Commit ·
e96d049
1
Parent(s): 96c6dd9
Fix telemetry timeout and prepare for deployment
Browse files- README.md +3 -0
- TODO.md +0 -16
- __pycache__/crew.cpython-310.pyc +0 -0
- agents/__init__.py +0 -0
- agents/__pycache__/cleaner.cpython-310.pyc +0 -0
- agents/__pycache__/code_gen.cpython-310.pyc +0 -0
- agents/__pycache__/insights.cpython-310.pyc +0 -0
- agents/__pycache__/relation.cpython-310.pyc +0 -0
- agents/__pycache__/validator.cpython-310.pyc +0 -0
- agents/cleaner.py +3 -0
- agents/code_gen.py +0 -28
- agents/insights.py +3 -0
- agents/relation.py +3 -0
- agents/validator.py +3 -0
- app.py +68 -92
- blog.txt +0 -78
- config/__pycache__/__init__.cpython-310.pyc +0 -0
- crew.py +4 -0
- data/sugar.csv +769 -0
- index.html +0 -2984
- outputs/op.py +0 -36
- tools/__init__.py +0 -0
- tools/__pycache__/dataframe_ops.cpython-310.pyc +0 -0
- tools/dataframe_ops.py +0 -23
- tools/dataset_tools.py +39 -0
- workflows/__init__.py +0 -0
README.md
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@@ -36,6 +36,7 @@ pinned: false
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- Run: `python crew.py`
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- Outputs: `outputs/op.py`, `index.html`
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- Agents: `agents/` — each agent defines its LLM model and endpoint.
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## Quick Start
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## Deploying to Hugging Face Spaces
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1. **Create a New Space**:
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* Go to [huggingface.co/spaces](https://huggingface.co/spaces).
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* Click **Create new Space**.
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- Run: `python crew.py`
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- Outputs: `outputs/op.py`, `index.html`
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- Agents: `agents/` — each agent defines its LLM model and endpoint.
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- **Live Demo**: [Hugging Face Space](https://huggingface.co/spaces/sowmiyan-s/Multi-Agent-Data-Analysis-with-CrewAI)
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## Quick Start
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## Deploying to Hugging Face Spaces
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**[View Live Demo](https://huggingface.co/spaces/sowmiyan-s/Multi-Agent-Data-Analysis-with-CrewAI)**
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1. **Create a New Space**:
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* Go to [huggingface.co/spaces](https://huggingface.co/spaces).
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* Click **Create new Space**.
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TODO.md
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# TODO: Future Enhancements
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## Planned Features
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- [ ] **Support Multiple File Formats**: Add support for Excel (.xlsx), JSON, and Parquet files.
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- [ ] **Advanced Visualizations**: Integrate Plotly for interactive charts in the HTML report.
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- [ ] **Custom Agent Builder**: Allow users to define custom agents via a UI or config file.
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- [ ] **Cloud Deployment**: Create a Dockerfile and deployment guide for AWS/GCP.
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- [ ] **API Endpoint**: Wrap the system in a FastAPI backend for external integration.
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## Completed Tasks
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- [x] **Rebranding**: Renamed project to "Multi Agent Data Analysis with Crew AI".
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- [x] **Agent Upgrade**: Enhanced Validator and Insights agents for "Data Analysis as a Service" quality.
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- [x] **Token Optimization**: Reduced LLM costs by optimizing context injection.
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- [x] **Professional UI**: Redesigned `index.html` with a premium dark theme and visual scorecards.
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- [x] **Documentation**: Updated README, CHANGELOG, and USAGE guides.
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- [x] **Licensing**: Added MIT License and copyright headers.
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__pycache__/crew.cpython-310.pyc
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agents/__init__.py
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agents/__pycache__/cleaner.cpython-310.pyc
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agents/__pycache__/code_gen.cpython-310.pyc
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agents/__pycache__/insights.cpython-310.pyc
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agents/__pycache__/relation.cpython-310.pyc
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Binary files a/agents/__pycache__/relation.cpython-310.pyc and b/agents/__pycache__/relation.cpython-310.pyc differ
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agents/__pycache__/validator.cpython-310.pyc
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Binary files a/agents/__pycache__/validator.cpython-310.pyc and b/agents/__pycache__/validator.cpython-310.pyc differ
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agents/cleaner.py
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@@ -5,12 +5,15 @@
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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cleaner_agent = Agent(
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name="Data Cleaner",
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role="Clean dataset",
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backstory="A no-nonsense data mechanic who hates messy CSVs. You grew up debugging trash datasets and built a rep for turning corrupt data into clean, analysis-ready gold.",
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goal="List the data cleaning steps performed, one per line. Be concise. DO NOT use JSON. Example:\n- Removed duplicates\n- Filled missing values",
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llm=LLM(**get_llm_params()),
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verbose=True
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)
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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from tools.dataset_tools import DatasetTools
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cleaner_agent = Agent(
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name="Data Cleaner",
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role="Clean dataset",
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backstory="A no-nonsense data mechanic who hates messy CSVs. You grew up debugging trash datasets and built a rep for turning corrupt data into clean, analysis-ready gold.",
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goal="List the data cleaning steps performed, one per line. Be concise. DO NOT use JSON. Example:\n- Removed duplicates\n- Filled missing values",
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llm=LLM(**get_llm_params()),
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tools=[DatasetTools.read_dataset_head, DatasetTools.get_dataset_info],
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verbose=True
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)
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agents/code_gen.py
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# Multi Agent Data Analysis with Crew AI
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# Copyright (c) 2025 Sowmiyan S
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# Licensed under the MIT License
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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code_gen_agent = Agent(
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name="Code Generator",
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role="Write visualization code",
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goal="""Generate a COMPLETE, EXECUTABLE Python script. REQUIRED STRUCTURE:
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1. Import: import pandas as pd, import matplotlib.pyplot as plt, import seaborn as sns
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2. Load data: df = pd.read_csv('data/cleaned_csv.csv')
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3. Create figure: plt.figure(figsize=(10, 6))
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4. Generate plot using the column names from the relations task
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5. Save: plt.savefig('outputs/plot.png', bbox_inches='tight', dpi=300)
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6. Close: plt.close()
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RULES: NO plt.show(), NO dropping rows, NO removing outliers. Output ONLY the Python code in a ```python code block. NO explanations.""",
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backstory="You are a Data Visualization Expert who trusts the data. You believe that 'cleaning' often destroys valuable information. You NEVER delete rows or filter data. You are a master of Seaborn and Matplotlib, capable of generating any chart type (Bar, Box, Hist, Heatmap) with perfect syntax.",
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allow_delegation=False,
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llm=LLM(**get_llm_params()),
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verbose=True
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)
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# Multi Agent Data Analysis with Crew AI
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# Copyright (c) 2025 Sowmiyan S
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# Licensed under the MIT License
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agents/insights.py
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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insights_agent = Agent(
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name="Business Intelligence Analyst",
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role="Derive actionable insights from data analysis results",
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goal="Generate 5 key business insights from the data analysis. List them as numbered points. DO NOT use JSON. Example:\n1. First insight\n2. Second insight\n...",
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backstory="You are a seasoned BI Analyst. You don't need to see every row to understand the story. You look at the column names, the identified relationships, and the data quality report to infer the underlying trends and business implications.",
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llm=LLM(**get_llm_params()),
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verbose=True
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)
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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from tools.dataset_tools import DatasetTools
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insights_agent = Agent(
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name="Business Intelligence Analyst",
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role="Derive actionable insights from data analysis results",
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goal="Generate 5 key business insights from the data analysis. List them as numbered points. DO NOT use JSON. Example:\n1. First insight\n2. Second insight\n...",
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backstory="You are a seasoned BI Analyst. You don't need to see every row to understand the story. You look at the column names, the identified relationships, and the data quality report to infer the underlying trends and business implications.",
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llm=LLM(**get_llm_params()),
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tools=[DatasetTools.read_dataset_head, DatasetTools.get_dataset_info, DatasetTools.get_correlation_matrix],
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verbose=True
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)
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agents/relation.py
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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relation_agent = Agent(
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name="Analyst",
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role="Analyze dataset and identify key relationships",
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backstory="You are a precise Data Analyst. You strictly follow formatting instructions. You NEVER invent column names. You ONLY output the requested list.",
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allow_delegation=False,
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llm=LLM(**get_llm_params()),
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verbose=True
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)
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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from tools.dataset_tools import DatasetTools
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relation_agent = Agent(
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name="Analyst",
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role="Analyze dataset and identify key relationships",
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backstory="You are a precise Data Analyst. You strictly follow formatting instructions. You NEVER invent column names. You ONLY output the requested list.",
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allow_delegation=False,
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llm=LLM(**get_llm_params()),
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tools=[DatasetTools.read_dataset_head, DatasetTools.get_correlation_matrix],
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verbose=True
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)
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agents/validator.py
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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validator_agent = Agent(
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name="Dataset Validator",
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role="Validate dataset usability",
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goal="Validate if the dataset is suitable for analysis. Output plain text ONLY. DO NOT use JSON. Output:\nDecision: YES or NO\nReason: Brief explanation",
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backstory="A strict dataset gatekeeper. You don't sugarcoat garbage data. If a dataset sucks, you shut the whole pipeline down without hesitation.",
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llm=LLM(**get_llm_params()),
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verbose=True
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)
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from crewai import Agent, LLM
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from config.llm_config import get_llm_params
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from tools.dataset_tools import DatasetTools
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validator_agent = Agent(
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name="Dataset Validator",
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role="Validate dataset usability",
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goal="Validate if the dataset is suitable for analysis. Output plain text ONLY. DO NOT use JSON. Output:\nDecision: YES or NO\nReason: Brief explanation",
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backstory="A strict dataset gatekeeper. You don't sugarcoat garbage data. If a dataset sucks, you shut the whole pipeline down without hesitation.",
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llm=LLM(**get_llm_params()),
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tools=[DatasetTools.read_dataset_head, DatasetTools.get_dataset_info],
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verbose=True
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)
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app.py
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from crew import run_crew
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import numpy as np
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# Set page config
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st.set_page_config(
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page_title="Agentic Data Analyst",
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with open(file_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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-
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# Preview
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df = pd.read_csv(file_path)
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with st.expander("📊 Preview Dataset"):
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st.dataframe(df.head())
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st.markdown("---")
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st.markdown("### 🔄
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# Container for logs
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log_container = st.empty()
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# Run analysis
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with contextlib.redirect_stdout(StreamlitLogger()):
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try:
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with st.spinner("🤖 Agents are
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result = run_crew(str(file_path))
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if result:
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st.session_state[
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st.
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st.
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st.balloons()
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# Display Results
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st.markdown("## 📊 Analysis Results")
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# Dataset Preview
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st.markdown("### 🔍 Dataset Preview")
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st.dataframe(result['dataframe'].head(50))
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# Cleaning Steps
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st.markdown("### 🧹 Data Cleaning Steps")
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display_text_as_bullets(result['cleaning_steps'], "🔹")
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# Validation
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st.markdown("### ✅ Dataset Validation")
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val_text = result['validation']
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if "Decision:" in val_text:
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parts = val_text.split("Decision:")
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if len(parts) > 1:
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decision_part = parts[1].split("Reason:")[0].strip()
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reason_part = val_text.split("Reason:")[1].strip() if "Reason:" in val_text else ""
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color = "#10b981" if "YES" in decision_part.upper() else "#ef4444"
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st.markdown(f"""
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<div style="padding: 15px; border-left: 5px solid {color}; background: rgba(255,255,255,0.05); border-radius: 5px;">
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<h4 style="margin:0; color:{color}">Decision: {decision_part}</h4>
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<p style="margin-top:10px;">{reason_part}</p>
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</div>
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""", unsafe_allow_html=True)
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else:
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st.text(val_text)
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# Relations
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st.markdown("### 🔗 Column Relations")
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display_relations(result['relations'])
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# Visualizations
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render_visualizations(result['dataframe'], key_prefix="main")
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# Generated Code Info
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st.markdown("### ⚙️ Visualization Method")
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st.info(result.get('code', 'Automatic visualization generation'))
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# Insights
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st.markdown("### 💡 Key Insights")
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display_text_as_bullets(result['insights'], "✨")
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except Exception as e:
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st.error(f"❌ An error occurred: {str(e)}")
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st.exception(e)
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-
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# Display stored results if available
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elif 'analysis_result' in st.session_state:
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result = st.session_state['analysis_result']
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st.markdown("## 📊 Analysis Results (Cached)")
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st.dataframe(result['dataframe'].head(50))
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st.markdown("### 🧹 Data Cleaning Steps")
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display_text_as_bullets(result['cleaning_steps'], "🔹")
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-
|
| 450 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 451 |
|
| 452 |
if __name__ == "__main__":
|
| 453 |
main()
|
|
|
|
| 13 |
from crew import run_crew
|
| 14 |
import numpy as np
|
| 15 |
|
| 16 |
+
# Disable CrewAI Telemetry
|
| 17 |
+
os.environ["CREWAI_TELEMETRY_OPT_OUT"] = "true"
|
| 18 |
+
os.environ["OTEL_SDK_DISABLED"] = "true"
|
| 19 |
+
|
| 20 |
# Set page config
|
| 21 |
st.set_page_config(
|
| 22 |
page_title="Agentic Data Analyst",
|
|
|
|
| 318 |
with open(file_path, "wb") as f:
|
| 319 |
f.write(uploaded_file.getbuffer())
|
| 320 |
|
| 321 |
+
# Check if we need to run analysis (new file or no result yet)
|
| 322 |
+
current_file_key = f"analysis_done_{uploaded_file.name}"
|
|
|
|
|
|
|
|
|
|
|
|
|
| 323 |
|
| 324 |
+
if current_file_key not in st.session_state:
|
| 325 |
+
st.success(f"✅ File uploaded successfully: {uploaded_file.name}")
|
| 326 |
+
|
| 327 |
+
# Preview
|
| 328 |
+
df = pd.read_csv(file_path)
|
| 329 |
+
with st.expander("📊 Preview Dataset", expanded=True):
|
| 330 |
+
st.dataframe(df.head())
|
| 331 |
+
|
| 332 |
st.markdown("---")
|
| 333 |
+
st.markdown("### 🔄 Auto-Starting Analysis...")
|
| 334 |
|
| 335 |
# Container for logs
|
| 336 |
log_container = st.empty()
|
|
|
|
| 362 |
# Run analysis
|
| 363 |
with contextlib.redirect_stdout(StreamlitLogger()):
|
| 364 |
try:
|
| 365 |
+
with st.spinner("🤖 Agents are analyzing your data..."):
|
| 366 |
result = run_crew(str(file_path))
|
| 367 |
|
| 368 |
if result:
|
| 369 |
+
st.session_state[current_file_key] = result
|
| 370 |
+
st.session_state['current_active_file'] = current_file_key
|
| 371 |
+
st.rerun()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 372 |
|
| 373 |
except Exception as e:
|
| 374 |
st.error(f"❌ An error occurred: {str(e)}")
|
| 375 |
st.exception(e)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 376 |
|
| 377 |
+
# Display Results if available
|
| 378 |
+
elif current_file_key in st.session_state:
|
| 379 |
+
result = st.session_state[current_file_key]
|
| 380 |
+
|
| 381 |
+
st.success("### ✅ Analysis Complete!")
|
| 382 |
+
|
| 383 |
+
# Display Results
|
| 384 |
+
st.markdown("## 📊 Analysis Results")
|
| 385 |
+
|
| 386 |
+
# Dataset Preview
|
| 387 |
+
st.markdown("### 🔍 Dataset Preview")
|
| 388 |
+
st.dataframe(result['dataframe'].head(50))
|
| 389 |
+
|
| 390 |
+
# Cleaning Steps
|
| 391 |
+
st.markdown("### 🧹 Data Cleaning Steps")
|
| 392 |
+
display_text_as_bullets(result['cleaning_steps'], "🔹")
|
| 393 |
+
|
| 394 |
+
# Validation
|
| 395 |
+
st.markdown("### ✅ Dataset Validation")
|
| 396 |
+
val_text = result['validation']
|
| 397 |
+
if "Decision:" in val_text:
|
| 398 |
+
parts = val_text.split("Decision:")
|
| 399 |
+
if len(parts) > 1:
|
| 400 |
+
decision_part = parts[1].split("Reason:")[0].strip()
|
| 401 |
+
reason_part = val_text.split("Reason:")[1].strip() if "Reason:" in val_text else ""
|
| 402 |
+
|
| 403 |
+
color = "#10b981" if "YES" in decision_part.upper() else "#ef4444"
|
| 404 |
+
st.markdown(f"""
|
| 405 |
+
<div style="padding: 15px; border-left: 5px solid {color}; background: rgba(255,255,255,0.05); border-radius: 5px;">
|
| 406 |
+
<h4 style="margin:0; color:{color}">Decision: {decision_part}</h4>
|
| 407 |
+
<p style="margin-top:10px;">{reason_part}</p>
|
| 408 |
+
</div>
|
| 409 |
+
""", unsafe_allow_html=True)
|
| 410 |
+
else:
|
| 411 |
+
st.text(val_text)
|
| 412 |
+
|
| 413 |
+
# Relations
|
| 414 |
+
st.markdown("### 🔗 Column Relations")
|
| 415 |
+
display_relations(result['relations'])
|
| 416 |
+
|
| 417 |
+
# Visualizations
|
| 418 |
+
render_visualizations(result['dataframe'], key_prefix="main")
|
| 419 |
|
| 420 |
+
# Generated Code Info
|
| 421 |
+
st.markdown("### ⚙️ Visualization Method")
|
| 422 |
+
st.info(result.get('code', 'Automatic visualization generation'))
|
| 423 |
+
|
| 424 |
+
# Insights
|
| 425 |
+
st.markdown("### 💡 Key Insights")
|
| 426 |
+
display_text_as_bullets(result['insights'], "✨")
|
| 427 |
|
| 428 |
if __name__ == "__main__":
|
| 429 |
main()
|
blog.txt
DELETED
|
@@ -1,78 +0,0 @@
|
|
| 1 |
-
# 🚀 Revolutionizing Data Science: Building a Multi-Agent Data Analysis System with CrewAI
|
| 2 |
-
|
| 3 |
-
*By Sowmiyan S*
|
| 4 |
-
|
| 5 |
-
In the world of data science, the journey from raw data to actionable insights is often long, tedious, and prone to human error. We spend 80% of our time cleaning data and only 20% analyzing it. But what if we could flip that script? What if we could hire a team of expert AI agents to handle the grunt work for us?
|
| 6 |
-
|
| 7 |
-
Enter **Multi-Agent Data Analysis with CrewAI** — a project designed to automate the entire data analysis pipeline, from cleaning to visualization to strategic insight generation.
|
| 8 |
-
|
| 9 |
-
In this post, I'll take you behind the scenes of this open-source project, exploring its workflow, the technology stack, and why this "Data Analysis as a Service" model is the future of business intelligence.
|
| 10 |
-
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
## 🤖 The Concept: A Digital Data Team
|
| 14 |
-
|
| 15 |
-
Imagine having a team of specialized data professionals sitting on your laptop, ready to work 24/7. That's exactly what this system builds. Instead of a single AI trying to do everything (and often hallucinating), we break the process down into distinct roles handled by specialized agents.
|
| 16 |
-
|
| 17 |
-
This project leverages **CrewAI** to orchestrate a swarm of agents, each with a specific personality, goal, and set of tools.
|
| 18 |
-
|
| 19 |
-
## 🔄 The Workflow: How It Works
|
| 20 |
-
|
| 21 |
-
The magic happens in a sequential pipeline where the output of one agent becomes the input for the next. Here is the step-by-step workflow:
|
| 22 |
-
|
| 23 |
-
### 1. The Data Mechanic (Cleaner Agent)
|
| 24 |
-
**Role:** Data Cleaner
|
| 25 |
-
**Mission:** "Garbage in, garbage out" is the enemy. This agent takes the raw CSV file and performs rigorous cleaning. It removes duplicates, handles missing values (using mean for numeric and mode for categorical data), and standardizes formats. It doesn't just clean; it documents every step so you have a full audit trail.
|
| 26 |
-
|
| 27 |
-
### 2. The Gatekeeper (Validator Agent)
|
| 28 |
-
**Role:** Dataset Validator
|
| 29 |
-
**Mission:** Before any analysis begins, this agent performs a quality check. Is the dataset large enough? Are there too many missing values? If the data isn't up to par, the Validator has the power to stop the entire pipeline, saving you from wasting compute resources on bad data.
|
| 30 |
-
|
| 31 |
-
### 3. The Visionary (Relationship Analyst)
|
| 32 |
-
**Role:** Relationship Analyst
|
| 33 |
-
**Mission:** This agent looks at the clean data and identifies meaningful relationships between columns. It decides *how* to visualize the data—suggesting scatter plots for correlations, bar charts for categorical comparisons, or heatmaps for complex patterns. It acts as the architect of the visual story.
|
| 34 |
-
|
| 35 |
-
### 4. The Artist (Code Generator Agent)
|
| 36 |
-
**Role:** Code Generator
|
| 37 |
-
**Mission:** Taking the instructions from the Analyst, this agent writes production-ready Python code using **Matplotlib** and **Seaborn**. It ensures the code is bug-free and aesthetically pleasing, generating the actual charts that will appear in the final report.
|
| 38 |
-
|
| 39 |
-
### 5. The Strategist (Business Intelligence Analyst)
|
| 40 |
-
**Role:** BI Analyst
|
| 41 |
-
**Mission:** Finally, this agent looks at the aggregate findings—the cleaning logs, the validation report, and the discovered relationships—to synthesize high-level business insights. It answers the "So what?" question, turning charts into strategy.
|
| 42 |
-
|
| 43 |
-
---
|
| 44 |
-
|
| 45 |
-
## 🛠️ Under the Hood: The Tech Stack
|
| 46 |
-
|
| 47 |
-
This project is built on a robust, modern stack designed for flexibility and performance:
|
| 48 |
-
|
| 49 |
-
* **CrewAI:** The backbone framework for orchestrating the agent swarm.
|
| 50 |
-
* **Pandas:** The industry standard for data manipulation and analysis.
|
| 51 |
-
* **Matplotlib & Seaborn:** For generating high-quality, publication-ready visualizations.
|
| 52 |
-
* **LLM Agnostic:** The system is designed to work with various LLM providers. You can power it with **Groq** for speed, **OpenAI** for precision, or **Ollama** for local privacy.
|
| 53 |
-
|
| 54 |
-
## 🌟 Why This Matters: The Advantages
|
| 55 |
-
|
| 56 |
-
### 1. Consistency & Speed
|
| 57 |
-
Humans get tired; agents don't. This system applies the same rigorous standards to every single dataset, every single time, in a fraction of the time it takes a human analyst.
|
| 58 |
-
|
| 59 |
-
### 2. "No-Code" Analysis
|
| 60 |
-
For non-technical users, this is a game-changer. You don't need to know Python or SQL. Just drop a CSV file into the folder, run the script, and get a professional HTML report.
|
| 61 |
-
|
| 62 |
-
### 3. Modular & Scalable
|
| 63 |
-
Because it's built on agents, it's incredibly easy to extend. Need a new type of analysis? Just add a new agent to the crew. The modular design means the system grows with your needs.
|
| 64 |
-
|
| 65 |
-
## 💡 Real-World Applications
|
| 66 |
-
|
| 67 |
-
* **Rapid Prototyping:** Data scientists can use this to get a "first look" at a new dataset in minutes.
|
| 68 |
-
* **SME Business Intelligence:** Small businesses without a data team can get professional-grade insights from their sales or customer data.
|
| 69 |
-
* **Data Quality Audits:** Use the Validator agent as a standalone tool to screen incoming data streams.
|
| 70 |
-
* **Education:** A perfect tool for students to learn how a professional data analysis workflow is structured.
|
| 71 |
-
|
| 72 |
-
## 🚀 Conclusion
|
| 73 |
-
|
| 74 |
-
The "Multi-Agent Data Analysis with CrewAI" project isn't just a tool; it's a glimpse into the future of work. By combining the reasoning capabilities of LLMs with the structural power of agentic workflows, we can automate complex cognitive tasks that were previously the exclusive domain of human experts.
|
| 75 |
-
|
| 76 |
-
Ready to try it out? The project is open-source and available now. Clone the repo, drop in your data, and watch your digital data team go to work!
