New Poll: Which Data Science / Machine Learning methods and tools you used?

Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.
By Gregory Piatetsky, KDnuggets.
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New KDnuggets Poll is asking:


Which
Poll
Which
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


Current Results

Here are the results of a similar 2016 poll:
Top 10 algorithms/methods uses
Top Algorithms and Methods Used by data Scientists

See also a very interesting Kaggle survey The State of data Science & Machine Learning in 2017, which also included a section on algorithms.

Kaggle survey asked: What data science methods are used at work? and the top answers were
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Neural Networks
  • Bayesian Techniques
  • Ensemble Methods
  • SVMs
  • Gradient Boosted Machines
  • CNNs