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Python Machine Learning predictive model to route the best payment service provider for credit cards in online transactions.

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Credit Card Routing System

This project is divided by 2 files (main.ipynb and app.py). The main.ipynb file is subdivided by:

  • Main Imports: import all the libraries needed for the project
  • Read Data: read the data from the excel sheet dataset
  • Exploratory Data Analysis: explore the data for consequent analysis and development of the model
  • Data Preparation
  • Modeling
  • Evaluation
  • End-user GUI

Use the app.py to run the project by executing the command: >> python -m streamlit run app.py

Below some screenshot results of the project:

Raw Data

Unnamed: 0 tmsp country amount success PSP 3D_secured card
0 0 2019-01-01 00:01:11 Germany 89 0 UK_Card 0 Visa
1 1 2019-01-01 00:01:17 Germany 89 1 UK_Card 0 Visa
2 2 2019-01-01 00:02:49 Germany 238 0 UK_Card 1 Diners
3 3 2019-01-01 00:03:13 Germany 238 1 UK_Card 1 Diners
4 4 2019-01-01 00:04:33 Austria 124 0 Simplecard 0 Diners

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Feature Engineering Result Data

Unnamed: 0 tmsp country amount success PSP 3D_secured card retry hour day_of_week month PSP_code transaction_cost
9238 9238 2019-01-10 03:49:12 Austria 6 0 Moneycard 0 Diners 0 3 3 1 0 4.5
9239 9239 2019-01-10 03:49:37 Austria 6 0 Simplecard 0 Diners 1 3 3 1 3 1.0
22742 22742 2019-01-27 14:01:11 Austria 6 1 Simplecard 0 Master 0 14 6 1 3 1.5
33737 33737 2019-02-08 05:02:33 Austria 6 0 UK_Card 0 Diners 0 5 4 2 2 1.5
33738 33738 2019-02-08 05:02:37 Austria 6 0 UK_Card 0 Diners 1 5 4 2 2 1.5

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Python Machine Learning predictive model to route the best payment service provider for credit cards in online transactions.

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