DACs_classification.py:
Where all of your support vector and random forest classifier code for the
disadvantaged communities dataset will reside. In this file, you only need to implement the initialization and training of your classifiers as given in the assignment details section. You should print confusion matrix, precision, recall, specificity, and accuracy score for this dataset and copy it in your assignment report.
evaluation.py:
The evaluation file where you will implement your code to load your
classifier, run it on test cases for the selected dataset, and print the precision, recall, specificity, confusion matrix and accuracy score.
SvmClassifier.sav and RfClassifier.sav:
This will be the files for the trained models (SVM) and (Random Forest)
on the disadvantaged communities dataset that you will produce in your DACs_classification.py file using Pickle library (see description in the following section)