This project performs an exploratory data analysis (EDA) on a flight booking dataset using Python. The objective is to visualize data, compute descriptive statistics, and derive meaningful insights from booking patterns.
- Analyze flight booking data using descriptive statistics
- Visualize data distributions and relationships
- Identify trends, patterns, and possible outliers
- Interpret results using simple and clear analysis
The dataset contains flight booking details with the following key attributes:
- Passenger Age
- Gender
- Travel Class
- Source and Destination
- Flight Distance
- Ticket Price
- Booking Channel
The data is clean and structured, suitable for visualization and statistical analysis.
The following techniques were applied:
- Descriptive statistics (mean, median, standard deviation, min, max)
- Histogram to understand ticket price distribution
- Bar chart to analyze booking counts by travel class
- Scatter plot to study the relationship between flight distance and ticket price
- Boxplot to detect outliers in ticket prices
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
- GitHub
- Most ticket prices are concentrated in the lower range, indicating economy-class dominance.
- Ticket price generally increases with flight distance.
- Business and First-Class bookings appear as high-value outliers.
- Economy class has the highest number of bookings.
This project demonstrates how visualization and basic statistics can be used to understand real-world datasets and extract actionable insights. It highlights the importance of exploratory data analysis before applying advanced techniques.