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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By : Perrier
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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By: Perrier

Overview of this book

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (10 chapters)
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Examining data statistics 


When Amazon ML created the data source, it carried out a basic statistical analysis of the different variables. For each variable, it estimated the following information:

  • Correlation of each attribute to the target
  • Number of missing values
  • Number of invalid values
  • Distribution of numeric variables with histogram and box plot 
  • Range, mean, and median for numeric variables
  • Most and least frequent categories for categorical variables
  • Word counts for text variables
  • Percentage of true values for binary variables

Go to the Datasource dashboard, and click on the new datasource you just created in order to access the data summary page. The left side menu lets you access data statistics for the target and different attributes, grouped by data types. The following screenshot shows data insights for the Numeric attributes. The age and fare variables are worth looking at more closely:

Two things stand out:

  • age has 20% missing values. We should replace these missing values by the mean...

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