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

• pima-indians-diabetes.data - Pima Indians Diabetes Database ( https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes )

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.