Rèvolange the Ideal Fashion Suggestion for Customers by IRJET Journal

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page192

Key Words: ClothRecommendationSystem

1. INTRODUCTION The fashion industry is one of the most underrated industries. Fashion has always been an important part of howpeopleexpressthemselvesanditisapowerfultoolof influence. The fashion industry designs manufacture and market clothing wear, footwear, and access. Why does fashionmatter?Becausefashionnotonlyenhanceshuman life but also allows us to be independent in our thinking. Allowing us to express ourselves, our creativity, and the fashion industry contributes a lot. Fashion acts as a force thataffectstheprocessoftheworldasaforcethatmakes things move very quickly. But people ignore fashion and apparelOnehascostumes,specialhavewardrobe.infashionneedhugefcompaniesimpacttheyhavenevertakenitonaseriousnoteorconsideredthestorywanttoexpressthroughtheirclothes.Theeconomicofthefashionindustryishumongousasfashionmakeupamultibilliondollarindustry.Theashionindustrygeneratesagreaternumberofjobsandamountsofmoney.Somepartsofthefashionindustryimprovementasthefashionindustryisstillnotperfect.Theconceptofecommercehasrevolutionizedtheindustryinmanyways.OnecanshopfromanywheretheworldandmaketheirfavoritebrandenterintotheirThisisthereasonforEcommerceportalsthatboostedsalesofregionalapparelinIndia,rightfromvariantsofethnicweddingdressestotraditionalintheinitialstageofshoppinginthedigitalerabroughtIndianhandicraftheritageintothelimelight.ofthebiggestreasonsfortraditionalandregionalbecomingfashionableistheriseofecommerce.In

themtobetreatedasseveralothercustomers.Ifthere’sno propercareprovided,eventuallythey’llfindanotherbrand.

2. LITERATURE SURVEY

Affirms that the framework will want to prescribe the clienttopickfurnishesthatsuittheircharactertolessenthe outfitchoiceandbuyingtime[1].Theframeworkdepends ontwomodules:thefirstistodiscoverthecomponentfor the use of outfits like conventional, western, daytime or night, and so forth, and the subsequent element is to compute the body estimation boundaries. The proposed frameworkwillhavepicturecatchingbyutilizingtheHAAR componentorinfogadgetwhichgetsbodyboundariesfrom clients.HIGENMINERcalculationisutilizedtoagroupand concentratethemostidealoutfitsfromtheframework.

Rèvolange the Ideal Fashion Suggestion for Customers

Product recommendation, personalized home page resultsaccuratetheMachinepinpointalgorithmstheirrecommendationssuggestions,dealsandofferssuggestions,personalizedemailwillmakethecustomersactivelyplacepurchaseonaparticularbrand.Machinelearningrecommendcustomers,somuchthattheycanaccuratelytheproductstheyarelookingfor.learningalgorithmsareusedinthesearchprocess;searchprocessbecomeseasierforthecustomertogetproductsthattheyhavetypedinthesearchbar.Thewillalsobemoremeaningfulandrelated.

M. Nishant1 , K. Smethaa2 , K. Lohit Shakthi3

1 3

Department of Information Science and Engineering, Kumaraguru College of Technology, Coimbatore 641048, Tamil Nadu, India. *** Abstract Fashion is about self expression only, self empowerment and confidence.Fashionreflectsthecultureofa country. One of the main drawbacks in online fashion retail stores is lack of personalization, bad user experience, poor search engine approximation, missing product information, missing fake product reviews, poor tracking, lack of support, and no live chat opportunity. This causes the customer to lose faith and interest in E commerce and M commerce, making them lethargic about fashion. Another major drawback is people consume ample time surfing through products online and a few end up with products that don’t go well with their bodies. To get a clear picture of this scenario, a model is suggested that recommends the most suitable clothes by considering parameterslike gender,bodysize,bodyshape,and skin tone.