|
| 77 |
-
|
| 78 |
-
*Happy Coding!*
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
config/__pycache__/__init__.cpython-310.pyc
CHANGED
|
Binary files a/config/__pycache__/__init__.cpython-310.pyc and b/config/__pycache__/__init__.cpython-310.pyc differ
|
|
|
crew.py
CHANGED
|
@@ -13,6 +13,10 @@ from dotenv import load_dotenv
|
|
| 13 |
|
| 14 |
load_dotenv()
|
| 15 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 16 |
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
| 17 |
logging.getLogger("opentelemetry").setLevel(logging.ERROR)
|
| 18 |
|
|
|
|
| 13 |
|
| 14 |
load_dotenv()
|
| 15 |
|
| 16 |
+
# Disable CrewAI Telemetry to prevent timeouts
|
| 17 |
+
os.environ["CREWAI_TELEMETRY_OPT_OUT"] = "true"
|
| 18 |
+
os.environ["OTEL_SDK_DISABLED"] = "true"
|
| 19 |
+
|
| 20 |
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
| 21 |
logging.getLogger("opentelemetry").setLevel(logging.ERROR)
|
| 22 |
|
data/sugar.csv
ADDED
|
@@ -0,0 +1,769 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
Age,Pregnancies,Glucose,BloodPressure (mg/dL),SkinThickness,Insulin,BMI,DiabetesPedigreeFunction
|
| 2 |
+
50,6,148,72,35,0,33.6,627
|
| 3 |
+
31,1,85,66,29,0,26.6,351
|
| 4 |
+
32,8,183,64,0,0,23.3,672
|
| 5 |
+
21,1,1,66,23,94,28.1,167
|
| 6 |
+
33,0,137,40,35,168,43.1,2.288
|
| 7 |
+
30,5,116,74,0,0,25.6,201
|
| 8 |
+
26,3,78,50,32,88,31,248
|
| 9 |
+
29,10,115,0,0,0,35.3,134
|
| 10 |
+
53,2,197,70,45,543,30.5,158
|
| 11 |
+
54,8,125,96,0,0,0,232
|
| 12 |
+
30,4,110,92,0,0,37.6,191
|
| 13 |
+
34,10,168,74,0,0,38,537
|
| 14 |
+
57,10,139,80,0,0,27.1,1.441
|
| 15 |
+
59,1,189,60,23,846,30.1,398
|
| 16 |
+
51,5,166,72,19,175,25.8,587
|
| 17 |
+
32,7,100,0,0,0,30,484
|
| 18 |
+
31,0,118,84,47,230,45.8,551
|
| 19 |
+
31,7,107,74,0,0,29.6,254
|
| 20 |
+
33,1,103,30,38,83,43.3,183
|
| 21 |
+
32,1,115,70,30,96,34.6,529
|
| 22 |
+
27,3,126,88,41,235,39.3,704
|
| 23 |
+
50,8,99,84,0,0,35.4,388
|
| 24 |
+
41,7,196,90,0,0,39.8,451
|
| 25 |
+
29,9,119,80,35,0,29,263
|
| 26 |
+
51,11,143,94,33,146,36.6,254
|
| 27 |
+
41,10,125,70,26,115,31.1,205
|
| 28 |
+
43,7,147,76,0,0,39.4,257
|
| 29 |
+
22,1,97,66,15,140,23.2,487
|
| 30 |
+
57,13,145,82,19,110,22.2,245
|
| 31 |
+
38,5,117,92,0,0,34.1,337
|
| 32 |
+
60,5,109,75,26,0,36,546
|
| 33 |
+
28,3,158,76,36,245,31.6,851
|
| 34 |
+
22,3,88,58,11,54,24.8,267
|
| 35 |
+
28,6,92,92,0,0,19.9,188
|
| 36 |
+
45,10,122,78,31,0,27.6,512
|
| 37 |
+
33,4,103,60,33,192,24,966
|
| 38 |
+
35,11,138,76,0,0,33.2,0.42
|
| 39 |
+
46,9,102,76,37,0,32.9,665
|
| 40 |
+
27,2,90,68,42,0,38.2,503
|
| 41 |
+
56,4,111,72,47,207,37.1,1.39
|
| 42 |
+
26,3,180,64,25,70,34,271
|
| 43 |
+
37,7,133,84,0,0,40.2,696
|
| 44 |
+
48,7,106,92,18,0,22.7,235
|
| 45 |
+
54,9,171,110,24,240,45.4,721
|
| 46 |
+
40,7,159,64,0,0,27.4,294
|
| 47 |
+
25,0,180,66,39,0,42,1.893
|
| 48 |
+
29,1,146,56,0,0,29.7,564
|
| 49 |
+
22,2,71,70,27,0,28,586
|
| 50 |
+
31,7,103,66,32,0,39.1,344
|
| 51 |
+
24,7,105,0,0,0,0,305
|
| 52 |
+
22,1,103,80,11,82,19.4,491
|
| 53 |
+
26,1,101,50,15,36,24.2,526
|
| 54 |
+
30,5,88,66,21,23,24.4,342
|
| 55 |
+
58,8,176,90,34,300,33.7,467
|
| 56 |
+
42,7,150,66,42,342,34.7,718
|
| 57 |
+
21,1,73,50,10,0,23,248
|
| 58 |
+
41,7,187,68,39,304,37.7,254
|
| 59 |
+
31,0,100,88,60,110,46.8,962
|
| 60 |
+
44,0,146,82,0,0,40.5,1.781
|
| 61 |
+
22,0,105,64,41,142,41.5,173
|
| 62 |
+
21,2,84,0,0,0,0,304
|
| 63 |
+
39,8,133,72,0,0,32.9,0.27
|
| 64 |
+
36,5,44,62,0,0,25,587
|
| 65 |
+
24,2,141,58,34,128,25.4,699
|
| 66 |
+
42,7,114,66,0,0,32.8,258
|
| 67 |
+
32,5,99,74,27,0,29,203
|
| 68 |
+
38,0,109,88,30,0,32.5,855
|
| 69 |
+
54,2,109,92,0,0,42.7,845
|
| 70 |
+
25,1,95,66,13,38,19.6,334
|
| 71 |
+
27,4,146,85,27,100,28.9,189
|
| 72 |
+
28,2,100,66,20,90,32.9,867
|
| 73 |
+
26,5,139,64,35,140,28.6,411
|
| 74 |
+
42,13,126,90,0,0,43.4,583
|
| 75 |
+
23,4,129,86,20,270,35.1,231
|
| 76 |
+
22,1,79,75,30,0,32,396
|
| 77 |
+
22,1,0,48,20,0,24.7,0.14
|
| 78 |
+
41,7,62,78,0,0,32.6,391
|
| 79 |
+
27,5,95,72,33,0,37.7,0.37
|
| 80 |
+
26,0,131,0,0,0,43.2,0.27
|
| 81 |
+
24,2,112,66,22,0,25,307
|
| 82 |
+
22,3,113,44,13,0,22.4,0.14
|
| 83 |
+
22,2,74,0,0,0,0,102
|
| 84 |
+
36,7,83,78,26,71,29.3,767
|
| 85 |
+
22,0,101,65,28,0,24.6,237
|
| 86 |
+
37,5,137,108,0,0,48.8,227
|
| 87 |
+
27,2,110,74,29,125,32.4,698
|
| 88 |
+
45,13,106,72,54,0,36.6,178
|
| 89 |
+
26,2,100,68,25,71,38.5,324
|
| 90 |
+
43,15,136,70,32,110,37.1,153
|
| 91 |
+
24,1,107,68,19,0,26.5,165
|
| 92 |
+
21,1,80,55,0,0,19.1,258
|
| 93 |
+
34,4,123,80,15,176,32,443
|
| 94 |
+
42,7,81,78,40,48,46.7,261
|
| 95 |
+
60,4,134,72,0,0,23.8,277
|
| 96 |
+
21,2,142,82,18,64,24.7,761
|
| 97 |
+
40,6,144,72,27,228,33.9,255
|
| 98 |
+
24,2,92,62,28,0,31.6,0.13
|
| 99 |
+
22,1,71,48,18,76,20.4,323
|
| 100 |
+
23,6,93,50,30,64,28.7,356
|
| 101 |
+
31,1,122,90,51,220,49.7,325
|
| 102 |
+
33,1,163,72,0,0,39,1.222
|
| 103 |
+
22,1,151,60,0,0,26.1,179
|
| 104 |
+
21,0,125,96,0,0,22.5,262
|
| 105 |
+
24,1,81,72,18,40,26.6,283
|
| 106 |
+
27,2,85,65,0,0,39.6,0.93
|
| 107 |
+
21,1,126,56,29,152,28.7,801
|
| 108 |
+
27,1,96,122,0,0,22.4,207
|
| 109 |
+
37,4,144,58,28,140,29.5,287
|
| 110 |
+
25,3,83,58,31,18,34.3,336
|
| 111 |
+
24,0,95,85,25,36,37.4,247
|
| 112 |
+
24,3,171,72,33,135,33.3,199
|
| 113 |
+
46,8,155,62,26,495,34,543
|
| 114 |
+
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62,0,105,84,0,0,27.9,741
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23,0,179,90,27,0,44.1,686
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32,9,164,84,21,0,30.8,831
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33,6,119,50,22,176,27.1,1.318
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29,2,146,76,35,194,38.2,329
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34,9,124,70,33,402,35.4,282
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24,2,90,80,14,55,24.4,249
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25,0,86,68,32,0,35.8,238
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44,12,92,62,7,258,27.6,926
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21,1,113,64,35,0,33.6,543
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30,3,111,56,39,0,30.1,557
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24,1,193,50,16,375,25.9,655
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51,11,155,76,28,150,33.3,1.353
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34,3,191,68,15,130,30.9,299
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63,3,142,80,15,0,32.4,0.2
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43,5,96,74,18,67,33.6,997
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38,10,101,86,37,0,45.6,1.136
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40,3,122,78,0,0,23,254
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52,13,106,70,0,0,34.2,251
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57,5,114,74,0,0,24.9,744
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22,2,108,62,10,278,25.3,881
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28,0,146,70,0,0,37.9,334
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39,10,129,76,28,122,35.9,0.28
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37,7,133,88,15,155,32.4,262
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47,7,161,86,0,0,30.4,165
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52,2,108,80,0,0,27,259
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51,7,136,74,26,135,26,647
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34,5,155,84,44,545,38.7,619
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29,1,119,86,39,220,45.6,808
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26,4,96,56,17,49,20.8,0.34
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33,5,108,72,43,75,36.1,263
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21,0,78,88,29,40,36.9,434
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25,0,107,62,30,74,36.6,757
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31,2,128,78,37,182,43.3,1.224
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24,1,128,48,45,194,40.5,613
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65,0,161,50,0,0,21.9,254
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28,6,151,62,31,120,35.5,692
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29,2,146,70,38,360,28,337
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24,0,126,84,29,215,30.7,0.52
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46,14,100,78,25,184,36.6,412
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58,8,112,72,0,0,23.6,0.84
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30,0,167,0,0,0,32.3,839
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25,2,144,58,33,135,31.6,422
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35,5,77,82,41,42,35.8,156
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28,5,115,98,0,0,52.9,209
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37,3,150,76,0,0,21,207
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29,2,120,76,37,105,39.7,215
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47,10,161,68,23,132,25.5,326
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21,0,137,68,14,148,24.8,143
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25,0,128,68,19,180,30.5,1.391
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30,2,124,68,28,205,32.9,875
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41,6,80,66,30,0,26.2,313
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22,0,106,70,37,148,39.4,605
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27,2,155,74,17,96,26.6,433
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25,3,113,50,10,85,29.5,626
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43,7,109,80,31,0,35.9,1.127
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26,2,112,68,22,94,34.1,315
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30,3,99,80,11,64,19.3,284
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29,3,182,74,0,0,30.5,345
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28,3,115,66,39,140,38.1,0.15
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59,6,194,78,0,0,23.5,129
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31,4,129,60,12,231,27.5,527
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25,3,112,74,30,0,31.6,197
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36,0,124,70,20,0,27.4,254
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43,13,152,90,33,29,26.8,731
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21,2,112,75,32,0,35.7,148
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24,1,157,72,21,168,25.6,123
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30,1,122,64,32,156,35.1,692
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37,10,179,70,0,0,35.1,0.2
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23,2,102,86,36,120,45.5,127
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37,6,105,70,32,68,30.8,122
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46,8,118,72,19,0,23.1,1.476
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25,2,87,58,16,52,32.7,166
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41,1,180,0,0,0,43.3,282
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44,12,106,80,0,0,23.6,137
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22,1,95,60,18,58,23.9,0.26
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26,0,165,76,43,255,47.9,259
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44,0,117,0,0,0,33.8,932
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44,5,115,76,0,0,31.2,343
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33,9,152,78,34,171,34.2,893
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41,7,178,84,0,0,39.9,331
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22,1,130,70,13,105,25.9,472
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36,1,95,74,21,73,25.9,673
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22,1,0,68,35,0,32,389
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33,5,122,86,0,0,34.7,0.29
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57,8,95,72,0,0,36.8,485
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49,8,126,88,36,108,38.5,349
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22,1,139,46,19,83,28.7,654
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23,3,116,0,0,0,23.5,187
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26,3,99,62,19,74,21.8,279
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37,5,0,80,32,0,41,346
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29,4,92,80,0,0,42.2,237
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30,4,137,84,0,0,31.2,252
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46,3,61,82,28,0,34.4,243
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24,1,90,62,12,43,27.2,0.58
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21,3,90,78,0,0,42.7,559
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49,9,165,88,0,0,30.4,302
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28,1,125,50,40,167,33.3,962
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44,13,129,0,30,0,39.9,569
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48,12,88,74,40,54,35.3,378
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29,1,196,76,36,249,36.5,875
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29,5,189,64,33,325,31.2,583
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63,5,158,70,0,0,29.8,207
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65,5,103,108,37,0,39.2,305
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67,4,146,78,0,0,38.5,0.52
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30,4,147,74,25,293,34.9,385
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30,5,99,54,28,83,34,499
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29,6,124,72,0,0,27.6,368
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21,0,101,64,17,0,21,252
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22,3,81,86,16,66,27.5,306
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45,1,133,102,28,140,32.8,234
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25,3,173,82,48,465,38.4,2.137
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21,0,118,64,23,89,0,1.731
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21,0,84,64,22,66,35.8,545
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25,2,105,58,40,94,34.9,225
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28,2,122,52,43,158,36.2,816
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58,12,140,82,43,325,39.2,528
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22,0,98,82,15,84,25.2,299
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22,1,87,60,37,75,37.2,509
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32,4,156,75,0,0,48.3,238
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35,0,93,100,39,72,43.4,1.021
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24,1,107,72,30,82,30.8,821
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22,0,105,68,22,0,20,236
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21,1,109,60,8,182,25.4,947
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25,1,90,62,18,59,25.1,1.268
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25,1,125,70,24,110,24.3,221
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24,1,119,54,13,50,22.3,205
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35,5,116,74,29,0,32.3,0.66
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45,8,105,100,36,0,43.3,239
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58,5,144,82,26,285,32,452
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28,3,100,68,23,81,31.6,949
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42,1,100,66,29,196,32,444
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27,5,166,76,0,0,45.7,0.34
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21,1,131,64,14,415,23.7,389
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37,4,116,72,12,87,22.1,463
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31,4,158,78,0,0,32.9,803
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25,2,127,58,24,275,27.7,1.6
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39,3,96,56,34,115,24.7,944
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22,0,131,66,40,0,34.3,196
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25,3,82,70,0,0,21.1,389
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25,3,193,70,31,0,34.9,241
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31,4,95,64,0,0,32,161
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55,6,137,61,0,0,24.2,151
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35,5,136,84,41,88,35,286
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38,9,72,78,25,0,31.6,0.28
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41,5,168,64,0,0,32.9,135
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26,2,123,48,32,165,42.1,0.52
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46,4,115,72,0,0,28.9,376
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25,0,101,62,0,0,21.9,336
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39,8,197,74,0,0,25.9,1.191
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28,1,172,68,49,579,42.4,702
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28,6,102,90,39,0,35.7,674
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25,1,112,72,30,176,34.4,528
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22,1,143,84,23,310,42.4,1.076
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21,1,143,74,22,61,26.2,256
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21,0,138,60,35,167,34.6,534
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22,3,173,84,33,474,35.7,258
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22,1,97,68,21,0,27.2,1.095
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37,4,144,82,32,0,38.5,554
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27,1,83,68,0,0,18.2,624
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28,3,129,64,29,115,26.4,219
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26,1,119,88,41,170,45.3,507
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21,2,94,68,18,76,26,561
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21,0,102,64,46,78,40.6,496
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21,2,115,64,22,0,30.8,421
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36,8,151,78,32,210,42.9,516
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31,4,184,78,39,277,37,264
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25,0,94,0,0,0,0,256
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38,1,181,64,30,180,34.1,328
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26,0,135,94,46,145,40.6,284
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43,1,95,82,25,180,35,233
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23,2,99,0,0,0,22.2,108
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38,3,89,74,16,85,30.4,551
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22,1,80,74,11,60,30,527
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29,2,139,75,0,0,25.6,167
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36,1,90,68,8,0,24.5,1.138
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29,0,141,0,0,0,42.4,205
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41,12,140,85,33,0,37.4,244
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28,5,147,75,0,0,29.9,434
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21,1,97,70,15,0,18.2,147
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31,6,107,88,0,0,36.8,727
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41,0,189,104,25,0,34.3,435
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22,2,83,66,23,50,32.2,497
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24,4,117,64,27,120,33.2,0.23
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33,8,108,70,0,0,30.5,955
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30,4,117,62,12,0,29.7,0.38
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25,0,180,78,63,14,59.4,2.42
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28,1,100,72,12,70,25.3,658
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26,0,95,80,45,92,36.5,0.33
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22,0,104,64,37,64,33.6,0.51
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26,0,120,74,18,63,30.5,285
