https://github.com/xre22zax/airline-analysis
Travel agency and need to know the ins and outs of airline prices for your clients
https://github.com/xre22zax/airline-analysis
data-analysis data-visualization python python3 visualization
Last synced: 3 months ago
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Travel agency and need to know the ins and outs of airline prices for your clients
- Host: GitHub
- URL: https://github.com/xre22zax/airline-analysis
- Owner: xre22zax
- Created: 2023-10-22T09:57:32.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-13T16:17:06.000Z (over 2 years ago)
- Last Synced: 2025-02-17T15:16:34.935Z (over 1 year ago)
- Topics: data-analysis, data-visualization, python, python3, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 4.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
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README
# Uncover the Best Airline Deals with Data-Driven Insights
## Overview
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.
## Key Findings
- **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.
## Methods Employed
**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`
## Libraries Used
- Pandas
- NumPy
- Seaborn
- Matplotlib.pyplot
- Statsmodels
- Math
- SciPy.stats
## Get Started in 3 Steps
1. Clone this repository.
2. Install required libraries: `pip install pandas numpy seaborn matplotlib re`
3. Run the main Python script: `Airline Analysis.ipynb`
## Usage
- Explore the generated visualizations to gain insights into the data.
- Modify the code to experiment with different visualizations and analyses.
## Contributing
Feel free to submit issues or pull requests for improvements or additions.
## Author
[Reza sadeghi](https://github.com/xre22zax/)