EDA Guide
Def:
Learning useful information or formulating new questions about a population based upon an associated
Main:
Process:
- Examine Data information
- Handle anomalies and missing values (NA, NAN)
- Drop the error data or replace it
- Identify the data type and process it
- Process numeric features, such as normalizing or scaling
- Process categorical features, such as encoding
- Create few features for advanced graphics or models
- Examine the features correlation relation
EDA Tools:
Visualization:
- Designing Principles
- Choose the right plot for the right data
- identify good design and bad design
- Implementing in Python:
- Difference between EDA vs Visualization:
- EDA is for exploring
- visualization is for Present