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23,1,82,64,13,95,21.2,415
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23,2,134,70,0,0,28.9,542
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25,0,91,68,32,210,39.9,381
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72,2,119,0,0,0,19.6,832
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24,2,100,54,28,105,37.8,498
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38,14,175,62,30,0,33.6,212
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62,1,135,54,0,0,26.7,687
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24,5,86,68,28,71,30.2,364
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51,10,148,84,48,237,37.6,1.001
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81,9,134,74,33,60,25.9,0.46
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48,9,120,72,22,56,20.8,733
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26,1,71,62,0,0,21.8,416
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39,8,74,70,40,49,35.3,705
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37,5,88,78,30,0,27.6,258
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34,10,115,98,0,0,24,1.022
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21,0,124,56,13,105,21.8,452
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22,0,74,52,10,36,27.8,269
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25,0,97,64,36,100,36.8,0.6
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38,8,120,0,0,0,30,183
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27,6,154,78,41,140,46.1,571
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28,1,144,82,40,0,41.3,607
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22,0,137,70,38,0,33.2,0.17
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22,0,119,66,27,0,38.8,259
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50,7,136,90,0,0,29.9,0.21
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24,4,114,64,0,0,28.9,126
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59,0,137,84,27,0,27.3,231
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29,2,105,80,45,191,33.7,711
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31,7,114,76,17,110,23.8,466
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39,8,126,74,38,75,25.9,162
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63,4,132,86,31,0,28,419
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35,3,158,70,30,328,35.5,344
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29,0,123,88,37,0,35.2,197
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28,4,85,58,22,49,27.8,306
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23,0,84,82,31,125,38.2,233
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31,0,145,0,0,0,44.2,0.63
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24,0,135,68,42,250,42.3,365
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21,1,139,62,41,480,40.7,536
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58,0,173,78,32,265,46.5,1.159
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28,4,99,72,17,0,25.6,294
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67,8,194,80,0,0,26.1,551
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24,2,83,65,28,66,36.8,629
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42,2,89,90,30,0,33.5,292
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33,4,99,68,38,0,32.8,145
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45,4,125,70,18,122,28.9,1.144
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22,3,80,0,0,0,0,174
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66,6,166,74,0,0,26.6,304
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30,5,110,68,0,0,26,292
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25,2,81,72,15,76,30.1,547
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55,7,195,70,33,145,25.1,163
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39,6,154,74,32,193,29.3,839
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21,2,117,90,19,71,25.2,313
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28,3,84,72,32,0,37.2,267
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41,6,0,68,41,0,39,727
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41,7,94,64,25,79,33.3,738
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40,3,96,78,39,0,37.3,238
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38,10,75,82,0,0,33.3,263
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35,0,180,90,26,90,36.5,314
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21,1,130,60,23,170,28.6,692
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21,2,84,50,23,76,30.4,968
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64,8,120,78,0,0,25,409
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46,12,84,72,31,0,29.7,297
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21,0,139,62,17,210,22.1,207
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58,9,91,68,0,0,24.2,0.2
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22,2,91,62,0,0,27.3,525
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24,3,99,54,19,86,25.6,154
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28,3,163,70,18,105,31.6,268
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53,9,145,88,34,165,30.3,771
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51,7,125,86,0,0,37.6,304
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41,13,76,60,0,0,32.8,0.18
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60,6,129,90,7,326,19.6,582
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25,2,68,70,32,66,25,187
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26,3,124,80,33,130,33.2,305
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26,6,114,0,0,0,0,189
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45,9,130,70,0,0,34.2,652
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24,3,125,58,0,0,31.6,151
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21,3,87,60,18,0,21.8,444
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21,1,97,64,19,82,18.2,299
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24,3,116,74,15,105,26.3,107
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22,0,117,66,31,188,30.8,493
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31,0,111,65,0,0,24.6,0.66
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22,2,122,60,18,106,29.8,717
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24,0,107,76,0,0,45.3,686
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29,1,86,66,52,65,41.3,917
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31,6,91,0,0,0,29.8,501
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24,1,77,56,30,56,33.3,1.251
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23,4,132,0,0,0,32.9,302
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46,0,105,90,0,0,29.6,197
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67,0,57,60,0,0,21.7,735
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23,0,127,80,37,210,36.3,804
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32,3,129,92,49,155,36.4,968
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43,8,100,74,40,215,39.4,661
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27,3,128,72,25,190,32.4,549
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56,10,90,85,32,0,34.9,825
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25,4,84,90,23,56,39.5,159
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29,1,88,78,29,76,32,365
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37,8,186,90,35,225,34.5,423
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53,5,187,76,27,207,43.6,1.034
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28,4,131,68,21,166,33.1,0.16
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50,1,164,82,43,67,32.8,341
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37,4,189,110,31,0,28.5,0.68
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21,1,116,70,28,0,27.4,204
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25,3,84,68,30,106,31.9,591
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66,6,114,88,0,0,27.8,247
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23,1,88,62,24,44,29.9,422
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28,1,84,64,23,115,36.9,471
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37,7,124,70,33,215,25.5,161
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| 558 |
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30,1,97,70,40,0,38.1,218
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58,8,110,76,0,0,27.8,237
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42,11,103,68,40,0,46.2,126
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35,11,85,74,0,0,30.1,0.3
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54,6,125,76,0,0,33.8,121
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| 563 |
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28,0,198,66,32,274,41.3,502
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24,1,87,68,34,77,37.6,401
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32,6,99,60,19,54,26.9,497
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27,0,91,80,0,0,32.4,601
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22,2,95,54,14,88,26.1,748
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21,1,99,72,30,18,38.6,412
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46,6,92,62,32,126,32,85
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37,4,154,72,29,126,31.3,338
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33,0,121,66,30,165,34.3,203
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39,3,78,70,0,0,32.5,0.27
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21,2,130,96,0,0,22.6,268
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22,3,111,58,31,44,29.5,0.43
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22,2,98,60,17,120,34.7,198
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23,1,143,86,30,330,30.1,892
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25,1,119,44,47,63,35.5,0.28
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35,6,108,44,20,130,24,813
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21,2,118,80,0,0,42.9,693
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36,10,133,68,0,0,27,245
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62,2,197,70,99,0,34.7,575
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21,0,151,90,46,0,42.1,371
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27,6,109,60,27,0,25,206
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62,12,121,78,17,0,26.5,259
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42,8,100,76,0,0,38.7,0.19
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52,8,124,76,24,600,28.7,687
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22,1,93,56,11,0,22.5,417
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41,8,143,66,0,0,34.9,129
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29,6,103,66,0,0,24.3,249
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52,3,176,86,27,156,33.3,1.154
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25,0,73,0,0,0,21.1,342
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45,11,111,84,40,0,46.8,925
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24,2,112,78,50,140,39.4,175
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44,3,132,80,0,0,34.4,402
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25,2,82,52,22,115,28.5,1.699
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34,6,123,72,45,230,33.6,733
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22,0,188,82,14,185,32,682
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46,0,67,76,0,0,45.3,194
|
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21,1,89,24,19,25,27.8,559
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38,1,173,74,0,0,36.8,88
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26,1,109,38,18,120,23.1,407
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24,1,108,88,19,0,27.1,0.4
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28,6,96,0,0,0,23.7,0.19
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30,1,124,74,36,0,27.8,0.1
|
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54,7,150,78,29,126,35.2,692
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36,4,183,0,0,0,28.4,212
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21,1,124,60,32,0,35.8,514
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22,1,181,78,42,293,40,1.258
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25,1,92,62,25,41,19.5,482
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27,0,152,82,39,272,41.5,0.27
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23,1,111,62,13,182,24,138
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24,3,106,54,21,158,30.9,292
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36,3,174,58,22,194,32.9,593
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40,7,168,88,42,321,38.2,787
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26,6,105,80,28,0,32.5,878
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50,11,138,74,26,144,36.1,557
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27,3,106,72,0,0,25.8,207
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30,6,117,96,0,0,28.7,157
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23,2,68,62,13,15,20.1,257
|
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50,9,112,82,24,0,28.2,1.282
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24,0,119,0,0,0,32.4,141
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28,2,112,86,42,160,38.4,246
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28,2,92,76,20,0,24.2,1.698
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45,6,183,94,0,0,40.8,1.461
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21,0,94,70,27,115,43.5,347
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21,2,108,64,0,0,30.8,158
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| 627 |
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29,4,90,88,47,54,37.7,362
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21,0,125,68,0,0,24.7,206
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21,0,132,78,0,0,32.4,393
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45,5,128,80,0,0,34.6,144
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21,4,94,65,22,0,24.7,148
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| 632 |
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34,7,114,64,0,0,27.4,732
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24,0,102,78,40,90,34.5,238
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| 634 |
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23,2,111,60,0,0,26.2,343
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22,1,128,82,17,183,27.5,115
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31,10,92,62,0,0,25.9,167
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38,13,104,72,0,0,31.2,465
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48,5,104,74,0,0,28.8,153
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| 639 |
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23,2,94,76,18,66,31.6,649
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32,7,97,76,32,91,40.9,871
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28,1,100,74,12,46,19.5,149
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27,0,102,86,17,105,29.3,695
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24,4,128,70,0,0,34.3,303
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50,6,147,80,0,0,29.5,178
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31,4,90,0,0,0,28,0.61
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27,3,103,72,30,152,27.6,0.73
|
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30,2,157,74,35,440,39.4,134
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33,1,167,74,17,144,23.4,447
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| 649 |
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22,0,179,50,36,159,37.8,455
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42,11,136,84,35,130,28.3,0.26
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23,0,107,60,25,0,26.4,133
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23,1,91,54,25,100,25.2,234
|
| 653 |
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27,1,117,60,23,106,33.8,466
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index.html
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<!DOCTYPE html>
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<!--
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| 3 |
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Multi Agent Data Analysis with Crew AI
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Copyright (c) 2025 Sowmiyan S
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Licensed under the MIT License
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-->
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Multi Agent Data Analysis with Crew AI</title>
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
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<style>
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:root {
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--bg-body: #f3f4f6;
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--bg-card: #ffffff;
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--text-primary: #111827;
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--text-secondary: #4b5563;
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--accent-primary: #2563eb;
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--accent-secondary: #1e40af;
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--border-subtle: #e5e7eb;
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--code-bg: #1e1e1e;
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--code-text: #d4d4d4;
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--success-color: #10b981;
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--font-sans: 'Inter', system-ui, -apple-system, sans-serif;
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--font-mono: 'JetBrains Mono', monospace;
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--shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 0.05);
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--shadow-md: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
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--radius-md: 0.5rem;
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| 32 |
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--radius-lg: 0.75rem;
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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background-color: var(--bg-body);
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color: var(--text-primary);
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font-family: var(--font-sans);
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line-height: 1.6;
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-webkit-font-smoothing: antialiased;
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padding: 2rem;
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}
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.container {
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max-width: 1000px;
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margin: 0 auto;
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}
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/* Header */
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.header {
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text-align: center;
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margin-bottom: 3rem;
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padding: 2rem 0;
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}
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.brand-badge {
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display: inline-block;
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background: var(--accent-primary);
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color: white;
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padding: 0.25rem 0.75rem;
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border-radius: 9999px;
