The Bankruptcy Prediction project is a machine learning project that aims to predict if an organization is likely to go bankrupt based on a set of attributes related to the organization's financial data. The project used a data set of 250 records, each with 6 nominal attributes, to train and test machine learning models. The machine learning techniques used in this project included Linear and Non-Linear Support Vector Machines (SVM), Decision Tree Classifier, and Gaussian Naive Bayes. The goal of the project was to develop a model that could accurately predict bankruptcy based on the financial data provided.
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Using data mining techniques to predict if the organization is prone to bankruptcy using the data with 250 records and 6 nominal attributes per record. Machine learning techniques used: Linear and non-linear SVM, Decision Tree Classifier, Gaussian Naive Bayes.
IrakliXYZ/Bankruptcy-Prediction
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Using data mining techniques to predict if the organization is prone to bankruptcy using the data with 250 records and 6 nominal attributes per record. Machine learning techniques used: Linear and non-linear SVM, Decision Tree Classifier, Gaussian Naive Bayes.