A repo of the analysis carried out for the final project on the PLUS Course on the Introduction to Data Science and Machine Learning for Geospatial Data taught by Euro Beinat. This analysis used the HURDAT2 Database, containing tropical storm paths from 1851 till date.
Jupyter Notebook 100.00%
plus_machine-learning-eot's Introduction
End of Term Project for the Introduction to Data Science and Machine Learning for Geospatial Data. After an exploratory visual analysis of the data, two Recurrent Neural Networks architectures -- LSTM and GRU were tested for their performance in predicting the trajectory of the hurricane.
Group Member : Hira Zafar, Rufai Omowunmi Balogun
There are two jupyter notebooks one for EDA and the other shows the model creation and comparison