<jupyter_start><jupyter_text>**Prophet Model**<jupyter_code>df = data.rename(columns={'date':'ds','sales':'y'})
df_1 = df[df.company=='Beam Global ']
df_1
# Training
model=Prophet(yearly_seasonality=False,
        weekly_seasonality=False,
        daily_seasonality=False, 
        interval_width=0.95, 
        mcmc_samples = 500)
model.add_seasonality(
        name='weekly', 
        period=7.5, 
        fourier_order=5
    )
model.fit(df_1)
# Prediction
forecast=model.make_future_dataframe(freq='W', periods = 4)
forecast=model.predict(forecast)
forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]]
# Plotting
figure=model.plot(forecast,xlabel='Date',ylabel='Sales')<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup