Smote Analysis-Based Classifier and Data Equalisation Of Bankruptcy Using Logistic Regression Classification Technique Through Machine Learning Applications

Within the domain of financial risk assessment, the accurate anticipation of bankruptcy has significant importance in safeguarding the stability of economic systems. One of the longstanding issues in this particular field pertains to unbalanced datasets, whereby the number of instances representing bankruptcy is much lower in comparison to non-bankrupt cases. The combination of Synthetic Minority Over-sampling Technique (SMOTE) with logistic regression classification presents a robust approach to tackle the issue of class imbalance, resulting in improved predicted accuracy of models. Financial statistics often demonstrate a disparity in class distribution, whereby occurrences of bankruptcy are comparatively few in comparison to instances of solvency. The existing disparity and social policy are a significant obstacle for machine learning algorithms, since these models tend to exhibit a bias towards the dominant class, leading to less-than-ideal accuracy when forecasting the minority class (bankruptcy). The main aim of this study is to access the Logistic regression classifier in order to classify Bankruptcy detection. The target will be achieved by the use of SMOTE Analysis, a technique designed to address the issue of unbalanced data. The anticipated accuracy of 84 percent will be employed using the classification report and the confusion matrix, both of which will be utilised in the proposed research study to visualise the results.