使用 Python 进行人工智能机器学习

VISVESVARAYATECHNOLOGICAL"JnanaSangama",Belgavi-590018,Karnataka,AnInternshipReport“MachineLearningWithAIusingAlongwiththe“ADMISSIONPREDICTIONSubmittedinPartialFulfillmentoftherequirementfortheawardofthedegreeBACHELOROFCOMPUTERSCIENCEANDSubmittedName:VenkateshMurthySRUSN:1SJ18CS117UndertheguidanceInternal ExternalProf.SrinathG Dr.PurbadriAssistant MentorDept.OfCSE, KnowledgeSolutionsSJCINSTITUTEOFTECHNOLOGYDEPARTMENTOFCOMPUTERSCIENCEANDENGINEERINGllJaiSrI(;uru(k·`,IISriAdichunchanagiriShiksl;;ana1'rustS.J.CINSTl1l'1EOl?TEC1八01.0GY,Chickh:1ll11pu-r1)cimrtmcntofComputcrScicnc`.am1卜：ngir1ccrillg

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．．．．．．．．．．．．．．．．．．．．．．♦它气言，总'I/S气言，总'I/Principal,气Signaturewith气COMPANYPAGE\*romanPAGE\*romaniI,VenkateshMurthySR,studentofVIIIsemesterB.EinComputerscience&EngineeringatSJCInstituteofTechnology,Chickballapur,herebydeclarethattheInternshipworkentitled“ADMISSIONPREDICTIONANALYSIS”hasbeenindependentlycarriedoutbymeunderthesupervisionofProf.SrinathGM,AssistantProfessorofDepartmentofCSE,andthecoordinatorProf.SwethaTAssistantProfessor,submittedinpartialfulfillmentofthecourserequirementfortheawardofdegreeinBachelorofEngineeringinComputerScience&EngineeringofVisveswarayaTechnologicalUniversity,Belgaviduringtheyear2021-2022.IfurtherdeclarethatthereporthasnotbeensubmittedtoanyotherUniversityfortheawardofanyotherdegree.PLACE:CHICKBALLAPUR STUDENTNAME:VenkateshMurthySRDate:11May2022 USN:1SJ18CS117Manystudentsnowadaysarepursuingtheireducationoutsideoftheirhomenations.TheseinternationalstudentsareprimarilyinterestedintheUnitedStatesofAmerica,Canada,Ireland,andGermany.IndiaandChinaaccountforthemajorityofinternationalstudentsintheUnitedStates.ThenumberofIndianstudentspursuingpostgraduateeducationintheUnitedStateshassurgeddramaticallyduringthelastdecade.WiththegrowingnumberofinternationalstudentsstudyingintheUnitedStates,eachcandidatemustcompetefiercelyforadmissiontotheirpreferreduniversity.Ineducationalinstitutions,theissueofstudentadmittanceiscritical.Thisresearchfocusesonusingmachinelearningmodelstopredictastudent'schancesofbeingacceptedintoamaster'sdegree.Studentswillbeabletoseeaheadoftimeiftheyhaveaprobabilityofbeingadmitted.Thisprojectpredictsastudent'sadmittancebasedonavarietyoffactorssuchastheuniversity'srating,thestudent'sundergraduateGPA,GREscore,researchexperience,andsoon.Thisforecastswhetherornotthestudentwillgetadmittedtotheuniversityofhischoice.Iemployedavarietyofmethodsinthisstudy,includinglinearregression,artificialneuralnetworks(ANN),randomforestregression,anddecisiontreeregression.Finally,IputthismodelonaWeb-basedGUItocheckastudent'sacceptancepossibilities,anditworkedperfectly.Withreverentialpranam,weexpressmysinceregratitudeandsalutationstothefeetofhisholinessByravaikyaPadmabhushanaSriSriSriDr.BalagangadharanathaMahaSwamiji,&hisholinessJagadguruSriSriSriDr.NirmalanandanathaSwamijiofSriAdichunchanagiriMuttfortheirunlimitedblessings.Firstandforemost,wewishtoexpressmydeepsincerefeelingsofgratitudetoourinstitution,SriJagadguruChandrashekaranathaSwamijiInstituteofTechnology.Forprovidingmeanopportunityforcompletingmyinternshipworksuccessfully.IextenddeepsenseofsinceregratitudetoDr.GTRaju,Principal,SJCInstituteofTechnology,Chickballapur,forprovidinganopportunitytocompletetheInternshipWork.Iextendspecialin-depth,heartfelt,andsinceregratitudetoourHODDr.ManjunathaKumarBH,ProfessorandHeadoftheDepartment,ComputerScienceandEngineering,SJCInstituteofTechnology,Chickballapur,forhisconstantsupportandvaluableguidanceoftheInternshipWork.IconveyoursincerethankstoInternshipInternalGuideProf.SrinathGM,AssistantProfessor,DepartmentofComputerScienceandEngineering,SJCInstituteofTechnology,forhisconstantsupport,valuableguidanceandsuggestionsoftheInternshipWork.IamthankfultoInternshipExternalGuideDr.PurbadriGhosal,KnowledgeSolutionsIndia,forprovidingvaluableguidanceandencouragementoftheInternshipWork.IalsofeelimmensepleasuretoexpressdeepandprofoundgratitudetoourInternshipCoordinatorProf.SwethaT,AssistantProfessor,DepartmentofComputerScienceandEngineering,SJCInstituteofTechnology,forhisguidanceandsuggestionsoftheInternshipFinally,IwouldliketothankallfacultymembersofDepartmentofComputerScienceandEngineering,SJCInstituteofTechnology,Chickballapurfortheirsupport.Ialsothankallthosewhoextendedtheirsupportandco-operationwhilebringingoutthisInternshipReport.VenkateshMurthySRiListofChapterChapterPage1COMPANYHistoryofthe Operationsofthe Major Structureofthe Services 2ABOUTTHE2.1SpecificFunctionalitiesofthe62.2Process62.372.4Structureofthe72.5RolesandResponsibilitiesof8 TASK 44.14.2TechnicalPAGE\*romanPAGE\*romanv4.2.1SystemRequirementSystemAnalysisand ExistingDisadvantagesoftheExistingProposedAdvantagesoftheProposedSystem DataFlowUMLUSECASEClassSequenceActivity Screen 55AppendixA:LISTOFFigureNameofthePageDataFlowStructureofthe7HomeInputCHAPTER-COMPANYHistoryoftheKnowledgeSolutionsIndiaiscorporationthatspecializesincertificationandtraining.TheyprovideinternationalcertificationsfromMicrosoft,Apple,Adobe,ECCouncil,Autodesk,Quickbooks,andothersasMicrosoftAuthorisedEducationPartnersandCertiportCATC.AndcollaboratecloselywithcollegesanduniversitiesaroundtheAnothercompany,QuantumLearnings,isaMicrosoftGlobalTrainingPartnerandtheirdaughtercompany,andMentorrBuddyistheirownplacementassistancetoolthathelpsstudentsfindthebestjobpreparationandapplications.ThesubjectmatterexpertsatKSIarehighlyqualified.Theseexpertshaveextensiveexperienceintheirrespectivefieldsandarealsocertified.Theyareenthusiasticaboutthesubjectstheyteach,andtheirwebinarsandcoursesreflectthisenthusiasm.Theyhavethebesttechnicaltrainingdeliveredacrossthecountry,whetheranyonelookingforMobileapplicationdevelopment,ProgressiveWebapplicationdevelopment,AngularJs,Python,IoT,ML,AI,DataScience,AdvExcel,orDigitalMarketing.Alloftheircoursesbeginwithnoprerequisites,andtheirteammakeseveryefforttoensurethatacandidateonlycompletestheprogramafteracquiringthenecessaryskills.1AdmissionPredictionAdmissionPredictionCompanyDept.ofCSE,Dept.ofCSE,Theirgoalistoconsistentlydeliversuccesstostudentsbygoingtheextramile.Tohelptheirstudentsmeettheirtechnologicalskillsandcareeropportunities,theyoffertherightpeople,solutions,andservices.Byleveragingleadingtechnologiesandindustrybestpractices,theyprovidetheirstudentswiththemostefficientandeffectivetraining.OperationoftheTheracefordigitaltransformationison.Inthisgloballyconnectedon-demandworldwithrapidadvancementsininternettechnologies,businessesworldwideareunderconstantpressuretoaddinnovativereal-timecapabilitiestotheirapplicationstorespondtomarketopportunities.Everybusinessworldwideisbuildingevent-driven,real-timeapplications-fromfinancialservices,transportation,andenergy,toretail,healthcare,andGamingOurendeavoristomakeiteasytodevelopinnovativereal-timeapplicationsandefficienttooperatetheminproduction.Wehaveaprovenrecordofbuildinghighlyscalable,world-classconsultingprocessesthatoffertremendousbusinessadvantagestoourclientsintheformofhugecost-benefits,definitiveresultsandconsistentprojectdeliveriesacrosstheglobe.Weprominentlystrivetoimproveyourbusinessbydeliveringthefullrangeofcompetenciesincludingoperationalperformance,developingandapplyingbusinessstrategiestoimprovefinancialreports,definingstrategicgoalsandmeasureandmanagethosegoalsalongwithmeasuringandmanagingthem.MajorSkillshavebecometheglobalcurrencyofthe21stcentury.Inaworldwherecompetitionforjobs,payincreases,andacademicsuccesscontinuestoincrease,certificationsofferhopebecausetheyareacredible,third-partyassessmentofone’sskillandknowledgeforagivensubject.SomeofthekeybenefitsachievedbythestudentsbycertificationareValidationofknowledge,Increasedmarketability,Increasedearningpower,Enhancedacademicperformance,Improvedreputation,Enhancedcredibility,Increasedconfidence,Respectfrompeers.ByKnowledgeSolutionIndia’scertification,studentshasimprovedacademicperformancehaving,highergradepointaverageforcertifiedcollegestudentsfrom6.9to7.8,highergraduationratesforcertifiedcollegestudents:78.4%to94.5%andthedropoutratesarereducedto0.2%to1.0%.StructureoftheOursuperenergeticandmassiveteamatKSIisourcorestrength,forminganexcellentblendofITmindswithacreativebent.TheirgoalistokeepimprovinganddeliveringtheskillsthatwillhelpstudentshaveasuccessfulcareerintheITindustry.Takingadvantageofourhighlyskilledandexperiencedtrainers.Weareprimarilyastudent-centeredorganizationdedicatedtoexceedingstudents'expectationsintermsofmeetingtheirneeds.Theysuccessfullyhostedagroupofseasonedprofessionals.Trainerswhocollaborateinordertoprovidetheirstudentswiththeknowledgetheyneedtoadvanceintheircareers.Theytakeprideinbeingasought-afterSkilldevelopmentafterdeliveringsuccessfulinternships.Theyhavesuccessfullydeliveredvaluetoourstudentsaswellascollegesovertheyears.Theytrulybelievethatthesuccessoftheirstudentsistheirsuccess,andtheydonotconsiderthemselvestobeavendorfortheirprogram.We'dliketohearsomeoftheirstoriesandlearnhowfarthey'vegonetoensurethesuccessofourstudents,andthey'lldoeverythingtheycantomakethathappen.ServicesTraining/Internshipsformaveryimportantpartofstudentsoveralldevelopmentthat'swhyAICTEandUniversitieshavemadeitmandatoryforeveryengineerandMCAtoundergothesame,wehelpstudentsinachievingthisgoalbyhelpingthemacquirelatestskillsandprovidethemwithhandsonprojects.MachineLearningandInternshipLearnMachinelearning,anapplicationofartificialintelligence(AI)thatprovidessystemstheabilitytoautomaticallylearnandimprovefromexperiencewithoutbeingexplicitlyprogrammed,bundledwithMicrosoftMTACertificationDataScienceandInternshipLearnDatascienceandhowtousescientificmethods,processes,algorithmsandsystemstoextractknowledgeandinsightsfromstructuredandunstructureddataasoneofthehottestprofessionsinthemarkettoday,bundledwithMicrosoftMTACertificationJavaCertificateLearnJavaoneofthemostpopularprogramminglanguagesusedinthedevelopmentofWebandMobileapplications.Itisdesignedforflexibility,allowingdeveloperstowritecodethatwouldrunonanymachine,regardlessofarchitectureorplatformBundledwithMicrosoftMTACertificationCyberSecurityCertifiedLearntheethicalwayofhowtodopenetrationtestingandothertestingmethodologiesthatensuresthesecurityofanorganization’sinformationsystems,bundledwithMicrosoftMTACertification.InternetofLearnhowtoworkwithconnecteddevicesusesensorsandraspberryPI3andconnectthesedevicestocloudtoidentifypatternsandextractmeaning-fullinformationoutofit,bundledwithMicrosoftMTACertificationBusinessLearnBusinessAnalyticsandhowitenablescompaniestoautomateandoptimizetheirbusinessprocessesin-factData-drivencompaniestreattheirdataasacorporateassetandleverageitforacompetitiveadvantageastheyareabletousetheinsightstofindnewpatternsandrelationships.DigitalLearnDigitalMarketingandhowitsusedforpromotingproductsorservicesonlineviainternet,companiesaregaininghigherprofitabilityandreturnoninvestmentbyhavingtheirDigitalmarketingstrategiesinplacetheprogramisbundledwithGoogle66CHAPTER–ABOUTTHESpecificFunctionalitiesoftheOurdepartmentoftechsupportmajorlyfocusedonmanage,maintainandrepairITsystems.TheSpecialfunctionalitiesincludeUnderstandingtheworktobePlanningtheassignedactivitiesinmoredetailif Informingtheprojectmanagerofissues,scopechanges,riskandqualityProactivelycommunicatingstatusandmanagingProcessThedepartmentaimstofirstunderstandtheuserrequirements.Furtheron,abasicstructureoftheproductthatneedstobebuiltisdrawnandunderstood.Eventually,thetechnologiesthatwouldbesthelpindevelopingtheproductareunderstood.Iftheproducthasdatabaserequirements,theschemaandthedatabasedesignareworkedupon.Thedepartmentbelievesin“Thinkbeforeyoucode”-therequirementsandlogicsarefirstunderstoodoverapaperandthenaremovedtoacodeform.Agileprocessesgenerallypromoteadisciplinedprojectmanagementprocessthatencouragesfrequentinspectionandadaptation,aleadershipphilosophythatencouragesteamwork,self-organizationandaccountability,asetofengineeringbestpracticesintendedtoallowforrapiddeliveryofhigh-qualitysoftware,andabusinessapproachthatalignsdevelopmentwithcustomerneedsandcompanygoals.AgiledevelopmentreferstoanydevelopmentprocessthatisalignedwiththeconceptsoftheAgileManifesto.TheManifestowasdevelopedbyagroupfourteenleadingfiguresinthesoftwareindustry,andreflectstheirexperienceofwhatapproachesdoanddonotworkforsoftwareAdmissionPredictionAdmissionPredictionAbouttheDept.ofCSE,Dept.ofCSE,TestingwasdoneaccordingtotheCorporateStandards.Aseachcomponentwasbeingbuilt,Unittestingwasperformedinordertocheckifthedesiredfunctionalityisobtained.Eachcomponentinturnistestedwithmultipletestcasestoverifyifitisproperlyworking.Theseunittestedcomponentsareintegratedwiththeexistingbuiltcomponentsandthenintegrationtestingisperformed.Hereagain,multipletestcasesareruntoensurethenewlybuiltcomponentrunsinco-ordinationwiththeexistingcomponents.UnitandIntegrationtestingareiterativelyperformeduntilthecompleteproductisbuilt.Oncethecompleteproductisbuilt,itisagaintestedagainstmultipletestcasesandallthefunctionalities.Theproductcouldbeworkingfineinthedeveloper’senvironmentbutmightnotnecessarilyworkwellinallotherenvironmentsthattheuserscouldbeusing.Hence,theproductisalsotestedundermultipleenvironments(Variousoperatingsystemsanddevices).Ateverystep,ifaflawisobserved,thecomponentisrebuilttofixthebugs.Thisway,testingisdonehierarchicallyanditeratively.StructureoftheFigure2.4.1StructureoftheRolesandResponsibilitiesofSincetheinternshipwasremotelyconductedbythecompany,toensureeasyonboardingofinterns,thecompanyhadindividualswhotookcareofthesmoothrunofonlinetraining.OperationandStrategyHead-Ensuredtherewerenodifficultiesforinternswhileonboarding.Bestofmentorsanddoubtclarifyingsessionswerearrangedtoo.TechnicalLead-Ensuredthetechnicalitiesofonlinetrainingtobesmooth.Bestplatformswerearrangedforourmeetingsandtrainings.Mentors-Theyhavehelpedustounderstandtheconcepts,gaveustaskstogetpracticaltakeawayandclarifieddoubtstothebest.Interns-WorkedthroughthetasksgiveneitherindividuallyorinaPAGEPAGE9CHAPTER–TASKInthisInternshipMachineLearningwithPythonusingAIitwasdividedintotwopartsoneisfrontenddevelopmentandonemoreisbackendcourse.TrainingTheinternshipisaplatformwherethetraineesareassignedwiththespecifictask.Intheinitialdaysoftheinternship,Iwastrainedonthefollowing:PythonArtificialMachineLearningDATAThissectiondescribes,inbrief,thedatathathasbeenusedfortheresearch.Datafrommultiplesourceswasusedinthisproject,themajoramountofdatawasextractedfrompublicwebsiteYocket(Y),dataregardingtherankings,feesandenrolmentincollegeswasobtainedfromaleadingeducationalconsultancyfirmTheMentorsCircleinIndia.Datafromboththesourceswasintegratedtogethertoformastagingdata-set.Forpredictingthechanceofastudentgettingshortlistedinuniversitiesthefinaldata-setwasdividedintomultipledata-setseachrepresentingaparticularuniversity.Forpredictingthelistofuniversitiessuitableforstudentsbasedontheirprofiledataofallthestudentsthestagingdata-setwasupdatedonlytohaverecordsofstudentswhohadsuccessfullysecuredadmissionintheuniversities.Belowtableshowsthedifferentfeaturesofthedata-sets. MarksscoredbythestudentinTOEFLMarksscoredbythestudentinEnglishProficiencyTheUniversityQualityofStatementofPurposeorStatementofQualityofLetterofRecommendationsResultofthestudentintheirUndergraduateRelevantexperienceinResearchAdmissionPredictionAdmissionPredictionTaskDept.ofCSE,Dept.ofCSE,2021-DATASETEXTRACTIONANDDatarelatedtothecollegerankingwascollectedin.csvformat,thedatarelatedtostudents’profilewasextractedusingdataextractiontoolprovidedby(Mozenda(n.d.))in.csvfiles.Databeingfrompublicportalhadmultiplerecordswithmissingandirrelevantvalues;datacleaningwasperformedinMicrosoftExcelbydeletingtherecordshavingunwantedandmissingvalues.Unwantedcolumnswereremovedfromthedata-set.Oncethedata-setwascleaneddatawastransformedtobesuitableforthemodel.Theoriginaldata-sethadTOEFLscoreasarepresentationoflanguage,tohaveaconsistentmetricsforthelanguagescore.Similarly,theUndergraduatescoreofthestudentswererepresentedintermsofpercentageandCGPA;alltherecordsofpercentagewereconvertedtoCGPAbymultiplyingpercentagescoreby9.5.LinearLinearRegressionisamachinelearningalgorithmbasedonsupervisedlearning.Itperformsaregressiontask.Regressionmodelsatargetpredictionvaluebasedonindependentvariables.Itismostlyusedforfindingouttherelationshipbetweenvariablesandforecasting.Differentregressionmodelsdifferbasedon–thekindofrelationshipbetweendependentandindependentvariablestheyareconsidering,andthenumberofindependentvariablesgettingused.ArtificialNeuralItintendedtosimulatethebehaviorofbiologicalsystemscomposedof“neurons”.ANNsarecomputationalmodelsinspiredbyananimal’scentralnervoussystems.Itiscapableofmachinelearningaswellaspatternrecognition.Thesepresentedassystemsofinterconnected“neurons”whichcancomputevaluesfrominputs.Aneuralnetworkisanorientedgraph.Itconsistsofnodeswhichinthebiologicalanalogyrepresentneurons,connectedbyarcs.Itcorrespondstodendritesandsynapses.Eacharcassociatedwithaweightwhileateachnode.ApplythevaluesreceivedasinputbythenodeanddefineActivationfunctionalongtheincomingarcs,adjustedbytheweightsofthearcs.Aneuralnetworkisamachinelearningalgorithmbasedonthemodelofahumanneuron.Thehumanbrainconsistsofmillionsofneurons.Itsendsandprocesssignalsintheformofelectricalandchemicalsignals.Theseneuronsareconnectedwithaspecialstructureknownassynapses.Synapsesallowneuronstopasssignals.Fromlargenumbersofsimulatedneuronsneuralnetworksforms.InmySixweeksInternshipIhaveundergonethroughthreeTrainingDesigningandDevelopmentTestingandMaintenanceAsthefinaltask,amainprojectwasdevelopedusingmachinelearningmodelstopredictthechanceofastudenttobeadmittedtoamaster’sprogram.Thiswillassiststudentstoknowinadvanceiftheyhaveachancetogetaccepted.Thisprojectpredictstheadmissionofastudentbasedondifferentfeaturesincludinguniversityrating,student’sundergraduateGPA,GREscore,researchexperienceandetc.Thispredictsthathowmuchchancesaretherethatthestudentwillgetadmissioninhisselecteduniversityornot.InthisprojectIhaveusedmultiplealgorithmsincludinglinearregression,artificialneuralnetwork(ANN),randomforestregressor,decisiontreeregressor.IntheendIhavedeployedthismodelonaWebBasedGUItocheckstudent’sadmissionchancesandthesemodelsareworkingfine.CHAPTER–REFLECTIONAccordingtoourinternshipexperience,KnowledgeSolutionsIndiaoffersapositiveworkcultureandcourteouspersonnelatalllevels,fromstafftomanagement.Theinstructorsareknowledgeableintheirsubjectsandtreateveryonefairly.Therearenodistinctionsmadebetweennewgraduatesandcorporateexecutives,andeveryoneistreatedequally.Everyactivity,nomatterhowdifficultorsimple,requiresalotofteamwork,andthemoodisalwayspeacefulandwelcoming.Becauseoftheexcellentcommunicationandsupportavailable,thereisalotofroomforself-improvement.Internswerewelltreatedandeducated,andallofourquestionsandconcernsaboutthetrainingorthefirmswereaddressed.Allinall,KnowledgeSolutionsIndiawasagreatplaceforafreshertostartcareerandalsoforacorporatetoboosthis/hercareer.Ithasbeenagreatexperiencetobeaninterninsuchareputedorganization.TechnicalSystemRequirementsandSpecificationHARDWAREREQUIREMENTS: :x86orHardDisk 216GBor :512MB(minimum),1SOFTWAREOperating WindowsorDevelopmentEnvironment AnacondaNavigator(JupiterNotebookorAdmissionPredictionAdmissionPredictionDept.Dept.ofCSE,2021-SystemAnalysisandExisting(Bibodietal.