/predicting_equipment_failure

Project leveraging machine learning to anticipate equipment maintenance schedules.

Primary LanguageHTMLMIT LicenseMIT

Predicting Equipment Failure

Description

Version: 0.0

  • For an overview of the project, see the overview markdown file.
  • The final deployment dashboard can be seen here.

Authors

Dependencies

  • numpy
  • pytz
  • pandas
  • flask
  • psycopg2
  • numpy
  • googlemaps
  • sklearn
  • scipy

Getting Started

Prerequisites
Installation

First, clone the project repo from Github. Then, change directories into the cloned repository. To accomplish this, execute these commands:

$ git clone https://github.com/kurtrm/predicting_equipment_failure.git

$ cd predicting_equipment_failure

Now that you have cloned your repo and changed directories into the project, create a virtual environment named "ENV", and install the project requirements into your VE. (Or your preferred environment manager.)

$ python3 -m venv ENV

$ source ENV/bin/activate

$ pip install -r requirements.txt

Test Suite

Running Tests

This application uses pytest as a testing suite. To run tests, run:

Until a config file is made, execute the following: $ cd src

$ pytest ../tests/test.py

To view test coverage, add --cov to the above command.

Test Files

The testing files for this project are:

File Name Description
./tests/test.py Test this.

Development Tools

  • python - programming language
  • flask - web framework
  • psycopg2 - DB management system

License

This project is licensed under MIT License - see the LICENSE.md file for details.

Acknowledgements

This README was generated using writeme.