{"id":23832238,"url":"https://github.com/dheerajgits/skyhack","last_synced_at":"2026-02-06T05:02:10.394Z","repository":{"id":270608828,"uuid":"869094584","full_name":"dheerajGits/skyhack","owner":"dheerajGits","description":"This repository supports a project aimed improve the efficiency of United Airlines' call center by predicting call handle times using machine learning. It analyzes factors like customer tone, call reason, and high-volume call times. 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The project includes two Jupyter notebooks for data analysis and model training, as well as a Flask application to serve predictions.\n\nPresentation Link:-\n\nhttps://www.canva.com/design/DAGTJm15k2k/UzHF9TO3m4twIsXDYgOlwQ/edit?utm_content=DAGTJm15k2k\u0026utm_campaign=designshare\u0026utm_medium=link2\u0026utm_source=sharebutton\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Usage](#usage)\n- [Project Structure](#project-structure)\n- [Endpoints](#endpoints)\n- [Data Requirements](#data-requirements)\n- [How to Run the Application](#how-to-run-the-application)\n- [Example Requests](#example-requests)\n- [License](#license)\n\n## Installation\n\n1. Clone the repository:\n\n   ```bash\n   git clone \u003crepository_url\u003e\n   cd \u003crepository_directory\u003e\n\n   ```\n\n## Usage\n\n### Project Structure\n\n- `main.ipynb`: Contains data analysis and exploration of the datasets.\n- `model.ipynb`: Contains the predictive model for calculating handle time.\n- `app.py`: The main Flask application for serving predictions.\n\n### Endpoints\n\n1.  **Predict Endpoint**\n\n- URL: `/predict`\n- Method: `POST`\n- **Request Body**:\n\n        json\n        `{\n          \"customer_id\": \"12345\",\n          \"primary_call_reason\": \"Flight Inquiry\"\n         }`\n\n- **Response**:\n\n        json\n        `{\n           \"transferred_to_agent\": \"agent_id_x\",\n           \"least_handle_time\": 300.5\n         }`\n\n2.  **Call Complete Endpoint**\n\n- URL: `/call_complete`\n- Method: `POST`\n- **Request Body**:\n\n        json\n        `{\n           \"agent_id\": \"agent_id_x\"\n         }`\n\n- **Response**:\n\n        json\n        `{\n            \"message\": \"Agent status updated to available.\"\n         }`\n\n3.  **Update Availability Endpoint**\n\n- URL: `/update_availability`\n- Method: `POST`\n- **Request Body**:\n\n      json\n      `{\n         \"agent_id\": \"agent_id_x\",\n         \"availability\": true\n      }`\n\n- **Response**:\n\n        json\n        `{\n            \"message\": \"Agent {agent_id} availability updated to {availability}.\"\n         }`\n\n## Data Requirements\n\n``Ensure you have the following datasets in the `dataset` directory:\n\n    `customer.csv`: Contains information about the customers.\n    `calls.csv`: Contains details about the calls.\n    `reason.csv`: Contains reasons for the calls.\n    `sentiment_statistics.csv`: Contains sentiment analysis statistics.``\n\n## How to Run the Application\n\n1.  Navigate to the project directory:\n\n    ```bash\n    `cd \u003crepository_directory\u003e`\n\n2.  Run the Jupyter notebooks `main.ipynb` and `model.ipynb` to perform data analysis and train the predictive model.\n\n3.  Run the Flask application:\n\n    ```bash\n    `python app.py`\n\n\n## Example Requests\n\n### Predict Handle Time\n\n    ```bash\n     `curl -X POST http://127.0.0.1:5000/predict \\\n      -H \"Content-Type: application/json\" \\\n      -d '{\"customer_id\": \"12345\", \"primary_call_reason\": \"Flight Inquiry\"}'`\n\n### Update Agent Status\n\n    ```bash\n    `curl -X POST http://127.0.0.1:5000/call_complete \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\"agent_id\": \"agent_id_x\"}'\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdheerajgits%2Fskyhack","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdheerajgits%2Fskyhack","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdheerajgits%2Fskyhack/lists"}