Welcome to the Seoul Bike Share Dataset Project! As a dedicated data scientist, I am excited to present this project, which encompasses data management, data visualization, and machine learning techniques applied to the Seoul Bike Share dataset.
If you are new to GitHub click here to view the project.
In this project, we dive into the Seoul Bike Share dataset, aiming to gain valuable insights and make predictions related to bike rental patterns in Seoul. By leveraging our data management, visualization, and machine learning skills, we uncover trends, explore relationships, and develop models to optimize bike sharing operations.
The Seoul Bike Share dataset contains extensive information on bike rentals in Seoul, including weather conditions, rental time, temperature, humidity, and more. This rich dataset allows us to analyze various factors influencing bike rentals and gain a comprehensive understanding of the bike sharing system.
This project encompasses three main phases, each focusing on a crucial aspect of data science:
In the data management phase, we preprocess and clean the dataset, ensuring data quality, resolving missing values, and transforming variables where necessary. This step lays the foundation for accurate and reliable analysis.
In the data visualization phase, we create insightful visualizations to understand the bike sharing patterns, identify seasonality, and explore the impact of weather conditions and other factors on bike rentals. Interactive visualizations using libraries such as Matplotlib and Seaborn allow us to showcase trends and correlations effectively.
In the machine learning phase, we employ various algorithms to develop predictive models for bike rental demand such as Logistic Regression, Random Forest Regressor, and Explainable Boosting Regressor. We also compare and contrast these models as we tune perform hyper-parameter tuning to maximize the R^2 value. By considering features such as weather conditions, day of the week, and time of day, we aim to accurately forecast bike rental requirements. This information can help optimize bike allocation, maintenance schedules, and overall operational efficiency.
Within this GitHub repository, you will find the following components:
- Jupyter Notebooks: Detailed notebooks containing the code for data management, data visualization, and machine learning techniques applied to the Seoul Bike Share dataset.
- Datasets: The Seoul Bike Share dataset used in the project.
If you have any questions, suggestions, or potential collaborations related to this project, I would love to hear from you. Feel free to connect with me on LinkedIn.
Thank you for exploring the Seoul Bike Share Dataset Project, and I hope you find the insights and models developed in this project valuable for optimizing bike sharing operations.
Happy cycling!
Best regards,
David Shields