A real world application of a Recurrent Neural Network on a binary classification of time series data
What is this
This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data cleanup, model creation, fitting, and testing/reporting and was designed and analysed in less than 24 hours.
Challenge and input
Three input files were provided for this challenge:
- aigua.csv
- aire.csv
- amoni.csv (amoni_pred.csv is the same thing with integers rather than booleans)
The objective is to train a Machine Learning classifier that can predict dangerous drift on amoni.
Analysis procedure
Gretl has benn used to analyze the data.
Ideally, fuzzing techniques would be applied that would remove the input noise on amoni from the correlation with aigua.csv and