Regression Model to Predict Earthquakes Using Laboratory Generated Acoustic Seismic Signal Data

This project is envisaged to conduct an analysis in the form of a case study to understand the process of earthquake prediction using a machine learning-based approach. The project explores various supervised learning algorithms to predict the time remaining before laboratory earthquakes occur. The other aspects of earthquake prediction related to location, magnitude, severity, duration, and impact of the earthquake are considered out of the scope of this case study. The primary goal of the case study is to demonstrate how machine learning algorithms can be used to create a model to predict the time of a seismic event (using laboratory earthquake data). This project is undertaken to accomplish the following objectives: Demonstrate the knowledge gained on predictive analytics and machine learning techniques during the data science course by creating a seismic event prediction model. Learn more about supervised learning algorithms for regression modeling, feature extraction and forecasting methods. Leverage and enhance my knowledge of big data processing and data validation using cross validation techniques. Gain a deeper, more detailed understanding python and how its libraries can be used to create a regression-based prediction model.