├── .dockerignore ├── state_of_the_business ├── src │ ├── __init__.py │ ├── tools │ │ ├── __init__.py │ │ ├── llama_csv_file_read_tool.py │ │ ├── file_read_tool.py │ │ └── custom_tool.py │ ├── crews │ │ ├── mkt_ops_crew │ │ │ ├── __init__.py │ │ │ ├── config │ │ │ │ ├── agents.yaml │ │ │ │ └── tasks.yaml │ │ │ └── mkt_ops_crew.py │ │ ├── sales_ops_bb_crew │ │ │ ├── __init__.py │ │ │ ├── config │ │ │ │ ├── agents.yaml │ │ │ │ └── tasks.yaml │ │ │ └── sales_ops_b2b_crew.py │ │ ├── sales_ops_bc_crew │ │ │ ├── __init__.py │ │ │ ├── config │ │ │ │ ├── basic_task.yaml │ │ │ │ ├── basic_agent.yaml │ │ │ │ ├── agents.yaml │ │ │ │ └── tasks.yaml │ │ │ └── sales_ops_b2c_crew.py │ │ └── customer_success_ops │ │ │ ├── __init__.py │ │ │ ├── config │ │ │ ├── agents.yaml │ │ │ └── tasks.yaml │ │ │ └── customer_success_ops_crew.py │ ├── pydantic_models │ │ ├── marketing_opps.py │ │ ├── sales_ops.py │ │ └── customer_success.py │ ├── utils │ │ ├── logging_config.py │ │ ├── file_validation.py │ │ └── llm_config.py │ ├── main_sync.py │ ├── main_async.py │ └── flows │ │ ├── customer_success.py │ │ ├── marketing_ops.py │ │ └── sales_ops.py ├── mlruns │ └── 0 │ │ ├── traces │ │ ├── 1391b46db9b24fc298005400bfc9960a │ │ │ ├── tags │ │ │ │ ├── mlflow.source.type │ │ │ │ ├── mlflow.traceName │ │ │ │ ├── mlflow.source.name │ │ │ │ └── mlflow.artifactLocation │ │ │ ├── request_metadata │ │ │ │ ├── mlflow.trace_schema.version │ │ │ │ ├── mlflow.traceInputs │ │ │ │ └── mlflow.traceOutputs │ │ │ └── trace_info.yaml │ │ ├── 6039b296d75844cfa3c37f9371f2e5bb │ │ │ ├── tags │ │ │ │ ├── mlflow.source.type │ │ │ │ ├── mlflow.traceName │ │ │ │ ├── mlflow.source.name │ │ │ │ └── mlflow.artifactLocation │ │ │ ├── request_metadata │ │ │ │ ├── mlflow.trace_schema.version │ │ │ │ ├── mlflow.traceInputs │ │ │ │ └── mlflow.traceOutputs │ │ │ └── trace_info.yaml │ │ ├── 9ee4d4771590409caf8f3f0f6bc21de0 │ │ │ ├── tags │ │ │ │ ├── mlflow.source.type │ │ │ │ ├── mlflow.traceName │ │ │ │ ├── mlflow.source.name │ │ │ │ └── mlflow.artifactLocation │ │ │ ├── request_metadata │ │ │ │ ├── mlflow.trace_schema.version │ │ │ │ ├── mlflow.traceInputs │ │ │ │ └── mlflow.traceOutputs │ │ │ └── trace_info.yaml │ │ └── a241630a1cc34ddda3c7123313b915a6 │ │ │ ├── tags │ │ │ ├── mlflow.source.type │ │ │ ├── mlflow.traceName │ │ │ ├── mlflow.source.name │ │ │ └── mlflow.artifactLocation │ │ │ ├── request_metadata │ │ │ ├── mlflow.trace_schema.version │ │ │ ├── mlflow.traceInputs │ │ │ └── mlflow.traceOutputs │ │ │ └── trace_info.yaml │ │ └── meta.yaml ├── example_reports │ ├── marketing_report.md │ ├── b2c_report.md │ ├── b2b_report.md │ └── customer_success_report.md └── datasets │ └── spaceoutfitters_marketing_campaigns.csv ├── entrypoint_sync.sh ├── entrypoint_async.sh ├── mlruns └── 0 │ └── meta.yaml ├── .env_example ├── docker-compose.yml ├── Dockerfile.sync ├── Dockerfile.async ├── .github └── workflows │ └── docker-images.yaml ├── README.md ├── .gitignore ├── requirements.txt └── LICENSE /.dockerignore: -------------------------------------------------------------------------------- 1 | .venv 2 | __pycache__ -------------------------------------------------------------------------------- /state_of_the_business/src/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /state_of_the_business/src/tools/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/mkt_ops_crew/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bb_crew/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/customer_success_ops/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/tags/mlflow.source.type: -------------------------------------------------------------------------------- 1 | LOCAL -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/tags/mlflow.source.type: -------------------------------------------------------------------------------- 1 | LOCAL -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/tags/mlflow.source.type: -------------------------------------------------------------------------------- 1 | LOCAL -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/tags/mlflow.source.type: -------------------------------------------------------------------------------- 1 | LOCAL -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/tags/mlflow.traceName: -------------------------------------------------------------------------------- 1 | Crew.kickoff -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/tags/mlflow.traceName: -------------------------------------------------------------------------------- 1 | Crew.kickoff -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/tags/mlflow.traceName: -------------------------------------------------------------------------------- 1 | Crew.kickoff -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/tags/mlflow.traceName: -------------------------------------------------------------------------------- 1 | Crew.kickoff -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/request_metadata/mlflow.trace_schema.version: -------------------------------------------------------------------------------- 1 | 2 -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/request_metadata/mlflow.trace_schema.version: -------------------------------------------------------------------------------- 1 | 2 -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/request_metadata/mlflow.trace_schema.version: -------------------------------------------------------------------------------- 1 | 2 -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/request_metadata/mlflow.trace_schema.version: -------------------------------------------------------------------------------- 1 | 2 -------------------------------------------------------------------------------- /entrypoint_sync.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | source /app/.uvenv/bin/activate 3 | cd /app/state_of_the_business 4 | python -m src.main_sync -------------------------------------------------------------------------------- /entrypoint_async.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | source /app/.uvenv/bin/activate 3 | cd /app/state_of_the_business 4 | python -m src.main_async -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/tags/mlflow.source.name: -------------------------------------------------------------------------------- 1 | /app/state_of_the_business/src/main.py -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/tags/mlflow.source.name: -------------------------------------------------------------------------------- 1 | /app/state_of_the_business/src/main.py -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/tags/mlflow.source.name: -------------------------------------------------------------------------------- 1 | /app/state_of_the_business/src/main.py -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/tags/mlflow.source.name: -------------------------------------------------------------------------------- 1 | /app/state_of_the_business/src/main.py -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/tags/mlflow.artifactLocation: -------------------------------------------------------------------------------- 1 | file:///app/state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/artifacts -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/tags/mlflow.artifactLocation: -------------------------------------------------------------------------------- 1 | file:///app/state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/artifacts -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/tags/mlflow.artifactLocation: -------------------------------------------------------------------------------- 1 | file:///app/state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/artifacts -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/tags/mlflow.artifactLocation: -------------------------------------------------------------------------------- 1 | file:///app/state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/artifacts -------------------------------------------------------------------------------- /mlruns/0/meta.yaml: -------------------------------------------------------------------------------- 1 | artifact_location: mlflow-artifacts:/0 2 | creation_time: 1745332099004 3 | experiment_id: '0' 4 | last_update_time: 1745332099004 5 | lifecycle_stage: active 6 | name: Default 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/request_metadata/mlflow.traceInputs: -------------------------------------------------------------------------------- 1 | {"inputs": {"month": "03", "year": "2025", "b2c_sales_data": "/app/state_of_the_business/datasets/spaceoutfitters_sales.csv"}} -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/request_metadata/mlflow.traceInputs: -------------------------------------------------------------------------------- 1 | {"inputs": {"month": "03", "year": "2025", "b2c_sales_data": "/app/state_of_the_business/datasets/spaceoutfitters_sales.csv"}} -------------------------------------------------------------------------------- /.env_example: -------------------------------------------------------------------------------- 1 | ## Lambda API 2 | LAMBDA_API_KEY="" 3 | LAMBDA_API_BASE="https://api.lambda.ai/v1" 4 | LAMBDA_MODEL="openai/llama-4-maverick-17b-128e-instruct-fp8" 5 | 6 | #LAMBDA_MODEL="deepseek-r1-671b" -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/request_metadata/mlflow.traceInputs: -------------------------------------------------------------------------------- 1 | {"inputs": {"month": "03", "year": "2025", "b2b_sales_data": "/app/state_of_the_business/datasets/spaceoutfitters_b2b_sales_lite.csv"}} -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/request_metadata/mlflow.traceInputs: -------------------------------------------------------------------------------- 1 | {"inputs": {"month": "03", "year": "2025", "b2b_sales_data": "/app/state_of_the_business/datasets/spaceoutfitters_b2b_sales_lite.csv"}} -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/meta.yaml: -------------------------------------------------------------------------------- 1 | artifact_location: file:///app/state_of_the_business/mlruns/0 2 | creation_time: 1745360289945 3 | experiment_id: '0' 4 | last_update_time: 1745360289945 5 | lifecycle_stage: active 6 | name: Default 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/trace_info.yaml: -------------------------------------------------------------------------------- 1 | assessments: [] 2 | execution_time_ms: 14639 3 | experiment_id: '0' 4 | request_id: 1391b46db9b24fc298005400bfc9960a 5 | status: OK 6 | timestamp_ms: 1745360289949 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/trace_info.yaml: -------------------------------------------------------------------------------- 1 | assessments: [] 2 | execution_time_ms: 66641 3 | experiment_id: '0' 4 | request_id: 6039b296d75844cfa3c37f9371f2e5bb 5 | status: OK 6 | timestamp_ms: 1745360304606 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/trace_info.yaml: -------------------------------------------------------------------------------- 1 | assessments: [] 2 | execution_time_ms: 83914 3 | experiment_id: '0' 4 | request_id: 9ee4d4771590409caf8f3f0f6bc21de0 5 | status: OK 6 | timestamp_ms: 1745360637536 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/trace_info.yaml: -------------------------------------------------------------------------------- 1 | assessments: [] 2 | execution_time_ms: 13965 3 | experiment_id: '0' 4 | request_id: a241630a1cc34ddda3c7123313b915a6 5 | status: OK 6 | timestamp_ms: 1745360623551 7 | -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/1391b46db9b24fc298005400bfc9960a/request_metadata/mlflow.traceOutputs: -------------------------------------------------------------------------------- 1 | {"raw": "## Executive Summary\n* The sales data analysis for March 2025 could not be performed due to the unavailability of the dataset.\n* The file 'spaceoutfitters_sales.csv' was not found at the specified path.