![]() This research provides a comprehensive review of machine learning techniques for predicting house prices. ![]() Real estate is one of the most critical investments in the household portfolio, and represents the greatest proportion of wealth of the private households in highly developed countries. Regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8%įor kSVM and 64.8% for LR showing that kSVM offered the best result. Zindi.africa competition and their performances are checked using not only accuracy and precision metrics butĪlso recall, and F1 score metrics, all displayed on the confusion matrix. Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from This study therefore,Ĭompares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi AfricaĬompetition which is said to be from Olusola Insurance Company in Lagos Nigeria. Long history and have also got some modifications for optimum functionality. ![]() Various classification algorithms have been used since they have a Machine Learning (ML) whenīrought into the field of insurance would enable seamless formulation of insurance policies with a better Various factors affect house insurance claims, some of which contribute toįormulating insurance policies including specific features that the house has. In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the
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