OnTrack is a data analysis project that explores ride booking patterns using an Uber rides dataset. The objective of this project is to analyze different aspects of ride data such as booking category, trip purpose, booking time, trip distance, and ride frequency across different days and months.
The analysis is performed using Python and popular data analysis libraries to extract meaningful insights from the dataset.
This project uses the Uber ride dataset obtained from Kaggle for exploratory data analysis.
Main columns in the dataset include:
- Start Date
- End Date
- Category
- Start Location
- Stop Location
- Miles
- Purpose
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
The project answers the following questions:
- In which category do people book Uber rides the most?
- For what purpose do people book Uber rides the most?
- At what time do people book cabs the most?
- In which months do people book Uber rides less frequently?
- On which days of the week do people book Uber rides the most?
- How many miles do people usually book a cab for?
- Business category rides occur more frequently than personal rides.
- Many rides are booked for meetings and business-related purposes.
- Evening hours tend to have higher ride bookings.
- Most trips are short-distance rides.
- Certain days of the week show higher ride demand.
OnTrack-Data-Analysis │ ├── ontrack_data_analysis.ipynb ├── dataset.csv └── README.md
- Clone the repository
- Install required Python libraries
- Open the Jupyter Notebook file
- Run all cells to see the analysis and visualizations
- Build an interactive dashboard for ride analysis
- Perform deeper statistical analysis
- Apply machine learning models for ride prediction
Ashlesha Mishra B.Tech Computer Science and Data Science