Exploratory Data Analysis for Feature Selection in Machine ...

Exploratory Data Analysis for Feature Selection in Machine learning Contents About this guide 3. 1. Introduction 4. 2. statistical data Analysis 4. Descriptive Analysis (univariate Analysis ) 4. Correlation Analysis (bivariate Analysis ) 5. Qualitative Analysis 6. Quantitative Analysis 7. Contextual Analysis 9. Time-based Analysis 9. Agent-based Analysis 10. 3. Visualization for data Analysis 12. 4. Feature Selection and engineering 13. Feature Selection based on descriptive Analysis 13. Feature Selection based on correlation Analysis 16. Feature Selection based on contextual Analysis 17. 5. EDA tools ecosystem 18. Existing tools 18. Feature comparison 19.

Machine learning (ML) projects typically start with a comprehensive exploration of the provided datasets. It is critical that ML practitioners gain a deep understanding of: The properties of the data : schema, statistical properties, and so on The quality of the data : missing values, inconsistent data types, and so on

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  Feature, Statistical, Learning

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