Vehicle Price Prediction By Aggregating decision tree model With Boosting Model

TL;DR

This paper proposes a vehicle price prediction method that combines decision tree and gradient boosting models, utilizing data preprocessing to improve accuracy and promising performance results.

Contribution

It introduces a hybrid approach combining decision tree and gradient boosting models for used vehicle price prediction, evaluated on a specific dataset.

Findings

The combined model achieved promising prediction accuracy.

Data preprocessing improved model performance.

The dataset is valuable for future research in vehicle price prediction.

Abstract

Predicting the price of used vehicles is a more interesting and needed problem by many users. Vehicle price prediction can be a challenging task due to the high number of attributes that should be considered for accurate prediction. The major step in the prediction process is the collection and pre-processing of the data. In this project, python scripts were built to normalize, standardize, and clean data to avoid unnecessary noise for machine learning algorithms. The data set used in this project can be very valuable in conducting similar research using different prediction techniques. Many assumptions were made on the basis of the data set. The proposed system uses a Decision tree model and Gradient boosting predictive model, which are combined in other to get closed to accurate prediction, the proposed model was evaluated and it gives a promising performance. The future price prediction of used vehicles with the help of the same data set will comprise different models.