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<img src="https://dev.arenametrix.fr/assets/logo_ax-806e8204f49bcc2c5e8cd34e9748d16a6038404e37fdb2dc9d61455bb06c6461.png" width=300> <img src="https://dev.arenametrix.fr/assets/logo_ax-806e8204f49bcc2c5e8cd34e9748d16a6038404e37fdb2dc9d61455bb06c6461.png" width=300>
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<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/LOGO-ENSAE.png/900px-LOGO-ENSAE.png" width=300>
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## Team 1 ## Team 1
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### How run the code ### How run the code
Codes have to be run in an order following their numbers. Each of them is described below : Codes have to be run in an order following their numbers. Each of them is described below :
- 1_Input_cleaning.py \ - '1_Input_cleaning.py' \
Clean raw data and generate dataframes that will be used to build datasets with insightful variables. Datasets are exported to location 0_Input/. Clean raw data and generate dataframes that will be used to build datasets with insightful variables. Datasets are exported to location 0_Input/.
- 2_Datasets_generation.py \ - '2_Datasets_generation.py' \
Use dataframes previously created and aggregate them to create test and train set for each company. Databases are exported to location 1_Temp/1_0_Modelling_Datasets/ in a folder containing all 5 databases for a type of activity. Use dataframes previously created and aggregate them to create test and train set for each company. Databases are exported to location 1_Temp/1_0_Modelling_Datasets/ in a folder containing all 5 databases for a type of activity.
- 3_Modelling_datasets.py \ - 3_Modelling_datasets.py \
For each type of activity, the test and train sets of the 5 tenants are concatenated. Databases are exported to location 1_Temp/1_0_Modelling_Datasets/. For each type of activity, the test and train sets of the 5 tenants are concatenated. Databases are exported to location 1_Temp/1_0_Modelling_Datasets/.