This project is about Deep Learning, the sample came out of this site:
https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)
Why doing this?, because I find it fun and educative.
This is a dataset that describes sonar chirp returns bouncing off different services. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders.
You can learn more about this dataset on the UCI Machine Learning repository. You can download the dataset for free and place it in your working directory with the filename sonar.csv.
It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.
A benefit of using this dataset is that it is a standard benchmark problem. This means that we have some idea of the expected skill of a good model. Using cross-validation, a neural network should be able to achieve performance around 84% with an upper bound on accuracy for custom models at around 88%.
Another usefull link here: https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/