havelhakimi/bitcoinheistransomware

Decision Trees, Ensembling, Adaboost and Random Forest on Bitcoin Heist Ransomware Address Dataset

Machine Learning Models for Bitcoin Heist Ransomware Address Dataset

This is a solution notebook to an assignment question given in a Data Mining graduate course. Each code block is accompanied by relevant analysis wherever required.
Dataset link : https://archive.ics.uci.edu/ml/datasets/BitcoinHeistRansomwareAddressDataset
Broadly, the following steps have been performed in this solution notebook:

• A custom designed train-test split method which splits the data into training, validation and test set (70:15:15).
• Decision Tree trained using both the Gini index and the Entropy by changing the max-depth as [4,8,10,15,20].
• The splitting criteria (gini/entropy) selected is the one which gives better accuracy on test set with the chosen depth.
• Ensembling method is a method to combine multiple not-so-good models to get a better performing model.
• Created 100 different decision stumps (max-depth 3). For each stump trained it on a randomly selected 50% of the training data i.e. selelct data for each stump separately
• Finally predicted the test samples's labels by taking a majority vote of the output of the stumps.
• Used the sklearn Adaboost algorithm on the above dataset and reported the testing accuracy.
• Decision tree is used as the base estimator and with a number of estimators as [4,8,10,15,20].
• Further compared the Random Forest and Adaboost results.

These above assumptions and the flow of work is according to the questions asked in assignment.