Top50MachineLearningProjectsforBeginnersin2025MachineLearningProjectsIdeasforBeginnerswithSourceCodeinPython2025-Interestingmachinelearningprojectideastokick-startacareerinmachinelearning.
"WhatprojectscanIdowithmachinelearning"Thatisacommonquestionforbeginnersinthefield.OurindustryexpertsatProjectProrecommendexploringvariousMLprojectideasacrossdifferentbusinessdomains.Thesemachinelearningprojectsprovidehands-onexperiencewiththeskillsyou'velearnedandtheopportunitytosolvereal-worldproblems.
Considerasituationwhereyouwanttobuyorsellahouseoraremovingtoanewcityandwanttorentahome,butyouneedtoknowwheretostart.Sometimes,youknowwheretostartbutmustcheckthesource'scredibility.SomepeoplefromMicrosoftalsofelttheneedtocreateareliableplacetoprovideallthisinformationonline,and"Zillow"wasbornin2006.Zillowintroduceda"Zestimate"featureafewyearslater,completelychangingthemarket.Zestimateisatoolthatprovidesthehouse'sworthbasedonvariousattributeslikepublicandsalesdata.Zestimatehasinformationonmorethan97millionhomessndasperZillow,Zestimatesarewithintherangeof10%ofthesellingpriceofhomes.
ProjectIdea:InthisMachineLearningprojectforstudents,youwillusetheZillowsEconomicsdatasettobuildahousepricepredictionmodelwithXGBoostbasedonfactorslikeaverageincome,crimerate,numberofhospitals,numberofschools,etc.HavingcompletedthistopMLproject,oneshouldbeabletoanswerquestionsliketopstateswiththehighestrentvalues,whereyoushouldbuy/rentahouse,Zestimatepersquarefeet,themedianrentalpriceforallhomes,etc.
Industry:RealEstate
ProjectIdea:ThebigMartsalesdatasetisatreasuretroveoflearningopportunities.Itconsistsof2013salesdatafor1559productsacrosstenoutletsindifferentcities.YourgoalinthisMLprojectistobuildaregressionmodelthatcanpredictthesalesofeachofthese1559productsforthefollowingyearineachofthe10differentBigMartoutlets.Thedatasetalsoincludesspecificattributesforeachproductandstore,providingvaluableinsightsintothefactorsinfluencingsales.ThisprojectisafantasticwaytounderstandhowmachinelearningcanhelpbusinesseslikeBigMartincreasetheirsales.
Industry:Multiple
Industry:Entertainment
Industry:Medicine
Industry:Finance
It’sknownthattheolderthewine,thebetterthetaste.However,severalfactorsotherthanagegointowinequalitycertification,whichincludesphysiochemicaltestslikealcoholquantity,fixedacidity,volatileacidity,determinationofdensity,pH,andmore.
Industry:Viticulture
ProjectIdea:Youcangetstartedworkingwiththisdatasetbybuildingaworld-cloudvisualizationofmovietitlestobuildamovierecommendersystem.
BostonHousePricesDatasetconsistsofhousingpricesacrossdifferentplacesinBoston.Thedatasetalsoconsistsofinformationonareasofnon-retailbusiness(INDUS),crimerate(CRIM),ageofpeoplewhoownahouse(AGE),andseveralotherattributes(thedatasethasatotalof14attributes).
ProjectIdea:TheBostonHousingdatasetcanbedownloadedfromtheUCIMachineLearningRepository.Thismachinelearningprojectaimstopredictthesellingpriceofanewhomebyapplyingbasicmachinelearningconceptstothehousingpricedata.Thisdatasetistoosmall,with506observations,andisconsideredagoodstartformachinelearningbeginnerstokick-starttheirhands-onpracticeonregressionconcepts.Ifyouareabeginnerindeeplearning,youcanalsousethisdatasettoexperimentwithdeeplearningalgorithmsandbuildadeeplearningproject.
Afteralongdayofwork,wealllookforwardtoreturningtoourhomesandgettingcomfortinthosefamiliarwalls.Evenmoresonow,withthepandemicchangingtheworkcultureandencouragingmoreofustoworkfromhome,findingacozyandaccommodatinghousehasbecomeparamount.Goingthroughlonglistsofoptionsonrentalsitescanbeverytiringandresultinonesettlingforahomethatneedstobebetter.
ProjectIdea:Byperformingasentimentalanalysisoftheviewersforvariousrentallistings,itispossibletodeterminetheirreactionstowardscertainhousesand,accordingly,understandthepopularityofhomesthatareupforrent.Itcanfurtherpredicttheinterestlevelsofnewplacestobelisted.Thisknowledgealsobenefitstheownerssotheycanplanbasedonthepredictionsforthenumberofinquiriesexpected.Thechallengehereistogroupandmakesenseofthepastdata.Inthismanner,itwillallowforbetterhandlingoffraudcontrol,identifypotentialqualityissuesorconcernsthatmayarisewhilelisting,andalsohelptheownersandagentstogetabetterideaofwhatattractsrenters.
CouponMarketingisastrategybusinessesusetolurecustomerstobuytheirproducts.Couponsareeasyandcommonlyusedacrossseveraldomainsfordiscountsandpromocodes.Apartfromtheusuale-commercesites,couponscouldbenefitthetravelindustrybyofferingdealsonflightsandhotelbookings,thehealthsectorbyprovidingdiscountedconsultations,andeveneducationalplatformssothatexpectedclientscanunderstandthebusiness.Thismarketingstrategywillbehelpfulonlyifitreachestheintendedaudience.
