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https://github.com/sumdiboii/loan-prediction-decision-trees

A Decision Tree Classifier was implemented to predict personal loan acceptance using a dataset of 5,000 customers. Key features included income, education, mortgage, and credit card usage. The model achieved 97% accuracy, with 92% precision and 76% recall for positive loan predictions, validated using a classification report and confusion matrix.
https://github.com/sumdiboii/loan-prediction-decision-trees

classification data-visualisation decision-trees loan-prediction machine-learning python scikit-learn supervised-learning

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A Decision Tree Classifier was implemented to predict personal loan acceptance using a dataset of 5,000 customers. Key features included income, education, mortgage, and credit card usage. The model achieved 97% accuracy, with 92% precision and 76% recall for positive loan predictions, validated using a classification report and confusion matrix.

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