-machine-learning-on-streaming-data-using-Kafka-and-Docker

I have completed my first project that machine learning on streaming data using Kafka and Docker. You can check-up my GitHub repository for codes.

Steps of process:

1- install vscode

2- install anaconda

3- create nev enviroment on conda

4- install docker

5- install docker-compose

6- install docker desktop (https://www.youtube.com/watch?v=Vplj9b0L_1Y)

7- download docker-compose.yaml (https://github.com/cigdemkadakoglu/apache-kafka)

8- docker-composeup -d

• https://www.youtube.com/playlist?list=PLZYKO7600KN9ttvG0mFYzP82AbIdeJYwq this video very useful but Turkish. (sonunda bu espriyi yapabildim.)

9- you can check containers with docker ps or docker stats -a

10- create topic

• I am really hardned with creating topic. Because of sh bash or another concepts are far to my domain.
• But, our container named kafdrop also have create topic function.
• You can write on the browser bar: https://localhost:9000/
• create a new topic it is easier way for create topics.

11- I love pycaret library

   * setup()
   * create_model()
   * tune_model()
   * save_model()
    * Here is complex at API docs, you have to save the model with path_name just like pandas save function.
   * load_model()
    * And also when loading model, you must take path_name.
   * How it easy, yeah?

11 - I created random data generator named as data_generator.py

      * I used iris dataset, you can change this.

12 - I used this module for producer.py

        * same topic, proper ports

13 - Then, I used consumer.py

        * same topic, proper ports
        * load_model
        * print result

Coming soon....

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