Car Price Prediction using Linear Regression, RandomForest Regressor, and GradientBoost Regressor

This project focuses on predicting car prices based on various features using machine learning algorithms. We have implemented three popular regression algorithms - Linear Regression, RandomForest Regressor, and GradientBoost Regressor. The project also includes exploratory analysis of the dataset to gain insights into the data.

Car price prediction is a common problem in the automotive industry and can be useful for various applications such as insurance, valuation, and sales forecasting. In this project, we utilize machine learning algorithms to build predictive models that estimate the price of a car based on its features.

Dataset The dataset used in this project contains information about various cars, including features like make, model, year, mileage, engine size, fuel type, and more. The dataset is stored in a CSV file named car_data.csv.

Exploratory Analysis The exploratory analysis section of the project involves understanding the dataset, performing data cleaning and preprocessing, and visualizing the data. The analysis helps in gaining insights into the dataset and identifying any patterns or correlations that can be useful in building accurate models.

Machine Learning Algorithms We have implemented the following three regression algorithms for car price prediction:

Linear Regression: This algorithm assumes a linear relationship between the features and the target variable. It estimates the coefficients for each feature and uses them to make predictions.

RandomForest Regressor: RandomForest is an ensemble learning algorithm that creates a multitude of decision trees and combines their predictions to obtain the final result. It is capable of capturing complex relationships between features and the target variable.

GradientBoost Regressor: GradientBoost is another ensemble learning algorithm that builds a series of weak prediction models (typically decision trees) and combines their predictions in a weighted manner. It sequentially improves the models by minimizing the errors of the previous models.

Contributing Contributions to this project are welcome. If you have any suggestions, bug reports, or feature requests, please open an issue on the GitHub repository. If you would like to contribute code, feel free to fork the repository and submit a pull request.