Car Price Prediction Using Machine Learning

1. Car Price Prediction: An Application of Machine Learning

Keywords

Car price Prediction, Machine Learning, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Train Data, Validation Data

            In our approach, a regularization technique is integrated with hyperparameter tuning methods to address the issue of overfitting. A major aim of our approach is to develop a framework to forecast the user car price. We constructed several methods such as linear regression, lasso regression, ridge regression, elastic net regression, RF, DT and SVM with hyperparameters. Results show that, support vector regressor is considered as the best method.

2. Metaverse: Design of the Car Price Prediction Model Through a Machine-learning Approach

Keywords

Metaverse, rental car price prediction, random forest regression, multilayer perceptron, convolution neural network, autoregressive-moving-average model, long short-term memory

            We recommended a car price forecasting system in the metaverse by employing ML techniques in our article. We extracted essential features by utilizing various methods including random forest regression, multilayer perceptron, convolution or recurrent neural networks and autoregressive moving average. We conclude that, our suggested framework offers useful information in the metaverse rental car prices and it can be utilized by rental car industries.

3. Predicting the Prices of the Used Cars using Machine Learning for Resale

Keywords

Forecast (predict), Random Forest, Decision Tree, Extra Tree Regressor, Bagging Regressor, Accuracy

            To predict a second hand car prices, we proposed a supervised ML framework in our study. We utilized various historical data to predict the car price. We carried out the forecasting process by employing methods like Random Forest Regressor, Extra Tree Regressor, Bagging Regressor, Decision Tree and the XG Boost. Then, we compared these methods to find out the best framework. As a consequence, Random Forest method outperformed other methods.

4. Performance Evaluation of Popular Machine Learning Models for Used Car Price Prediction

Keywords

Cars, Efficient use of resources, Transport, Road network, Linear regression, KNN regression, Decision tree regression, XGBoost regression, Used car price prediction, Basic services

            Various innovative ML techniques such as XGBoost, KNN, random forest, decision tree, and linear regression are evaluated in our research to forecast the used car price. In terms of different metrics, we examined the efficiency of each technique. As a result, XGBoost achieved highest outcomes when compared with others. Finally, we stated the significance of the utilized ML techniques through the research findings.

5. Used Car Price Prediction using Different Machine Learning Algorithms

Keywords

 Regression techniques, lasso regression, ridge regression

            A major concentration of our approach is to forecast the used car price through the utilization of various regression methods. To forecast the price very precisely, we examined various factors. We utilized several supervised learning ML methods such as linear regression, lasso and ridge regression to develop a used car price forecasting model. We employed ML based libraries such as Numpy, Pandas, and Sklearn. In that, Lasso outperformed others.

6. Prediction of Used Car Prices Using Artificial Neural Networks and Machine Learning

Keywords

ANN, Keras, Regression, Ridge, LASSO

            We constructed a ML based Random Forest and a supervised learning based Artificial Neural Network architecture in our study for the prediction process of used car price. The development of ANN framework is carried out by employing Keras Regressor and we also developed ML methods like Random Forest, Lasso, Ridge, Linear regressions. We investigated an experimental analysis, in that; Random Forest provides greater end results.

7. Second Sale Car Price Prediction using Machine Learning Algorithm

Keywords

Hyperparameter Tuning, Categorical data, Randomised Search CV, Prediction Model

            An ultimate aim of our research is to build a forecasting model for the purpose of used car selling price prediction. Here we utilized ML based method i.e Random Forest Regression to forecast the selling price of used car. Then we employed Feature engineering approach like Extra Trees Regression to fit the number of decision trees. We conclude that, our suggested model offers efficient results.

8. Prediction of the price of used cars based on machine learning algorithms

Keywords

Neural network, XGBoost, SVM, price of used cars

            Several ML based approaches including SVM, XGBoost and neutral networks are employed in our paper to predict the car price. We compared the efficiency of methods according to the findings in terms of various metrics. From the conclusion, we considered SVM as an optimal method for the car price prediction process.

9. Car Price Prediction Using Machine Learning Algorithms

Keywords

            A main goal of our work is to forecast the car price. We mainly concentrated on the discovery of important factors which is essential to forecast the used car price. We carried out this process by utilizing several ML techniques such as linear regression, ridge regression, lasso regression, KNN regressor, random forest regressor, bagging regressor, AdaBoost regressor, and XGBoost. Results show that, RF and XGBoost Regressor methods outperformed others.

10. Machine Learning Based Solution for Asymmetric Information in Prediction of Used Car Prices

Keywords

            Firstly, we investigated the markets with asymmetric data about the used car price. Here, we preprocessed the data by managing the missing data. For the forecasting of car price, we employed different ML approaches like LR, Random Forest, Extra Tree Regressor and Extreme Gradient Boosting Regression. In that, Extra Tree Regressor offers highest end results. At last, we constructed a cloud related application that offers a prediction price of a specific car.