Learning predictive analytics with Python gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python
Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Get to grips with the basics of Predictive Analytics with Py...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2016.
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| Edición: | First edition |
| Colección: | Community experience distilled.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630102206719 |
Tabla de Contenidos:
- Cover ; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Predictive Modelling ; Introducing predictive modelling; Scope of predictive modelling; Ensemble of statistical algorithms; Statistical tools; Historical data; Mathematical function; Business context; Knowledge matrix for predictive modelling; Task matrix for predictive modelling; Applications and examples of predictive modelling; LinkedIn's ""People also viewed"" feature; What it does?; How is it done?
- Correct targeting of online adsHow is it done?; Santa Cruz predictive policing; How is it done?; Determining the activity of a smartphone user using accelerometer data; How is it done?; Sport and fantasy leagues; How was it done?; Python and its packages - download and installation; Anaconda; Standalone Python; Installing a Python package; Installing pip; Installing Python packages with pip; Python and its packages for predictive modelling; IDEs for Python; Summary; Chapter 2: Data Cleaning ; Reading the data - variations and examples; Data frames; Delimiters
- Various methods of importing data in PythonCase 1 - reading a dataset using the read_csv method; The read_csv method; Use cases of the read_csv method; Case 2 - reading a dataset using the open method of Python; Reading a dataset line by line; Changing the delimiter of a dataset; Case 3 - reading data from a URL; Case 4 - miscellaneous cases; Reading from an .xls or .xlsx file; Writing to a CSV or Excel file; Basics - summary, dimensions, structure; Handling missing values; Checking for missing values; What constitutes missing data?; How missing values are generated and propagated
- Treating missing valuesDeletion; Imputation; Creating dummy variables; Visualizing a dataset by basic plotting; Scatter plots; Histograms; Boxplots; Summary; Chapter 3: Data Wrangling ; Subsetting a dataset; Selecting columns; Selecting rows; Selecting a combination of rows and columns; Creating new columns; Generating random numbers and their usage; Various methods for generating random numbers; Seeding a random number; Generating random numbers following probability distributions; Probability density function; Cumulative density function; Uniform distribution; Normal distribution
- Using the Monte-Carlo simulation to find the value of piGeometry and mathematics behind the calculation of pi; Generating a dummy data frame; Grouping the data - aggregation, filtering, and transformation; Aggregation; Filtering; Transformation; Miscellaneous operations; Random sampling - splitting a dataset in training and testing datasets; Method 1 - using the Customer Churn Model; Method 2 - using sklearn; Method 3 - using the shuffle function; Concatenating and appending data; Merging/joining datasets; Inner Join; Left Join; Right Join; An example of the Inner Join
- An example of the Left Join