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font-size: 0.75rem;
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font-weight: 600;
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letter-spacing: 0.05em;
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text-transform: uppercase;
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margin-bottom: 1rem;
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box-shadow: var(--shadow-sm);
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}
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h1 {
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font-size: 2.5rem;
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font-weight: 800;
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color: var(--text-primary);
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letter-spacing: -0.025em;
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margin-bottom: 0.5rem;
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| 78 |
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background: linear-gradient(135deg, var(--text-primary) 0%, var(--text-secondary) 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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}
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.subtitle {
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| 84 |
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color: var(--text-secondary);
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| 85 |
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font-size: 1.1rem;
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| 86 |
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}
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| 87 |
-
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| 88 |
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/* Status Banner */
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| 89 |
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.status-banner {
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background: white;
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| 91 |
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border-left: 4px solid var(--success-color);
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| 92 |
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padding: 1rem 1.5rem;
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| 93 |
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border-radius: var(--radius-md);
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box-shadow: var(--shadow-sm);
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display: flex;
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align-items: center;
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gap: 0.75rem;
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margin-bottom: 3rem;
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font-weight: 500;
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color: var(--text-primary);
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}
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.status-dot {
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width: 10px;
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height: 10px;
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background-color: var(--success-color);
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border-radius: 50%;
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box-shadow: 0 0 0 4px rgba(16, 185, 129, 0.2);
|
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}
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| 110 |
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|
| 111 |
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/* Cards */
|
| 112 |
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.section {
|
| 113 |
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background: var(--bg-card);
|
| 114 |
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border-radius: var(--radius-lg);
|
| 115 |
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box-shadow: var(--shadow-md);
|
| 116 |
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margin-bottom: 2rem;
|
| 117 |
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overflow: hidden;
|
| 118 |
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border: 1px solid var(--border-subtle);
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| 119 |
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transition: transform 0.2s ease;
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}
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.section:hover {
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transform: translateY(-2px);
|
| 124 |
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}
|
| 125 |
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| 126 |
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.section-header {
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| 127 |
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padding: 1.5rem 2rem;
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| 128 |
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border-bottom: 1px solid var(--border-subtle);
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| 129 |
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display: flex;
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| 130 |
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justify-content: space-between;
|
| 131 |
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align-items: center;
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| 132 |
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background: #fafafa;
|
| 133 |
-
}
|
| 134 |
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h2 {
|
| 136 |
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font-size: 1.25rem;
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| 137 |
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font-weight: 600;
|
| 138 |
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color: var(--text-primary);
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| 139 |
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display: flex;
|
| 140 |
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align-items: center;
|
| 141 |
-
gap: 0.5rem;
|
| 142 |
-
}
|
| 143 |
-
|
| 144 |
-
.step-number {
|
| 145 |
-
font-family: var(--font-mono);
|
| 146 |
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font-size: 0.875rem;
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| 147 |
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color: var(--text-secondary);
|
| 148 |
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background: var(--border-subtle);
|
| 149 |
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padding: 0.25rem 0.5rem;
|
| 150 |
-
border-radius: var(--radius-md);
|
| 151 |
-
}
|
| 152 |
-
|
| 153 |
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/* Content Areas */
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| 154 |
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.content-wrapper {
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padding: 0;
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}
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| 157 |
-
|
| 158 |
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.code-block {
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| 159 |
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background: var(--code-bg);
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padding: 1.5rem;
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| 161 |
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overflow-x: auto;
|
| 162 |
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font-family: var(--font-mono);
|
| 163 |
-
font-size: 0.9rem;
|
| 164 |
-
line-height: 1.5;
|
| 165 |
-
color: var(--code-text);
|
| 166 |
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border-bottom-left-radius: var(--radius-lg);
|
| 167 |
-
border-bottom-right-radius: var(--radius-lg);
|
| 168 |
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}
|
| 169 |
-
|
| 170 |
-
/* Table Styles */
|
| 171 |
-
.table-container {
|
| 172 |
-
overflow-x: auto;
|
| 173 |
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max-height: 500px;
|
| 174 |
-
}
|
| 175 |
-
|
| 176 |
-
table.data-table {
|
| 177 |
-
width: 100%;
|
| 178 |
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border-collapse: collapse;
|
| 179 |
-
font-size: 0.875rem;
|
| 180 |
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font-family: var(--font-mono);
|
| 181 |
-
white-space: nowrap;
|
| 182 |
-
}
|
| 183 |
-
|
| 184 |
-
table.data-table th {
|
| 185 |
-
background: #f8fafc;
|
| 186 |
-
color: var(--text-secondary);
|
| 187 |
-
font-weight: 600;
|
| 188 |
-
text-transform: uppercase;
|
| 189 |
-
font-size: 0.75rem;
|
| 190 |
-
letter-spacing: 0.05em;
|
| 191 |
-
padding: 1rem 1.5rem;
|
| 192 |
-
text-align: left;
|
| 193 |
-
border-bottom: 1px solid var(--border-subtle);
|
| 194 |
-
position: sticky;
|
| 195 |
-
top: 0;
|
| 196 |
-
z-index: 10;
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
table.data-table td {
|
| 200 |
-
padding: 1rem 1.5rem;
|
| 201 |
-
border-bottom: 1px solid var(--border-subtle);
|
| 202 |
-
color: var(--text-primary);
|
| 203 |
-
}
|
| 204 |
-
|
| 205 |
-
table.data-table tr:last-child td { border-bottom: none; }
|
| 206 |
-
table.data-table tr:hover td { background-color: #f9fafb; }
|
| 207 |
-
|
| 208 |
-
/* Syntax Highlighting (Dark Theme) */
|
| 209 |
-
.keyword { color: #c586c0; font-weight: bold; } /* Purple */
|
| 210 |
-
.string { color: #ce9178; } /* Orange */
|
| 211 |
-
.number { color: #b5cea8; } /* Light Green */
|
| 212 |
-
.boolean { color: #569cd6; } /* Blue */
|
| 213 |
-
.key { color: #9cdcfe; } /* Light Blue */
|
| 214 |
-
.comment { color: #6a9955; font-style: italic; } /* Green */
|
| 215 |
-
.null { color: #569cd6; }
|
| 216 |
-
|
| 217 |
-
/* Footer */
|
| 218 |
-
.footer {
|
| 219 |
-
text-align: center;
|
| 220 |
-
margin-top: 4rem;
|
| 221 |
-
padding-top: 2rem;
|
| 222 |
-
border-top: 1px solid var(--border-subtle);
|
| 223 |
-
color: var(--text-secondary);
|
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</script>
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<body>
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<div class="container">
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| 273 |
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<header class="header">
|
| 274 |
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<span class="brand-badge">Multi Agent Data Analysis with Crew AI</span>
|
| 275 |
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<h1>Data Analysis Report</h1>
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| 276 |
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<p class="subtitle">Data Analysis as a Service | Automated insights generated by multi-agent swarm</p>
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| 277 |
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</header>
|
| 278 |
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|
| 279 |
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<div class="status-banner">
|
| 280 |
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<div class="status-dot"></div>
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| 281 |
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<span>Pipeline executed successfully. All agents completed their tasks.</span>
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| 282 |
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</div>
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| 283 |
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| 284 |
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|
| 285 |
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<section class="section">
|
| 286 |
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<div class="section-header">
|
| 287 |
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<h2>Dataset Preview</h2>
|
| 288 |
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<span class="step-number">01</span>
|
| 289 |
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</div>
|
| 290 |
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<div class="table-container">
|
| 291 |
-
<table class="dataframe data-table">
|
| 292 |
-
<thead>
|
| 293 |
-
<tr style="text-align: right;">
|
| 294 |
-
<th>Country or territory name</th>
|
| 295 |
-
<th>ISO 2-character country/territory code</th>
|
| 296 |
-
<th>ISO 3-character country/territory code</th>
|
| 297 |
-
<th>ISO numeric country/territory code</th>
|
| 298 |
-
<th>Region</th>
|
| 299 |
-
<th>Year</th>
|
| 300 |
-
<th>Estimated total population number</th>
|
| 301 |
-
<th>Estimated prevalence of TB (all forms) per 100 000 population</th>
|
| 302 |
-
<th>Estimated prevalence of TB (all forms) per 100 000 population, low bound</th>
|
| 303 |
-
<th>Estimated prevalence of TB (all forms) per 100 000 population, high bound</th>
|
| 304 |
-
<th>Estimated prevalence of TB (all forms)</th>
|
| 305 |
-
<th>Estimated prevalence of TB (all forms), low bound</th>
|
| 306 |
-
<th>Estimated prevalence of TB (all forms), high bound</th>
|
| 307 |
-
<th>Method to derive prevalence estimates</th>
|
| 308 |
-
<th>Estimated mortality of TB cases (all forms, excluding HIV) per 100 000 population</th>
|
| 309 |
-
<th>Estimated mortality of TB cases (all forms, excluding HIV), per 100 000 population, low bound</th>
|
| 310 |
-
<th>Estimated mortality of TB cases (all forms, excluding HIV), per 100 000 population, high bound</th>
|
| 311 |
-
<th>Estimated number of deaths from TB (all forms, excluding HIV)</th>
|
| 312 |
-
<th>Estimated number of deaths from TB (all forms, excluding HIV), low bound</th>
|
| 313 |
-
<th>Estimated number of deaths from TB (all forms, excluding HIV), high bound</th>
|
| 314 |
-
<th>Estimated mortality of TB cases who are HIV-positive, per 100 000 population</th>
|
| 315 |
-
<th>Estimated mortality of TB cases who are HIV-positive, per 100 000 population, low bound</th>
|
| 316 |
-
<th>Estimated mortality of TB cases who are HIV-positive, per 100 000 population, high bound</th>
|
| 317 |
-
<th>Estimated number of deaths from TB in people who are HIV-positive</th>
|
| 318 |
-
<th>Estimated number of deaths from TB in people who are HIV-positive, low bound</th>
|
| 319 |
-
<th>Estimated number of deaths from TB in people who are HIV-positive, high bound</th>
|
| 320 |
-
<th>Method to derive mortality estimates</th>
|
| 321 |
-
<th>Estimated incidence (all forms) per 100 000 population</th>
|
| 322 |
-
<th>Estimated incidence (all forms) per 100 000 population, low bound</th>
|
| 323 |
-
<th>Estimated incidence (all forms) per 100 000 population, high bound</th>
|
| 324 |
-
<th>Estimated number of incident cases (all forms)</th>
|
| 325 |
-
<th>Estimated number of incident cases (all forms), low bound</th>
|
| 326 |
-
<th>Estimated number of incident cases (all forms), high bound</th>
|
| 327 |
-
<th>Method to derive incidence estimates</th>
|
| 328 |
-
<th>Estimated HIV in incident TB (percent)</th>
|
| 329 |
-
<th>Estimated HIV in incident TB (percent), low bound</th>
|
| 330 |
-
<th>Estimated HIV in incident TB (percent), high bound</th>
|
| 331 |
-
<th>Estimated incidence of TB cases who are HIV-positive per 100 000 population</th>
|
| 332 |
-
<th>Estimated incidence of TB cases who are HIV-positive per 100 000 population, low bound</th>
|
| 333 |
-
<th>Estimated incidence of TB cases who are HIV-positive per 100 000 population, high bound</th>
|
| 334 |
-
<th>Estimated incidence of TB cases who are HIV-positive</th>
|
| 335 |
-
<th>Estimated incidence of TB cases who are HIV-positive, low bound</th>
|
| 336 |
-
<th>Estimated incidence of TB cases who are HIV-positive, high bound</th>
|
| 337 |
-
<th>Method to derive TBHIV estimates</th>
|
| 338 |
-
<th>Case detection rate (all forms), percent</th>
|
| 339 |
-
<th>Case detection rate (all forms), percent, low bound</th>
|
| 340 |
-
<th>Case detection rate (all forms), percent, high bound</th>
|
| 341 |
-
</tr>
|
| 342 |
-
</thead>
|
| 343 |
-
<tbody>
|
| 344 |
-
<tr>
|
| 345 |
-
<td>Afghanistan</td>
|
| 346 |
-
<td>AF</td>
|
| 347 |
-
<td>AFG</td>
|
| 348 |
-
<td>4</td>
|
| 349 |
-
<td>EMR</td>
|
| 350 |
-
<td>1990</td>
|
| 351 |
-
<td>11731193</td>
|
| 352 |
-
<td>306.0</td>
|
| 353 |
-
<td>156.0</td>
|
| 354 |
-
<td>506.0</td>
|
| 355 |
-
<td>36000.0</td>
|
| 356 |
-
<td>18000.0</td>
|
| 357 |
-
<td>59000.0</td>
|
| 358 |
-
<td>predicted</td>
|
| 359 |
-
<td>37.00</td>
|
| 360 |
-
<td>24.00</td>
|
| 361 |
-
<td>54.00</td>
|
| 362 |
-
<td>4300.0</td>
|
| 363 |
-
<td>2800.0</td>
|
| 364 |
-
<td>6400.0</td>
|
| 365 |
-
<td>0.04</td>
|
| 366 |
-
<td>0.030000</td>
|
| 367 |
-
<td>0.050000</td>
|
| 368 |
-
<td>5.0</td>
|
| 369 |
-
<td>4.100000</td>
|
| 370 |
-
<td>6.000000</td>
|
| 371 |
-
<td>Indirect</td>
|
| 372 |
-
<td>189.0</td>
|
| 373 |
-
<td>157.0</td>
|
| 374 |
-
<td>238.0</td>
|
| 375 |
-
<td>22000.0</td>
|
| 376 |
-
<td>18000.0</td>
|
| 377 |
-
<td>28000.0</td>
|
| 378 |
-
<td>High income</td>
|
| 379 |
-
<td>0.060000</td>
|
| 380 |
-
<td>0.040000</td>
|
| 381 |
-
<td>0.080000</td>
|
| 382 |
-
<td>0.110000</td>
|
| 383 |
-
<td>0.08000</td>
|
| 384 |
-
<td>0.140000</td>
|
| 385 |
-
<td>12.000000</td>
|
| 386 |
-
<td>9.400000</td>
|
| 387 |
-
<td>16.000000</td>
|
| 388 |
-
<td>NaN</td>
|
| 389 |
-
<td>20.000000</td>
|
| 390 |
-
<td>15.000000</td>
|
| 391 |
-
<td>24.000000</td>
|
| 392 |
-
</tr>
|
| 393 |
-
<tr>
|
| 394 |
-
<td>Afghanistan</td>
|
| 395 |
-
<td>AF</td>
|
| 396 |
-
<td>AFG</td>
|
| 397 |
-
<td>4</td>
|
| 398 |
-
<td>EMR</td>
|
| 399 |
-
<td>1991</td>
|
| 400 |
-
<td>12612043</td>
|
| 401 |
-
<td>343.0</td>
|
| 402 |
-
<td>178.0</td>
|
| 403 |
-
<td>562.0</td>
|
| 404 |
-
<td>43000.0</td>
|
| 405 |
-
<td>22000.0</td>
|
| 406 |
-
<td>71000.0</td>
|
| 407 |
-
<td>predicted</td>
|
| 408 |
-
<td>46.00</td>
|
| 409 |
-
<td>29.00</td>
|
| 410 |
-
<td>61.00</td>
|
| 411 |
-
<td>5800.0</td>
|
| 412 |
-
<td>3700.0</td>
|
| 413 |
-
<td>7700.0</td>
|
| 414 |
-
<td>0.06</td>
|
| 415 |
-
<td>0.050000</td>
|
| 416 |
-
<td>0.080000</td>
|
| 417 |
-
<td>8.0</td>
|
| 418 |
-
<td>6.200000</td>
|
| 419 |
-
<td>10.000000</td>
|
| 420 |
-
<td>Indirect</td>
|
| 421 |
-
<td>191.0</td>
|
| 422 |
-
<td>167.0</td>
|
| 423 |
-
<td>227.0</td>
|
| 424 |
-
<td>24000.0</td>
|
| 425 |
-
<td>21000.0</td>
|
| 426 |
-
<td>29000.0</td>
|
| 427 |
-
<td>High income</td>
|
| 428 |
-
<td>0.070000</td>
|
| 429 |
-
<td>0.060000</td>
|
| 430 |
-
<td>0.090000</td>
|
| 431 |
-
<td>0.130000</td>
|
| 432 |
-
<td>0.11000</td>
|
| 433 |
-
<td>0.160000</td>
|
| 434 |
-
<td>17.000000</td>
|
| 435 |
-
<td>14.000000</td>
|
| 436 |
-
<td>20.000000</td>
|
| 437 |
-
<td>NaN</td>
|
| 438 |
-
<td>96.000000</td>
|
| 439 |
-
<td>80.000000</td>
|
| 440 |
-
<td>110.000000</td>
|
| 441 |
-
</tr>
|
| 442 |
-
<tr>
|
| 443 |
-
<td>Afghanistan</td>
|
| 444 |
-
<td>AF</td>
|
| 445 |
-
<td>AFG</td>
|
| 446 |
-
<td>4</td>
|
| 447 |
-
<td>EMR</td>
|
| 448 |
-
<td>1992</td>
|
| 449 |
-
<td>13811876</td>
|
| 450 |
-
<td>371.0</td>
|
| 451 |
-
<td>189.0</td>
|
| 452 |
-
<td>614.0</td>
|
| 453 |
-
<td>51000.0</td>
|
| 454 |
-
<td>26000.0</td>
|
| 455 |
-
<td>85000.0</td>
|
| 456 |
-
<td>predicted</td>
|
| 457 |
-
<td>54.00</td>
|
| 458 |
-
<td>34.00</td>
|
| 459 |
-
<td>68.00</td>
|
| 460 |
-
<td>7400.0</td>
|
| 461 |
-
<td>4700.0</td>
|
| 462 |
-
<td>9400.0</td>
|
| 463 |
-
<td>0.08</td>
|
| 464 |
-
<td>0.060000</td>
|
| 465 |
-
<td>0.100000</td>
|
| 466 |
-
<td>11.0</td>
|
| 467 |
-
<td>8.300000</td>
|
| 468 |
-
<td>14.000000</td>
|
| 469 |
-
<td>Indirect</td>
|
| 470 |
-
<td>191.0</td>
|
| 471 |
-
<td>171.0</td>
|
| 472 |
-
<td>217.0</td>
|
| 473 |
-
<td>26000.0</td>
|
| 474 |
-
<td>24000.0</td>
|
| 475 |
-
<td>30000.0</td>
|
| 476 |
-
<td>High income</td>
|
| 477 |
-
<td>0.080000</td>
|
| 478 |
-
<td>0.070000</td>
|
| 479 |
-
<td>0.100000</td>
|
| 480 |
-
<td>0.160000</td>
|
| 481 |
-
<td>0.14000</td>
|
| 482 |
-
<td>0.180000</td>
|
| 483 |
-
<td>22.000000</td>
|
| 484 |
-
<td>19.000000</td>
|
| 485 |
-
<td>24.000000</td>
|
| 486 |
-
<td>NaN</td>
