The used car market needs an accurate prediction of vehicle resale values to such stakeholders as sellers, buyers, and dealers. Conventional approaches to vehicle valuation are highly subjective in judgment and data limit. With this, the proposed approach aimed at facilitating the application of machine learning in predicting vehicle prices concerning multiple factors. This would assist in making better pricing decisions at dealerships along with inventory optimization.
Week 1-2: Initial Planning and Requirement Analysis
Define project objectives, scope, and high-level requirements. Collect the required data and resources.
Week 3-4: Data Collection and Preprocessing Phase
Collecting and preprocessing historical data regarding vehicle sales.
Extract features and clean the data.
Week 5-6: Model Development Phase
Develop and train machine learning models for price prediction.
Test model accuracy and performance on test data.
Week 7-8: System Implementation Phase
Implement the Vehicle Price Prediction Model.
Integrate the model into a user-friendly interface.
Test and refine the system based on defined performance metrics and user feedback.
Week 9-10: System Integration and Testing
Test end-to-end to avoid incorrect predictions of prices.
System performance and user interaction monitoring.
Continual improvement utilizing analytics and feedback.
Week 11-12: Final Evaluation and Reporting Phase
Provide final evaluations and validation of the Vehicle Price Prediction System.
Final compilation of the project report together with its documentation.
Presentations of individual reports by students.
