Credit Card Approval Prediciton using Logistic Regression

A python project on predicting the approval for credit cards using logistic regression.

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this notebook, an automatic credit card approval predictor is built using machine learning techniques, just like the real banks do. [Credit Card Approval dataset](http://archive.ics.uci.edu/ml/datasets/credit+approval\) from the UCI Machine Learning Repository is used. This project is referenced from datacamp.

The metohds used in this project are:

1. EDA using sklearn summary statistics methods.
2. Handling the missing data by imputation.
3. Converting nonumeric data into numeric and scaling the numeric values.
4. Fitting the Logistic Regression model.
5. Evaluating the accuracy.
6. Hyperparameter tuning.