Tagged machine-learning
Rebuilding evaluation calculations for recommendation systems
Evaluation metrics describe how well a model predicts on unseen data, and are used in fine-tuning. In the Python package "implicit", the evaluation module returned errors for common metrics, such as Normalised Discounted Cumulative Gain (NDCG).
User churn model
Digital media rely on user activity to determine marketable audiences. Being able to predict churning users allow for retention efforts to be put in place. In this project, I defined churn as a 30-day streak of a logged-out state, or 30-day streak of non-listenership.
Ad Request Optimisation
Supply-Side Platforms manage ad space by deciding which instances of web traffic would be good to show ads to; however, not all traffic is profitable, and sending these instances to the auction (real-time bidding) can be costly.
Talked about basic Categorical Variable Handling
Covered the introduction to data types, exploratory analyses, encoding, feature engineering, and machine learning options for categorical variables.