1.Debitron Challenge
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|__ RESULT
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| |__DDB.xlsx
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|__ Scripts
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| |__ .ipynb_checkpoints
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| |__Serialised Models
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| | |__LogRmodel_1
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| |__BankingIndustry.ipynb
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| |__production_model.ipynb
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| |__Untitled.ipynb
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| |__production_model.py
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| |__model_training.py
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|__ Testing Data
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| |__testdata.xlsx
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|__ Training Data
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| |__CompiledDDBs_Challenge_v3_2.xlsx
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| |__DataDictionary_Challenge_v2.xlsx
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|__ Readme.md
1.Python 2.7
2.Pandas library
3.sklearn library
4.Jupyter NoteBook or Anaconda
5.Pickle Library
1.First Way 1.Change folder paths accordingly in model_training.py and production_model.py
2.Then run production_model.py from cmd
3.It will create a LogRmodel_1 named file in serialised_model folder
4.then run model_training.py from cmd
5.It will generate DDB.xlsx file in the RESULT folder
2.Second Way 1.start Jupyter NoteBook from Debitron Challenge folder
2.start BankingIndustry.ipynb and run cells upto model_2
3.model_2 is actually incomplete
4.It wwill also generate LogRmodel in serialised folder
5. then start production_model.ipynb in notebook and execute all cells
6.Same as above It will generate DDB.xlsx in Result folder
1.I had trained my model on first 20000 data points in excel
2. Then i validated them against 10000 remaining in dataset
3. I had got 85.5 accuracy
4.So i serialised model
5.Given a new dataset my model will classify each user forced debit will get accepted or rejected
6.Then i moved would_be_rejected people into top rows and sorted both rejected / aprroved guys each of them by their amount due .