{"id":18993961,"url":"https://github.com/fahrettinsolak/ai-customer-segmentation-project","last_synced_at":"2026-05-13T13:50:13.696Z","repository":{"id":257988458,"uuid":"873266395","full_name":"Fahrettinsolak/AI-Customer-Segmentation-Project","owner":"Fahrettinsolak","description":"This project demonstrates the use of K-Means clustering to segment customers based on their annual income and spending score. 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The dataset used is `Avm_Customers.csv`, which contains information about customers' annual income and their spending score.\n\n## Project Overview\n\n1. **Data Exploration**: \n   - Load the dataset and preview the data.\n   - Visualize the distribution of customers based on annual income and spending score.\n\n2. **Data Preprocessing**:\n   - Rename columns for simplicity.\n   - Normalize the data using `MinMaxScaler`.\n\n3. **Clustering**:\n   - Determine the optimal number of clusters using the Elbow Method.\n   - Apply K-Means clustering with the optimal number of clusters.\n   - Analyze and visualize the clustering results.\n\n4. **Visualization**:\n   - Plot the clustered data points and cluster centroids.\n\n## Files\n\n- `Avm_Customers.csv`: The dataset used for clustering.\n- `K-MeansClustering-Customer_Segmentation.ipynb`: The Python script implementing the K-Means clustering algorithm and visualizations.\n\n## Installation\n\nTo run this project, you will need Python and the following libraries:\n\n- `numpy`\n- `pandas`\n- `scikit-learn`\n- `matplotlib`\n\nYou can install the required libraries using pip:\n\n\npip install numpy pandas scikit-learn matplotlib\n\n## Usage\n\n1. **Clone this repository:**\n\n    ```bash\n    git clone https://github.com/Semssolak/repository.git\n    ```\n\n2. **Navigate to the project directory:**\n\n    ```bash\n    cd repository\n    ```\n\n3. **Run the Python script:**\n\n    ```bash\n    python K-MeansClustering-Customer_Segmentation.ipynb\n    ```\n\n## Results\n\n- **Elbow Method Plot**: This plot shows the distortion value (inertia) for different numbers of clusters, helping to determine the optimal number of clusters.\n- **Clustered Data Plot**: A scatter plot visualizing the clusters, with different colors representing different clusters. The centroids of the clusters are marked with blue 'X' markers.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffahrettinsolak%2Fai-customer-segmentation-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffahrettinsolak%2Fai-customer-segmentation-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffahrettinsolak%2Fai-customer-segmentation-project/lists"}