yash0422 / python-for-data-science-ml

SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - 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

WILL BE HAVING ATLEAST ONE FILE FOR BELOW METHODS & ALGORITHMS

If you didn't find a file for any of the below topics please feel free to email me, so I will upload the same. email: [email protected]

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SUPERVISED LEARNING:

REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - Smoothing Technique

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UNSUPERVISED LEARNING:

CLUSTERING: K-Means - Agglomerative - Mean-Shift - Fuzzy C-Mean - DBSCAN - Hierarchical - Canopy PATTERN SEARCH: Apriori - FP-Growth - Euclat DIMENSION REDUCTION: PCA - LSA - SVD - LDA - t-SNE RECOMMENDATION ENGINE: Association Rules - Market Basket Analysis - Apriori Algorithm - Real Rating Matrix - IBCF - (Item) - User-Based Collaborative Filtering UBCF - Method & Model

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ENSEMBLE METHODS: BOOSTING: AdaBoost - XG Boost - LightGBM - CatBoost. BAGGING: Random Forest STACKING