Analyzed 64 financial ratios of 6 thousand companies that went bankrupt and built a Logistic Regression classification model to predict bankruptcy. Feature selection is performed with Simulated Annealing to extract the optimal subset of features that maximizes Accuracy rate.
Analyzed 64 financial ratios of 6 thousand companies that went bankrupt and built a Logistic Regression classification model to predict bankruptcy. Feature selection is performed with Simulated Annealing to extract the optimal subset of features that maximizes Accuracy rate.
Link to data source: https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data
Analyzed 64 financial ratios of 6 thousand companies that went bankrupt and built a Logistic Regression classification model to predict bankruptcy. Feature selection is performed with Simulated Annealing to extract the optimal subset of features that maximizes Accuracy rate.