Industry:E-commerce
Loansarewhatmaketheworldgoround.Theyarethecorebusinessforbankssincetheirmainprofitcomesfrominterestonloans.Sometimes,tobeabletotakerisksofthissortandsometimes,eventohavesomeworldlypleasures,itbecomesnecessaryforonetoapplyforaloan.Banksusuallyhavearigorousprocesstofollowbeforealoancanbeapproved.Andtheycanleveragemachinelearningmethodstopredicttheeligibilityforaloanthatsomeoneappliesforsothattherecanbebetterplanningbeyondtheloanbeingapprovedorrejected.
ProjectIdea:Themodelfordeterminingloaneligibilitypredictionhastobetrainedusingadatasetthatconsistsofdataincludingdatasuchassex,maritalstatus,numberofdependents,income,qualifications,creditcardhistoryandloanamounttonameafew.Forthisproject,wemakeuseofthedatasetfromSYLbank.TheSYLbankisoneofAustralia’slargestbanks.Thisprojectwillrequiretrainingandtestingthedatamodelusingthemethodofcrossvalidation.Afterusingdatavisualizationtechniques,cleanthedataandfillinthemissingvalues.ThisprojectisanexcellentmeanstolearnhowtobuildstatisticalmodelssuchasGradientBoostingandXGBoost,andalsotounderstandmetricssuchasROCCurve,MCCscorerandthelike.
Industry:FinancialServices
PreparingsufficientinventoryisataskthatnotonlyrestaurantsregisteredonZomatohavetocomplete.Mostcompaniesthatofferproductshavetoensuretheyhaveenoughtosatisfyalltheircustomers.Itisessentialtohavearoughestimateofhowmuchpreparationwouldbeenough.Thisestimationcanbeachievedbywhatwecalldemandforecasting.Ademandforecastisvitalforallbusinessdecisions:sales,finance,productionmanagement,logistics,andmarketing.Iftheseforecastsarecorrectlypredicted,theycanhelpbusinessesgrowsignificantlybyallowingthemtoreachcustomerswiththerightproductsattherighttime.Itcanalsohelpcompaniesinavoidingunnecessarywastageoftheirresources.
ProjectIdea:ByapplyingrelevantalgorithmssuchasBagging,Boosting,XGBoost,GradientBoostingMachine(GBM),SupportVectorMachines,andmore,businessescanmakeaccuratepredictionsaboutcustomerdemand.Thiscansignificantlyimprovetheirinventorymanagementandoveralloperations.
ProjectIdea:IfyouarewonderinghoweverythingisrelatedtoaMachineLearningproject,don’tbesurprisedtoknowthatKagglehasaviralchallengerelatedtotheTitanicship.Thetaskistopredictwhichpassengersontheshipwillsurvivegiventheirname,age,gender,socio-economicstatus,etc.Youcanuseanymachinelearningmodelyouliketomodelthegivendatasetanddeterminewhichbestcorrelatesthepassengercharacteristicstothechancesoftheirsurvivalontheship.
Thebestwaytolearnmachinelearningistoimplementbeginner—toadvanced-levelprojectsbasedonmachinelearning.HereareafewmachinelearningprojectideasforbeginnersinPython.
Industry:Hospitality
Customersareacompany'sgreatestasset,andretainingcustomersisvitalforanybusinesstoboostrevenueandbuildlong-lasting,meaningfulrelationshipswithcustomers.Moreover,thecostofacquiringanewcustomerisfivetimesmorethanthatofretaininganexistingcustomer.Identifyingifandwhenacustomerwillchurnandquicklydeliveringactionableinformationaimedatcustomerretentioniscriticaltoreducingchurn.Machinelearningprovidespracticalmethodsforidentifyingchurn'sunderlyingfactorsandprescriptivetoolsforaddressingthem.
ImageCredit.:gallery.azure.ai
ImageCredit:medium.com
Forexample,twoaccountswiththesamemonthlyclosingbalanceinthebankingindustrycanbechallengingtodifferentiateregardingchurnprediction.However,featureengineeringcanaddatimedimensiontothisdatasothatMLalgorithmscandetermineifthemonthlyclosingbalancehasdeviatedfromwhatisusuallyexpectedfromacustomer.Indicatorslikedormantaccounts,increasingwithdrawals,usagetrends,andnetbalanceoutflowoverthelastfewdayscanbeearlywarningsignsofchurn.Thisinternaldata,combinedwithexternaldatalikecompetitoroffers,canhelppredictcustomerchurn.Havingidentifiedthefeatures,thenextstepistounderstandwhychurnsoccurinabusinesscontextandremovethefeaturesthatarenotstrongpredictorstoreducedimensionality.
ProjectIdea:Moreawarenessofthesalesandpricesofavocadoscanbenefitthevendors,producers,associations,andcompanies.Pricepredictionbasedonsaleswouldbeagoodinputinthemarkettodetermineshiftingofproducetolocationswherethefruitismoreindemandorevenencouragementofconsumptioninplaceswheredemandisnotuptothemark.Theideahereistopredictfuturepricesbasedondatacollectedofpastpricesbasedongeographicallocation,weatherchanges,andseasonalavailabilityofavocados.
