Cogni Value: Unveiling the Future-A Journey into used Car Price Forecasting with ANN and ML

Authors : Dr. B. Krishna; Teja Chalikanti; Bobbili Sreeja Reddy

Volume/Issue : Volume 8 - 2023, Issue 7 - July

Google Scholar :

Scribd :


In the rapidly expanding domain of the used car market, accurately forecasting prices present a significant challenge, necessitating innovative solutions. This research paper introduces a ground-breaking methodology that combines the power of Artificial Neural Networks (ANNs) and machine learning algorithms to achieve highly precise predictions of used car prices. A comprehensive dataset is meticulously compiled, encompassing influential attributes such as make, model, year, mileage, condition, location, and additional features. Rigorous pre-processing techniques are applied to address missing values, outliers, and categorical variables, ensuring the dataset is optimized for accurate model performance. An intricate feature selection technique is employed to identify the most significant attributes driving used car prices. The model complexity is reduced by eliminating irrelevant and redundant features, leading to improved prediction accuracy and efficiency. The predictive models integrate ANNs with various machine learning algorithms, including linear regression, decision trees, random forests, and gradient boosting. ANNs play a central role in this framework, adeptly capturing the complex non- linear relationships inherent in the data. The empirical evaluation is conducted on a diverse dataset comprisinga wide range of used car transactions, encompassing different makes, models, and geographical locations. Random partitioning divides the dataset into training and testing subsets, enabling thorough model training and comprehensive evaluation of predictive performance. The empirical findings unequivocally demonstrate the superiority of the proposed approach, with ANNs surpassing traditional machine learning algorithms in accurately predicting used car prices. ANNs excel in unravelling the intricate patterns and nuances within the data. Additionally, the inclusion of multiple machine learning algorithms provides valuable insights, enabling comparative analysis and a deeper understanding of their performances. The implications of this research are significant, benefiting used car dealerships, buyers, and sellers.Accurate price predictions facilitate fair negotiations and empower stakeholders to make well-informed decisions, ultimately enhancing market efficiency and customer satisfaction. As progress continues topropel the field forward, this research represents a pivotal milestone in predicting used car prices, leveragingthe synergy ofANNand machine learning algorithms. The proposed approach hasthe potential to redefine pricing strategies in the industry, introducing reliability and efficiency into the dynamic used car market.

Keywords : Artificial Neural Network, Decision Tree Regressor, Linear Regression.


Paper Submission Last Date
30 - November - 2023

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.