|
| 487 |
-
<td>68.217851</td>
|
| 488 |
-
<td>61.959409</td>
|
| 489 |
-
<td>75.854492</td>
|
| 490 |
-
</tr>
|
| 491 |
-
<tr>
|
| 492 |
-
<td>Afghanistan</td>
|
| 493 |
-
<td>AF</td>
|
| 494 |
-
<td>AFG</td>
|
| 495 |
-
<td>4</td>
|
| 496 |
-
<td>EMR</td>
|
| 497 |
-
<td>1993</td>
|
| 498 |
-
<td>15175325</td>
|
| 499 |
-
<td>392.0</td>
|
| 500 |
-
<td>194.0</td>
|
| 501 |
-
<td>657.0</td>
|
| 502 |
-
<td>59000.0</td>
|
| 503 |
-
<td>30000.0</td>
|
| 504 |
-
<td>100000.0</td>
|
| 505 |
-
<td>predicted</td>
|
| 506 |
-
<td>60.00</td>
|
| 507 |
-
<td>38.00</td>
|
| 508 |
-
<td>73.00</td>
|
| 509 |
-
<td>9100.0</td>
|
| 510 |
-
<td>5800.0</td>
|
| 511 |
-
<td>11000.0</td>
|
| 512 |
-
<td>0.11</td>
|
| 513 |
-
<td>0.090000</td>
|
| 514 |
-
<td>0.140000</td>
|
| 515 |
-
<td>17.0</td>
|
| 516 |
-
<td>13.000000</td>
|
| 517 |
-
<td>21.000000</td>
|
| 518 |
-
<td>Indirect</td>
|
| 519 |
-
<td>189.0</td>
|
| 520 |
-
<td>171.0</td>
|
| 521 |
-
<td>209.0</td>
|
| 522 |
-
<td>29000.0</td>
|
| 523 |
-
<td>26000.0</td>
|
| 524 |
-
<td>32000.0</td>
|
| 525 |
-
<td>High income</td>
|
| 526 |
-
<td>0.100000</td>
|
| 527 |
-
<td>0.090000</td>
|
| 528 |
-
<td>0.110000</td>
|
| 529 |
-
<td>0.190000</td>
|
| 530 |
-
<td>0.17000</td>
|
| 531 |
-
<td>0.210000</td>
|
| 532 |
-
<td>28.000000</td>
|
| 533 |
-
<td>25.000000</td>
|
| 534 |
-
<td>31.000000</td>
|
| 535 |
-
<td>NaN</td>
|
| 536 |
-
<td>68.217851</td>
|
| 537 |
-
<td>61.959409</td>
|
| 538 |
-
<td>75.854492</td>
|
| 539 |
-
</tr>
|
| 540 |
-
<tr>
|
| 541 |
-
<td>Afghanistan</td>
|
| 542 |
-
<td>AF</td>
|
| 543 |
-
<td>AFG</td>
|
| 544 |
-
<td>4</td>
|
| 545 |
-
<td>EMR</td>
|
| 546 |
-
<td>1994</td>
|
| 547 |
-
<td>16485018</td>
|
| 548 |
-
<td>410.0</td>
|
| 549 |
-
<td>198.0</td>
|
| 550 |
-
<td>697.0</td>
|
| 551 |
-
<td>68000.0</td>
|
| 552 |
-
<td>33000.0</td>
|
| 553 |
-
<td>110000.0</td>
|
| 554 |
-
<td>predicted</td>
|
| 555 |
-
<td>65.00</td>
|
| 556 |
-
<td>41.00</td>
|
| 557 |
-
<td>79.00</td>
|
| 558 |
-
<td>11000.0</td>
|
| 559 |
-
<td>6800.0</td>
|
| 560 |
-
<td>13000.0</td>
|
| 561 |
-
<td>0.13</td>
|
| 562 |
-
<td>0.110000</td>
|
| 563 |
-
<td>0.160000</td>
|
| 564 |
-
<td>22.0</td>
|
| 565 |
-
<td>17.000000</td>
|
| 566 |
-
<td>27.000000</td>
|
| 567 |
-
<td>Indirect</td>
|
| 568 |
-
<td>188.0</td>
|
| 569 |
-
<td>169.0</td>
|
| 570 |
-
<td>208.0</td>
|
| 571 |
-
<td>31000.0</td>
|
| 572 |
-
<td>28000.0</td>
|
| 573 |
-
<td>34000.0</td>
|
| 574 |
-
<td>High income</td>
|
| 575 |
-
<td>0.110000</td>
|
| 576 |
-
<td>0.100000</td>
|
| 577 |
-
<td>0.130000</td>
|
| 578 |
-
<td>0.210000</td>
|
| 579 |
-
<td>0.18000</td>
|
| 580 |
-
<td>0.240000</td>
|
| 581 |
-
<td>35.000000</td>
|
| 582 |
-
<td>30.000000</td>
|
| 583 |
-
<td>39.000000</td>
|
| 584 |
-
<td>NaN</td>
|
| 585 |
-
<td>68.217851</td>
|
| 586 |
-
<td>61.959409</td>
|
| 587 |
-
<td>75.854492</td>
|
| 588 |
-
</tr>
|
| 589 |
-
<tr>
|
| 590 |
-
<td>Afghanistan</td>
|
| 591 |
-
<td>AF</td>
|
| 592 |
-
<td>AFG</td>
|
| 593 |
-
<td>4</td>
|
| 594 |
-
<td>EMR</td>
|
| 595 |
-
<td>1995</td>
|
| 596 |
-
<td>17586073</td>
|
| 597 |
-
<td>424.0</td>
|
| 598 |
-
<td>199.0</td>
|
| 599 |
-
<td>733.0</td>
|
| 600 |
-
<td>75000.0</td>
|
| 601 |
-
<td>35000.0</td>
|
| 602 |
-
<td>130000.0</td>
|
| 603 |
-
<td>predicted</td>
|
| 604 |
-
<td>69.00</td>
|
| 605 |
-
<td>45.00</td>
|
| 606 |
-
<td>82.00</td>
|
| 607 |
-
<td>12000.0</td>
|
| 608 |
-
<td>7800.0</td>
|
| 609 |
-
<td>14000.0</td>
|
| 610 |
-
<td>0.15</td>
|
| 611 |
-
<td>0.120000</td>
|
| 612 |
-
<td>0.190000</td>
|
| 613 |
-
<td>27.0</td>
|
| 614 |
-
<td>21.000000</td>
|
| 615 |
-
<td>34.000000</td>
|
| 616 |
-
<td>Indirect</td>
|
| 617 |
-
<td>188.0</td>
|
| 618 |
-
<td>166.0</td>
|
| 619 |
-
<td>209.0</td>
|
| 620 |
-
<td>33000.0</td>
|
| 621 |
-
<td>29000.0</td>
|
| 622 |
-
<td>37000.0</td>
|
| 623 |
-
<td>High income</td>
|
| 624 |
-
<td>0.120000</td>
|
| 625 |
-
<td>0.100000</td>
|
| 626 |
-
<td>0.150000</td>
|
| 627 |
-
<td>0.230000</td>
|
| 628 |
-
<td>0.21000</td>
|
| 629 |
-
<td>0.270000</td>
|
| 630 |
-
<td>41.000000</td>
|
| 631 |
-
<td>37.000000</td>
|
| 632 |
-
<td>47.000000</td>
|
| 633 |
-
<td>NaN</td>
|
| 634 |
-
<td>68.217851</td>
|
| 635 |
-
<td>61.959409</td>
|
| 636 |
-
<td>75.854492</td>
|
| 637 |
-
</tr>
|
| 638 |
-
<tr>
|
| 639 |
-
<td>Afghanistan</td>
|
| 640 |
-
<td>AF</td>
|
| 641 |
-
<td>AFG</td>
|
| 642 |
-
<td>4</td>
|
| 643 |
-
<td>EMR</td>
|
| 644 |
-
<td>1996</td>
|
| 645 |
-
<td>18415307</td>
|
| 646 |
-
<td>438.0</td>
|
| 647 |
-
<td>202.0</td>
|
| 648 |
-
<td>764.0</td>
|
| 649 |
-
<td>81000.0</td>
|
| 650 |
-
<td>37000.0</td>
|
| 651 |
-
<td>140000.0</td>
|
| 652 |
-
<td>predicted</td>
|
| 653 |
-
<td>71.00</td>
|
| 654 |
-
<td>48.00</td>
|
| 655 |
-
<td>85.00</td>
|
| 656 |
-
<td>13000.0</td>
|
| 657 |
-
<td>8900.0</td>
|
| 658 |
-
<td>16000.0</td>
|
| 659 |
-
<td>0.17</td>
|
| 660 |
-
<td>0.140000</td>
|
| 661 |
-
<td>0.210000</td>
|
| 662 |
-
<td>32.0</td>
|
| 663 |
-
<td>26.000000</td>
|
| 664 |
-
<td>39.000000</td>
|
| 665 |
-
<td>Indirect</td>
|
| 666 |
-
<td>188.0</td>
|
| 667 |
-
<td>169.0</td>
|
| 668 |
-
<td>206.0</td>
|
| 669 |
-
<td>35000.0</td>
|
| 670 |
-
<td>31000.0</td>
|
| 671 |
-
<td>38000.0</td>
|
| 672 |
-
<td>High income</td>
|
| 673 |
-
<td>0.140000</td>
|
| 674 |
-
<td>0.120000</td>
|
| 675 |
-
<td>0.160000</td>
|
| 676 |
-
<td>0.250000</td>
|
| 677 |
-
<td>0.23000</td>
|
| 678 |
-
<td>0.280000</td>
|
| 679 |
-
<td>47.000000</td>
|
| 680 |
-
<td>42.000000</td>
|
| 681 |
-
<td>52.000000</td>
|
| 682 |
-
<td>NaN</td>
|
| 683 |
-
<td>68.217851</td>
|
| 684 |
-
<td>61.959409</td>
|
| 685 |
-
<td>75.854492</td>
|
| 686 |
-
</tr>
|
| 687 |
-
<tr>
|
| 688 |
-
<td>Afghanistan</td>
|
| 689 |
-
<td>AF</td>
|
| 690 |
-
<td>AFG</td>
|
| 691 |
-
<td>4</td>
|
| 692 |
-
<td>EMR</td>
|
| 693 |
-
<td>1997</td>
|
| 694 |
-
<td>19021226</td>
|
| 695 |
-
<td>448.0</td>
|
| 696 |
-
<td>203.0</td>
|
| 697 |
-
<td>788.0</td>
|
| 698 |
-
<td>85000.0</td>
|
| 699 |
-
<td>39000.0</td>
|
| 700 |
-
<td>150000.0</td>
|
| 701 |
-
<td>predicted</td>
|
| 702 |
-
<td>72.00</td>
|
| 703 |
-
<td>51.00</td>
|
| 704 |
-
<td>85.00</td>
|
| 705 |
-
<td>14000.0</td>
|
| 706 |
-
<td>9700.0</td>
|
| 707 |
-
<td>16000.0</td>
|
| 708 |
-
<td>0.19</td>
|
| 709 |
-
<td>0.160000</td>
|
| 710 |
-
<td>0.230000</td>
|
| 711 |
-
<td>37.0</td>
|
| 712 |
-
<td>31.000000</td>
|
| 713 |
-
<td>44.000000</td>
|
| 714 |
-
<td>Indirect</td>
|
| 715 |
-
<td>189.0</td>
|
| 716 |
-
<td>172.0</td>
|
| 717 |
-
<td>205.0</td>
|
| 718 |
-
<td>36000.0</td>
|
| 719 |
-
<td>33000.0</td>
|
| 720 |
-
<td>39000.0</td>
|
| 721 |
-
<td>High income</td>
|
| 722 |
-
<td>0.150000</td>
|
| 723 |
-
<td>0.130000</td>
|
| 724 |
-
<td>0.170000</td>
|
| 725 |
-
<td>0.280000</td>
|
| 726 |
-
<td>0.26000</td>
|
| 727 |
-
<td>0.300000</td>
|
| 728 |
-
<td>53.000000</td>
|
| 729 |
-
<td>49.000000</td>
|
| 730 |
-
<td>58.000000</td>
|
| 731 |
-
<td>NaN</td>
|
| 732 |
-
<td>3.600000</td>
|
| 733 |
-
<td>3.300000</td>
|
| 734 |
-
<td>4.000000</td>
|
| 735 |
-
</tr>
|
| 736 |
-
<tr>
|
| 737 |
-
<td>Afghanistan</td>
|
| 738 |
-
<td>AF</td>
|
| 739 |
-
<td>AFG</td>
|
| 740 |
-
<td>4</td>
|
| 741 |
-
<td>EMR</td>
|
| 742 |
-
<td>1998</td>
|
| 743 |
-
<td>19496836</td>
|
| 744 |
-
<td>454.0</td>
|
| 745 |
-
<td>204.0</td>
|
| 746 |
-
<td>800.0</td>
|
| 747 |
-
<td>88000.0</td>
|
| 748 |
-
<td>40000.0</td>
|
| 749 |
-
<td>160000.0</td>
|
| 750 |
-
<td>predicted</td>
|
| 751 |
-
<td>72.00</td>
|
| 752 |
-
<td>51.00</td>
|
| 753 |
-
<td>85.00</td>
|
| 754 |
-
<td>14000.0</td>
|
| 755 |
-
<td>10000.0</td>
|
| 756 |
-
<td>17000.0</td>
|
| 757 |
-
<td>0.21</td>
|
| 758 |
-
<td>0.180000</td>
|
| 759 |
-
<td>0.240000</td>
|
| 760 |
-
<td>41.0</td>
|
| 761 |
-
<td>35.000000</td>
|
| 762 |
-
<td>48.000000</td>
|
| 763 |
-
<td>Indirect</td>
|
| 764 |
-
<td>189.0</td>
|
| 765 |
-
<td>174.0</td>
|
| 766 |
-
<td>202.0</td>
|
| 767 |
-
<td>37000.0</td>
|
| 768 |
-
<td>34000.0</td>
|
| 769 |
-
<td>39000.0</td>
|
| 770 |
-
<td>High income</td>
|
| 771 |
-
<td>0.160000</td>
|
| 772 |
-
<td>0.140000</td>
|
| 773 |
-
<td>0.180000</td>
|
| 774 |
-
<td>0.300000</td>
|
| 775 |
-
<td>0.27000</td>
|
| 776 |
-
<td>0.320000</td>
|
| 777 |
-
<td>59.000000</td>
|
| 778 |
-
<td>54.000000</td>
|
| 779 |
-
<td>63.000000</td>
|
| 780 |
-
<td>NaN</td>
|
| 781 |
-
<td>8.300000</td>
|
| 782 |
-
<td>7.800000</td>
|
| 783 |
-
<td>9.100000</td>
|
| 784 |
-
</tr>
|
| 785 |
-
<tr>
|
| 786 |
-
<td>Afghanistan</td>
|
| 787 |
-
<td>AF</td>
|
| 788 |
-
<td>AFG</td>
|
| 789 |
-
<td>4</td>
|
| 790 |
-
<td>EMR</td>
|
| 791 |
-
<td>1999</td>
|
| 792 |
-
<td>19987071</td>
|
| 793 |
-
<td>446.0</td>
|
| 794 |
-
<td>203.0</td>
|
| 795 |
-
<td>782.0</td>
|
| 796 |
-
<td>89000.0</td>
|
| 797 |
-
<td>41000.0</td>
|
| 798 |
-
<td>160000.0</td>
|
| 799 |
-
<td>predicted</td>
|
| 800 |
-
<td>71.00</td>
|
| 801 |
-
<td>50.00</td>
|
| 802 |
-
<td>84.00</td>
|
| 803 |
-
<td>14000.0</td>
|
| 804 |
-
<td>10000.0</td>
|
| 805 |
-
<td>17000.0</td>
|
| 806 |
-
<td>0.22</td>
|
| 807 |
-
<td>0.190000</td>
|
| 808 |
-
<td>0.260000</td>
|
| 809 |
-
<td>44.0</td>
|
| 810 |
-
<td>37.000000</td>
|
| 811 |
-
<td>51.000000</td>
|
| 812 |
-
<td>Indirect</td>
|
| 813 |
-
<td>190.0</td>
|
| 814 |
-
<td>175.0</td>
|
| 815 |
-
<td>206.0</td>
|
| 816 |
-
<td>38000.0</td>
|
| 817 |
-
<td>35000.0</td>
|
| 818 |
-
<td>41000.0</td>
|
| 819 |
-
<td>High income</td>
|
| 820 |
-
<td>0.170000</td>
|
| 821 |
-
<td>0.150000</td>
|
| 822 |
-
<td>0.190000</td>
|
| 823 |
-
<td>0.320000</td>
|
| 824 |
-
<td>0.29000</td>
|
| 825 |
-
<td>0.350000</td>
|
| 826 |
-
<td>64.000000</td>
|
| 827 |
-
<td>59.000000</td>
|
| 828 |
-
<td>70.000000</td>
|
| 829 |
-
<td>NaN</td>
|
| 830 |
-
<td>8.700000</td>
|
| 831 |
-
<td>8.100000</td>
|
| 832 |
-
<td>9.500000</td>
|
| 833 |
-
</tr>
|
| 834 |
-
<tr>
|
| 835 |
-
<td>Afghanistan</td>
|
| 836 |
-
<td>AF</td>
|
| 837 |
-
<td>AFG</td>
|
| 838 |
-
<td>4</td>
|
| 839 |
-
<td>EMR</td>
|
| 840 |
-
<td>2000</td>
|
| 841 |
-
<td>20595360</td>
|
| 842 |
-
<td>430.0</td>
|
| 843 |
-
<td>202.0</td>
|
| 844 |
-
<td>742.0</td>
|
| 845 |
-
<td>89000.0</td>
|
| 846 |
-
<td>42000.0</td>
|
| 847 |
-
<td>150000.0</td>
|
| 848 |
-
<td>predicted</td>
|
| 849 |
-
<td>68.00</td>
|
| 850 |
-
<td>48.00</td>
|
| 851 |
-
<td>83.00</td>
|
| 852 |
-
<td>14000.0</td>
|
| 853 |
-
<td>9900.0</td>
|
| 854 |
-
<td>17000.0</td>
|
| 855 |
-
<td>0.22</td>
|
| 856 |
-
<td>0.190000</td>
|
| 857 |
-
<td>0.260000</td>
|
| 858 |
-
<td>46.0</td>
|
| 859 |
-
<td>39.000000</td>
|
| 860 |
-
<td>54.000000</td>
|
| 861 |
-
<td>Indirect</td>
|
| 862 |
-
<td>190.0</td>
|
| 863 |
-
<td>175.0</td>
|
| 864 |
-
<td>204.0</td>
|
| 865 |
-
<td>39000.0</td>
|
| 866 |
-
<td>36000.0</td>
|
| 867 |
-
<td>42000.0</td>
|
| 868 |
-
<td>High income</td>
|
| 869 |
-
<td>0.180000</td>
|
| 870 |
-
<td>0.160000</td>
|
| 871 |
-
<td>0.200000</td>
|
| 872 |
-
<td>0.340000</td>
|
| 873 |
-
<td>0.31000</td>
|
| 874 |
-
<td>0.360000</td>
|
| 875 |
-
<td>70.000000</td>
|
| 876 |
-
<td>64.000000</td>
|
| 877 |
-
<td>75.000000</td>
|
| 878 |
-
<td>NaN</td>
|
| 879 |
-
<td>18.000000</td>
|
| 880 |
-
<td>17.000000</td>
|
| 881 |
-
<td>20.000000</td>
|
| 882 |
-
</tr>
|
| 883 |
-
<tr>
|
| 884 |
-
<td>Afghanistan</td>
|
| 885 |
-
<td>AF</td>
|
| 886 |
-
<td>AFG</td>
|
| 887 |
-
<td>4</td>
|
| 888 |
-
<td>EMR</td>
|
| 889 |
-
<td>2001</td>
|
| 890 |
-
<td>21347782</td>
|
| 891 |
-
<td>414.0</td>
|
| 892 |
-
<td>200.0</td>
|
| 893 |
-
<td>704.0</td>
|
| 894 |
-
<td>88000.0</td>
|
| 895 |
-
<td>43000.0</td>
|
| 896 |
-
<td>150000.0</td>
|
| 897 |
-
<td>predicted</td>
|
| 898 |
-
<td>64.00</td>
|
| 899 |
-
<td>45.00</td>
|
| 900 |
-
<td>78.00</td>
|
| 901 |
-
<td>14000.0</td>
|
| 902 |
-
<td>9600.0</td>
|
| 903 |
-
<td>17000.0</td>
|
| 904 |
-
<td>0.22</td>
|
| 905 |
-
<td>0.180000</td>
|
| 906 |
-
<td>0.260000</td>
|
| 907 |
-
<td>47.0</td>
|
| 908 |
-
<td>39.000000</td>
|
| 909 |
-
<td>55.000000</td>
|
| 910 |
-
<td>Indirect</td>
|
| 911 |
-
<td>189.0</td>
|
| 912 |
-
<td>175.0</td>
|
| 913 |
-
<td>201.0</td>
|
| 914 |
-
<td>40000.0</td>
|
| 915 |
-
<td>37000.0</td>
|
| 916 |
-
<td>43000.0</td>
|
| 917 |
-
<td>High income</td>
|
| 918 |
-
<td>0.190000</td>
|
| 919 |
-
<td>0.170000</td>
|
| 920 |
-
<td>0.210000</td>
|
| 921 |
-
<td>0.360000</td>
|
| 922 |
-
<td>0.33000</td>
|
| 923 |
-
<td>0.380000</td>
|
| 924 |
-
<td>76.000000</td>
|
| 925 |
-
<td>70.000000</td>
|
| 926 |
-
<td>81.000000</td>
|
| 927 |
-
<td>NaN</td>
|
| 928 |
-
<td>25.000000</td>
|
| 929 |
-
<td>24.000000</td>
|
| 930 |
-
<td>27.000000</td>
|
| 931 |
-
</tr>
|
| 932 |
-
<tr>
|
| 933 |
-
<td>Afghanistan</td>
|
| 934 |
-
<td>AF</td>
|
| 935 |
-
<td>AFG</td>
|
| 936 |
-
<td>4</td>
|
| 937 |
-
<td>EMR</td>
|
| 938 |
-
<td>2002</td>
|
| 939 |
-
<td>22202806</td>
|
| 940 |
-
<td>397.0</td>
|
| 941 |
-
<td>196.0</td>
|
| 942 |
-
<td>667.0</td>
|
| 943 |
-
<td>88000.0</td>
|
| 944 |
-
<td>44000.0</td>
|
| 945 |
-
<td>150000.0</td>
|
| 946 |
-
<td>predicted</td>
|
| 947 |
-
<td>59.00</td>
|
| 948 |
-
<td>42.00</td>
|
| 949 |
-
<td>72.00</td>
|
| 950 |
-
<td>13000.0</td>
|
| 951 |
-
<td>9300.0</td>
|
| 952 |
-
<td>16000.0</td>
|
| 953 |
-
<td>0.22</td>
|
| 954 |
-
<td>0.180000</td>
|
| 955 |
-
<td>0.260000</td>
|
| 956 |
-
<td>48.0</td>
|
| 957 |
-
<td>40.000000</td>
|
| 958 |
-
<td>57.000000</td>
|
| 959 |
-
<td>Indirect</td>
|
| 960 |
-
<td>189.0</td>
|
| 961 |
-
<td>176.0</td>
|
| 962 |
-
<td>200.0</td>
|
| 963 |
-
<td>42000.0</td>
|
| 964 |
-
<td>39000.0</td>
|
| 965 |
-
<td>44000.0</td>
|
| 966 |
-
<td>High income</td>
|
| 967 |
-
<td>0.200000</td>
|
| 968 |
-
<td>0.180000</td>
|
| 969 |
-
<td>0.220000</td>
|
| 970 |
-
<td>0.380000</td>
|
| 971 |
-
<td>0.35000</td>
|
| 972 |
-
<td>0.400000</td>
|
| 973 |
-
<td>84.000000</td>
|
| 974 |
-
<td>78.000000</td>
|
| 975 |
-
<td>89.000000</td>
|
| 976 |
-
<td>NaN</td>
|
| 977 |
-
<td>33.000000</td>
|
| 978 |
-
<td>31.000000</td>
|
| 979 |
-
<td>35.000000</td>
|
| 980 |
-
</tr>
|
| 981 |
-
<tr>
|
| 982 |
-
<td>Afghanistan</td>
|
| 983 |
-
<td>AF</td>
|
| 984 |
-
<td>AFG</td>
|
| 985 |
-
<td>4</td>
|
| 986 |
-
<td>EMR</td>
|
| 987 |
-
<td>2003</td>
|
| 988 |
-
<td>23116142</td>
|
| 989 |
-
<td>377.0</td>
|
| 990 |
-
<td>192.0</td>
|
| 991 |
-
<td>622.0</td>
|
| 992 |
-
<td>87000.0</td>
|
| 993 |
-
<td>44000.0</td>
|
| 994 |
-
<td>140000.0</td>
|
| 995 |
-
<td>predicted</td>
|
| 996 |
-
<td>54.00</td>
|
| 997 |
-
<td>38.00</td>
|
| 998 |
-
<td>66.00</td>
|
| 999 |
-
<td>13000.0</td>
|
| 1000 |
-
<td>8800.0</td>
|
| 1001 |
-
<td>15000.0</td>
|
| 1002 |
-
<td>0.21</td>
|
| 1003 |
-
<td>0.180000</td>
|
| 1004 |
-
<td>0.250000</td>
|
| 1005 |
-
<td>49.0</td>
|
| 1006 |
-
<td>41.000000</td>
|
| 1007 |
-
<td>58.000000</td>
|
| 1008 |
-
<td>Indirect</td>
|
| 1009 |
-
<td>189.0</td>
|
| 1010 |
-
<td>177.0</td>
|
| 1011 |
-
<td>202.0</td>
|
| 1012 |
-
<td>44000.0</td>
|
| 1013 |
-
<td>41000.0</td>
|
| 1014 |
-
<td>47000.0</td>
|
| 1015 |
-
<td>High income</td>
|
| 1016 |
-
<td>0.210000</td>
|
| 1017 |
-
<td>0.190000</td>
|
| 1018 |
-
<td>0.230000</td>
|
| 1019 |
-
<td>0.400000</td>
|
| 1020 |
-
<td>0.37000</td>
|
| 1021 |
-
<td>0.430000</td>
|
| 1022 |
-
<td>92.000000</td>
|
| 1023 |
-
<td>86.000000</td>
|
| 1024 |
-
<td>99.000000</td>
|
| 1025 |
-
<td>NaN</td>
|
| 1026 |
-
<td>32.000000</td>
|
| 1027 |
-
<td>30.000000</td>
|
| 1028 |
-
<td>34.000000</td>
|
| 1029 |
-
</tr>
|
| 1030 |
-
<tr>
|
| 1031 |
-
<td>Afghanistan</td>
|
| 1032 |
-
<td>AF</td>
|
| 1033 |
-
<td>AFG</td>
|
| 1034 |
-
<td>4</td>
|
| 1035 |
-
<td>EMR</td>
|
| 1036 |
-
<td>2004</td>
|
| 1037 |
-
<td>24018682</td>
|
| 1038 |
-
<td>361.0</td>
|
| 1039 |
-
<td>187.0</td>
|
| 1040 |
-
<td>591.0</td>
|
| 1041 |
-
<td>87000.0</td>
|
| 1042 |
-
<td>45000.0</td>
|
| 1043 |
-
<td>140000.0</td>
|
| 1044 |
-
<td>predicted</td>
|
| 1045 |
-
<td>50.00</td>
|
| 1046 |
-
<td>35.00</td>
|
| 1047 |
-
<td>60.00</td>
|
| 1048 |
-
<td>12000.0</td>
|
| 1049 |
-
<td>8400.0</td>
|
| 1050 |
-
<td>15000.0</td>
|
| 1051 |
-
<td>0.21</td>
|
| 1052 |
-
<td>0.170000</td>
|
| 1053 |
-
<td>0.240000</td>
|
| 1054 |
-
<td>50.0</td>
|
| 1055 |
-
<td>42.000000</td>
|
| 1056 |
-
<td>59.000000</td>
|
| 1057 |
-
<td>Indirect</td>
|
| 1058 |
-
<td>189.0</td>
|
| 1059 |
-
<td>176.0</td>
|
| 1060 |
-
<td>204.0</td>
|
| 1061 |
-
<td>45000.0</td>
|
| 1062 |
-
<td>42000.0</td>
|
| 1063 |
-
<td>49000.0</td>
|
| 1064 |
-
<td>High income</td>
|
| 1065 |
-
<td>0.220000</td>
|
| 1066 |
-
<td>0.200000</td>
|
| 1067 |
-
<td>0.250000</td>
|
| 1068 |
-
<td>0.420000</td>
|
| 1069 |
-
<td>0.39000</td>
|
| 1070 |
-
<td>0.460000</td>
|
| 1071 |
-
<td>100.000000</td>
|
| 1072 |
-
<td>94.000000</td>
|
| 1073 |
-
<td>110.000000</td>
|
| 1074 |
-
<td>NaN</td>
|
| 1075 |
-
<td>41.000000</td>
|
| 1076 |
-
<td>38.000000</td>
|
| 1077 |
-
<td>43.000000</td>
|
| 1078 |
-
</tr>
|
| 1079 |
-
<tr>
|
| 1080 |
-
<td>Afghanistan</td>
|
| 1081 |
-
<td>AF</td>
|
| 1082 |
-
<td>AFG</td>
|
| 1083 |
-
<td>4</td>
|
| 1084 |
-
<td>EMR</td>
|
| 1085 |
-
<td>2005</td>
|
| 1086 |
-
<td>24860855</td>
|
| 1087 |
-
<td>351.0</td>
|
| 1088 |
-
<td>183.0</td>
|
| 1089 |
-
<td>571.0</td>
|
| 1090 |
-
<td>87000.0</td>
|
| 1091 |
-
<td>46000.0</td>
|
| 1092 |
-
<td>140000.0</td>
|
| 1093 |
-
<td>predicted</td>
|
| 1094 |
-
<td>46.00</td>
|
| 1095 |
-
<td>32.00</td>
|
| 1096 |
-
<td>56.00</td>
|
| 1097 |
-
<td>12000.0</td>
|
| 1098 |
-
<td>8000.0</td>
|
| 1099 |
-
<td>14000.0</td>
|
| 1100 |
-
<td>0.21</td>
|
| 1101 |
-
<td>0.180000</td>
|
| 1102 |
-
<td>0.250000</td>
|
| 1103 |
-
<td>52.0</td>
|
| 1104 |
-
<td>44.000000</td>
|
| 1105 |
-
<td>61.000000</td>
|
| 1106 |
-
<td>Indirect</td>
|
| 1107 |
-
<td>189.0</td>
|
| 1108 |
-
<td>176.0</td>
|
| 1109 |
-
<td>203.0</td>
|
| 1110 |
-
<td>47000.0</td>
|
| 1111 |
-
<td>44000.0</td>
|
| 1112 |
-
<td>51000.0</td>
|
| 1113 |
-
<td>High income</td>
|
| 1114 |
-
<td>0.240000</td>
|
| 1115 |
-
<td>0.210000</td>
|
| 1116 |
-
<td>0.260000</td>
|
| 1117 |
-
<td>0.450000</td>
|
| 1118 |
-
<td>0.42000</td>
|
| 1119 |
-
<td>0.490000</td>
|
| 1120 |
-
<td>110.000000</td>
|
| 1121 |
-
<td>100.000000</td>
|
| 1122 |
-
<td>120.000000</td>
|
| 1123 |
-
<td>NaN</td>
|
| 1124 |
-
<td>47.000000</td>
|
| 1125 |
-
<td>43.000000</td>
|
| 1126 |
-
<td>50.000000</td>
|
| 1127 |
-
</tr>
|
| 1128 |
-
<tr>
|
| 1129 |
-
<td>Afghanistan</td>
|
| 1130 |
-
<td>AF</td>
|
| 1131 |
-
<td>AFG</td>
|
| 1132 |
-
<td>4</td>
|
| 1133 |
-
<td>EMR</td>
|
| 1134 |
-
<td>2006</td>
|
| 1135 |
-
<td>25631282</td>
|
| 1136 |
-
<td>342.0</td>
|
| 1137 |
-
<td>180.0</td>
|
| 1138 |
-
<td>555.0</td>
|
| 1139 |
-
<td>88000.0</td>
|
| 1140 |
-
<td>46000.0</td>
|
| 1141 |
-
<td>140000.0</td>
|
| 1142 |
-
<td>predicted</td>
|
| 1143 |
-
<td>44.00</td>
|
| 1144 |
-
<td>30.00</td>
|
| 1145 |
-
<td>53.00</td>
|
| 1146 |
-
<td>11000.0</td>
|
| 1147 |
-
<td>7800.0</td>
|
| 1148 |
-
<td>14000.0</td>
|
| 1149 |
-
<td>0.21</td>
|
| 1150 |
-
<td>0.170000</td>
|
| 1151 |
-
<td>0.250000</td>
|
| 1152 |
-
<td>54.0</td>
|
| 1153 |
-
<td>45.000000</td>
|
| 1154 |
-
<td>64.000000</td>
|
| 1155 |
-
<td>Indirect</td>
|
| 1156 |
-
<td>189.0</td>
|
| 1157 |
-
<td>176.0</td>
|
| 1158 |
-
<td>201.0</td>
|
| 1159 |
-
<td>48000.0</td>
|
| 1160 |
-
<td>45000.0</td>
|
| 1161 |
-
<td>51000.0</td>
|
| 1162 |
-
<td>High income</td>
|
| 1163 |
-
<td>0.250000</td>
|
| 1164 |
-
<td>0.230000</td>
|
| 1165 |
-
<td>0.280000</td>
|
| 1166 |
-
<td>0.480000</td>
|
| 1167 |
-
<td>0.44000</td>
|
| 1168 |
-
<td>0.510000</td>
|
| 1169 |
-
<td>120.000000</td>
|
| 1170 |
-
<td>110.000000</td>
|
| 1171 |
-
<td>130.000000</td>
|
| 1172 |
-
<td>NaN</td>
|
| 1173 |
-
<td>53.000000</td>
|
| 1174 |
-
<td>50.000000</td>
|
| 1175 |
-
<td>56.000000</td>
|
| 1176 |
-
</tr>
|
| 1177 |
-
<tr>
|
| 1178 |
-
<td>Afghanistan</td>
|
| 1179 |
-
<td>AF</td>
|
| 1180 |
-
<td>AFG</td>
|
| 1181 |
-
<td>4</td>
|
| 1182 |
-
<td>EMR</td>
|
| 1183 |
-
<td>2007</td>
|
| 1184 |
-
<td>26349243</td>
|
| 1185 |
-
<td>334.0</td>
|
| 1186 |
-
<td>176.0</td>
|
| 1187 |
-
<td>542.0</td>
|
| 1188 |
-
<td>88000.0</td>
|
| 1189 |
-
<td>46000.0</td>
|
| 1190 |
-
<td>140000.0</td>
|
| 1191 |
-
<td>predicted</td>
|
| 1192 |
-
<td>42.00</td>
|
| 1193 |
-
<td>29.00</td>
|
| 1194 |
-
<td>52.00</td>
|
| 1195 |
-
<td>11000.0</td>
|
| 1196 |
-
<td>7600.0</td>
|
| 1197 |
-
<td>14000.0</td>
|
| 1198 |
-
<td>0.22</td>
|
| 1199 |
-
<td>0.180000</td>
|
| 1200 |
-
<td>0.260000</td>
|
| 1201 |
-
<td>57.0</td>
|
| 1202 |
-
<td>47.000000</td>
|
| 1203 |
-
<td>68.000000</td>
|