Industry:FoodandBeverages
Thisisoneofthetopmachinelearningprojectsthataimstopredictcustomerswhowilldefaultonaloan.Banksmayexperiencelossoncreditcardproductsfromvarioussources,andonepossiblereasonforthelossiswhencustomersdefaultontheirdebt,preventingbanksfromcollectingpaymentsfortheservicesrendered.
ProjectIdea:Inthismachinelearningproject,youwillexamineasliceofthecustomerdatabasetodeterminehowmanycustomerswillbedelinquentinmakingpaymentsinthenexttwoyears.Therearevariousmachinelearningmodelsforpredictingwhichcustomersdefaultonaloansothebankscancancelcreditlinesforriskycustomersordecreasethecreditlimitonthecardtominimizelosses.Thesemodelswillalsohelpbanksscreenwhichcustomerscanbeapprovedforacreditcard.
Thesmartphonedatasetconsistsoffitnessactivityrecordingsof30peoplecapturedthroughsmartphone-enabledinertialsensors.
ProjectIdea:Thisprojectonmachinelearningaimstobuildaclassificationmodelthatcanpreciselyidentifyhumanfitnessactivities.Workingonthismachinelearningprojectwillhelpyouunderstandhowtosolvemulti-classificationproblems.
ThisimpressivemachinelearningprojectideaisanexcellentopportunityforBotanystudentstoexploretheworldofDataScience.Itusesmachinelearningalgorithmstocorrectlyidentify99plantspeciesthroughthebinaryleafimagesandevaluatedfeatures.Thesefeaturesincludeshape,margin,andtexture.
Hereareimportantmachinelearningprojectsthatwilltestnotonlyyourproductionengineeringskillsbutalsotheoreticalmachinelearningknowledge.
Incomeinequalityhasbeenofgreatconcerninrecentyears,andcensusdatacanbebeneficialinpredictingdatalikethehealthandincomeofeveryindividualbasedonhistoricalrecords.Thisprojectonmachinelearningaimstousetheadultcensusincomedatasettopredictwhetherincomeexceeds50Kyrbasedoncensusdatalikeeducationlevel,relationship,hoursofworkperweek,andotherattributes.
ProjectIdea:TheAdultCensusIncomedatasetisinterestingbecauseofitsrichnessanddiversityofdata,fromaperson'seducationleveltotheirrelationshiplevel.Withover32Krowsand15columnsdescribingvariousattributesofpeople,theAdultCensusIncomeDatasetisaperfectblendofmissingvaluesnumericalandcategoricaldata,makingitanexcellentchoiceforbuildingaclassifier.
Thepandemichascompelledustoanalyzeemotionsincommunication,asallwehavetodayisvirtualcommunication.Thus,detectingthecorrectemotionsbecomesaherculeantask.
ProjectIdea:Thereisnodefinitivewaytodeterminetheemotionsfromspeech.Hence,theSpeechEmotionRecognition(SER)systemwasdefinedasacombinationofdifferentframeworksandworksbasedonanalyzingaudiosignalstoidentifyemotions.Thehumanbraingenerallyseparatesemotionsfromspeechbydividingspeechintothreeparts:theacoustic,lexical,andvocalparts.Wecanuseoneorcombineotherpartstoreachthecorrectemotion,butinthisfunmachine-learningproject,wewillusetheacousticpartofspeech,includingpitch,jitter,tone,etc.
Industry:Communication,Entertainment
AccordingtoInvestopedia,atimeseriesisasequenceofdatapointsoccurringinsuccessiveorderovertime.Timeseriesanalysisaimstolookatdatacharacteristicsoveracertainperiodandusethattomakefuturisticcalculations.Thismeansthatfutureeventsmaybepredictedbyconsideringpreviouseventsthathaverepeatedlyoccurredoveraparticularperiodoroccurduetocertainotherphenomenabyanalyzingatimeseries.
ProjectIdea:TimeSeriesAnalysisisdonetofindhiddenpatternsinthedata.Thesehiddenpatternscanbeduetospecifictrends,oritcanbeseenthatthereisaseasonalvariationinthepatterns.Theanalysiscanalsohelptoidentifyanomaliesinthedatabyobservingunexpectedoccurrencesanddeterminingwhathascausedthem.ThisprojectisanadvancedmachinelearningprojectinwhichtimeseriesmodelingisdoneusingProphet,anopen-sourceforecastingtoolbuiltbyFacebook.
Goodinventorymanagementisprimarilyaboutmanagingdemandandsupply.Havingagoodideaofthestoresalescanhelptogetagoodideaofthedemandforvariousproductsinthemarketand,hence,stockupwiththecorrectamountofgoods.Itisespeciallycriticalintermsofperishablegoodssincetheyhavetobesoldfromstoresbeforetheirshelflife;otherwise,theywillbewastedandcausealossforthestores.Eveninthecaseofnon-perishablegoods,itisessentialtohavestockclosetotheamountssoldsincemanyotherproductscangooutofstyle,too.
ProjectIdea:Storesalescanbeinfluencedbymanyfactors,includingpromotions,thepresenceofcompetitors,holidays,seasonality,andlocality.Machinelearningcanbeusedtoidentifypatternsinthesetrendsanddeterminehowtheyinfluencesales.
