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Methodology in report.ipynb

Requirements

Python and Miniconda/Anaconda installed https://docs.conda.io/en/latest/miniconda.html

conda env create -f env.yml

CLI to train model

conda activate $ENV_NAME [default: conda activate juvo_test_envi]
python main.py

Usage: main.py [OPTIONS]

Options:
-l, --loans TEXT                Path to loans dataset  [default: datasets/Br
                                azil_DS_loans_2019-11-10_2019-12-05.csv]
-lp, --loans_prev TEXT          Path to previous loans dataset  [default:
                                datasets/Brazil_DS_prev_loans.csv]
-r, --recharges TEXT            Path to recharges dataset  [default: dataset
                                s/Brazil_DS_recharges_2019-08-10_2019-12-05.
                                csv]
-m, --metric TEXT               Metric to evaluate the model. AUC[default] =
                                roc_auc, Recall = recall, F1=f1,
                                Accuracy=accuracy  [default: roc_auc]
-i, --inicial_date TEXT         Inicial date to train the model. Format:
                                YYYY-MM-DD  [default: 2019-01-01]
-f, --final_date TEXT           Final date to train the model. Format: YYYY-
                                MM-DD  [default: 2019-12-31]
--plot, --no-plot               Show plots. --no-plot to disable  [default:
                                True]
--install-completion [bash|zsh|fish|powershell|pwsh]
                                Install completion for the specified shell.
--show-completion [bash|zsh|fish|powershell|pwsh]
                                Show completion for the specified shell, to
                                copy it or customize the installation.
--help                          Show this message and exit.

Datasets

You will find 3 csv files. The files were created from our database as of 2020 Feb 05.

Brazil_DS_loans_2019-11-10_2019-12-05

It has the loans made for a period of 25 days with following important fields

  • Loan_id - unique identifier for a loan
  • Uuid - user identifier
  • Created_at - time when loan was created
  • Paid_at - time when it was paid. If it is missing then loan was not paid as of file creation date
  • Amount - amount of loan

A loan is considered repaid if it's paid within 60 days.The objective is to create a predictive model for loan repayment based on the labels in this file.

Brazil_DS_prev_loans

This has the previous loans taken for users in the above file and should have the same schema.

Brazil_DS_recharges_2019-08-10_2019-12-05

A user pays for loans by making recharges after taking a loan. This file contains recharges for all users for about 4 months.

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