Car Price Prediction is a Python program developed in Google Colab that utilizes machine learning techniques to predict car prices based on various features and attributes. This project demonstrates the application of regression models for price prediction.
To run the Car Price Prediction program locally or in your own Colab environment, follow these steps:
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Clone this repository to your local machine or open it in Google Colab.
git clone https://github.com/yourusername/car-price-prediction.git
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Open the Jupyter notebook
car_price_prediction.ipynbin Google Colab or a Jupyter notebook environment. -
Follow the instructions and code within the notebook to train and evaluate the car price prediction model.
The primary purpose of this project is to demonstrate how to build a car price prediction model using machine learning techniques. You can use the provided Jupyter notebook car_price_prediction.ipynb as a guide to understand the following:
- Data preprocessing and exploration.
- Feature selection and engineering.
- Building, training, and evaluating regression models.
- Making predictions and analyzing model performance.
Feel free to adapt the code and techniques to your specific dataset or use case.
The dataset used for this project is available in the data folder. It contains car-related features such as mileage, year, engine size, and more, along with their corresponding prices. You can replace this dataset with your own data if needed.
This project employs various regression models, including Linear Regression, Random Forest Regression, and XGBoost Regression, to predict car prices. Model performance metrics and evaluation are discussed in the notebook.
This project is open-source and available under the MIT License. You are free to use, modify, and distribute the code for your own purposes as long as you include the original license in your distribution.