Cargando…
Learning predictive analytics with python : gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
[2016]
Ã2016 |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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.