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README.md

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+# Clustering Algorithms for Customer Segmentation
+
+This repository contains a comprehensive implementation of various clustering algorithms to perform customer segmentation on a synthetic dataset. The project explores K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models (GMM) to identify distinct customer groups based on age and income.
+
+## Project Structure
+
+- `implementation.ipynb`: The main Jupyter notebook containing the entire analysis, from data generation to model evaluation and visualization.
+- `data/`: Contains the synthetic `customer_data.csv` file.
+- `models/`: Stores the trained clustering models and the data scaler.
+- `results/`: Includes the algorithm comparison, detailed analysis, and experiment summary.
+- `visualizations/`: Contains the output plots, such as the elbow method analysis and cluster comparisons.
+
+## Features
+
+- **Data Generation**: A synthetic customer dataset is generated with clear cluster structures for effective model training and evaluation.
+- **Multiple Algorithms**: Implements and compares four popular clustering algorithms:
+    - K-Means
+    - Hierarchical Clustering
+    - DBSCAN
+    - Gaussian Mixture Models (GMM)
+- **Model Evaluation**: Uses the elbow method and silhouette scores to determine the optimal number of clusters and evaluate performance.
+- **Comprehensive Visualization**: Generates plots to visualize the clusters, compare algorithm performance, and analyze the optimal 'k'.
+
+## How to Use
+
+1.  **Clone the repository:**
+    ```bash
+    git clone https://github.com/GruheshKurra/ClusteringAlgorithms.git
+    ```
+2.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    ```
+3.  **Run the notebook:**
+    Open and run the `implementation.ipynb` notebook in a Jupyter environment to see the full analysis.
+
+## License
+
+This project is licensed under the MIT License. 

README_HF.md

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+---
+license: mit
+---
+
+# Clustering Algorithms for Customer Segmentation
+
+This repository hosts a project on customer segmentation using various clustering algorithms. It includes the code, a synthetic dataset, trained models, and visualizations.
+
+## Project Overview
+
+This project implements and compares the following clustering algorithms for customer segmentation:
+- K-Means
+- Hierarchical Clustering
+- DBSCAN
+- Gaussian Mixture Models (GMM)
+
+The goal is to identify distinct customer groups based on their age and income.
+
+## Repository Contents
+
+- `implementation.ipynb`: The main Jupyter notebook with the complete analysis.
+- `data/customer_data.csv`: The synthetic dataset used for clustering.
+- `models/`: Contains the saved models for each algorithm and the data scaler.
+- `results/`: Contains detailed analysis and comparison of the algorithms.
+- `visualizations/`: Includes plots for cluster visualization and analysis.
+
+## How to Use
+
+You can use the trained models and the dataset from this repository for your own analysis. To get started, you can clone the repository and explore the `implementation.ipynb` notebook.
+
+```bash
+# Clone the repository
+git clone https://huggingface.co/karthik-2905/ClusteringAlgorithms
+```
+
+## License
+
+This project is licensed under the MIT License.