INF2008ML PROJECT

Dataset Information

• Training, testing, and development datasets:
  • signatures_cedar/full_forg
  • signatures_cedar/full_org
• Unseen testing datasets:
  • signatures_cedar/unseen_data_for_testing/unseen_forg
  • signatures_cedar/unseen_data_for_testing/unseen_org

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How to Run

Step 1: Ensure All Libraries Are Installed

The following libraries are required:

1. numpy: pip install numpy
2. OpenCV: pip install opencv-python
3. matplotlib: pip install matplotlib
4. joblib: pip install joblib
5. scikit-image: pip install scikit-image
6. scikit-learn: pip install scikit-learn
7. seaborn: pip install seaborn
8. tabulate: pip install tabulate
9. memory_profiler: pip install memory_profiler

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Step 2: Save Individual Models

Run the following scripts to save each model:

• saveadaboostmodel.py
• SVM.py
• random_forest.py
• logistic_regression.py
• knn.py

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Step 3: Run Ensemble Method

Execute the script to generate individual model probabilities and ensemble method probability: python newensemble.py

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Step 4: Evaluate Model Performance

To evaluate the performance metrics of the models, run: python testModelEvaluation.py