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Sentiment Labeling (VADER-based)\n    \n2.  Exploratory Data Analysis (EDA)\n    \n3.  Monthly Sentiment Score Calculation\n    \n4.  Employee Ranking (Top 3 Positive/Negative)\n    \n5.  Flight Risk Identification\n    \n6.  Predictive Modeling (Linear Regression)\n    \n\n### Key Results\n\n*   **Top Positive Employees (latest month):** Employee\\_A, Employee\\_B, Employee\\_C\n    \n*   **Top Negative Employees (latest month):** Employee\\_X, Employee\\_Y, Employee\\_Z\n    \n*   **Flight Risk Employees:** Employee\\_M, Employee\\_N\n    \n*   **Model Performance:** RMSE ≈ \\[value\\], R² ≈ \\[value\\]\n    \n\n### Repository Structure\n\n```bash\nemployee_sentiment_project/\n│\n├── data/                  # raw + processed datasets\n├── visualization/          # generated plots\n├── notebooks/              # main_notebook.ipynb\n├── report/                 # Employee_Sentiment_Analysis_Report.docx\n├── README.md               # summary file\n└── requirements.txt        # dependencies\n```\n\n### How to Run\n\n1.  Install dependencies: pip install -r requirements.txt\n    \n2.  Open notebooks/main\\_notebook.ipynb\n    \n3.  Run cells step by step to reproduce results","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finvincible1602%2Femployee_sentiment_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finvincible1602%2Femployee_sentiment_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finvincible1602%2Femployee_sentiment_project/lists"}