ChurnDataset: city, state, county zip, Lat, Lng, Population, Area, Timezone, children, Age, Income, Marital, Gender, Outage_sec_perweek, Email, contacts, Yearly_equip_failure, Techie

Time Series of below information

Day

Revenue

kindly provide details python code for where codes is required

Part I: Research Question

A.Describe the purpose of this data analysis by doing the following:

1.Summarizeoneresearch question that is relevant to a real-world organizational situation captured in the selected data set and that you will answer using time series modeling techniques.

2.Define the objectives or goals of the data analysis. Ensure your objectives or goals are reasonable within the scope of the scenario and are represented in the available data.

Part II: Method Justification

B.Summarize the assumptions of a time series model including stationarity and autocorrelated data.

Part III: Data Preparation

C.Summarize the data cleaning process by doing the following:

1.Provide a line graph visualizing the realization of the time series.

2.Describe the time step formatting of the realization, includinganygaps in measurement and the length of the sequence.

3.Evaluate the stationarity of the time series.

4.Explain the steps you used to prepare the data for analysis, including the training and test set split.

5.Provide a copy of the cleaned data set.

Part IV: Model Identification and Analysis

D.Analyze the time series data set by doing the following:

1.Report the annotated findings with visualizations of your data analysis, including the following elements:

the presence or lack of a seasonal component

trends

the autocorrelation function

the spectral density

the decomposed time series

confirmation of the lack of trends in the residuals of the decomposed series

2.Identify an autoregressive integrated moving average (ARIMA) model that accounts for the observed trend and seasonality of the time series data.

3.Perform a forecast using the derived ARIMA model identified in part D2.

4.Provide the output and calculations of the analysis you performed.

5.Provide the code used to support the implementation of the time series model.

Part V: Data Summary and Implications

E.Summarize your findings and assumptions by doing the following:

1. Discuss the results of your data analysis, including the following points:

the selection of an ARIMA model

the prediction interval of the forecast

a justification of the forecast length

the model evaluation procedure and error metric

2.Provide an annotated visualization of the forecast of the final model compared to the test set.

3.Recommend a course of action based on your results.