Predictive Analysis of Products for Quick-Commerce Dark Stores Using Machine Learning

A machine learning system that analyses and predicts product demand for quick-commerce dark stores.

A machine learning system that analyses and predicts product demand for quick-commerce dark stores. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Razorpay, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.