Boschisaworld-renownedengineeringandtechnologycompanythatdealsinfourbusinesssectors:mobility,consumergoods,industrialtechnology,andenergyandbuildingtechnology.Forsuchacompany,oneofthebiggestchallengesistokeepacheckontheproductionofthecompany’smechanicalcomponents.AndBoschachievesthisbycarefullyobservingthesecomponentsastheyproceedthroughthemanufacturingprocesses.Thecompanycollectsdataforeverystepalongtheassemblylines,andthiscollectionmakesitpossibletoutilizeadvancedanalyticaltechniquestoimprovisethemanufacturingprocesses.
ProjectIdea:So,asyoumusthaveguessedbynow,inthismachinelearningproject,youareexpectedtopredictfailuresinthemanufacturingofthecomponentsalongtheassemblyline.Thedifficultyindealingwiththisprojectliesinimplementingthoseanalyticaltechniques,astheproductionlinesarecomplex,andthedataisonlysometimesinanalyst-friendlyform.Andthischallengeiswhatmakesthisprojectonmachine-learninginteresting.It’sokayifyouneedaguideonhowtoimplementthisprojectinaprogramminglanguage.
Industry:ProductManufacturing
ProjectIdea:AtOla,choosingthesuitableforecastingmethodologyforausecaselikebikeriderequestdemanddependsonseveralfactors,suchashowmuchdataisavailableandthebusinessrequirements.Otherexternalfactors,suchasweather,playavitalrole.Inthismachinelearningproject,youwillchoosethebestmachinelearningapproachtopredictOlabikeriderequestdemandforagivenlatitudeandlongitudeforfuturetimeduration.
Industry:Taxi
Ride-sharingandfooddeliveryservicesworldwiderelyondriveravailabilitytooperatesmoothly.Predictingtheavailabilityofdriversinaparticularlocalitysothatusershaveinformationonwhetheracabwillarriveandwhatthetentativewaitingtimeforthearrivaliswillhelpefficientlyallocatedriverstolocationswherethereisdemand.
Marketbasketanalysisreferstoabetterunderstandingofcombinationsinwhichcustomerspurchasevariouscommodities.Itisadataminingtechniquethatobservespurchasingpatternsinconsumerstounderstandthembetterand,intheprocess,increasesales.Ingrocerystores,theaislescanbearrangedaccordingtoproductsthatareobservedtobepurchasedtogetherfrequently.Marketbasketanalysiscanhelpimproveabusiness'ssales.Evenmenuscanbewrittenupwiththeresultsdrawnfromthisanalysis.
ProjectIdea:Theideahereisthatifacustomerpurchasesanitemoragroupofitems,sayproduct'A,'thenthisincreasesthechancesthatthecustomerwouldalsobeinterestedinbuyinganotheritemoranothergroupofitems,'B';AninterestinAimpliesaninterestinBbasedonthebehaviorsofpreviouscustomers.MarketBasketAnalysiscanbeusedfortargetedpromotions,personalizedcustomerrecommendations,andcross-selling.Forexample,offeringadiscountonaproduct'B'foracustomerwhopurchases'A,'oradvertisingAandBtogether.AllthesepatternscanberealizedusingMachinelearningtechniqueslikeFpogrowthandApriorialgorithm.
Withtheemergenceoftheinternet,ithasbecomepossibleforfamilyandfriendsfromacrosstheglobetostayintouchwitheachotherandcontinuallybeupdatedwithwhat’shappeningontheothersideoftheworld.Similarly,evennewsseemstobetravelingatlightningspeednow.Ithasproventobehelpfulinmanysituations.However,justlikehowtheinternethashelpedusreacttonewsandemergenciesmuchfaster,ithasalsoresultedintheunwantedspreadofmisinformationacrossplatforms.Asopposedtopreviously,wherearticleswerecheckedmultipletimesbyeditors,andthenewssourcecouldeasilybetraced,peoplenowrelyonsocialmediaplatforms,blogs,andothernewsplatformsonlinefornews.
Fakenewscanbeofthefollowingtypes:
ProjectIdea:DuetothesheervolumeandspeedofdataacrosstheInternet,analyzingeverynewsclipasanexpertisimpossible.Hence,atechniquetodeterminefakenewsbyapplyingmethodsbasedonNaturalLanguageProcessingisproposedtoidentifyfakenewsinreal-timeandpreventthespreadofmisinformation.
Industry:Communication&IT
RecruitersfromcompaniesandHRneedhelpreviewingmanyresumeswheneverajobopens.Incasesofhighdemandforjobroles,manyjobapplicationscomeflowingin.Sometimes,whenskimmingthroughresumes,thereisapossibilitythatanidealcandidate’sresumedoesnotreceivethenecessaryattention,ormaybeitismissedduetotheenormouspileofapplications.Thatmakesthingsdifficultforthejobapplicantsandthecompanywheretheywouldhavebeenmoresuitedtowork.ItisagoodapplicationforML,asitcanhelppeoplebrowsethroughresumes.
