One of the famous regression problems in machine learning field is car price prediction. Its main objective is to predict the car price depends on various features includes creating a model, age, mileage, features and more. We apply various models and imply many tools so that we can get the apt result. The trending topics will be selected as per your interest, only after your acknowledgement we move to the next step.
To approach this project, we mentioned below the step-by-step guidelines,
We create a machine learning model to predict the price of car based on their characteristics.
This datasets consist the features of car and their respective prices. We use landscapes like Kaggle is even host datasets which adapt for this problem. Data includes features like mileage, horsepower, fuel type, brand, model, year, transmission and more.
We solve the regression problem by some tools like,
Grid search or random search algorithms are used to fine and re-tune our model parameters. It executes the feature selection techniques for enhancing the performance of the model and minimizes the chances for over fitting.
We utilize this model through a web or mobile application for the users to load the car details and receive the prediction of car price. If any transformations or scaling is applied, not to forget about converting the models result and then retreat to the original price of the scale.
Fetch the users review to predict perfectly. Update our model regularly with new data that should grasp the latest modifying trends in the car market.
The project’s findings, methodologies, and lessons are learned and filed by us. It designs the capable future advancements including external factors such as new car model launches, economic indicators, or integrating image-based predictions from the provided car pictures .
Through this project, we learn and understand the all-inclusive regression techniques, feature engineering and challenges in predicting car prices. Thus, you can get a wide variety of research assistance from us. Journal Writing are well written by our journal writing department an error free paper will be hand over to scholars as they are well versed in English.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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