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general ML steps

• Gathering data sampling
• EDA

- understanding data
- outliers           | IQR, anomaly detection
- relations          | statistical tests

• Preprocessing

- handling missing, wrong, null, duplicates
- feature scaling    | standardization vs normalization
- feature selection  |
- feature extraction | dimension reduction      | PCA, SVD
- encoding           | dummy categorical fields
- discretization     | binning continuous fields

• Sampling train-test split
• Model

- Regression         | supervised   | predict continuous features
- Classification     | supervised   | predict categorized features 
- Clustering         | unsupervised | discover groups, density estimation, dimension reduction