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Advanced Customer Segmentation

📌 Project Overview

This project applies various clustering and classification techniques to segment customers based on their characteristics. It includes K-Means, Hierarchical Clustering, DBSCAN, Decision Trees, Random Forests, Neural Networks, and SVM, providing a comprehensive approach to customer segmentation.

By leveraging unsupervised and supervised learning models, this project delivers an automated, scalable, and interpretable segmentation solution.

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📊 Initial Data Analysis & Visualization

Before applying clustering techniques, exploratory data analysis (EDA) is performed to understand the structure and distribution of customer attributes.

📌 Distribution of Features:

📌 Pairwise Relationship Plot:

📌 Density Plot:

📌 Box Plot:

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🚀 Clustering Techniques Implemented

1️⃣ K-Means Clustering

• K-Means is a centroid-based clustering method that partitions data into K clusters based on similarity.
• The Elbow Method and Silhouette Analysis are used to determine the optimal number of clusters.

📌 Elbow Method for Optimal K:

📌 K-Means Cluster Visualization:

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2️⃣ Hierarchical Clustering

• Hierarchical clustering builds a tree-like structure (dendrogram) to visualize relationships between customers.
• Ward’s method is used to minimize variance within clusters.

📌 Dendrogram (Hierarchical Clustering):

📌 Hierarchical Clusters Visualization:

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1️⃣ DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

• DBSCAN is a density-based clustering algorithm that groups core samples and detects noise.
• This method is effective for identifying arbitrary-shaped clusters and handling outliers.

📌 DBSCAN Output:

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2️⃣ Decision Tree (CART - Classification and Regression Tree)

• A Decision Tree is trained to classify customers into clusters based on feature splits.
• It provides an interpretable model for understanding customer segmentation.

📌 Decision Tree Output:

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3️⃣ Random Forests

• A Random Forest classifier is used to improve classification accuracy.
• Feature importance ranking helps understand which attributes drive customer segmentation.

📌 Feature Importance Plot:

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5️⃣ Support Vector Machines

• SVM is used to find the best decision boundary for segmenting customers.
• Kernel trick is applied to handle non-linearity in data.

📌 SVM Decision Boundary:

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⚙ How to Run the Project

1️⃣ Clone the Repository:

git clone https://github.com/yourusername/customer-segmentation.git
cd customer-segmentation

2️⃣ Install Required Libraries in R:

install.packages(c("tidyverse", "dbscan", "factoextra", "cluster", 
                   "ggplot2", "rpart", "rpart.plot", "randomForest", 
                   "nnet", "e1071"))

3️⃣ Run the Segmentation Script:

source("customer_segmentation.R")

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🎯 Customization and Parameter Tuning

• DBSCAN: Modify eps and minPts to adjust cluster density.
• Decision Trees & Random Forests: Tune ntree and mtry for better classification.
• Neural Networks: Adjust hidden layers (size), regularization (decay), and iterations (maxit).
• SVM: Change kernel type (linear, radial, polynomial) to improve separation.

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📜 License

This project is open-source and available under the MIT License.

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✅ This project provides a comprehensive approach to customer segmentation using advanced machine learning techniques.
💡 Use this repository to improve your understanding of clustering, classification, and model evaluation! 🚀

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🔗 Connect & Follow

👨‍💻 Author: Joel Mande 📧 Email: joelwanjala09@gmail.com.com
🌐 LinkedIn: Joel Mande

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