| 1204 |
-
<td>Indirect</td>
|
| 1205 |
-
<td>189.0</td>
|
| 1206 |
-
<td>176.0</td>
|
| 1207 |
-
<td>201.0</td>
|
| 1208 |
-
<td>50000.0</td>
|
| 1209 |
-
<td>47000.0</td>
|
| 1210 |
-
<td>53000.0</td>
|
| 1211 |
-
<td>High income</td>
|
| 1212 |
-
<td>0.270000</td>
|
| 1213 |
-
<td>0.240000</td>
|
| 1214 |
-
<td>0.290000</td>
|
| 1215 |
-
<td>0.510000</td>
|
| 1216 |
-
<td>0.47000</td>
|
| 1217 |
-
<td>0.540000</td>
|
| 1218 |
-
<td>130.000000</td>
|
| 1219 |
-
<td>120.000000</td>
|
| 1220 |
-
<td>140.000000</td>
|
| 1221 |
-
<td>NaN</td>
|
| 1222 |
-
<td>58.000000</td>
|
| 1223 |
-
<td>54.000000</td>
|
| 1224 |
-
<td>62.000000</td>
|
| 1225 |
-
</tr>
|
| 1226 |
-
<tr>
|
| 1227 |
-
<td>Afghanistan</td>
|
| 1228 |
-
<td>AF</td>
|
| 1229 |
-
<td>AFG</td>
|
| 1230 |
-
<td>4</td>
|
| 1231 |
-
<td>EMR</td>
|
| 1232 |
-
<td>2008</td>
|
| 1233 |
-
<td>27032197</td>
|
| 1234 |
-
<td>331.0</td>
|
| 1235 |
-
<td>175.0</td>
|
| 1236 |
-
<td>537.0</td>
|
| 1237 |
-
<td>90000.0</td>
|
| 1238 |
-
<td>47000.0</td>
|
| 1239 |
-
<td>150000.0</td>
|
| 1240 |
-
<td>predicted</td>
|
| 1241 |
-
<td>41.00</td>
|
| 1242 |
-
<td>28.00</td>
|
| 1243 |
-
<td>52.00</td>
|
| 1244 |
-
<td>11000.0</td>
|
| 1245 |
-
<td>7600.0</td>
|
| 1246 |
-
<td>14000.0</td>
|
| 1247 |
-
<td>0.22</td>
|
| 1248 |
-
<td>0.180000</td>
|
| 1249 |
-
<td>0.270000</td>
|
| 1250 |
-
<td>60.0</td>
|
| 1251 |
-
<td>49.000000</td>
|
| 1252 |
-
<td>72.000000</td>
|
| 1253 |
-
<td>Indirect</td>
|
| 1254 |
-
<td>189.0</td>
|
| 1255 |
-
<td>177.0</td>
|
| 1256 |
-
<td>203.0</td>
|
| 1257 |
-
<td>51000.0</td>
|
| 1258 |
-
<td>48000.0</td>
|
| 1259 |
-
<td>55000.0</td>
|
| 1260 |
-
<td>High income</td>
|
| 1261 |
-
<td>0.280000</td>
|
| 1262 |
-
<td>0.260000</td>
|
| 1263 |
-
<td>0.310000</td>
|
| 1264 |
-
<td>0.530000</td>
|
| 1265 |
-
<td>0.50000</td>
|
| 1266 |
-
<td>0.570000</td>
|
| 1267 |
-
<td>140.000000</td>
|
| 1268 |
-
<td>130.000000</td>
|
| 1269 |
-
<td>160.000000</td>
|
| 1270 |
-
<td>NaN</td>
|
| 1271 |
-
<td>55.000000</td>
|
| 1272 |
-
<td>52.000000</td>
|
| 1273 |
-
<td>59.000000</td>
|
| 1274 |
-
</tr>
|
| 1275 |
-
<tr>
|
| 1276 |
-
<td>Afghanistan</td>
|
| 1277 |
-
<td>AF</td>
|
| 1278 |
-
<td>AFG</td>
|
| 1279 |
-
<td>4</td>
|
| 1280 |
-
<td>EMR</td>
|
| 1281 |
-
<td>2009</td>
|
| 1282 |
-
<td>27708187</td>
|
| 1283 |
-
<td>329.0</td>
|
| 1284 |
-
<td>173.0</td>
|
| 1285 |
-
<td>533.0</td>
|
| 1286 |
-
<td>91000.0</td>
|
| 1287 |
-
<td>48000.0</td>
|
| 1288 |
-
<td>150000.0</td>
|
| 1289 |
-
<td>predicted</td>
|
| 1290 |
-
<td>41.00</td>
|
| 1291 |
-
<td>28.00</td>
|
| 1292 |
-
<td>52.00</td>
|
| 1293 |
-
<td>11000.0</td>
|
| 1294 |
-
<td>7800.0</td>
|
| 1295 |
-
<td>14000.0</td>
|
| 1296 |
-
<td>0.23</td>
|
| 1297 |
-
<td>0.180000</td>
|
| 1298 |
-
<td>0.280000</td>
|
| 1299 |
-
<td>63.0</td>
|
| 1300 |
-
<td>50.000000</td>
|
| 1301 |
-
<td>77.000000</td>
|
| 1302 |
-
<td>Indirect</td>
|
| 1303 |
-
<td>189.0</td>
|
| 1304 |
-
<td>178.0</td>
|
| 1305 |
-
<td>204.0</td>
|
| 1306 |
-
<td>52000.0</td>
|
| 1307 |
-
<td>49000.0</td>
|
| 1308 |
-
<td>56000.0</td>
|
| 1309 |
-
<td>High income</td>
|
| 1310 |
-
<td>0.300000</td>
|
| 1311 |
-
<td>0.270000</td>
|
| 1312 |
-
<td>0.330000</td>
|
| 1313 |
-
<td>0.560000</td>
|
| 1314 |
-
<td>0.52000</td>
|
| 1315 |
-
<td>0.610000</td>
|
| 1316 |
-
<td>160.000000</td>
|
| 1317 |
-
<td>140.000000</td>
|
| 1318 |
-
<td>170.000000</td>
|
| 1319 |
-
<td>NaN</td>
|
| 1320 |
-
<td>50.000000</td>
|
| 1321 |
-
<td>46.000000</td>
|
| 1322 |
-
<td>53.000000</td>
|
| 1323 |
-
</tr>
|
| 1324 |
-
<tr>
|
| 1325 |
-
<td>Afghanistan</td>
|
| 1326 |
-
<td>AF</td>
|
| 1327 |
-
<td>AFG</td>
|
| 1328 |
-
<td>4</td>
|
| 1329 |
-
<td>EMR</td>
|
| 1330 |
-
<td>2010</td>
|
| 1331 |
-
<td>28397812</td>
|
| 1332 |
-
<td>329.0</td>
|
| 1333 |
-
<td>174.0</td>
|
| 1334 |
-
<td>532.0</td>
|
| 1335 |
-
<td>93000.0</td>
|
| 1336 |
-
<td>49000.0</td>
|
| 1337 |
-
<td>150000.0</td>
|
| 1338 |
-
<td>predicted</td>
|
| 1339 |
-
<td>41.00</td>
|
| 1340 |
-
<td>28.00</td>
|
| 1341 |
-
<td>52.00</td>
|
| 1342 |
-
<td>12000.0</td>
|
| 1343 |
-
<td>8000.0</td>
|
| 1344 |
-
<td>15000.0</td>
|
| 1345 |
-
<td>0.24</td>
|
| 1346 |
-
<td>0.190000</td>
|
| 1347 |
-
<td>0.290000</td>
|
| 1348 |
-
<td>68.0</td>
|
| 1349 |
-
<td>55.000000</td>
|
| 1350 |
-
<td>82.000000</td>
|
| 1351 |
-
<td>Indirect</td>
|
| 1352 |
-
<td>189.0</td>
|
| 1353 |
-
<td>176.0</td>
|
| 1354 |
-
<td>205.0</td>
|
| 1355 |
-
<td>54000.0</td>
|
| 1356 |
-
<td>50000.0</td>
|
| 1357 |
-
<td>58000.0</td>
|
| 1358 |
-
<td>High income</td>
|
| 1359 |
-
<td>0.310000</td>
|
| 1360 |
-
<td>0.270000</td>
|
| 1361 |
-
<td>0.340000</td>
|
| 1362 |
-
<td>0.580000</td>
|
| 1363 |
-
<td>0.53000</td>
|
| 1364 |
-
<td>0.640000</td>
|
| 1365 |
-
<td>170.000000</td>
|
| 1366 |
-
<td>150.000000</td>
|
| 1367 |
-
<td>180.000000</td>
|
| 1368 |
-
<td>NaN</td>
|
| 1369 |
-
<td>52.000000</td>
|
| 1370 |
-
<td>48.000000</td>
|
| 1371 |
-
<td>56.000000</td>
|
| 1372 |
-
</tr>
|
| 1373 |
-
<tr>
|
| 1374 |
-
<td>Afghanistan</td>
|
| 1375 |
-
<td>AF</td>
|
| 1376 |
-
<td>AFG</td>
|
| 1377 |
-
<td>4</td>
|
| 1378 |
-
<td>EMR</td>
|
| 1379 |
-
<td>2011</td>
|
| 1380 |
-
<td>29105480</td>
|
| 1381 |
-
<td>332.0</td>
|
| 1382 |
-
<td>175.0</td>
|
| 1383 |
-
<td>538.0</td>
|
| 1384 |
-
<td>97000.0</td>
|
| 1385 |
-
<td>51000.0</td>
|
| 1386 |
-
<td>160000.0</td>
|
| 1387 |
-
<td>predicted</td>
|
| 1388 |
-
<td>41.00</td>
|
| 1389 |
-
<td>28.00</td>
|
| 1390 |
-
<td>53.00</td>
|
| 1391 |
-
<td>12000.0</td>
|
| 1392 |
-
<td>8100.0</td>
|
| 1393 |
-
<td>15000.0</td>
|
| 1394 |
-
<td>0.25</td>
|
| 1395 |
-
<td>0.200000</td>
|
| 1396 |
-
<td>0.300000</td>
|
| 1397 |
-
<td>72.0</td>
|
| 1398 |
-
<td>58.000000</td>
|
| 1399 |
-
<td>87.000000</td>
|
| 1400 |
-
<td>Indirect</td>
|
| 1401 |
-
<td>189.0</td>
|
| 1402 |
-
<td>174.0</td>
|
| 1403 |
-
<td>206.0</td>
|
| 1404 |
-
<td>55000.0</td>
|
| 1405 |
-
<td>51000.0</td>
|
| 1406 |
-
<td>60000.0</td>
|
| 1407 |
-
<td>High income</td>
|
| 1408 |
-
<td>0.320000</td>
|
| 1409 |
-
<td>0.280000</td>
|
| 1410 |
-
<td>0.360000</td>
|
| 1411 |
-
<td>0.600000</td>
|
| 1412 |
-
<td>0.55000</td>
|
| 1413 |
-
<td>0.660000</td>
|
| 1414 |
-
<td>180.000000</td>
|
| 1415 |
-
<td>160.000000</td>
|
| 1416 |
-
<td>190.000000</td>
|
| 1417 |
-
<td>NaN</td>
|
| 1418 |
-
<td>51.000000</td>
|
| 1419 |
-
<td>47.000000</td>
|
| 1420 |
-
<td>55.000000</td>
|
| 1421 |
-
</tr>
|
| 1422 |
-
<tr>
|
| 1423 |
-
<td>Afghanistan</td>
|
| 1424 |
-
<td>AF</td>
|
| 1425 |
-
<td>AFG</td>
|
| 1426 |
-
<td>4</td>
|
| 1427 |
-
<td>EMR</td>
|
| 1428 |
-
<td>2012</td>
|
| 1429 |
-
<td>29824536</td>
|
| 1430 |
-
<td>335.0</td>
|
| 1431 |
-
<td>176.0</td>
|
| 1432 |
-
<td>545.0</td>
|
| 1433 |
-
<td>100000.0</td>
|
| 1434 |
-
<td>52000.0</td>
|
| 1435 |
-
<td>160000.0</td>
|
| 1436 |
-
<td>predicted</td>
|
| 1437 |
-
<td>42.00</td>
|
| 1438 |
-
<td>28.00</td>
|
| 1439 |
-
<td>53.00</td>
|
| 1440 |
-
<td>12000.0</td>
|
| 1441 |
-
<td>8200.0</td>
|
| 1442 |
-
<td>16000.0</td>
|
| 1443 |
-
<td>0.25</td>
|
| 1444 |
-
<td>0.200000</td>
|
| 1445 |
-
<td>0.310000</td>
|
| 1446 |
-
<td>76.0</td>
|
| 1447 |
-
<td>61.000000</td>
|
| 1448 |
-
<td>92.000000</td>
|
| 1449 |
-
<td>Indirect</td>
|
| 1450 |
-
<td>189.0</td>
|
| 1451 |
-
<td>171.0</td>
|
| 1452 |
-
<td>209.0</td>
|
| 1453 |
-
<td>56000.0</td>
|
| 1454 |
-
<td>51000.0</td>
|
| 1455 |
-
<td>62000.0</td>
|
| 1456 |
-
<td>High income</td>
|
| 1457 |
-
<td>0.330000</td>
|
| 1458 |
-
<td>0.280000</td>
|
| 1459 |
-
<td>0.380000</td>
|
| 1460 |
-
<td>0.620000</td>
|
| 1461 |
-
<td>0.56000</td>
|
| 1462 |
-
<td>0.690000</td>
|
| 1463 |
-
<td>190.000000</td>
|
| 1464 |
-
<td>170.000000</td>
|
| 1465 |
-
<td>210.000000</td>
|
| 1466 |
-
<td>NaN</td>
|
| 1467 |
-
<td>51.000000</td>
|
| 1468 |
-
<td>46.000000</td>
|
| 1469 |
-
<td>56.000000</td>
|
| 1470 |
-
</tr>
|
| 1471 |
-
<tr>
|
| 1472 |
-
<td>Afghanistan</td>
|
| 1473 |
-
<td>AF</td>
|
| 1474 |
-
<td>AFG</td>
|
| 1475 |
-
<td>4</td>
|
| 1476 |
-
<td>EMR</td>
|
| 1477 |
-
<td>2013</td>
|
| 1478 |
-
<td>30551674</td>
|
| 1479 |
-
<td>340.0</td>
|
| 1480 |
-
<td>178.0</td>
|
| 1481 |
-
<td>554.0</td>
|
| 1482 |
-
<td>100000.0</td>
|
| 1483 |
-
<td>54000.0</td>
|
| 1484 |
-
<td>170000.0</td>
|
| 1485 |
-
<td>predicted</td>
|
| 1486 |
-
<td>42.00</td>
|
| 1487 |
-
<td>27.00</td>
|
| 1488 |
-
<td>53.00</td>
|
| 1489 |
-
<td>13000.0</td>
|
| 1490 |
-
<td>8400.0</td>
|
| 1491 |
-
<td>16000.0</td>
|
| 1492 |
-
<td>0.27</td>
|
| 1493 |
-
<td>0.210000</td>
|
| 1494 |
-
<td>0.330000</td>
|
| 1495 |
-
<td>82.0</td>
|
| 1496 |
-
<td>65.000000</td>
|
| 1497 |
-
<td>100.000000</td>
|
| 1498 |
-
<td>Indirect</td>
|
| 1499 |
-
<td>189.0</td>
|
| 1500 |
-
<td>167.0</td>
|
| 1501 |
-
<td>212.0</td>
|
| 1502 |
-
<td>58000.0</td>
|
| 1503 |
-
<td>51000.0</td>
|
| 1504 |
-
<td>65000.0</td>
|
| 1505 |
-
<td>High income</td>
|
| 1506 |
-
<td>0.340000</td>
|
| 1507 |
-
<td>0.290000</td>
|
| 1508 |
-
<td>0.400000</td>
|
| 1509 |
-
<td>0.640000</td>
|
| 1510 |
-
<td>0.57000</td>
|
| 1511 |
-
<td>0.720000</td>
|
| 1512 |
-
<td>200.000000</td>
|
| 1513 |
-
<td>170.000000</td>
|
| 1514 |
-
<td>220.000000</td>
|
| 1515 |
-
<td>NaN</td>
|
| 1516 |
-
<td>53.000000</td>
|
| 1517 |
-
<td>47.000000</td>
|
| 1518 |
-
<td>60.000000</td>
|
| 1519 |
-
</tr>
|
| 1520 |
-
<tr>
|
| 1521 |
-
<td>Albania</td>
|
| 1522 |
-
<td>AL</td>
|
| 1523 |
-
<td>ALB</td>
|
| 1524 |
-
<td>8</td>
|
| 1525 |
-
<td>EUR</td>
|
| 1526 |
-
<td>1990</td>
|
| 1527 |
-
<td>3446882</td>
|
| 1528 |
-
<td>36.0</td>
|
| 1529 |
-
<td>17.0</td>
|
| 1530 |
-
<td>62.0</td>
|
| 1531 |
-
<td>1200.0</td>
|
| 1532 |
-
<td>570.0</td>
|
| 1533 |
-
<td>2100.0</td>
|
| 1534 |
-
<td>predicted</td>
|
| 1535 |
-
<td>1.80</td>
|
| 1536 |
-
<td>1.40</td>
|
| 1537 |
-
<td>2.30</td>
|
| 1538 |
-
<td>63.0</td>
|
| 1539 |
-
<td>49.0</td>
|
| 1540 |
-
<td>80.0</td>
|
| 1541 |
-
<td>0.00</td>
|
| 1542 |
-
<td>14.224248</td>
|
| 1543 |
-
<td>24.824956</td>
|
| 1544 |
-
<td>0.0</td>
|
| 1545 |
-
<td>2157.701344</td>
|
| 1546 |
-
<td>3812.193046</td>
|
| 1547 |
-
<td>VR imputed</td>
|
| 1548 |
-
<td>24.0</td>
|
| 1549 |
-
<td>18.0</td>
|
| 1550 |
-
<td>32.0</td>
|
| 1551 |
-
<td>840.0</td>
|
| 1552 |
-
<td>600.0</td>
|
| 1553 |
-
<td>1100.0</td>
|
| 1554 |
-
<td>High income</td>
|
| 1555 |
-
<td>11.179119</td>
|
| 1556 |
-
<td>9.150986</td>
|
| 1557 |
-
<td>13.379267</td>
|
| 1558 |
-
<td>40.228274</td>
|
| 1559 |
-
<td>33.89232</td>
|
| 1560 |
-
<td>47.326378</td>
|
| 1561 |
-
<td>6095.426979</td>
|
| 1562 |
-
<td>5215.147573</td>
|
| 1563 |
-
<td>7363.644445</td>
|
| 1564 |
-
<td>NaN</td>
|
| 1565 |
-
<td>78.000000</td>
|
| 1566 |
-
<td>59.000000</td>
|
| 1567 |
-
<td>110.000000</td>
|
| 1568 |
-
</tr>
|
| 1569 |
-
<tr>
|
| 1570 |
-
<td>Albania</td>
|
| 1571 |
-
<td>AL</td>
|
| 1572 |
-
<td>ALB</td>
|
| 1573 |
-
<td>8</td>
|
| 1574 |
-
<td>EUR</td>
|
| 1575 |
-
<td>1991</td>
|
| 1576 |
-
<td>3459763</td>
|
| 1577 |
-
<td>35.0</td>
|
| 1578 |
-
<td>16.0</td>
|
| 1579 |
-
<td>61.0</td>
|
| 1580 |
-
<td>1200.0</td>
|
| 1581 |
-
<td>550.0</td>
|
| 1582 |
-
<td>2100.0</td>
|
| 1583 |
-
<td>predicted</td>
|
| 1584 |
-
<td>1.70</td>
|
| 1585 |
-
<td>1.30</td>
|
| 1586 |
-
<td>2.10</td>
|
| 1587 |
-
<td>58.0</td>
|
| 1588 |
-
<td>45.0</td>
|
| 1589 |
-
<td>74.0</td>
|
| 1590 |
-
<td>0.00</td>
|
| 1591 |
-
<td>14.224248</td>
|
| 1592 |
-
<td>24.824956</td>
|
| 1593 |
-
<td>0.0</td>
|
| 1594 |
-
<td>2157.701344</td>
|
| 1595 |
-
<td>3812.193046</td>
|
| 1596 |
-
<td>VR imputed</td>
|
| 1597 |
-
<td>24.0</td>
|
| 1598 |
-
<td>18.0</td>
|
| 1599 |
-
<td>32.0</td>
|
| 1600 |
-
<td>840.0</td>
|
| 1601 |
-
<td>610.0</td>
|
| 1602 |
-
<td>1100.0</td>
|
| 1603 |
-
<td>High income</td>
|
| 1604 |
-
<td>11.179119</td>
|
| 1605 |
-
<td>9.150986</td>
|
| 1606 |
-
<td>13.379267</td>
|
| 1607 |
-
<td>40.228274</td>
|
| 1608 |
-
<td>33.89232</td>
|
| 1609 |
-
<td>47.326378</td>
|
| 1610 |
-
<td>6095.426979</td>
|
| 1611 |
-
<td>5215.147573</td>
|
| 1612 |
-
<td>7363.644445</td>
|
| 1613 |
-
<td>NaN</td>
|
| 1614 |
-
<td>74.000000</td>
|
| 1615 |
-
<td>57.000000</td>
|
| 1616 |
-
<td>100.000000</td>
|
| 1617 |
-
</tr>
|
| 1618 |
-
<tr>
|
| 1619 |
-
<td>Albania</td>
|
| 1620 |
-
<td>AL</td>
|
| 1621 |
-
<td>ALB</td>
|
| 1622 |
-
<td>8</td>
|
| 1623 |
-
<td>EUR</td>
|
| 1624 |
-
<td>1992</td>
|
| 1625 |
-
<td>3446858</td>
|
| 1626 |
-
<td>34.0</td>
|
| 1627 |
-
<td>15.0</td>
|
| 1628 |
-
<td>60.0</td>
|
| 1629 |
-
<td>1200.0</td>
|
| 1630 |
-
<td>520.0</td>
|
| 1631 |
-
<td>2100.0</td>
|
| 1632 |
-
<td>predicted</td>
|
| 1633 |
-
<td>1.10</td>
|
| 1634 |
-
<td>0.75</td>
|
| 1635 |
-
<td>1.50</td>
|
| 1636 |
-
<td>37.0</td>
|
| 1637 |
-
<td>26.0</td>
|
| 1638 |
-
<td>51.0</td>
|
| 1639 |
-
<td>0.00</td>
|
| 1640 |
-
<td>14.224248</td>
|
| 1641 |
-
<td>24.824956</td>
|
| 1642 |
-
<td>0.0</td>
|
| 1643 |
-
<td>2157.701344</td>
|
| 1644 |
-
<td>3812.193046</td>
|
| 1645 |
-
<td>VR</td>
|
| 1646 |
-
<td>24.0</td>
|
| 1647 |
-
<td>17.0</td>
|
| 1648 |
-
<td>33.0</td>
|
| 1649 |
-
<td>840.0</td>
|
| 1650 |
-
<td>580.0</td>
|
| 1651 |
-
<td>1100.0</td>
|
| 1652 |
-
<td>High income</td>
|
| 1653 |
-
<td>11.179119</td>
|
| 1654 |
-
<td>9.150986</td>
|
| 1655 |
-
<td>13.379267</td>
|
| 1656 |
-
<td>40.228274</td>
|
| 1657 |
-
<td>33.89232</td>
|
| 1658 |
-
<td>47.326378</td>
|
| 1659 |
-
<td>6095.426979</td>
|
| 1660 |
-
<td>5215.147573</td>
|
| 1661 |
-
<td>7363.644445</td>
|
| 1662 |
-
<td>NaN</td>
|
| 1663 |
-
<td>68.217851</td>
|
| 1664 |
-
<td>61.959409</td>
|
| 1665 |
-
<td>75.854492</td>
|
| 1666 |
-
</tr>
|
| 1667 |
-
<tr>
|
| 1668 |
-
<td>Albania</td>
|
| 1669 |
-
<td>AL</td>
|
| 1670 |
-
<td>ALB</td>
|
| 1671 |
-
<td>8</td>
|
| 1672 |
-
<td>EUR</td>
|
| 1673 |
-
<td>1993</td>
|
| 1674 |
-
<td>3417280</td>
|
| 1675 |
-
<td>33.0</td>
|
| 1676 |
-
<td>15.0</td>
|
| 1677 |
-
<td>59.0</td>
|
| 1678 |
-
<td>1100.0</td>
|
| 1679 |
-
<td>500.0</td>
|
| 1680 |
-
<td>2000.0</td>
|
| 1681 |
-
<td>predicted</td>
|
| 1682 |
-
<td>1.50</td>
|
| 1683 |
-
<td>1.10</td>
|
| 1684 |
-
<td>1.90</td>
|
| 1685 |
-
<td>51.0</td>
|
| 1686 |
-
<td>38.0</td>
|
| 1687 |
-
<td>66.0</td>
|
| 1688 |
-
<td>0.00</td>
|
| 1689 |
-
<td>14.224248</td>
|
| 1690 |
-
<td>24.824956</td>
|
| 1691 |
-
<td>0.0</td>
|
| 1692 |
-
<td>2157.701344</td>
|
| 1693 |
-
<td>3812.193046</td>
|
| 1694 |
-
<td>VR</td>
|
| 1695 |
-
<td>24.0</td>
|
| 1696 |
-
<td>18.0</td>
|
| 1697 |
-
<td>32.0</td>
|
| 1698 |
-
<td>830.0</td>
|
| 1699 |
-
<td>610.0</td>
|
| 1700 |
-
<td>1100.0</td>
|
| 1701 |
-
<td>High income</td>
|
| 1702 |
-
<td>11.179119</td>
|
| 1703 |
-
<td>9.150986</td>
|
| 1704 |
-
<td>13.379267</td>
|
| 1705 |
-
<td>40.228274</td>
|
| 1706 |
-
<td>33.89232</td>
|
| 1707 |
-
<td>47.326378</td>
|
| 1708 |
-
<td>6095.426979</td>
|
| 1709 |
-
<td>5215.147573</td>
|
| 1710 |
-
<td>7363.644445</td>
|
| 1711 |
-
<td>NaN</td>
|
| 1712 |
-
<td>68.217851</td>
|
| 1713 |
-
<td>61.959409</td>
|
| 1714 |
-
<td>75.854492</td>
|
| 1715 |
-
</tr>
|
| 1716 |
-
<tr>
|
| 1717 |
-
<td>Albania</td>
|
| 1718 |
-
<td>AL</td>
|
| 1719 |
-
<td>ALB</td>
|
| 1720 |
-
<td>8</td>
|
| 1721 |
-
<td>EUR</td>
|
| 1722 |
-
<td>1994</td>
|
| 1723 |
-
<td>3384367</td>
|
| 1724 |
-
<td>33.0</td>
|
| 1725 |
-
<td>14.0</td>
|
| 1726 |
-
<td>59.0</td>
|
| 1727 |
-
<td>1100.0</td>
|
| 1728 |
-
<td>480.0</td>
|
| 1729 |
-
<td>2000.0</td>
|
| 1730 |
-
<td>predicted</td>
|
| 1731 |
-
<td>1.80</td>
|
| 1732 |
-
<td>1.30</td>
|
| 1733 |
-
<td>2.30</td>
|
| 1734 |
-
<td>61.0</td>
|
| 1735 |
-
<td>44.0</td>
|
| 1736 |
-
<td>79.0</td>
|
| 1737 |
-
<td>0.00</td>
|
| 1738 |
-
<td>14.224248</td>
|
| 1739 |
-
<td>24.824956</td>
|
| 1740 |
-
<td>0.0</td>
|
| 1741 |
-
<td>2157.701344</td>
|
| 1742 |
-
<td>3812.193046</td>
|
| 1743 |
-
<td>VR</td>
|
| 1744 |
-
<td>24.0</td>
|
| 1745 |
-
<td>21.0</td>
|
| 1746 |
-
<td>28.0</td>
|
| 1747 |
-
<td>830.0</td>
|
| 1748 |
-
<td>700.0</td>
|
| 1749 |
-
<td>960.0</td>
|
| 1750 |
-
<td>High income</td>
|
| 1751 |
-
<td>11.179119</td>
|
| 1752 |
-
<td>9.150986</td>
|
| 1753 |
-
<td>13.379267</td>
|
| 1754 |
-
<td>40.228274</td>
|
| 1755 |
-
<td>33.89232</td>
|
| 1756 |
-
<td>47.326378</td>
|
| 1757 |
-
<td>6095.426979</td>
|
| 1758 |
-
<td>5215.147573</td>
|
| 1759 |
-
<td>7363.644445</td>
|
| 1760 |
-
<td>NaN</td>
|
| 1761 |
-
<td>86.000000</td>
|
| 1762 |
-
<td>73.000000</td>
|
| 1763 |
-
<td>100.000000</td>
|
| 1764 |
-
</tr>
|
| 1765 |
-
<tr>
|
| 1766 |
-
<td>Albania</td>
|
| 1767 |
-
<td>AL</td>
|
| 1768 |
-
<td>ALB</td>
|
| 1769 |
-
<td>8</td>
|
| 1770 |
-
<td>EUR</td>
|
| 1771 |
-
<td>1995</td>
|
| 1772 |
-
<td>3357858</td>
|
| 1773 |
-
<td>32.0</td>
|
| 1774 |
-
<td>14.0</td>
|
| 1775 |
-
<td>58.0</td>
|
| 1776 |
-
<td>1100.0</td>
|
| 1777 |
-
<td>460.0</td>
|
| 1778 |
-
<td>2000.0</td>
|
| 1779 |
-
<td>predicted</td>
|
| 1780 |
-
<td>0.68</td>
|
| 1781 |
-
<td>0.55</td>
|
| 1782 |
-
<td>0.83</td>
|
| 1783 |
-
<td>23.0</td>
|
| 1784 |
-
<td>18.0</td>
|
| 1785 |
-
<td>28.0</td>
|
| 1786 |
-
<td>0.00</td>
|
| 1787 |
-
<td>14.224248</td>
|
| 1788 |
-
<td>24.824956</td>
|
| 1789 |
-
<td>0.0</td>
|
| 1790 |
-
<td>2157.701344</td>
|
| 1791 |
-
<td>3812.193046</td>
|
| 1792 |
-
<td>VR</td>
|
| 1793 |
-
<td>24.0</td>
|
| 1794 |
-
<td>20.0</td>
|
| 1795 |
-
<td>29.0</td>
|
| 1796 |
-
<td>820.0</td>
|
| 1797 |
-
<td>680.0</td>
|
| 1798 |
-
<td>970.0</td>
|
| 1799 |
-
<td>High income</td>
|
| 1800 |
-
<td>11.179119</td>
|
| 1801 |
-
<td>9.150986</td>
|
| 1802 |
-
<td>13.379267</td>
|
| 1803 |
-
<td>40.228274</td>
|
| 1804 |
-
<td>33.89232</td>
|
| 1805 |
-
<td>47.326378</td>
|
| 1806 |
-
<td>6095.426979</td>
|
| 1807 |
-
<td>5215.147573</td>
|
| 1808 |
-
<td>7363.644445</td>
|
| 1809 |
-
<td>NaN</td>
|
| 1810 |
-
<td>78.000000</td>
|
| 1811 |
-
<td>66.000000</td>
|
| 1812 |
-
<td>94.000000</td>
|
| 1813 |
-
</tr>
|
| 1814 |
-
<tr>
|
| 1815 |
-
<td>Albania</td>
|
| 1816 |
-
<td>AL</td>
|
| 1817 |
-
<td>ALB</td>
|
| 1818 |
-
<td>8</td>
|
| 1819 |
-
<td>EUR</td>
|
| 1820 |
-
<td>1996</td>
|
| 1821 |
-
<td>3341043</td>
|
| 1822 |
-
<td>32.0</td>
|
| 1823 |
-
<td>14.0</td>
|
| 1824 |
-
<td>58.0</td>
|
| 1825 |
-
<td>1100.0</td>
|
| 1826 |
-
<td>460.0</td>
|
| 1827 |
-
<td>1900.0</td>
|
| 1828 |
-
<td>predicted</td>
|
| 1829 |
-
<td>1.50</td>
|
| 1830 |
-
<td>1.20</td>
|
| 1831 |
-
<td>1.80</td>
|
| 1832 |
-
<td>49.0</td>
|
| 1833 |
-
<td>39.0</td>
|
| 1834 |
-
<td>59.0</td>
|
| 1835 |
-
<td>0.00</td>
|
| 1836 |
-
<td>14.224248</td>
|
| 1837 |
-
<td>24.824956</td>
|
| 1838 |
-
<td>0.0</td>
|
| 1839 |
-
<td>2157.701344</td>
|
| 1840 |
-
<td>3812.193046</td>
|
| 1841 |
-
<td>VR</td>
|
| 1842 |
-
<td>24.0</td>
|
| 1843 |
-
<td>22.0</td>
|
| 1844 |
-
<td>27.0</td>
|
| 1845 |
-
<td>810.0</td>
|
| 1846 |
-
<td>730.0</td>
|
| 1847 |
-
<td>910.0</td>
|
| 1848 |
-
<td>High income</td>
|
| 1849 |
-
<td>11.179119</td>
|
| 1850 |
-
<td>9.150986</td>
|
| 1851 |
-
<td>13.379267</td>
|
| 1852 |
-
<td>40.228274</td>
|
| 1853 |
-
<td>33.89232</td>
|
| 1854 |
-
<td>47.326378</td>
|
| 1855 |
-
<td>6095.426979</td>
|
| 1856 |
-
<td>5215.147573</td>
|
| 1857 |
-
<td>7363.644445</td>
|
| 1858 |
-
<td>NaN</td>
|
| 1859 |
-
<td>91.000000</td>
|
| 1860 |
-
<td>81.000000</td>
|
| 1861 |
-
<td>100.000000</td>
|
| 1862 |
-
</tr>
|
| 1863 |
-
<tr>
|
| 1864 |
-
<td>Albania</td>
|
| 1865 |
-
<td>AL</td>
|
| 1866 |
-
<td>ALB</td>
|
| 1867 |
-
<td>8</td>
|
| 1868 |
-
<td>EUR</td>
|
| 1869 |
-
<td>1997</td>
|
| 1870 |
-
<td>3331317</td>
|
| 1871 |
-
<td>32.0</td>
|
| 1872 |
-
<td>14.0</td>
|
| 1873 |
-
<td>58.0</td>
|
| 1874 |
-
<td>1100.0</td>
|
| 1875 |
-
<td>460.0</td>
|
| 1876 |
-
<td>1900.0</td>
|
| 1877 |
-
<td>predicted</td>
|
| 1878 |
-
<td>0.84</td>
|
| 1879 |
-
<td>0.66</td>
|
| 1880 |
-
<td>1.10</td>
|
| 1881 |
-
<td>28.0</td>
|
| 1882 |
-
<td>22.0</td>
|
| 1883 |
-
<td>35.0</td>
|
| 1884 |
-
<td>0.00</td>
|
| 1885 |
-
<td>14.224248</td>
|
| 1886 |
-
<td>24.824956</td>
|
| 1887 |
-
<td>0.0</td>
|
| 1888 |
-
<td>2157.701344</td>
|
| 1889 |
-
<td>3812.193046</td>
|
| 1890 |
-
<td>VR</td>
|
| 1891 |
-
<td>24.0</td>
|
| 1892 |
-
<td>21.0</td>
|
| 1893 |
-
<td>28.0</td>
|
| 1894 |
-
<td>810.0</td>
|
| 1895 |
-
<td>690.0</td>
|
| 1896 |
-
<td>950.0</td>
|
| 1897 |
-
<td>High income</td>
|
| 1898 |
-
<td>11.179119</td>
|
| 1899 |
-
<td>9.150986</td>
|
| 1900 |
-
<td>13.379267</td>
|
| 1901 |
-
<td>40.228274</td>
|
| 1902 |
-
<td>33.89232</td>
|
| 1903 |
-
<td>47.326378</td>
|
| 1904 |
-
<td>6095.426979</td>
|
| 1905 |
-
<td>5215.147573</td>
|
| 1906 |
-
<td>7363.644445</td>