ProjectIdea:Usingmachinelearningandnaturallanguageprocessingtechniquesinsuchascenariocanreducemanuallaborandincreaseefficiency.Aresumeparsercanbebuilttoparsetherequiredfieldsandcategorizetheapplicantsbasedontheirresumes.Buildingaresumeparserischallengingsinceindividualsfollowmanydifferentlayouts.Eachinformationblockwouldideallybeassignedalabelandthensortedintoacorrespondingcategorysuchasworkhistory,education,qualifications,orevencontactinformation.Thelackoffixedpatternsinsuchascenarioaddstothechallenge.
Whilebrowsingtheinternet,youmusthaveencounteredvariousmemepagesthatmakefunofGoogleAssistant,Apple’sSiri,andAmazon’sAlexa.Whataretheseapplications,andwhyarepeoplemakingfunofthemTheseapplicationsarecalledChatbots,robotsthatcanchatwithahumanlikeahuman.Andtheseapplicationsarebeingmadefunofbecausesometimes,theycannotrespondlikeahuman.Forinstance,whenasked'Whatisthemeaningoflife',aChatbotmightrespondwith'42',areferencetothefamousbook'TheHitchhiker'sGuidetotheGalaxy'.Bytheway,theirfunnyresponsesaren’ttheonlyreasontheyarebecomingpopular.MostwebsitesarenowbuildingsimplerversionsoftheseChatbotstosupportcustomerqueries.
BERT(BidirectionalEncoderRepresentationsfromTransformers)isanMLalgorithmusedwidelytosolveNaturalLanguageProcessingproblems.Ithasatransformer-basedarchitectureandwasdevelopedbyGoogle.Ithasbeentrainedon2,500millionwordsandisabiasofmostNLPresearchersamongNLPmodels.However,recently,improvementshavebeenmadetothisstate-of-the-artlanguagemodel,andinthisproject,youwillexploretwosuchmodels:RoBERTaandXLNet.
Topicmodelingisanunsupervisedlearningtechniquefortextanalysis.Ithelpsorganizationsgarnervaluableinsightsfromdatabyunderstandingcustomers'likesanddislikes,findingathemeacrossproductreviews,analyzingonlineconversations,etc.Thisanalysishelpsbusinessesfocusonfurtherimprovementsandprepareforthefuture.Bydetectingpatternslikethedistancebetweenwordsandthefrequencyofwords,atopicmodelingalgorithmwillgroupsimilarfeedbackandexpressionsthatappearmostoftentohelpdeducewhatcustomersarefrequentlytalkingabout.
ProjectIdea:ThisNaturalLanguageProcessingProjectusestheRACEdatasettoapplyLatentDirichletAllocation(LDA)TopicModellingwithPython.RACEisanextensivedatasetofover28Kcomprehensionswitharound100,000questions.Eachdocumentinthedatasetwillcompriseatleastonetopic,ifnotmultipletopics.
ProjectIdea:Tobeginworkingintheseareas,youmuststartwithasimpleandmanageabledatasetliketheMNISTdataset.Workingwithimagedataoverflatrelationaldataischallenging;asabeginner,youcanpickupandsolvetheMNISTHandwrittenDigitClassificationChallenge.TheMNISTdatasetisbeginner-friendlyandsmallenoughtofitintoyourPCmemory.
Industry:IT
Withthepopularityofe-commerce,ithasbecomeveryconvenienttoorderitemsattheclickofabuttoninthecomfortofourhomes.However,insuchcases,weneedtoknowthenameoftheitemwewanttopurchase.Itwouldbeevenmoreconvenienttoseesomethingwelike,clickapicture,andthenfindsimilarimagesoftheitemone-commercesites.Thisisoneoftheobjectivesofthisinterestingmachinelearningproject.
ProjectIdea:Thegoalhereistoclickapictureandbepresentedwithmorepicturesthatmatchthecontentintheoriginalpicture.Inthisproject,itisimportantforthesystemtorecognizeproductsaccuratelybasedontheimage.Themodelhastobetrainedtoidentifyanddetectsimilarimagessothatthefinalmodelcanpickupimagesthatmatchtheoriginalimageautomaticallyandasaccuratelyaspossible.
Industry:ITandCommunications,Entertainment
Asurgicalprocedureisnojoke.Therearerisksandcomplicationsinvolved,nottomentionthepost-surgeryrecovery.Post-surgerypainisalsoanissuethatmanypatientshavetoface.Currently,paininadultsismanagedbyusingmedicines,whichhavetheirownsetofsideeffects.Usingultrasoundnervesegmentation,thesourceofthepaincanbefound,andthepaincanbetreatedatthesourceratherthanwithdrugsthatwillonlytemporarilynumbthepain.
ProjectIdea:Accurateidentificationofnervestructuresinultrasoundimagescanhelpdeterminethesourceofthepainand,accordingly,insertacatheterforbetterpainmanagement.Thenervestructuresmustbeanalyzedasaccuratelyaspossiblesincethisanalysisdealsdirectlywithapatient,andlivesareatstake.Mistakes,whichcanleadtoincorrectinsertion,canresultinmorepatientproblemslater.Thisprojectinvolvesgatheringimagesthatcontainnervesthatdonotshowanysignsofdamagetocomparethemwiththosethatshowsignsofabnormality,whichcouldindicatepain.Imageswillhavetobebrokendownintoamatrixforanalysis.
Inthissection,youwillfindexcitingMLbasedprojectsthatdifferslightlyfromthoselistedintheprevioussections.Theseareafewofthebestmachinelearningprojectsfromourrepository,sodonothesitatetoexplorethedetailsoftheseMLprojectideasbyclickingonthelinks.
