This Uber Data Analysis project aims to uncover insights into rush hour patterns and peak pickup times in New York City. By analyzing six months of data, I will explore Uber rides during peak hours to identify the busiest times and locations. Using advanced statistical techniques and visualizations, the project seeks to reveal trends and correlations to optimize operations and enhance the Uber experience for drivers and riders.
The codes were executed using Jupyter notebook.
- Pandas :- Collect and modify data
- Numpy :- Carry out numeric features
- Matplotlib :- Base model for data visualization
- Seaborn :- Beautiful and fast plots
- Ploty :- Dynamic plot
Data pre-processing/Data cleaning was carried out.
- Checking and dropping duplicated values
- Checking for missing values
- Converting the "Pickup_date" column to a data-time data type from object data type
Which month have the maximum Uber pickups in New York City?
Lets find out hourly rush in New York on all days
Which base-number has most number of active vehicles?
At what locations in New York City we are getting rush?
Examine rush on Hour and Weekday
Peak activity consistently occurs during regular working hours, especially mid-day, and the mid-day and evening periods are consistently the busiest across all days.