CAR PRICE PREDICTION by IRJET Journal

CAR PRICE PREDICTION

Aditya Arora, Akriti Singh, Aman Goel, Kirti Kushwah

Adiya Arora, Dept. of CSE, Inderprastha Engineering College, Sahibabad, Ghaziabad, India

Akriti Singh, Dept. of CSE, Inderprastha Engineering College, Sahibabad, Ghaziabad, India

Aman Goel, Dept. of CSE, Inderprastha Engineering College, Sahibabad, Ghaziabad, India

Kirti Kushwah, Dept. of CSE, Inderprastha Engineering College, Sahibabad, Ghaziabad, India

Abstract - Over 70 million passenger automobiles were created in 2016. The number of cars produced has been rising overthelasttenyears. The secondhand automobile market has emerged as a result of this, and on its own has grown to be a prosperous sector. The emergence of online portals has made it easier for both buyers and sellers to learn more about the patterns and trends that influence a used car's market value. Our goal is to create a statistical model that can forecast the price of a used car by utilizing machine learning algorithms like regression trees, multiple regression,andLasso regression. This model will be based on past consumer data and a predetermined set of features. We intend to additionally contrasting these models' prediction accuracy in order to identify the best one. In the industry, the manufacturer sets the price of a new car, with the government bearingsomeadditional expenses in the form of taxes. Customers who purchase new cars can therefore be sure that theirfinancial investment is worthwhile. However, sales of used automobiles are rising globally as a result of rising new car prices and consumers' inability to afford them. A usedcar price predictionsystemthat accurately assesses the car's worthiness based on a range of factors is therefore desperately needed. The current systemhas a procedure where a vendor choosesa price at random and a buyeras no notionwhatthe car is worth in the current market. In actuality, neither the car's current worth nor the asking price are known to the seller. We have createdamodelthatwillwork incredibly welltosolve this issue. The reason machine learning algorithms are chosen is that their output is continuousrather than categorized. This makes it feasible to forecast an automobile's true cost rather than just its pricing range. Additionally, auser interface has been created that takes input from any user and displays the price of a car based on that input.

Keywords CarPrice,MLAlgorithm,Regression,Prediction, Category.

1.INTRODUCTION

Therearesomanyvariablesthatinfluenceausedcar's pricing on the market, it can be difficult to determine whetherthequotedpriceisaccurate.Thisproject'smaingoal is to createmachine learning models that can effectively estimateausedcar'spricebasedonitsattributes,enabling them to make well- informeddecisions.Weuseandassess learningmethodologiesonadatasetcomprisingtheselling

pricesofvariousmodelsandmanufactures.Weassesshow well machine learning algorithms perform. Regression in LinearForm.Variousfactorswillbetakenintoconsideration whiledeterminingthecar'spricing.RegressionThereason algorithms are utilized is that their output is a continuous valueratherthanacategorizedvalue,whichmakesiteasyto estimatethetruecostofanautomobile ratherthanjustthe rangeofprices.Additionally,auserinterfacethatgathersinput fromusersandshowscarpricesbasedoninputfromusershas beenbuilt.Themarketforoldcarsisexpandingrapidly;in the previous several years, its market value has nearly doubled.TheriseofinternetportalslikeCarDheko,Quicker, Carwale,Cars24,andnumerousothershasmadeiteasierfor buyersand sellers to learn more about the patterns and trendsthatinfluenceausedcar'smarketvalue.Basedona certainsetoffeatures,machinelearningalgorithmscanbe used to anticipate an automobile's retail value. Various websitesThereisn'tasinglealgorithmutilizedtodetermine the pricing because different companies use different algorithms to create the retail price of used cars. Without actuallyenteringthedetailsintothedesiredwebsite,onecan easily get a reasonable estimate of the price by training statisticalmodelsforpriceprediction.Kagglegeneratedthe datasetthatwasutilizedinthepredictionmodels[1].9104 automobilerecordswithcomputedretailpricesareincluded inthedata.

