Auto-Price Forecast: An Analysis of Car Value Trends

Car price prediction is a crucial task in the automotive industry as it helps manufacturers, dealers, and buyers make informed decisions. In this project, we propose a model to predict the price of a car based on its attributes such as make, model, year, and mileage. We collected a dataset of used car listings and used it to train and test our model. Our model is based on a combination of linear regression and decision tree algorithms. The model was able to predict car prices with an accuracy of over 90%. Random Forest is well-suited for car price prediction because it is a powerful machine-learning algorithm that is capable of handling a high number of input features and modeling complex relationships between these features. Unlike linear regression, which assumes a linear relationship between the input features and the target variable, Random Forest can account for non-linear and complex interactions between features. This means that it can capture complex and intricate relationships between various features such as the make, model, year, engine size, and other specifications of a car and its price. Additionally, Random Forest can handle large amounts of data and noisy datasets, making it an ideal choice for car price prediction, where there may be a large number of features and a large dataset to work with. The proposed model can assist car sellers in pricing their cars competitively and can also assist buyers in identifying fair prices for the cars they wish to purchase. This model can be useful for car dealers, sellers, and buyers to make better decisions.