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Authentication:
Since we are dealing with user-specific data, we need to use SpotifyOAuth to authenticate.
Data Retrieval:
Use Spotipy and your playlist ID to fetch playlist tracks.
playlist_id = 'YOUR_PLAYLIST_ID'results = sp.playlist_tracks(playlist_id)tracks = results['items']
Feature Extraction:
Extract relevant features from the playlist data. In this case, we want to extract name, artist and danceability.
Anomaly Detection Algorithm
Use anomaly detection algorithm such as One-Class SVM to detect anomalies.
Threshold Setting:
Set appropriate thresholds for anomaly detection. This involves determining what level of deviation from normal behavior should be considered an anomaly.
Alerting Mechanism:
Let user know of the anomalies.
if anomalies > threshold: alert_user("Anomaly detected in your playlist!")
Visualization:
Create visualizations through time series graphs to help users understand the detected anomalies.
plot_anomalies(features, anomalies)
FlorentLvr
commented
5 months ago
Authentication: Since we are dealing with user-specific data, we need to use
SpotifyOAuthto authenticate.Data Retrieval: Use Spotipy and your playlist ID to fetch playlist tracks.
playlist_id = 'YOUR_PLAYLIST_ID'results = sp.playlist_tracks(playlist_id)tracks = results['items']Feature Extraction: Extract relevant features from the playlist data. In this case, we want to extract name, artist and danceability.
Anomaly Detection Algorithm Use anomaly detection algorithm such as One-Class SVM to detect anomalies.
Threshold Setting: Set appropriate thresholds for anomaly detection. This involves determining what level of deviation from normal behavior should be considered an anomaly.
Alerting Mechanism: Let user know of the anomalies.
if anomalies > threshold: alert_user("Anomaly detected in your playlist!")Visualization: Create visualizations through time series graphs to help users understand the detected anomalies.
plot_anomalies(features, anomalies)