Santander-PR/
├── Models
│ ├── #1-Voting
│ │ ├── reproduce
│ │ │ ├── Product-Wise
│ │ │ │ ├── product-wise-lag.ipynb
│ │ │ │ ├── product-wise-lag.py
│ │ │ │ └── proposed-sampling-month.pptx
│ │ │ └── XGBoost
│ │ │ ├── xgboost.ipynb
│ │ │ └── xgboost.py
│ │ ├── voting.ipynb
│ │ └── voting.py
│ └── EDA
│ ├── EDA.ipynb
│ └── EDA.py
├── README.txt
└── Santander_Product_Recommendation.pdf
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The report is saved as 'Santander_Product_Recommendation.pdf'
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We have submitted the best model. You can find them in 'Models' folder
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The top submission of ours is in 'Voting folder'
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For Voting we had used multiple models to get a consolidated score.
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To reproduce those CSVs please look at 'reproduce' folder
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In 'reproduce' folder you can find two sub folders XGBoost and Product-Wise
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'Product-Wise' folder needs to be reproduced for every product separately.
- We had used the Product Wise analysis to check which month is most suitable for training and trained accordingly
- But as mentioned in the report, the accuracy is more dependant on the latest month than this.
- We have also attached a PPT consisting of the inferences we found from running all such notebooks
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On running the 'XGBoost' code we get it's respective CSV
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Each folder has it's jupyter notebook(for better visualisation) and it's relavent python code.
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Incase you want to skip the part where you run all the notebooks to get the CSVs, we have CSVs stored in drive
- The second best submission is for XGBoost.
- It's relevant script is saved as 'xgboost.py'. We had also used it for voting