Prediction of Car Sales Price Trends using Ensembling Models

Predicting car prices accurately is crucial for both consumers and sellers in the automotive industry. This paper delves into the utilization of machine learning methods for forecasting car prices By analyzing sales, price datas and various factors influencing sales, brand of the car, type of the fuel, mileage, Price it covers We construct predictive frameworks employing machine learning algorithms and ensemble learning algorithms like Gradient boosting, Catboost, Voting Regressor, Bagging Regression, Ridge Regression Through feature engineering, we extract relevant information to improve the accuracy of our predictions and novelty between the algorithms to get more ac-curated results. The results demonstrate the effectiveness of the proposed approach in providing valuable insights for car manufacturers and dealerships to optimize their strategies and operations. This helps businesses make informed decisions and stay competitive in the market. This research study contributes to the growing body of knowledge with the integration of Machine Learning (ML) analytics. By evaluating the performance of various predictive models and identifying the key factors influencing car sales, this study intends to provide useful suggestions for both industry professionals and researchers, encouraging innovation and effectiveness in the automotive field.