https://github.com/visvav/flight-delay-prediction-analysis-using-logistic-regression
A statistical project analyzing flight delay predictions using historical data. It employs logistic regression to classify flights as delayed or on-time based on factors like departure time, carrier, origin airport, weather conditions, and past delay patterns.
https://github.com/visvav/flight-delay-prediction-analysis-using-logistic-regression
Last synced: 10 months ago
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A statistical project analyzing flight delay predictions using historical data. It employs logistic regression to classify flights as delayed or on-time based on factors like departure time, carrier, origin airport, weather conditions, and past delay patterns.
- Host: GitHub
- URL: https://github.com/visvav/flight-delay-prediction-analysis-using-logistic-regression
- Owner: VisvaV
- License: mit
- Created: 2025-03-22T09:44:23.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-22T09:45:41.000Z (about 1 year ago)
- Last Synced: 2025-05-18T05:15:48.949Z (about 1 year ago)
- Language: R
- Size: 9.77 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Flight Delay Prediction Analysis
A statistical project analyzing flight delay predictions using historical data. It employs logistic regression to classify flights as delayed or on-time based on factors like departure time, carrier, origin airport, weather conditions, and past delay patterns.
## Features
- **Data Exploration**: Analyzes the `flights` and `weather` datasets to identify trends and missing values.
- **Data Preprocessing**: Merges datasets, extracts relevant features, and converts data into suitable formats for modeling.
- **Logistic Regression Model**: Trains a logistic regression model to predict flight delays based on selected features.
- **Model Evaluation**: Uses metrics like accuracy, sensitivity, and specificity to evaluate model performance.
- **Visualization**: Plots confusion matrices, feature importance, and delay rates by carrier, hour, and weather conditions.
## Requirements
- R 4.0+
- `tidyverse` for data manipulation
- `tidymodels` for modeling
- `nycflights13` for flight data
- `ggplot2` for visualization
## Installation
1. Clone the repository: `git clone https://github.com/yourusername/flight-delay-prediction-analysis.git`
2. Navigate into the project directory: `cd flight-delay-prediction-analysis`
3. Install dependencies: `install.packages(c("tidyverse", "tidymodels", "nycflights13", "ggplot2"))`
## Usage
1. Run the application: `Rscript main.R`
2. Follow the prompts to explore data, train the model, and visualize results.
## Contributing
Contributions are welcome! Please submit a pull request with your changes.
## License
[MIT License](https://opensource.org/licenses/MIT)