There　are　many　different　kinds　of　prediction　models　that　have　different　performances　when　faced　with　different　kinds　of　data.　This　essay　focus　on　the　comparison　of　the　performance　of　multiple　regressions,　SVM　with　RBF　kernel　function,　and　Random　Forest　when　predicting　the　house　prices　of　Boston　and　using　score　function,　k-fold　cross-validation　and　shuffle　cross-validation　to　evaluate　their　performance　respectively.　Finally,　parameter　adjustment,　grid　search,　and　forward　selection　are　introduced　to　improve　their　performance.　By　combining　the　result　given　by　three　evaluating　methods,　SVM　with　RBF　kernel　function　is　the　better　model　and　the　Random　Forest　is　the　worst　one,　whose　scores　are　higher　than　0.7　and　lower　than　0.1　respectively.　And　all　of　these　three　functions　can　slightly　improve　the　performance,　especially,　the　effect　of　the　grid　search　is　the　best　one,　which　can　improve　the　score　by　0.023　higher　than　the　original　score.　©　2022　IEEE.