Banknote Authentication using K-means Clustering

A simple machine learning project that uses K-Means Clustering to detect potential counterfeit banknotes based on their statistical properties. This project demonstrates unsupervised learning using real-world data and visualises the clustering process.

Project Structure

• data/: Contains the dataset used for analysis and prediction.
• notebooks/: Jupyter notebooks for data analysis, feature engineering, and model building.
• README.md: Project overview and usage instructions.

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Features

• Load and explore a dataset of banknotes
• Preprocess data for clustering
• Apply K-Means Clustering to group genuine and counterfeit bills
• Visualise the clusters using Matplotlib and Seaborn
• Evaluate clustering results using inertia and silhouette score

Tools & Libraries

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

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How to Use

1. Clone this repository:

   git clone https://github.com/nurulashraf/kmeans-counterfeit-bills.git
   cd kmeans-counterfeit-bills

2. Install dependencies:

   pip install pandas numpy matplotlib seaborn scikit-learn scipy

3. Place your dataset (fake_bills.csv) in the data folder.

4. Open the notebook:

   jupyter notebook kmeans_counterfeit_bills.ipynb

5. Run the cells and explore the analysis.

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License

This project is licensed under the MIT License.