This project focuses on developing machine learning models to assist medical insurance companies in risk assessment. The goal is to enable rapid decision-making regarding pricing and provide tailored coverage based on each client's risk profile. By leveraging machine learning techniques such as Softmax Regression, Support Vector Machines (SVM), and Gradient Boosting Classifier, we aim to enhance the accuracy and efficiency of risk assessment processes.
The motivation behind this project is to support insurance companies in making informed decisions while granting policies to applicants. By accurately assessing risk, insurers can ensure their financial solvency and provide tailored coverage to clients.
The dataset used in this project is sourced from Kaggle and consists of medical insurance risk assessment data, including demographic and medical information of clients. The dataset is available here.
Three machine learning models were employed in this project:
- Softmax Regression: A classification algorithm suitable for multiclass problems.
- Support Vector Machines (SVM): A popular classification algorithm effective for binary classification tasks.
- Gradient Boosting Classifier: A machine learning algorithm that combines decision trees to improve accuracy.
The models were trained and evaluated using various evaluation metrics such as ROC curves, balanced accuracy scores, and confusion matrices. The Gradient Boosting Classifier demonstrated superior performance, achieving high balanced accuracy scores and minimizing false positive and false negative rates.
Machine learning techniques have shown promise in assisting medical insurance companies with risk assessment. By utilizing advanced models and evaluation metrics, insurers can make rapid and informed decisions regarding pricing and coverage for their clients. This project highlights the potential of machine learning in enhancing the accuracy and efficiency of risk assessment processes.