arminrmt / decision-tree-flight-satisfaction-predictor Goto Github PK

Predict flight satisfaction using a decision tree algorithm. This repository includes code for training a decision tree model, evaluating its accuracy, and visualizing key insights such as information gain and correlation matrix.

This repository contains Python code for predicting flight satisfaction using a decision tree algorithm. The decision tree model is trained on a dataset containing information about various flight parameters and passenger satisfaction levels.

1. Clone the repository to your local machine.

2. Install the required dependencies using pip install -r requirements.txt.

3. Run the main.py script to execute the flight satisfaction prediction experiment.

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   python main.py

   The experiment is run for two different subsets of data (2000 and 5000 rows) with random state 42.

• decision_tree.py: Contains the implementation of the DecisionTree class for building and using a decision tree model.
• flight_satisfaction.csv: Dataset containing information about flight parameters and passenger satisfaction.
• main.py: The main script to run the flight satisfaction prediction experiment.
• results/: Directory to store experiment results, including correlation matrix heatmap, distribution plots, and information gain analysis.

• The accuracy of the decision tree model for different subsets of data is printed to the console.
• Correlation matrix heatmap and distribution plots are saved in the results/ directory.
• Information gain analysis results, including a plot, are saved in the results/rows_{number}/ directory.

1. Clone the repository:

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   git clone https://github.com/your-username/Decision-Tree-Flight-Satisfaction-Predictor.git

2. Navigate to the repository directory:

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   cd Decision-Tree-Flight-Satisfaction-Predictor

3. Install dependencies:

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   pip install -r requirements.txt

4. Run the experiment:

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   python main.py

• Python 3.x
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• tqdm

Feel free to modify the code and experiment with different parameters or datasets. Contributions are welcome!

This project is licensed under the MIT License - see the LICENSE file for details.