Preprocessing and Machine Learning classification for targets extracted from Multi Beam Echo Sounder data

This code was written for the purpose of preprocessing exported targets coming from Multibeam Echosounder data for machine learning classification of the objects in gas, fish schools, platform, and noise. The notebook.py file is exported from a Jupyter notebook used to perform preprocessing and exploratory data analysis on a .csv file of exported targets from raw data, partially labeled. The pseudocode and ML procedure to be executed in Orange (Conda suite) is also uploaded with this code (Machine_Learning_Workflow.pdf file). The raw MBES data is released under CC-BY 4.0 license and it is available here: https://doi.org/10.17882/79142

Keywords

Inorganic Chemistry, machine learning, Science Policy, Information Systems not elsewhere classified, Multi Beam Echosounder data, Plant Biology, exploratory data analysis, Cell Biology, preprocessing, Molecular Biology, Biological Sciences not elsewhere classified, Python

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