Case Study: This case study considers the Credit approval dataset. This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. 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: +,- (class attribute) Question: Apply the KNN algorithm to predict the acceptance of rejection of a credit card application
Case Study:
This case study considers the Credit approval dataset. This file concerns credit card applications. All
attribute names and values have been changed to meaningless symbols to protect confidentiality of
the data.
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: +,- (class attribute)
Question: Apply the KNN algorithm to predict the acceptance of rejection of a credit card
application
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