This project analyzes data from a leading air transportation company to address challenges affecting profitability and growth. Using SQLite and Python, the aim is to increase aircraft occupancy rates and maximize average profit per seat.
The airline faces challenges including:
- Stricter environmental regulations
- Higher flight taxes
- Increased labor costs
- Rising fuel prices
The goal is to use data analytics to enhance aircraft occupancy rates and identify opportunities to increase profitability per seat.
- SQLite
- Python 3.1.
- Jupyter Notebook
- pandas
- numpy
- matplotlib
- seaborn
- json
The data is stored in an SQLite database, containing tables related to flights, aircrafts, tickets, bookings, and boarding passes.
- Database analysis using SQL queries
- Data visualization of temporal trends
- Calculation of revenue and occupancy rates
- Analysis of fare conditions and pricing
- Clone this repository
- Install required libraries:
pip install pandas numpy matplotlib seaborn - Ensure you have Jupyter Notebook installed
- Open the
.ipynbfile in Jupyter Notebook
Navigate through the Jupyter Notebook to see the step-by-step analysis, including:
- Exploratory Data Analysis
- Revenue calculations
- Occupancy rate analysis
- Visualizations of key metrics
- Aircraft models and their seating capacities
- Temporal trends in ticket bookings and revenue
- Average costs for different fare conditions
- Revenue analysis per aircraft
- Occupancy rates and their impact on turnover
This analysis provides actionable insights for maximizing profitability while considering factors such as consumer satisfaction and safety. The project demonstrates the value of a data-driven approach in the airline industry for sustainable growth and success.
Contributions to improve the analysis or extend the project are welcome. Please fork the repository and submit a pull request with your proposed changes.
##Volunteer Contributors Emanuele Merveille G