# Context This data was compiled as part of our undergrad project that used machine learning to classify songs based on themes or activities songs are associated with. For the project we, four activities were choose. 1. Dinner: Songs that sound good when played in a dinner setting or at a restaurant. 2. Sleep: Songs that promote sleep when they are played. 3. Party: Songs that sound good when played at a party. 4. Workout: Songs that sound good when one is exercising/ working out. The collection of data started with collecting playlist details form Spotify. Spotify web API was used for the collection of the playlist of each category. Track title, album name and artist names were used to extract low level and high level Audio features like MFCC, Spectral centroid, Spectral Roll-off, Spectral Bandwidth, Tempo, Spectral Contrast and Root Mean Square Energy of the songs. For ease of computation, the mean of the values were calculated and added to the tables. Data was also curated using Spotify's audio analysis API. A larger set of songs is part of this data set. # Content The data set has eight tables. 1. Four tables with names *playlist*_audio_features have the signal processing features like MFCC, spectral centroid etc. 2. Four more tables with names *playlist*_spotify_features have the data extracted from Spotify's audio feature API. These tables have larger number of features. The data set size is quite large. ##Description of the *"playlist"*_audio_features columns: 1. The first column has the simple integer id if the track. (This id is local to that file). 2. The second column has the name of the track. 3. The third column name *mfcc* has the mean of the calculated MFCC for that track. 20 MFC coefficients were extracted from one frame of the track. 4. The forth column is named *scem*: This is the mean of Spectral centroid. Spectral centroid was calculated for each frame. 5. The fifth column is named *scom*: This is the mean of Spectral contrast. Spectral contrast was calculated for each frame. 6. The sixth column is named *srom*: This is the mean of Spectral Roll-off. Spectral roll-off was calculated for each frame. 7. The seventh column is named *sbwm*: This is the mean of Spectral Bandwidth. Spectral Bandwidth was calculated for each frame. 8. The eight column is name *tempo*: This is the estimated tempo of the track. 9. The ninth column is name *rmse*: This is the mean of the RSME was calculated for each frame. ##Description of the