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, for example. 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 project, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
- Supervised Learning with scikit-learn
- pandas Foundations
- The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.
-
Credit card applications
-
Inspecting the applications
-
Handling the missing values (part i)
-
Handling the missing values (part ii)
-
Handling the missing values (part iii)
-
Preprocessing the data (part i)
-
Splitting the dataset into train and test sets
-
Preprocessing the data (part ii)
-
Fitting a logistic regression model to the train set
-
Making predictions and evaluating performance
-
Grid searching and making the model perform better
-
Finding the best performing model