New_York_Uber_Ride_Data_Analysis

This Project analyzes Uber ride data, providing visualizations and insights on ride counts by date, hour, and day of the week. The script utilizes popular Python packages such as Pandas, NumPy, Matplotlib, Seaborn, and Folium for data manipulation and visualization.

Prerequisites

Make sure you have the following packages installed:

• pandas
• numpy
• matplotlib
• seaborn

Usage

1. Clone this repository.
2. Place the Uber ride data CSV files in the same directory as the script.
3. Update the file paths in the script to match your file locations.
4. Run the script in your preferred Python environment.

Features

• Data concatenation: Combines Uber ride data from multiple CSV files.
• Date and time manipulation: Converts the date/time column to datetime format and extracts additional columns for date, hour, and day of the week.
• Count calculations: Computes counts of occurrences for dates, hours, and days of the week.
• Visualization: Generates bar plots, line plots, and heatmaps to visualize ride counts and ride density.
• Insights: Provides insights on peak and pit days, as well as ride density on specific days of the week.

Acknowledgments

The Uber ride data used in this script is sourced from Kaggle.

Special thanks to the developers of Pandas, NumPy, Matplotlib, Seaborn, and Folium for their amazing packages.

Contact

For any questions or suggestions, please feel free to reach out to LinkedIn.