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Zadania realizowane w ramach zajęć Inżynieria Uczenia Maszynowego
20 Commits 2 Branches 0 Tags 76 KiB
Jupyter Notebook 77.8%
Python 17.6%
Shell 4.1%
Dockerfile 0.5%
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Marcin Kostrzewski 3ba963864b
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Use archived dataset and trigger training
2022-05-05 21:29:59 +02:00
.gitignore Added dataset download script 2022-03-20 17:46:53 +01:00
Dockerfile Dockerfile runs training 2022-04-24 22:27:58 +02:00
download_dataset.sh Save artifacts 2022-03-27 23:29:37 +02:00
Jenkinsfile Use archived dataset and trigger training 2022-05-05 21:29:59 +02:00
Jenkinsfile-docker Use archived dataset and trigger training 2022-05-05 21:29:59 +02:00
Jenkinsfile-stats Add stats jenkinsfile 2022-04-01 23:02:13 +02:00
Jenkinsfile-stats-docker Moved chmod to Dockerfile 2022-04-02 17:27:15 +02:00
power_plant_data_stats.ipynb Added first solution 2022-03-20 18:07:34 +01:00
power_plant_data_stats.py Dockerization 2022-04-01 22:25:05 +02:00
README.md Added first solution 2022-03-20 18:07:34 +01:00
requirements.txt Added regression model training 2022-04-24 22:20:14 +02:00
stats.sh Added statistics script 2022-03-27 23:46:51 +02:00
train_model.py Model saving 2022-04-24 22:23:53 +02:00

README.md

ium_444409

Zadania realizowane w ramach zajęć Inżynieria Uczenia Maszynowego.

Zbiór

Solar Power Generation Data https://www.kaggle.com/datasets/anikannal/solar-power-generation-data?select=Plant_1_Generation_Data.csv

Wymagania

  • python3
  • pip
  • API token z kaggle.com

Uruchamianie

  • Instalujemy potrzebne pakiety:
$ pip install -r requirements.txt
  • Pobieramy zbiór danych z Kaggle. Skorzystamy ze skryptu w repo, który pobierze i podzieli dane na podzbiory:
$ ./download_dataset.sh