elbowK 1.0

Automatically find the optimal number of clusters (k) for KMeans using the Elbow Method and visualize the result.

elbowK

This package provides tools for determining the optimal number of clusters in K-Means clustering using the Elbow Method. It automatically calculates the sum of squared errors (SSE) for different values of k, detects the optimal k, and visualizes the results with an elbow plot.

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Installation

Install from requirements.txt

pip install -r requirements.txt

Install in development mode (local)

pip install -e .

Install from PyPI

pip install elbowK

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Usage

To use the package, import the main function and pass your scaled data:

import pandas as pd
from sklearn.preprocessing import StandardScaler
from elbowK.elbow import find_best_k

# Example dataset
data = pd.DataFrame({
    'Income_$': [15, 16, 17, 18, 19, 20],
    'SpendingScore': [39, 81, 6, 77, 40, 76]
})

# Scale the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(data)

# Find the best k and save the elbow plot
best_k = find_best_k(X_scaled, max_k=10, save_plot=True)
print(f"Optimal k: {best_k}")
# The elbow plot will be saved as 'elbow_plot.png' in your working directory.

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Package Structure

elbowK/
    __init__.py       # Initializes the package
    elbow.py          # Core functionality for determining optimal clusters
setup.py              # Package metadata and setup configuration
requirements.txt      # Dependencies required
tests/
    elbow_test.py     # Unit tests

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License

This project is licensed under the MIT License – see the LICENSE file for details.