Price Prediction on Finding Retail Prices of Used Motorbikes

Abstract

Motorcycles are one of the most popular modes of transportation because they are quick, inexpensive, and convenient. Many distinct attributes are examined in order to make a reliable and accurate prediction. This thesis presents about Motor Bike prediction system by using the supervised machine learning techniques. Pricing of the motorcycles is one of the major concerns for motorcycle Sellers. The main purpose of this study is to construct a model to determine the price of motorcycle based on significant factors and various features of selected Motorcycle from secondary data sources such as. ‘Motorcycle brand, ‘Model’, ‘Year’, ‘Owner Type’, ‘Transmission’ were the significant factors which were used & helpful to find the correct values. The author's proposed method includes adaptive features as well as predictive analysis components based on statistical algorithms and machine learning. A web application prototype is created so end-user can easily make predictions. When the application is launched, it will be given a startup introduction to what needs to be done before making a prediction. A questionnaire was sent to the target group to test the prototype and see if any updates were required, with the hope that the End user would have a positive effect. The functional and non-functional criteria were tested, and the results were positive. This study demonstrates that the prediction model's accuracy can be improved. Finally the author has examined whether goals and objectives are well set out properly. Author has painted a picture how Bike as public transportation is a good solution when it comes to traffic and bad roads.