<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/3b374e0a7cb94456a7008cbdb84b5f77&quot; frameborder=&quot;0&quot; width=&quot;1728&quot; height=&quot;1296&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1296</height><width>1728</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1296</thumbnail_height><thumbnail_width>1728</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3b374e0a7cb94456a7008cbdb84b5f77-dd907b41c0a241c9.gif</thumbnail_url><duration>345.25</duration><title>Dealership and Sales Forecasting Project Overview</title><description>In this video, I discuss my project on dealership and sales forecasting for my DSI course, focusing on the best-selling cars. I&apos;ve learned about various machine learning models, data engineering, and feature selection through UDA data analysis. One of the main challenges I faced was dealing with heavily cleaned data that didn&apos;t reflect real-world scenarios, which led us to introduce synthetic data for better efficiency. I also aim to enhance our model design by incorporating deep learning concepts and building the project in a Docker environment for broader accessibility. I encourage viewers to consider how we can make our project more configurable and professional, moving beyond just notebooks to a more product-oriented approach.</description></oembed>