LEVELUP: Your One-Stop Fitness Shop with Personalized Diet Tips by IRJET Journal

LEVELUP: Your One-Stop Fitness Shop with Personalized Diet Tips

Shivam Raina1 , Vedang Mule2 , Deepanshu3 , Dr Dilip Motwani4

1,2,3Student, Dept. of Information Technology, Vidyalankar Institute Technology, Mumbai Maharashtra, India

4Prof. Dept of Information Technology, Vidyalankar Institute of Technology,Mumbai,Maharashtra,India

Abstract In today’s era as people increasingly focus on their fitness, there's a growing need for a convenient and personalizedsolution.ThisprojectintroducesLEVELUP,aonestop online store for gym equipment, supplements, and accessories. It's created to meet the demand for a simple and comprehensive platform where fitness enthusiasts can find everythingtheyneed. LEVELUPgoes beyondjustshopping;it's a helpful resource for fitness information and support. To make it even more user-friendly, LEVELUP uses advanced technology called machine learning. This helps provide personalized diet recommendations based on each person's specificdetailsandpreferences. Bysuggestingtherightfitness equipmentand offeringtailoreddietplans,LEVELUPensuresa personalizedandwell-roundedfitnessexperienceforeveryone.

Key Words: Fitness Enthusiasts, Personalized Solutions, Online Store, Gym Equipment, Supplements, Accessories, Machine Learning, Personalized Diet Recommendations.

1. INTRODUCTION

Inaworldincreasinglyfocusedonphysicalfitness,recent global events have emphasized the critical importance of health and wellness. Against this backdrop, the pursuit of one'sfittestselfhasbecomeauniversalaspiration,driving thedemandforinnovativesolutionstosupportindividuals ontheirtransformativejourneys.Inresponsetothisneed,we proudlyintroduce"LEVELUP"–notjustanothere-commerce website, but a cutting-edge platform designed to be your trustedfitnesscompanion.

AtLEVELUP,ourmissionisclear:tocreateauser-friendly, all-encompassing e-commerce platform that leverages the latest advancements in web development technology. ThroughtheintegrationoftoolssuchasHTML,CSS,React, TailwindCSS,Node.js,Express.js,Pythonetc.wehavebuilta platformthatsetsanewstandardinfitnesscommerce.

Ourcommitmenttoembracingthedigitalagetranslates intoaseamlessandconvenientfitnessshoppingexperience for our users. With LEVELUP, individuals can effortlessly browse,compare,andpurchaseacomprehensiveselectionof gym equipment, supplements, and accessories from an extensivearrayofleadingbrands–allconvenientlylocatedin oneplace.We'veredefinedtheconceptofaone-stopshop, ensuringthatouruserscaneasilyaccesstheproductsthey needtopursuetheirfitnessgoals.

Butwedon'tstopthere.AtLEVELUP,wetranscendthe limitations of traditional e-commerce platforms by seamlessly integrating advanced machine learning capabilities[3,4].This integrationmarksasignificantstep forward, enabling us to provide highly personalized diet recommendationstailoredtotheuniqueneedsandgoalsof eachuser.Leveragingsophisticatedalgorithms,ourplatform meticulouslyanalysesuserdataandpreferences,allowingus togeneratecustomdietplans,recommendthemostsuitable fitness equipment, and even offer food suggestions complete withrecipes.Byharnessingthepowerofmachinelearning, LEVELUPensuresthateachuser'sjourneytowardsfitnessis notjustpersonalized,butoptimizedforsuccess[3,5].

In this the incorporation of Body Mass Index (BMI) calculations. This metric provides users with valuable insightsintotheiroverallhealthandfitnesslevels,allowing themtotracktheirprogressandmakeinformeddecisions regardingtheirdietaryandfitnessroutines.Byintegrating BMIcalculationsintoourplatform,weempoweruserstotake controloftheirhealthandmakepositivelifestylechanges[6].

Whether you're an experienced fitness enthusiast or just embarking on your journey, LEVELUP ensures that your experience is as individualized and effective as possible. Furthermore, our platform empowers users by offering featuressuchasaccountregistration,ordertracking,andthe ability to contribute product reviews, fostering a community-driven environment where users can share insights and support one another. In essence, LEVELUP represents the convergence of cutting-edge technology, ecommerce convenience, and personalized fitness support. We are committed to helping individuals achieve their fitnessdreams,onestepatatime.