\n* As a result, key performance m... -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/6039b296d75844cfa3c37f9371f2e5bb/request_metadata/mlflow.traceOutputs: -------------------------------------------------------------------------------- 1 | {"raw": "It appears that you are trying to analyze the B2B sales data for March 2025 from a given dataset. Since I can only provide a final answer in a specific markdown format and I don't have the capability to directly read or manipulate the fil... -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/9ee4d4771590409caf8f3f0f6bc21de0/request_metadata/mlflow.traceOutputs: -------------------------------------------------------------------------------- 1 | {"raw": "# Executive Summary\n* Total Closed-Won Revenue increased by 25% MoM\n* Number of Deals Closed increased by 25% MoM\n* Average Deal Size remained the same\n* Sales Cycle Length increased by 11.11% MoM\n* Pipeline Coverage decreased by 9.0... -------------------------------------------------------------------------------- /state_of_the_business/mlruns/0/traces/a241630a1cc34ddda3c7123313b915a6/request_metadata/mlflow.traceOutputs: -------------------------------------------------------------------------------- 1 | {"raw": "## Executive Summary\n* The sales data for March 2025 was to be analyzed to determine key performance metrics such as total revenue, number of orders, average order value (AOV), top-selling products, and customer acquisition trends.\n* Th... -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/config/basic_task.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_b2c_report: 2 | description: > 3 | Write a report on the ecommerce sales outcomes for the company. 4 | Ensure the report is accurate and up to date for the last full month of sales. 5 | expected_output: > 6 | A report on the ecommerce sales outcomes for the company. 7 | agent: sales_ops_agent -------------------------------------------------------------------------------- /state_of_the_business/src/pydantic_models/marketing_opps.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from pathlib import Path 3 | 4 | 5 | class MarketingOpsState(BaseModel): 6 | """State containing all the data and reports for marketing operations.""" 7 | 8 | datasets_dir: Path = Path(__file__).parent.parent.parent / "datasets" 9 | reports_dir: Path = Path(__file__).parent.parent.parent / "reports" 10 | marketing_report: str = "" 11 | -------------------------------------------------------------------------------- /state_of_the_business/src/pydantic_models/sales_ops.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from pathlib import Path 3 | 4 | 5 | class SalesOpsState(BaseModel): 6 | """State containing all the data and reports for sales operations.""" 7 | 8 | datasets_dir: Path = Path(__file__).parent.parent.parent / "datasets" 9 | reports_dir: Path = Path(__file__).parent.parent.parent / "reports" 10 | b2c_report: str = "" 11 | b2b_report: str = "" 12 | -------------------------------------------------------------------------------- /state_of_the_business/src/pydantic_models/customer_success.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | from pathlib import Path 3 | 4 | 5 | class CustomerSuccessOpsState(BaseModel): 6 | """State containing all the data and reports for customer success operations.""" 7 | 8 | datasets_dir: Path = Path(__file__).parent.parent.parent / "datasets" 9 | reports_dir: Path = Path(__file__).parent.parent.parent / "reports" 10 | customer_success_report: str = "" 11 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/config/basic_agent.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_agent: 2 | role: > 3 | Sales Ops Agent 4 | goal: > 5 | Build reports on the sales outcomes for the company 6 | backstory: > 7 | You're a sales operations agent with a talent for expertly analyzing sales data 8 | and creating reports that accurately represent sales data. You are known for your 9 | ability to analyze data and create reports that are easy to understand and use. 10 | -------------------------------------------------------------------------------- /state_of_the_business/src/tools/llama_csv_file_read_tool.py: -------------------------------------------------------------------------------- 1 | from crewai.tools import BaseTool 2 | from llama_index.readers.file import CSVReader 3 | 4 | 5 | 6 | class LlamaCSVFileReadTool(BaseTool): 7 | name: str = "LlamaIndex CSV Tool" 8 | description: str = "Uses LlamaIndex to read and parse a CSV file" 9 | 10 | def _run(self, file_path: str) -> str: 11 | reader = CSVReader() 12 | documents = reader.load_data(file_path) 13 | return "\n".join([doc.text for doc in documents]) 14 | -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | services: 2 | app: 3 | build: 4 | context: . 5 | dockerfile: Dockerfile.async 6 | volumes: 7 | - ./state_of_the_business:/app/state_of_the_business 8 | depends_on: 9 | - mlflow 10 | networks: 11 | - app-network 12 | 13 | mlflow: 14 | image: ghcr.io/mlflow/mlflow:latest 15 | ports: 16 | - "8000:8000" 17 | command: mlflow server --host 0.0.0.0 --port 8000 18 | networks: 19 | - app-network 20 | 21 | networks: 22 | app-network: 23 | -------------------------------------------------------------------------------- /state_of_the_business/src/tools/file_read_tool.py: -------------------------------------------------------------------------------- 1 | from crewai_tools import FileReadTool 2 | 3 | 4 | class DatasetFileReadTool(FileReadTool): 5 | """A tool for reading files in the datasets directory.""" 6 | 7 | def __init__(self): 8 | """Initialize the tool without a specific file path.""" 9 | super().__init__() 10 | 11 | def set_file_path(self, file_path: str): 12 | """Set the file path to read. 13 | 14 | Args: 15 | file_path (str): Path to the file to read 16 | """ 17 | self._file_path = file_path 18 | -------------------------------------------------------------------------------- /state_of_the_business/src/tools/custom_tool.py: -------------------------------------------------------------------------------- 1 | from typing import Type 2 | 3 | from crewai_tools import BaseTool 4 | from pydantic import BaseModel, Field 5 | 6 | 7 | class MyCustomToolInput(BaseModel): 8 | """Input schema for MyCustomTool.""" 9 | 10 | argument: str = Field(..., description="Description of the argument.") 11 | 12 | 13 | class MyCustomTool(BaseTool): 14 | name: str = "Name of my tool" 15 | description: str = ( 16 | "Clear description for what this tool is useful for, you agent will need this information to use it." 17 | ) 18 | args_schema: Type[BaseModel] = MyCustomToolInput 19 | 20 | def _run(self, argument: str) -> str: 21 | # Implementation goes here 22 | return "this is an example of a tool output, ignore it and move along." 23 | -------------------------------------------------------------------------------- /Dockerfile.sync: -------------------------------------------------------------------------------- 1 | # Build stage 2 | FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder 3 | 4 | # Set working directory 5 | WORKDIR /app 6 | 7 | # Install UV from official image 8 | COPY --from=ghcr.io/astral-sh/uv:0.6.14 /uv /bin/uv 9 | 10 | # Copy the requirements.txt into the container 11 | COPY requirements.txt ./ 12 | 13 | # Create and activate the uv virtual environment 14 | RUN uv venv /app/.uvenv # Creates a new virtual environment inside the container 15 | RUN . /app/.uvenv/bin/activate && uv pip install --no-cache-dir -r requirements.txt 16 | 17 | # Copy application code 18 | COPY . . 19 | 20 | # Copy the entrypoint_sync.sh script into the container 21 | RUN chmod +x /app/entrypoint_sync.sh 22 | 23 | # Set environment variables 24 | ENV PYTHONPATH=/app 25 | 26 | # Set the entrypoint to the entrypoint_sync.sh script 27 | ENTRYPOINT ["/app/entrypoint_sync.sh"] 28 | 29 | ## debugging 30 | # CMD ["/bin/bash"] -------------------------------------------------------------------------------- /Dockerfile.async: -------------------------------------------------------------------------------- 1 | # Build stage 2 | FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder 3 | 4 | # Set working directory 5 | WORKDIR /app 6 | 7 | # Install UV from official image 8 | COPY --from=ghcr.io/astral-sh/uv:0.6.14 /uv /bin/uv 9 | 10 | # Copy the requirements.txt into the container 11 | COPY requirements.txt ./ 12 | 13 | # Create and activate the uv virtual environment 14 | RUN uv venv /app/.uvenv # Creates a new virtual environment inside the container 15 | RUN . /app/.uvenv/bin/activate && uv pip install --no-cache-dir -r requirements.txt 16 | 17 | # Copy application code 18 | COPY . . 19 | 20 | # Copy the entrypoint_async.sh script into the container 21 | RUN chmod +x /app/entrypoint_async.sh 22 | 23 | # Set environment variables 24 | ENV PYTHONPATH=/app 25 | 26 | # Set the entrypoint to the entrypoint_async.sh script 27 | ENTRYPOINT ["/app/entrypoint_async.sh"] 28 | 29 | ## debugging 30 | # CMD ["/bin/bash"] -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bb_crew/config/agents.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_agent: 2 | role: > 3 | Sales Operations Reporting Specialist Focused On Revenue Analytics and Pipeline Forecasting 4 | goal: > 5 | Deliver clear, accurate, and actionable sales performance reports that provide leadership and sales 6 | teams with timely insights into revenue trends for B2B sales, pipeline health, 7 | rep productivity, and forecasting accuracy. Ensure data is structured, relevant, and visualized 8 | to drive informed decision-making. 9 | backstory: > 10 | You have 10+ years of experience in Sales Operations at high-growth Retail companies, supporting 11 | executive leadership and sales teams with data-driven insights. You specialize in Salesforce reporting, 12 | forecasting best practices, and dashboard creation. You believe that the best sales ops reports 13 | are not just accurate, but also easy to understand, actionable, and aligned with business goals. 14 | You are detail-obsessed, think from a CROs perspective, and prioritize clarity, 15 | consistency, and strategic relevance in all reporting. 16 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/config/agents.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_agent: 2 | role: > 3 | Sales Operations Reporting Specialist Focused On Revenue Analytics and Pipeline Forecasting 4 | goal: > 5 | Deliver clear, accurate, and actionable sales performance reports that provide leadership and sales 6 | teams with timely insights into revenue trends for direct sales, pipeline health, 7 | rep productivity, and forecasting accuracy. Ensure data is structured, relevant, and visualized 8 | to drive informed decision-making. 9 | backstory: > 10 | You have 10+ years of experience in Sales Operations at high-growth retail companies, supporting 11 | executive leadership and sales teams with data-driven insights. You specialize in Salesforce reporting, 12 | forecasting best practices, and dashboard creation. You believe that the best sales ops reports 13 | are not just accurate, but also easy to understand, actionable, and aligned with business goals. 14 | You are detail-obsessed, think from a CROs perspective, and prioritize clarity, consistency, 15 | and strategic relevance in all reporting. 16 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/customer_success_ops/config/agents.yaml: -------------------------------------------------------------------------------- 1 | customer_success_ops_agent: 2 | role: > 3 | Customer Success Reporting Specialist Focused On Board-Level Operational Analytics 4 | goal: > 5 | Generate high-impact Customer Success reports that provide executive leadership with clear 6 | insights into customer satisfaction, SLA compliance, operational efficiency, agent performance, 7 | and customer risk factors. Ensure reports are concise, actionable, and structured to drive 8 | strategic decisions and improvements in customer retention and support experience. 9 | backstory: > 10 | You have 10+ years of experience in Customer Success Operations for high-growth Retail 11 | and service companies. You are an expert in designing board-level reporting frameworks 12 | that balance operational detail with strategic insight. You excel at identifying patterns 13 | in customer behavior, pinpointing operational bottlenecks, and surfacing early risk indicators. 14 | You believe that Customer Success reporting should not only reflect past performance but 15 | also guide proactive action, reduce churn, and maximize customer satisfaction and lifetime value. 16 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/mkt_ops_crew/config/agents.yaml: -------------------------------------------------------------------------------- 1 | mkt_ops_agent: 2 | role: > 3 | Marketing Operations Reporting Specialist Focused On Campaign Analytics and Customer Retention Insights 4 | goal: > 5 | Produce accurate, insightful marketing performance reports that analyze campaign effectiveness, 6 | customer lifetime value, promotional impact, web engagement, and churn risks. Ensure insights 7 | are accurate, actionable, clearly structured, and aligned to business growth and retention goals. 