<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Load the data
For this work, we're going to use the same retail sales data that we've used before. It can be found in the examples directory of this repository.
Step1:  Like all good modeling projects, we need to take a look at the data to get an idea of what it looks like.
Step2:  It's pretty clear from this data that we are looking at a trending dataset with some seasonality. This is actually a pretty good datset for prophet since the additive model and prophet's implemention does well with this type of data.  
With that in mind, let's take look at what prophet does from a modeling standpoint to compare with the dynamic linear regression model.  For more details on this, you can take a look at my blog post titled Forecasting Time Series data with Prophet – Part 4 (http
Step3:  With our prophet model ready for comparison, let's build a model with pyflux's dynamic linear regresion model.
<ASSISTANT_TASK:>
Python Code:
sales_df = pd.read_csv('../examples/retail_sales.csv', index_col='date', parse_dates=True)
sales_df.head()
Explanation: Load the data
For this work, we're going to use the same retail sales data that we've used before. It can be found in the examples directory of this repository.
End of explanation
sales_df.plot()
Explanation: Like all good modeling projects, we need to take a look at the data to get an idea of what it looks like.
End of explanation
# Prep data for prophet and run prophet
df = sales_df.reset_index()
df=df.rename(columns={'date':'ds', 'sales':'y'})
model = Prophet(weekly_seasonality=True)
model.fit(df);
future = model.make_future_dataframe(periods=24, freq = 'm')
future.tail()
forecast = model.predict(future)
model.plot(forecast);
Explanation: It's pretty clear from this data that we are looking at a trending dataset with some seasonality. This is actually a pretty good datset for prophet since the additive model and prophet's implemention does well with this type of data.  
With that in mind, let's take look at what prophet does from a modeling standpoint to compare with the dynamic linear regression model.  For more details on this, you can take a look at my blog post titled Forecasting Time Series data with Prophet – Part 4 (http://pythondata.com/forecasting-time-series-data-prophet-part-4/)
End of explanation
sales_df['revenue'] = sales_df.sales
model = pf.DynReg('revenue ~ sales', data=sales_df)
model.plot_fit(figsize=(15,15))
Explanation: With our prophet model ready for comparison, let's build a model with pyflux's dynamic linear regresion model.
End of explanation
<END_TASK>