Harness the power of data to make informed travel decisions! This project delves into comprehensive airline price analysis, unearthing patterns and insights to help travelers secure the best deals and airlines optimize pricing strategies.
- Price benchmarks and flight duration:
- Explore typical prices for coach and 8-hour flights.
- Understand price variations across weekdays and weekends.
- Delay patterns:
- Discover the most common types of delays to anticipate travel disruptions.
- Pricing correlations:
- Investigate the relationship between coach and first-class prices.
- Identify key features that impact prices most.
- Passenger preferences:
- Analyze passenger count variations based on flight duration to understand travel patterns.
- Overnight flight savings:
- Uncover how overnight flights offer significant price advantages, especially on weekends.
Data Manipulation:
Median,mean,IQR,list comprehension,value_counts
Data Visualization:
Box plot,histogram plot,pie chart,lmplot,axvline,line_kws,autopct,hue,figsize,linestyle,label,color,x_jitter,scatter_kws,alpha,fit_reg, legend
- Pandas
- NumPy
- Seaborn
- Matplotlib.pyplot
- Statsmodels
- Math
- SciPy.stats
- Clone this repository.
- Install required libraries:
pip install pandas numpy seaborn matplotlib re - Run the main Python script:
Airline Analysis.ipynb
- Explore the generated visualizations to gain insights into the data.
- Modify the code to experiment with different visualizations and analyses.
Feel free to submit issues or pull requests for improvements or additions.