video1.0<iframe src="https://www.loom.com/embed/cdda1f4155d84e51b517708cc1e6f167" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/cdda1f4155d84e51b517708cc1e6f167-ecf2f8884552732e.gif309.59In this video, I share my Databricks hackathon project focused on Netflix Analytics and Popularity Prediction, where I analyzed over 8,800 Netflix titles to uncover content patterns and built an ML model to predict show popularity. I imported the data from Kaggle, cleaned it, and ran SQL queries to identify key insights, such as 70% of the content being movies and a notable spike in content acquisition from 2019 to 2020. My classification model, which initially showed around 80% accuracy, was fine-tuned to achieve 86% accuracy using random forest algorithms. I also developed an interactive dashboard for stakeholders to explore the data visually. I encourage viewers to engage with the dashboard and utilize the insights for decision-making.