeneltch, Nathan.Python Data Mining Quick Start Guide : A Beginner's Guide to Extracting Valuable Insights from Your Data.Birmingham : Packt Publishing, Limited, 짤20199781789800265
대출바로가기http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2111782

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