Watch variables, use conditional breakpoints, call stack, exception breakpoint, etc— when working on larger projects — Quick pulse check — Still using print() statements to debug your code? Hey, no shame in admitting if you do. If it works, it works (especially for smaller scripts)! Having said that, it’s not a sustainable solution when working with larger projects — involving a buttload of third-party packages, util…

With a bonus sample script at the end that lets you show off your tech skills discreetly! — This is Part 2 of the Interview Question series that I recently started. In Part 1, we talked about another important data science interview question pertaining to scaling your ML model. Be sure to check that out! Interviews can be intimidating, but explaining a project you put your blood and sweat in, shouldn’t be!

Tips and tricks I use for writing clean codes on a day-to-day basis — In this article, I wanted to share a few tips for writing cleaner codes that I have absorbed in the last year — mainly from pair programming. …

Bonus intro to keywords like seasonality, trend, autocorrelation, and much more. — Right off the bat, time-series data is not your average dataset! You might have worked with housing data wherein each row represents features of a particular house (such as total area, number of bedrooms, year in which it was built) or student dataset wherein each row represents such…

Taking model from Jupyter notebook to Flask app, testing API endpoint using Postman, and Heroku deployment — Note: This is the second part of a two-part series implementing a deep-learning project from scratch. Part 1 covers the setup for the problem statement, data preprocessing, the intuition behind transfer learning, feature extraction, fine-tuning, and model evaluation. Part 2 covers the implementation of the Flask app and its subsequent…