2. LITERATURE SURVEY

Before developing the project,wereviewed other solutionstoourproblemstatementandexaminedhowthe integration of machine learning in our website enhances the userexperience.

Thispaper[1]givesanintroductiontomostofthe popular machine learning algorithms which enable computers to learn and perform tasks without explicit programming. Supervised algorithms like support vector machines and decision trees handle classification and

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

regression tasks, while Naive Bayes is useful for text classification. Unsupervised methods like Principal ComponentAnalysisandK-MeansClusteringlearnfeatures from data. Reinforcement learning aims for complex goal achievement, while neural networks mimic human brain functions. Techniques like bagging and instance-based learning enhance accuracy and stability. Machine learning finds applications in diverse fields from online recommendations to social networking updates. Machine LearningcanbeSupervisedorUnsupervised.Ifyouhavea lesseramountofdataandclearlylabelleddatafortraining, optforSupervisedLearning.UnsupervisedLearningwould generallygivebetterperformanceandresultsforlargedata sets.Ifyouhaveahugedataseteasilyavailable,gofordeep learningtechniques.YoualsohavelearnedReinforcement LearningandDeepReinforcementLearning.Younowknow what Neural Networks are, their applications and limitations. This paper surveys various machine learning algorithms. Today each person is using machine learning knowingly or unknowingly. From getting a recommended product in online shopping to updating photos in social networkingsites.

Intheirstudy[2],VrajShethandcolleagues(2022) conducted a comparative analysis of machine learning algorithmsforclassification,focusingonNaiveBayesian,Knearest neighbor, Support Vector Machine (SVM), and Decision Trees. Evaluating these algorithms on different datasets, they assessed performance metrics such as accuracy,recall,precision,andF1score.Thestudyinvolved five datasets, with classification models handling the predictionofclasses.Whileeachalgorithmexhibitedminor differences in performance, Naive Bayesian consistently outperformedothersintermsofaccuracy,recall,precision, andF1score. K-NearestNeighborfollowedasthesecondbestclassifier,withSVMandDecisionTreestrailing.Their research aimed to select the best classifier from popular techniques,withpotentialforconsideringothermodelsin future comparisons. They suggested exploring additional noisereductionstrategiestoenhanceresultsandemploying various measures to compare algorithm performance in subsequent studies. This research contributes to understandingtheeffectivenessofclassificationalgorithms andlaysgroundworkforfutureinvestigationsintorefining classificationmethodologies.

Thepaper[5]discussestheutilizationofpersonal informationlikeage,height,weight,anddietarypreferences inrecommendingfooditemsthroughtheKNNalgorithm.It underscoresthesignificanceoftailoredmealplansandthe

advancement of AI applications in health diet recommendations.Thesystemgathersuserdata,processes it, and crafts nutrition plans considering factors like allergies, blood pressure, age, weight, and dietary preferences. Employing machine learning algorithms and content-basedfiltering,itfurnishesappropriatesuggestions. Data collection and pre-processing are vital phases in developing machine learning models. The system employs a hybridfilteringapproachwiththeKNNalgorithmtosuggest food items based on user similarities. Its objective is to enhance health outcomes via personalized diet recommendations. The system assists users by recommendingdietstailoredtotheirhealthneeds,including conditionslikehypertensionanddiabetes.Byutilizinguserprovided information during registration, the system employsahybridfilteringtechniquewiththeKNNalgorithm to suggest food items, presenting them as output to the users.

Thestudyexamines[7]therecommendationoffood items tailored for thyroid patients using a content based KNN method. It highlights the growing popularity of recommendation frameworks in e-commerce and online selectionservices.Theproposedmodelintegratescontentbased and K-Nearest Neighbor methods to deliver personalized food recommendations based on user preferencesandhealthconditions.Theresearchexploresthe impactofdietaryfactorsonhealthissuesandestablishesa balance between health conditions and food preferences. With a93% precisionrate acrossall user profiles,the model employs a classification approach to generate recommended food lists. Future directions may involve exploring alternativerecommendationalgorithmsandincorporating additional features such as meal cost and seasonal variations.Theproposedrecommendermodelsuggestsfood lists based on user preferences and health conditions. It combinesaclassificationmodelwithcontentbasedandKNN methodstocreatepersonalizedfoodrecommendations.By evaluating prediction scores, the model's performance is assessed. Future work may explore different recommendation algorithms and additional features like mealcostandseasonality.

