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Data-Driven Marketing Specialist

CitiBike Case Study - Kellstadt Graduate Business School, DePaul University

R studio + Excel Solver + Microsoft PowerPoint

This project involves analyzing Citi Bike's demand data to optimize daily bike allocation at various stations in NYC, using descriptive, predictive, and prescriptive analytics.

Key takeaways:

• Simulated multiple regression model to predict bike renting demand for each station

• Combined marketing and sales data with a linear optimization model to maximize the number of total trips by 37%

You can download the data and the optimization model here:

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