Added ML project on PGA prediction of Earthquake data#52
Open
subhmm wants to merge 1 commit intolazyprogrammer:masterfrom
Open
Added ML project on PGA prediction of Earthquake data#52subhmm wants to merge 1 commit intolazyprogrammer:masterfrom
subhmm wants to merge 1 commit intolazyprogrammer:masterfrom
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Iran is one of the most seismic countries of the world. It is situated over the Himalayan-Alpide seismic belt and is one of those countries, which have lost many human lives and a lot of money due to the occurrence of earthquakes. Here a model is built using Machine Learning to predict PGA in this region.
The dataset was split into input part and the PGA value which was supposed to be predicted. The input part was standardised using StandardScalar. ● Now, the entire dataset was split into test set and training set. ● We trained the model using Logistic Regression, K-Nearest Neighbours(KNN) and Random Forest Regression using the training set. ● The trained models were tested with the test set to predict the efficiency of the model. ● Finally, two Ensemble models were created combining the previous models, one is the Averaging technique and the other is Blending technique. The efficiency of the models were predicted by calculating the Mean Average Error MAE , Mean Squared Error- MSE and Root Mean Squared Error- R MSE. Lower the value, the higher is the efficiency.