Car Price Predictor is a machine learning project that aims to predict the price of used cars based on various features. It utilizes a dataset containing information about different cars, such as their make, model, year of manufacture, mileage, fuel type, and more.
Predicting the price of used cars can be useful for both buyers and sellers. By analyzing the characteristics of a car, such as its age, mileage, and brand, we can estimate its market value. This project utilizes machine learning techniques to develop a model that predicts car prices based on the provided dataset.
The dataset used for this project is located in the data directory. It contains information about various cars, such as their make, model, year of manufacture, mileage, fuel type, and more. The dataset is in CSV format and named car_data.csv.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/subhradip-bo/Python-project-Scripts.git
- Navigate to the project directory:
cd Python-project-Scripts/MachineLearning\ Projects/Car\ Price\ Predictor/
- Install the required dependencies:
pip install -r requirements.txt
Once you have installed the necessary dependencies, you can use the project as follows:
- Make sure you are in the project directory:
cd Python-project-Scripts/MachineLearning\ Projects/Car\ Price\ Predictor/
- Run the main Python script:
python car_price_predictor.py
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The program will prompt you to enter the details of the car for which you want to predict the price. Provide the required information and press Enter.
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The program will display the predicted price of the car based on the trained machine learning model.
The machine learning model used in this project is trained using the car_data.csv dataset. The model training code can be found in the car_price_predictor.py script. It follows the standard steps of a typical machine learning workflow, including data preprocessing, feature engineering, model selection, and evaluation.
The accuracy and performance of the trained model may vary depending on the dataset and the specific machine learning algorithms used. It is important to note that the predictions provided by this model are estimates and should be used as a reference rather than absolute values.
Contributions to this project are welcome. If you find any issues or want to enhance the functionality, feel free to open a pull request with your changes. Please ensure to follow the project's code of conduct.
The code in this project is available under the MIT License. Feel free to use and modify it as per your needs.