In this notebook, we will build an automatic credit card approval predictor using machine learning techniques. We'll use the Credit Card Approval dataset from the UCI Machine Learning Repository and build a machine learning model that can predict if an individual's application for a credit card will be accepted.
I have used different classifiers to model the approval behaviour of banks towards credit card applications, including logistic regression, support vector machines and k-nearest neighbours.
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All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.
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This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.
Attribute Information:
- A1: b, a.
- A2: continuous.
- A3: continuous.
- A4: u, y, l, t.
- A5: g, p, gg.
- A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff.
- A7: v, h, bb, j, n, z, dd, ff, o.
- A8: continuous.
- A9: t, f.
- A10: t, f.
- A11: continuous.
- A12: t, f.
- A13: g, p, s.
- A14: continuous.
- A15: continuous.
- A16: +,- (target variable)