The dataset “scatter_data. csv” contains measured properties of sonar waves scattered off objects. The objects of interest could be metallic (M) or rocky, (R) as given in the last column. In the data
given, there are 60 observed signal characteristics, and the last column is M or R for Metal or Rock.
The task here is to develop a model that can predict the type of object being scanned based on reflected
sonar wave properties.
Use a Naive Bayes classifier to develop a model for this classification problem. Use a random seed of
42 and shuffle the data first. Take the first 40 rows of the shuffled set as test data and the rest as training
data. Train the model with the training data and evaluate the model’s accuracy based on the test dataset.
Submit a runnable jupyter notebook. Follow the template I gave you, retaining the comments in
the markups.