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Pour l'instant fait pas grand chose

Project description

Forecastor for Buildings' Consumption

Go check ce lien pour rédiger le README: Github-flavored Markdown

1. Choses à faire

  • [ ] FAUT MODIFIER LE SETUP.PY? VOILA VOILA
  • [ ] Ajouter des features
    • [ ] Gérer les exceptions un peu partout pour que ce soit user friendly *_* (simley avec des étoiles dans les yeux)
  • [ ] Ajouter un modele (voir 2 ou 3 soyons fous)
  • [ ] comprendre plus encore le sujet mdr

2. Features' List for preprocessing data

  • day_of_week(date_serie): Input: serie of dates ; Output: serie of corresponding week days (['Monday', 'Tuesday', ...])
  • cycle_time(df): From the 3rd competitor of the Forecast challenge by Schneider Electric ; turns time in a continous element (no break between 23h59 and 00h01) by applying cosinus and sinus functions
  • add_weather(df, weather_df): From the 3rd competitor of the Forecast challenge by Schneider Electric ; add the weather data to the training dataset (df here) merging the two dataframes on the 'Timestamp'

3. Requirements for dataframes

  • The column containing dates must be labeled 'Timestamp'

4. Model functions

  • building_regressor(): returns a linear regressor from Scikit-learn
  • building_train(reg, X, y): train the regressor with X the data and Y the targeted values
  • building_prediction(reg, X): returns a dataframe showing the prediction of the regressor reg given the data X

5. To install the package

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