https://github.com/nancyhamdan/demil-degree-prediction

Predicting the Demilitarization Degree Required for Military Items
https://github.com/nancyhamdan/demil-degree-prediction

deep-learning exploratory-data-analysis keras multiclass-classification

Last synced: 1 day ago
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Predicting the Demilitarization Degree Required for Military Items

• Host: GitHub
• URL: https://github.com/nancyhamdan/demil-degree-prediction
• Owner: nancyhamdan
• Created: 2022-03-28T22:07:07.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-08T12:10:32.000Z (over 1 year ago)
• Last Synced: 2024-01-18T08:22:37.545Z (10 months ago)
• Topics: deep-learning, exploratory-data-analysis, keras, multiclass-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 8.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting the Demilitarization Degree of Military Items - Multiclass Classification

This project presents findings from exploring the Military Equipment for Local Law Enforcement

dataset that is provided from The Defense Logistics Agency (DLA) in the United States. Excess

military equipment and devices from the Department of Defense in the US are handled by the

DLA which transfers this excess equipment to federal, state, and local law enforcement agencies

in the US as part of the LESO program (Law Enforcement Support Office program). The dataset

contains 130958 records representing the transfers that happened between 01/01/1980 and

30/09/2021.

All military items that are transferred must be demilitarized to some degree; the degree of

demilitarization required is represented by the DEMIL Code attribute in the dataset. Each DEMIL

Code has a corresponding DEMIL IC representing the integrity of the DEMIL Code registered for

the transfer. This project details the process that was followed in building a high performing deep

learning predictor of the DEMIL Code and in deriving key insights about the data.

You can find the dataset that was used [here](./AllStatesAndTerritoriesQTR4FY21.xlsx) along with its dictionary [here](./MeaningOfFieldsInTheDataset.pdf)