This Python script estimates the price of a car model based on its year and mileage. It utilizes web scraping techniques to extract data from a website and uses the scikit-learn library for machine learning-based prediction.
- Python 3.x
- Requests library (
pip install requests) - BeautifulSoup library (
pip install beautifulsoup4) - scikit-learn library (
pip install scikit-learn)
- Run the script.
- Input the car company (brand) when prompted.
- Input the car model when prompted.
- Input your car's model year and mileage when prompted.
- The script prompts the user to input the car company (brand) and model.
- It then requests data from the website https://www.cazoo.co.uk/used-cars/ for the specified car brand and model.
- The script extracts relevant information such as year, mileage, and price using BeautifulSoup.
- It prepares the data for estimation by converting it into a suitable format.
- Machine learning, utilizing a decision tree classifier from scikit-learn, is employed to estimate the car's price based on year and mileage.
- The user is prompted to input their car's model year and mileage.
- The script predicts the price of the user's car based on the input data and displays the result.
- The accuracy of the price estimation heavily depends on the availability and accuracy of data from the website.
- This code serves as an educational example of web scraping and basic machine learning techniques for estimating car prices and may require further refinement for practical use.
- This script is provided for educational purposes only.
- The accuracy of the estimated prices may vary, and it is recommended to consult multiple sources for accurate pricing information when buying or selling a car.