This repository contains a web service that uses a machine learning approach to make accurate predictions of wind turbine power from given wind speed based on the data set powerproduction.
The project has the following features:
- Jupyter notebook with three models trained on the data set powerproduction.
- Python script running a web service based on the best performing model.
- Dockerfile that builds and runs the web service in a container.
Submitted by: Olga Rozhdestvina (Student No: G00387844)
Lecturer: Ian McLoughlin
Programming Language used: Python
Applications used for completion of the project are The Jupyter Notebook, Visual Studio Code, cmder
Distribution of the Python used is Anaconda Python distribution.
- Make sure that you have Python installed
- Download or clone current repository "Machine-Learning-and-Statistics-Project"
- Open Command Interpreter and get into correct directory
- Install packages and run the app:
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If using a virtual environment:
Linux
pip install -r requirements.txt export FLASK_APP=flask_server python3 -m flask run
Windows
pip install -r requirements.txt set FLASK_APP=flask_server python -m flask run
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If using Docker:
docker build . -t wind-power docker run -d -p 5000:5000 wind-power
In case of an error, change the first line of the Docker file for your version of Python.
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- To view the model analysis run Jupyter Notebook and open Power_production_models.ipynb.
This project is licensed under the MIT License - see the LICENSE.md file for details