Predicting tornado magnitude with Machine Learning

Repository containing the Capstone project I performed at General Assembly, London, for the Data Science Immersive course (June-Sept 2017)

This capstone project consists of 5 notebooks:

Part 1: First analysis of the data and modelling (Logistic Regression, Random Forest) on a subset of 500 tornadoes.
Part 2 / Web Scraping: Importing a bigger dataset from the web and saving as a local PostgreSQL
Part 3: work on the dataset of tornadoes which magnitude was measured with EF scale (2007-2017). Final EDA, more modelling (Recurrent Neural Network, Stochastic Gradient Descent), NLP and time series analysis.
Part 4 / Pre-Processing class: Gathering of all the pre-processing steps of part 3 in a single class.
Part 5: Upsampling, further NLP testing and a first glance at predicting damage costs.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
images		images
README.md		README.md
tornadoes_part1_project_introduction.ipynb		tornadoes_part1_project_introduction.ipynb
tornadoes_part2_webscraping_posgres.ipynb		tornadoes_part2_webscraping_posgres.ipynb
tornadoes_part3_eda_modelling_nlp_tsa.ipynb		tornadoes_part3_eda_modelling_nlp_tsa.ipynb
tornadoes_part4_pre_processing_class.ipynb		tornadoes_part4_pre_processing_class.ipynb
tornadoes_part5_modelling_upsampling_nlp.ipynb		tornadoes_part5_modelling_upsampling_nlp.ipynb

Provide feedback