Team Members: Neha Rana, Amisha Dubey, Ashika Kotia, Vansh Desai
The goal of this project was to develop predictive models for assessing financial risks and to identify business opportunities through advanced machine learning techniques. To achieve this goal, students employed Logistic Regression and Random Forest algorithms, complimented by sophisticated feature selection and model validation strategies. The team’s methodology also incorporated several advanced techniques: permutation importance for feature selection, SHAP (SHapley Additive exPlanations) analysis for model interpretability, and a Simple Imputer to prevent data leakage. In addition, students conducted rigorous hyperparameter tuning to optimize model performance and utilized time series analysis to engineer innovative new features, enhancing the predictive capabilities of the models. The modeling approach yielded impressive results, with the risk prediction model achieving 93% accuracy and the business opportunity classification model demonstrating high precision. The statistical performance metrics were also particularly strong and included an R² of 0.81 for score prediction, a Root Mean Square Error (RMSE) of 0.86, and an F1 score of 0.92 for risk trend classification. By integrating advanced statistical methods, machine learning algorithms, and sophisticated analytical techniques, the team successfully developed a comprehensive predictive modeling framework capable of providing nuanced insights into business potential and financial risk.