AutoTune [unstable.ver-1]

NOTE:
- This README can be subjected to change at anytime. So don't be suprise if you come back tomorrow and you find that the documentation for this applciation has changed COMPLETELY
- Support Checked for Python 3.6 and 3.7 only

AutoTune is a Machine Learning tool that is useful in designing machine learning models using a few lines of python code, and with it, create, train, and evaluate machine learning pipelines.

Principles

To guide me (and hopefully other contributors ;)) in knowing why I am building the application in the first place.

1. Flexibility
   The tool should allow [advanced] users to make quick changes to their created pipeline.

   So doing things like changing the models used in a pipeline. Adding imputation techniques (few of which are pre-added). Modifying how to deal with imbalanced data

2. Extensibility
   Making it easy to add more features and models that don't yet exist. It should also make it possible to add pytorch and tensorflow created models to be used in the pipeline

3. Seemless Pipelining
   This goes hand-in-hand with Less Coding. You should make it possible to create Maching learning pipelines by making simple code additions/modifications.

   (this may not change, or reflect what is inside. this is just an example)

   import ...
   
   kaggle_pipeline = SupervisedPipeline(task='regression', metrics='accuracy')
   
   at_model = kaggle_pipeline
               .feed_data('somefile.csv')
               .impute(column='age', method='mean')
               .oversample(by='target')
               .train(model=GaussianNB(...))
               .output()
   
   test_data = ...
   at_model.predict(test_data)

4. Less Coding Well, Duhh?!

5. AutoML-ing [well yes but actually no. May be later]
   Although, it is debatable on the usefulness of having this, this option could be added as a bonus or last resort feature in the application. I could try to make this feature make use of the pipeline. May be

6. [Optional] If I do get to use this in the future more that not, find a way to store the analysis data in such a way that an AutoML approach can be obtain by understanding how humans make decisions through experience (and not calculate every possiblity)