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.

• Number of Instances: 690

• Number of Attributes: 15 + class attribute

• 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)

• 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

• 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 [email protected]