Correlation/Pattern Recognition in Lists

I am looking for algorithms to find pattern or more precise correlations in lists compared to an Output. Let us assume I have a Database like this:

  1. Input: [A,C,D,E...], Output: Positive
  2. Input: [A,B,C,E,F...], Output: Negative

The Problem is that the distinct Input values are roughly 1000 and not just 6 like in my example (A-F). The output is binary though.

Do you know of any algorithm that detects correlations in the Inputs to finally detect the most critical Inputs that lead to a Positive Output?

Topic pattern-recognition correlation python machine-learning

Category Data Science

1
answered at

It clearly states that you are dealing with simple classification problem. So you don't need to go with CNN you can use Machine learning classification Algorithms like

  1. Logistic Regression.
  2. SVM
  3. KNN (k-Nearest Neighbor)
1
answered at

You could start off with something as simple as logistic regression to model your problem. You could then experiment with Random Forest Classifiers and then graduate to CNNs and RNNs.

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