(n.d.))usedmultiplemachinelearningmodelstocreateasystemthatwouldhelpthestudentstoshortlisttheuniversitiessuitableforthemalsoasecondmodelwascreatedtohelpthecollegestodecideonenrolmentofthestudent.NaveBayesalgorithmwasusedtopredictthelikelihoodofsuccessofanapplication,andmultipleclassificationalgorithmslikeDecisionTree,RandomForest,NaveBayesandSVMwerecomparedandevaluatedbasedontheiraccuracytoselectthebestcandidatesforthecollege.GRADEsystemwasdevelopedby(WatersandMiikkulainen(2013))tosupporttheadmissionprocessforthegraduatestudentsintheUniversityofTexasAustinDepartmentofComputerScience.Themainobjectiveoftheprojectwastodevelopasystemthatcanhelptheadmissioncommitteeoftheuniversitytotakebetterandfasterdecisions.LogisticregressionandSVMwereusedtocreatethemodel,bothmodelsperformedequallywellandthefinalsystemwasdevelopedusingLogisticregressionduetoitssimplicity.Thetimerequiredbytheadmissioncommitteetoreviewtheapplicationswasreducedby74%buthumaninterventionwasrequiredtomakethefinaldecisiononstatusiftheapplication.(Nandeshwaretal.(2014))createdasimilarmodeltopredicttheenrolmentofthestudentintheuniversitybasedonthefactorslikeSATscore,GPAscore,residencyraceetc.TheModelwascreatedusingtheMultipleLogisticregressionalgorithm,itwasabletoachieveaccuracyrateof67%only.DisadvantagesoftheExistingLimitationofthissystemonlyreliedontheGRE,TOEFLandUndergraduateScoreofthestudentandmissedontakingintoconsiderationotherimportantfactorslikeSOPandLOR.TheexistingsystemlaggedthefactoroftheresearchworkintherelatedThismodelachievedonly67%Proposed AdmissionPredictionAdmissionPredictionDept.ofDept.ofCSE,2021-TheprincipalobjectiveoftheresearchistohelpthestudentswhoareaspiringtopursuetheireducationintheUSA.TheGraduateAdmissionsPredictionsystemwillhelpthemtoevaluatethechancesofsuccessinanyuniversitywithoutbeingdependentonanyeducationconsultancyfirm.Itwillhelptheminsavingahugeamountoftimeandmoneyspentintheapplicationprocess.Also,itwillhelpthemtolimitthenumberofapplicationsmadebythestudentsbysuggestingthemthebestuniversitieswheretheyhavehighchancesofsecuringadmissiontherebybysavingtheamountofmoneyspentbythestudentsbyapplyinginuniversitieswheretheyhavelesschancetosecureadmitbasedontheirprofile.AdvantagesoftheProposedInformationaboutthepredictionanalysisiscleartoenteralltherequiredinformationtopredicttheadmission.TheuserinterfacecodewillinteractwiththeLinearRegression,ANN,randomforestregressor,decisiontreeregressortoprovidetheuserswiththerequiredresult.TheANNalgorithmandLinearRegressionAlgorithmwillbeusedtodeterminethechanceofthestudentofsecuringadmissioninaparticularuniversitybasedonhis/herOncethemodelshavebeenexecutedtheresultwillbeprovidedtothestudentastheoutputontheuserinterface.System4.3.1DataFlowThemachinelearningmodelsaretrainedwiththegivendataset.Themachinelearningmodelsusedinthisprojectarelinearregression,artificialneuralnetwork(ANN),randomforestregressor,decisiontreeregressor.Oncethemodelsaretrained,thestudent’sprofiledetailsareenteredtopredictthechancesofgettingtheadmittotheuniversity.FigureDataflowdiagramofAdmissionExploratoryDataAnalysisinMachineDataTrainingandTrainandEvaluateLinearTrainandEvaluateArtificialNeuralMODULESExploratoryDataAnalysis:Performedinitialinvestigationsondatasoastodiscoverpatterns,tospotanomalies,totesthypothesisandtocheckassumptionswiththehelpofsummarystatisticsandgraphicalrepresentations.DataVisualization:Usingdatavisualization,Isummarizedthedatawithgraphs,picturesandmaps,sothatthehumanmindhasaneasiertimeprocessingandunderstandingthegivendata.Datavisualizationplaysasignific