Yulu is facing challenges in aligning its bike availability with fluctuating rental demand. Inefficient distribution of bikes can lead to high customer wait times at popular stations or overstocking at less frequented locations. To enhance operational efficiency, the company seeks to predict bike rental demand with high accuracy.
By forecasting demand, the company can optimize bike distribution, ensure timely availability at high-demand locations, and reduce operational costs. Ultimately, improved demand predictions can lead to increased customer satisfaction and higher utilization rates.
Develop a robust predictive model to forecast bike rental demand by leveraging historical data. The model should address missing data, employ sophisticated feature engineering techniques, and rigorously evaluate performance. The goal is to generate actionable insights for optimizing bike distribution and improving service levels across the bike-sharing network.
A Note on the Reference Solution
We strongly encourage you to attempt the assignment independently before consulting the reference. The goal is to evaluate your unique approach. The solution is provided as a learning tool to compare your own work against a potential implementation after you have completed the task.
The dataset for this task, along with a reference solution notebook, can be found at the following link:
- Please submit your work as a notebook (`.ipynb`) or Python script (`.py`).
- If you have any questions or need clarification, feel free to reach out.