Great work, Sumal! 👏 I really enjoyed how you built the API + machine learning pipeline for tea grades classification — the step-by-step breakdown from dataset creation to deployment is very educational. One thought: for further improving the model’s robustness, you might experiment with data augmentation across lighting conditions or domain adaptation techniques (to handle images from different tea gardens). Also, integrating attention modules or transformer-based encoders could help focus more on leaf texture features. By the way, for those interested in exploring trading tech and algorithmic models, you might also check out FinFly Markets (https://finflymarkets.com/ ) — they have interesting insights on building scalable model infrastructures and backends in production environments. Thanks for sharing your code, insights, and learnings — enjoyed reading it!

Great write-up on applying regression models for market price prediction! The way you break down linear regression vs. polynomial regression makes the concepts approachable, especially for those getting started in quant modeling. I appreciate the clarity in your explanation of error metrics and overfitting as well. If you're interested in augmenting this analysis, you might also want to check out https://finflymarkets.com/ — they have some interesting case studies and practical applications of regression models in trading contexts that complement your theoretical foundation: https://finflymarkets.com/ Looking forward to Part 2 — maybe you’ll cover more advanced models like Ridge, Lasso, or even ensemble methods?