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Visualization and Prediction model of Paris traffic ( random Forests ) from data published by the OpenData-Paris project

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Paris road traffic Prediction

thank to data published by OpenData-Paris : OpenData-Paris-Traffic . look Pièces jointes rubric

gps localisation of sensors : OpenData-Paris-GPS-sensors .

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Full video : https://vimeo.com/248162106

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Mandarina Project

To parse and store to SQLite database

you simply need to personalize the parameters of the only function call at the end of the file , then run :
python3 parseFill_BD_Pandas.py

Create an HTML/JavaScript Mapping file:

you simply need to uncomment function call at the end of the file to get the selected map
python3 mapPrinter.py

Create Decision Tree or Random Forest ( predictions )

you can edit the Query of the data train Set who fit the model , by default we predict values of the month of november2017
pyhton3 predict2.py

Show the decision tree :

dot -Tx11 my_dot_file.dot
dot -Tpng my_dot_file.dot > output.png

Plot the year X , run :

python3 plotFileAverage.py 2013
python3 plotFileAverage.py 2014
python3 plotFileAverage.py 2015
python3 plotFileAverage.py 2016
python3 plotFileAverage.py 2017

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Visualization and Prediction model of Paris traffic ( random Forests ) from data published by the OpenData-Paris project

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