raghavrama21/auction-price-prediction

Christie's Online Auction Price Predictor

This project uses web scraping tools, including Selenium and Beautiful Soup, to extract auction prices from Christie's online platform. The collected data is then used to train a machine learning model to predict auction prices.

Setup

To run this project, you will need to have Python installed on your machine along with the following libraries:

• Selenium
• Beautiful Soup
• Pandas
• Scikit-learn

Usage

To use the scraper, modify the script to specify the auctions you are interested in and run it to extract the data. The extracted data can then be preprocessed and used to train a machine learning model using scikit-learn.

Evaluation

The performance of the model can be evaluated using appropriate metrics and testing techniques, and the model can be refined based on feedback and further data analysis.

Legal Considerations

It is important to ensure that the data collection and modeling methods used in this project are ethical and comply with applicable laws and regulations.

Conclusion

This project can provide valuable insights for buyers and sellers in the art market. By using machine learning to predict auction prices, users can make more informed decisions when buying or selling artwork through Christie's online auction platform.