the case of offline shopping, the sales assistants show personal attention to their customers and give complete detailsontheproduct.Ifthereareanyqueries,customers can ask right there while purchasing their product and apersonalizedcustomizedcommerceissolutiontechnologyperfectscrollingshoppingcustomersreadtheplacedecideaboutpurchasingordenyingit.Butthisdoesnottakeinthecaseofonlineshopping.Customerscanonlyseepicture,allowingthemtocheckcustomerreviewsandthedescription.Duetothelackofpersonalization,endupspendingtoomuchtimeonlinelike,especiallyforfashioncanturnintoamarathonofandclickingdownrabbitholes,searchingfortheoutfit.Theremaybeabillionproblemsbutwiththehelpofwecannoweffortlesslycomeupwithazillions.Oneofwhichisbeingimplementedinthisprojectmachinelearningalgorithmsbecauseitallowsebusinessestocreateamorepersonalizedandexperience.Today,theuserspreferahighlycustomerexperienceaspersonalizationkeepscustomerloyaltoabrand.Thecustomeralsodoesn'twant

Affirms that the framework will assist clients with observing reasonable sets of clothes considering

shopsystemmakethebehavsimilarkindfindsusingrecommendeditemtomethods.wearingideaofwidesharingpartners,aschoices.andhelpfulclosetsinnovationtaskconsideringsizehowever,thistimetheframeworkhaschangedalongsidetheandcomprehensionofthecommercialcentretoo.Byabasiccourseofchoosingwhattowear,thisgivesastagetotalkaboutthelikelyeffectoftheinregulardailyexistenceconduct[6].Cannyarealmostnormalthesedaysandareexceptionallytotheshoppers.SitesaregivenbyclothingretailersareutilizedtourgeindividualstosettleonbuyingWebbasedmediaorganizingislikewiseconsideredtheyassociatetheirusetospeakwiththeircompanions,friends,educators,andfamiliesinvariouswaysbyphotographs,calls,recordings,areas,status,andarangeofotherpartsoftheirlife.Thefundamentalpointthisventureistoconfigure,create,andassessanoriginalfordealingwiththepurchasing,stockpiling,andofclothesbyutilizingclientfocusedstrategiesandThenewmethodologiesintherecommenderSystemtryforecastthepreferenceofwhatuserswouldgivetoan[7].BasedonpreviouspurchasesbytheuserthesystemtriestopredictasimilarproductbyaContentBasedapproach.CollaborativeFilteringtheuser'spreviouspurchasesandpredictssimilarsofproducts.Infashion,domainusersmaynotlookforproductswhichhavebeenpurchasedearlier.So,thisiorimpliesthattheitem'scontentsimilaritybetweenitemsalreadypurchasedbytheuserisnotenoughtoaccuratepredictions.InthisRecommendationSystemforoutfitSelection,thesuggeststheappropriateoutfitswhichwillsuit pers’character[8].TheRecommendationforthechoice oftheiroutfitsisbasedupondifferentrealboundariesthat createwiththelearningofaccessiblenamedandunlabelled information.Therearetwo modulesinthisinteraction;In thefirstmoduleistoperceivetheelementsforutilizationof outfitslikeconventional,western,useful,daytimeornight, andsoforth,thesecondmoduleisforcomputingthebody estimation boundaries. The system will have picture catching by involving HAAR element or info gadget for gettingbodyboundariesappropriately.Wemeantoagroup andconcentratethemostidealoutfitsfromthesystemby utilizing the HIGEN MINER calculation. By gathering not many insights concerning the user to prescribe proper outfitstothem. AbetterrecommendationcalculationnamedAdvanced User based Collaborative Filtering (AUCF) calculation is proposed and is executed in the dress recommendation system [9]. A successful recommendation system is genuinely necessary for clients. User based Collaborative rundown.FintricaciesbroadlyinclinationsFiltering(UCF)calculationisgenerallyusedtoanticipatetheoftheclients.Asinternetbusinessinnovationisdeveloping.Wecanconquertheissueofhugetimethrough,AdvancedUserbasedCollaborativeiltering(AUCF)calculationpresentsauserthingconnectedStylefirmshaveallowedtheirplanofactionto

The review on the internet shopping and consumer loyaltyonMyntra.Myntra,anIndian styleinternetbusiness commercialcentersettledinBangalore,Karnataka,India.In thispaper,ithasbeenclarifiedhowInternetMarketinghas upgradedorganizationsallovertheplanet[3].Web based Marketing in its least complex terms alludes to the advertisingandsellingoflaborandproductsinvolvingthe webasthedealsandappropriationmedium. TheInternet has decreased the world into a worldwide town and has madedistanceinsignificantandtimeregionsminimalmore than a bother. Affirms that the suggestion of clothes is accomplished utilizing the Advanced client based cooperative separating (AUCF) calculation [4]. The AUCF calculation presents a client thing connected rundown, which can beat the issue of the huge time intricacy. Considering the effect of various notoriety of things, the Advanced client based cooperative separating (AUCF) calculation is equipped for distributing the adverse consequences of famous things, they can build the suggestioninclusion.

