I need to develop a small project for a data science bootcamp interview next week (insightdatascience.com).

One idea I had was to create an art valuation bot and art generation bot. The outline would look something like this:

1. Scrape abstract art data (image and price data) from an art sales website.
2. Use a pretrained network and fine-tune it on my dataset for price regression.
3. Greate a GAN on the same dataset, then uses it to generate novel art images.
4. Evaluate GAN with the price regression model to determine the newly generated pieces with the highest predicted value.

Some potential challenges I see:

1. Image size: Resizing the dataset to have a standard resolution will really affect the appearance of the newly generated images. Can I do without this? Perhaps I will resize while maintaining the original aspect ratio.
2. Noise: Given that I don’t have access to an art valuation dataset and am just trying to train based on an art sales website, the price does not necessarily indicate that the art piece is valuable. Hopefully, there is enough signal in the dataset to offset this effect.

Does anyone have thoughts on project-based data science boot camps like Insight Data Science (https://www.insightdatascience.com/ )

If you see any more potential issues with my project outline, or have a suggestion for improving it, please leave a comment or PM 🙂