Table of Contents
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Preparing the data
Data Cleaning
Data Quality issues,
- Missing values
- Duplicate data
- Inconsistent data
- Noise
- Outliers
Addressing Data Quality Issues
Some techniques,
- Remove data with missing values
- Merge duplicate records
- Generate best estimate for invalid values
Cleaning Data
Getting Data in Shape
Data Wrangling
Feature Selection,
- Combining Features
- Adding / Removing features
Feature Transformation,
- Scaling
- Dimensionality Reduction
Remember
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