🧠 McDonald's Market Segmentation (Fast Food Case Study)

A machine learning project to replicate a market segmentation case study on McDonald's customers using clustering techniques, powered by Python and scikit-learn.

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✨ Features

• 💬 Customer segmentation using unsupervised learning (KMeans)
• 📊 Feature preprocessing (scaling, encoding)
• 🔍 Elbow method to determine optimal number of clusters
• 🧬 PCA-based cluster visualization
• 📄 Modular and scalable codebase structured like an ML pipeline
• 🚀 Ready for deployment and GitHub publication

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🚀 Tech Stack

Technology	Purpose
Python	Core programming language
pandas	Data manipulation and I/O
scikit-learn	Clustering and preprocessing
matplotlib	Visualizations
Flask (optional)	Web deployment interface
Docker (optional)	Containerization
GitHub	Version control and collaboration

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🏗️ Architecture

┌────────────────────────┐     ┌────────────────────────┐
│  Dataset (CSV File)    │     │   main.py              │
└────────────┬───────────┘     └────────┬───────────────┘
             │                          │
             ▼                          ▼
   ┌──────────────────┐        ┌──────────────────────┐
   │ Load & Preprocess│◀──────▶│   src/ modules      │
   └────────┬─────────┘        └────────┬─────────────┘
            ▼                           ▼
   ┌──────────────────┐        ┌──────────────────────┐
   │ Clustering Logic │        │   Visualization      │
   └────────┬─────────┘        └────────┬─────────────┘
            ▼                           ▼
     Outputs: Cluster labels, PCA plots, Elbow chart

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📁 Project Structure

fastfood_segmentation/
├── data/                         # Dataset CSV
├── models/                       # (Optional) Saved models
├── outputs/                      # Plots and analysis results
├── src/
│   ├── data/                     # Data loading
│   ├── preprocessing/            # Data cleaning, encoding, scaling
│   ├── model/                    # Clustering (KMeans)
│   └── visualization/            # Elbow & cluster plots
├── main.py                       # Pipeline entry point
├── requirements.txt              # Python dependencies
└── README.md                     # Project overview

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🔧 Installation

Prerequisites

• Python 3.10+

🛠 Manual Setup

git clone https://github.com/your-username/mcdonalds-segmentation.git
cd mcdonalds-segmentation
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

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🚀 Usage

Run the full pipeline:

python main.py

Expected outputs:

• outputs/elbow_plot.png
• outputs/cluster_plot.png

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📄 Dataset Description

Column	Type	Description
yummy, cheap...	Categorical	Yes/No features about food perception
Like	Integer	Rating from -3 to +3
Age	Integer	Customer age
VisitFrequency	Categorical	Visit frequency (encoded)
Gender	Categorical	Gender (encoded)

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📈 Output Samples

Below are examples of the plots generated by the analysis:

Elbow Plot for Optimal Clusters:

PCA Cluster Plot for Customer Visualization (Example with K=3):

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🧪 Testing (Optional)

If tests are added:

python -m pytest tests/

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📝 License

MIT License

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🙏 Acknowledgements

• Book: Market Segmentation Analysis
• scikit-learn open source community
• Inspiration from McDonald's case study

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📞 Contact

For questions or suggestions, open an issue or contact: Vishal Gorule – [gorulevishal984@gmail.com] – Vision Expo