8 | Always provide detailed methodology, calculations, and reasoning behind all conclusions to ensure 9 | transparency and reproducibility of analysis. 10 | backstory: > 11 | You have over 8 years of experience in Marketing Operations at leading e-commerce and B2B companies. 12 | You specialize in translating complex marketing data into executive-ready reports that inform both 13 | strategic and tactical decisions. Your expertise lies in campaign analysis, customer segmentation, 14 | promotional effectiveness, and web funnel optimization. You believe that the best marketing insights 15 | are those that directly impact revenue growth, customer retention, and marketing ROI. You are known 16 | for your meticulous documentation of analysis methods and clear explanation of your reasoning process. 17 | -------------------------------------------------------------------------------- /.github/workflows/docker-images.yaml: -------------------------------------------------------------------------------- 1 | name: Build and Push Docker Images 2 | 3 | on: 4 | push: 5 | branches: 6 | - part1 7 | - workshop_part_2 8 | jobs: 9 | build-and-push: 10 | runs-on: ubuntu-latest 11 | 12 | steps: 13 | - name: Checkout repository 14 | uses: actions/checkout@v4 15 | 16 | - name: Set up Docker Build Variables 17 | id: vars 18 | run: | 19 | BRANCH="${GITHUB_REF##*/}" 20 | if [[ "$BRANCH" == "part1" ]]; then 21 | echo "DOCKERFILE=Dockerfile.sync" >> $GITHUB_ENV 22 | echo "IMAGE_NAME=nickharvey/lambda-ai-agent-masterclass-sync" >> $GITHUB_ENV 23 | elif [[ "$BRANCH" == "workshop_part_2" ]]; then 24 | echo "DOCKERFILE=Dockerfile.async" >> $GITHUB_ENV 25 | echo "IMAGE_NAME=nickharvey/lambda-ai-agent-masterclass-async" >> $GITHUB_ENV 26 | else 27 | echo "Unsupported branch: $BRANCH" 28 | exit 1 29 | fi 30 | 31 | - name: Log in to Docker Hub 32 | uses: docker/login-action@v3 33 | with: 34 | username: ${{ secrets.DOCKERHUB_USERNAME }} 35 | password: ${{ secrets.DOCKERHUB_TOKEN }} 36 | 37 | - name: Build Docker image 38 | run: | 39 | docker build -f $DOCKERFILE -t $IMAGE_NAME:latest . 40 | 41 | - name: Push Docker image 42 | run: | 43 | docker push $IMAGE_NAME:latest -------------------------------------------------------------------------------- /state_of_the_business/src/utils/logging_config.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from pathlib import Path 3 | 4 | 5 | def setup_logging(): 6 | # Create logs directory if it doesn't exist 7 | logs_dir = Path(__file__).parent.parent.parent / "logs" 8 | logs_dir.mkdir(exist_ok=True) 9 | 10 | # Configure root logger 11 | root_logger = logging.getLogger() 12 | root_logger.setLevel(logging.INFO) 13 | 14 | # Remove any existing handlers 15 | for handler in root_logger.handlers[:]: 16 | root_logger.removeHandler(handler) 17 | 18 | # Create formatters 19 | file_formatter = logging.Formatter( 20 | "%(asctime)s - %(name)s - %(levelname)s - %(message)s" 21 | ) 22 | console_formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") 23 | 24 | # File handler 25 | file_handler = logging.FileHandler(logs_dir / "primary_flow.log", encoding="utf-8") 26 | file_handler.setFormatter(file_formatter) 27 | file_handler.setLevel(logging.INFO) 28 | 29 | # Console handler 30 | console_handler = logging.StreamHandler() 31 | console_handler.setFormatter(console_formatter) 32 | console_handler.setLevel(logging.INFO) 33 | 34 | # Add handlers to root logger 35 | root_logger.addHandler(file_handler) 36 | root_logger.addHandler(console_handler) 37 | 38 | return root_logger 39 | 40 | 41 | # Get logger for this module 42 | logger = logging.getLogger(__name__) 43 | -------------------------------------------------------------------------------- /state_of_the_business/src/utils/file_validation.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from pathlib import Path 3 | from llama_index.readers.file import CSVReader 4 | from src.utils.logging_config import logger 5 | 6 | # Get logger for this module 7 | logger = logging.getLogger(__name__) 8 | 9 | 10 | def validate_csv_files(): 11 | """ 12 | Validate all CSV files in the datasets directory. 13 | """ 14 | 15 | logger.info("Starting company data validation...") 16 | 17 | # get all csv files 18 | datasets_dir = Path(__file__).parent.parent.parent / "datasets" 19 | print(datasets_dir) 20 | csv_files = list(datasets_dir.glob("*.csv")) 21 | 22 | for fp in csv_files: 23 | if not fp.exists(): 24 | error_msg = f"Required file not found: {fp}" 25 | logger.error(error_msg) 26 | raise FileNotFoundError(error_msg) 27 | 28 | try: 29 | # Use LlamaIndex's CSVReader 30 | reader = CSVReader() 31 | documents = reader.load_data(file=fp) 32 | 33 | if not documents or len(documents) == 0: 34 | logger.error(f"Empty or malformed data in {fp}") 35 | else: 36 | logger.info(f"Successfully validated {fp}") 37 | logger.info(f"Found {len(documents)} rows of data") 38 | except Exception as e: 39 | logger.error(f"Failed to validate {fp} data: {str(e)}", exc_info=True) 40 | raise 41 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/config/tasks.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_b2c_report: 2 | description: > 3 | Analyze the ecommerce (B2C) sales data for {month} {year} in this dataset: {b2c_sales_data}. 4 | Your analysis should focus on key performance metrics such as total revenue, number of orders, average order value (AOV), top-selling products, and customer acquisition trends. 5 | Identify month-over-month changes compared to the previous month and highlight any notable patterns or anomalies. 6 | Ensure data is accurate, up-to-date, and presented in a clear, executive-friendly format. 7 | expected_output: > 8 | A structured markdown report including: 9 | 1. Executive Summary (3-5 bullet points) 10 | 2. Key Metrics Table (Revenue, Orders, AOV, Customer Acquisition, Conversion Rate) 11 | 3. Top 5 Best-Selling Products (with units sold and revenue generated) 12 | 4. Month-over-Month Performance Comparison 13 | 5. Notable Trends or Anomalies (brief analysis) 14 | 6. Recommendations (if applicable) 15 | 16 | For each section include a sub-section called "Methodology" with the following: 17 | - The methodology used to analyze the data 18 | - Key calculations and formulas applied 19 | - Reasoning behind conclusions drawn 20 | - Supporting data points and metrics 21 | - Any assumptions made during analysis 22 | - Data validation steps taken 23 | - Statistical significance of trends identified 24 | agent: sales_ops_agent -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bb_crew/config/tasks.yaml: -------------------------------------------------------------------------------- 1 | sales_ops_b2b_report: 2 | description: > 3 | Analyze the B2B sales data for {month}/{year} in this dataset: {b2b_sales_data}. 4 | Your analysis should include total closed-won revenue, number of deals closed, average deal size, sales cycle length, pipeline coverage, and new logo acquisition. 5 | Compare performance to the prior month and identify any material deviations, emerging trends, or areas of concern. 6 | Focus on delivering insights that are actionable for both sales leadership and executive stakeholders. 7 | expected_output: > 8 | A structured markdown report including: 9 | 1. Executive Summary (3-5 bullet points) 10 | 2. Key Metrics Table (Closed-Won Revenue, Deal Count, Average Deal Size, Sales Cycle Length, Pipeline Coverage, New Logos) 11 | 3. Month-over-Month Performance Comparison 12 | 4. Notable Trends, Risks, or Anomalies 13 | 5. Recommendations for Sales Leadership 14 | 15 | For each section include a sub-section called "Methodology" with the following: 16 | - The methodology used to analyze the data 17 | - Key calculations and formulas applied 18 | - Reasoning behind conclusions drawn 19 | - Supporting data points and metrics 20 | - Any assumptions made during analysis 21 | - Data validation steps taken 22 | - Statistical significance of trends identified 23 | - Pipeline analysis methodology 24 | - Deal stage transition analysis 25 | agent: sales_ops_agent -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bb_crew/sales_ops_b2b_crew.py: -------------------------------------------------------------------------------- 1 | from crewai import Agent, Crew, Process, Task 2 | from crewai.project import CrewBase, agent, crew, task 3 | from src.utils.llm_config import LLMConfig 4 | # from src.tools.llama_csv_file_read_tool import LlamaCSVFileReadTool 5 | from src.tools.file_read_tool import DatasetFileReadTool 6 | 7 | llm = LLMConfig().llm 8 | 9 | 10 | @CrewBase 11 | class SalesOpsB2BCrew: 12 | """Crew for generating the B2B sales report.""" 13 | 14 | agents_config = "config/agents.yaml" 15 | tasks_config = "config/tasks.yaml" 16 | 17 | @agent 18 | def sales_ops_agent(self) -> Agent: 19 | return Agent( 20 | config=self.agents_config["sales_ops_agent"], 21 | # tools=[LlamaCSVFileReadTool()], 22 | tools=[DatasetFileReadTool()], 23 | llm=llm, 24 | verbose=True, 25 | ) 26 | 27 | @task 28 | def sales_ops_b2b_report(self) -> Task: 29 | return Task( 30 | config=self.tasks_config["sales_ops_b2b_report"], 31 | ) 32 | 33 | @crew 34 | def crew(self) -> Crew: 35 | """Creates the Sales Ops Crew""" 36 | return Crew( 37 | agents=self.agents, # Automatically created by the @agent decorator 38 | tasks=self.tasks, # Automatically created by the @task decorator 39 | process=Process.sequential, 40 | verbose=True, 41 | full_output=True, 42 | planning=True, 43 | planning_llm=llm, 44 | ) 45 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/sales_ops_bc_crew/sales_ops_b2c_crew.py: -------------------------------------------------------------------------------- 1 | from crewai import Agent, Crew, Process, Task 2 | from crewai.project import CrewBase, agent, crew, task 3 | 4 | from src.utils.llm_config import LLMConfig 5 | # from src.tools.llama_csv_file_read_tool import LlamaCSVFileReadTool 6 | from src.tools.file_read_tool import DatasetFileReadTool 7 | 8 | llm = LLMConfig().llm 9 | 10 | @CrewBase 11 | class SalesOpsB2CCrew: 12 | """Crew for generating the B2C sales report.""" 13 | 14 | agents_config = "config/agents.yaml" 15 | tasks_config = "config/tasks.yaml" 16 | 17 | @agent 18 | def sales_ops_agent(self) -> Agent: 19 | return Agent( 20 | config=self.agents_config["sales_ops_agent"], 21 | # tools=[LlamaCSVFileReadTool()], 22 | tools=[DatasetFileReadTool()], 23 | llm=llm, 24 | verbose=True, 25 | ) 26 | 27 | @task 28 | def sales_ops_b2c_report(self) -> Task: 29 | return Task( 30 | config=self.tasks_config["sales_ops_b2c_report"], 31 | ) 32 | 33 | @crew 34 | def crew(self) -> Crew: 35 | """Creates the Sales Ops Crew""" 36 | return Crew( 37 | agents=self.agents, # Automatically created by the @agent decorator 38 | tasks=self.tasks, # Automatically created by the @task decorator 39 | process=Process.sequential, 40 | verbose=True, 41 | full_output=True, 42 | planning=True, 43 | planning_llm=llm, 44 | ) 45 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/customer_success_ops/customer_success_ops_crew.py: -------------------------------------------------------------------------------- 1 | from crewai import Agent, Crew, Process, Task 2 | from crewai.project import CrewBase, agent, crew, task 3 | from src.utils.llm_config import LLMConfig 4 | # from src.tools.llama_csv_file_read_tool import LlamaCSVFileReadTool 5 | from src.tools.file_read_tool import DatasetFileReadTool 6 | 7 | 8 | llm = LLMConfig().llm 9 | 10 | 11 | @CrewBase 12 | class CustomerSuccessOpsCrew: 13 | """Customer Success Ops Crew""" 14 | 15 | agents_config = "config/agents.yaml" 16 | tasks_config = "config/tasks.yaml" 17 | 18 | @agent 19 | def customer_success_ops_agent(self) -> Agent: 20 | return Agent( 21 | config=self.agents_config["customer_success_ops_agent"], 22 | # tools=[LlamaCSVFileReadTool()], 23 | tools=[DatasetFileReadTool()], 24 | llm=llm, 25 | verbose=True, 26 | ) 27 | 28 | @task 29 | def cs_ops_performance_report(self) -> Task: 30 | return Task( 31 | config=self.tasks_config["cs_ops_board_report"], 32 | ) 33 | 34 | @crew 35 | def crew(self) -> Crew: 36 | """Creates the Customer Success Ops Crew""" 37 | return Crew( 38 | agents=self.agents, # Automatically created by the @agent decorator 39 | tasks=self.tasks, # Automatically created by the @task decorator 40 | process=Process.sequential, 41 | verbose=True, 42 | full_output=True, 43 | planning=True, 44 | planning_llm=llm, 45 | ) 46 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/mkt_ops_crew/mkt_ops_crew.py: -------------------------------------------------------------------------------- 1 | from crewai import Agent, Crew, Process, Task 2 | from crewai.project import CrewBase, agent, crew, task 3 | from src.utils.llm_config import LLMConfig 4 | # from src.tools.llama_csv_file_read_tool import LlamaCSVFileReadTool 5 | from src.tools.file_read_tool import DatasetFileReadTool 6 | 7 | llm = LLMConfig().llm 8 | 9 | 10 | @CrewBase 11 | class MktOpsCrew: 12 | """Marketing Ops Crew""" 13 | 14 | agents_config = "config/agents.yaml" 15 | tasks_config = "config/tasks.yaml" 16 | 17 | @agent 18 | def mkt_ops_agent(self) -> Agent: 19 | return Agent( 20 | config=self.agents_config["mkt_ops_agent"], 21 | # tools=[LlamaCSVFileReadTool()], 22 | tools=[DatasetFileReadTool()], 23 | llm=llm, 24 | verbose=True, 25 | ) 26 | 27 | @task 28 | def mkt_ops_performance_report(self) -> Task: 29 | return Task( 30 | config=self.tasks_config["mkt_ops_performance_report"], 31 | ) 32 | 33 | @task 34 | def mkt_ops_retention_risk_report(self) -> Task: 35 | return Task( 36 | config=self.tasks_config["mkt_ops_retention_risk_report"], 37 | ) 38 | 39 | @crew 40 | def crew(self) -> Crew: 41 | """Creates the Marketing Ops Crew""" 42 | return Crew( 43 | agents=self.agents, # Automatically created by the @agent decorator 44 | tasks=self.tasks, # Automatically created by the @task decorator 45 | process=Process.sequential, 46 | verbose=True, 47 | full_output=True, 48 | planning=True, 49 | planning_llm=llm, 50 | ) 51 | -------------------------------------------------------------------------------- /state_of_the_business/src/utils/llm_config.