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The goal is to identify different customer segments based on their purchasing behavior.\n\n## Dataset\nThe dataset contains transactional data from an online retail store, including fields such as InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country.\n\n## Requirements\n- **pandas**: Data manipulation and analysis\n- **matplotlib**: Data visualization\n- **seaborn**: Statistical data visualization\n- **scikit-learn**: Machine learning algorithms\n- **openpyxl**: Excel file handling\n\nInstall the dependencies using the following command:\n```bash\npip install pandas matplotlib seaborn scikit-learn openpyxl\n```\n\n## Features\n1. **Data Preprocessing**: Handles missing values and removes duplicates.\n2. **Feature Engineering**: Creates RFM (Recency, Frequency, Monetary) features.\n3. **Outlier Detection**: Uses IQR to remove outliers.\n4. **Scaling**: Standardizes features for clustering.\n5. **K-means Clustering**: Implements K-means for customer segmentation.\n6. **Visualization**: Plots clusters to interpret results.\n\n## Project Workflow\n1. **Import Libraries**: Load required Python libraries.\n2. **Load Dataset**: Import data from Excel file.\n3. **Data Cleaning**: Handle missing values and duplicates.\n4. **Feature Engineering**: Calculate RFM metrics for customers.\n5. **Outlier Removal**: Detect and remove outliers using the IQR method.\n6. **Scaling**: Standardize features for clustering.\n7. **K-means Clustering**: Determine the optimal number of clusters using the Elbow method and fit the model.\n8. **Visualization**: Plot cluster distributions and interpret results.\n\n## Results\nThe output includes visualizations of clusters and insights into customer segmentation based on purchasing behavior.\n\n## Future Improvements\n- Implementing hierarchical clustering and DBSCAN for comparison.\n- Automating identifying customer label for future data by automation.\n- Adding dashboards for interactive visualization.\n\n## Resources  \n- Online retail mining paper: [https://link.springer.com/article/10.1057/dbm.2012.17](https://link.springer.com/article/10.1057/dbm.2012.17)  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshindcoder%2Fonline_retail_data_clustering_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharshindcoder%2Fonline_retail_data_clustering_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshindcoder%2Fonline_retail_data_clustering_project/lists"}