Projects
Recommender systems
Building and evaluating recommendation models.
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.
Google Analytics Raw Data: Wrangling
At the nascency of this digital radio product, I focused on establishing big data pipelines to meet business needs for reporting, visualisation, and machine learning applications.
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.
3-Step ETL Reporting Tool
Management makes use of spend, revenue, and eCommerce metrics to make marketing decisions. The stakeholders had a preference for tables that could be explored by date range and broken down by date.
Exploring mental health incidents in New Zealand Police Data
In 2021, the New Zealand Police expressed a need to approach call-outs related to mental health issues. To free up the workload of the police force in that regard, it would be good to explore existing trends on mental health-related issues.
Youtube API Data Extraction
For my first Python project, I wanted to understand popular video topics in a certain niche, and explore Youtube as a data source. One of the largest limitations to the project is the rate limit, which compelled me to consider an ETL process to avoid redundant extraction.