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Code for Weather Prediction Kaggle Competation as part of DMA18 course at UCB. https://www.kaggle.com/c/cal-dma-2018

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DMA18 Lab 05 Kaggle Competition

https://www.kaggle.com/c/cal-dma-2018 Invite Link: https://www.kaggle.com/t/b176d1b772f64e2c9854aa7bfa8a0a2f

How to contribute

[1] Use precise and meaningful commit message that tell the others what was changed.

[2] Respect the folder structure.

- /data/ .............. (the extracted data from the kaggle competition. in .gitignore)
- /models/ ............ (stored models)
- /src/ ............... (put iphython notebooks here)

Getting Started

[1] Download the data

The /data/ folder is in the .gitignore meaning that nothing it is pushed to the remote repository. After cloning the repository, download and extract the source data from https://www.kaggle.com/c/cal-dma-2018/data into the /data/ folder.

[2] Bootstrap Environment

To start you will need virtualenv, to bootstrap our environment. It's a more flexible way of managing environments as opposed to anaconda.

Intitial Installation

Do this one time: Run the following commands

pip install virtualenv
virtualenv --python=/usr/local/bin/python3 .lab05
source .lab05/bin/activate
pip install -r requirements.txt

Then start jupyter (jupyter notebook) and check, whether everything works by running all cells in the AssertInstallation.ipynb.

Activate environment

Do this everytime.

source .lab05/bin/activate

Installing new packages

Do this everytime you use pip install within the environment to install a new package. It saves all required packages into the requirements.txt file, which you should push to the remote repository then.

pip freeze > requirements.txt

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Code for Weather Prediction Kaggle Competation as part of DMA18 course at UCB. https://www.kaggle.com/c/cal-dma-2018

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