Car Price Prediction Using Machine Learning

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,

  1. Objective Definition

We create a machine learning model to predict the price of car based on their characteristics.

  1. Data Collection

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.

  1. Data Exploration and Cleaning
  • Understanding the Data: We examine the data structures, types and basic statistics.
  • Missing Values: By using methods (mean, median), we control the missing values or separate the rows or columns.
  • Outliers: The anomalies are detected and tackle by us, it figures out by using scatter plots or box plots.
  1. Exploratory Data Analysis (EDA)
  • Univariate Analysis: This understands the allotment of individual variables. Specifically, it main objective is target variable (the price).
  • Bivariate Analysis: The connections between the price and other attributes are examined by us. For example, how does the car’s age relate to its price?
  • Correlation Analysis: It observes how the features are related with the cost and depends on one another.
  1. Feature Engineering
  • Encoding Categorical Variables: Using label encoding or one-hot encoding, we convert the categorical variables to numerical format.
  • Feature Creation: It extracts the possible new attributes. Such as age from the year of production.
  • Feature Scaling: If the sensitive algorithms of feature scale like SVM (Support Vector Machine) and linear regression are using by us to normalize or organize the attributes.
  1. Model Selection

We solve the regression problem by some tools like,

  • Linear Regression
  • Neural Networks is used for more difficult datasets.
  • Decision Trees and Random Forest
  • Gradient Boosting Machines such as, XGBoost, LightGBM.
  • Support Vector Machines associated with a regression kernel.
  1. Training and Validation
  • Data Splitting: The data is divided into training, authentication and test sets.
  • Model Training: Training dataset are used by us to train the models.
  • Model Validation: Models are examined with the help of validation set.
  1. Model Evaluation
  • Regression Metrics: The calculation of our model is done by using Mean Absolute Error (MAE), RMSE (Root Mean Squared Error), R^2 etc.
  • Residual Analysis: Residuals or particles are evaluated to make sure that theory of regression is being met.
  1. Optimization and Hyper parameter Tuning

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.

  1. Deployment

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.

  1. Feedback and Continuous Learning

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.

  1. Conclusion & Future Work

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 .


  • Temporal Factors: The temporary factor in car pricing such as inflation adjustments.
  • Domain Knowledge: We deploy our field knowledge to understand the powerful attributes of a car .For example; luxury car brands might have various undervalue curve compares to economy brand.
  • Data Quality: Make sure of data reliability, the prices of each are different based on their regional factors, import or export duties etc.

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. 

Car Price Prediction Using Machine Learning Thesis Topics

Various thesis ideas from professionals’ point of view are shared, get our thesis guidance to perform best in your research career. We are glad to offer you a professional thesis support our researchers are well versed in machine learning domain so you can expect a good outcome from our end. Thesis ideas will also be shared from leading journals.

Car Price Prediction Using Machine Learning Ideas
  1. Car Price Prediction: An Application of Machine Learning


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.

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


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.

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


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.

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


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.

  1. Used Car Price Prediction using Different Machine Learning Algorithms


 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.

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


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.

  1. Second Sale Car Price Prediction using Machine Learning Algorithm


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.

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


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.

  1. Car Price Prediction Using Machine Learning Algorithms


            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.

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


            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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