Thehistoryofcomputer-generatedmusictracesbackto1957,with"TheSilverScale"byMathews'MusicIsoftware.Today,advancementslikeOpenAI'sJukeBoxshowcasethepotentialofGenerativeAImodelsinmusiccomposition.
ProjectIdea:Inthisproject,wesuggestleveragingthepotentialofgenerativeadversarialnetworks(GANs)formusiccomposition.BytrainingaGANmodelonacorpusofclassicalmusic,weaimtogenerateincreasinglylifelikecompositions.LeveragingLSTMandGANneuralnetworks,we'llexplorethecreationofmusicthatrivalshuman-madecompositions,invitingreaderstoassessthequalityofthegeneratedpiecesfirsthand.
ProjectIdea:"ReneWind,"acompanydedicatedtooptimizingwindenergyproductionprocesses,hasaccumulatedsensordataongeneratorfailuresinwindturbines.With40predictorsand40,000observationsinthetrainingsetand10,000inthetestset,theobjectiveistodevelopclassificationmodelstoidentifypotentialfailures.Bytuningandevaluatingthesemodels,theaimistominimizemaintenancecostsbypredictingfailuresaccurately.Maintenancecostmetrics,factoringinrepair,replacement,andinspectioncosts,willguidemodeloptimizationtoachievethehighestpossiblecostreductionratio.
Industry:RenewableEnergy
Earthquakesareasignificantthreattolivesandinfrastructure,showcasingtheimportanceofaccuratepredictivemodelsfordisasterpreparednessandmitigation.Byestimatingthepotentialdamagetohomes,resourcesandaidcanbeallocatedeffectively,assistingdisastermanagementefforts.
ProjectIdea:ThisprojectuseshistoricalseismicdatatoforecastearthquakemagnitudeandoccurrenceprobabilitiesinCalifornia,UnitedStates.Machinelearningmodelswillbetrainedtopredictseismicactivityusingthe"SOCREarthquakeDataset,"whichincludesearthquakedetailssuchasdate,time,location,depth,andmagnitude.Withover37,000earthquakeeventsspanningfromJanuary2017toDecember2019,thedatasetoffersampleopportunitiesforanalysisandprediction.Machinelearningtechniquessuchastimeseriesanalysis,clustering,andregressionwillbeusedtobuildearthquakepredictionandmagnitudeestimationmodels,contributingtoenhancedseismicriskassessmentanddisasterpreparednessstrategies.
Industry:Seismology
UsingtheMLprojectideasmentionedbelow,youcanfurtherexcelintheamazingdomainofmachinelearning.Werecommendyoucheckouttheseprojectsafterimplementingvariousbeginnermachinelearningprojectideas.
Languagedetectionisvitalinvariousapplicationstoday,facilitatingmultilingualsupport,contentfiltering,andinformationretrieval.Witharichhistoryspanningfromearlyrule-basedsystemstomodernmachine-learningapproaches,languagedetectionsystemshaveevolvedsignificantlytomeetthedemandsofaglobalizedworld.
Intoday'sdigitizedworld,theonlinesaleofpropertieshasbecomeincreasinglycommon,presentingrealestatecompanieswiththechallengeofaccuratelypricingeachpropertybasedonvariousfactors.ThischallengeissolvedthroughMachineLearningtechniques.Byleveragingpredictivemodels,realestatebusinessescanmorepreciselypredictthepricerangeofnewlylistedproperties,consideringattributessuchasarea,apartmenttype,amenities,andmore.
ProjectIdea:TheprojectfocusesonpredictingthepricerangeofpropertiesinPune,Maharashtra,India,utilizingadatasetcomprisinginformationon200properties.Theprojectaimstobuildrobustpredictivemodelsthroughdatapreprocessing,includingcategoricalandcontinuousdatacleaning,outliertreatment,andfeatureextraction,followedbytextdataprocessingtechniqueslikePartsofSpeechTaggingandCountVectorization.TheprojectwilldevelopaccuratepricingmodelsusingatechstackconsistingofPythonandvariousmachinelearninglibrarieslikepandas,numpy,sklearn,andmore,alongsidemethodologiessuchasLinearRegression,Regularization,andVotingRegressor.Ultimately,theprojectaimstodeploythesemodelsthroughAPIsandawebapplicationdevelopedusingFastAPIandhostedonHeroku,facilitatingreal-timepropertypriceprediction.
afiki,theAI-poweredchatbotdevelopedbyIntelliverseAI,offerspersonalizedmentalhealthsupportaroundtheclock.Usingadvancednaturallanguageprocessing,Rafikicomprehendsthesubtletiesofhumancommunication,enablingittoprovideempatheticresponsestailoredtousers'moodsandpreferences.ByengaginginconversationswithRafiki,usersreceiveimmediateemotionalsupportandcontributetoRafiki'slearningprocess,refiningitsabilitytoofferpersonalizedguidanceovertime.Rafiki'sgoalistoassistusersinnavigatingemotionalcrisesinrealtimethroughcompassionateconversations,offeringcustomizedtechniquestohelpusersself-sootheandmanagetheiremotionseffectively.
