{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/eead9e5a626e44cea48b951ccfc49251\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/eead9e5a626e44cea48b951ccfc49251-a29b5108cf961356.gif","duration":348.459,"title":"Retail Sales Forecasting Portfolio Overview 🚀","description":"Hi, I am Sudhir Dubey, and this is my retail sales forecasting portfolio project. I loaded and checked the dataset, with 188,340 rows and 10 columns in train, and 22,265 rows and 8 columns in test, with no missing values or duplicates. I created calendar and encoded features, then ran EDA and tested 5 hypotheses on discount, holiday, store type, location type, and the orders to sales relationship. GradientBoosting performed best with MAE 8577.30, RMSE 12818.88, and RSOF 0.5816. I turned the model into a simple Flask app, built a Tableau dashboard, and prepared notebook, repo, blog, and deployment."}