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Which Data Science / Machine Learning methods and tools you used in the past 12 months for a real-world application? (choose up 20)
Anomaly/Deviation detection
Association rules
Bagging
Bayesian networks/methods
Boosting
Clustering
Convolutional Neural Nets
Decision Trees/Rules
Deep Learning Networks
EM
Ensemble methods
Factor Analysis
Generative Adversarial Networks (GAN)
Genetic algorithms/Evolutionary methods
Gradient Boosted Machines
Graph / Link / Social Network Analysis
Hidden Markov Models (HMM)
K-nearest neighbors
Markov Logic Networks
Neural networks (not DL)
Optimization
PCA
Random Forests
Recurrent Neural Networks (RNN)
Regression
Reinforcement Learning
Support Vector Machine (SVM)
Singular Value Decomposition (SVD)
Statistics - Descriptive
Survival Analysis
Text Mining
Time series/Sequence analysis
Visualization
Uplift modeling
Other methods


Your affiliation:
Industry/Self-employed
Researcher/Academia
Student
Government/non-profit
other


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