video1.0<iframe src="https://www.loom.com/embed/d5eaddf3ce714ff1b88065bb6d996615" frameborder="0" width="1658" height="1243" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12431658Loomhttps://www.loom.com12431658https://cdn.loom.com/sessions/thumbnails/d5eaddf3ce714ff1b88065bb6d996615-f676e2099bc0acbc.gif309.206Hi everyone, I am Joanna, and I implemented an updated Music Recommendation System as an extension of Module 3, where I originally scored 18 songs from the provided CSV. In this version, I added RAC, more data using the Kaggle Spotify dataset with 114 songs, and a reliability indicator. I collect user preference data, score and sort to get the top 50, then run a second layer to compute match scores and return the top 5. The UI shows match score and details like title, artist, genre, mood, and energy, plus a Why These Songs explanation. I also built 7 unit test cases to ensure results are above 0.7 match score out of 1.