Data Science coding challenge time! The popular Data Science competition website Kaggle has an ongoing competition to solve the problem of earthquake prediction. Given a dataset of seismographic activity from a laboratory simulation, participants are asked to create a predictive model for earthquakes. In this video, I'll attempt the challenge as a way to teach 3 concepts; the Data Science mindset, Categorical Boosting, and Support Vector Regression models. I'll be coding this using python from start to finish in the online Google colab environment. Enjoy!

Code for this video:
https://github.com/llSourcell/Kaggle_Earthquake_challenge

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More learning resources:
https://www.kaggle.com/c/LANL-Earthquake-Prediction/data
https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/
https://blog.griddynamics.com/xgboost-vs-catboost-vs-lightgbm-which-is-best-for-price-prediction/
https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db
https://accio.github.io/machinelearning/2018/05/30/catboost.html
http://kernelsvm.tripod.com/
https://www.saedsayad.com/support_vector_machine_reg.htm
https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html

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