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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](http://archive.ics.uci.edu/ml/datasets/credit+approval) from the UCI Machine Learning Repository.
- 1Credit card applications
- 2Inspecting the applications
- 3Splitting the dataset into train and test sets
- 4Handling the missing values (part i)
- 5Handling the missing values (part ii)
- 6Handling the missing values (part iii)
- 7Preprocessing the data (part i)
- 8Preprocessing the data (part ii)
- 9Fitting a logistic regression model to the train set
- 10Making predictions and evaluating performance
- 11Grid searching and making the model perform better
- 12Finding the best performing model
Machine Learning Engineer at Carted
Sayak is currently a Machine Learning Engineer at Carted working with large data regimes. His subject of interest lies in the area of semi-supervised learning, self-supervised learning, and model robustness. He enjoys applying deep learning to solve real-world problems. Off the work, Sayak likes to blog about different topics in machine learning and speak at developer meetups. Check out his site to find out more about him and how to contact him.
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