My ranking of the top 26 algorithms for practical data science, breaking down their strengths, quirks, and when (or if) you should use them. Algorithm List: Latent Dirichlet Allocation, GANs, Boruta, Hierarchical Clustering, RNN/LSTMs, CART, PPO, Prophet, SVM, Isolation Forest, Autoencoders, T-SNE, ARIMA, Naive Bayes, CNNs, kNN, UMAP, HDBSCAN, PCA, Neural Networks, Linear Regression, KMeans, Random Forest, Logistic Regression, Transformers, Gradient Boosting.

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