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The objective of the project was to develop a predictive model that combines various econometric measures to foresee a financial condition (Bankruptcy or not) of a firm based on a financial balance sheet data set available on Kaggle. This was done as part of the Data Mining course at the Krannert Business Analytics Program, Purdue University - Team: Zaid Ahmed, Mohinder Goyal and Maharshi Dutta

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The objective of the project was to develop a predictive model that combines various econometric measures to foresee a financial condition (Bankruptcy or not) of a firm based on a financial balance sheet data set available on Kaggle.
We used multiple algorithms to apply Logistic regression and found out that the XGBOOST Gradient boosting algorithm gave the best results leading to the most accurate classification of a firms financial health.

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