This project focuses on analyzing datasets provided by Capital Bikeshare, a bike-sharing company. Through a series of data wrangling, data cleaning, exploratory data analysis (EDA), and machine learning (ML) techniques, valuable insights were extracted from the data.

• Conducted thorough data wrangling and data cleaning to ensure data quality and accuracy.
• Performed exploratory data analysis (EDA) to uncover patterns, trends, and relationships within the dataset.
• Utilized machine learning algorithms to develop predictive models and gain deeper insights.
• Created visually appealing and informative geographical visualizations to better understand the data and consumer behavior.

By applying these techniques, we gained valuable insights into bike usage patterns, user preferences, and other important factors that can drive business decisions for Capital Bikeshare.

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Geopy
• Folium

To get a comprehensive overview of the project, please check out the presentation I created on Google Drive.

To ensure a well-structured project, I have deployed the HTML maps I created for this portfolio in a separate repository. You can access the deployed maps by following this repository.

This project serves as a demonstration of my data analysis skills and showcases my ability to extract meaningful insights from complex datasets.