A credit approval application which decides if a bank client should get credit or not. KNN and KMeans algorithms are used and implemented in C-language. For the validation of solution, K-fold cross validation is used.
Dataset concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.
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Number of Instances: 690
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Number of Attributes: 15 + class attribute
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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: +,- (class attribute)
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Missing Attribute Values: 37 cases (5%) have one or more missing values. The missing values from particular attributes are:
- A1: 12
- A2: 12
- A4: 6
- A5: 6
- A6: 9
- A7: 9
- A14: 13
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Class Distribution:
- +: 307 (44.5%)
- -: 383 (55.5%)
- https://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.names
- https://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.data
- Dataset submitted by quinlan@cs.su.oz.au