Theexaminationresultsshowhowthe AUCF calculation altogether builds the proposal inclusion andThisaccuracy.isabout the conduct of the buyer for attire, the shopper item purchasing conduct is affected by the significant three elements like social, psychological, and flipplanhaspurchaseofandonlineshoppingindividualelements[5].Intheunderlyingphasesofinternet,buyerswerenotkeenonpurchasingclothesasithasnumerousconstraints.Inanycase,inthisdayage,thecommercialcentrecanvanquishalargenumberthelimitsandassemblecertaintyamongtheshopperstoontheweb.Presentlythepatternofeshoppingbecomeessentialpeculiaritieswiththepurchasers.TheofactionoftheIndianebusinessistakingaroundtriptoreturntowhereitbegantoitsunderlyingstages,

exceptionallycomplexsubtletieslikestyle,designs,colors, surfaces,andsoonlikewiserememberingclient’screditslike age,complexion,favoritecolor,andsoon[2].Itattemptsto assisttheclientwithwearingclothesthatareappropriate for eventsandassists theclientwith buyingthoseclothes that would suit their style. The suggestion of clothes is accomplished utilizing PCST; Latent SVM; Apriori; Fuzzy Logic.

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072

Fig -1:ArchitectureofReal TimeClothing Recommendation

examinationtoacquirehighercorrectness’sandadditionally

quality decrease which is performed to analyze the exactness’stheyget.

Fig 3:ComparisonofRecall(UCF,AUCF)

Theexactnessoftheordereddatasetutilizingdifferent information mining procedures like bunching, grouping AdaboostM1.zeroR,characterchisquaredAttributeEval.cfsSubsetEval,ToassistbuildTakingaffiliationmining,andinformationminingcalculations[11].thebestchoicewhilebuyingandadditionallyingthebusiness,utilizingarecommendationsystemtobuyerswithchoosingtheiroutfitwithoutanyproblem.performpropertydecrease,theyhaveutilizedconsistencySubsetEvalandThecalculationsthatareutilizedtoizethedatasetareRandomForest,NaiveBayes,MultilayerPerceptron,RBFNetwork,andThedatasetisadjustedutilizingSMOTE

clothesbasedontheuser’sinterestandthesecondtomatch therecommendedclothesformorepersonalization.Thisis achievedusingadeepfullyconnectedneuralnetworkand deep convoluted neural network [15]. Asserts that the clothes are recommended to users not only based on the user’sfeaturesbutalsobasedontheclothes’review.Their modelincludestwostages,onetoextracttheuser’sfeatures andthesecondtoclassifytheclothesasgoodorbad.Thisis achieved using a deep neural network. More datasets are usedtotrainthisartificialintelligencemodel[16]

Fig 2:ComparisonofPrecision(UCF,AUCF)

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072

undertakennetworksArecommendationalgoritdifficultChoosinglearningfilteringrecommendationcompanycomplementshoppinganddesignrecommendationThisitemstoadministrations.thatRecommendationsystemsareaninventivearrangementconquerstherestrictionsoftheinternetbusinessTheyuseclientconductdata,anditemdatarecognizetheclientinclinations,andproactivelyproposethattheyaremostlikelyintriguedtopurchase[12].paperisaboutagenuineworldcollaborativefilteringsystemexecutedgenerallyinKoreanorganizationswhichsellsstyleitemsbothonthewebdisconnected.Itstatesthatclothesarerecommendedbasedonuserhistory.Userscanbuyclothestoreplaceortheclothesthattheyalreadybought.Also,theirsellsthesameproductsbothonlineandoffline.Theofclothesisachievedusingcollaborative[13].Affirmsthatthereareplentyofmachinealgorithmsthatareusedforrecommendation.thecorrectalgorithmforanapplicationisaprocess.ThispaperconcludesthattheBayesianhmandDecisiontreealgorithmarewidelyusedinduetotheirrelativesimplicity[14]ssertsthatthereisarapidgrowthinfashionfocusedsocialandonlineshopping.Theauthorofthispaperhastwochallenges,onetorecommendindividual

