Welcome to Seoul Bike Sharing Demand! This data science project aims to visualize and model the sharing demand of bikes in Seoul. We were able to achieve an R2 score of 0.927. This project is the result of the collaboration of 3 persons, namely Anis Oufkir, Elias Ferreira and Clement Florval
Welcome to the Seoul Bike Sharing Data Analysis project! This initiative aims to address the dynamic challenges associated with urban transportation by leveraging comprehensive data on bike sharing systems in Seoul. As urban populations continue to grow, and concerns about environmental sustainability rise, understanding and optimizing transportation systems become increasingly crucial.
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Usage Patterns: Analyze temporal and wheather patterns of bike sharing to identify peak hours and wheather influence.
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User Behavior: Understand user behavior and preferences to optimize services and enhance user experience.
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Seasonal Variations: Investigate how bike sharing patterns vary across seasons, weather conditions.
By delving into the Seoul Bike Sharing Data, we aim to contribute to urban planning, improve infrastructure, and promote eco-friendly transportation alternatives. The insights gained from this analysis can guide policymakers, city planners, and bike-sharing service providers in enhancing the overall transportation ecosystem in Seoul.
Here are the features of the base dataset found in Datasets/data.csv
- Date
- Rented Bike Count: The target variable
- Hour
- Temperature
- Humidity
- Wind speed
- Visibility
- Dew point temperature
- Solar radiation
- Rainfall
- Snowfall
- Seasons
- Holiday
- Functioning Day
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/ClementFrvl/DataAnalysisProject.git- Install dependencies:
This project depends on mainstream libraries, for plotting, modeling and data manipulation
- Enjoy !
To use the streamlit app, use the command
streamlit run ./Streamlit/streamlit_board.pyNormal jupyter notebook