Spotify Recommendation Sytem
Feature Engineering and Data Processing
Popularity-Based Recommendation Systems
User Similarity-Based Collaborative Filtering
Matrix Factorization
As a data scientist, I worked on a Data Set for Spotify leading to a recommendation system music recommendation system for the streaming platform, I took the task of creating a data science solution to enhance the user experience by developing a personalized recommendation system for Spotify.
Through this Project, I have developed skills at :
Gathered data on user listening history, preferences, and interactions with the platform.Obtained metadata on songs, including genres, artists, and release dates.
Data Preprocessing: Cleaned and standardized the collected data to remove inconsistencies and errors.Handled missing values and outliers appropriately to ensure data quality.
Feature Engineering: Extracted relevant features from the dataset, such as user demographics, song attributes, and listening habits.
Engineered new features to capture complex relationships between users, songs, and playlists.Model Selection: Explored various recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid approaches. Conducted experiments to evaluate the performance of different models using metrics like accuracy, precision, and recall.
Model Training: Implemented the selected recommendation model using scalable machine learning libraries like TensorFlow or PyTorch. Trained the model on a large dataset to capture diverse user preferences and music tastes.
Evaluation and Testing: Split the dataset into training and testing sets to assess the model’s performance. Conducted A/B testing to compare the new recommendation system with the existing one and gather user feedback.