give customized encounters to their clients by utilizing advanced CAD apparatuses like CLO 3D, Marvellous Designer, Brow wear, Lectra, and a lot something else for planningthepieceofclothingandconstruct3 Dimensional symbolforthemodifiedarticleofclothingaswellasonline administrations to be brought together with the web and portable based applications [10]. The system structure is planned on the user's biometric profile and recorded informationof item requests oftheclients.Thedesignfor this recommendation system is based on various Data Mining methods like bunching, grouping, and affiliation mining.Thecontent based(CB)filteringmethodreadiesthe inuser'srecommendationbymatchingthecomparablecontentoftheidealthings.TheresultsoftheexperimentsarebelowFigures2and3.

Proclaimsthatfashion orientedonlineshoppingisafast growingfield.Also,alargenumberofclothesareshownto usersononlineshoppingplatforms.Todesignandprovide clothes according to users’ needs one should implement machinelearningalgorithmstorecommendclothes.Thisis additionallychangeclassificationdescription.basedsystem.problem,reviewsproblems.clothesismixedrecommendation,achievedusinganaturallanguageprocessingalgorithm.Forthecorrectdatasetsareretrievedusingtypeclusteringalgorithms[17].Proclaimsthatsizefitmoreimportantcomparedtostylefit.MorereturnsofhappeninonlineshoppingduetosizeandfitAlso,theuserhastoonlyrelyonimagesandinonlineshopping.TobringasolutiontothistheauthorproposesasizerecommendationThissystemrecommendsthecorrectsizetousersonpastorderhistoryandthecontentintheclothesThisisachievedusinggradientboostingmodelandgrambasedword2vecmodel[18]AtteststheelementswhichareaffectingtheclientstotoMcommercestagesfromEcommercesites.ItstateshowMyntraperformsthroughM

System  Module

Table 1: AlgorithmComparison Decision Tree and Random(Proposed)Forest

DT: TreelikeStructure and easy to implement in classifying products cause the scatter plots are in groups

 Quick

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072

correctclothesaccordingtoone’sbodyshape[21].Asserts that body shape plays a key role in fashion dressing. This paperisastudyofthecorrelationbetweenbodyshapesand cloth garments and this is achieved using a novel and vigorousmulti photographwaytodealwithassessthebody statesofeveryuserandassemblethecontingentmodelof dressing classifications with a given body shape [22] Proclaims that fashion is a series of short trends. Fashion reflects how people define themselves. Choosing the right outfit in the right color brightens and smoothens the complexion, minimizes the face lines, and gives a healthy glow to the skin. Some people struggle to find the right (in)b)categories:psychologyaboutexpressingbelieve[23]tonesclothesforthemselves.Thispaperisastudyondifferentskinandthedressesthatmatchtheskintoneaccordingly.Proclaimsthatpeoplebuyclothesaccordingtowhattheyintheirclothestoexpress.Clothingisawayofthemselves.Thispaperisapsychologicalstudyclothescolorandwhypeoplewearthem.Thebehindclothingisclassifiedintothreethematica)Themeaningofcolorsinclothingpsychology;Thesociopsychologicalimpactofclothing;andc)GenderEqualityregardingclothing[24]



RF:  Toincreaseaccuracyinrealtime Complications

Commerce stages in the style attire industry [19]. Affirms thatinternetmarketingisquitepopularthesedays.Doing business on the internet has great advantages like more profit, more sales, and more production. This paper ClothingAdiscusseshowMyntraperformsinthefashionindustry[20]ssertsthatitisnotaneasytasktochoosewhattowear.isanintegralpartoflife.Anessentialtipistodress

groupsplotstheproductsinComplicationsclassifyingcausescatterarenotin

 Module

Fig -4:Clothes

not

accordingtoone’sbodyshape.Wearingclothesaccordingto thegivesbodyshapeacceleratesgoodfeaturesinthebody.Thispaperaclearpictureofthecompatibilityofbodyshapeswithrespectivetypesofclothes.Thegoalistorecommend

