  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.
