Uber Data Analysis Welcome to my Uber Data Analysis project! In this project, I dive into Uber’s trip data to uncover insights that can help optimize the service and understand ride patterns better. Using Python and popular libraries like Pandas, Matplotlib, and Seaborn, I analyzed various aspects like ride durations, fare amounts, and how ride demand fluctuates throughout the day and year.
By exploring this dataset, I aimed to identify trends that could help improve Uber’s operations and create more efficient strategies for both riders and drivers.
What You’ll Find: Data Cleaning: The raw data has been cleaned to ensure it’s ready for analysis. This includes handling missing values, removing duplicates, and converting the data into a more usable format.
Exploratory Data Analysis (EDA): Visualizations that highlight interesting patterns in the Uber ecosystem, such as peak times, the most common ride categories, and how things change across different days and months.
Categorical Breakdown: I took a deep dive into the distribution of Uber's service categories (like UberX and UberPOOL) and their specific usage purposes to understand trends in rider preferences.
Seasonal Insights: From weekday patterns to seasonal variations, I’ve looked at how Uber trips vary by time of day and month.
Fare Analysis: I also explored how factors like distance and ride duration impact fare amounts to help understand fare structures better.
Dataset: This dataset contains key columns that track important metrics:
START_DATE & END_DATE: When each trip started and ended.
MILES: The distance of each trip.
CATEGORY: The type of ride (e.g., UberX, UberPOOL).
PURPOSE: Why the ride was taken (e.g., business, leisure).
Fare Data: Information about the fare amounts.
How to Get Started: Clone or download this repository to your local machine.
Install the necessary libraries:
bash pip install -r requirements.txt Open the UberDataAnalysis.ipynb notebook in Jupyter or your preferred IDE to explore the analysis and visualizations.
License: This project is licensed under the MIT License. You can check out the full license details in the LICENSE file.