Bike Price Prediction System Using Machine Learning

• Author(s):

  BALAVIGNESH T | S.Anushalakshmi

• Keywords:

  Bike Price Prediction, Machine Learning, Regression Analysis, Data Analytics, XGBoost, Random Forest, Vehicle Valuation, Price Estimation, Streamlit Deployment.

• Abstract:

  The Valuation Of Used Consumer Assets, Particularly Pre-owned Two-wheeled Vehicles, Remains A Major Secondary-market And Socioeconomic Challenge. In Rapidly Developing Urban Transport Ecosystems, Frequent Fluctuations In Marketplace Demand, Regional Brand Preferences, Seasonal Consumer Choices, And Macroeconomic Inflationary Changes Contribute Heavily To Unstable Price Valuations, Significant Financial Losses For Individual Traders, And Extended Structural Negotiation Delays. Accurate Estimation And Real-time Management Of Vehicle Depreciations Across Transactional Digital Platforms Are Therefore Essential For Contemporary Asset Lifecycle Management, Transparent Consumer Electronics And Automotive Commerce, And Automated Municipal E-marketplace Quality Control. Conventional Valuation Methodologies, Including Manual Inspections By Local Technicians, Subjective Dealer-broker Assessments, And Static Depreciation Lookup Tables, Are Often Time-consuming To Execute, Heavily Vulnerable To Human Bias Or Incomplete Vehicle Information, And Difficult To Scale Effectively Across High Throughput, Cloud-integrated Consumer-to-consumer (C2C) Transaction Pipelines. To Overcome These Financial And Computational Limitations, This Study Proposes An Automated, Data-driven Bike Valuation And Asset Screening Framework Using Multi-parametric Vehicle Feature Engineering And Advanced Ensemble Machine Learning Techniques. Tabular Historical Sales Records Capturing Extensive Transaction Instances From Online Marketplaces And Kaggle Repositories Are Ingested And Preprocessed Through Automated Missing-value Handling, Categorical Encoding, And Min-max Feature Normalization To Resolve Baseline Input Variances Across Disparate Digital Platform Entries. Discriminative Physical And Administrative Parameters—specifically Focusing On Brand Equity Indexes, Model Classifications, Manufacture Years, Cumulative Kilometers Driven, Engine Displacement Capacity, Fuel Type Variants, Ownership Histories, Insurance Validities, And Geographical Sales Locations—are Extracted And Engineered To Establish A Structured Asset Feature Matrix. Predictive Modeling Is Executed Through A Comparative Optimization Of Three Distinct Mathematical Architectures: Linear Regression, Random Forest Regressor, And Extreme Gradient Boosting (XGBoost) Regressor, Which Are Trained To Solve The Continuous Target Optimization Task Of Estimating Accurate Market Prices. Model Execution Is Measured Comprehensively Using Standardized Validation Criteria, Evaluating Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), And The R-squared (R²) Coefficient To Ensure Prediction And Evaluation Stability. Experimental Observations Suggest That The Integrated XGBoost Framework Yields A Predictive R² Score Of 0.9512, Providing An Objective, Scalable, And Non-invasive Decision Support Pipeline. The System Is Deployed As An Interactive Application Via A Streamlit Web Interface, Integrating Responsive Data Entry Fields, Correlation Charts, Feature Importance Modules, And Real-time Price Prediction Alerts For Public Vehicle Marketplaces.

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Other Details

• Paper id:

  IJSARTV12I6105697

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-18

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