3. PROPOSED WORK 3.1 Clothes Recommendation System

4. CONCLUSION

haveinformationshowbeownadditionallysomethingwaitingtodirectlyshoppingauthenticated.fakebuyerTheremisrepresentsomeplaceitemconfinedoverandconsideringpurchaseinformationuseorderedandneedComfortableshoppingisgraduallybecomingareality,allyouisadevicethatneedstobeconnectedtotheinternetaconvenientaddresswhereyoucanreceiveyouritems.Providingrecommendationsbasedonar'spreviousorderhistory,preferences,andpersonalwillmakeiteasierfortheusertomakeaandthisappmakesthenecessarysuggestionsbyparameterslikegender,bodysize,bodyshape,skintoneusingMLmodels.Whenpurchasingproductstheinternet,youhavethemostoptions.Youarenottowhatasolitarystoresellsorstocks.Assuminganexists,youcanmorelikelythannotgetitonlinehowevermakingawebbasedbuy,ationandwholesalefraudareasteadygamble.arealsosignificantconcernsabouttheprivacyofdataprovidedtoretailers.Toensuresafeshopping,productsandretailersmustbeadministrativelyUnlessyouchooseinstorepickup,onlinemeansthattheordereditemsaredeliveredtoyourdoor.Youdon'thavetoworryaboutdrivingyourdestination,payingforgas,findingparking,orinlinetobeservedinanycase,whenyoupurchaseontheweb,youcan'tgiveitashotfirst.Youcan'tcontactorseeanitemveryclosewithyoureyes.Withregardstoparticularsortsofitems,thiscanahugeburden.Ourappwillbeprogrammedtoproductsthatarerelevanttotheuser'spersonalandpreferences.Asaresult,theuserdoesnottobeconcernedaboutproductfit.

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The clothes recommendation system recommends clothes usingthedetailsthataregivenbytheusers.Theuserdetails include name,address,phone number, email id, body size, modules.gender.tobodyshape,skintone,gender.Theclothesarerecommendedusersbasedonbodysize,bodyshape,skintone,andThisrecommendationsystemisdividedintotwo Recommendation 1: Thisprovidesusersanormalshopping experience in which the clothes are not personalizedandnotcustomized. 2: Thisrecommendsclothestousersfrom the details that are given by the user. The recommendation of clothes is achieved using machinelearningalgorithmssuchas theDecision tree cart algorithm, Random Forest, and Logistic regression. k NearestAlgorithmNeighbor(Existing) RegressioLinearn(Existing)

[11] ZengAShuklaSharma,LudovicKoehl,PascalBruniaux,Xianyi ,“CustomizedGarmentFashionRecommendation System Using Data Mining Techniques,” GEMTEX Laboratory,France.

[15] Tong He, Yang Hu, “FashionNet: Personalized Outfit Recommendation with Deep Neural Network”, University of California, Los Angeles, University of ElectronicScienceandTechnologyofChina

[17] MariaTh.Kotouza,“TowardsFashionRecommendation: AnAISystemforClothingDataRetrievalandAnalysis,” Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume584). [18] G. Mohammed Abdulla, Sumit Borar, “Size Recommendation System for Fashion E commerce,” MyntraDesigns,India.

[7] Dina Etebari, “Intelligent Wardrobe: Using mobile devices,Recommendersystemsandsocialnetworksto adviseonClothingChoice,”UniversityofBirmingham [8] Gabriele Prato, “New Methodologies for Fashion Recommender Systems,” School of Electrical EngineeringandComputerScience.

[16] Ying Huang A, Tao Huang, “Outfit Recommendation SystemBasedonDeepLearning,”SchoolofInformation Engineering,WuhanUniversityofTechnology,Wuhan 430070,China.

[12] N.SrinivasaGupta,D.SaiThanishviandB.Valarmathi, “Outfit Selection Recommendation System using Classification Techniques”, Vellore Institute of Technology,Vellore(TamilNadu),India.

[13] Hyunwoo Hwangbo, Yang Sok Kim, Kyung Jin Cha, “Recommendation system development for fashion retail e commerce,” Graduate School of Information, YonseiUniversity, 50 Yonsei ro, Seodaemun Gu, Seoul 03722,SouthKorea.

