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  ▼a  1838552162

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  ▼a  9781838552169
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  ▼z  9781838552862
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  ▼a  9781838552169
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  ▼a  QA76.9.D343

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• 100

  ▼a  Chopra, Rohan.

• 245

  ▼a  Data Science with Python:
  ▼b  Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2019.

• 300

  ▼a  1 online resource (426 pages).

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  ▼a  text
  ▼b  txt
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  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 500

  ▼a  Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model

• 505

  ▼a  Cover; FM; Copyright; Table of Contents; Preface; Chapter 1: Introduction to Data Science and Data Pre-Processing; Introduction; Python Libraries; Roadmap for Building Machine Learning Models; Data Representation; Independent and Target Variables; Exercise 1: Loading a Sample Dataset and Creating the Feature Matrix and Target Matrix; Data Cleaning; Exercise 2: Removing Missing Data; Exercise 3: Imputing Missing Data; Exercise 4: Finding and Removing Outliers in Data; Data Integration; Exercise 5: Integrating Data; Data Transformation; Handling Categorical Data

• 505

  ▼a  Exercise 6: Simple Replacement of Categorical Data with a NumberExercise 7: Converting Categorical Data to Numerical Data Using Label Encoding; Exercise 8: Converting Categorical Data to Numerical Data Using One-Hot Encoding; Data in Different Scales; Exercise 9: Implementing Scaling Using the Standard Scaler Method; Exercise 10: Implementing Scaling Using the MinMax Scaler Method; Data Discretization; Exercise 11: Discretization of Continuous Data; Train and Test Data; Exercise 12: Splitting Data into Train and Test Sets

• 505

  ▼a  Activity 1: Pre-Processing Using the Bank Marketing Subscription DatasetSupervised Learning; Unsupervised Learning; Reinforcement Learning; Performance Metrics; Summary; Chapter 2: Data Visualization; Introduction; Functional Approach; Exercise 13: Functional Approach -- Line Plot; Exercise 14: Functional Approach -- Add a Second Line to the Line Plot; Activity 2: Line Plot; Exercise 15: Creating a Bar Plot; Activity 3: Bar Plot; Exercise 16: Functional Approach -- Histogram; Exercise 17: Functional Approach -- Box-and-Whisker plot; Exercise 18: Scatterplot

• 505

  ▼a  Object-Oriented Approach Using SubplotsExercise 19: Single Line Plot using Subplots; Exercise 20: Multiple Line Plots Using Subplots; Activity 4: Multiple Plot Types Using Subplots; Summary; Chapter 3: Introduction to Machine Learning via Scikit-Learn; Introduction; Introduction to Linear and Logistic Regression; Simple Linear Regression; Exercise 21: Preparing Data for a Linear Regression Model; Exercise 22: Fitting a Simple Linear Regression Model and Determining the Intercept and Coefficient

• 505

  ▼a  Exercise 23: Generating Predictions and Evaluating the Performance of a Simple Linear Regression ModelMultiple Linear Regression; Exercise 24: Fitting a Multiple Linear Regression Model and Determining the Intercept and Coefficients; Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model; Logistic Regression; Exercise 25: Fitting a Logistic Regression Model and Determining the Intercept and Coefficients; Exercise 26: Generating Predictions and Evaluating the Performance of a Logistic Regression Model

• 520

  ▼a  Data Science with Python will help you get comfortable with using the Python environment for data science. You will learn all the libraries that a data scientist uses on a daily basis. By the end of this course, you will be able to take a large raw dataset, clean it, manipulate it, and run machine learning algorithms to obtain results that ...

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  ▼a  Print version record.

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  ▼a  Added to collection customer.56279.3

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  ▼a  Machine learning.

• 650

  ▼a  Data mining.

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Data mining.
  ▼2  fast
  ▼0  (OCoLC)fst00887946

• 650

  ▼a  Machine learning.
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

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  ▼a  Electronic books.

• 700

  ▼a  England, Aaron.

• 700

  ▼a  Alaudeen, Mohamed Noordeen.

• 776

  ▼i  Print version:
  ▼a  Chopra, Rohan.
  ▼t  Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data.
  ▼d  Birmingham : Packt Publishing, Limited, ©2019,
  ▼z  9781838552862

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  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2204654

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  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  BDZ0040275734

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  ▼a  ProQuest Ebook Central
  ▼b  EBLB
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  ▼a  YBP Library Services
  ▼b  YANK
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  ▼a  EBSCOhost
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  ▼a  강리원

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  ▼a  eBook

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