The Car Price Prediction project uses three supervised learning algorithms – Random Forest, LightGBM, and XGBoost – to predict the prices of cars based on their features such as make, model, mileage, and age. The models are trained on a dataset of historical car prices and features to develop accurate prediction models.
The project has several use cases in the automotive industry. Car dealerships can use the models to estimate the value of cars they plan to sell or trade-in. Car buyers can use the models to verify if the price they are paying for a used car is fair. Insurance companies can use the models to determine the premium for car insurance. Car manufacturers can use the models to aid in pricing decisions for new cars, while car rental companies can use the models to estimate the value of their rental cars.
To evaluate the models’ performance, the project uses Python and various libraries such as scikit-learn, pandas, and matplotlib. The project involves pre-processing the data, splitting it into training and testing sets, and training the models using the training data. The trained models are then evaluated using various performance metrics such as mean absolute error and mean squared error.
Overall, the Car Price Prediction project is a machine learning project that has various use cases in the automotive industry. It uses three powerful algorithms and can be evaluated using Python and various performance metrics to develop accurate prediction models.