The paper [8] discusses the importance of a recommender system for providing personalized food recommendations based on user preferences and health goals.Itemphasizestheroleofanutritiousdietandphysical activityinmaintainingahealthyweight.Variousalgorithms likeRandomForestandK-meansareusedfordataanalysis andclustering.ThesystemcalculatesBodyMassIndex(BMI)

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

and recommends diet plans based on user input. Machine learning and artificial intelligence are highlighted as key technologiesdrivingadvancementsintheITindustry.For training of the system, the initial process involves the segregation of food items depending upon the meal for whichtheyareconsumedi.e.Breakfast,LunchandDinner. Theclusteringofvariousnutrientsdependinguponwhich are essential for weight loss, weight gain and health is performed.Aftertheclusteringisperformed,usingRandom Forestclassifier,thenearestfooditemsarepredictedwhich best suited for the appropriate diet. The diet recommendation system allows users to basically get the desiredhealthydietonthebasesofBMItogetbalanceddiet plans.

The food recommendation system developed in paper[9]offersatailoredsolutiontoaddressthegrowing concernofobesitybyfocusingondietmanagement.Utilizing a content filtering algorithm, the system analyzes user profilestosuggesthealthyfoodchoicesbasedonnutritional value.Bycollectingdataonfooditemswithmilkorfishas primary ingredients and providing detailed nutritional information, the system ensures that users have access to relevantandbeneficialdietaryoptions.Throughsortingfood items by their calorie content, the system not only assists users in making informed decisions but also offers personalized recommendations that align with individual dietaryneedsandgoals.Thisweb-basedapplicationcaters to a local audience, offering a convenient and accessible platform for users to navigate towards healthier eating habits. Overall, the system serves as a proactive tool in promoting nutrition awareness and supporting users in achievingtheirdietaryobjectivesamidsttheglobalchallenge ofrisingobesityrates.

3. PROPOSED SYSTEM

The proposed systemis a user-friendly e-commerce platform tailored to meet the diverse needs of fitness enthusiastsandhealth-consciousindividuals.Inadditionto traditional e-commerce functionalities, such as product listings,shoppingcart,andcheckout,LEVELUPintegratesa personalized diet recommendation system. Leveraging a suite of modern web development tools including HTML, CSS, React, Tailwind CSS, Node.js, and Express.js, this platform offers a seamless and engaging user experience. Thedietrecommendationsystemutilizesadvancedmachine learning techniques to provide customized dietary plans based on user preferences and fitness goals. This paper outlinesthecorefeaturesofLEVELUP,emphasizingits

innovative approach to combining e-commerce with personalizedhealthsolutions.

Theprimaryobjectivesandfunctionalitiesofthissystemare asfollows:

•WideProductRange:

LEVELUP offers an extensive selection of gym equipment, supplements, and accessories from various brands, providinguserswithacomprehensiverangeofproductsto supporttheirfitnessjourney.

•User-FriendlyInterface:

The platform boasts a user-friendly interface, developed using HTML, CSS, React, Tailwind CSS, Node.js, and Express.js, ensuring seamless navigation and an engaging shoppingexperienceforusers.

• UserRegistration:

Users have the option to register and create personalized profilesonLEVELUP.Thisregistrationprocessservesasa gatewaytoaccesspersonalizeddietrecommendationsand tailoredproductsuggestions.

•ShoppingCart:

LEVELUPincorporatesashoppingcartfeaturethatallows users to add desired products, facilitating easy management andpurchaseofselecteditems.

•OrderTracking:

Users can track the status of their orders in real-time through the platform,enhancing transparencyandensuring asmoothpurchasingprocess.

•ProductRating:

Reviews and Blogs: Users are encouraged to rate, and reviewpurchasedproducts,providingvaluablefeedbackfor other buyers, and fostering a sense of community within the platform.Blogsarealsoprovidedrelatedtofitnessactivities.

