1. Pick any dataset relevant to your major that you would like to analyze. Avoid using the same or similar datasets to one you use for your final project. The dataset should have at least tree features (input columns) and exactly one classification or output column. The output column must be an example of business consequences (categorical or continuous business outcome).
2. Randomly divide it into two chunks 80% and 20% of records.
3. Use Python or any other other tool to apply appropriate decision trees method to train a decision tree on a larger part of the dataset.
4. For the remaining 20% of the records from the remaining smaller part of the dataset use the trained tree model to classify the data. Compare the model's outcome to the actual classes in the testing sample. Calculate accuracy of the model (number of correctly and incorrectly identified cases in each class).
5. Submit a Word report describing each step and a result of this process, include relevant scripts, tree model, and outputs produced by the tool you use.