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Optional 3 | from dotenv import load_dotenv 4 | from crewai import LLM 5 | 6 | 7 | class LLMConfig: 8 | """Configuration class for managing LLM settings and interactions.""" 9 | 10 | _instance: Optional["LLMConfig"] = None 11 | _llm: Optional[LLM] = None 12 | 13 | def __new__(cls): 14 | if cls._instance is None: 15 | cls._instance = super(LLMConfig, cls).__new__(cls) 16 | return cls._instance 17 | 18 | def __init__(self): 19 | """Initialize the LLM configuration.""" 20 | if self._llm is None: 21 | load_dotenv() # Load environment variables 22 | 23 | base_url = os.getenv("LAMBDA_API_BASE") 24 | api_key = os.getenv("LAMBDA_API_KEY") 25 | model = os.getenv("LAMBDA_MODEL") 26 | 27 | if not base_url or not api_key: 28 | raise ValueError( 29 | "Missing required environment variables. " 30 | "Please set LAMBDA_API_BASE and LAMBDA_API_KEY." 31 | ) 32 | 33 | self._llm = LLM( 34 | base_url=base_url, 35 | model=model, 36 | api_key=api_key, 37 | ) 38 | 39 | @property 40 | def llm(self) -> LLM: 41 | """Get the configured LLM instance.""" 42 | if self._llm is None: 43 | raise RuntimeError("LLM not initialized. Please call __init__ first.") 44 | return self._llm 45 | 46 | def get_llm(self) -> LLM: 47 | """Get the LLM instance (alternative to property).""" 48 | return self.llm 49 | 50 | 51 | # llm = LLM( 52 | # base_url=os.getenv("LAMBDA_API_BASE"), 53 | # model="openai/llama-4-maverick", 54 | # api_key=os.getenv("LAMBDA_API_KEY"), 55 | # ) 56 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🧠 Agent Workshop 2 | 3 | ## Requirements 4 | - [Docker](https://www.docker.com/) 5 | - [Valid Lambda API Key](https://lambda.ai/inference) 6 | 7 | ## Setup Instructions 8 | *(If you are attending the in-person workshop, please checkout to `part_1` or `workshop_part_2` and **ignore the instructions below**.)* 9 | 10 | 1. Clone the Repository and Checkout the Correct Branch 11 | ``` 12 | git clone https://github.com/LambdaLabsML/AI_Agent_Masterclass.git 13 | cd /AI_Agent_Masterclass 14 | ``` 15 | 16 | 2. Create a `.env` File in the Root Directory 17 | ``` 18 | LAMBDA_API_BASE="https://api.lambda.ai/v1" 19 | LAMBDA_API_KEY= 20 | LAMBDA_MODEL="openai/llama-4-maverick-17b-128e-instruct-fp8" 21 | ``` 22 | 23 | 3. Test that you can connect to Lambda's Inference API 24 | ``` 25 | source .env 26 | curl https://api.lambdalabs.com/v1/lambda/models -H "Authorization: Bearer $LAMBDA_API_KEY" 27 | ``` 28 | 29 | 4. Run with Docker Compose (Recommended) 30 | ``` 31 | # Default (crews will run async and MLFlow will be launched) 32 | docker compose up --build 33 | 34 | # Once the container is up and running, you can visit the following link in your browser, to track the execution traces on MLFlow: 35 | http://0.0.0.0:8000/ 36 | ``` 37 | 38 | 5. (Optional) Run the Crews Synchronously Without MLflow 39 | ``` 40 | # Build the Docker image 41 | docker build -f Dockerfile.sync -t ml_agent_workshop_sync . 42 | 43 | # Run the container 44 | docker run ml_agent_workshop_sync 45 | 46 | # After the Docker Container is finished, get container ID 47 | docker ps -a 48 | 49 | # Copy reports from container to local machine 50 | docker cp :/app/state_of_the_business/reports 51 | ``` 52 | 53 | 6. (Optional) Run the Crews Asynchronously Without MLflow 54 | ``` 55 | # Build the Docker image 56 | docker build -f Dockerfile.async -t ml_agent_workshop_async . 57 | 58 | # Run the container 59 | docker run ml_agent_workshop_async 60 | 61 | # After the Docker Container is finished, get container ID 62 | docker ps -a 63 | 64 | # Copy reports from container to local machine 65 | docker cp :/app/state_of_the_business/reports 66 | ``` -------------------------------------------------------------------------------- /state_of_the_business/example_reports/marketing_report.md: -------------------------------------------------------------------------------- 1 | # Marketing Performance Report for SpaceOutfitters (03/2025) 2 | 3 | ## Executive Summary 4 | * Overall churn risk rate: 0.12% 5 | * Churn risk rates by Education: 6 | Basic 0.125 7 | Graduate 0.105 8 | High School 0.115 9 | PhD 0.135 10 | Postgraduate 0.095 11 | dtype: float64 12 | * Churn risk rates by Marital Status: 13 | Divorced 0.130 14 | Married 0.100 15 | Separated 0.140 16 | Single 0.110 17 | Widow 0.120 18 | dtype: float64 19 | * Number of at-risk customers by Education and Marital Status: 20 | Education Marital 21 | Basic Divorced 10 22 | Married 8 23 | Separated 6 24 | Single 4 25 | Widow 12 26 | Graduate Divorced 8 27 | Married 6 28 | Separated 4 29 | Single 6 30 | Widow 2 31 | High School Divorced 12 32 | Married 8 33 | Separated 6 34 | Single 8 35 | Widow 4 36 | PhD Divorced 14 37 | Married 6 38 | Separated 10 39 | Single 8 40 | Widow 6 41 | Postgraduate Divorced 10 42 | Married 8 43 | Separated 8 44 | Single 6 45 | Widow 4 46 | dtype: int64 47 | 48 | ## Churn Risk Identification Methodology 49 | ### Methodology 50 | * Used Recency and Complain columns to identify high-risk customers. 51 | * Applied threshold of 90 days for Recency. 52 | 53 | ## Churn Risk Rates Overall and by Segment 54 | ### Methodology 55 | * Calculated overall churn risk rate by dividing the number of high-risk customers by the total number of customers. 56 | * Segmented churn risk rates by Education and Marital Status using groupby operations. 57 | 58 | ## At-Risk Customer Profile Summary 59 | ### Methodology 60 | * Identified at-risk customers based on Recency and Complain columns. 61 | * Summarized at-risk customer profiles by Education and Marital Status. 62 | 63 | ## Retention Strategy Recommendations 64 | ### Methodology 65 | * Recommended retention strategies based on the analysis of at-risk customers. 66 | * Suggested targeting customers with Recency > 90 days and those who have lodged complaints with personalized retention strategies. -------------------------------------------------------------------------------- /state_of_the_business/src/main_sync.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from src.flows.sales_ops import SalesOpsFlow 4 | from src.flows.marketing_ops import MarketingOpsFlow 5 | from src.flows.customer_success import CustomerSuccessOpsFlow 6 | from src.utils.logging_config import setup_logging, logger 7 | from src.utils.file_validation import validate_csv_files 8 | 9 | # Get logger for this module 10 | logger = logging.getLogger(__name__) 11 | 12 | # TODO: 13 | # If you are running the docker-compose.yml file, uncomment the following lines. 14 | # However, if your running the individual Dockerfiles, leave these commented out as the ML Flow Server will not be running. 15 | 16 | # # Enable MLFlow logging 17 | # import mlflow 18 | # mlflow.crewai.autolog() 19 | 20 | # mlflow.set_tracking_uri("http://mlflow:8000") 21 | # mlflow.set_experiment("Autopilot for Executive Reporting") 22 | 23 | 24 | def kickoff(): 25 | # Setup logging 26 | setup_logging() 27 | 28 | logger.info("🚀 Kicking off primary flow...") 29 | 30 | # Pre-async run validation of all CSV files 31 | validate_csv_files() 32 | 33 | # Run all flows 34 | flows_to_run = [ 35 | [SalesOpsFlow(), "Sales Operations Flow"], 36 | [MarketingOpsFlow(), "Marketing Operations Flow"], 37 | [CustomerSuccessOpsFlow(), "Customer Success Operations Flow"], 38 | ] 39 | 40 | flow_results_list = [] 41 | for flow in flows_to_run: 42 | logger.info(f"- Starting {flow[1]}") 43 | flow_result = flow[0].kickoff() 44 | flow_class = flow[0] 45 | logger.info(f"- {flow[1]} completed successfully!") 46 | flow_results_list.append(flow_class) 47 | 48 | # Combine results into a structured output 49 | sales_ops_flow = flow_results_list[0] 50 | marketing_ops_flow = flow_results_list[1] 51 | customer_success_ops_flow = flow_results_list[2] 52 | 53 | combined_result = { 54 | "sales_ops": { 55 | "b2c_report": sales_ops_flow.state.b2c_report, 56 | "b2b_report": sales_ops_flow.state.b2b_report, 57 | }, 58 | "marketing_ops": { 59 | "marketing_report": marketing_ops_flow.state.marketing_report, 60 | }, 61 | "customer_success_ops": { 62 | "customer_success_report": customer_success_ops_flow.state.customer_success_report, 63 | }, 64 | } 65 | 66 | return combined_result 67 | 68 | 69 | def plot(): 70 | sales_ops_flow = SalesOpsFlow() 71 | sales_ops_flow.plot() 72 | 73 | 74 | if __name__ == "__main__": 75 | kickoff() 76 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/customer_success_ops/config/tasks.yaml: -------------------------------------------------------------------------------- 1 | cs_ops_board_report: 2 | description: > 3 | Create a comprehensive Customer Success board report for {month}/{year}. 4 | use the following data: {cs_ops_data}. 5 | Your report must include: 6 | - Customer Satisfaction Summary (CSAT distribution and trends by channel and product group) 7 | - SLA Compliance Analysis (first response and resolution compliance rates by priority and agent group) 8 | - Ticket Volume and Load Trends (total tickets created, by channel, spaceport, and time trends) 9 | - Agent and Team Performance Review (first response time, resolution time, agent interaction counts, CSAT by agent) 10 | - Customer Risk and Escalation Assessment (low CSAT cases, SLA violation patterns, high-risk customers) 11 | 12 | Highlight major positive trends, emerging risks, and operational bottlenecks. Provide strategic recommendations for leadership. 13 | expected_output: > 14 | A structured markdown report including: 15 | 1. Executive Summary (5-7 bullet points) 16 | 2. Section 1: Customer Satisfaction Overview 17 | - Avg CSAT, CSAT by Channel, CSAT by Product Group 18 | - CSAT Distribution Table (1–5 stars) 19 | 3. Section 2: SLA Compliance Report 20 | - First Response SLA Compliance % (Overall and by Priority) 21 | - Resolution SLA Compliance % (Overall and by Agent Group) 22 | 4. Section 3: Ticket Volume and Load Trends 23 | - Total Tickets, Volume by Source and Spaceport 24 | - Ticket creation volume trends (week/month) 25 | 5. Section 4: Agent and Team Performance 26 | - Avg First Response and Resolution Times by Agent 27 | - Avg Agent Interactions per Ticket 28 | - CSAT by Agent 29 | 6. Section 5: Risk and Escalation Report 30 | - Low CSAT Ticket % (Scores 1–2) 31 | - SLA Violation Impact on Satisfaction 32 | - High-risk customer profiles (spaceport, topic, agent group) 33 | 7. Section 6: Strategic Recommendations 34 | - 3–5 actionable suggestions for improving CSAT, SLA compliance, and retention 35 | 36 | For each section include a sub-section called "Methodology" with the following: 37 | - The methodology used to analyze the data 38 | - Key calculations and formulas applied 39 | - Reasoning behind conclusions drawn 40 | - Supporting data points and metrics 41 | - Any assumptions made during analysis 42 | - Data validation steps taken 43 | - Statistical significance of trends identified 44 | - Correlation analysis between metrics 45 | - Root cause analysis methodology 46 | - Trend analysis methodology 47 | agent: customer_success_ops_agent -------------------------------------------------------------------------------- /state_of_the_business/src/main_async.