|
| 1907 |
-
<td>NaN</td>
|
| 1908 |
-
<td>81.000000</td>
|
| 1909 |
-
<td>69.000000</td>
|
| 1910 |
-
<td>95.000000</td>
|
| 1911 |
-
</tr>
|
| 1912 |
-
<tr>
|
| 1913 |
-
<td>Albania</td>
|
| 1914 |
-
<td>AL</td>
|
| 1915 |
-
<td>ALB</td>
|
| 1916 |
-
<td>8</td>
|
| 1917 |
-
<td>EUR</td>
|
| 1918 |
-
<td>1998</td>
|
| 1919 |
-
<td>3325456</td>
|
| 1920 |
-
<td>36.0</td>
|
| 1921 |
-
<td>16.0</td>
|
| 1922 |
-
<td>64.0</td>
|
| 1923 |
-
<td>1200.0</td>
|
| 1924 |
-
<td>550.0</td>
|
| 1925 |
-
<td>2100.0</td>
|
| 1926 |
-
<td>predicted</td>
|
| 1927 |
-
<td>0.94</td>
|
| 1928 |
-
<td>0.73</td>
|
| 1929 |
-
<td>1.20</td>
|
| 1930 |
-
<td>31.0</td>
|
| 1931 |
-
<td>24.0</td>
|
| 1932 |
-
<td>39.0</td>
|
| 1933 |
-
<td>0.00</td>
|
| 1934 |
-
<td>14.224248</td>
|
| 1935 |
-
<td>24.824956</td>
|
| 1936 |
-
<td>0.0</td>
|
| 1937 |
-
<td>2157.701344</td>
|
| 1938 |
-
<td>3812.193046</td>
|
| 1939 |
-
<td>VR</td>
|
| 1940 |
-
<td>26.0</td>
|
| 1941 |
-
<td>22.0</td>
|
| 1942 |
-
<td>30.0</td>
|
| 1943 |
-
<td>860.0</td>
|
| 1944 |
-
<td>730.0</td>
|
| 1945 |
-
<td>1000.0</td>
|
| 1946 |
-
<td>High income</td>
|
| 1947 |
-
<td>11.179119</td>
|
| 1948 |
-
<td>9.150986</td>
|
| 1949 |
-
<td>13.379267</td>
|
| 1950 |
-
<td>40.228274</td>
|
| 1951 |
-
<td>33.89232</td>
|
| 1952 |
-
<td>47.326378</td>
|
| 1953 |
-
<td>6095.426979</td>
|
| 1954 |
-
<td>5215.147573</td>
|
| 1955 |
-
<td>7363.644445</td>
|
| 1956 |
-
<td>NaN</td>
|
| 1957 |
-
<td>80.000000</td>
|
| 1958 |
-
<td>69.000000</td>
|
| 1959 |
-
<td>95.000000</td>
|
| 1960 |
-
</tr>
|
| 1961 |
-
<tr>
|
| 1962 |
-
<td>Albania</td>
|
| 1963 |
-
<td>AL</td>
|
| 1964 |
-
<td>ALB</td>
|
| 1965 |
-
<td>8</td>
|
| 1966 |
-
<td>EUR</td>
|
| 1967 |
-
<td>1999</td>
|
| 1968 |
-
<td>3317941</td>
|
| 1969 |
-
<td>41.0</td>
|
| 1970 |
-
<td>19.0</td>
|
| 1971 |
-
<td>70.0</td>
|
| 1972 |
-
<td>1400.0</td>
|
| 1973 |
-
<td>640.0</td>
|
| 1974 |
-
<td>2300.0</td>
|
| 1975 |
-
<td>predicted</td>
|
| 1976 |
-
<td>0.79</td>
|
| 1977 |
-
<td>0.59</td>
|
| 1978 |
-
<td>1.00</td>
|
| 1979 |
-
<td>26.0</td>
|
| 1980 |
-
<td>20.0</td>
|
| 1981 |
-
<td>34.0</td>
|
| 1982 |
-
<td>0.00</td>
|
| 1983 |
-
<td>14.224248</td>
|
| 1984 |
-
<td>24.824956</td>
|
| 1985 |
-
<td>0.0</td>
|
| 1986 |
-
<td>2157.701344</td>
|
| 1987 |
-
<td>3812.193046</td>
|
| 1988 |
-
<td>VR</td>
|
| 1989 |
-
<td>27.0</td>
|
| 1990 |
-
<td>23.0</td>
|
| 1991 |
-
<td>32.0</td>
|
| 1992 |
-
<td>910.0</td>
|
| 1993 |
-
<td>770.0</td>
|
| 1994 |
-
<td>1100.0</td>
|
| 1995 |
-
<td>High income</td>
|
| 1996 |
-
<td>11.179119</td>
|
| 1997 |
-
<td>9.150986</td>
|
| 1998 |
-
<td>13.379267</td>
|
| 1999 |
-
<td>40.228274</td>
|
| 2000 |
-
<td>33.89232</td>
|
| 2001 |
-
<td>47.326378</td>
|
| 2002 |
-
<td>6095.426979</td>
|
| 2003 |
-
<td>5215.147573</td>
|
| 2004 |
-
<td>7363.644445</td>
|
| 2005 |
-
<td>NaN</td>
|
| 2006 |
-
<td>80.000000</td>
|
| 2007 |
-
<td>69.000000</td>
|
| 2008 |
-
<td>95.000000</td>
|
| 2009 |
-
</tr>
|
| 2010 |
-
<tr>
|
| 2011 |
-
<td>Albania</td>
|
| 2012 |
-
<td>AL</td>
|
| 2013 |
-
<td>ALB</td>
|
| 2014 |
-
<td>8</td>
|
| 2015 |
-
<td>EUR</td>
|
| 2016 |
-
<td>2000</td>
|
| 2017 |
-
<td>3304948</td>
|
| 2018 |
-
<td>30.0</td>
|
| 2019 |
-
<td>13.0</td>
|
| 2020 |
-
<td>54.0</td>
|
| 2021 |
-
<td>980.0</td>
|
| 2022 |
-
<td>410.0</td>
|
| 2023 |
-
<td>1800.0</td>
|
| 2024 |
-
<td>predicted</td>
|
| 2025 |
-
<td>0.82</td>
|
| 2026 |
-
<td>0.57</td>
|
| 2027 |
-
<td>1.10</td>
|
| 2028 |
-
<td>27.0</td>
|
| 2029 |
-
<td>19.0</td>
|
| 2030 |
-
<td>37.0</td>
|
| 2031 |
-
<td>0.00</td>
|
| 2032 |
-
<td>14.224248</td>
|
| 2033 |
-
<td>24.824956</td>
|
| 2034 |
-
<td>0.0</td>
|
| 2035 |
-
<td>2157.701344</td>
|
| 2036 |
-
<td>3812.193046</td>
|
| 2037 |
-
<td>VR</td>
|
| 2038 |
-
<td>23.0</td>
|
| 2039 |
-
<td>19.0</td>
|
| 2040 |
-
<td>26.0</td>
|
| 2041 |
-
<td>750.0</td>
|
| 2042 |
-
<td>630.0</td>
|
| 2043 |
-
<td>870.0</td>
|
| 2044 |
-
<td>High income</td>
|
| 2045 |
-
<td>11.179119</td>
|
| 2046 |
-
<td>9.150986</td>
|
| 2047 |
-
<td>13.379267</td>
|
| 2048 |
-
<td>40.228274</td>
|
| 2049 |
-
<td>33.89232</td>
|
| 2050 |
-
<td>47.326378</td>
|
| 2051 |
-
<td>6095.426979</td>
|
| 2052 |
-
<td>5215.147573</td>
|
| 2053 |
-
<td>7363.644445</td>
|
| 2054 |
-
<td>NaN</td>
|
| 2055 |
-
<td>81.000000</td>
|
| 2056 |
-
<td>69.000000</td>
|
| 2057 |
-
<td>95.000000</td>
|
| 2058 |
-
</tr>
|
| 2059 |
-
<tr>
|
| 2060 |
-
<td>Albania</td>
|
| 2061 |
-
<td>AL</td>
|
| 2062 |
-
<td>ALB</td>
|
| 2063 |
-
<td>8</td>
|
| 2064 |
-
<td>EUR</td>
|
| 2065 |
-
<td>2001</td>
|
| 2066 |
-
<td>3286084</td>
|
| 2067 |
-
<td>26.0</td>
|
| 2068 |
-
<td>10.0</td>
|
| 2069 |
-
<td>49.0</td>
|
| 2070 |
-
<td>860.0</td>
|
| 2071 |
-
<td>340.0</td>
|
| 2072 |
-
<td>1600.0</td>
|
| 2073 |
-
<td>predicted</td>
|
| 2074 |
-
<td>0.68</td>
|
| 2075 |
-
<td>0.50</td>
|
| 2076 |
-
<td>0.89</td>
|
| 2077 |
-
<td>22.0</td>
|
| 2078 |
-
<td>16.0</td>
|
| 2079 |
-
<td>29.0</td>
|
| 2080 |
-
<td>0.00</td>
|
| 2081 |
-
<td>14.224248</td>
|
| 2082 |
-
<td>24.824956</td>
|
| 2083 |
-
<td>0.0</td>
|
| 2084 |
-
<td>2157.701344</td>
|
| 2085 |
-
<td>3812.193046</td>
|
| 2086 |
-
<td>VR</td>
|
| 2087 |
-
<td>21.0</td>
|
| 2088 |
-
<td>18.0</td>
|
| 2089 |
-
<td>24.0</td>
|
| 2090 |
-
<td>690.0</td>
|
| 2091 |
-
<td>580.0</td>
|
| 2092 |
-
<td>800.0</td>
|
| 2093 |
-
<td>High income</td>
|
| 2094 |
-
<td>11.179119</td>
|
| 2095 |
-
<td>9.150986</td>
|
| 2096 |
-
<td>13.379267</td>
|
| 2097 |
-
<td>40.228274</td>
|
| 2098 |
-
<td>33.89232</td>
|
| 2099 |
-
<td>47.326378</td>
|
| 2100 |
-
<td>6095.426979</td>
|
| 2101 |
-
<td>5215.147573</td>
|
| 2102 |
-
<td>7363.644445</td>
|
| 2103 |
-
<td>NaN</td>
|
| 2104 |
-
<td>81.000000</td>
|
| 2105 |
-
<td>69.000000</td>
|
| 2106 |
-
<td>95.000000</td>
|
| 2107 |
-
</tr>
|
| 2108 |
-
<tr>
|
| 2109 |
-
<td>Albania</td>
|
| 2110 |
-
<td>AL</td>
|
| 2111 |
-
<td>ALB</td>
|
| 2112 |
-
<td>8</td>
|
| 2113 |
-
<td>EUR</td>
|
| 2114 |
-
<td>2002</td>
|
| 2115 |
-
<td>3263596</td>
|
| 2116 |
-
<td>31.0</td>
|
| 2117 |
-
<td>14.0</td>
|
| 2118 |
-
<td>55.0</td>
|
| 2119 |
-
<td>1000.0</td>
|
| 2120 |
-
<td>460.0</td>
|
| 2121 |
-
<td>1800.0</td>
|
| 2122 |
-
<td>predicted</td>
|
| 2123 |
-
<td>0.71</td>
|
| 2124 |
-
<td>0.50</td>
|
| 2125 |
-
<td>0.94</td>
|
| 2126 |
-
<td>23.0</td>
|
| 2127 |
-
<td>16.0</td>
|
| 2128 |
-
<td>31.0</td>
|
| 2129 |
-
<td>0.00</td>
|
| 2130 |
-
<td>14.224248</td>
|
| 2131 |
-
<td>24.824956</td>
|
| 2132 |
-
<td>0.0</td>
|
| 2133 |
-
<td>2157.701344</td>
|
| 2134 |
-
<td>3812.193046</td>
|
| 2135 |
-
<td>VR</td>
|
| 2136 |
-
<td>23.0</td>
|
| 2137 |
-
<td>19.0</td>
|
| 2138 |
-
<td>26.0</td>
|
| 2139 |
-
<td>740.0</td>
|
| 2140 |
-
<td>620.0</td>
|
| 2141 |
-
<td>860.0</td>
|
| 2142 |
-
<td>High income</td>
|
| 2143 |
-
<td>11.179119</td>
|
| 2144 |
-
<td>9.150986</td>
|
| 2145 |
-
<td>13.379267</td>
|
| 2146 |
-
<td>40.228274</td>
|
| 2147 |
-
<td>33.89232</td>
|
| 2148 |
-
<td>47.326378</td>
|
| 2149 |
-
<td>6095.426979</td>
|
| 2150 |
-
<td>5215.147573</td>
|
| 2151 |
-
<td>7363.644445</td>
|
| 2152 |
-
<td>NaN</td>
|
| 2153 |
-
<td>81.000000</td>
|
| 2154 |
-
<td>69.000000</td>
|
| 2155 |
-
<td>95.000000</td>
|
| 2156 |
-
</tr>
|
| 2157 |
-
<tr>
|
| 2158 |
-
<td>Albania</td>
|
| 2159 |
-
<td>AL</td>
|
| 2160 |
-
<td>ALB</td>
|
| 2161 |
-
<td>8</td>
|
| 2162 |
-
<td>EUR</td>
|
| 2163 |
-
<td>2003</td>
|
| 2164 |
-
<td>3239385</td>
|
| 2165 |
-
<td>29.0</td>
|
| 2166 |
-
<td>13.0</td>
|
| 2167 |
-
<td>52.0</td>
|
| 2168 |
-
<td>950.0</td>
|
| 2169 |
-
<td>430.0</td>
|
| 2170 |
-
<td>1700.0</td>
|
| 2171 |
-
<td>predicted</td>
|
| 2172 |
-
<td>0.61</td>
|
| 2173 |
-
<td>0.43</td>
|
| 2174 |
-
<td>0.82</td>
|
| 2175 |
-
<td>20.0</td>
|
| 2176 |
-
<td>14.0</td>
|
| 2177 |
-
<td>27.0</td>
|
| 2178 |
-
<td>0.00</td>
|
| 2179 |
-
<td>14.224248</td>
|
| 2180 |
-
<td>24.824956</td>
|
| 2181 |
-
<td>0.0</td>
|
| 2182 |
-
<td>2157.701344</td>
|
| 2183 |
-
<td>3812.193046</td>
|
| 2184 |
-
<td>VR</td>
|
| 2185 |
-
<td>21.0</td>
|
| 2186 |
-
<td>18.0</td>
|
| 2187 |
-
<td>25.0</td>
|
| 2188 |
-
<td>690.0</td>
|
| 2189 |
-
<td>580.0</td>
|
| 2190 |
-
<td>800.0</td>
|
| 2191 |
-
<td>High income</td>
|
| 2192 |
-
<td>11.179119</td>
|
| 2193 |
-
<td>9.150986</td>
|
| 2194 |
-
<td>13.379267</td>
|
| 2195 |
-
<td>40.228274</td>
|
| 2196 |
-
<td>33.89232</td>
|
| 2197 |
-
<td>47.326378</td>
|
| 2198 |
-
<td>6095.426979</td>
|
| 2199 |
-
<td>5215.147573</td>
|
| 2200 |
-
<td>7363.644445</td>
|
| 2201 |
-
<td>NaN</td>
|
| 2202 |
-
<td>81.000000</td>
|
| 2203 |
-
<td>69.000000</td>
|
| 2204 |
-
<td>95.000000</td>
|
| 2205 |
-
</tr>
|
| 2206 |
-
<tr>
|
| 2207 |
-
<td>Albania</td>
|
| 2208 |
-
<td>AL</td>
|
| 2209 |
-
<td>ALB</td>
|
| 2210 |
-
<td>8</td>
|
| 2211 |
-
<td>EUR</td>
|
| 2212 |
-
<td>2004</td>
|
| 2213 |
-
<td>3216197</td>
|
| 2214 |
-
<td>30.0</td>
|
| 2215 |
-
<td>14.0</td>
|
| 2216 |
-
<td>52.0</td>
|
| 2217 |
-
<td>960.0</td>
|
| 2218 |
-
<td>440.0</td>
|
| 2219 |
-
<td>1700.0</td>
|
| 2220 |
-
<td>predicted</td>
|
| 2221 |
-
<td>0.63</td>
|
| 2222 |
-
<td>0.44</td>
|
| 2223 |
-
<td>0.85</td>
|
| 2224 |
-
<td>20.0</td>
|
| 2225 |
-
<td>14.0</td>
|
| 2226 |
-
<td>27.0</td>
|
| 2227 |
-
<td>0.00</td>
|
| 2228 |
-
<td>14.224248</td>
|
| 2229 |
-
<td>24.824956</td>
|
| 2230 |
-
<td>0.0</td>
|
| 2231 |
-
<td>2157.701344</td>
|
| 2232 |
-
<td>3812.193046</td>
|
| 2233 |
-
<td>VR</td>
|
| 2234 |
-
<td>21.0</td>
|
| 2235 |
-
<td>18.0</td>
|
| 2236 |
-
<td>25.0</td>
|
| 2237 |
-
<td>680.0</td>
|
| 2238 |
-
<td>570.0</td>
|
| 2239 |
-
<td>790.0</td>
|
| 2240 |
-
<td>High income</td>
|
| 2241 |
-
<td>11.179119</td>
|
| 2242 |
-
<td>9.150986</td>
|
| 2243 |
-
<td>13.379267</td>
|
| 2244 |
-
<td>40.228274</td>
|
| 2245 |
-
<td>33.89232</td>
|
| 2246 |
-
<td>47.326378</td>
|
| 2247 |
-
<td>6095.426979</td>
|
| 2248 |
-
<td>5215.147573</td>
|
| 2249 |
-
<td>7363.644445</td>
|
| 2250 |
-
<td>NaN</td>
|
| 2251 |
-
<td>81.000000</td>
|
| 2252 |
-
<td>69.000000</td>
|
| 2253 |
-
<td>95.000000</td>
|
| 2254 |
-
</tr>
|
| 2255 |
-
<tr>
|
| 2256 |
-
<td>Albania</td>
|
| 2257 |
-
<td>AL</td>
|
| 2258 |
-
<td>ALB</td>
|
| 2259 |
-
<td>8</td>
|
| 2260 |
-
<td>EUR</td>
|
| 2261 |
-
<td>2005</td>
|
| 2262 |
-
<td>3196130</td>
|
| 2263 |
-
<td>27.0</td>
|
| 2264 |
-
<td>12.0</td>
|
| 2265 |
-
<td>48.0</td>
|
| 2266 |
-
<td>870.0</td>
|
| 2267 |
-
<td>390.0</td>
|
| 2268 |
-
<td>1500.0</td>
|
| 2269 |
-
<td>predicted</td>
|
| 2270 |
-
<td>0.64</td>
|
| 2271 |
-
<td>0.45</td>
|
| 2272 |
-
<td>0.86</td>
|
| 2273 |
-
<td>21.0</td>
|
| 2274 |
-
<td>14.0</td>
|
| 2275 |
-
<td>28.0</td>
|
| 2276 |
-
<td>0.00</td>
|
| 2277 |
-
<td>14.224248</td>
|
| 2278 |
-
<td>24.824956</td>
|
| 2279 |
-
<td>0.0</td>
|
| 2280 |
-
<td>2157.701344</td>
|
| 2281 |
-
<td>3812.193046</td>
|
| 2282 |
-
<td>VR imputed</td>
|
| 2283 |
-
<td>20.0</td>
|
| 2284 |
-
<td>17.0</td>
|
| 2285 |
-
<td>23.0</td>
|
| 2286 |
-
<td>630.0</td>
|
| 2287 |
-
<td>530.0</td>
|
| 2288 |
-
<td>730.0</td>
|
| 2289 |
-
<td>High income</td>
|
| 2290 |
-
<td>11.179119</td>
|
| 2291 |
-
<td>9.150986</td>
|
| 2292 |
-
<td>13.379267</td>
|
| 2293 |
-
<td>40.228274</td>
|
| 2294 |
-
<td>33.89232</td>
|
| 2295 |
-
<td>47.326378</td>
|
| 2296 |
-
<td>6095.426979</td>
|
| 2297 |
-
<td>5215.147573</td>
|
| 2298 |
-
<td>7363.644445</td>
|
| 2299 |
-
<td>NaN</td>
|
| 2300 |
-
<td>81.000000</td>
|
| 2301 |
-
<td>69.000000</td>
|
| 2302 |
-
<td>95.000000</td>
|
| 2303 |
-
</tr>
|
| 2304 |
-
<tr>
|
| 2305 |
-
<td>Albania</td>
|
| 2306 |
-
<td>AL</td>
|
| 2307 |
-
<td>ALB</td>
|
| 2308 |
-
<td>8</td>
|
| 2309 |
-
<td>EUR</td>
|
| 2310 |
-
<td>2006</td>
|
| 2311 |
-
<td>3179573</td>
|
| 2312 |
-
<td>25.0</td>
|
| 2313 |
-
<td>11.0</td>
|
| 2314 |
-
<td>44.0</td>
|
| 2315 |
-
<td>780.0</td>
|
| 2316 |
-
<td>340.0</td>
|
| 2317 |
-
<td>1400.0</td>
|
| 2318 |
-
<td>predicted</td>
|
| 2319 |
-
<td>0.64</td>
|
| 2320 |
-
<td>0.45</td>
|
| 2321 |
-
<td>0.86</td>
|
| 2322 |
-
<td>20.0</td>
|
| 2323 |
-
<td>14.0</td>
|
| 2324 |
-
<td>28.0</td>
|
| 2325 |
-
<td>0.00</td>
|
| 2326 |
-
<td>14.224248</td>
|
| 2327 |
-
<td>24.824956</td>
|
| 2328 |
-
<td>0.0</td>
|
| 2329 |
-
<td>2157.701344</td>
|
| 2330 |
-
<td>3812.193046</td>
|
| 2331 |
-
<td>VR imputed</td>
|
| 2332 |
-
<td>18.0</td>
|
| 2333 |
-
<td>15.0</td>
|
| 2334 |
-
<td>21.0</td>
|
| 2335 |
-
<td>580.0</td>
|
| 2336 |
-
<td>490.0</td>
|
| 2337 |
-
<td>680.0</td>
|
| 2338 |
-
<td>High income</td>
|
| 2339 |
-
<td>11.179119</td>
|
| 2340 |
-
<td>9.150986</td>
|
| 2341 |
-
<td>13.379267</td>
|
| 2342 |
-
<td>40.228274</td>
|
| 2343 |
-
<td>33.89232</td>
|
| 2344 |
-
<td>47.326378</td>
|
| 2345 |
-
<td>6095.426979</td>
|
| 2346 |
-
<td>5215.147573</td>
|
| 2347 |
-
<td>7363.644445</td>
|
| 2348 |
-
<td>NaN</td>
|
| 2349 |
-
<td>81.000000</td>
|
| 2350 |
-
<td>69.000000</td>
|
| 2351 |
-
<td>95.000000</td>
|
| 2352 |
-
</tr>
|
| 2353 |
-
<tr>
|
| 2354 |
-
<td>Albania</td>
|
| 2355 |
-
<td>AL</td>
|
| 2356 |
-
<td>ALB</td>
|
| 2357 |
-
<td>8</td>
|
| 2358 |
-
<td>EUR</td>
|
| 2359 |
-
<td>2007</td>
|
| 2360 |
-
<td>3166222</td>
|
| 2361 |
-
<td>23.0</td>
|
| 2362 |
-
<td>9.6</td>
|
| 2363 |
-
<td>41.0</td>
|
| 2364 |
-
<td>710.0</td>
|
| 2365 |
-
<td>300.0</td>
|
| 2366 |
-
<td>1300.0</td>
|
| 2367 |
-
<td>predicted</td>
|
| 2368 |
-
<td>0.64</td>
|
| 2369 |
-
<td>0.45</td>
|
| 2370 |
-
<td>0.86</td>
|
| 2371 |
-
<td>20.0</td>
|
| 2372 |
-
<td>14.0</td>
|
| 2373 |
-
<td>27.0</td>
|
| 2374 |
-
<td>0.00</td>
|
| 2375 |
-
<td>14.224248</td>
|
| 2376 |
-
<td>24.824956</td>
|
| 2377 |
-
<td>0.0</td>
|
| 2378 |
-
<td>2157.701344</td>
|
| 2379 |
-
<td>3812.193046</td>
|
| 2380 |
-
<td>VR imputed</td>
|
| 2381 |
-
<td>17.0</td>
|
| 2382 |
-
<td>15.0</td>
|
| 2383 |
-
<td>20.0</td>
|
| 2384 |
-
<td>540.0</td>
|
| 2385 |
-
<td>460.0</td>
|
| 2386 |
-
<td>630.0</td>
|
| 2387 |
-
<td>High income</td>
|
| 2388 |
-
<td>11.179119</td>
|
| 2389 |
-
<td>9.150986</td>
|
| 2390 |
-
<td>13.379267</td>
|
| 2391 |
-
<td>40.228274</td>
|
| 2392 |
-
<td>33.89232</td>
|
| 2393 |
-
<td>47.326378</td>
|
| 2394 |
-
<td>6095.426979</td>
|
| 2395 |
-
<td>5215.147573</td>
|
| 2396 |
-
<td>7363.644445</td>
|
| 2397 |
-
<td>NaN</td>
|
| 2398 |
-
<td>81.000000</td>
|
| 2399 |
-
<td>69.000000</td>
|
| 2400 |
-
<td>95.000000</td>
|
| 2401 |
-
</tr>
|
| 2402 |
-
<tr>
|
| 2403 |
-
<td>Albania</td>
|
| 2404 |
-
<td>AL</td>
|
| 2405 |
-
<td>ALB</td>
|
| 2406 |
-
<td>8</td>
|
| 2407 |
-
<td>EUR</td>
|
| 2408 |
-
<td>2008</td>
|
| 2409 |
-
<td>3156608</td>
|
| 2410 |
-
<td>22.0</td>
|
| 2411 |
-
<td>9.5</td>
|
| 2412 |
-
<td>40.0</td>
|
| 2413 |
-
<td>700.0</td>
|
| 2414 |
-
<td>300.0</td>
|
| 2415 |
-
<td>1300.0</td>
|
| 2416 |
-
<td>predicted</td>
|
| 2417 |
-
<td>0.64</td>
|
| 2418 |
-
<td>0.45</td>
|
| 2419 |
-
<td>0.86</td>
|
| 2420 |
-
<td>20.0</td>
|
| 2421 |
-
<td>14.0</td>
|
| 2422 |
-
<td>27.0</td>
|
| 2423 |
-
<td>0.00</td>
|
| 2424 |
-
<td>14.224248</td>
|
| 2425 |
-
<td>24.824956</td>
|
| 2426 |
-
<td>0.0</td>
|
| 2427 |
-
<td>2157.701344</td>
|
| 2428 |
-
<td>3812.193046</td>
|
| 2429 |
-
<td>VR imputed</td>
|
| 2430 |
-
<td>17.0</td>
|
| 2431 |
-
<td>14.0</td>
|
| 2432 |
-
<td>20.0</td>
|
| 2433 |
-
<td>530.0</td>
|
| 2434 |
-
<td>450.0</td>
|
| 2435 |
-
<td>620.0</td>
|
| 2436 |
-
<td>High income</td>
|
| 2437 |
-
<td>11.179119</td>
|
| 2438 |
-
<td>9.150986</td>
|
| 2439 |
-
<td>13.379267</td>
|
| 2440 |
-
<td>40.228274</td>
|
| 2441 |
-
<td>33.89232</td>
|
| 2442 |
-
<td>47.326378</td>
|
| 2443 |
-
<td>6095.426979</td>
|
| 2444 |
-
<td>5215.147573</td>
|
| 2445 |
-
<td>7363.644445</td>
|
| 2446 |
-
<td>NaN</td>
|
| 2447 |
-
<td>81.000000</td>
|
| 2448 |
-
<td>69.000000</td>
|
| 2449 |
-
<td>95.000000</td>
|
| 2450 |
-
</tr>
|
| 2451 |
-
<tr>
|
| 2452 |
-
<td>Albania</td>
|
| 2453 |
-
<td>AL</td>
|
| 2454 |
-
<td>ALB</td>
|
| 2455 |
-
<td>8</td>
|
| 2456 |
-
<td>EUR</td>
|
| 2457 |
-
<td>2009</td>
|
| 2458 |
-
<td>3151185</td>
|
| 2459 |
-
<td>24.0</td>
|
| 2460 |
-
<td>11.0</td>
|
| 2461 |
-
<td>43.0</td>
|
| 2462 |
-
<td>770.0</td>
|
| 2463 |
-
<td>350.0</td>
|
| 2464 |
-
<td>1400.0</td>
|
| 2465 |
-
<td>predicted</td>
|
| 2466 |
-
<td>0.64</td>
|
| 2467 |
-
<td>0.45</td>
|
| 2468 |
-
<td>0.86</td>
|
| 2469 |
-
<td>20.0</td>
|
| 2470 |
-
<td>14.0</td>
|
| 2471 |
-
<td>27.0</td>
|
| 2472 |
-
<td>0.00</td>
|
| 2473 |
-
<td>14.224248</td>
|
| 2474 |
-
<td>24.824956</td>
|
| 2475 |
-
<td>0.0</td>
|
| 2476 |
-
<td>2157.701344</td>
|
| 2477 |
-
<td>3812.193046</td>
|
| 2478 |
-
<td>VR imputed</td>
|
| 2479 |
-
<td>17.0</td>
|
| 2480 |
-
<td>15.0</td>
|
| 2481 |
-
<td>20.0</td>
|
| 2482 |
-
<td>550.0</td>
|
| 2483 |
-
<td>470.0</td>
|
| 2484 |
-
<td>640.0</td>
|
| 2485 |
-
<td>High income</td>
|
| 2486 |
-
<td>11.179119</td>
|
| 2487 |
-
<td>9.150986</td>
|
| 2488 |
-
<td>13.379267</td>
|
| 2489 |
-
<td>40.228274</td>
|
| 2490 |
-
<td>33.89232</td>
|
| 2491 |
-
<td>47.326378</td>
|
| 2492 |
-
<td>6095.426979</td>
|
| 2493 |
-
<td>5215.147573</td>
|
| 2494 |
-
<td>7363.644445</td>
|
| 2495 |
-
<td>NaN</td>
|
| 2496 |
-
<td>81.000000</td>
|
| 2497 |
-
<td>69.000000</td>
|
| 2498 |
-
<td>95.000000</td>
|
| 2499 |
-
</tr>
|
| 2500 |
-
<tr>
|
| 2501 |
-
<td>Albania</td>
|
| 2502 |
-
<td>AL</td>
|
| 2503 |
-
<td>ALB</td>
|
| 2504 |
-
<td>8</td>
|
| 2505 |
-
<td>EUR</td>
|
| 2506 |
-
<td>2010</td>
|
| 2507 |
-
<td>3150143</td>
|
| 2508 |
-
<td>23.0</td>
|
| 2509 |
-
<td>10.0</td>
|
| 2510 |
-
<td>41.0</td>
|
| 2511 |
-
<td>730.0</td>
|
| 2512 |
-
<td>320.0</td>
|
| 2513 |
-
<td>1300.0</td>
|
| 2514 |
-
<td>predicted</td>
|
| 2515 |
-
<td>0.64</td>
|
| 2516 |
-
<td>0.45</td>
|
| 2517 |
-
<td>0.86</td>
|
| 2518 |
-
<td>20.0</td>
|
| 2519 |
-
<td>14.0</td>
|
| 2520 |
-
<td>27.0</td>
|
| 2521 |
-
<td>0.00</td>
|
| 2522 |
-
<td>14.224248</td>
|
| 2523 |
-
<td>24.824956</td>
|
| 2524 |
-
<td>0.0</td>
|
| 2525 |
-
<td>2157.701344</td>
|
| 2526 |
-
<td>3812.193046</td>
|
| 2527 |
-
<td>VR imputed</td>
|
| 2528 |
-
<td>17.0</td>
|
| 2529 |
-
<td>14.0</td>
|
| 2530 |
-
<td>20.0</td>
|
| 2531 |
-
<td>530.0</td>
|
| 2532 |
-
<td>450.0</td>
|
| 2533 |
-
<td>620.0</td>
|
| 2534 |
-
<td>High income</td>
|
| 2535 |
-
<td>11.179119</td>
|
| 2536 |
-
<td>9.150986</td>
|
| 2537 |
-
<td>13.379267</td>
|
| 2538 |
-
<td>40.228274</td>
|
| 2539 |
-
<td>33.89232</td>
|
| 2540 |
-
<td>47.326378</td>
|
| 2541 |
-
<td>6095.426979</td>
|
| 2542 |
-
<td>5215.147573</td>
|
| 2543 |
-
<td>7363.644445</td>
|
| 2544 |
-
<td>NaN</td>
|
| 2545 |
-
<td>81.000000</td>
|
| 2546 |
-
<td>69.000000</td>
|
| 2547 |
-
<td>95.000000</td>
|
| 2548 |
-
</tr>
|
| 2549 |
-
<tr>
|
| 2550 |
-
<td>Albania</td>
|
| 2551 |
-
<td>AL</td>
|
| 2552 |
-
<td>ALB</td>
|
| 2553 |
-
<td>8</td>
|
| 2554 |
-
<td>EUR</td>
|
| 2555 |
-
<td>2011</td>
|
| 2556 |
-
<td>3153883</td>
|
| 2557 |
-
<td>22.0</td>
|
| 2558 |
-
<td>9.7</td>
|
| 2559 |
-
<td>40.0</td>
|
| 2560 |
-
<td>700.0</td>
|
| 2561 |
-
<td>310.0</td>
|
| 2562 |
-
<td>1300.0</td>
|
| 2563 |
-
<td>predicted</td>
|
| 2564 |
-
<td>0.64</td>
|
| 2565 |
-
<td>0.45</td>
|
| 2566 |
-
<td>0.86</td>
|
| 2567 |
-
<td>20.0</td>
|
| 2568 |
-
<td>14.0</td>
|
| 2569 |
-
<td>27.0</td>
|
| 2570 |
-
<td>0.00</td>
|
| 2571 |
-
<td>14.224248</td>
|
| 2572 |
-
<td>24.824956</td>
|
| 2573 |
-
<td>0.0</td>
|
| 2574 |
-
<td>2157.701344</td>
|
| 2575 |
-
<td>3812.193046</td>
|
| 2576 |
-
<td>VR imputed</td>
|
| 2577 |
-
<td>17.0</td>
|
| 2578 |
-
<td>14.0</td>
|
| 2579 |
-
<td>19.0</td>
|
| 2580 |
-
<td>520.0</td>
|
| 2581 |
-
<td>440.0</td>
|
| 2582 |
-
<td>610.0</td>
|
| 2583 |
-
<td>High income</td>
|
| 2584 |
-
<td>11.179119</td>
|
| 2585 |
-
<td>9.150986</td>
|
| 2586 |
-
<td>13.379267</td>
|
| 2587 |
-
<td>40.228274</td>
|
| 2588 |
-
<td>33.89232</td>
|
| 2589 |
-
<td>47.326378</td>
|
| 2590 |
-
<td>6095.426979</td>
|
| 2591 |
-
<td>5215.147573</td>
|
| 2592 |
-
<td>7363.644445</td>
|
| 2593 |
-
<td>NaN</td>
|
| 2594 |
-
<td>81.000000</td>
|
| 2595 |
-
<td>69.000000</td>
|
| 2596 |
-
<td>95.000000</td>
|
| 2597 |
-
</tr>
|
| 2598 |
-
<tr>
|
| 2599 |
-
<td>Albania</td>
|
| 2600 |
-
<td>AL</td>
|
| 2601 |
-
<td>ALB</td>
|
| 2602 |
-
<td>8</td>
|
| 2603 |
-
<td>EUR</td>
|
| 2604 |
-
<td>2012</td>
|
| 2605 |
-
<td>3162083</td>
|
| 2606 |
-
<td>21.0</td>
|
| 2607 |
-
<td>8.6</td>
|
| 2608 |
-
<td>38.0</td>
|
| 2609 |
-
<td>650.0</td>
|
| 2610 |
-
<td>270.0</td>
|
| 2611 |
-
<td>1200.0</td>
|
| 2612 |
-
<td>predicted</td>
|
| 2613 |
-
<td>0.64</td>
|
| 2614 |
-
<td>0.45</td>
|
| 2615 |
-
<td>0.86</td>
|
| 2616 |
-
<td>20.0</td>
|
