AI Data Science 24
Credit Cards Defaulters
To build a classification methodology to determine whether a person defaults the credit card payment for the next month.
Architect
Data Description
The client will send data in multiple sets of files in batches at a given location. The data has been extracted from the census bureau.
The data contains 32561 instances with the following attributes:
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Features:
1. LIMIT_BAL: continuous. Credit Limit of the person.
2. SEX: Categorical: 1 = male; 2 = female
3. EDUCATION: Categorical: 1 = graduate school; 2 = university; 3 = high school; 4 = others
4. MARRIAGE: 1 = married; 2 = single; 3 = others
5. AGE-num: continuous.
6. PAY_0 to PAY_6: History of past payment. We tracked the past monthly payment records (from April to September, 2005)
7. BILL_AMT1 to BILL_AMT6: Amount of bill statements.
Model Training
Data Export from Db
The data in a stored database is exported as a CSV file to be used for model training.
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Data Preprocessing
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Check for null values in the columns. If present, impute the null values using the categorical imputer.
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Scale the numeric values using the standard scaler.
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Check for correlation.
Clustering
KMeans algorithm is used to create clusters in the preprocessed data. The optimum number of clusters is selected by plotting the elbow plot, and for the dynamic selection of the number of clusters, we are using "KneeLocator" function. The idea behind clustering is to implement different algorithms. The Kmeans model is trained over preprocessed data, and the model is saved for further use in prediction.
Model Selection
After the clusters have been created, we find the best model for each cluster. We are using two algorithms, “Naïve Bayes” and "XGBoost". For each cluster, both the algorithms are passed with the best parameters derived from GridSearch. We calculate the AUC scores for both models and select the model with the best score. Similarly, the model is selected for each cluster. All the models for every cluster are saved for use in prediction.