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Python scikit-learn Machine Learning Examples

This project is created to learn Machine Learning using Python scikit-learn. This project consists of the following example:

  • Predictive Modeling on pima-indians-diabetes data set.

Data Sets

Frameworks/Libraries

  • scikit-learn

Getting Started

These instructions will get you a brief idea on setting up the environment and running on your local machine for development and testing purposes.

Prerequisities

  • python2.7
  • scikit-learn
  • numpy
  • pandas
  • matplotlib

Setup and running tests

  1. Run python -V to check the installation

  2. Install all the required libraries.

  3. Execute the following commands from terminal to run the tests:

    python main.py

###Modules/Classes Please start exploring from module main.py

All modules in this project are listed below:

  • dataset_loader.py - Contains class DatasetLoader to load data set. Class contains the following methods:

    	  `load(self)`
	  `print_shape(self, data)`

  • data_explorer.py - Contains class DataExplorer to understand data with descriptive statistics and visualization. Class contains the following method.

        `print_data_statistics(self, data)`
    `visualize(self, data)`

  • data_preprocessor.py - Contains class DataPreprocessor to perform pre-processing on data set. Class contains the following methods:

    	  `split_dataset(self, data, start, end, n)` 
	  `display_dataset(self)`
	  `summarize(self, data, start, end, precision)`

  • evaluator.py - Contains class Evaluator to performs cross validation and evaluation. Class contains the following methods:

    	  `validate(self, model, input_set, output_set, folds, seed)`
	  `evaluate(self, model, input_set, output_set, folds, seed, scoring)`

  • model_selector.py - Contains class ModelSelector that compares a set of models to select the best one. Class contains the following method.

        `select_model(self, models, input_data, output_data, folds, seed)`

  • accuracy_improver.py - Contains class AccuracyImprover to performs tuning and ensemble prediction. Class contains the following methods:

    	  `tuning(self, model, input_set, output_set)` 
	  `ensemble_prediction(self, model, input_data, output_data, folds, seed)`

  • model_finalizer.py - Contains class ModelFinalizer to finalize and save model for prediction. Class contains the following methods:

    	  `split_train_test_sets(self, input_data, output_data, test_size, seed)` 
	  `finalize_and_save(self, model, filename, input_train, output_train)`
	  `predict(self, model_filename, input_test, output_test)`

  • main.py - Contains Main class to test and run the classes in this project.