Counterfeit-Bills-Detection-ML-Visualization

Given data set that provides 4 features per bill and whether the bill was genuine or counterfeit. Using Machine Learning algorithms and the measurements provided, I built a predictor to determine whether a bill is genuine or counterfeit in machineLearningAlgorithms.py In addition, a descriptive statistical analysis and pair plot is created for visualization on the data set interactions in statisticalAnalysis_visualization.py

Algorithms

• Perceptron
• Logistic Regression
• Support Vector Machine
• Decision Tree Learning
• Random Forest
• K-Nearest Neighbor

Results

• Best Prediction was K-Nearest Neighbor with Accuracy 100% & Combined Accuracy 99.93%

Visualization

• Pair Plot
• Cross Covariance

Requirements

• Python 3

Packages

• numpy version 1.16.5 or +
• pandas version 1.0.0 or +
• scipy version 1.3.1 or +
• matplotlib version 1.3 or +
• scikit-learn version 0.21.3 or +
• seaborn version 0.9.0 or +

• I used the Anaconda Environment to install these packages with additional, with Jupyter Notebook and Spyder IDE in addtion
• Other method to just download packages is Miniconda

Data Set Bill Features data_banknote_authentication.txt

1. variance of Wavelet Transformed image (continuous)
2. skewness of Wavelet Transformed image (continuous)
3. curtosis of Wavelet Transformed image (continuous)
4. entropy of image (continuous)
5. class (integer)