Welcome to the Capital Bikeshare Geographical Data Analysis repository! This project is dedicated to the analysis of datasets provided by Capital Bikeshare, a prominent bike-sharing company. Through meticulous data wrangling, data cleaning, exploratory data analysis (EDA), and advanced machine learning (ML) techniques, this project extracts invaluable insights from the data.
- Thorough data wrangling and data cleaning techniques are employed to ensure high data quality and accuracy.
- Exploratory data analysis (EDA) uncovers patterns, trends, and relationships within the dataset, shedding light on various aspects of bike-sharing behavior.
- Utilization of machine learning algorithms enables the development of predictive models, contributing to deeper insights and more accurate predictions.
- The project also presents visually appealing and informative geographical visualizations, providing enhanced understanding of the data and consumer behavior.
The following technologies were utilized in this project:
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Geopy
- Folium
To gain a comprehensive overview of the project, please refer to the detailed presentation created on Google Drive, which covers the analysis methodologies, results, and conclusions.
In order to provide an interactive and immersive experience, the HTML maps generated from the analysis have been deployed in a separate repository. These visualizations offer valuable insights into the geographical aspects of the Capital Bikeshare data.
You can access the deployed maps by following these links:
- Index with all maps
- Map 1 - Heatmap of the most used start stations
- Map 2 - Heatmap of the most used end stations
- Map 3 - Map of the top 10 most used routes
Please utilize the provided links to explore the interactive maps and gain a deeper understanding of the geographical aspects analyzed in this project.
The Capital Bikeshare Geographical Data Analysis project serves as a demonstration of advanced data analysis skills, showcasing the ability to extract meaningful insights from complex datasets. By implementing various analytical techniques, this project uncovers bike usage patterns, user preferences, and other crucial factors that can inform strategic business decisions for Capital Bikeshare.
Feel free to explore the repository, delve into the analysis, and leverage the insights gained to drive data-informed decision-making in the bike-sharing industry.