This project analyzes Uber trips taken by drivers using Exploratory Data Analysis (EDA) techniques. The dataset includes details on trip start and stop locations, mileage, trip purposes, and categories, enabling insights into travel patterns, popular routes, and usage trends.
This analysis was completed as part of a Data Analysis course to practice data manipulation and visualization using Python. The project addresses various aspects of Uber trips, including:
- Identifying the most popular starting and ending locations.
- Analyzing the purpose and category of trips.
- Visualizing the total distance traveled for different purposes.
- Calculating the percentage of miles for business vs. personal trips.
The dataset contains Uber trip details with the following columns:
- Date: Date of the trip.
- Start: Starting location of the trip.
- Stop: Ending location of the trip.
- Category: Trip type (Business or Personal).
- Miles: Distance traveled in miles.
- Purpose: Purpose of the trip (e.g., Meeting, Errand, Customer Visit).
- Start Locations: 176 unique start points.
- Stop Locations: 187 unique stop points.
- Most common purpose: Meeting
- Other notable purposes: Customer Visit, Meal/Entertainment
- 94.12% of the miles were driven for business purposes.
- 5.88% of the miles were driven for personal purposes.
- A bar plot showing the total miles traveled for different purposes.
- A count plot showing the frequency of trips by category (Business vs. Personal).
- Python Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Techniques: Data cleaning, grouping, and summarizing
- Visualizations: Bar plots, count plots
- Data Analysis Concepts: EDA, route analysis, purpose-driven mileage insights
Working on this project provided valuable experience in data cleaning, handling missing values, and performing exploratory data analysis. I learned how to identify patterns within datasets and present findings through visualization, enhancing my skills in data storytelling and Python programming.