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Zadania realizowane w ramach zajęć Inżynieria Uczenia Maszynowego
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Marcin Kostrzewski d494e86141 Add eval dependency
2022-06-05 22:30:54 +02:00
.dvc Add remote 2022-06-05 22:00:22 +02:00
.dvcignore DVC init 2022-06-05 21:40:34 +02:00
.gitignore Add data folder to DVC 2022-06-05 21:42:59 +02:00
data.dvc Add data folder to DVC 2022-06-05 21:42:59 +02:00
Dockerfile Improved caching layers 2022-05-09 17:04:58 +02:00
download_dataset.sh Save artifacts 2022-03-27 23:29:37 +02:00
dvc.lock DVC Fixes 2022-06-05 22:19:45 +02:00
dvc.yaml Add eval dependency 2022-06-05 22:30:54 +02:00
environment.yml Added conda environment file 2022-05-21 13:38:49 +02:00
eval_model.py Move common methods and functions to model.py 2022-05-06 21:51:49 +02:00
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model.py Floatify 2022-05-11 19:10:26 +02:00
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power_plant_data_stats.ipynb Added first solution 2022-03-20 18:07:34 +01:00
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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