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| ML techniques | Advantages | Disadvantages | Related articles |
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| Polynomial regression (PR) | (i) Works on any size of the dataset
(ii) Gives information about the relevance of features | (i) The correct polynomial degree should be chosen for a good bias | [24, 25, 27, 30, 31, 33, 34, 40–42, 45, 47] |
| Neural networks (NN) | (i) Efficiency
(ii) Continuous learning
(iii) Data retrieval
(iv) Multitasking | (i) Hardware dependent
(ii) Complex algorithms
(iii) Black-box nature
(iv) Approximate results
(v) Data-dependency | [12, 35, 36, 42, 43, 46, 48, 49] |
| Support vector machine (SVM) | (i) Works very well on nonlinear problems
(ii) Easily adaptable
(iii) Ignores outliers | (i) Requires feature scaling | [38, 46] |
| Gradient boost (GBoost) | (i) Performs very well on medium and small datasets
(ii) Easy to interpret
(iii) Prevents overfitting
(iv) A great approach for enhancing classification and regression solutions | (i) Sensitive to outliers
(ii) Hardly scalable
(iii) Poor results on unstructured data | [42] |
| Random forest (RF) | (i) Accurate
(ii) Powerful
(iii) Works very well on linear/nonlinear problems | (i) The number of trees should be chosen
(ii) No interpretability | [42] |
| K-nearest neighbor (KNN) | (i) Fast
(ii) Easy to implement | (i) Need feature scaling
(ii) Data should be cleaned | [42] |
| Hierarchical clustering (HC) | (i) Easy to implement
(ii) Does not require the number of clusters | (i) Long runtime | [26, 30, 51] |
| K-means | (i) Easy to implement
(ii) Large datasets
(iii) Convergence
(iv) Different shapes and sizes with generalization | (i) Requires the number of clusters
(ii) Requires initial values
(iii) Requires dimensionality reduction tools if the number of dimensions is high
(iv) Does not ignore outliers | [37, 50] |
| Spatial clustering (SC) | (i) Does not require the number of clusters
(ii) Different shaped clusters
(iii) Ignores outliers | (i) Poor results for datasets with various densities
(ii) Poor results for unstructured data
(iii) Not deterministic | [32] |
| Principal component analysis (PCA) | (i) Eliminates correlated features
(ii) Enhances algorithm performance
(iii) Reduces overfitting
(iv) Enhances visualization | (i) Less interpretable
(ii) Data has to be uniformed
(iii) Loss of information | [26, 27, 44, 52] |
| Kernel density estimator (KDE) | (i) Smooth visualization
(ii) Works with various shapes and sizes | (i) Biased at the boundaries
(ii) Information loss by oversmoothing | [28] |
| Self-organizing maps (SOM) | (i) Interpretable
(ii) Applicable for large datasets | (i) Requires initial weights | [29] |
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