1. Pick any dataset relevant that you would like to analyze. 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. 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.
3. For the remaining of the records from the remaining smaller part of the dataset use the trained tree model to classify the data. Compare the models outcome to the actual classes in the testing sample. Calculate accuracy of the model (number of correctly and incorrectly identified cases in each class).
4. Describe each step and a result of this process, include relevant scripts, tree model, and outputs produced by the tool you use.
5. 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.
6. 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 models outcome to the actual classes in the testing sample. Calculate accuracy of the model (number of correctly and incorrectly identified cases in each class).
7. Describe each step and a result of this process, include relevant scripts, tree model, and outputs produced by the tool you use.