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Developed as a course project in my third year.\n\n## Core Technologies:\n  - Python (3.8 +)\n  - Google Custom Search JSON API\n  - Matplotlib (for visualisation)\n  - Concurrent execution via `ThreadPoolExecutor`\n\n## Key Features\n\n- **Multi-mode Search Capability**:\n  - General web search\n  - Crime reporting papers analysis\n  - Deep learning journal papers analysis\n\n- **Structured Data Extraction**:\n  - Identifies 100+ crime-reporting features (incident details, victim info, evidence, etc.)\n  - Detects academic paper sub-headings in deep learning publications\n\n- **Visual Analytics**:\n  - Automated bar chart generation for feature frequency analysis\n  - Threaded processing for concurrent data extraction/visualization\n\n## Usage\n\n1. **Configure API**:\n   - Replace placeholder credentials in `utils.py`:\n     \n     ```python\n     API_KEY = \"your_google_api_key\"\n     SEARCH_ENGINE_ID = \"your_search_engine_id\"\n     ```\n\n2. **Run the program**:\n   ```bash\n   python main.py\n   ```\n\n3. **Select analysis mode:**\n   - General search\n   - Crime reporting analysis\n   - Deep learning paper analysis\n  \n## Usage Examples\n\n**Crime-Reporting Mode:**\n\n```bash\nCopy\n\u003e Select mode: 2\n\u003e Query: \"homicide investigation techniques 2023\"\n\nAnalyzing 10 results...\n- Extracted 23 incident_details features\n- Found 8 weapon/evidence references\n- Generated visualization: crime_features.png\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv3l0s-c0d3r%2Fserp-analyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fv3l0s-c0d3r%2Fserp-analyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv3l0s-c0d3r%2Fserp-analyzer/lists"}