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I am a Python beginner, just getting into machine learning and need advice on the approach i should use for my problem.
Here is an example of my data-set.
Where the RESULT is a corresponding INDEX in each VALUES array and every row is a separate array, i need to find the probability of each index of an array being the RESULT based on its configuration, where the overall configuration and distribution is most important rather than any individual value.
You are looking at a Classification problem.
Logistic regression, Decision trees, SVM. Any of the above can solve the job for you. But selecting the best model depends upon how good it is able to predict the test set. Use cross-validations and see which model can give you better accuracy. So split you test and train set accordingly. You don't expect the model to predict, if you haven't trained it. See stratified sampling for starters.
Though your predictors might be categorical or ordinals, they can be treated as numericals.
You have built in functions like predict_proba to find class probability. The references for this can be found in above links. But go through how they work and what is the policy for selecting best plane in any classification, so that you don't feel like you are using a black-box. Since you are saying that you are a beginner, pandas will be a very useful module for reading data into dataframes and mending it as you like.
Hope this clears something.
