Python-for-Data-Science-ML
SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - NaΓ―ve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - NaΓ―ve - Smoothing Technique UNSUPERVISED LEARNING: CLUSTERING: K-Means - Agglomerative - Mean-Shift - Fuzzy C-Mean - DBSCAN - Hierarchical - Canopy DIMENSION REDUCTION: PCA - LSA - SVD - LDA - t-SNE PATTERN SEARCH: Apriori - FP-Growth - Euclat RECOMMENDATION ENGINE: Association Rules - Market Basket Analysis - Apriori Algorithm - Real Rating Matrix - IBCF - (Item) - User-Based Collaborative Filtering UBCF - Method & Model ENSEMBLE METHODS: BOOSTING: AdaBoost - XG Boost - LightGBM - CatBoost. BAGGING: Random Forest STACKING
