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Uber Rides Data Analysis (Python Project)

Monthly trips trend

1. Project Overview​​​​​

The aim was to investigate business travel patterns and discover insights that can help:

  • Uber improve operational planning and resource allocation

  • Uber drivers maximize earnings by focusing on high-demand areas and times

  • Corporate customers optimize travel costs and scheduling efficiency

By analyzing trip timestamps, miles travelled, locations, and ride purpose, the study reveals clear business-driven mobility trends and opportunities for data-driven decision-making.

2. Tools & Skills Used​​​​​

  • Python (Jupyter Notebook – VS Code)

  • Pandas, NumPy

  • Matplotlib, Seaborn

  • Data cleaning & preparation

  • Exploratory data analysis (EDA)

  • Feature engineering

3. Data Preparation $ Engineering Workflow​​​​

Before analyzing the data, several preprocessing steps were performed to ensure accuracy, remove noise, and enrich the dataset for insights.

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Data Loading

  • Imported the Public Kaggle dataset (by Zeeshan-ul-hassan Usmani) into Python using Pandas

  • Performed initial checks (shape, info, head, null-value summary)

  • Scanned for inconsistencies and anomalies

 

 Data Cleaning

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       Cleaning Step                                                                                           Result

       Removed entries with missing timestamps or locations                      -1 row removed

       Standardized column names (removed “*”)                                          All columns cleaned

       Filled 502 missing Purpose values                                                          Replaced with "Unknown"

       Converted date columns to datetime format                                        Enabled time-based feature extraction

 

Feature Engineering

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       New Feature                                                                                             Purpose

       START_HOUR                                                                                           Identify peak usage hours

       START_WEEKDAY                                                                                    Detect weekday/weekend patterns

       START_MONTH                                                                                       Analyze seasonal trends

       DURATION_MINS                                                                                    Compare travel distance vs. time

       ROUTE (Start → Stop)                                                                             Discover most common business routes

       â€‹

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âž¡ These transformations enabled time-based demand analysis, routing insights, and purpose classification trends.

4. Key Findings​​​​​

94% of the rides are for business purposes

Business Usage Dominates

  • 93% of all trips are business-related

  • 94% of total miles driven for business
    → Uber is mostly used for corporate travel in this dataset​​

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Business purpose travel dominates

Most Business-Driven Purposes

  • Common purposes: Meetings, Meal/Entertainment, Errands

  • 43% Unknown → opportunity to improve data capture

trips-by-day-of-the-week.png

Busiest day is Friday and peak hours are from 11.00am - 2.00pm

Busiest Day & Time

  • Friday is the busiest day

  • Peak travel: 11:00 AM – 2:00 PM

  • Very low late-night usage → not leisure activity

top-10-starting-locations.png

Uber should ensure the availability of drivers for these busy routes

Demand by Location

  • High-demand starting areas:

  • Cary

  • Morrisville

  • Whitebridge

  • Some Unknown locations → need better standardization

Distance distribution

​​Typical Travel Distance

  • Most rides < 50 miles (urban commuting)

  • Some very long trips 200–300+ miles
    → Potential airport/inter-city travel cost review

Find more visuals and analysis in notebook in my GitHub:

🔗 Uber rides analysis

5. Insights & Recommendations​​​​​

Takeaway:

Uber demand in this dataset is strongly driven by weekday business activity, not leisure.
Aligning drivers, pricing, and travel planning to these patterns supports efficiency, revenue growth, and cost savings.

Who benefits from this analysis:

  • Uber → better resource planning

  • Drivers → increased earnings

  • Organizations & customers→ travel cost optimization

6. Project Files​​

This project is available on GitHub including:

  • Full Jupyter Notebook with code and visualizations

  • Data folder (with a link to download the dataset)

  • Project documentation (README)

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🔗 GitHub:

       https://github.com/manpb/uber-rides-analysis-python.git

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