I'm currently working on a machine learning project where I'm using a CSV file as a makeshift database. I have products and their attributes, like price, description, and category, stored in this file. However, I'm struggling with how to render this information effectively since the CSV structure is quite limiting compared to a traditional relational database. My mentor insisted on using CSV, but I'm curious if it's feasible to manage the data this way or if I should switch to a proper database system. I'm using React for the frontend and Python for the backend, so any advice on best practices in handling this would be greatly appreciated!
2 Answers
Consider using libraries like Polars or Pandas. They can help you organize your CSV data into a structured format, allowing you to query it more effectively. That way, you can manipulate the data as needed for your project without relying on a traditional database.
Just to clarify, using a CSV file as a database in a ML project raises some questions. It sounds like you're trying to query input data rather than interacting directly with the ML model. Understanding the intended use of the data might help clarify if a CSV is suitable. Are you looking to train the model or simply utilize the product catalog in your app?
Yes, it’s an educational project. I'm not querying the ML model, just fetching data from the product catalog that the model utilizes for the user interface.
Thanks for the quick answer! I'll definitely give that a try!!!