Happy-learning

Happy Learning is a toolbox for reinforced developing of machine learning models in Python. It is designed to evolve and optimize machine learning models using evolutionary algorithms, and covers all aspects of the developing process such as feature engineering, feature and model selection, and hyper parameter optimization. It is able to process tabular data smartly, and combines both the feature engineering module and the genetic algorithm module to create a reinforcement learning environment. It also includes a feature tournament process to evaluate the importance of each feature, and a feature selector module to select the best features. Lastly, it has a model generator module to generate supervised machine learning models and a network generator module to generate neural network architectures.