I hope you enjoy viewing each project which offers unique insights into various aspects of data science and machine learning, coded in Python and SQL. If you have any questions or wish to discuss collaboration opportunities, feel free to reach out.
📧 Contact Information
Email: [email protected]
Website: sabrinarenna.com
Key performance indicators and regional trends for an e-commerce client are identified, which are in turn used to formulate a data-driven sales strategy.
As a Product Analyst, I conducted A/B tests to measure changes in click-through rates (CTR) and average time spent on the mobile app. This involved statistical hypothesis testing to make data-driven decisions, ultimately leading to feature improvements.
To conduct predictive modeling for housing prices in King City, Ontario, this project delves deep into regression and regularization techniques. I employed Ordinary Least Squares (OLS) regression, LASSO, Ridge, and Elastic Net regression methods to predict house prices based on a range of features. Advanced model evaluation and feature engineering were required to identify the main drivers of the housing market.
Web visit churn is examined to extract insights into user behavior. By conducting cohort analysis, funnel analysis, and user segmentation, I identified factors influencing user checkout behavior. This involved advanced techniques such as retention rate calculation and product recommendation systems.
Tackling data imbalance challenges in machine learning, a high-risk digital subscription business is provided with EDA and predictive modeling to reduce chargebacks, refunds, and failed payments. Techniques such as oversampling, SMOTE, and ensemble methods were employed to handle imbalanced datasets effectively. Extensive feature engineering is conducted.
This grocery subscription churn analysis provides an in-depth view of end-to-end classification in machine learning. This includes data cleaning, feature engineering, model selection, hyperparameter tuning, and model evaluation. Additionally, the project delves into the business implications of the analysis, providing a comprehensive understanding of customer churn and retention.
Sales data for a bakery is analyzed leveraging advanced SQL concepts for data aggregation, filtering, and summarization.
By scraping weather data from the Government of Canada's Weather Alerts directory and funneling it into a data pipeline, I enabled daily weather alerts for a logistics company. This project acquires data from unconventional sources in the absence of a developed API and demonstrates automated data workflows.
Click on each project to explore comprehensive descriptions, detailed code samples, and my systematic approach to solving complex data-related challenges.
Thank you for exploring my portfolio, and I look forward to connecting with you!