FormodernSoftware-as-a-Service(SaaS)companies,classifyingsoftwarebugsisimportantforensuringapplicationqualityandminimizingunforeseenoutcomes.Softwaredefects,rangingfromerrorstoflawsorbugswithinapplications,significantlyimpactdevelopmentcosts,releaseschedules,andoverallsoftwarequality.Byleveragingpredictivemodels,organizationscaneffectivelycategorizesoftwaremodulesasdefectiveornon-defective,enablingdeveloperstoextractvaluableinsightsandanalyzedatafromdiverseperspectives.EarlydefectdetectionthroughSDPenhancesresourceefficiency,reducingdevelopmenttimeandexpenses.
ProjectIdea:ThisprojectaimstopredictsoftwarebugsusingabugpredictiondatasetcollectedattheUniversityofGeneva,Switzerland,encompassingvarioussoftwaresystemssuchasEclipseJDTCore,EclipsePDEUI,andLucene.Byanalyzingsoftwarepropertieslikelinesofcode,methods,andattributes,theobjectiveistoforecastthenumberofbugsinadvance,facilitatingproactivedefectmanagementandriskmitigation.Theprojectinvolvescomprehensivedataanalysis,preprocessing,andvisualization,followedbyadvancedexploratorydataanalysis(EDA)employingMLanddimensionalityreductionalgorithms.Addressingchallengessuchashyperparametertuningandclassimbalance,theprojectseekstoclassifysoftwaredatabasedonbugseverity,rangingfromnobugstomultiplebugs,ultimatelyenhancingsoftwaremaintenanceandreleaseprocesses.
Delhi'ssevereairpollutionpromptstheurgentneedforaccurateAirQualityIndex(AQI)predictions,particularlyinwinter.AQI,rangingfrom0to500,highlightspollutionlevels,withhighervaluesindicatingmoresignificanthealthrisks.ThisprojectusesmachinelearningtoforecastAQIlevels,aidingintimelyalertsandpreventiveactions.
ProjectIdea:UtilizingdatasetsfrommajorIndiancitieslikeNewDelhi,Bangalore,Kolkata,andHyderabad,theprojectinvolvesthoroughdataprocessing,algorithmictraining,andmodelevaluation.TechniquessuchastheSyntheticMinorityOversamplingTechnique(SMOTE)addressimbalancesinAQI_Bucketvalues,ensuringreliablepredictions.TheprojectaimstoprovidepreciseAQIforecaststhroughsystematicassessmentandcomparison,empoweringauthoritiesandcommunitiestotackleairpollutioneffectively.
Industry:Energy
TheCOVID-19pandemichasunderscoredtheurgentneedforinnovativeapproachestodrugdiscovery.Traditionally,drugdevelopmenthasbeenalaboriousprocessmarkedbyhighcostsandlengthytimelines.However,recentyearshavewitnessedaconvergenceofmedicaldataproliferationandadvancementsincomputationalhardware,pavingthewayforaneweraindrugdiscovery.Withaccesstovastrepositoriesofbiologicalandchemicaldataandthecomputationalpoweraffordedbycloudcomputing,GPUs,andTPUs,machinelearning(ML)hasemergedasapromisingtoolinacceleratingthedrugdiscoverypipeline.
ProjectIdea:InthisML-drivendrugdiscoveryproject,datafromtheChEMBLdatabaseisharnessedtotargettheSARScoronavirus3C-likeproteinase,akeyenzymeinviralreplication.Leveragingthewealthofinformationavailable,datapreprocessingtechniquesareappliedtopreparethedatasetforanalysis.Exploratorydataanalysisdelvesintothechemicalspaceofpotentialdrugcandidates,employingLipinskidescriptorstoassessdrug-likeness.Subsequently,bioactivityfingerprintdescriptorsarecomputed,enablingtheconstructionofregressionmodelsusingalgorithmssuchasrandomforest.Throughrigorousmodelevaluationandcomparison,theprojectaimstopredictthepotencyofcandidatecompoundsandfacilitatetheidentificationofpromisingdrugcandidatesforfurtherexperimentalvalidation.
ThisprojectaimstoleverageKubernetesandKubeflowtostreamlinethedeploymentofmachinelearningworkflowsontheGoogleCloudPlatform.Kubernetes,alsoknownasK8s,isemployedtoautomatecontainerizedapplications'deployment,maintenance,andscaling.Atthesametime,KubeflowisutilizedtosimplifydeployingmachinelearningmodelsonKubernetes.Theprojectfocusesondevelopinganddeployingadeep-learningmodelfortextdetectioninimagesusingPython.ThetechstackincludesPythonlibrariesliketqdm,torch,opencv,andothers,alongwithFlask,Docker,andGCPservices.PriorknowledgeofFlask,Docker,CloudBuild,CloudRun,CloudSourceRepository,Kubernetes,andPython-baseddeeplearningprojectsisrecommendedforbetterunderstandingandimplementation.
