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 Best 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.