02237nas a2200277   4500000000100000000000100001008004100002260001200043653002100055653001500076653001500091100002500106700001300131700002900144700002400173700002300197700002600220700002400246700001100270245009700281856007900378300001000457490000600467520147200473022001401945       2022                            d  c03/202210aMachine Learning10aPrediction10aRegression1 aChukwuebuka J. Ejiyi1 aZhen Qin1 aAbdulhaq Adetunji Salako1 aMonday Nkanta Happy1 aGrace Ugochi Nneji1 aChiagoziem C. Ukwuoma1 aIjeoma A. Chikwendu1 aJi Gen00aComparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms  uhttps://www.ijimai.org/journal/sites/default/files/2022-02/ijimai7_3_7.pdf  a75-850 v73 aIn finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result.  a1989-1660