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  200919s2020 xx o ||| 0 eng d

• 019

  ▼a  1192971225

• 020

  ▼a  9781800569409

• 020

  ▼a  1800569408

• 035

  ▼a  2589264
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1193116825
  ▼z  (OCoLC)1192971225

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼c  EBLCP
  ▼d  EBLCP
  ▼d  NLW
  ▼d  UKAHL
  ▼d  YDX
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  Q325.5
  ▼b  .S62 2020b

• 066

  ▼c  (S

• 082

  ▼a  006.31
  ▼2  23

• 100

  ▼a  So, Anthony.

• 245

  ▼a  The the Data Science Workshop
  ▼h  [electronic resource]:
  ▼b  Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition.

• 250

  ▼a  2nd ed.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2020.

• 300

  ▼a  1 online resource (823 p.).

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Correlation Matrix and Visualization

• 505

  ▼a  Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Data Science in Python -- Introduction -- Application of Data Science -- What Is Machine Learning? -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Overview of Python -- Types of Variable -- Numeric Variables -- Text Variables -- Python List -- Python Dictionary -- Exercise 1.01: Creating a Dictionary That Will Contain Machine Learning Algorithms -- Python for Data Science -- The pandas Package -- DataFrame and Series -- CSV Files -- Excel Spreadsheets -- JSON

• 505

  ▼a  Exercise 2.01: Loading and Preparing the Data for Analysis -- The Correlation Coefficient -- Exercise 2.02: Graphical Investigation of Linear Relationships Using Python -- Exercise 2.03: Examining a Possible Log-Linear Relationship Using Python -- The Statsmodels formula API -- Exercise 2.04: Fitting a Simple Linear Regression Model Using the Statsmodels formula API -- Analyzing the Model Summary -- The Model Formula Language -- Intercept Handling -- Activity 2.01: Fitting a Log-Linear Model Using the Statsmodels Formula API -- Multiple Regression Analysis

• 505

  ▼a  Exercise 2.05: Fitting a Multiple Linear Regression Model Using the Statsmodels Formula API -- Assumptions of Regression Analysis -- Activity 2.02: Fitting a Multiple Log-Linear Regression Model -- Explaining the Results of Regression Analysis -- Regression Analysis Checks and Balances -- The F-test -- The t-test -- Summary -- Chapter 3: Binary Classification -- Introduction -- Understanding the Business Context -- Business Discovery -- Exercise 3.01: Loading and Exploring the Data from the Dataset -- Testing Business Hypotheses Using Exploratory Data Analysis

• 505

  ▼a  Visualization for Exploratory Data Analysis -- Exercise 3.02: Business Hypothesis Testing for Age versus Propensity for a Term Loan -- Intuitions from the Exploratory Analysis -- Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits -- Feature Engineering -- Business-Driven Feature Engineering -- Exercise 3.03: Feature Engineering -- Exploration of Individual Features -- Exercise 3.04: Feature Engineering -- Creating New Features from Existing Ones -- Data-Driven Feature Engineering -- A Quick Peek at Data Types and a Descriptive Summary

• 520

  ▼a  The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You'll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world.

• 590

  ▼a  Master record variable field(s) change: 050, 082, 650 - OCLC control number change

• 650

  ▼a  Programming & scripting languages: general.
  ▼2  bicssc

• 650

  ▼a  Data capture & analysis.
  ▼2  bicssc

• 650

  ▼a  Information visualization.
  ▼2  bicssc

• 650

  ▼a  Computers
  ▼x  Data Processing.
  ▼2  bisacsh

• 650

  ▼a  Computers
  ▼x  Programming Languages
  ▼x  Python.
  ▼2  bisacsh

• 650

  ▼a  Machine learning.

• 650

  ▼a  Electronic data processing.

• 650

  ▼a  Statistics
  ▼x  Data processing.

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Application software
  ▼x  Development.

• 655

  ▼a  Electronic books.

• 700

  ▼a  Joseph, Thomas V.

• 700

  ▼a  John, Robert Thas.

• 700

  ▼a  Worsley, Andrew.

• 700

  ▼a  Asare, Samuel.

• 776

  ▼i  Print version:
  ▼a  So, Anthony
  ▼t  The the Data Science Workshop : Learn How You Can Build Machine Learning Models and Create Your Own Real-World Data Science Projects, 2nd Edition.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2589264

• 880

  ▼6  505-00
  ▼a  Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame -- Scikit-Learn -- What Is a Model-- Model Hyperparameters -- The sklearn API -- Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn -- Activity 1.01: Train a Spam Detector Algorithm -- Summary -- Chapter 2: Regression -- Introduction -- Simple Linear Regression -- The Method of Least Squares -- Multiple Linear Regression -- Estimating the Regression Coefficients (β0, β1, β2 and β3) -- Logarithmic Transformations of Variables -- Correlation Matrices -- Conducting Regression Analysis Using Python

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH37727423

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6326389

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  301489357

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2589264

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T