Predictive Pricing Model for Commercial Vehicles using Supervised Learning
Keywords:
Support Vector Machines, K Nearest Neighbor Regression, Lasso Regression, Machine learning Model, Artificial Neural NetworkAbstract
Despite the fact that the manufacturer determines the market price for new automobiles, some governments in specific nations incur additional fees in the form of taxes. Customers may be certain they will receive value for their money when purchasing a new vehicle. Used vehicle sales are rising globally, meanwhile, as new automobile costs climb and more people are unable to afford to acquire them. A system that reliably forecasts the price of automobiles based on multiple attributes is therefore urgently needed. In the current system, sellers arbitrarily choose prices without considering the true condition of the car, while purchasers are uninformed of the vehicle's actual market worth. In actuality, neither the asking price nor the value of the automobile are known to the seller. It has highly efficient way to address this issue and this circumstance. Supervised machine learning methods including KNN (K Nearest Neighbor) Regression, Lasso Regression, ANN (Artificial Neural Network), and SVM (Support Vector Machine) are provided for the analysis of used car expenditure. To train the model, we used pre-owned automobile data that we gathered from a number of websites utilizing a single source. In this experiment, a variety of training-to-test ratios were used to analyses the date. As a consequence, the proposed model's accuracy significantly increased, and it was modified to become the ideal model.
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