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It simulates a dynamic retail environment where sales data is continuously streamed, processed, and visualized.\n\nThe system consists of a Kafka producer that feeds new sales records, a Kafka consumer that stores the data in a database, a trained ML model that predicts future demand, and a Streamlit dashboard that displays the top 10 latest entries with predictions in real time.\n\n📦 Tech Stack\n\nApache Kafka – for real-time data streaming\n\nPython – for scripting, data processing, and ML\n\nscikit-learn – for building the predictive model\n\nSQLite (bigmart.db) – for storing incoming data\n\nPandas \u0026 NumPy – for data manipulation\n\nStreamlit – for creating a web-based dashboard\n\nDocker – (optional) for containerized deployment\n\n🔄 Workflow\n\nKafka Producer\n\nReads from bigmartsales.csv line-by-line\n\nSends records to a Kafka topic every few seconds\n\nKafka Consumer\n\nListens to the topic\n\nWrites the data into bigmart.db SQLite database\n\nML Model\n\nA regression model (e.g., Linear Regression) is trained on historical sales data\n\nPredicts Item_Outlet_Sales for each new record\n\nStreamlit Dashboard\n\nFetches the top 10 most recent records from the database\n\nDisplays actual and predicted sales dynamically with refresh capability\n\n📁 Project Structure\n\n.\n├── app\n│   └── streamlit_app.py         # Streamlit UI logic\n├── consumer.py                  # Kafka consumer script\n├── producer.py                  # Kafka producer script\n├── model.pkl                    # Trained ML model\n├── bigmart.db                   # SQLite database storing new data\n├── bigmartsalws.csv             # Sample data used by the producer\n├── requirements.txt             # Dependencies for the project\n└── README.md                    # Project documentation\n\n💻 How to Run Locally\n\n1. Start Kafka and Zookeeper\n\nUse Docker or local Kafka setup to start Zookeeper and Kafka broker.\n\n2. Start Producer and Consumer\n\npython producer.py\npython consumer.py\n\n3. Launch the Dashboard\n\nstreamlit run app/streamlit_app.py\n\n📊 Output Preview\n\nTop 10 latest sales records\n\nReal-time updates every few seconds\n\nActual vs Predicted sales\n\n🌐 Deployment\n\nDeployed using Streamlit Cloud. Visit the live app here: https://bigmartflow.streamlit.app\n\n✨ Highlights\n\nReal-time data streaming \u0026 visualization\n\nEnd-to-end ML integration\n\nLightweight and scalable design\n\n🤝 Contributions\n\nFeel free to fork the repo, raise issues, or submit PRs. Suggestions to improve real-time accuracy, visualization, or deployment are welcome!\n\n📧 Contact\n\nDeveloped by Praneeth Goud📬 Email: praneethgoud2510@gmail.com 🔗 GitHub: pranee123\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpranee123%2Fbigmartflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpranee123%2Fbigmartflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpranee123%2Fbigmartflow/lists"}