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import asyncio 3 | 4 | from src.flows.sales_ops import SalesOpsFlow 5 | from src.flows.marketing_ops import MarketingOpsFlow 6 | from src.flows.customer_success import CustomerSuccessOpsFlow 7 | from src.utils.logging_config import setup_logging, logger 8 | from src.utils.file_validation import validate_csv_files 9 | 10 | # Get logger for this module 11 | logger = logging.getLogger(__name__) 12 | 13 | # TODO: 14 | # If you are running the docker-compose.yml file, uncomment the following lines. 15 | # However, if your running the individual Dockerfiles, leave these commented out as the ML Flow Server will not be running. 16 | 17 | # # Enable MLFlow logging 18 | # import mlflow 19 | # mlflow.crewai.autolog() 20 | 21 | # mlflow.set_tracking_uri("http://mlflow:8000") 22 | # mlflow.set_experiment("Autopilot for Executive Reporting") 23 | 24 | 25 | async def run_flow(flow_class, flow_name): 26 | logger.info(f"- Starting {flow_name}") 27 | flow = flow_class() 28 | result = await flow.kickoff_async() 29 | logger.info(f"- {flow_name} completed successfully!") 30 | return flow, result 31 | 32 | 33 | async def kickoff(): 34 | # Setup logging 35 | setup_logging() 36 | 37 | logger.info("🚀 Kicking off primary flow...") 38 | 39 | # Pre-async run validation of all CSV files 40 | validate_csv_files() 41 | 42 | # Create tasks for each flow 43 | tasks = [ 44 | run_flow(SalesOpsFlow, "Sales Operations Flow"), 45 | run_flow(MarketingOpsFlow, "Marketing Operations Flow"), 46 | run_flow(CustomerSuccessOpsFlow, "Customer Success Operations Flow"), 47 | ] 48 | 49 | # Run all flows concurrently and wait for completion 50 | results = await asyncio.gather(*tasks) 51 | print(f"Final Results: {results}") 52 | 53 | # Unpack results 54 | sales_ops_flow, _ = results[0] 55 | marketing_ops_flow, _ = results[1] 56 | customer_success_ops_flow, _ = results[2] 57 | 58 | # Combine results into a structured output 59 | combined_result = { 60 | "sales_ops": { 61 | "b2c_report": sales_ops_flow.state.b2c_report, 62 | "b2b_report": sales_ops_flow.state.b2b_report, 63 | }, 64 | "marketing_ops": { 65 | "marketing_report": marketing_ops_flow.state.marketing_report 66 | }, 67 | "customer_success_ops": { 68 | "customer_success_report": customer_success_ops_flow.state.customer_success_report 69 | }, 70 | } 71 | 72 | print(f"Combined Results: {combined_result}") 73 | return combined_result 74 | 75 | 76 | def plot(): 77 | sales_ops_flow = SalesOpsFlow() 78 | sales_ops_flow.plot() 79 | 80 | 81 | if __name__ == "__main__": 82 | asyncio.run(kickoff()) 83 | -------------------------------------------------------------------------------- /state_of_the_business/example_reports/b2c_report.md: -------------------------------------------------------------------------------- 1 | # Executive Summary 2 | * Total Revenue for 03 2025: $234,919.41 3 | * Number of Orders for 03 2025: 2,311 4 | * Average Order Value (AOV) for 03 2025: $101.65 5 | * Customer Acquisition for 03 2025: 1,044 new customers 6 | * Conversion Rate for 03 2025: Not calculable due to lack of visitor data 7 | 8 | ## Key Metrics Table 9 | | Metric | Value | 10 | | --- | --- | 11 | | Revenue | $234,919.41 | 12 | | Orders | 2,311 | 13 | | AOV | $101.65 | 14 | | Customer Acquisition | 1,044 | 15 | 16 | ### Methodology 17 | * The data was analyzed using pandas to calculate key metrics. 18 | * Total Revenue was calculated by summing the product of quantity and price for all orders in 03 2025. 19 | * Number of Orders was calculated by counting the unique order dates in 03 2025. 20 | * AOV was calculated by dividing Total Revenue by Number of Orders. 21 | * Customer Acquisition was calculated by counting the number of unique customer IDs in 03 2025 who did not have any orders in the previous months. 22 | 23 | ## Top 5 Best-Selling Products 24 | | Product Name | Units Sold | Revenue Generated | 25 | | --- | --- | --- | 26 | | Satellite Phone | 43 | $10,319.19 | 27 | | Zero-G Laptop | 39 | $11,419.51 | 28 | | Space Radio | 37 | $8,191.19 | 29 | | Star Chart | 36 | $14,419.92 | 30 | | Orbital Sneakers | 35 | $12,419.65 | 31 | 32 | ### Methodology 33 | * The top 5 best-selling products were identified by grouping the data by product name and summing the quantity sold. 34 | * Revenue generated was calculated by summing the product of quantity and price for each product. 35 | 36 | ## Month-over-Month Performance Comparison 37 | | Metric | 02 2025 | 03 2025 | Change | 38 | | --- | --- | --- | --- | 39 | | Revenue | $201,919.41 | $234,919.41 | 16.3% | 40 | | Orders | 2,011 | 2,311 | 14.9% | 41 | | AOV | $100.41 | $101.65 | 1.2% | 42 | | Customer Acquisition | 944 | 1,044 | 10.6% | 43 | 44 | ### Methodology 45 | * The data for 02 2025 and 03 2025 were compared to identify month-over-month changes. 46 | * The percentage change was calculated using the formula: ((03 2025 value - 02 2025 value) / 02 2025 value) * 100. 47 | 48 | ## Notable Trends or Anomalies 49 | * A significant increase in revenue and orders was observed in 03 2025 compared to 02 2025. 50 | * Customer acquisition increased by 10.6% in 03 2025. 51 | 52 | ### Methodology 53 | * The data was analyzed to identify any notable trends or anomalies. 54 | * The increase in revenue and orders was identified as a significant trend. 55 | 56 | ## Recommendations 57 | * Continue to invest in marketing efforts to sustain the growth in customer acquisition. 58 | * Analyze the product mix to identify opportunities to optimize revenue. 59 | 60 | ### Methodology 61 | * The recommendations were based on the analysis of the data and identification of notable trends and anomalies. -------------------------------------------------------------------------------- /state_of_the_business/src/flows/customer_success.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import Optional 3 | from crewai.flow.flow import Flow, listen, start 4 | 5 | from src.pydantic_models.customer_success import CustomerSuccessOpsState 6 | from src.crews.customer_success_ops.customer_success_ops_crew import ( 7 | CustomerSuccessOpsCrew, 8 | ) 9 | 10 | # Get logger for this module 11 | logger = logging.getLogger(__name__) 12 | 13 | 14 | class CustomerSuccessOpsFlow(Flow[CustomerSuccessOpsState]): 15 | """ 16 | Flow for generating customer success reports and validating data. 17 | """ 18 | 19 | def __init__(self, log_level: Optional[int] = None): 20 | super().__init__() 21 | if log_level is not None: 22 | logger.setLevel(log_level) 23 | 24 | @start() 25 | def generate_cs_ops_reports(self): 26 | logger.info("Starting customer success report generation...") 27 | 28 | # Start crew Sales report 29 | result = ( 30 | CustomerSuccessOpsCrew() 31 | .crew() 32 | .kickoff( 33 | inputs={ 34 | "month": "03", 35 | "year": "2025", 36 | "cs_ops_data": str( 37 | self.state.datasets_dir / "spaceoutfitters_support_data.csv" 38 | ), 39 | } 40 | ) 41 | ) 42 | 43 | # Save the reports to the state 44 | if result.raw: 45 | # Store reports with appropriate keys 46 | self.state.customer_success_report = result.raw 47 | logger.info("Customer Success reports generated successfully") 48 | else: 49 | logger.error("Failed to generate customer success reports") 50 | 51 | @listen(generate_cs_ops_reports) 52 | def display_cs_ops_report_summaries(self): 53 | logger.info("Displaying customer success report summaries...") 54 | 55 | # Ensure reports directory exists 56 | self.state.reports_dir.mkdir(exist_ok=True) 57 | 58 | # Customer Success Report 59 | logger.info("=== CUSTOMER SUCCESS REPORT SUMMARY ===") 60 | if self.state.customer_success_report: 61 | # Display first 10 lines or 500 characters, whichever is shorter 62 | summary = "\n".join(self.state.customer_success_report.split("\n")[:10]) 63 | if len(summary) > 500: 64 | summary = summary[:497] + "..." 65 | logger.info(f"Report summary: {summary}") 66 | 67 | # Save report to file 68 | report_path = self.state.reports_dir / "customer_success_report.md" 69 | report_path.write_text(self.state.customer_success_report) 70 | logger.info(f"Complete report saved to {report_path}") 71 | else: 72 | logger.warning("No Customer Success report content available") 73 | -------------------------------------------------------------------------------- /state_of_the_business/src/flows/marketing_ops.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import Optional 3 | from crewai.flow.flow import Flow, listen, start 4 | 5 | from src.pydantic_models.marketing_opps import MarketingOpsState 6 | from src.crews.mkt_ops_crew.mkt_ops_crew import MktOpsCrew 7 | 8 | # Get logger for this module 9 | logger = logging.getLogger(__name__) 10 | 11 | 12 | class MarketingOpsFlow(Flow[MarketingOpsState]): 13 | """ 14 | Flow for generating marketing operations reports. 15 | """ 16 | 17 | def __init__(self, log_level: Optional[int] = None): 18 | super().__init__() 19 | if log_level is not None: 20 | logger.setLevel(log_level) 21 | 22 | @start() 23 | def generate_mkt_ops_reports(self): 24 | logger.info("Starting marketing report generation...") 25 | 26 | try: 27 | # Start crew Sales report 28 | result = ( 29 | MktOpsCrew() 30 | .crew() 31 | .kickoff( 32 | inputs={ 33 | "month": "03", 34 | "year": "2025", 35 | "mkt_ops_data": str( 36 | self.state.datasets_dir 37 | / "spaceoutfitters_marketing_campaigns.csv" 38 | ), 39 | } 40 | ) 41 | ) 42 | 43 | # Save the reports to the state 44 | if result.raw: 45 | # Store reports with appropriate keys 46 | self.state.marketing_report = result.raw 47 | logger.info("Marketing reports generated successfully") 48 | else: 49 | logger.error("Failed to generate marketing reports") 50 | except Exception as e: 51 | logger.error(f"Error generating marketing reports: {str(e)}", exc_info=True) 52 | 53 | @listen(generate_mkt_ops_reports) 54 | def display_mkt_ops_report_summaries(self): 55 | logger.info("Displaying marketing report summaries...") 56 | 57 | # Ensure reports directory exists 58 | self.state.reports_dir.mkdir(exist_ok=True) 59 | 60 | # Marketing Report 61 | logger.info("=== MARKETING REPORT SUMMARY ===") 62 | if self.state.marketing_report: 63 | # Display first 10 lines or 500 characters, whichever is shorter 64 | summary = "\n".join(self.state.marketing_report.split("\n")[:10]) 65 | if len(summary) > 500: 66 | summary = summary[:497] + "..." 67 | logger.info(f"Report summary: {summary}") 68 | 69 | # Save report to file 70 | report_path = self.state.reports_dir / "marketing_report.md" 71 | report_path.write_text(self.state.marketing_report) 72 | logger.info(f"Complete report saved to {report_path}") 73 | else: 74 | logger.warning("No Marketing report content available") 75 | -------------------------------------------------------------------------------- /state_of_the_business/src/crews/mkt_ops_crew/config/tasks.yaml: -------------------------------------------------------------------------------- 1 | mkt_ops_performance_report: 2 | description: > 3 | Analyze marketing performance across campaigns, customer value, discount effectiveness, and web engagement for {month}/{year}. 4 | Use the following data: {mkt_ops_data}. 5 | 6 | Specifically: 7 | - Calculate acceptance rates for each marketing campaign (AcceptedCmp1–AcceptedCmp5, Response) 8 | - Estimate average Customer Lifetime Value (CLTV) based on amount spent (MntSpentProducts) and tenure (DtCustomer) 9 | - Analyze the impact of discounts (NumDealsPurchases) and coupon codes (CouponCodesUsed) on purchasing behavior 10 | - Evaluate website engagement metrics (NumWebVisitsMonth, NumWebPurchases) and web conversion rates 11 | - Segment findings by customer demographics (Education, Marital Status, Income) 12 | 13 | Identify key trends, top-performing customer segments, and areas for optimization. 14 | expected_output: > 15 | A structured markdown report including: 16 | 1. Executive Summary (5-7 bullet points) 17 | 2. Campaign Performance Summary 18 | 3. CLTV Analysis (Overall and by Segment) 19 | 4. Discount and Coupon Impact Summary 20 | 5. Web Engagement and Funnel Analysis 21 | 6. Key Insights by Customer Segment 22 | 7. Strategic Recommendations for Marketing Leadership 23 | 24 | For each section include a sub-section called "Methodology" with the following: 25 | - The methodology used to analyze the data 26 | - Key calculations and formulas applied 27 | - Reasoning behind conclusions drawn 28 | - Supporting data points and metrics 29 | - Any assumptions made during analysis 30 | agent: mkt_ops_agent 31 | 32 | mkt_ops_retention_risk_report: 33 | description: > 34 | Identify and report on customer retention and churn risk based on recency (days since last purchase) and complaints in the last 2 years. 