Machine Learning for Anomaly Detection in Elemental Impurities Analysis

In this video, I will demonstrate how to use OneClassSVM to predict if a soil sample is an outlier. The data in the Python project was produced from ICP-OES. I will use PCA scores plots to visualize the data.
Steps:
Import the necessary libraries.
Load the data.
Split the data into a training and testing set.
Train the OneClassSVM model on the training set.
Predict the outliers on the testing set.
Visualize the data using PCA scores plots.
Conclusion:
OneClassSVM is a powerful tool for detecting outliers in data. In this video, I showed you how to use it to predict soil outliers. I hope you found this video helpful!