build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
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Updated
Nov 9, 2019 - Jupyter Notebook
build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
Build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
I used pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
I have built a machine learning model to predict if a credit card application will get approved.
I have built an automatic credit card approval predictor using machine learning techniques, just like the real banks do. This is a guided project under one of the courses that I took online.
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Applied finance notes
Build a machine learning model to predict if a credit card application will get approved.
Analyze stock risk-return for investment decisions using Sharpe Ratio
A machine learning model to predict if a credit card application will get approved.
Build a machine learning model to predict if a credit card application will get approved
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio
Building a machine learning model to predict if a credit card application will get approved
Commercial banks receive many applications for credit cards. Fortunately, this task can be automated with the power of machine learning, and virtually all commercial banks do it today. In this project, an automatic credit card approval predictor is built using machine learning techniques, just like real banks do.
Build an ML-based credit card approval predictor for commercial banks to automate application analysis, saving time and reducing errors. High loan balances, low income, or excessive credit inquiries often lead to rejections. This project replicates real banks' automation for efficient, accurate, and faster decision-making.
Built a machine learning model to predict if a credit card application will get approved.
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