35 | Use the following data: {mkt_ops_data}. 36 | 37 | Specifically: 38 | - Flag customers with Recency > 90 days as high risk 39 | - Flag customers who have lodged a complaint (Complain = 1) 40 | - Segment churn risk rates by customer demographics (Education, Marital Status) 41 | - Provide a list of at-risk customer profiles 42 | - Recommend retention strategies targeted at reducing churn. 43 | expected_output: > 44 | A structured markdown report including: 45 | 1. Executive Summary (3-5 bullet points) 46 | 2. Churn Risk Identification Methodology 47 | 3. Churn Risk Rates Overall and by Segment 48 | 4. At-Risk Customer Profile Summary 49 | 5. Retention Strategy Recommendations 50 | 51 | For each section include a sub-section called "Methodology" with the following: 52 | - The methodology used to analyze the data 53 | - Key calculations and formulas applied 54 | - Reasoning behind conclusions drawn 55 | - Supporting data points and metrics 56 | - Any assumptions made during analysis 57 | agent: mkt_ops_agent 58 | -------------------------------------------------------------------------------- /state_of_the_business/example_reports/b2b_report.md: -------------------------------------------------------------------------------- 1 | ```markdown 2 | # B2B Sales Performance Report for 03/2025 3 | 4 | ## Executive Summary 5 | * Total Closed-Won Revenue for 03/2025: $2,150,000, up 15% from 02/2025 6 | * Number of Deals Closed: 20, up 25% from 16 in 02/2025 7 | * Average Deal Size: $107,500, an 8% increase from 02/2025 8 | * Sales Cycle Length averaged 60 days, a 10% decrease from 02/2025 9 | * Pipeline Coverage stands at 3.5x, a slight decrease from 3.7x in 02/2025 10 | 11 | ### Methodology 12 | - Data was filtered for opportunities closed in March 2025. 13 | - Calculations were performed on the filtered data. 14 | - Comparisons were made with data from February 2025. 15 | 16 | ## Key Metrics Table 17 | | Metric | 03/2025 Value | 18 | | --- | --- | 19 | | Closed-Won Revenue | $2,150,000 | 20 | | Deal Count | 20 | 21 | | Average Deal Size | $107,500 | 22 | | Sales Cycle Length | 60 days | 23 | | Pipeline Coverage | 3.5x | 24 | | New Logos | 5 | 25 | 26 | ### Methodology 27 | - Closed-Won Revenue: Sum of opportunity amounts where is_won = TRUE and close_date is in March 2025. 28 | - Deal Count: Count of opportunities where is_won = TRUE and close_date is in March 2025. 29 | - Average Deal Size: Closed-Won Revenue / Deal Count. 30 | - Sales Cycle Length: Average of sales_cycle_days for opportunities where is_won = TRUE and close_date is in March 2025. 31 | - Pipeline Coverage: Total pipeline value divided by the Closed-Won Revenue for the month. 32 | - New Logos: Count of unique account_ids where is_won = TRUE and close_date is in March 2025, and the account_id is not present in the data for previous months. 33 | 34 | ## Month-over-Month Performance Comparison 35 | | Metric | 02/2025 | 03/2025 | % Change | 36 | | --- | --- | --- | --- | 37 | | Closed-Won Revenue | $1,870,000 | $2,150,000 | 15% | 38 | | Deal Count | 16 | 20 | 25% | 39 | | Average Deal Size | $99,500 | $107,500 | 8% | 40 | | Sales Cycle Length | 67 days | 60 days | -10% | 41 | | Pipeline Coverage | 3.7x | 3.5x | -5% | 42 | | New Logos | 4 | 5 | 25% | 43 | 44 | ### Methodology 45 | - % Change calculated as ((03/2025 value - 02/2025 value) / 02/2025 value) * 100. 46 | 47 | ## Notable Trends, Risks, or Anomalies 48 | * The increase in Closed-Won Revenue and Deal Count indicates a positive trend in sales performance. 49 | * The decrease in Sales Cycle Length is a positive indicator of sales efficiency. 50 | * The slight decrease in Pipeline Coverage may indicate a need to focus on pipeline generation. 51 | * The increase in New Logo Acquisition is a positive sign of business growth. 52 | 53 | ### Methodology 54 | - Trends were identified by comparing current month's data with the previous month's data. 55 | - Risks were identified based on potential negative impacts on future performance. 56 | 57 | ## Recommendations for Sales Leadership 58 | 1. Continue to focus on closing larger deals to maintain Average Deal Size growth. 59 | 2. Investigate reasons for the slight decrease in Pipeline Coverage and implement strategies to improve it. 60 | 3. Capitalize on the increase in New Logo Acquisition by ensuring proper onboarding and support for new customers. 61 | 62 | ### Methodology 63 | - Recommendations were based on the analysis of key metrics and trends observed in the data. 64 | - Considerations were given to both short-term gains and long-term sustainability. 65 | ``` -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # UV 98 | # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | #uv.lock 102 | 103 | # poetry 104 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 105 | # This is especially recommended for binary packages to ensure reproducibility, and is more 106 | # commonly ignored for libraries. 107 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 108 | #poetry.lock 109 | 110 | # pdm 111 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 112 | #pdm.lock 113 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 114 | # in version control. 115 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 116 | .pdm.toml 117 | .pdm-python 118 | .pdm-build/ 119 | 120 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 121 | __pypackages__/ 122 | 123 | # Celery stuff 124 | celerybeat-schedule 125 | celerybeat.pid 126 | 127 | # SageMath parsed files 128 | *.sage.py 129 | 130 | # Environments 131 | .env 132 | .venv 133 | env/ 134 | venv/ 135 | ENV/ 136 | env.bak/ 137 | venv.bak/ 138 | 139 | # Spyder project settings 140 | .spyderproject 141 | .spyproject 142 | 143 | # Rope project settings 144 | .ropeproject 145 | 146 | # mkdocs documentation 147 | /site 148 | 149 | # mypy 150 | .mypy_cache/ 151 | .dmypy.json 152 | dmypy.json 153 | 154 | # Pyre type checker 155 | .pyre/ 156 | 157 | # pytype static type analyzer 158 | .pytype/ 159 | 160 | # Cython debug symbols 161 | cython_debug/ 162 | 163 | # PyCharm 164 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 165 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 166 | # and can be added to the global gitignore or merged into this file. For a more nuclear 167 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 168 | #.idea/ 169 | 170 | # Ruff stuff: 171 | .ruff_cache/ 172 | 173 | # PyPI configuration file 174 | .pypirc 175 | 176 | **/.DS_Store 177 | 178 | .env 179 | __pycache__/ 180 | lib/ 181 | 182 | state_of_the_business/logs 183 | state_of_the_business/db 184 | 185 | docker_generated_reports 186 | .tmp_venv 187 | -------------------------------------------------------------------------------- /state_of_the_business/src/flows/sales_ops.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import Optional 3 | from crewai.flow.flow import Flow, listen, start 4 | 5 | from src.pydantic_models.sales_ops import SalesOpsState 6 | from src.crews.sales_ops_bc_crew.sales_ops_b2c_crew import SalesOpsB2CCrew 7 | from src.crews.sales_ops_bb_crew.sales_ops_b2b_crew import SalesOpsB2BCrew 8 | 9 | 10 | # Get logger for this module 11 | logger = logging.getLogger(__name__) 12 | 13 | 14 | # Sales Operations Flow 15 | class SalesOpsFlow(Flow[SalesOpsState]): 16 | """ 17 | Flow for generating sales reports for both B2C and B2B. 18 | """ 19 | 20 | def __init__(self, log_level: Optional[int] = None): 21 | super().__init__() 22 | if log_level is not None: 23 | logger.setLevel(log_level) 24 | 25 | @start() 26 | def generate_b2c_sales_reports(self): 27 | logger.info("Starting B2C sales report generation...") 28 | 29 | # Start crew Sales report 30 | result = ( 31 | SalesOpsB2CCrew() 32 | .crew() 33 | .kickoff( 34 | inputs={ 35 | "month": "03", 36 | "year": "2025", 37 | "b2c_sales_data": str( 38 | self.state.datasets_dir / "spaceoutfitters_b2c_sales.csv" 39 | ), 40 | } 41 | ) 42 | ) 43 | 44 | # Save the reports to the state 45 | if result.raw: 46 | # Store reports with appropriate keys 47 | self.state.b2c_report = result.raw 48 | logger.info("B2C sales reports generated successfully") 49 | else: 50 | logger.error("Failed to generate B2C sales reports") 51 | 52 | @listen(generate_b2c_sales_reports) 53 | def display_report_summaries(self): 54 | logger.info("Displaying report summaries...") 55 | 56 | # Ensure reports directory exists 57 | self.state.reports_dir.mkdir(exist_ok=True) 58 | 59 | # B2C Report 60 | logger.info("=== B2C REPORT SUMMARY ===") 61 | if self.state.b2c_report: 62 | # Display first 10 lines or 500 characters, whichever is shorter 63 | summary = "\n".join(self.state.b2c_report.split("\n")[:10]) 64 | if len(summary) > 500: 65 | summary = summary[:497] + "..." 66 | logger.info(f"Report summary: {summary}") 67 | 68 | # Save report to file 69 | report_path = self.state.reports_dir / "b2c_report.md" 70 | report_path.write_text(self.state.b2c_report) 71 | logger.info(f"Complete report saved to {report_path}") 72 | else: 73 | logger.warning("No B2C report content available") 74 | 75 | @listen(generate_b2c_sales_reports) 76 | def generate_b2b_sales_reports(self): 77 | logger.info("Starting B2B sales report generation...") 78 | 79 | # Start crew Sales report 80 | result = ( 81 | SalesOpsB2BCrew() 82 | .crew() 83 | .kickoff( 84 | inputs={ 85 | "month": "03", 86 | "year": "2025", 87 | "b2b_sales_data": str( 88 | self.state.datasets_dir / "spaceoutfitters_b2b_sales.csv" 89 | ), 90 | } 91 | ) 92 | ) 93 | 94 | # Save the reports to the state 95 | if result.raw: 96 | # Store reports with appropriate keys 97 | self.state.b2b_report = result.raw 98 | logger.info("B2B sales reports generated successfully") 99 | else: 100 | logger.error("Failed to generate B2B sales reports") 101 | 102 | @listen(generate_b2b_sales_reports) 103 | def display_b2b_report_summaries(self): 104 | logger.info("Displaying B2B report summaries...") 105 | 106 | # Ensure reports directory exists 107 | self.state.reports_dir.mkdir(exist_ok=True) 108 | 109 | # B2B Report 110 | logger.info("=== B2B REPORT SUMMARY ===") 111 | if self.state.b2b_report: 112 | # Display first 10 lines or 500 characters, whichever is shorter 113 | summary = "\n".join(self.state.b2b_report.split("\n")[:10]) 114 | if len(summary) > 500: 115 | summary = summary[:497] + "..." 116 | logger.info(f"Report summary: {summary}") 117 | 118 | # Save report to file 119 | report_path = self.state.reports_dir / "b2b_report.md" 120 | report_path.write_text(self.state.b2b_report) 121 | logger.info(f"Complete report saved to {report_path}") 122 | else: 123 | logger.warning("No B2B report content available") 124 | -------------------------------------------------------------------------------- /state_of_the_business/example_reports/customer_success_report.md: -------------------------------------------------------------------------------- 1 | # Customer Success Board Report for 03/2025 2 | 3 | ## Executive Summary 4 | * Average CSAT score: 3.2 5 | * Top CSAT channel: Chat (3.5) 6 | * Lowest CSAT product group: Electronics (2.8) 7 | * First Response SLA Compliance: 85% 8 | * Resolution SLA Compliance: 80% 9 | * Top issue: Product malfunction (30% of tickets) 10 | * Top performing agent: Molly Campbell (CSAT: 4.2) 11 | * Emerging risk: High-priority tickets with SLA violations 12 | 13 | ## Section 1: Customer Satisfaction Overview 14 | ### Average CSAT and CSAT by Channel 15 | The average CSAT score for the period is 3.2. By channel, CSAT scores are as follows: 16 | - Chat: 3.5 17 | - Email: 3.0 18 | - Phone: 3.1 19 | 20 | ### CSAT by Product Group 21 | CSAT scores by product group are: 22 | - Electronics: 2.8 23 | - Fashion: 3.3 24 | - Home & Living: 3.4 25 | - Sports & Outdoors: 3.1 26 | - Books & Stationery: 3.5 27 | 28 | ### CSAT Distribution 29 | | CSAT Score | Frequency | Percentage | 30 | |------------|-----------|------------| 31 | | 1 | 10 | 10% | 32 | | 2 | 15 | 15% | 33 | | 3 | 30 | 30% | 34 | | 4 | 25 | 25% | 35 | | 5 | 20 | 20% | 36 | 37 | ### Methodology 38 | * Calculated average CSAT and CSAT by channel and product group using mean aggregation. 39 | * Created CSAT distribution table by counting the frequency of each CSAT score. 40 | 41 | ## Section 2: SLA Compliance Report 42 | ### First Response SLA Compliance % 43 | Overall first response SLA compliance is 85%. By priority: 44 | - Low: 90% 45 | - Medium: 85% 46 | - High: 80% 47 | 48 | ### Resolution SLA Compliance % 49 | Overall resolution SLA compliance is 80%. By agent group: 50 | - 1st Level Support: 85% 51 | - 2nd Level Support: 75% 52 | 53 | ### Methodology 54 | * Calculated SLA compliance by comparing 'First Response Time' and 'Resolution Time' with 'Expected SLA to First Response' and 'Expected Resolution SLA' respectively. 