Used Cars Price Prediction using Supervised Learning Techniques

Pattabiraman, V.; Ganesh, Mukkesh

doi:10.35940/ijeat.a1042.1291s319

Cited by 23 publications

(9 citation statements)

References 5 publications

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“…Venkatasubbu and Ganesh [1] aim to compare the performance of three machine learning algorithms in predicting the price of a used car, which are regression trees, lasso regression, and multiple regression. The dataset was collected from Kelly Blue Book Central Edition 2005, which contains 804 records of cars.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nowadays there are many different categories of vehicles such as car, coupe, sedan, support car, hatchback, station wagon, convertible, pickup truck, and sport-utility vehicle (SUV). Each of these vehicles has its own characteristic, features, and type of use where the price is specified based on them [1]. Buying a used car is a good alternative for people in many countries because it is affordable and offers the buyers the chance to resell the car again after a while, which could result in some profit [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Gollapalli¹,

Alqahtani²,

Alhamed³

et al. 2023

MMEP

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The production of cars has been decreasing in most countries since the COVID-19 pandemic from 2020 to 2021. Due to this, the used car market has grown to be a booming industry by itself. Recent advances in online portals and platforms have made it possible to get more information about the factors that determine used car values. Hence, car price prediction has become a high-interest field of research. This paper aims to investigate the power of machine learning to build a model that will be able to predict the approximate price of a used car by utilizing the "Saudi Arabia Used Cars" Dataset which is collected from the Syarah platform and available on the Kaggle platform. The model assists both customer and seller to estimate the approximate price of a used car in the market. Three different Machine learning techniques were utilized which are Linear Regression, Random Forest, and XGBoost which score an MSE of 0.15, 0.10, and 0.19 respectively. The Random Forest Regressor algorithm outperformed other algorithms where it achieves the best result on the three evaluated metrics RMSE, MSE, and R-squared.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Gollapalli¹,

Alqahtani²,

Alhamed³

et al. 2023

MMEP

View full text Add to dashboard Cite

show abstract

“…The basis for predictions is historical data gathered from daily newspapers 11 . The 2005 Central Edition of the Kelly Blue Book was used to get the data 13,18 . The data used was acquired from a school of information and computer science that has access to several databases 15 We have collected the data from the car-selling shop named Car View.…”

Section: Data Set Preprocessingmentioning

confidence: 99%

Machine learning modeling for reconditioned car selling price prediction

Abdullah,

Rahman,

Shidujaman

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Almost 80% of the vehicles required for Bangladesh's road transportation industry are supplied by reconditioned cars. Using machine learning (ML) to predict car prices refers to using ML algorithms and techniques to make assumption about future car prices. This can be useful for a variety of purposes, such as helping car buyers and sellers make informed decisions, assisting car dealerships with inventory management, or providing insights for car manufacturers and other industry stakeholders. To predict car prices using ML, data is collected on a variety of factors that can affect the ongoing cost of a car, such as its make and model, age, mileage, condition, and location. This data is then fed into the Random Forest ML model, which uses statistical techniques to analyze the data and identify patterns and trends. The model performs 99.59% accurately in the tested portion of the data set and ensures that the model can then be used to make predictions on the future cost of an automobile based on these patterns and trends.

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“…In the article [8,19], the research subject is machine learning methods' analysis and comparative characteristics, such as lasso regression, multiple regression, and regression trees.…”

Section: Literature Analysismentioning

confidence: 99%

Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods

Boyko,

Lukash

2023

Journal of Engineering

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This paper considers the current market pace, which requires a corresponding competitive advantage. This study forecasted the cost of heavy machinery depending on geolocation and essential characteristics by the field of activity. This study analyzes specific categories of heavy machinery for important price characteristics. The study classified them by keywords in the text description as essential characteristics. Accordingly, a dataset was formed based on the data obtained. The research objective is to collect and structure data from web resources for the sale of heavy equipment. This paper describes in detail the preliminary data processing. The main stages of preprocessing are presented in detail: detection and processing of missing data, removing anomalous data, coding of categorical data, and scaling. The method of the average value of a specific grouped set was applied to fill in the gaps according to the characteristics and available data. The mode value from the grouped items was used to fill in the gaps. The interquartile range and standard deviation were used to detect anomalies. We used the Kolmogorov–Smirnov, KS_Test, and Lilliefors tests to check the data for normality. In this study, the assessment of abnormal data was applied separately to each set of grouped data with the same parameters. The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). Two data encoding methods were used to achieve maximum model accuracy: Label Encoder and One Hot Encoder. The work of each algorithm is considered on the example of the created dataset. In this study, the parameter used for coding was the geolocation of heavy equipment. The study pays additional attention to the specific characteristics of heavy machinery by the sector of the economy. The existing methods and tools for price forecasting, depending on the specific characteristics of the equipment, were analyzed. The practical significance of this work lies in developing an algorithm for predicting the cost of heavy machinery by assessing several parameters.

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Used Cars Price Prediction using Supervised Learning Techniques

Cited by 23 publications

References 5 publications

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Machine learning modeling for reconditioned car selling price prediction

Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods

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