https://github.com/kmohamedalie/credit-approval

Credit Approval using Xgboost and GridSearch CV
https://github.com/kmohamedalie/credit-approval

banking classification finance gridsearchcv hyperparameter-tuning loan-eligibility xgboost-classifier

Last synced: 6 days ago
JSON representation

Credit Approval using Xgboost and GridSearch CV

• Host: GitHub
• URL: https://github.com/kmohamedalie/credit-approval
• Owner: Kmohamedalie
• License: mit
• Created: 2023-08-02T10:46:11.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-08-22T13:54:44.000Z (about 1 year ago)
• Last Synced: 2023-08-22T17:44:59.035Z (about 1 year ago)
• Topics: banking, classification, finance, gridsearchcv, hyperparameter-tuning, loan-eligibility, xgboost-classifier
• Language: Jupyter Notebook
• Homepage: https://github.com/Kmohamedalie/Credit-Approval/tree/master
• Size: 359 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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](https://archive.ics.uci.edu/dataset/27/credit+approval) , [Kaggle](https://www.kaggle.com/datasets/impapan/credit-approval-data-set)

**Developers' Guide:** [Amazon Machine Learning](https://docs.aws.amazon.com/pdfs/machine-learning/latest/dg/machinelearning-dg.pdf#cross-validation)                                             

**Complete notebook:** [Credit-approval Xgboost](https://github.com/Kmohamedalie/Credit-Approval/blob/master/Notebook/Credit_Approval_Xgboost.ipynb)

**Metrics achieved:** 

| Algorithm | Precision | Recall | F1-score | Accuracy |

|-----------|-----------|--------|----------|----------|

| Xgboost (GridSearchCV)  | 85%       |  85%   | 85%      | 85%    |

![image](https://github.com/Kmohamedalie/Credit-Approval/assets/63104472/869a8b95-07cf-4769-b9e1-e8ce81d1f7d5)

## **Additional Information:**

1. **Title:** Credit Approval

2. Sources: 

    (confidential)

    Submitted by [email protected]

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%)