Music Scheduling &
Personalization:
Implemented machine learning algorithms (K-Means
Clustering and
Collaborative
Filtering) to curate personalized playlists based on listener
preferences,
historical data, and
real-time feedback.
Automated DJ
Tasks:
Developed an AI-based module to automate DJ tasks, such
as
announcing tracks,
adjusting playlists based on live listener interaction, and providing
dynamic
transitions between
segments.
Intelligent Content
Recommendations:
Used transformer models (like BERT) to recommend relevant
news,
advertisements, and
radio shows, ensuring content is aligned with listener interests and
demographic
data.
Audience Engagement
Optimization:
Utilized reinforcement learning algorithms to optimize
listener
engagement by
adjusting playlist content, timing, and show recommendations based on
real-time
feedback.
Tools:
Python, TensorFlow/Keras, PyTorch, Scikit-learn, Pandas,
Numpy,
OpenAI API,
Collaborative Filtering Models.
