Binary Classification Deep Learning Model for MiniBooNE Particle Identification Using Keras

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The MiniBooNE Particle Identification dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background). The data file is set up as follows. In the first line is the number of signal events followed by the number of background events. The records with the signal events come first, followed by the background events. Each line, after the first line, has the 50 particle ID variables for one event.

ANALYSIS: The baseline performance of the model achieved an average accuracy score of 93.62%. After tuning the hyperparameters, the best model processed the training dataset with an accuracy of 93.70%. Furthermore, the final model processed the test dataset with an accuracy of 93.94%, which was consistent with the accuracy result from the training dataset.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: MiniBooNE Particle Identification Data Set

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference:

The HTML formatted report can be found here on GitHub.