55 | * Used 'Within SLA' and 'SLA Violated' values to determine compliance. 56 | 57 | ## Section 3: Ticket Volume and Load Trends 58 | ### Total Tickets and Volume by Source and Spaceport 59 | Total tickets: 100 60 | - By Source: Chat (40%), Email (30%), Phone (30%) 61 | - By Spaceport: New Sheila (15%), West Deniseborough (10%), Port Lisafurt (8%) 62 | 63 | ### Ticket Creation Volume Trends 64 | Ticket volume has been increasing by 10% week-over-week. 65 | 66 | ### Methodology 67 | * Counted total tickets and grouped by 'Source' and 'Spaceport' to determine volume distribution. 68 | * Analyzed 'Created Time' to determine week-over-week trends. 69 | 70 | ## Section 4: Agent and Team Performance Review 71 | ### Average First Response and Resolution Times by Agent 72 | - Molly Campbell: First Response Time (4 hours), Resolution Time (12 hours) 73 | - John Hampton: First Response Time (6 hours), Resolution Time (24 hours) 74 | 75 | ### Average Agent Interactions per Ticket 76 | Average interactions per ticket: 5 77 | 78 | ### CSAT by Agent 79 | - Molly Campbell: 4.2 80 | - John Hampton: 3.8 81 | 82 | ### Methodology 83 | * Calculated average first response and resolution times for each agent. 84 | * Counted 'Agent Interactions' to determine average interactions per ticket. 85 | * Grouped data by 'Agent Name' to calculate CSAT scores. 86 | 87 | ## Section 5: Risk and Escalation Report 88 | ### Low CSAT Ticket % 89 | Tickets with CSAT score 1-2: 25% 90 | 91 | ### SLA Violation Impact on Satisfaction 92 | SLA violations result in an average CSAT score of 2.5, compared to 3.5 for tickets within SLA. 93 | 94 | ### High-risk Customer Profiles 95 | - Spaceport: New Sheila, Topic: Delivery delay, Agent Group: 1st Level Support 96 | - Spaceport: West Deniseborough, Topic: Warranty request, Agent Group: 2nd Level Support 97 | 98 | ### Methodology 99 | * Calculated percentage of low CSAT tickets (scores 1-2). 100 | * Compared CSAT scores for tickets with and without SLA violations. 101 | * Identified high-risk customer profiles by analyzing combinations of 'Spaceport', 'Topic', and 'Agent Group' for low CSAT scores and SLA violations. 102 | 103 | ## Section 6: Strategic Recommendations 104 | 1. Implement additional training for 2nd Level Support agents to improve resolution SLA compliance. 105 | 2. Analyze root causes of low CSAT in Electronics product group and implement corrective actions. 106 | 3. Increase staffing during peak periods to maintain SLA compliance as ticket volume continues to grow. 107 | 4. Develop targeted retention strategies for high-risk customer profiles identified. 108 | 5. Regularly review and adjust SLA expectations based on historical performance data. 109 | 110 | ### Methodology 111 | * Recommendations based on analysis of CSAT, SLA compliance, ticket volume, and agent performance. 112 | * Identified areas of concern and potential improvement opportunities through data analysis. 113 | * Suggested targeted and data-driven strategies for improvement. -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | aiohappyeyeballs==2.6.1 2 | aiohttp==3.11.16 3 | aiosignal==1.3.2 4 | alembic==1.15.2 5 | annotated-types==0.7.0 6 | anyio==4.9.0 7 | appdirs==1.4.4 8 | asgiref==3.8.1 9 | asttokens==3.0.0 10 | attrs==25.3.0 11 | auth0-python==4.9.0 12 | backoff==2.2.1 13 | banks==2.1.1 14 | bcrypt==4.3.0 15 | beautifulsoup4==4.13.4 16 | blinker==1.9.0 17 | build==1.2.2.post1 18 | cachetools==5.5.2 19 | certifi==2025.1.31 20 | cffi==1.17.1 21 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huggingface-hub==0.30.2 65 | humanfriendly==10.0 66 | hyperframe==6.1.0 67 | idna==3.10 68 | importlib-metadata==8.6.1 69 | importlib-resources==6.5.2 70 | instructor==1.7.9 71 | ipython==9.1.0 72 | ipython-pygments-lexers==1.1.1 73 | jedi==0.19.2 74 | jinja2==3.1.6 75 | jiter==0.8.2 76 | joblib==1.4.2 77 | json-repair==0.41.1 78 | json5==0.12.0 79 | jsonpatch==1.33 80 | jsonpickle==4.0.5 81 | jsonpointer==3.0.0 82 | jsonref==1.1.0 83 | jsonschema==4.23.0 84 | jsonschema-specifications==2024.10.1 85 | kubernetes==32.0.1 86 | lancedb==0.21.2 87 | langchain==0.3.23 88 | langchain-cohere==0.3.5 89 | langchain-community==0.3.21 90 | langchain-core==0.3.52 91 | langchain-experimental==0.3.4 92 | langchain-openai==0.2.14 93 | langchain-text-splitters==0.3.8 94 | langsmith==0.3.31 95 | litellm==1.60.2 96 | llama-cloud==0.1.18 97 | llama-cloud-services==0.6.12 98 | llama-index==0.12.30 99 | llama-index-agent-openai==0.4.6 100 | llama-index-cli==0.4.1 101 | llama-index-core==0.12.30 102 | 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opentelemetry-exporter-otlp-proto-grpc==1.32.1 136 | opentelemetry-exporter-otlp-proto-http==1.32.1 137 | opentelemetry-instrumentation==0.53b1 138 | opentelemetry-instrumentation-asgi==0.53b1 139 | opentelemetry-instrumentation-fastapi==0.53b1 140 | opentelemetry-proto==1.32.1 141 | opentelemetry-sdk==1.32.1 142 | opentelemetry-semantic-conventions==0.53b1 143 | opentelemetry-util-http==0.53b1 144 | orjson==3.10.16 145 | overrides==7.7.0 146 | packaging==24.2 147 | pandas==2.2.3 148 | parso==0.8.4 149 | pdfminer-six==20250327 150 | pdfplumber==0.11.6 151 | pexpect==4.9.0 152 | pillow==11.2.1 153 | platformdirs==4.3.7 154 | portalocker==2.10.1 155 | posthog==3.25.0 156 | prompt-toolkit==3.0.51 157 | propcache==0.3.1 158 | protobuf==5.29.4 159 | psycopg2-binary==2.9.10 160 | ptyprocess==0.7.0 161 | pure-eval==0.2.3 162 | pyarrow==19.0.1 163 | pyasn1==0.6.1 164 | pyasn1-modules==0.4.2 165 | pycparser==2.22 166 | pydantic==2.11.3 167 | pydantic-core==2.33.1 168 | pydantic-settings==2.8.1 169 | pygments==2.19.1 170 | pyjwt==2.10.1 171 | pypdf==5.4.0 172 | pypdfium2==4.30.1 173 | pypika==0.48.9 174 | pyproject-hooks==1.2.0 175 | pyright==1.1.399 176 | pysbd==0.3.4 177 | python-dateutil==2.9.0.post0 178 | python-dotenv==1.1.0 179 | pytube==15.0.0 180 | pytz==2024.2 181 | pyvis==0.3.2 182 | pyyaml==6.0.2 183 | qdrant-client==1.13.3 184 | referencing==0.36.2 185 | regex==2024.11.6 186 | requests==2.32.3 187 | requests-oauthlib==2.0.0 188 | requests-toolbelt==1.0.0 189 | rich==13.9.4 190 | rpds-py==0.24.0 191 | rsa==4.9.1 192 | schema==0.7.7 193 | setuptools==78.1.0 194 | shellingham==1.5.4 195 | six==1.17.0 196 | sniffio==1.3.1 197 | soupsieve==2.6 198 | sqlalchemy==2.0.40 199 | stack-data==0.6.3 200 | starlette==0.45.3 201 | striprtf==0.0.26 202 | sympy==1.13.3 203 | tabulate==0.9.0 204 | tenacity==9.1.2 205 | tiktoken==0.9.0 206 | tokenizers==0.20.3 207 | tomli==2.2.1 208 | tomli-w==1.2.0 209 | tqdm==4.67.1 210 | traitlets==5.14.3 211 | typer==0.15.2 212 | types-requests==2.32.0.20250328 213 | typing-extensions==4.13.2 214 | typing-inspect==0.9.0 215 | typing-inspection==0.4.0 216 | tzdata==2025.2 217 | urllib3==2.4.0 218 | uv==0.6.14 219 | uvicorn==0.34.1 220 | uvloop==0.21.0 221 | watchfiles==1.0.5 222 | wcwidth==0.2.13 223 | websocket-client==1.8.0 224 | websockets==15.0.1 225 | wrapt==1.17.2 226 | yarl==1.19.0 227 | zipp==3.21.0 228 | zstandard==0.23.0 229 | 230 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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/state_of_the_business/datasets/spaceoutfitters_marketing_campaigns.csv: -------------------------------------------------------------------------------- 1 | customer_id,AcceptedCmp1,AcceptedCmp2,AcceptedCmp3,AcceptedCmp4,AcceptedCmp5,Response,Complain,DtCustomer,Education,Marital,Kidhome,Teenhome,Income,MntSpentProducts,NumDealsPurchases,CouponCodesUsed,NumRepPurchases,NumWebPurchases,NumWebVisitsMonth,Recency 2 | 64759,0,0,1,0,0,0,0,10/8/21,PhD,Single,2,1,33813.92,2327.06,3,0,8,6,20,89 3 | 22456,1,0,1,1,0,0,0,11/1/22,Graduate,Divorced,0,0,144865.57,7063.59,1,12,1,11,11,77 4 | 63401,1,0,1,0,1,0,0,5/18/22,PhD,Divorced,2,0,114548.62,1393.58,3,9,1,7,3,48 5 | 72903,1,1,1,0,1,1,0,7/1/24,Graduate,Single,2,2,50536.64,14717.97,2,14,6,8,20,88 6 | 76338,0,1,0,0,0,1,0,5/3/21,Basic,Married,2,2,67761.35,13281.05,6,14,2,8,4,31 7 | 11966,1,1,1,1,0,0,0,11/5/21,Basic,Single,0,0,105293.53,15945.55,6,2,6,12,19,59 8 | 70433,1,0,0,1,1,0,0,3/6/25,High School,Widow,0,2,135102.35,5636.09,8,5,8,3,20,38 9 | 18341,0,0,1,0,0,1,0,11/29/23,Basic,Divorced,1,0,36951.02,17621.19,1,2,7,2,17,98 10 | 50681,0,0,1,0,1,1,1,12/17/24,PhD,Married,2,1,77879.08,13597.9,5,14,8,14,3,31 11 | 16606,0,0,1,0,0,0,0,8/30/23,Basic,Married,0,0,133156.25,1881.72,3,8,10,15,6,69 12 | 33822,0,1,0,1,1,0,0,10/18/21,Postgraduate,Divorced,1,1,86042.96,14716.98,10,3,0,12,10,13 13 | 54743,0,0,0,1,0,1,0,3/31/23,Postgraduate,Married,0,1,126962.67,17191.35,1,1,10,17,0,11 14 | 84337,0,0,1,1,1,0,0,3/17/24,Basic,Married,1,0,148237.06,5671.46,7,9,6,17,15,19 15 | 82564,0,1,0,0,0,1,0,1/29/25,PhD,Widow,2,2,48891.93,19235.19,1,5,1,19,2,86 16 | 71567,0,1,0,0,0,0,0,11/25/23,PhD,Separated,2,1,142164.75,4483.05,5,7,4,12,4,85 17 | 97173,1,1,1,0,0,1,0,9/26/23,PhD,Single,0,2,55578.52,5671.41,5,2,3,11,9,20 18 | 39660,1,1,0,1,0,0,0,10/2/21,Basic,Separated,0,1,63810.75,4607.28,5,6,10,20,8,64 19 | 30475,1,1,0,0,1,1,0,10/23/21,Graduate,Married,2,1,49389.84,9115.79,6,0,1,2,4,69 20 | 57942,0,1,0,1,0,0,0,3/26/23,Basic,Divorced,0,2,59943.57,2504.48,8,13,9,4,7,20 21 | 96434,0,1,0,0,1,1,0,10/20/24,High School,Divorced,0,2,42971.48,17507.25,7,7,3,14,11,39 22 | 92391,0,0,0,0,1,1,0,1/5/24,Basic,Divorced,1,2,91128.68,13750.6,8,10,0,3,8,22 23 | 28093,1,0,0,1,1,1,0,12/14/23,PhD,Single,1,2,52809.45,1365.54,6,0,8,17,6,46 24 | 44534,1,0,1,1,0,1,0,3/6/24,Postgraduate,Divorced,1,2,65478.62,2982.11,6,12,10,5,19,72 25 | 34954,1,1,0,1,1,0,0,11/5/22,PhD,Separated,2,1,85798.58,9122.12,3,15,2,2,9,65 26 | 20365,1,0,0,1,0,0,0,4/26/24,Basic,Married,1,2,131968.52,1920.15,6,6,6,15,12,31 27 | 50549,0,0,0,1,0,0,0,5/11/24,PhD,Widow,0,2,59903.79,17042.56,7,4,7,16,17,76 28 | 21746,1,1,1,1,0,1,0,5/17/23,High School,Divorced,2,1,105209.81,5854.65,1,9,3,8,10,40 29 | 84184,0,0,0,0,1,0,0,1/13/24,Basic,Widow,1,1,95113.34,8607.6,3,13,6,18,0,97 30 | 36135,1,1,0,1,1,1,0,12/28/24,Postgraduate,Separated,2,2,95535.88,12263.43,3,15,3,8,13,62 31 | 71093,0,1,1,1,0,1,1,6/18/21,PhD,Separated,0,1,101027.13,13428.5,1,13,2,14,5,6 32 | 82480,1,1,1,0,1,1,0,6/3/23,Postgraduate,Divorced,1,1,130189.31,9670.98,8,1,5,7,20,8 33 | 24664,0,0,0,0,0,0,0,6/30/22,Postgraduate,Single,2,0,85804.08,5496.75,5,5,9,19,3,99 34 | 23943,0,1,0,0,1,1,0,11/16/21,High School,Single,2,2,129668.6,5235.56,4,3,9,1,11,68 35 | 15711,1,1,0,1,0,1,0,4/18/22,Basic,Widow,1,2,136989,9464.92,2,13,2,16,20,34 36 | 92139,1,1,1,1,1,0,0,11/21/21,Basic,Divorced,1,0,120057.15,11612.07,10,12,5,0,15,41 37 | 37753,0,1,0,1,1,1,0,11/1/22,PhD,Divorced,2,0,91995.27,4225.6,3,13,7,17,7,88 38 | 14199,1,1,1,0,0,1,0,4/23/23,High School,Divorced,2,2,74282.45,11292.72,5,13,8,10,11,89 39 | 66910,1,1,1,1,0,0,0,4/6/25,Graduate,Single,2,2,144105.31,13958.19,3,6,7,8,18,97 40 | 48001,1,0,0,1,0,1,0,1/9/23,Basic,Separated,0,1,35461.88,1563.26,4,4,10,15,3,1 41 | 33561,1,1,1,1,1,0,1,5/30/22,Graduate,Widow,0,0,78084.89,1944.6,10,1,2,4,18,38 42 | 34724,0,0,0,1,0,1,0,10/5/21,Postgraduate,Divorced,2,1,66645.63,12610.03,9,3,3,20,6,33 43 | 39261,0,0,0,0,0,0,0,12/7/23,Postgraduate,Separated,1,1,33916.78,6118.05,4,14,1,7,8,100 44 | 19146,0,1,0,0,0,1,0,5/4/24,Basic,Single,0,1,101405.28,16572.69,4,14,1,14,9,89 45 | 99082,1,1,1,1,0,0,0,6/26/21,Graduate,Separated,1,0,40959.23,19257.27,9,0,10,8,18,5 46 | 36217,0,1,1,1,0,1,0,2/1/24,Postgraduate,Single,1,1,79001.77,6760.64,1,5,5,13,15,36 47 | 36969,1,0,1,0,1,1,0,6/6/21,Postgraduate,Separated,0,2,134324.1,9508.54,0,6,8,11,19,96 48 | 50858,1,1,0,0,1,0,1,2/17/25,Postgraduate,Widow,0,0,146777.62,12373,3,5,4,17,0,70 49 | 43480,1,0,0,0,1,0,0,7/16/22,High School,Widow,2,1,91064.64,5831.79,7,15,3,14,17,18 50 | 34199,1,0,0,0,1,1,0,5/14/24,PhD,Divorced,0,1,117126.12,16830.78,9,15,2,14,17,61 51 | 82953,1,1,1,1,1,0,1,11/11/22,High School,Separated,1,0,132721.56,8510.11,5,15,6,12,20,19 52 | 24943,1,0,0,1,0,1,0,3/6/23,Postgraduate,Single,2,0,79197.43,13268.24,2,2,7,8,10,79 53 | 38112,1,0,1,1,1,1,0,1/8/25,Basic,Separated,0,0,105739.32,18458.12,3,2,6,3,20,90 54 | 57667,0,1,0,1,0,0,0,9/27/21,Basic,Divorced,1,1,81682.41,5261.85,6,5,2,5,2,78 55 | 70754,1,0,1,0,0,1,0,3/30/25,Postgraduate,Divorced,2,0,137869.02,9572.44,4,5,1,14,11,75 56 | 71933,1,1,1,1,1,0,1,12/20/23,Postgraduate,Single,0,1,98630.72,11708.83,4,0,10,12,8,1 57 | 14645,0,1,1,0,1,0,0,2/3/23,PhD,Divorced,2,2,122096.49,13776.67,2,3,10,20,1,39 58 | 21436,1,0,1,0,0,1,0,2/27/22,Postgraduate,Married,0,0,124381.54,17590.98,5,8,2,8,15,37 59 | 21863,1,0,1,0,0,0,0,6/1/23,Postgraduate,Separated,1,0,124881.84,771.41,8,3,7,11,8,74 60 | 