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 Ionosphere Signals dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.
Additional Notes: This is a replication of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python. I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.
INTRODUCTION: The dataset contains radar data collected by a system that is consists of a phased array of 16 high-frequency antennas. The targets were free electrons in the ionosphere. The system processed the signals using an autocorrelation function. Instances in this dataset are described by two attributes per pulse number, corresponding to the complex values returned by the function resulting from the electromagnetic signal. The “Good” radar returns are those showing evidence of some type of structure in the ionosphere. The “Bad” labels are those that do not as their signals pass through the ionosphere.
ANALYSIS: After setting up the deep learning model, the model processed the test dataset with an accuracy measurement of 87.93%.
CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.
Dataset Used: Ionosphere Signals Dataset
Dataset ML Model: Binary classification with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Ionosphere
One potential source of performance benchmarks: https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/
The HTML formatted report can be found here on GitHub.