Analyze and predict corona data with redis and machine learning

Analyze and predict corona data with redis and machine learning name is a university project that allows the work with a redis db to analyze and forecast corona data.

This project demonstrates how to work with a database and analyse the data using different algorithms (including the SARIMA machine learning algorithm).

It should be noted that this project mainly deals with the work of the redis database and that working with machine learning algorithms has been tested for the first time.

If you have any suggestions for improvement, please feel free to contact me.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed redis on your machine.
• Another option is to run a docker container on your machine or raspberryPi
• You have installed the requiered libaries listed in src\requirements.txt
• To do so you can try pip install -r requirements.txt
• It is recommended to install fbprophet via anaconda or via pip

  pip install fbprophet
  conda install -c conda-forge fbprophet

• This project was tested on python==3.8.0 and it is recommended to use this version

Installing Analyze and predict corona data with redis and machine learning

To install Analyze and predict corona data with redis and machine learning, follow these steps:

• Download this repository
• Unzip the downloaded file

Using Analyze and predict corona data with redis and machine learning

To use Analyze and predict corona data with redis and machine learning, follow these steps:

• Start your redis docker container or server.
• Change the parameters creating a RedisClient object to the specific information of your redis server.

    try:
        redisDB = RedisClient(_state=state, _redisHost=(localhost or IP), _redisPort="6379"(most likely), _redisPw=(yourRedisPW))
    except Exception as generalError:
        print(f"Somethin went wrong connecting to redis: {generalError}")
        return

• Start a terminal within the src folder of this project.
• Type the following command in your teminal with the parameters you want to use.
• Further explanation is given underneath.

C:\src>python main.py arg1 arg2 arg3 arg4

arg1: FutureCast          (Default = 10)
arg2: showAnalyzedData    (Default = True)
arg3: showForecastPlots   (Default = True)
arg4: prophetIncluded     (Default = True)

You can also change the values in your IDE. Therfore open the main.py file. Change the parameters shown in the picture below to your liking.

• FutureCast --> Describes the amount of days forecast methods should predict.
• showAnalyzedData --> Is True or False and decides, if plots regarding analyzing corona data should be shown.
• showForecastPlots --> Is True or False and decides, if plots regarding forecast of corona data should be shown.
• prophetIncluded --> Decides if the fbProphet algorithm is executed or not.

Possible Output

Analyzed Corona Data	FBProphet forecast	Other forecast methods

Run Unittests

• All unittest will be executed automatically at the end of the program
• If you want to run them seperately you find some test files in src directory
• To use them seperately add at the end of the file

if __name__ == '__main__':
    unittest.main()

• Now you can run the specific File and information should be printed in the terminal (see example underneath)

Contributing to Analyze and predict corona data with redis and machine learning

To contribute to Analyze and predict corona data with redis and machine learning, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin <project_name>/<location>
5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contact

If you want to contact me you can reach me at Linkedin.

License

This project uses the following license: MIT.