{"id":29089345,"url":"https://github.com/adityakumarda/kmeans-web-analytics","last_synced_at":"2026-04-10T15:34:37.667Z","repository":{"id":301133136,"uuid":"1007635513","full_name":"AdityakumarDA/kmeans-web-analytics","owner":"AdityakumarDA","description":"Built with Python, Pandas, and Scikit-learn, this machine learning project uses K-Means to cluster website users by behavior. 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It reveals patterns in engagement and bounce, helping drive data-informed decisions.\n\n---\n\n## Table of Contents\n\n- [Key Features and Benefits](#key-features-and-benefits)\n- [Prerequisites and Dependencies](#prerequisites-and-dependencies)\n- [Installation and Setup Instructions](#installation-and-setup-instructions)\n- [Usage Examples and API Documentation](#usage-examples-and-api-documentation)\n- [Configuration Options](#configuration-options)\n- [Contributing Guidelines](#contributing-guidelines)\n- [License Information](#license-information)\n- [Acknowledgments](#acknowledgments)\n- [Project Structure](#project-structure)\n- [Visual Output Snapshots](#Visual-Output-Snapshots)\n- [Future Enhancements](#future-enhancements)\n- [About Me](#about-me)\n\n---\n\n## Key Features and Benefits\n\n- **User Segmentation:** Divides website users into distinct clusters based on their behavior patterns.\n- **Behavioral Insights:** Identifies common engagement and bounce patterns within each cluster.\n- **Data-Driven Decisions:** Enables data-informed decisions regarding website optimization, marketing strategies, and user experience improvements.\n- **K-Means Clustering:** Employs the K-Means algorithm to effectively group users with similar behaviors.\n- **Python-Based:** Leverages the power and flexibility of Python for data analysis and machine learning.\n\n---\n\n## Prerequisites and Dependencies\n\nBefore running this project, ensure you have the following installed:\n\n- Python (3.6 or higher)\n- Pandas: `pip install pandas`\n- Scikit-learn: `pip install scikit-learn`\n- Jupyter Notebook (Optional): `pip install notebook`\n\n---\n\n## Installation and Setup Instructions\n1.  **Clone the Repository:**\n\n    ```bash\n    git clone https://github.com/AdityakumarDA/kmeans-web-analytics.git\n    cd kmeans-web-analytics\n    ```\n\n2.  **Install Dependencies:**\n\n    It is recommended to create a virtual environment for this project.\n\n    ```bash\n    # Create a virtual environment (optional)\n    python3 -m venv venv\n    source venv/bin/activate  # On Linux/macOS\n    # venv\\Scripts\\activate  # On Windows\n\n    # Install the required packages\n    pip install pandas scikit-learn notebook\n    ```\n\n3.  **Download the data**\n    Ensure that the `website_traffic_data.csv` is downloaded and placed in the project directory\n\n---\n\n## Usage Examples and API Documentation\n\nThis project primarily consists of a Jupyter Notebook (`ML_project.ipynb`) that demonstrates the usage of K-Means clustering.\n\n1.  **Run the Notebook:**\n\n    ```bash\n    jupyter notebook ML_project.ipynb\n    ```\n\n2.  **Follow the steps within the notebook:** The notebook guides you through data loading, preprocessing, K-Means model training, and cluster analysis.  It leverages Pandas and Scikit-learn functions directly.\n\n    Example snippet (from notebook concept):\n\n    ```python\n    import pandas as pd\n    from sklearn.cluster import KMeans\n    from sklearn.preprocessing import StandardScaler\n\n    # Load the data\n    data = pd.read_csv(\"website_traffic_data.csv\")\n\n    # Select features (e.g., 'engagement', 'bounce_rate')\n    features = ['engagement', 'bounce_rate']\n    X = data[features]\n\n    # Standardize the features\n    scaler = StandardScaler()\n    X_scaled = scaler.fit_transform(X)\n\n    # Apply K-Means clustering\n    kmeans = KMeans(n_clusters=3, random_state=42)  # Example: 3 clusters\n    data['cluster'] = kmeans.fit_predict(X_scaled)\n\n    # Analyze the clusters\n    print(data.groupby('cluster')[features].mean())\n    ```\n\n---\n\n## Configuration Options\n\nThe primary configurable option is the number of clusters (`n_clusters`) in the K-Means algorithm. This can be adjusted within the `ML_project.ipynb` notebook. Experiment with different values to find the optimal number of clusters for your dataset. Also the features that are used for clustering are configurable.