Indoor　air　quality　becomes　increasingly　important,partly　because　the　COVID-19　pandemic　increases　the　time　people　spend　indoors.Research　into　the　prediction　of　indoor　volatile　organic　compounds(VOCs)is　traditionally　confined　to　building　materials　and　furniture.Relatively　little　research　focuses　on　estimation　of　human-related　VOCs,which　have　been　shown　to　contribute　significantly　to　indoor　air　quality,especially　in　densely-occupied　environments.This　study　applies　a　machine　learning　approach　to　accurately　estimate　the　human-related　VOC　emissions　in　a　university　classroom.The　time-resolved　concentrations　of　two　typical　human-related(ozone-related)VOCs　in　the　classroom　over　a　five-day　period　were　analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By　comparing　the　results　for　6-MHO　concentration　predicted　via　five　machine　learning　approaches　including　the　random　forest　regression(RFR),adaptive　boosting(Adaboost),gradient　boosting　regression　tree(GBRT),extreme　gradient　boosting(XGboost),and　least　squares　support　vector　machine(LSSVM),we　find　that　the　LSSVM　approach　achieves　the　best　performance,by　using　multi-feature　parameters(number　of　occupants,ozone　concentration,temperature,relative　humidity)as　the　input.The　LSSVM　approach　is　then　used　to　predict　the　4-OPA　concentration,with　mean　absolute　percentage　error(MAPE)less　than　5%,indicating　high　accuracy.By　combining　the　LSSVM　with　a　kernel　density　estimation(KDE)method,we　further　establish　an　interval　prediction　model,which　can　provide　uncertainty　information　and　viable　option　for　decision-makers.The　machine　learning　approach　in　this　study　can　easily　incorporate　the　impact　of　various　factors　on　VOC　emission　behaviors,making　it　especially　suitable　for　concentration　prediction　and　exposure　assessment　in　realistic　indoor　settings.