[9] Shiv H. Sutar, Akshata H. Khade, “Recommendation SystemforOutfitSelection(RSOS),”UniversityofPune city Pune in Information Technology Nanded, Maharashtra,India.

REFERENCES

[19] Dr. Prathiba Devi R, Sindhu. P, “Fashion Recommendation Study on the Clothing Style for The Personal Body Shape,” PSG College of Technology, Coimbatore,641004,TamilNadu,India. [20] HosniehSatta,GerardPons Moll,MarioFritz,“Fashionis onTakingShape:UnderstandingClothingPreferenceBasedBodyShapefromOnlineSources ,” Max Planck InstituteforInformaticsSaarlandInformaticsCampus, Saarbrucken,Germany. [21] EconomyDujeKodzoman,“ThePsychologyofClothing: Meaning of Colors”, University of Zagreb, Faculty of TextileTechnology,Zagreb,Croatia [22] Venkatesh J, Vivekanandan. K, Balaji. D, “Fashion is onTakingShape:UnderstandingClothingPreferenceBasedBodyShapefromOnlineSources ,” School of Management Studies, Anna University of Technology Coimbatore, Jothipuram Post, Coimbatore 641 047. TamilNadu,India. [23] Patrizia Gazzola, Enrica Pavione, Roberta Pezzetti, Daniele Grechi, “Trends in the Fashion Industry. The PerceptionofSustainabilityandCircular” Department of Economics, University of Insubria, 21100 Varese, Italy. [24] W.M.B.S,FernandoH.S.M,KumaraH.H.S.N,MendisH.I.A,WettawaSamarasingheH.M.U.S.R

[14] IvensPortugal,PauloAlencar,DonaldCowan,“TheUse of Machine Learning Algorithms in Recommender Systems:ASystematicReview,”DavidR.CheritonSchool ofComputerScienceUniversityofWaterlooWaterloo, ON,Canada.

,“Relationshipamong emotions, mood, personality and clothing: an exploratorystudy” .

BIOGRAPHIES Mr. M. Nishant He is currently doing final year at Kumaraguru ScieHisCollegeofTechnology,CoimbatoremajoringinB.EInformationnceandEngineering.

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p ISSN: 2395 0072

[10] Yu Liu, Jingwen Nie, LexiXu,Yue Chen, Bingyu Xu, “ UserClothingRecommendationSystemBasedonAdvancedBasedCollaborativeFilteringAlgorithm ,”Beijing University of Posts and Telecommunications, Beijing 100876,China.

[5] Illa Pavan Kumar, Swathi Sambangi, “Content Based ApparelRecommendationSystemforFashionIndustry” , Kakatiya University and M.Tech (CSE) from K L University [6] Prof.MahalaxmiK.R.,NagamanikandanP.A,“Studyon Online Shopping Behavior for Apparel: Literature Review,” Anna University BIT Campus, Trichy 620024

[1] Atharv Pandit, ManavJain, Kunal Goel, Neha Katre, “A Review on Clothes Matching and Recommendation Systems based on User Attributes,” Department of Information Technology D. J. Sanghvi College of EngineeringMumbai,India [2] SamitChakraborty,Md.SaifulHoque,NaimurRahman Jeem, Manik Chandra Biswas, Deepayan Bardhan and Edgar Lobaton, “Fashion Recommendation Systems, Models and Methods: A Review”, Wilson College of Textiles, North Carolina State University, Raleigh, NC 27695,USA. [3] AnupindiSravani,“M CommercePlatformandFashion: Myntra.com,” Kirloskar Institute of Advanced ManagementStudiesHarihar 577601 [4] Dr.T.Shenbhagavadivu,M.Sharanya,S.SaiPriyanka,V. Ajithkumar, A. Irshad Khan, “A Study on Online Shopping and Customer Satisfaction on Myntra,” Sri KrishnaArtsandScienceCollege,Coimbatore 641008

Ms. K. Smethaa She is currently doing final year at Kumaraguru ScieHeCollegeofTechnology,Coimbatore.rmajoringinB.EInformationnceandEngineering.

Mr K Lohit Shakthi He is currently doing final year at Kumaraguru College of Technology, Coimbatore His majoring in B.E Information ScienceandEngineering.