Customer Segmentation using Hierarchical Clustering

This project applies Hierarchical Clustering to group customers based on their demographic attributes and spending behaviour. The goal is to help businesses better understand customer patterns and tailor marketing strategies for each group.

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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

• Loads and explores customer data.
• Visualises customer distributions using scatter plots and dendrograms.
• Applies Hierarchical Clustering (Agglomerative) for customer segmentation.
• Uses Elbow and Dendrogram methods to determine the optimal number of clusters.
• Plots the segmented clusters for easy interpretation.

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Tools & Libraries

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

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How to Use

1. Clone this repository:

   git clone https://github.com/nurulashraf/customer-segmentation-hierarchical-clustering.git
   cd customer-segmentation-hierarchical-clustering

2. Install dependencies:

   pip install pandas numpy matplotlib seaborn scikit-learn scipy

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

4. Open the notebook:

   jupyter notebook customer_segmentation_hierarchical_clustering.ipynb

5. Run the cells and explore the analysis.

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License

This project is licensed under the MIT License.