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AI reports (components)
With Shimoku you can create specific components with Machine Learning timeseries predictions or for Classification or Regression problems. Some examples follow
from os import getenv
from typing import Dict, List
import shimoku_api_python as shimoku
api_key: str = getenv('API_TOKEN')
universe_id: str = getenv('UNIVERSE_ID')
business_id: str = getenv('BUSINESS_ID')
environment: str = getenv('ENVIRONMENT')
df: pd.DataFrame = pd.read_csv('your_timeseries_revenue.csv')
shimoku = shimoku.Client(
config={'access_token': api_key},
universe_id=universe_id,
environment=environment,
)
shimoku.plt.set_business(business_id=business_id)
shimoku.plt.predictive_line(
data=df_.to_dict(orient='records'), x='date', y=['billing'],
date_col_name='date', y_col_name='billing', period=90, #90 days
min_value_mark=df['ds'].max().isoformat(),
max_value_mark=df_['date'].max().isoformat(),
menu_path='test/api-through-prediction',
order=0,
)
That results in
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