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Data Preparation

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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