Inthisproject,themachinelearningapplicationforBuildClassificationAlgorithmsforDigitalTransformation[Banking]willbedeployed.Hence,youareadvisedtoreviewthisprojectbeforehand.UtilizingAmazonEKS(cloudplatform),AmazonEC2,andElasticLoadBalancing,amongotherservices,AmazonEKS,afullymanagedservice,simplifiesthedeployment,management,andscalingofcontainerizedapplicationsusingKubernetesonAWS.Theaimistodeployamachinelearningmodeltoidentifypotentialborrowercustomersforfocusedmarketinganddeploythemthroughacloudprovider(AWS).ThetechstackincludesPythonandAWSservicessuchasEKS,ECR,Loadbalancer,codecommit,codedeploy,andcodepipeline.PriorknowledgeofFlask,AWSECR,ECS,EC2Loadbalancer,Codecommit,Build,Deploy,andPipelineisrecommended.Thesolutionapproachinvolves:
Thisprojectexploreswordembeddingstoimprovesearchengines,focusingonmedicalscience.Itaimstocreateanintelligentsearchenginethatunderstandstherelationshipsbetweenmedicalterms.UsingPython,NLTK,andAzureservices,theprojectdevelopsamachine-learningapplicationandadeploymentpipeline.Thesearchenginecanprovidemoreaccurateresultsbyunderstandinghowwordsrelate.Thegoalistoenhancesearchcapabilitiesbyanalyzingpatternsinmedicalterms.Thisprojectadoptsaformalapproach,employingvarioustechnologiestoachieveitsobjectives.
ThisprojectprovidesacomprehensiveguideonleveragingMicrosoftAzureservicestodevelopanefficientFAQchatbot.ItwalksyouthroughcreatingaknowledgebaseusingQnAMaker,whichinvolvesuploadingexistingFAQdocumentsormanuallyaddingquestionsandanswers.Then,itdemonstrateshowtointegratethisknowledgebasewithAzureBotServicetobuildaconversationalchatbotcapableofansweringuserqueriesinanaturallanguageformat.Additionally,theprojectmaycovertopicslikeconfiguringchannelsfordeployment,testingthebot'sfunctionality,andoptimizingitsperformanceusingAzureanalyticstools.
Wenowpresentafewbeginner-friendlytipsforworkingonamachine-learningproject.
Dependingonthenatureoftheproject,thisstepmighttakeafewdaysormonths.Inthemodelingstage,youdecidewhichmachinelearningalgorithmtouseandstarttrainingthemodelonthedata.Understandingthemeasureofaccuracy,error,andcorrectnessamachinelearningmodelshouldadheretoisessentialformodelselection.Havingtrainedthemodel,youevaluateitonvalidationdatatoanalyzeitsperformanceandpreventoverfitting.Modelevaluationiscriticalbecauseit'suselessifamodelworksperfectlywithhistoricaldataandreturnspoorperformancewithfuturedata.
Thisstepinvolvesdeployingsoftwareoranapptoenduserssonewdatacanflowintothemachinelearningmodelforfurtherlearning.Deployingthemachinelearningmodelisnotenough;youmustalsoensureitperformsasexpected.Youshouldretrainyourmodelonthenewliveproductiondatatoensureitsaccuracyorperformance—thisismodeltuning.Modeltuningalsorequiresvalidatingthemodeltoensureitisnotdriftingorbecomingbiased.
Theseprojectshavebeendevelopedforbeginnerstohelpthemenhancetheirappliedmachine-learningskillsquicklywhileallowingthemtoexploreinterestingbusinessusecasesacrossvariousdomains–Retail,Finance,Insurance,Manufacturing,andmore.So,ifyouwanttoenjoylearningmachinelearning,staymotivated,andmakequickprogress,thenProjectPro'sinterestingMLprojectsareforyou.Addthesemachinelearningprojectstoyourportfolioandlandatopgigwithahighersalaryandrewardingperks.
Understandably,manyaspiringMLpractitionersarejustlookingforadecentmachinelearningengineerjob.Withthatsaid,keepthosegoalsinmindasyouevaluatethesesourcesofmachinelearningprojects.Thereareseveralsourcesforfindingmachinelearningprojectsthataddbreadthtoyourmachinelearningportfolio,withthemostpopularonesbeingProjectProandKaggle.Ifyouwanttogenerateyourmachine-learningexperiencethatwillgetyouhired,workingonthisextensivelibraryof50+solvedend-to-enddatascienceandmachine-learningprojectsisthewaytogo.
Step1:DefiningtheMachineLearningProcess
Step2:Buildinganend-to-endMachineLearningPipeline
Step3:ModelDeployment
ThemostcommonquestionProjectAdvisorsgetaskedis:“HowdoIstartamachinelearningproject”.Hereisourbestadviceifyouarestartingamachinelearningproject:followthischecklist:
Thegoalofanymachinelearningprojectistomaximizethemodel'sperformanceandavoidoverfitting.Thus,trainingthemachinelearningmodelisthemostimportantMLproject,whereintrainingdataqualityplaysavitalrole.Withoutit,itisimpossibletotrainthemodeltomakethecorrectpredictions.Whentrainingamodel,itisalsoessentialtocarefullychoosethefeatures,modelparameters,andhyperparameterstogetaccurateresultsandavoidoverfittingthedevelopedmachinelearningmodel.
Hereareafewgoodmachinelearningprojectsthateverylearnermusttry:
Machinelearningprojectsmayappeardifficulttounderstandandimplementifyouhaven'tequippedyourselfwiththerightskillsbeforetryingthemout.Afterlearningthemathematicalbasics,aprogramminglanguagelikePython/R,andpopularalgorithms,youwillfinditeasytoimplementvariousprojectsinmachinelearning.