The objective is to report the prediction of the earthquake from the historical data. A machine learning model is trained with historical data of the world related to earthquakes from 1965-2016. The data includes geographical location and magnitude of the earthquakes (23.5k samples). The model predicts earthquake magnitude for the year of 2017. Finally, a dashboard is created to visualize the prediction in addition to the historical analysis on the data.

• The data preprocessing and analysis is done using PySpark.
• MongoDB is used for storing the processed data.
• MLlib is used for training and prediction.
• Finally, Tableau is used for reporting the results of the analysis. This Project’s GitHub Repository

• Visualization
• Big Data
• Regression
• Spark
• Tableau
• MongoDB