kmeans-web-analytics

Built with Python, Pandas, and Scikit-learn, this machine learning project uses K-Means to cluster website users by behavior. It reveals patterns in engagement and bounce, helping drive data-informed decisions.

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Table of Contents

• Key Features and Benefits
• Prerequisites and Dependencies
• Installation and Setup Instructions
• Usage Examples and API Documentation
• Configuration Options
• Contributing Guidelines
• License Information
• Acknowledgments
• Project Structure
• Visual Output Snapshots
• Future Enhancements
• About Me

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Key Features and Benefits

• User Segmentation: Divides website users into distinct clusters based on their behavior patterns.
• Behavioral Insights: Identifies common engagement and bounce patterns within each cluster.
• Data-Driven Decisions: Enables data-informed decisions regarding website optimization, marketing strategies, and user experience improvements.
• K-Means Clustering: Employs the K-Means algorithm to effectively group users with similar behaviors.
• Python-Based: Leverages the power and flexibility of Python for data analysis and machine learning.

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Prerequisites and Dependencies

Before running this project, ensure you have the following installed:

• Python (3.6 or higher)
• Pandas: pip install pandas
• Scikit-learn: pip install scikit-learn
• Jupyter Notebook (Optional): pip install notebook

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Installation and Setup Instructions

1. Clone the Repository:

   git clone https://github.com/AdityakumarDA/kmeans-web-analytics.git
   cd kmeans-web-analytics

2. Install Dependencies:

   It is recommended to create a virtual environment for this project.

   # Create a virtual environment (optional)
   python3 -m venv venv
   source venv/bin/activate  # On Linux/macOS
   # venv\Scripts\activate  # On Windows
   
   # Install the required packages
   pip install pandas scikit-learn notebook

3. Download the data Ensure that the website_traffic_data.csv is downloaded and placed in the project directory

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Usage Examples and API Documentation

This project primarily consists of a Jupyter Notebook (ML_project.ipynb) that demonstrates the usage of K-Means clustering.

1. Run the Notebook:

   jupyter notebook ML_project.ipynb

2. 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.

   Example snippet (from notebook concept):

   import pandas as pd
   from sklearn.cluster import KMeans
   from sklearn.preprocessing import StandardScaler
   
   # Load the data
   data = pd.read_csv("website_traffic_data.csv")
   
   # Select features (e.g., 'engagement', 'bounce_rate')
   features = ['engagement', 'bounce_rate']
   X = data[features]
   
   # Standardize the features
   scaler = StandardScaler()
   X_scaled = scaler.fit_transform(X)
   
   # Apply K-Means clustering
   kmeans = KMeans(n_clusters=3, random_state=42)  # Example: 3 clusters
   data['cluster'] = kmeans.fit_predict(X_scaled)
   
   # Analyze the clusters
   print(data.groupby('cluster')[features].mean())

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Configuration Options

The 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.

kmeans = KMeans(n_clusters=3, random_state=42) # change n_clusters

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Contributing Guidelines

Contributions are welcome! To contribute to this project:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with clear, descriptive messages.
4. Submit a pull request.

Please ensure your code adheres to Python coding standards and includes appropriate documentation.

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License Information

No license specified. All rights reserved by AdityakumarDA.

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Acknowledgments

• Scikit-learn - For the K-Means implementation.
• Pandas - For data manipulation and analysis. Pandas

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Project Structure

kmeans-web-analytics/
├── ML_project.ipynb
├── website_traffic_data.csv
├── README.md
└── images/
    ├── trafficcost_vs_Search_volume.png
    ├── elbow_plot.png
    └── cluster_scatter.png

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Visual Output Snapshots

1. 🔍 Search Volume vs Traffic Cost

This 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.

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2. 💡 Elbow Method to Determine Optimal Clusters

The 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.

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3. 📊 K-Means Cluster Scatter Plot (Search Volume vs Traffic)

This plot displays final K-Means clustering results, where:

• Each point is a data sample (a keyword or page).
• Different colors indicate different user segments (clusters).
• Red stars mark the centroids (mean position of each cluster).

It 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.

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Future Enhancements

• Add features like session duration, pages per session
• Use silhouette score for better cluster selection
• Deploy via Streamlit/Flask for interactivity
• Add time-series or location-based segmentation

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About Me

I'm Aditya Rajput, a data analyst passionate about storytelling with data, unsupervised learning, and real-world analytics.

• LinkedIn
• GitHub
• Tableau Public

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