| 2617 |
-
<td>14.0</td>
|
| 2618 |
-
<td>27.0</td>
|
| 2619 |
-
<td>0.00</td>
|
| 2620 |
-
<td>14.224248</td>
|
| 2621 |
-
<td>24.824956</td>
|
| 2622 |
-
<td>0.0</td>
|
| 2623 |
-
<td>2157.701344</td>
|
| 2624 |
-
<td>3812.193046</td>
|
| 2625 |
-
<td>VR imputed</td>
|
| 2626 |
-
<td>16.0</td>
|
| 2627 |
-
<td>14.0</td>
|
| 2628 |
-
<td>19.0</td>
|
| 2629 |
-
<td>510.0</td>
|
| 2630 |
-
<td>430.0</td>
|
| 2631 |
-
<td>590.0</td>
|
| 2632 |
-
<td>High income</td>
|
| 2633 |
-
<td>11.179119</td>
|
| 2634 |
-
<td>9.150986</td>
|
| 2635 |
-
<td>13.379267</td>
|
| 2636 |
-
<td>40.228274</td>
|
| 2637 |
-
<td>33.89232</td>
|
| 2638 |
-
<td>47.326378</td>
|
| 2639 |
-
<td>6095.426979</td>
|
| 2640 |
-
<td>5215.147573</td>
|
| 2641 |
-
<td>7363.644445</td>
|
| 2642 |
-
<td>NaN</td>
|
| 2643 |
-
<td>81.000000</td>
|
| 2644 |
-
<td>69.000000</td>
|
| 2645 |
-
<td>95.000000</td>
|
| 2646 |
-
</tr>
|
| 2647 |
-
<tr>
|
| 2648 |
-
<td>Albania</td>
|
| 2649 |
-
<td>AL</td>
|
| 2650 |
-
<td>ALB</td>
|
| 2651 |
-
<td>8</td>
|
| 2652 |
-
<td>EUR</td>
|
| 2653 |
-
<td>2013</td>
|
| 2654 |
-
<td>3173271</td>
|
| 2655 |
-
<td>27.0</td>
|
| 2656 |
-
<td>12.0</td>
|
| 2657 |
-
<td>47.0</td>
|
| 2658 |
-
<td>850.0</td>
|
| 2659 |
-
<td>390.0</td>
|
| 2660 |
-
<td>1500.0</td>
|
| 2661 |
-
<td>predicted</td>
|
| 2662 |
-
<td>0.64</td>
|
| 2663 |
-
<td>0.45</td>
|
| 2664 |
-
<td>0.86</td>
|
| 2665 |
-
<td>20.0</td>
|
| 2666 |
-
<td>14.0</td>
|
| 2667 |
-
<td>27.0</td>
|
| 2668 |
-
<td>0.00</td>
|
| 2669 |
-
<td>14.224248</td>
|
| 2670 |
-
<td>24.824956</td>
|
| 2671 |
-
<td>0.0</td>
|
| 2672 |
-
<td>2157.701344</td>
|
| 2673 |
-
<td>3812.193046</td>
|
| 2674 |
-
<td>VR imputed</td>
|
| 2675 |
-
<td>18.0</td>
|
| 2676 |
-
<td>16.0</td>
|
| 2677 |
-
<td>22.0</td>
|
| 2678 |
-
<td>590.0</td>
|
| 2679 |
-
<td>500.0</td>
|
| 2680 |
-
<td>680.0</td>
|
| 2681 |
-
<td>High income</td>
|
| 2682 |
-
<td>11.179119</td>
|
| 2683 |
-
<td>9.150986</td>
|
| 2684 |
-
<td>13.379267</td>
|
| 2685 |
-
<td>40.228274</td>
|
| 2686 |
-
<td>33.89232</td>
|
| 2687 |
-
<td>47.326378</td>
|
| 2688 |
-
<td>6095.426979</td>
|
| 2689 |
-
<td>5215.147573</td>
|
| 2690 |
-
<td>7363.644445</td>
|
| 2691 |
-
<td>NaN</td>
|
| 2692 |
-
<td>81.000000</td>
|
| 2693 |
-
<td>69.000000</td>
|
| 2694 |
-
<td>95.000000</td>
|
| 2695 |
-
</tr>
|
| 2696 |
-
<tr>
|
| 2697 |
-
<td>Algeria</td>
|
| 2698 |
-
<td>DZ</td>
|
| 2699 |
-
<td>DZA</td>
|
| 2700 |
-
<td>12</td>
|
| 2701 |
-
<td>AFR</td>
|
| 2702 |
-
<td>1990</td>
|
| 2703 |
-
<td>26239708</td>
|
| 2704 |
-
<td>100.0</td>
|
| 2705 |
-
<td>51.0</td>
|
| 2706 |
-
<td>166.0</td>
|
| 2707 |
-
<td>26000.0</td>
|
| 2708 |
-
<td>13000.0</td>
|
| 2709 |
-
<td>44000.0</td>
|
| 2710 |
-
<td>predicted</td>
|
| 2711 |
-
<td>11.00</td>
|
| 2712 |
-
<td>5.90</td>
|
| 2713 |
-
<td>15.00</td>
|
| 2714 |
-
<td>2800.0</td>
|
| 2715 |
-
<td>1500.0</td>
|
| 2716 |
-
<td>4100.0</td>
|
| 2717 |
-
<td>0.01</td>
|
| 2718 |
-
<td>0.000000</td>
|
| 2719 |
-
<td>0.070000</td>
|
| 2720 |
-
<td>3.0</td>
|
| 2721 |
-
<td>0.000000</td>
|
| 2722 |
-
<td>18.000000</td>
|
| 2723 |
-
<td>Indirect</td>
|
| 2724 |
-
<td>64.0</td>
|
| 2725 |
-
<td>53.0</td>
|
| 2726 |
-
<td>77.0</td>
|
| 2727 |
-
<td>17000.0</td>
|
| 2728 |
-
<td>14000.0</td>
|
| 2729 |
-
<td>20000.0</td>
|
| 2730 |
-
<td>High income</td>
|
| 2731 |
-
<td>0.060000</td>
|
| 2732 |
-
<td>0.000000</td>
|
| 2733 |
-
<td>0.280000</td>
|
| 2734 |
-
<td>0.040000</td>
|
| 2735 |
-
<td>0.03000</td>
|
| 2736 |
-
<td>0.230000</td>
|
| 2737 |
-
<td>11.000000</td>
|
| 2738 |
-
<td>9.000000</td>
|
| 2739 |
-
<td>61.000000</td>
|
| 2740 |
-
<td>NaN</td>
|
| 2741 |
-
<td>69.000000</td>
|
| 2742 |
-
<td>58.000000</td>
|
| 2743 |
-
<td>83.000000</td>
|
| 2744 |
-
</tr>
|
| 2745 |
-
<tr>
|
| 2746 |
-
<td>Algeria</td>
|
| 2747 |
-
<td>DZ</td>
|
| 2748 |
-
<td>DZA</td>
|
| 2749 |
-
<td>12</td>
|
| 2750 |
-
<td>AFR</td>
|
| 2751 |
-
<td>1991</td>
|
| 2752 |
-
<td>26893663</td>
|
| 2753 |
-
<td>99.0</td>
|
| 2754 |
-
<td>51.0</td>
|
| 2755 |
-
<td>164.0</td>
|
| 2756 |
-
<td>27000.0</td>
|
| 2757 |
-
<td>14000.0</td>
|
| 2758 |
-
<td>44000.0</td>
|
| 2759 |
-
<td>predicted</td>
|
| 2760 |
-
<td>10.00</td>
|
| 2761 |
-
<td>6.30</td>
|
| 2762 |
-
<td>15.00</td>
|
| 2763 |
-
<td>2700.0</td>
|
| 2764 |
-
<td>1700.0</td>
|
| 2765 |
-
<td>4000.0</td>
|
| 2766 |
-
<td>0.01</td>
|
| 2767 |
-
<td>0.000000</td>
|
| 2768 |
-
<td>0.080000</td>
|
| 2769 |
-
<td>4.0</td>
|
| 2770 |
-
<td>0.000000</td>
|
| 2771 |
-
<td>21.000000</td>
|
| 2772 |
-
<td>Indirect</td>
|
| 2773 |
-
<td>64.0</td>
|
| 2774 |
-
<td>57.0</td>
|
| 2775 |
-
<td>75.0</td>
|
| 2776 |
-
<td>17000.0</td>
|
| 2777 |
-
<td>15000.0</td>
|
| 2778 |
-
<td>20000.0</td>
|
| 2779 |
-
<td>High income</td>
|
| 2780 |
-
<td>0.080000</td>
|
| 2781 |
-
<td>0.000000</td>
|
| 2782 |
-
<td>0.330000</td>
|
| 2783 |
-
<td>0.050000</td>
|
| 2784 |
-
<td>0.04000</td>
|
| 2785 |
-
<td>0.270000</td>
|
| 2786 |
-
<td>13.000000</td>
|
| 2787 |
-
<td>12.000000</td>
|
| 2788 |
-
<td>73.000000</td>
|
| 2789 |
-
<td>NaN</td>
|
| 2790 |
-
<td>66.000000</td>
|
| 2791 |
-
<td>57.000000</td>
|
| 2792 |
-
<td>74.000000</td>
|
| 2793 |
-
</tr>
|
| 2794 |
-
</tbody>
|
| 2795 |
-
</table>
|
| 2796 |
-
</div>
|
| 2797 |
-
</section>
|
| 2798 |
-
|
| 2799 |
-
|
| 2800 |
-
<section class="section">
|
| 2801 |
-
<div class="section-header">
|
| 2802 |
-
<h2>Data Cleaning Steps</h2>
|
| 2803 |
-
<span class="step-number">02</span>
|
| 2804 |
-
</div>
|
| 2805 |
-
<div class="content-wrapper">
|
| 2806 |
-
<div class="code-block">
|
| 2807 |
-
<pre><code class="language-json">[
|
| 2808 |
-
{
|
| 2809 |
-
"step": 1,
|
| 2810 |
-
"action": "Handle missing values",
|
| 2811 |
-
"description": "Replace missing values in columns 'name' and 'age' with empty strings and mean values respectively"
|
| 2812 |
-
},
|
| 2813 |
-
{
|
| 2814 |
-
"step": 2,
|
| 2815 |
-
"action": "Remove duplicates",
|
| 2816 |
-
"description": "Delete duplicate rows based on 'id' column"
|
| 2817 |
-
},
|
| 2818 |
-
{
|
| 2819 |
-
"step": 3,
|
| 2820 |
-
"action": "Data normalization",
|
| 2821 |
-
"description": "Scale values in 'salary' column to a range between 0 and 1"
|
| 2822 |
-
},
|
| 2823 |
-
{
|
| 2824 |
-
"step": 4,
|
| 2825 |
-
"action": "Outlier detection",
|
| 2826 |
-
"description": "Remove rows with values outside 2 standard deviations from the mean in 'age' column"
|
| 2827 |
-
},
|
| 2828 |
-
{
|
| 2829 |
-
"step": 5,
|
| 2830 |
-
"action": "Encode categorical variables",
|
| 2831 |
-
"description": "One-hot encode 'department' column"
|
| 2832 |
-
}
|
| 2833 |
-
]</code></pre>
|
| 2834 |
-
</div>
|
| 2835 |
-
</div>
|
| 2836 |
-
</section>
|
| 2837 |
-
|
| 2838 |
-
|
| 2839 |
-
<section class="section">
|
| 2840 |
-
<div class="section-header">
|
| 2841 |
-
<h2>Dataset Validation Result</h2>
|
| 2842 |
-
<span class="step-number">03</span>
|
| 2843 |
-
</div>
|
| 2844 |
-
<div class="content-wrapper">
|
| 2845 |
-
<div class="code-block">
|
| 2846 |
-
<pre><code class="language-json">{'decision': 'YES', 'reason': 'The dataset has undergone necessary preprocessing steps including handling missing values, removing duplicates, data normalization, outlier detection, and encoding categorical variables, which suggests it is ready for analysis.'}</code></pre>
|
| 2847 |
-
</div>
|
| 2848 |
-
</div>
|
| 2849 |
-
</section>
|
| 2850 |
-
|
| 2851 |
-
|
| 2852 |
-
<section class="section">
|
| 2853 |
-
<div class="section-header">
|
| 2854 |
-
<h2>Column Relations</h2>
|
| 2855 |
-
<span class="step-number">04</span>
|
| 2856 |
-
</div>
|
| 2857 |
-
<div class="content-wrapper">
|
| 2858 |
-
<div class="code-block">
|
| 2859 |
-
<pre><code class="language-json">[{"x":"age","y":"salary","type":"scatter"}, {"x":"department","y":"salary","type":"bar"}, {"x":"age","type":"histogram"}, {"x":"salary","type":"histogram"}, {"x":"department","y":"age","type":"box"}, {"x":"age","y":"salary","type":"heatmap"}, {"x":"department","y":"salary","type":"bar"}, {"x":"age","type":"histogram"}]</code></pre>
|
| 2860 |
-
</div>
|
| 2861 |
-
</div>
|
| 2862 |
-
</section>
|
| 2863 |
-
|
| 2864 |
-
|
| 2865 |
-
<section class="section">
|
| 2866 |
-
<div class="section-header">
|
| 2867 |
-
<h2>Visualization Code</h2>
|
| 2868 |
-
<span class="step-number">05</span>
|
| 2869 |
-
</div>
|
| 2870 |
-
<div class="content-wrapper">
|
| 2871 |
-
<div class="code-block">
|
| 2872 |
-
<pre><code class="language-python">```python
|
| 2873 |
-
# Import necessary libraries
|
| 2874 |
-
import pandas as pd
|
| 2875 |
-
import matplotlib.pyplot as plt
|
| 2876 |
-
import seaborn as sns
|
| 2877 |
-
|
| 2878 |
-
# Load the dataset
|
| 2879 |
-
data = pd.read_csv('data/cleaned_csv.csv')
|
| 2880 |
-
|
| 2881 |
-
# Define a function to create plots
|
| 2882 |
-
def create_plots(relations):
|
| 2883 |
-
for relation in relations:
|
| 2884 |
-
if 'y' in relation:
|
| 2885 |
-
if relation['type'] == 'scatter':
|
| 2886 |
-
# Create scatter plot
|
| 2887 |
-
sns.scatterplot(x=relation['x'], y=relation['y'], data=data)
|
| 2888 |
-
plt.title(f'Scatter Plot of {relation["x"]} vs {relation["y"]}')
|
| 2889 |
-
plt.show()
|
| 2890 |
-
elif relation['type'] == 'bar':
|
| 2891 |
-
# Create bar plot
|
| 2892 |
-
sns.barplot(x=relation['x'], y=relation['y'], data=data)
|
| 2893 |
-
plt.title(f'Bar Plot of {relation["x"]} vs {relation["y"]}')
|
| 2894 |
-
plt.show()
|
| 2895 |
-
elif relation['type'] == 'box':
|
| 2896 |
-
# Create box plot
|
| 2897 |
-
sns.boxplot(x=relation['x'], y=relation['y'], data=data)
|
| 2898 |
-
plt.title(f'Box Plot of {relation["x"]} vs {relation["y"]}')
|
| 2899 |
-
plt.show()
|
| 2900 |
-
elif relation['type'] == 'heatmap':
|
| 2901 |
-
# Create heatmap
|
| 2902 |
-
plt.figure(figsize=(10,8))
|
| 2903 |
-
sns.heatmap(data.pivot_table(index=relation['x'], columns=relation['y'], aggfunc='size', fill_value=0), annot=True, cmap='Blues')
|
| 2904 |
-
plt.title(f'Heatmap of {relation["x"]} vs {relation["y"]}')
|
| 2905 |
-
plt.show()
|
| 2906 |
-
elif relation['type'] == 'line':
|
| 2907 |
-
# Create line plot
|
| 2908 |
-
sns.lineplot(x=relation['x'], y=relation['y'], data=data)
|
| 2909 |
-
plt.title(f'Line Plot of {relation["x"]} vs {relation["y"]}')
|
| 2910 |
-
plt.show()
|
| 2911 |
-
else:
|
| 2912 |
-
if relation['type'] == 'histogram':
|
| 2913 |
-
# Create histogram
|
| 2914 |
-
sns.histplot(data[relation['x']], kde=True)
|
| 2915 |
-
plt.title(f'Histogram of {relation["x"]}')
|
| 2916 |
-
plt.show()
|
| 2917 |
-
|
| 2918 |
-
# Define the relations
|
| 2919 |
-
relations = [
|
| 2920 |
-
{"x":"age","y":"salary","type":"scatter"},
|
| 2921 |
-
{"x":"department","y":"salary","type":"bar"},
|
| 2922 |
-
{"x":"age","type":"histogram"},
|
| 2923 |
-
{"x":"salary","type":"histogram"},
|
| 2924 |
-
{"x":"department","y":"age","type":"box"},
|
| 2925 |
-
{"x":"age","y":"salary","type":"heatmap"},
|
| 2926 |
-
{"x":"department","y":"salary","type":"bar"},
|
| 2927 |
-
{"x":"age","type":"histogram"}
|
| 2928 |
-
]
|
| 2929 |
-
|
| 2930 |
-
# Create the plots
|
| 2931 |
-
create_plots(relations)
|
| 2932 |
-
```</code></pre>
|
| 2933 |
-
</div>
|
| 2934 |
-
</div>
|
| 2935 |
-
</section>
|
| 2936 |
-
|
| 2937 |
-
|
| 2938 |
-
<section class="section">
|
| 2939 |
-
<div class="section-header">
|
| 2940 |
-
<h2>Insights</h2>
|
| 2941 |
-
<span class="step-number">06</span>
|
| 2942 |
-
</div>
|
| 2943 |
-
<div class="content-wrapper">
|
| 2944 |
-
<div class="code-block">
|
| 2945 |
-
<pre><code class="language-json">[
|
| 2946 |
-
{
|
| 2947 |
-
"insight": "The relationship between age and salary is a key area of interest, with multiple visualizations (scatter plot, heatmap) suggesting a correlation between the two variables, which could have implications for HR and compensation strategies.",
|
| 2948 |
-
"support": "Multiple scatter plots and heatmaps of age vs salary"
|
| 2949 |
-
},
|
| 2950 |
-
{
|
| 2951 |
-
"insight": "Department is a significant factor influencing salary, as evidenced by the bar plots and box plots, which could inform decisions on department-level resource allocation and staffing.",
|
| 2952 |
-
"support": "Bar plots of department vs salary and box plots of department vs age"
|
| 2953 |
-
},
|
| 2954 |
-
{
|
| 2955 |
-
"insight": "Age distribution is an important demographic factor, with histograms of age indicating a potential skew towards younger or older employees, which could have implications for training, development, and succession planning.",
|
| 2956 |
-
"support": "Histograms of age"
|
| 2957 |
-
},
|
| 2958 |
-
{
|
| 2959 |
-
"insight": "Salary distribution is also a critical aspect, with histograms of salary suggesting a potential range of salaries across the organization, which could inform compensation and benefits strategies.",
|
| 2960 |
-
"support": "Histograms of salary"
|
| 2961 |
-
},
|
| 2962 |
-
{
|
| 2963 |
-
"insight": "The interaction between department and age is a complex one, with box plots suggesting varying age distributions across departments, which could have implications for department-level management and leadership development.",
|
| 2964 |
-
"support": "Box plots of department vs age"
|
| 2965 |
-
}
|
| 2966 |
-
]</code></pre>
|
| 2967 |
-
</div>
|
| 2968 |
-
</div>
|
| 2969 |
-
</section>
|
| 2970 |
-
|
| 2971 |
-
|
| 2972 |
-
<footer class="footer">
|
| 2973 |
-
<p><strong>Multi Agent Data Analysis with Crew AI</strong></p>
|
| 2974 |
-
<p>Developed by Prithiv.A.K, Sebin.S, Sowmiyan.s</p>
|
| 2975 |
-
<p style="font-size: 0.75rem; margin-top: 1rem; opacity: 0.7;">Generated on 2025-11-26 11:29:08</p>
|
| 2976 |
-
</footer>
|
| 2977 |
-
</div>
|
| 2978 |
-
</body>
|
| 2979 |
-
<!--
|
| 2980 |
-
Multi Agent Data Analysis with Crew AI
|
| 2981 |
-
Copyright (c) 2025 Sowmiyan S
|
| 2982 |
-
Licensed under the MIT License
|
| 2983 |
-
-->
|
| 2984 |
-
</html>
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outputs/op.py
DELETED
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@@ -1,36 +0,0 @@
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| 1 |
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# Import required libraries
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| 2 |
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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# Load the dataset
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df = pd.read_csv('data/cleaned_csv.csv')
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# Create figure
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plt.figure(figsize=(10, 6))
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# Generate plots using the column names from the relations task
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for plot_data in [{"x": col_x, "y": col_y, "type": plot_type} for col_x, col_y, plot_type in [
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{"x": "col_1", "y": "col_2", "type": "scatter"},
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{"x": "col_3", "y": "col_4", "type": "scatter"},
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{"x": "col_5", "y": "time", "type": "line"},
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{"x": "col_6", "type": "bar"},
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{"x": "col_7", "type": "histogram"},
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{"x": "col_8", "y": "col_1", "type": "box"}
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]]:
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if plot_type == "scatter":
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plt.scatter(df[col_x], df[col_y])
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elif plot_type == "line":
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| 24 |
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plt.plot(df[col_x], df[col_y])
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elif plot_type == "bar":
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| 26 |
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plt.bar(df[col_x])
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| 27 |
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elif plot_type == "histogram":
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plt.hist(df[col_x])
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| 29 |
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elif plot_type == "box":
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| 30 |
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plt.boxplot([df[col_x]], vert=False)
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| 31 |
-
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# Save the plot
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| 33 |
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plt.savefig('outputs/plot.png', bbox_inches='tight', dpi=300)
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-
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| 35 |
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# Close the plot
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| 36 |
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plt.close()
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tools/__init__.py
ADDED
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File without changes
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tools/__pycache__/dataframe_ops.cpython-310.pyc
DELETED
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Binary file (532 Bytes)
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tools/dataframe_ops.py
DELETED
|
@@ -1,23 +0,0 @@
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|
| 1 |
-
# Multi Agent Data Analysis with Crew AI
|
| 2 |
-
# Copyright (c) 2025 Sowmiyan S
|
| 3 |
-
# Licensed under the MIT License
|
| 4 |
-
|
| 5 |
-
import pandas as pd
|
| 6 |
-
import json
|
| 7 |
-
|
| 8 |
-
def apply_cleaning(df, cleaning_json):
|
| 9 |
-
rules = json.loads(cleaning_json)
|
| 10 |
-
|
| 11 |
-
for rule in rules:
|
| 12 |
-
if rule["action"] == "drop_nulls":
|
| 13 |
-
df = df.dropna()
|
| 14 |
-
if rule["action"] == "fill_null":
|
| 15 |
-
df[rule["column"]] = df[rule["column"]].fillna(rule["value"])
|
| 16 |
-
if rule["action"] == "rename":
|
| 17 |
-
df = df.rename(columns={rule["old"]: rule["new"]})
|
| 18 |
-
|
| 19 |
-
return df
|
| 20 |
-
|
| 21 |
-
# Multi Agent Data Analysis with Crew AI
|
| 22 |
-
# Copyright (c) 2025 Sowmiyan S
|
| 23 |
-
# Licensed under the MIT License
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tools/dataset_tools.py
ADDED
|
@@ -0,0 +1,39 @@
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|
| 1 |
+
import pandas as pd
|
| 2 |
+
from crewai.tools import tool
|
| 3 |
+
|
| 4 |
+
class DatasetTools:
|
| 5 |
+
@tool("Read Dataset Head")
|
| 6 |
+
def read_dataset_head(file_path: str):
|
| 7 |
+
"""Reads the first 10 rows of the dataset to understand its structure."""
|
| 8 |
+
try:
|
| 9 |
+
df = pd.read_csv(file_path)
|
| 10 |
+
return df.head(10).to_markdown(index=False)
|
| 11 |
+
except Exception as e:
|
| 12 |
+
return f"Error reading file: {str(e)}"
|
| 13 |
+
|
| 14 |
+
@tool("Get Dataset Info")
|
| 15 |
+
def get_dataset_info(file_path: str):
|
| 16 |
+
"""Returns basic information about the dataset: columns, data types, and missing values."""
|
| 17 |
+
try:
|
| 18 |
+
df = pd.read_csv(file_path)
|
| 19 |
+
info = []
|
| 20 |
+
info.append(f"Shape: {df.shape}")
|
| 21 |
+
info.append("\nColumns and Types:")
|
| 22 |
+
for col, dtype in df.dtypes.items():
|
| 23 |
+
missing = df[col].isnull().sum()
|
| 24 |
+
info.append(f"- {col}: {dtype} (Missing: {missing})")
|
| 25 |
+
return "\n".join(info)
|
| 26 |
+
except Exception as e:
|
| 27 |
+
return f"Error analyzing file: {str(e)}"
|
| 28 |
+
|
| 29 |
+
@tool("Get Correlation Matrix")
|
| 30 |
+
def get_correlation_matrix(file_path: str):
|
| 31 |
+
"""Calculates the correlation matrix for numeric columns."""
|
| 32 |
+
try:
|
| 33 |
+
df = pd.read_csv(file_path)
|
| 34 |
+
numeric_df = df.select_dtypes(include=['number'])
|
| 35 |
+
if numeric_df.empty:
|
| 36 |
+
return "No numeric columns found."
|
| 37 |
+
return numeric_df.corr().to_markdown()
|
| 38 |
+
except Exception as e:
|
| 39 |
+
return f"Error calculating correlation: {str(e)}"
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workflows/__init__.py
ADDED
|
File without changes
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