Flight Delay prediction system
- Repository Structure
├── Report.pdf <- Assignment report file
├── ML-Assignment-1.ipynb <- jupyter notebook file
├── ML-Assignment-1.py <- python file
└── Readme.md
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How to use
Download ipynab file, upload the notebook to jupyter notebook environment ( or kaggle, google colab), import the dataset of your own and press run all.
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Description
In this report, first I read & analyze the given dataset of flight delays. Then preprocess it to make it suitable for computing. Preprocessing process includes adding new computed predictors from existing ones, deleting some of the columns, checking for nans, encoding into numerical values and splitting the dataset into train and test datasets. Then, I implement three machine learning models to predict the new delays. Afterall, I compare the efficiency of the models using some appropriate metrics. By reading this report, you can expect to gain knowledge on some practical data visualization, preprocessing and implementation of models.