Thevariablesthatareusedareasfollows:

3. Cost:TheGMvehiclescomputedretailcost.

4. Mileage:The total kilometersdriven bythevehicle driven;

5. Model:Theparticularmodelsforeachautomobile; Fuel:Thekindoffuelthecarrunson,suchaspetrol ordiesel.

6. Year: The year the actual owner of the car purchasedit.

2. LITERATURE REVIEW

2.1 UsingMachineLearningTechniquestoPredict Used Car Prices

Inthiswork,weexaminetheusageofsupervisedmachine learningmethodstoforecastMauritiususedvehicleprices The forecasts are predicated on historical information gathered from dailypublications.Thepredictionshavebeen

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

Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN: 2395-0072

madeusingausingavarietyofmethods,includingdecision trees, k-nearestneighbors,naïvebayes,andmultiplelinear regressionanalysis.Next,thePredictionsarecomparedand assessedtoseewhichyieldthebest resultsitprovestobe quite difficult to solve a seemingly simple problem with excellent precision.Each ofthe fourapproaches delivered performancethatwassimilar.Weplantomaketheforecasts usingadvancedalgorithmsinfuture

2.2 Machine Learning-Based Car PricePrediction

It necessitates observable effort and professional expertiseinthesubject,automobilepricepredictionresearch has garnered a lot of interest. A large variety of different characteristicsarelookedatinordertoprovideanaccurate and trustworthy prediction. We employed the Artificial Neural Network, Support Vector Machine, and Random Forest machine learning techniques tocreate a model that forecastsusedautomobilepricesinBosniaandHerzegovina. Nonetheless,the a forementioned methods were used in groupprojects.AwebscraperbuiltinPHPwasusedtogetthe datafortheforecastfromthewebsiteautopijaca.baonline. The optimal algorithm for the given data set was then determinedbycomparingtherespectiveperformancesofthe several methods. Additionally, Test data were used to evaluatethemodel,andanaccuracyof87.38%wasfound.

2.3 Regression Models for Predicting Used Car Prices

For this investigation, we carried out a comparison analysis on Regression usingsupervisedmachinelearning models is executed. Every model is trained with used automobilemarketdatagatheredfromGermanonlineretailers. Withmeanabsoluteerror(MSE)= 0.28, gradient enhanced regression trees perform the best as a consequence. Then camemultivariatelinearregression(MSE=0.55),andrandom forestregression(MSE=0.35).Usequalifiedqualitativedata and a knowledge- based system to forecast car prices. Summary: The machine learning process of pricing cars is closelylinkedtotheexpertsystem'sknowledgeacquisition process. Lately, the most common method of gaining knowledgehasbeenthelaboriousrecommendationposting processforpurchasinganautomobile.orsellingonwebsites for online markets. We can categorize the data into two groupsonceithasbeenfound:structuredandunstructured, whichbothneedknowledge-basedanalysis.Themethodsfor meaningextraction,datainference,andrulesforqualitative data will allbe covered in this study. The current study's primarygoalistoinvestigatevariousautomotivedatatypes inordertodevelopanautomatedmethodforpredictingcar pricing.

3. OBJECTIVE OF CAR PRICE PREDICTION SYSTEM

Creating a machine learning model that can reliably forecastanautomobile'spriceisthe maingoalofaproject thataimstopredictcarprices.onarangeofcharacteristics andelements.Withthehelpofthisinitiative,buyers,sellers, and other industry participants should be able to make moreinformedjudgementsaboutcarprice

1. Predictionaccuracycreateamodelthatcanreliably estimateautomobilepricesbasedonmanyfeatures suchasbrand,model,mileage,year,condition,etc.

2. Assistance in Making Decisions: Help vendors set competitive prices for their listings and buyers in determiningfairpricesforcarstheyareinterested inbuying.

3. Efficiency: Save time by providing a prompt estimate of car costs. in contrast to manual appraisalprocedures.

4. Insight Generation: Learn about thevariablesthat have a big impact on auto costs and comprehend marketdynamicstomakesmarterdecisions.

5. Scalability: Create a model that is flexible and scalableforvariousautomotiveindustryconditions, enabling it to be utilized for a broad variety of automobile listing. The ultimate goal is to use machine learning techniques to develop a trustworthy tool that, by projecting fair values based on numerous variables, improves comprehensionandefficiencyofthecarbuyingand sellingprocess.

4. METHODOLOGY

Thismethod'sprimaryobjectiveistoprovideconsumers withanaccurateestimateoftheamountthatmustbepaidfor thespecifiedcar.Themodelmightprovidethebuyerwitha listofoptionsfordifferentcarsdependingonthespecificsof the vehicle that the buyer desires. The system helps the customergetenough informationtoenablehimtomakea decision. The market for secondhand cars is growing exponentially, and car sellers may benefit from this by underpricingtheirvehiclesinordertotakeadvantageofthe demand.Consequently,thereisaneedforasystemthatcan forecastanautomobile'spricebasedonitsspecificationsand alsoaccount for the expenses ofrival models. Our method closesthegapsbygivinganestimateofthecar'svaluebasedon the most advanced price prediction system to buyers and sellers.

Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN: 2395-0072

4.1 LINEAR REGRESSION

Themethodofemployingindependentfactorsto predict adependentfactoriscalledregression.Usually,themethodis usedforestimatingandcomputingthecorrelationsbetween the independent and dependent variables. The regression models determine relationship between independentand dependentvariables.Regressionanalysisthatinvolvesonly one independentvariableandalinearconnectionbetween theindependent(x)anddependent(y)variablesisknownas linearregression.Theredlineintheaccompanyinggraphis referred to as the best fit straight line. Plotting a line that mostaccuratelypredictsthedatapointsgiventhedatapoints is our goal. The line can be represented by the linear equationgivenbelowyisequalto a0+ a1*x.

4.2 COST FUNCTION

Thegreatestviablevaluesfora0anda1arefoundusing thecostfunction,andthesevaluescanbeutilizedtocreatethe mostfeasiblefitlineforthedotsthatareplottedagainstthe data.Sinceourgoalistofindtheoptimalvaluesfora0anda1, we use this toformulatea minimizationprobleminwhich our goal is to reduce the difference between the expected (anticipated) and actual (truth) values. We employ the previouslystatedfunctiontominimize.Thedifferencebetween theexpectedandgroundtruthvaluesisusedtocalculatethe error difference. The error difference is squared, the datapointsareaddedtogether,andthesumisdividedbythe totalnumberofdatapoints.Youarenowpresentedwiththe average squared error for each of your data points. Consequently,theAnothertermforthiscostfunctionisthe MeanSquaredError(MSE)function.Thevaluesofa0anda1 willnowbeadjustedusingtheMSEfunctionuntiltheMSEvalueachievestheminimum.

a. RANDOM FOREST

A popular supervised machine learning algorithm for classificationandregressionissuesiscalledrandomforest. Itdevelopsdecisiontreesonseveralsamplesandusesthe averageinthecaseofregressionandthemajorityvotefor classification.Wheninterpretabilityisnotabigproblemand wehaveavastdataset,RandomForestisagoodfit.Decision treesarefarsimplertocomprehendandanalyses.Arandom forest is more challenging to read since it mixes several decisiontreeswhetherthereisalinearorexponential

4.3. Lasso Regression

Lasso Regression On the training data set, we first use Lassoregressiontoidentifythesubsetofattributesthatresult inthebest/leastsumofsquaredmistakewhenmakingaprice prediction. Ten-fold cross-validation is used to"lasso" the idealsubsetofattributes.L1regularizationisused.

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

Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN: 2395-0072

5. NOVELTY OF CAR PRICE PREDICTION

The automobile price prediction project is interesting becauseitcreativelyusesdataanalyticsandmachinelearning toanticipateautomotivepricingwithprecision.Thisproject utilizes advanced algorithms to provide real-time estimationsfor a variety of car variables,includingbrand, model,miles,year,andcondition.Thealgorithmsaretrained onlargedatasetstoaccommodatethedynamicnatureofthe automotiveindustry.Adaptabilityisincreasedbyitsabilityto tailormodelstodifferentautomotivetypesandlocal market preferences.Accuracyisalsoimprovedandnewinsightsinto changing market trends are fostered by ongoing refining through the integration of current data. This project is noteworthy for its capacity to provide insightful and predictive data that is based on data, so enabling giving stakeholders access to a user-centric tool that improves decision-making in the fiercely competitive and dynamic automotivesector.

6. ADVANTAGES AND DISADVANTAGES OF CAR PRICE PREDICTION SYSTEM

A. Machine learning is used in the automobile price prediction project. Algorithms to deliver athorough and effective solution that benefits the automobile market's suppliers as well as purchasers. This research provides precise car pricing estimates by examining a number of variables, including brand, model, mileage, year, and condition.Thishelpsdecision-makersmakewell-informed

choicesbyreducingthetimeandeffortrequiredformanual valuation procedures. By taking into account a variety of factors that affect prices, ithelps sellers set competitive prices, helps buyersnegotiate fairbargains,andimproves market understanding through insights into relevant dynamics. This project, which is scalable and constantly improvable, makes cars more accessible and guarantees ongoingimprovement,whichsimplifiesthecar-buyingand sellingprocessfor allpartiesinvolved.

