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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Examples
Importing libraries
Step1:  datacleaning
The datacleaning module is used to clean and organize the data into 51 CSV files corresponding to the 50 states of the US and the District of Columbia. 
The wrapping function clean_all_data takes all the data sets as input and sorts the data in to CSV files of the states.
The CSVs are stored in the Cleaned Data directory which is under the Data directory.
Step2:  missing_data
The missing_data module is used to estimate the missing data of the GDP (from 1960 - 1962) and determine the values of the predictors (from 2016-2020).
The wrapping function predict_all takes the CSV files of the states as input and stores the predicted missing values in the same CSV files.
The CSVs generated replace the previous CSV files in the Cleaned Data directory which is under the Data directory.
Step3:  ridge_prediction
The ridge_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using ridge regression.
The wrapping function ridge_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under Ridge Regression folder under the Predicted Data directory.
Step4:  svr_prediction
The svr_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using Support Vector Regression
The wrapping function SVR_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under SVR folder under the Predicted Data directory.
Step5:  plots
Visualizations is done using Tableau software. The Tableau workbook for the predicted data is included in the repository. The Tableau dashboard created for this data is illustrated below
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Python Code:
from ceo import data_cleaning
from ceo import missing_data
from ceo import svr_prediction
from ceo import ridge_prediction
Explanation: Examples
Importing libraries
End of explanation
data_cleaning.clean_all_data()
Explanation: datacleaning
The datacleaning module is used to clean and organize the data into 51 CSV files corresponding to the 50 states of the US and the District of Columbia. 
The wrapping function clean_all_data takes all the data sets as input and sorts the data in to CSV files of the states.
The CSVs are stored in the Cleaned Data directory which is under the Data directory.
End of explanation
missing_data.predict_all()
Explanation: missing_data
The missing_data module is used to estimate the missing data of the GDP (from 1960 - 1962) and determine the values of the predictors (from 2016-2020).
The wrapping function predict_all takes the CSV files of the states as input and stores the predicted missing values in the same CSV files.
The CSVs generated replace the previous CSV files in the Cleaned Data directory which is under the Data directory.
End of explanation
ridge_prediction.ridge_predict_all()
Explanation: ridge_prediction
The ridge_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using ridge regression.
The wrapping function ridge_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under Ridge Regression folder under the Predicted Data directory.
End of explanation
svr_prediction.SVR_predict_all()
Explanation: svr_prediction
The svr_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using Support Vector Regression
The wrapping function SVR_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under SVR folder under the Predicted Data directory.
End of explanation
%%HTML
<div class='tableauPlaceholder' id='viz1489609724011' style='position: relative'><noscript><a href='#'><img alt='Clean Energy Production in the contiguous United States(in million kWh) ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='path' value='shared&#47;PB87S38NW' /> <param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1489609724011');                    var vizElement = divElement.getElementsByTagName('object')[0];                    vizElement.style.width='1004px';vizElement.style.height='869px';                    var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>
Explanation: plots
Visualizations is done using Tableau software. The Tableau workbook for the predicted data is included in the repository. The Tableau dashboard created for this data is illustrated below:
End of explanation
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