hack26-project-health-and-behaviour-monitor/repo-manifest.json at main · Projecting-Success-Solutions-Portal/hack26-project-health-and-behaviour-monitor · GitHub

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{
  "event": {
    "name": "hack26",
    "slug": "hack26",
    "repo": "hack26",
    "topics": [
      "solution-centre",
      "hack26",
      "python",
      "azure",
      "power-bi",
      "github",
      "hackathon",
      "innovation",
      "data",
      "artificial-intelligence",
      "prototyping",
      "collaboration",
      "challenge1",
      "large-language-models",
      "machine-learning",
      "natural-language-processing",
      "risk-management",
      "tacit-knowledge",
      "heuristics",
      "project-risk"
    ],
    "technologies": [
      "python",
      "azure",
      "power-bi",
      "github",
      "large-language-models",
      "machine-learning",
      "natural-language-processing",
      "streamlit",
      "reportlab",
      "pandas",
      "microsoft-excel",
      "dataverse",
      "power-apps",
      "power-automate",
      "copilot-studio",
      "jupyter",
      "google-gemini",
      "openpyxl",
      "fuzzywuzzy",
      "json",
      "microsoft-copilot",
      "office-365",
      "project-scheduling-tools",
      "microsoft-office",
      "primavera-p6",
      "document-analysis",
      "knowledge-bases",
      "search",
      "pdfplumber",
      "sentence-transformers"
    ]
  },
  "visibility": {
    "event_repo": "public",
    "team_repos": "public"
  },
  "repos": [
    {
      "type": "team",
      "challenge_name": "Risky Resource Routines: Pinpointing Patterns for Preventing Poor Practices",
      "challenge_description": "This challenge, presented by Thales, focuses on improving project delivery by exposing and addressing poor resource planning and scheduling behaviours. Teams are invited to design behavioural analytics dashboards or tools that reveal hidden patterns in forecasting accuracy, resource over\u2011 and under\u2011utilisation, and reliance on generic placeholders, turning planning data into insight that drives better decisions and sustainable behaviour change.",
      "challenge_slug": "challenge5",
      "team_name": "Project Health and Behaviour Monitor",
      "team_slug": "c",
      "name": "hack26-project-health-and-behaviour-monitor",
      "child_repo_path": "submissions/hack26-challenge5-c",
      "url": "https://github.com/Projecting-Success-Solutions-Portal/hack26-project-health-and-behaviour-monitor.git",
      "description": "The team focused on establishing strong data quality and analytical foundations for a Project Health and Behaviour Monitor. Using a structured synthetic dataset, they demonstrated how task-level schedule, cost, and resource attributes can be cleaned, validated, and analysed to identify volatility, critical path risk, forecasting accuracy issues, and problematic resourcing behaviours.",
      "key_outcomes": "Improved reliability of planning data through systematic cleansing; clearer identification of behavioural risk indicators such as volatility and ignored dependencies; stronger analytical basis for predictive dashboards; reduced noise and ambiguity in schedule and resource analysis.",
      "important_files": [
        {
          "path": "Team 5c.ipynb",
          "purpose": "Notebook demonstrating data quality checks, cleansing, and exploratory analysis of schedule and resource behaviours."
        },
        {
          "path": "Source Data/Fake Dataset 26.05 Data Dictionary.docx",
          "purpose": "Defines the structure and meaning of the synthetic dataset used for behavioural analysis."
        },
        {
          "path": "Source Data/Supplementary Information Challenge 5.docx",
          "purpose": "Outlines extended behavioural metrics and future directions for a project health monitor."
        }
      ],
      "required_fields": {
        "summary": "Analytical groundwork for a Project Health and Behaviour Monitor focused on data quality.",
        "problem": "Poor data quality and inconsistency obscure true planning and resourcing behaviours.",
        "approach": "Clean, validate, and analyse task-level schedule and resource data to surface behavioural risk indicators.",
        "deliverables": "Cleaned analytical dataset, data quality notebook, and defined behavioural metrics."
      },
      "thumbnail_path": "Actual vs Predicted Duration using Random forest.png",
      "topics": [
        "solution-centre",
        "hack26",
        "challenge5",
        "python",
        "pandas",
        "jupyter",
        "data-analysis",
        "analytics",
        "resource-management",
        "schedule-forecasting",
        "behavioural-analytics",
        "data-quality",
        "project-controls"
      ],
      "technologies": [
        "python",
        "pandas",
        "jupyter",
        "data-analysis",
        "analytics"
      ],
      "members": []
    }
  ]
}