This project is focused on predicting flight prices using machine learning techniques. The analysis and model training are documented in the Jupyter notebook Rotem_Mihailovitch_Oren_Drori_Final_Project_python102.ipynb.
To run the notebook, you will need to install the following dependencies:
- numpy
- pandas
- matplotlib
- seaborn
- scikit-learn
- sklearn.preprocessing
- sklearn.cluster
- sklearn.model_selection
- sklearn.ensemble
- sklearn.metrics
- sklearn.tree
- matplotlib (version)
The notebook contains several functions and code blocks that:
- Load and preprocess the data
- Explore the data through visualizations
- Build and train machine learning models
- Evaluate the models and fine-tune them for better performance
The notebook provides insights into the factors that affect flight prices and how different models perform in predicting them.
To use this project, clone the repository, install the dependencies, and run the Jupyter notebook.
- Rotem Mihailovitch
- Oren Drori