ISSN :3049-2297
Millicent Auma Omondi, Josue Nguinabe, John Kamwele Mutinda, Amos kipkorir Langat, Leonard Sanya, Ouraga Aime Cervert Ballou and Jeremy Nlandu Mabiala
Jou. Artif. Intell. Auto. Intell., 2 (2):349-370
Millicent Auma Omondi : African Institute for Mathematical Sciences (AIMS) - Senegal
Josue Nguinabe : African Institute for Mathematical Sciences (AIMS), Senegal
John Kamwele Mutinda : University of Science and Technology of China (USTC)
Amos kipkorir Langat : Pan African University Institute for Basic Sciences
Leonard Sanya : Dedan Kimathi University of Technology
Ouraga Aime Cervert Ballou : African Institute for Mathematical Sciences (AIMS), Senegal
Jeremy Nlandu Mabiala : African Institute for Mathematical Sciences (AIMS), Senegal
Article History: Received on: 29-Apr-25, Accepted on: 22-Aug-25, Published on: 29-Aug-25
Corresponding Author: Millicent Auma Omondi
Email: momondi@aimsammi.org
Citation: Millicent Auma Omondi (2025). Interpretable Property Insurance Premium Prediction Using Machine Learning Models. Jou. Artif. Intell. Auto. Intell., 2 (2 ):349-370
Accurately predicting home insurance premiums is a critical challenge for insurers, as traditional methods struggle with the complexity and volume of modern data. This study leverages machine learning to address this problem, applying a range of supervised models—including linear regression, lasso regression, ridge regression, decision trees, random forests, gradient boosting, and extreme gradient boosting (XGBoost)—to a comprehensive home insurance dataset with 66 features