B. Predicting automobile prices has a number of intrinsic limitsdespiteitsbenefits.Relianceonthecomprehensiveness and quality of the dataset poses a serious problem, since faultyorInaccurateforecastsresultingfrombiasseddatamay affect user confidence and decision- making. Complex machinelearningmodelsmaybedifficulttouseincontexts withlimitedresourcesbothintermsofcomputedemandsand deployment. Additionally, the consistency and forecast accuracyofthemodelmaybecompromisedbythevolatility ofoutsidefactorsthataffectcarpricing,suchaschangesin the economy, market trends, or unanticipated events. Additionally, certaincomplexmodels'opacitycouldleadtoa lackoftransparency,whichcouldloweruserconfidenceand comprehension of how predictions are made. The maintenance of data quality, ongoing adaptation, and initiativestoimprovemodelinterpretabilityareessentialfor minimizingtheserestrictionsandguaranteeingtheproject's applicabilityandrelevanceintheever-changingautomotive industry

7. CASE STUDY OF CAR PRICEPREDICTION

Ausedautoe-commercecompanywantedtoimproveits pricingapproach,soitputinplaceamachinelearning-based carsystemforpredicting prices.Utilizingabroaddatasetthat includedvehicleattributesincludingmake,model,mileage, year,andcondition,theyusedensemblelearningtechniques tocreateapredictionmode

Thetechnologyusedextensivedataanalysistooffersellers with predicted listing pricing. Sales grew and customer satisfactionroseasaresultofthisinitiative'sstreamliningof the sales process. Because of the model's accuracy in determiningfairprices,theplatform'soverallconfidenceand openness increased, givingbuyers and sellers alike a more dependableandeffectivemarketplaceexperience.

8. CONCLUSION

Salesofusedautomobilesarerisinggloballyduetorising new car prices and consumers' inability to afford them. A usedcarpricepredictionsystemthataccuratelyassessesthe car's worthiness based on a range of factors is therefore desperatelyneeded.Thesuggestedmethodwillcontributeto the precise estimation of used automobile prices. Three distinct machine learning algorithms decision trees, randomforests,andvotingclassifiers arecomparedinthis work.

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

Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN: 2395-0072

9. FUTURE OF CAR PRICE PREDICTION

This machine learning model might eventually link to different websites that offer real-time data for price prediction. Additionally, we might include a significant amount of historical car price data to help the machine learningmodelbecomemoreaccurate.Asauserinterface, wecancreateanAndroidapptocommunicate with users. We intend tocarefullyconsiderthearchitectureofdeep learningnetworks,employadaptivelearningrates,andtrain ondataclustersinordertoimproveperformance.Insteadof theentiredatasets

10. TECHNICAL ARCHITECTURE OF CAR PRICE PREDICTION

Whatmakesupanautomobilepricepredictionsystem's technical architecture is Dataset creation comes after gathering information from multiple sources. This data is usedtofeedmachinelearningmodels,whichusealgorithms learnedonprocesseddata,suchasRandomForest,Gradient Boosting, or Neural Networks. The trained model is made availableviaanAPIorwebservicethatishousedonaserver orcloudplatformandcanbeaccessedviaanintuitivefrontend interface that allowsusers to enter information about theircarsandgetpriceestimates.Ongoingimprovementsare facilitatedviauserinputsystems,modelperformancereview, and continuous monitoring. Ensuring scalability, user privacy, and data security, the system combines multiple components to produce an accurate, efficient, and userfriendlycarpriceforecasttool.

11. REFERENCES

The "CAR PRICE PREDICTION" project's successful developmentdependsonastrongbaseofresearch,current knowledge,andpertinentsources.Thefollowingimportant sourcesofinformationandreferenceswillhelptoguideand assistourproject:

[1]“PredictingthePriceofUsedCarsusingMachineLearning Techniques"bySameerchandPudaruth;(IJICT2014)

[2] "CarPricePredictionUsingMachineLearning,"by Enis Gegic,BecirIsakovic,DinoKeco,ZerinaMasetic,andJasmin Kavcic;(TEMJournal)

[3] Ning Sun, Hongxi Bai, Yuxia Geng, and HuizhuShi, "BP Neural Network Theory-Based Price Evaluation Model in UsedCarSystem,"HaiUniversityChangzhou,China

[4] “Prediction of Prices for Used Car by using Regression Models,"byNitisMonburinon,PrajakChertchom,Thongchai Kaewkiriya,SuwatRungpheung,SabirBuya,andPitchayakit Boonpou(ICBIR2018)

[5]“Predictioncarpricesusingqualifyqualitativedataand knowledge-based system” (Hanoi National University) by

DoanVanThai,LuongNgocSon,PhamVuTien,NguyenNhat

Anh,andNguyenThiNgocAnh

[6] Journal of Statistics Education, 16:3 Shonda Kuiper (2008),IntroductionofMultipleRegression:Howmuch is yourcarworth?

[7] Bias vs Variance Decomposition for Regression and Classification, Guerts P. (2009). In: Rokach L., Maimon O. (eds) Information extraction and discovery Guidebook. Boston,MA8Springer.RegressionShrinkageandSelection throughtheLasso,RobertT.In:RoyalStatisticalSocietyof GreatBritainJournal,SeriesB(Methodological)