Nearest Neighbors	- Easy to explain	- Good as baseline	- Not good for large and high dimensional datasets	- non-linear time complexity Linear Models	- Good for large and high dimensional sparse datasets	- Usually fast	- Easy to explain	- Some can perform feature selection	- Sensible to scaling	- Sensible to parameter tuning	- Models are limited to hyperplanes Naive Bayes	- Very very fast	- Only for classification	- Good for large and high dimensional datasets	- Often less accurate than Linear Models Decision Trees	- Very fast	- Robust to scaling	- Very very easy to explain Random Forests	- Better than a Decision Tree alone	- Very robust and powerful	- Robust to scalin	- Not very good to high-dimensional sparse data Gradient Boosted Decision Trees	- Often better than Random Forests	- Slower to train tran Random Forests, but faster to predict and smaller in memory	- Often needs parameter tuning Support Vector Machines	- Poweful for medium-size datasets	- Requires scaling	- Very sensitive to parameter tuning Neural Networks	- Can build very complex models	- Sensitive to scaling of the data	- Sensitive to parameter tuning	- Long time to train 

Scaling	- StandardScaler	- RobustScaler	- MinMaxScaler	- Normalizer Dimensionality Reduction, Feature Extraction and Manifold Learning	- Principal Component Analysis (PCA)	- Non-Negative Matrix Factorization	- Manifold Learning with t-SNE Clustering	- *k*-Means Clustering	- Agglomerative Clustering	- DBSCAN Clustering Evaluation	- Adjusted Rand Index (ARI)	- Normalized Mutual Information (NMI)	- Sillhouette Coefficient	- Robustness-based clustering metrics	- Qualitative Method