Data mining problem types nach CRISP-DM

Quelle: CRISP-DM 1.0 Step-by-step data mining guide Pete Chapman (NCR), Julian Clinton (SPSS), Randy Kerber (NCR), Thomas Khabaza (SPSS), Thomas Reinartz (DaimlerChrysler), Colin Shearer (SPSS) and Rüdiger Wirth (DaimlerChrysler). © 2000 SPSS Inc. CRISPMWP-1104 CRISP-DM

problem type appropriate technique
2.1 Data description and summarization Iris: Attribute > pandas describe
2.2 Segmentation Clustering techniques Iris kNN 3D
Neural networks
Visualization
2.3 Concept descriptions Rule induction methods
Conceptual clustering
2.4 Classification Discriminant analysis
Rule induction methods CRISP-DM, S.68 unten: If SEX = male and AGE > 51 then CUSTOMER = loyal ...
Decision tree learning iris decision tree
Neural networks
K nearest neighbor https://medium.com/@srishtisawla/k-nearest-neighbors-f77f6ee6b7f5
Case-based reasoning
Genetic algorithms
2.5 Prediction Regression analysis analyticsvidhya ridge lasso > Regressionsgerade
Regression trees
Neural networks
K nearest neighbor
Box-Jenkins methods
Genetic algorithms
2.6 Dependency analysis Correlation analysis https://en.wikipedia.org/wiki/Correlation_and_dependence > Beispiele
Regression analysis
Association rules kdnugget > grocery transactions
Bayesian networks http://users.sussex.ac.uk/~christ/crs/kr-ist/lec09a.html > Reasoning as propagation
Inductive logic programming
Visualization techniques Tableau: Market Basket Analysis / Heatmap

Entscheidungsbaum zur Auswahl von Algorithmen: scikit-learn.org > scikit-learn algorithm cheat sheet