Customer Segmentation using Machine Learning 🚀📊

Welcome to the Customer Segmentation using Machine Learning project! This initiative focuses on segmenting customers based on their purchasing behavior, using clustering techniques like K-Means and Hierarchical Clustering.

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📝 Table of Contents

• About the Project
• Key Features
• Dataset
• Technologies Used
• Getting Started
• Results
• Output Visualizations
• Contributing
• License

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About the Project 📚

The Customer Segmentation project applies machine learning techniques to cluster customers based on their Annual Income and Spending Score. Through this project, we aim to:

• Perform data preprocessing and scaling.
• Apply K-Means Clustering and determine the optimal K using the Elbow Method.
• Apply Hierarchical Clustering and analyze the Dendrogram.
• Visualize clusters to uncover meaningful customer segments.

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Key Features 🎯

• Data Exploration: Understand customer spending habits and income groups.
• Visualization: Interactive visualizations for better insights.
• Machine Learning Models: K-Means and Hierarchical Clustering for segmentation.
• Business Insights: Practical recommendations for marketing strategies.

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Dataset 📂

The dataset, Mall_Customers.csv, contains customer information such as:

• Customer ID
• Age
• Gender
• Annual Income
• Spending Score

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Technologies Used 🛠️

• Programming Language: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Tools: Jupyter Notebook

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Getting Started 🚀

Prerequisites

• Python 3.8 or higher
• Required libraries installed (pip install -r requirements.txt)

🚀 Running the Code

1. Clone this repository: git clone https://github.com/QuantumCoderrr/Customer-Segmentation-ML.git

2. Install dependencies: pip install -r requirements.txt

3. Run the script: python src/clustering.py

📌 Results

Our analysis yielded the following insights:

Optimal Clusters: The Elbow Method determined the best number of clusters for segmentation. Customer Segments: Clear groups of customers based on income and spending habits. Model Evaluation: The clustering models successfully segmented customers for targeted marketing.

📸 Visualizations

1. Elbow Method for KMeans

2. K-Means Clustering

3. Dendrogram for Hierarchical Clustering

4. Hierarchical Clustering

Contributing

We welcome contributions from everyone! To learn how you can contribute, please see our Contributing Guidelines.

Code of Conduct

Please note that we have a Code of Conduct in place to ensure that all participants can contribute in a respectful and welcoming environment.

License 📜

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