•PersonalizedDietRecommendationSystem:

LEVELUPintegratesasophisticateddietrecommendation system that utilizes machine learning content-based algorithm that is nearest neighbors to analyze user data, including age, weight, height, dietary preferences, fitness goals,andanyhealthconditions.Basedonthisanalysis,the systemgeneratespersonalizeddietaryplanstailoredtoeach

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

user'sspecificneedsandobjectives.Therecommendation engineisbuiltusingNearestNeighborsalgorithmwhichis an unsupervised learner for implementing neighbor searches. It acts as a uniform interface to three different nearest neighbor’s algorithms: Ball Tree, KDTree, and a brute-forcealgorithmbasedonroutinesinsklearn.metrics. Pairwise. For our case, we used the brute-force algorithm usingcosinesimilarityduetoitsfastcomputationforsmall datasets.

•AdministrativePanel:

Theplatformincludesanadministrative panel thatallows site administrators to manage product listings, user accounts, orders, and other site functionalities efficiently usingdashboard.

Fig.1istheblock diagramofoursystem;ithasa detailed overview of how our system works and the services provided.

Theapplicationisdividedintouserinterfaceandservices provided.Adetailedoverviewofeachpartisshowninfig2 andfig3futher.

Fig.2.ProvidesanoverviewoftheUIofthesystemwhich upon entering the website, user authenticate itself by login/signinguptothewebsite.Thentheusercansurfthe productsavailableandaccordinglyaddhischoicestothecart forthepaymentprocedure.Theloginisprovidedtotheuser as well as the admin, admin login consists of a dashboard whichisused tohandle product,handle shipment,handle useraccordingly.Theadmincandynamicallyaddorremove theproductsfromthedatabase.Belowfig3givesoverviewof the services provided which include authentication, product management , orders , payment , notification and a diet recommendationsystemwhichisspecifictoeachusers

Fig.3.Providesablockdiagramofservicesprovidedbythe system developed. Services like authentication of the user at thetimeofloggingup,Productmanagementservicesforan admin user through the admin dynamic dashboard, Notification service at the time of payment, order confirmation to the registered user mail id and the diet recommendation for each user which makes use of the modeltomakeprediction.

Fig.1. BlockDiagram

Fig. 2 Userinterface

Fig. 3. Services

e-ISSN: 2395-0056 p-ISSN: 2395-0072 Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

4. IMPLEMENTATION

Theimplementationoftheprojectinvolvesutilizingastack oftechnologiessuchasinthefront-enddevelopmentphase, HTML,CSS,React,andTailwindCSSwereutilizedtocreate interactive user interfaces with streamlined styling. React facilitatedtheconstructionofdynamicandresponsiveweb pages,whileTailwindCSSprovidedautility-firstapproachto styling,ensuringefficientandconsistentdesignacrossthe platform. Cloudinary was employed for efficient image storage,enablingseamlessmanagementofproductimages andvisualcontent.

Forthebackend,Express.jsandNode.jswereemployedto develop a robust RESTful API, facilitating smooth clientserver communication and interaction with the MongoDB database.Express.jsprovidedasolidframeworkforbuilding webapplications,whileNode.jsensuredefficientexecution ofserver-sideJavaScriptcode. Theintegrationofmachinelearningalgorithmswasachieved using Python, particularly with the scikit-learn library for implementing the nearest neighbor’s algorithm. This algorithmanalyzeduserdatatogeneratepersonalizeddiet recommendations based on similarities to other users, enhancingtheuserexperiencebyprovidingtailoreddietary plansalignedwithindividualpreferencesandfitnessgoals.

5. RESULTS

A)Homepage:Thelandingpageofthewebsite

B)Products:alltheproductslistedinthewebsitecanbe accessed.Theirname,price,reviewsallareattachedwith theproductvisibleinthescreen.

C) Resource cart: user after shortlisting his required productscanaddthemintothecarttoseeallofitemsat onceinoneplaceandcanfurtherselectthequantityand movesonthepayment

Fig. 4. Homepage

Fig. 5. ProductPage

Fig. 6. Cart

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

D)AdminDashboard:theadminhasaseparateloginonthe website where he/she can dynamically add or remove productsalongwiththestatusoftheproductdelivery.

6. CONCLUSION

Inconclusion,LEVELUPistheultimatedestination forallthingsfitness-related,offeringaconvenientsolution for users seeking supplements, gym equipment, accessories, andpersonalizeddietrecommendationsallinoneplace.By consolidatingtheseofferings,LEVELUPeliminatesthehassle ofsearchingthroughmultiplewebsites.Withitsintegrated approach,userscanseamlesslynavigateadiverserangeof productswhilereceivingtailoreddietaryguidancebasedon their individual needs and goals. In essence, LEVELUP simplifiesthefitnessjourney,providinguserswiththetools andresourcestheyneedtoachievetheirhealthandwellness objectivesefficiently.

