Credit Approval using Xgboost and GridSearch CV

Task: Examples represent positive and negative instances of people who were and were not granted credit.

Dataset available on: UCI Machine Learning Credit Approval , Kaggle

Developers' Guide: Amazon Machine Learning
Complete notebook: Credit-approval Xgboost

Metrics achieved:

Algorithm	Precision	Recall	F1-score	Accuracy
Xgboost (GridSearchCV)	85%	85%	85%	85%

Additional Information:

1. Title: Credit Approval

2. Sources: (confidential) Submitted by quinlan@cs.su.oz.au

3. Past Usage:

   See Quinlan,

   • "Simplifying decision trees", Int J Man-Machine Studies 27, Dec 1987, pp. 221-234.
   • "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992

4. Relevant Information:

   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.

5. Number of Instances: 690

6. Number of Attributes: 15 + class attribute

7. 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)
   

8. 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

9. Class Distribution

   +: 307 (44.5%)
   -: 383 (55.5%)