Description
See PySpark program and dataset here
PySpark code for computationally efficient use of statistical learning and machine learning algorithms for the application scenario of personal credit evaluation with a performance comparison of models including logistic regression, decision tree, random forest, neural network, and support vector machine
PySpark code for computationally efficient use of statistical learning and machine learning algorithms for the application scenario of personal credit evaluation with a performance comparison of models including logistic regression, decision tree, random forest, neural network, and support vector machine
| Date made available | Oct 2023 |
|---|---|
| Publisher | GitHub |