7. FUTURE SCOPE

E) Personalized Diet Recommendation: here the user entersthedataintothegivenfieldssuchasheight,weight, choose weight loss plan etc. based on the input provided bytheuserthefooditemsarerecommended,whichcanbe seeninthefigurebelow.

1)EnhancingMachineLearning:

Bydelvingintoadvancedalgorithms,itaimstorefineitsdiet recommendationsystem,ensuringheightenedaccuracyand personalized guidance tailored to individual needs and preferences.

2)ExpandingProductRange:

Inabidtocatertodiversefitnessneeds,itplanstodiversify its product portfolio, including the addition of new categories such as apparel and digital programs, thereby providinguserswithacomprehensivearrayofoptions.

3)MobileAppDevelopment:

With the advent of mobile technology, it endeavors to develop a dedicated mobile application, enabling users to conveniently access the platform anytime, anywhere, and enhancingtheiroverallshoppingexperience.

Fig. 7.1.AdminDashboard

Fig. 7.2.AdminDashboard

Fig. 8.1.AdminDashboard

Fig. 8.2 Dietrecommendation.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net

4)CommunityFeatures:

Recognizing the significance of community engagement, it aimstofostera vibrantusercommunitybyimplementing featureslikeforumsandchallenges,facilitatinginteraction, motivation,andsupportamongusers.

5)DeviceIntegration:

Through integration with fitness trackers and health monitoring devices, it seeks to offer personalized recommendations based on real-time activity and health metrics,furtherenhancingitsutilityandeffectiveness.

6)Partnerships:

Collaborationswithindustryexpertsandinfluencersareon thehorizonforLEVELUP,pavingthewayforcuratedcontent and expert advice, thereby enriching the platform's value proposition,andattractingabroaderaudience.

ACKNOWLEDGEMENT

WewouldliketoextendoursincerethankstoDr.Dilip Motwani for his invaluable guidance and feedback which helpedinshapingthedevelopmentofLEVELUP.

REFERENCES

[1] Mahesh, Batta. (2019). Machine Learning Algorithms -A Review.10.21275/ART20203995.

[2] Sheth,Vraj&Tripathi,Urvashi&Sharma,Ankit.(2022). AComparativeAnalysisofMachineLearningAlgorithms forClassificationPurpose.ProcediaComputerScience. 215.422-431.10.1016/j.procs.2022.12.044.

[3] Enache, Maria. (2019). Machine Learning in Ecommerce.AnnalsofDunareadeJosUniversityofGalati. FascicleI.EconomicsandAppliedInformatics.25.169173.10.35219/eai1584040920.

[4] Portugal, Ivens & Alencar, Paulo & Cowan, Donald. (2015). The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review. Expert Systems with Applications. 97. 10.1016/j.eswa.2017.12.020.

[5] Marrapu,H.K.,Bhardwaj, S.S.,Tejaswi,P.,Varada,U.D. andRamya,M.,2022.Surveyon PersonalizedHealthy Diet Recommendation System Using Machine Learning. NeuroQuantology, 20(13),p.916.

[6] Ortega,Francisco&Lavie,Carl&Blair,Steven.(2016). Obesity and Cardiovascular Disease. Circulation

Research. 118. 1752-1770. 10.1161/CIRCRESAHA.115.306883.

[7] D.A. Adeniyi, Z. Wei, Y. Yongquan, Automated web usagedata mining and recommendation system using KNearest Neighbor (KNN) classification method, Applied Computing and Informatics, Volume 12, Issue 1,2016,Pages 90-108,ISSN 22108327,https://doi.org/10.1016/j.aci.2014.10.001.

[8] Kardam, S.S., Yadav, P., Thakkar, R. and Ingle, A., 2021. Website on diet recommendation using machine learning. Int Res J Eng Technol (IRJET), 2021, pp.3708-3711.

[9] Rana, Kundan Shumsher. “Food recommendation system based on content filtering algorithm.” Bachelor’s

Degree in Computer Science.Tribhuwan University (2016).