Predicting Credit Card Approvals

Right now we are in the last project (the sixth project), which is provided by DataCamp on the Data Scientist with Python Track. And in this project, we have implemented a number of these techniques, such as converting non-numeric data to numeric using get_dummies, scaling features to a range of 0 and 1 using the MinMaxScaler technique, and predicting the proportion of correct classifications and model performance using logistic regression and confusion matrix techniques. Not to forget, we are also required to apply grid searching to find the optimal values for the instructed parameters.

Project Description

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.

The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.

Project Tasks

1. Credit card applications
2. Inspecting the applications
3. Splitting the dataset into train and test sets
4. Handling the missing values (part i)
5. Handling the missing values (part ii)
6. Handling the missing values (part iii)
7. Preprocessing the data (part i)
8. Preprocessing the data (part ii)
9. Fitting a logistic regression model to the train set
10. Making predictions and evaluating performance
11. Grid searching and making the model perform better
12. Finding the best performing model