contact: [email protected], [email protected], [email protected]
University of British Columbia, Master's of Data Science (MDS)
Originated in Japan, the tradition to celebrate cherry blossoms have been embraced by different cultures for the recent decades. In participation in the George Mason’s Department of Statistics cherry blossom peak bloom prediction competition and for our passion to study this culturally and economically important event for many cities and regions across the globe, we built multiple supervised machine learning models and a novel model combining supervised and unsupervised learning algorithms to predict the peak bloom dates of cherry trees in Kyoto, Japan, Washington DC, USA, Vancouver, Canada and Liestal, Switzerland. The below figure demonstrates the workflow of our project.
The final report can be found here.
Our codes associated with the final report can be found here.
Our final predictions can be found here.
TBU A complete list of dependencies is available
here.
- Python 3.9.10 and Python packages:
- pandas==1.4.0
-
numpy==1.21.5
- altair_saver=0.5.0
- altair=4.1.0
-
scikit-learn=1.0.2
- catboost=1.0.4
conda env create --file blossom.yaml
Run the following command from the environment where you installed JupyterLab.
conda install nb_conda_kernels
If you are a windows user, run the following command inside the newly created environment to install vega-lite.
npm install -g vega vega-cli vega-lite canvas
For M1 mac users, make sure you are using the x86 version of conda and
not the arm64 version. See
here and
here for
more info.
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