This project performs exploratory data analysis (EDA) and predictive modeling on Uber ride data to understand ride demand patterns, peak usage hours, ride purposes, and distance trends. The project uses Python data science libraries to extract insights and visualize ride behaviors that can help improve operational planning and fleet management.
- Clean and preprocess Uber ride dataset
- Perform exploratory data analysis to identify ride patterns
- Visualize ride demand by hour, day, and month
- Analyze ride purpose and category distribution
- Build a machine learning model for ride distance prediction
- Generate business insights from the analysis
The dataset contains trip-level ride details including:
- Start Date
- End Date
- Category (Business / Personal)
- Start Location
- Stop Location
- Miles (Distance)
- Purpose of Ride
Time-based features such as hour, day, and month were extracted during preprocessing.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
EDA was performed to identify:
- Ride category distribution
- Ride purpose trends
- Peak ride hours
- Day-wise ride distribution
- Monthly ride demand trends
- Distance distribution and correlation analysis
A Linear Regression model was implemented to predict ride distance based on time-related features such as hour and month.
Model evaluation metrics:
- Mean Absolute Error (MAE)
- R² Score
- Evening hours show the highest ride demand.
- Business rides are more frequent than personal rides.
- Weekdays show higher ride frequency than weekends.
- Ride distance varies moderately with time-based features.
- Monthly demand shows seasonal variations.
- Driver allocation optimization during peak hours
- Better fleet management planning
- Demand-based pricing strategy improvements
- Ride demand forecasting using predictive analytics
Uber-Ride-Analysis/
│
├── data/
│ └── UberDataset.csv
├── notebook/
│ └── uber_analysis.ipynb
├── report/
│ └── Uber_Project_Report.pdf
└── README.md
Uber ride data analysis provides meaningful insights into customer travel behavior and demand patterns. Data-driven analysis helps ride-sharing companies improve operational efficiency, pricing strategies, and customer service planning.