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Predicting the price of used vehicles is a more interesting and neededproblem by many users. Vehicle price prediction can be a challenging task dueto the high number of attributes that should be considered for accurateprediction. The major step in the prediction process is the collection andpre-processing of the data. In this project, python scripts were built tonormalize, standardize, and clean data to avoid unnecessary noise for machinelearning algorithms. The data set used in this project can be very valuable inconducting similar research using different prediction techniques. Manyassumptions were made on the basis of the data set. The proposed system uses aDecision tree model and Gradient boosting predictive model, which are combinedin other to get closed to accurate prediction, the proposed model was evaluatedand it gives a promising performance. The future price prediction of usedvehicles with the help of the same data set will comprise different models.

Vehicle Price Prediction By Aggregating decision tree model With Boosting Model