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general ML steps

• Gathering data sampling
• EDA
  • understanding data
  • outliers (IRQ, anomaly detection)
  • relations statistical tests
• Preprocessing
  • handling missing, wrong, null, duplicates
  • feature scaling standardization vs normalization
  • feature selection
  • feature extraction (PCA, SVD)
  • encoding (dummy categorical fields)
  • discretization (binning continuous fields)
• Sampling train-test split