\n\n```python\nkmeans = KMeans(n_clusters=3, random_state=42) # change n_clusters\n```\n\n---\n\n## Contributing Guidelines\n\nContributions are welcome! To contribute to this project:\n\n1.  Fork the repository.\n2.  Create a new branch for your feature or bug fix.\n3.  Make your changes and commit them with clear, descriptive messages.\n4.  Submit a pull request.\n\nPlease ensure your code adheres to Python coding standards and includes appropriate documentation.\n\n---\n\n## License Information\n\nNo license specified. All rights reserved by **AdityakumarDA**.\n\n---\n\n## Acknowledgments\n\n*   [Scikit-learn](https://scikit-learn.org/stable/) - For the K-Means implementation.\n*   [Pandas](https://pandas.pydata.org/) - For data manipulation and analysis. [Pandas](https://pandas.pydata.org/)\n\n---\n\n## Project Structure\n\n```\nkmeans-web-analytics/\n├── ML_project.ipynb\n├── website_traffic_data.csv\n├── README.md\n└── images/\n    ├── trafficcost_vs_Search_volume.png\n    ├── elbow_plot.png\n    └── cluster_scatter.png\n```\n\n---\n\n## Visual Output Snapshots\n\n### 1. 🔍 Search Volume vs Traffic Cost\n\n![Search Volume vs Traffic Cost](ML_Images/trafficcost_vs_Search_volume.png)\n\nThis scatter plot visualizes how **Search Volume** impacts the **Traffic Cost** for various website keywords or landing pages. It helps identify outliers — e.g., terms with exceptionally high traffic costs or volume. This can assist in budget optimization for paid campaigns or SEO strategy.\n\n---\n\n### 2. 💡 Elbow Method to Determine Optimal Clusters\n\n![Elbow Plot](ML_Images/elbow_plot.png)\n\nThe **Elbow Method** helps us decide the optimal number of clusters (`n_clusters`) for K-Means. It plots the number of clusters vs the clustering inertia (error). The 'elbow point' (highlighted with a red star) indicates the most efficient number of clusters — beyond which performance gain diminishes. In this project, 2 clusters were optimal.\n\n---\n\n### 3. 📊 K-Means Cluster Scatter Plot (Search Volume vs Traffic)\n\n![Cluster Scatter Plot](ML_Images/cluster_scatter.png)\n\nThis plot displays final **K-Means clustering results**, where:\n- Each point is a data sample (a keyword or page).\n- Different colors indicate different user segments (clusters).\n- **Red stars** mark the centroids (mean position of each cluster).\n\nIt provides intuitive insights into user groupings like high-volume, low-cost vs low-volume, high-cost clusters. This is essential for personalized targeting and marketing strategies.\n\n---\n\n## Future Enhancements\n\n- Add features like session duration, pages per session\n- Use silhouette score for better cluster selection\n- Deploy via Streamlit/Flask for interactivity\n- Add time-series or location-based segmentation\n\n---\n\n## About Me\n\nI'm **Aditya Rajput**, a data analyst passionate about storytelling with data, unsupervised learning, and real-world analytics.\n\n- [LinkedIn](https://www.linkedin.com/in/adityakumarda/)  \n- [GitHub](https://github.com/AdityakumarDA)  \n- [Tableau Public](https://public.tableau.com/app/profile/adityakumarda)\n\nIf you liked this project, please ⭐ the repo!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityakumarda%2Fkmeans-web-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadityakumarda%2Fkmeans-web-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityakumarda%2Fkmeans-web-analytics/lists"}