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| 001 | | 000000317985 |
| 003 | | OCoLC |
| 005 | | 20230525183841 |
| 006 | | m d |
| 007 | | cr -n--------- |
| 008 | | 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 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a Q325.5
▼b .S62 2020b |
| 066 | |
▼c (S |
| 082 | 04 |
▼a 006.31
▼2 23 |
| 100 | 1 |
▼a So, Anthony. |
| 245 | 14 |
▼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 | 0 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 7 |
▼a Programming & scripting languages: general.
▼2 bicssc |
| 650 | 7 |
▼a Data capture & analysis.
▼2 bicssc |
| 650 | 7 |
▼a Information visualization.
▼2 bicssc |
| 650 | 7 |
▼a Computers
▼x Data Processing.
▼2 bisacsh |
| 650 | 7 |
▼a Computers
▼x Programming Languages
▼x Python.
▼2 bisacsh |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Electronic data processing. |
| 650 | 0 |
▼a Statistics
▼x Data processing. |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 0 |
▼a Application software
▼x Development. |
| 655 | 0 |
▼a Electronic books. |
| 700 | 1 |
▼a Joseph, Thomas V. |
| 700 | 1 |
▼a John, Robert Thas. |
| 700 | 1 |
▼a Worsley, Andrew. |
| 700 | 1 |
▼a Asare, Samuel. |
| 776 | 08 |
▼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 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2589264 |
| 880 | 8 |
▼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 (棺0, 棺1, 棺2 and 棺3) -- 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 관리자 |
| 994 | |
▼a 92
▼b N$T |