Uber User Data Analysis

An Exploratory Data Analysis (EDA) project based on Uber user/trip data. The project uses Python and popular data analysis and visualization libraries to understand trip patterns, mileage distribution, travel timing, monthly trends, and unusual observations in the dataset.

Project Overview

The dataset contains Uber trip records with information such as:

• Start and end date/time
• Trip category
• Starting location
• Destination
• Distance traveled in miles
• Trip purpose

The analysis also creates additional features such as date, time, day/night category, month, and day of the week.

Dataset

The original dataset contains 1,156 rows and 7 columns.

Original Columns

Column Description

---

START_DATE Trip start date and time END_DATE Trip end date and time CATEGORY Trip category START Trip starting location STOP Trip destination MILES Distance traveled PURPOSE Purpose of the trip

Additional Features

During preprocessing and feature engineering, the project creates:

• date -- extracted date from START_DATE
• time -- extracted hour from START_DATE
• day-night -- time-based category such as Morning, Afternoon, Evening, or Night
• MONTH -- month extracted from the trip date
• DAY -- day of the week

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Project Workflow

The analysis follows these main steps:

1. Import required Python libraries.
2. Load the Uber dataset using Pandas.
3. Explore the dataset using functions such as head() and basic inspection.
4. Convert date columns into datetime format.
5. Extract useful date and time features.
6. Categorize trips according to time of day.
7. Analyze monthly and daily trip patterns.
8. Analyze the MILES variable.
9. Detect potential outliers using boxplots.
10. Study mileage distribution using histograms and KDE.
11. Visualize important patterns using Matplotlib and Seaborn.

Exploratory Data Analysis

Mileage Analysis

The MILES column is analyzed to understand the typical distance of Uber trips.

A boxplot is used to identify unusually high mileage observations. The analysis shows that most trips are relatively short, while some trips have considerably higher mileage.

A filtered analysis of trips below 100 miles and below 40 miles is also used to examine the main distribution more clearly.

Distribution Analysis

A histogram with KDE is used to understand the distribution of trip distances.

The distribution of trips below 40 miles is concentrated toward lower mileage values and has a longer tail toward higher values, indicating a right-skewed distribution.

Time and Day/Night Analysis

The trip start time is extracted from START_DATE and grouped into categories such as:

• Morning
• Afternoon
• Evening
• Night

This helps analyze when trips are commonly made.

Monthly Analysis

The month is extracted from the trip start date and used to analyze trip patterns across the year.

Key Observations

• The dataset contains 1,156 trip records.
• Most recorded trips are relatively short-distance.
• Several high-mileage observations appear as potential outliers.
• The mileage distribution is positively/right-skewed.
• Date and time features can be used to study travel behavior across months, days, and different times of the day.

Repository Structure

Uber-User-Data-Analysis/
│
├── UberDataset.xlsx
├── project.ipynb
├── README.md

How to Run

1. Clone or download this repository.
2. Make sure Python is installed.
3. Install the required libraries:

pip install pandas numpy matplotlib seaborn openpyxl jupyter

4. Open the Jupyter Notebook:

jupyter notebook

5. Run the notebook cells sequentially.

Conclusion

This project demonstrates a practical Exploratory Data Analysis workflow using Uber trip data. It covers data preprocessing, feature engineering, statistical exploration, outlier detection, and visualization to understand trip behavior and mileage patterns.

Author

Niraj Kumar Maurya