Predicting Car Prices using Machine Learning

Using machine learning to predict automobile pricing entails creating a model that calculates a vehicle’s market worth based on a variety of attributes. A thorough dataset comprising characteristics like make, model, year, mileage, engine size, fuel type, and extra factors like colour and condition is first gathered. In order to prepare the dataset for analysis, data preparation is essential. This includes cleaning the dataset, addressing missing values, encoding categorical variables, and normalising numerical characteristics. The relationship between the input variables and car pricing can be modelled using a variety of machine learning algorithms, such as decision trees, linear regression, and ensemble techniques like random forests and gradient boosting.The model is trained using historical data, learning to identify patterns and factors that influence pricing. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared are used to assess the model’s accuracy and effectiveness. By accurately predicting car prices, machine learning models assist buyers, sellers, and dealerships in making informed decisions, optimizing pricing strategies, and enhancing the overall car trading process.