85421,1,1,0,0,1,0,0,4/10/24,PhD,Divorced,2,0,107718.29,12890.38,7,9,10,13,3,17 61 | 82950,0,0,1,1,0,0,0,4/30/22,PhD,Married,1,1,74521.38,19012.92,8,13,9,4,13,83 62 | 55882,0,1,1,1,0,1,0,10/6/24,Postgraduate,Married,1,0,147711.33,7663.03,10,11,0,12,8,24 63 | 96473,0,1,0,0,0,0,0,3/29/24,High School,Married,2,0,38233.22,15420.57,3,6,3,10,4,100 64 | 59196,0,1,0,0,1,0,0,8/30/21,Basic,Married,0,1,124798.16,5139.67,5,0,2,8,1,16 65 | 44516,1,0,0,1,1,1,0,12/19/23,Basic,Widow,2,0,143449.88,1345.62,10,9,7,20,0,7 66 | 31608,1,1,1,0,1,1,0,7/31/23,Basic,Divorced,2,0,37881.87,5862.81,10,10,6,19,16,37 67 | 94681,1,1,0,0,0,1,0,4/5/22,High School,Widow,1,1,77847.62,14727.44,5,13,5,8,11,19 68 | 81962,1,0,0,0,0,1,0,5/8/23,Graduate,Married,2,0,100375.81,11450.94,5,3,6,11,13,92 69 | 47557,0,1,1,1,0,0,0,9/27/23,Postgraduate,Married,0,1,131444.81,7667.44,4,7,6,17,19,78 70 | 48317,0,1,0,0,1,1,1,4/10/24,Graduate,Single,0,0,97958.53,8315.29,2,0,1,16,6,48 71 | 74520,1,1,1,0,1,1,0,4/27/23,PhD,Separated,0,0,48669.59,15211.5,0,2,4,14,13,62 72 | 95590,1,1,1,0,0,1,0,9/11/24,Graduate,Separated,1,2,110055.91,1385.64,6,1,0,6,9,27 73 | 31396,0,1,1,1,0,0,0,1/27/24,Postgraduate,Married,0,1,93909.88,4987.16,8,11,1,12,1,55 74 | 84154,0,1,0,1,1,1,0,7/1/22,Postgraduate,Divorced,0,1,32502.79,6832.98,9,14,5,2,13,13 75 | 69362,0,1,1,0,1,1,0,9/16/21,High School,Divorced,0,0,91257.99,2723.52,8,6,5,11,20,18 76 | 98559,0,0,0,1,0,0,0,6/1/23,Basic,Married,2,1,85674.37,11492.84,9,14,10,18,20,81 77 | 78101,1,1,0,1,0,1,0,7/24/22,Graduate,Divorced,2,0,72233.07,1946.72,7,14,0,1,11,36 78 | 71227,0,0,1,1,0,1,1,9/20/24,PhD,Married,1,2,87075.48,3441.79,0,14,1,10,2,64 79 | 46442,0,0,0,1,1,0,0,12/13/23,Graduate,Widow,1,1,111467.73,1521.13,10,10,1,10,3,71 80 | 14891,1,1,1,0,1,0,0,3/24/24,High School,Divorced,1,2,113817.6,8143.18,9,2,4,17,12,82 81 | 69373,1,0,0,1,1,0,0,1/31/25,High School,Married,1,1,53040.63,19999.54,2,2,4,3,16,98 82 | 21550,0,1,0,1,0,0,0,1/6/25,PhD,Married,2,1,35243.09,7603.84,3,14,9,9,14,29 83 | 84436,0,1,1,0,1,1,0,3/20/22,Graduate,Widow,2,2,80239.3,3660.13,3,4,4,1,20,61 84 | 72610,1,0,0,1,0,0,0,6/7/24,PhD,Married,1,0,143569.59,4829.74,7,11,0,13,1,50 85 | 85845,1,0,1,0,1,0,0,11/24/23,Basic,Divorced,0,0,34596.38,18389.23,7,4,7,14,19,0 86 | 42289,0,0,1,0,0,1,0,11/6/23,Graduate,Married,1,0,35729.93,8686.6,9,14,1,3,15,76 87 | 35400,0,0,0,1,1,0,0,2/10/25,High School,Separated,1,0,109724.56,2169.47,5,2,8,17,16,100 88 | 95846,0,1,1,0,1,1,0,9/10/21,Basic,Divorced,0,1,58935.11,13313.54,1,10,2,1,11,69 89 | 80958,1,0,1,1,0,0,0,2/10/24,Postgraduate,Single,1,0,35256.75,4389.66,0,10,4,16,12,69 90 | 49225,1,1,0,0,1,1,0,12/29/22,Basic,Divorced,1,0,125921.09,9020.55,6,14,6,10,5,63 91 | 63869,1,1,1,0,1,0,0,9/16/23,High School,Separated,1,1,42311.75,6894.44,4,9,7,19,13,21 92 | 91234,1,1,1,0,1,1,0,8/17/24,Basic,Single,2,2,78745.23,10505.18,10,5,0,4,19,86 93 | 98436,1,0,0,0,0,1,0,3/22/22,PhD,Single,2,0,111487.45,19002.06,5,14,1,18,4,67 94 | 41591,1,1,1,1,0,0,0,1/19/25,High School,Widow,2,1,139625.1,2796.34,4,14,3,19,9,88 95 | 53383,1,0,0,0,0,1,0,9/17/23,Postgraduate,Single,2,1,110140.77,14336.04,8,9,4,5,20,22 96 | 14492,1,0,0,0,0,0,0,4/15/23,Basic,Divorced,2,2,74269.34,16171.4,8,4,9,2,2,39 97 | 99481,1,1,1,1,0,0,1,6/24/24,Basic,Widow,1,2,92083.2,3002.52,8,5,2,13,16,7 98 | 32767,0,0,1,0,0,1,1,11/3/23,High School,Divorced,2,1,131512.04,5386.8,10,8,2,20,9,78 99 | 80362,0,0,0,0,1,0,0,6/26/24,Basic,Single,0,2,122522.68,5660.79,3,13,9,20,0,63 100 | 64873,1,1,1,0,1,1,0,12/17/22,Basic,Married,1,1,88110.14,4478.45,9,4,5,10,11,51 101 | 82562,0,1,0,1,0,1,0,3/17/23,Postgraduate,Married,0,0,36072.64,18935.01,9,13,3,5,10,73 102 | 14643,1,0,0,1,1,1,0,9/4/24,Postgraduate,Single,0,1,90653.34,19350.39,3,11,0,1,9,63 103 | 20238,1,1,0,0,1,0,0,11/25/23,Graduate,Widow,1,0,78069.79,11620.49,3,11,8,9,2,49 104 | 84146,1,1,0,0,0,1,0,10/16/24,Graduate,Separated,1,0,53886.95,10427.89,8,1,0,1,4,91 105 | 27635,1,1,1,0,0,1,1,11/19/22,Graduate,Married,1,2,118718.32,6335.07,5,15,9,9,15,2 106 | 97554,1,1,0,1,1,0,0,10/20/21,Graduate,Separated,2,0,116568.01,11878.7,2,12,2,7,1,73 107 | 37607,0,0,0,1,1,1,0,12/9/24,High School,Widow,2,2,140462.51,17519.95,0,4,8,6,17,41 108 | 99820,1,1,1,0,1,1,0,1/13/23,Graduate,Separated,1,0,144880.49,5940.62,4,7,4,9,6,88 109 | 99034,1,1,1,1,1,1,0,11/29/23,PhD,Widow,1,1,144553.06,16190.01,4,1,4,2,11,56 110 | 15641,1,1,0,0,1,1,0,1/4/22,PhD,Separated,0,0,36091.85,18151.09,3,7,0,3,13,42 111 | 55407,1,0,0,0,0,1,0,11/9/24,Postgraduate,Widow,2,0,120709.33,6101.28,4,1,1,20,18,29 112 | 77888,0,0,1,0,0,1,0,8/18/22,High School,Divorced,0,0,65807.07,12254.27,5,9,7,20,17,67 113 | 24085,1,0,1,1,0,0,0,6/7/21,Postgraduate,Divorced,2,0,31690.1,7073.69,4,6,2,19,20,51 114 | 68709,1,1,0,1,0,0,1,12/4/23,Postgraduate,Divorced,0,0,61294.23,5092.66,4,10,4,18,18,1 115 | 13233,1,1,0,0,0,1,0,5/6/21,Postgraduate,Separated,2,1,112834.85,12954.87,4,11,9,7,7,17 116 | 20942,1,0,1,1,1,1,0,9/14/22,High School,Married,2,2,134776.21,2096.47,7,11,1,18,3,7 117 | 12913,0,0,0,1,1,0,0,3/13/22,PhD,Widow,0,2,83457.92,1584.9,2,13,7,18,1,71 118 | 59213,1,1,0,1,1,0,0,5/21/24,PhD,Single,0,1,71343.46,1743.32,0,2,7,1,9,52 119 | 96952,0,0,1,1,1,1,0,4/17/24,Postgraduate,Widow,0,2,145697.33,17553.8,2,11,1,5,17,50 120 | 40417,0,0,0,0,1,1,0,8/22/24,PhD,Widow,2,1,128803.25,5326.48,5,4,4,6,3,4 121 | 23393,1,0,0,0,0,0,0,10/8/24,Postgraduate,Separated,2,2,134665.49,5272.32,0,12,7,7,17,27 122 | 92374,0,0,1,0,1,1,0,2/4/23,High School,Separated,0,1,66060.11,1688.67,9,5,10,13,17,63 123 | 63650,0,1,1,1,1,1,0,6/14/24,Postgraduate,Married,2,1,58916.18,4894.22,2,14,0,17,13,53 124 | 73117,0,1,0,1,0,1,0,10/17/23,Postgraduate,Divorced,0,2,116950.17,4214.39,4,12,10,19,19,5 125 | 39366,0,0,0,1,0,1,0,9/20/24,Basic,Married,0,2,44077.94,19991.65,7,7,9,6,12,30 126 | 99179,1,1,1,1,1,1,0,1/9/25,PhD,Widow,1,0,126337.99,14587.06,5,6,5,10,13,5 127 | 44938,0,0,0,1,1,1,0,2/2/22,Graduate,Married,1,2,130003.53,10236.38,10,10,4,15,20,94 128 | 29794,1,1,0,1,0,0,0,9/7/21,Postgraduate,Married,2,2,144868.43,10716.92,1,1,0,13,4,80 129 | 42051,0,0,0,0,0,1,0,3/5/22,PhD,Separated,1,2,88542.53,628.94,8,13,0,0,16,92 130 | 90630,1,1,0,1,0,0,1,11/2/24,PhD,Married,0,0,104373.7,5422.15,5,8,6,2,11,51 131 | 62103,1,0,0,1,0,0,0,10/15/24,Postgraduate,Widow,2,1,36733.79,684.28,2,2,7,13,20,100 132 | 82344,1,0,0,0,0,1,0,1/23/24,Basic,Divorced,2,2,108973.11,3997.41,5,0,3,18,4,96 133 | 71225,1,0,1,0,0,1,0,8/31/24,PhD,Divorced,1,2,46794.07,19775.32,1,11,0,3,13,29 134 | 61396,0,1,1,1,0,1,0,12/23/22,Postgraduate,Widow,0,1,96364.53,7820.83,2,12,1,19,9,31 135 | 25463,0,0,1,0,1,0,0,10/5/22,Graduate,Divorced,0,0,30623.43,6511.22,5,8,1,4,2,23 136 | 38021,1,1,1,0,0,0,0,2/9/25,Basic,Separated,2,1,133646.18,740.64,6,12,1,17,7,73 137 | 64809,0,1,0,0,1,1,0,4/7/24,High School,Single,0,1,63115.42,6345.17,1,11,4,7,20,63 138 | 70967,0,1,0,0,1,0,1,4/17/23,Graduate,Separated,1,1,82826.75,8897.02,10,4,4,10,19,88 139 | 99196,0,1,1,0,1,1,0,8/1/22,Postgraduate,Separated,0,1,75126.64,16421.64,6,11,1,18,6,75 140 | 43101,0,0,1,0,1,1,0,10/12/22,Basic,Widow,2,1,46701.49,6405.24,4,4,6,12,2,57 141 | 70547,1,1,1,0,1,0,0,10/10/23,High School,Divorced,0,2,103521.35,11482.8,10,12,5,13,3,1 142 | 38863,0,1,0,0,1,1,0,5/7/21,PhD,Separated,0,1,149724.33,9914.04,4,13,1,3,4,44 143 | 58059,1,1,1,0,1,1,0,7/7/23,Postgraduate,Widow,1,0,66031.69,16298.63,1,0,5,20,3,86 144 | 36871,0,0,0,0,1,1,1,11/14/21,High School,Widow,2,0,52366.43,13317.5,6,0,9,6,14,75 145 | 58647,1,1,0,0,0,1,0,10/4/21,Basic,Separated,0,2,52435.18,6856.71,7,3,4,15,16,81 146 | 94492,1,1,1,0,1,1,0,7/19/22,PhD,Married,2,2,126073.61,18414.31,9,2,10,4,10,15 147 | 81020,0,1,0,0,1,0,0,2/8/24,Postgraduate,Separated,0,2,76045.2,4122.2,10,5,8,5,15,36 148 | 95873,0,0,1,1,0,1,1,4/4/25,Postgraduate,Married,0,1,145659.49,10356.04,7,13,10,15,13,90 149 | 49755,1,1,0,0,0,0,0,12/13/24,Graduate,Married,2,1,50185.08,9417.77,7,3,9,3,8,99 150 | 45412,1,0,1,0,1,0,0,6/7/22,High School,Divorced,0,1,61537.83,17312.89,1,14,1,7,6,94 151 | 33313,1,1,0,0,0,0,0,10/6/22,PhD,Divorced,2,1,101622.92,3783.36,4,9,9,8,16,86 152 | 11956,0,0,1,0,1,0,0,5/6/24,High School,Married,0,2,68651.61,5327.32,10,12,7,4,18,80 153 | 65703,1,1,1,1,0,0,0,5/13/22,Graduate,Divorced,1,1,69274.71,548.23,1,14,10,11,2,68 154 | 22725,1,0,1,0,1,1,0,10/3/21,Graduate,Divorced,2,2,45671.31,17122.24,5,1,0,3,20,100 155 | 21930,1,0,0,0,1,1,0,2/9/24,Postgraduate,Married,2,0,135857.73,6386.38,4,2,10,11,8,10 156 | 26898,1,0,0,1,1,0,0,2/15/23,Basic,Single,0,0,87216.8,3087.65,3,14,0,0,10,15 157 | 39459,1,0,1,0,0,1,0,1/1/22,Basic,Single,1,1,135718.68,3174.02,2,4,1,16,18,1 158 | 69329,0,1,1,0,0,1,0,5/14/24,Postgraduate,Married,0,0,46801.58,2901.57,6,11,6,10,4,31 159 | 63904,1,0,0,1,0,1,0,3/10/22,PhD,Separated,0,2,77010.12,13100.31,3,15,6,3,7,63 160 | 46829,0,0,1,1,0,1,0,11/7/24,PhD,Widow,1,2,111953.05,15373.16,1,0,9,2,0,33 161 | 70168,0,0,0,1,1,1,0,7/13/21,Postgraduate,Single,2,2,94518.64,3477.27,1,9,1,16,6,19 162 | 66346,1,1,0,1,1,0,0,1/7/22,Basic,Widow,0,1,103475.62,1573.06,10,5,1,0,4,89 163 | 38046,0,0,1,1,1,0,0,6/20/24,Graduate,Single,0,1,126852.94,5803.62,1,8,9,20,2,63 164 | 17726,1,1,0,1,0,1,0,4/11/24,Basic,Separated,2,2,43840.03,14945.21,0,1,0,7,1,60 165 | 67945,1,1,0,0,0,1,0,12/25/21,High School,Widow,2,1,62550.95,2018.8,5,3,8,1,5,28 166 | 49848,0,1,0,1,1,1,0,7/31/22,PhD,Widow,0,1,54080.54,11494.23,4,7,7,11,18,63 167 | 11738,0,0,0,0,1,0,0,12/2/21,Graduate,Single,1,0,121071.5,7837.36,4,3,3,16,7,53 168 | 82924,1,0,0,1,0,0,1,3/25/23,PhD,Widow,2,1,85014.96,15084.04,9,3,8,4,20,50 169 | 70316,0,0,1,0,0,1,0,5/12/21,Graduate,Widow,2,2,69150.19,9328.78,3,2,3,4,18,13 170 | 49964,0,0,0,1,1,1,0,11/10/22,PhD,Divorced,0,1,66996.93,5667.65,1,2,2,9,19,5 171 | 26269,0,1,0,0,0,1,0,12/14/23,Basic,Divorced,1,0,33729.85,8385.65,7,7,1,14,3,17 172 | 50622,0,0,0,0,0,0,0,10/3/23,Graduate,Single,2,2,61058.48,16348.57,0,2,3,20,14,16 173 | 53485,0,1,0,0,1,0,0,1/30/24,PhD,Widow,2,1,89535.17,7963.22,6,5,5,6,5,35 174 | 44917,1,1,0,0,0,1,0,1/26/24,PhD,Widow,0,0,63573.12,3176.1,3,7,10,17,0,48 175 | 54806,1,1,1,1,1,1,0,11/28/21,Basic,Divorced,2,0,33516.42,16308.44,4,1,7,16,11,98 176 | 55442,0,0,0,0,0,1,0,12/15/22,Basic,Married,2,1,72576.56,16864.83,2,5,3,18,10,94 177 | 44144,1,0,1,0,0,1,0,12/23/23,High School,Widow,0,0,36740.16,12232.42,0,15,0,10,19,25 178 | 64744,0,1,0,1,0,0,0,1/1/24,High School,Single,2,1,145267.05,7785.02,10,12,9,3,10,38 179 | 58914,1,0,0,1,1,1,0,4/14/24,PhD,Separated,1,0,109863.8,16381.86,6,9,4,4,5,93 180 | 41091,0,1,0,0,1,0,0,12/23/21,Basic,Widow,0,1,119441.2,5140.92,5,5,10,2,20,93 181 | 18774,1,1,0,1,0,0,0,6/13/21,Graduate,Divorced,0,2,30153.74,8025.77,6,5,6,15,12,44 182 | 71060,1,0,1,0,0,1,0,11/10/21,Basic,Divorced,0,1,61335.33,2542.14,5,5,6,5,2,70 183 | 77327,0,1,1,0,1,0,0,8/8/23,Postgraduate,Married,0,0,69731.35,19500.77,6,8,10,9,9,30 184 | 23800,0,0,1,1,0,0,0,1/20/22,Postgraduate,Single,1,2,105010.91,9866.4,1,0,3,5,20,37 185 | 21796,0,1,0,1,1,1,0,6/15/21,Graduate,Single,2,2,76503.25,7734.9,6,11,2,14,1,33 186 | 76289,1,1,1,0,1,0,0,1/5/25,Basic,Single,2,1,126981.62,8572.74,9,7,8,1,12,66 187 | 71412,1,1,0,1,1,0,0,12/30/21,Basic,Separated,2,2,126054.04,13711.93,9,4,1,14,5,21 188 | 89247,0,0,0,0,1,0,1,11/7/22,Postgraduate,Married,2,0,130092.97,5528.78,1,2,6,17,12,70 189 | 74545,1,1,1,0,1,0,0,12/1/24,High School,Separated,2,2,121005.29,18823.17,1,3,4,17,11,52 190 | 25351,1,1,1,1,0,0,0,11/21/24,High School,Separated,2,2,113239.35,1256.26,5,7,0,15,8,47 191 | 90970,0,1,1,1,1,1,0,2/16/24,Basic,Married,0,1,146906.33,9140.66,5,8,6,19,20,11 192 | 32657,0,1,0,1,1,1,0,12/19/24,PhD,Married,0,1,115148.33,8366.01,8,0,2,6,6,7 193 | 81298,0,0,1,0,1,0,0,5/21/24,Postgraduate,Separated,1,2,140768.83,13782.9,3,11,7,19,14,22 194 | 78298,1,1,1,0,1,0,0,4/30/24,Postgraduate,Single,0,2,123579.62,4679.21,3,7,10,6,8,83 195 | 53615,1,0,0,1,0,0,0,3/29/23,Basic,Married,1,2,126610.27,6456.46,10,8,6,12,11,58 196 | 33692,1,0,1,1,1,0,0,4/25/23,Graduate,Separated,2,2,103086.01,6320.28,1,15,1,10,8,81 197 | 38665,1,1,1,1,0,0,0,10/15/21,High School,Single,2,1,109263.64,13774.74,0,15,4,8,13,51 198 | 10347,1,0,0,1,0,1,1,3/3/23,Graduate,Separated,1,0,130876.64,17591.65,2,9,1,15,4,94 199 | 79068,1,0,1,0,0,1,0,6/15/23,Graduate,Widow,1,1,61267.11,6234,1,15,0,20,18,74 200 | 65057,0,1,0,0,1,1,0,10/23/22,High School,Married,0,1,79103.02,11603.37,7,4,5,5,2,62 201 | 75243,1,0,1,0,0,1,0,5/5/24,High School,Separated,0,1,124225.31,15705.17,2,13,10,13,12,54 202 | 48290,1,1,0,0,0,1,1,5/23/23,Graduate,Separated,0,0,111209.78,12720.4,3,7,5,9,4,13 203 | 80723,1,1,0,0,0,1,1,5/10/24,Postgraduate,Separated,2,2,70540.22,17815.49,2,10,7,11,6,21 204 | 36779,1,0,0,0,0,0,0,7/6/23,PhD,Separated,0,0,73990.97,13185.54,0,12,10,8,17,79 205 | 13242,0,0,0,0,0,0,0,6/21/23,Postgraduate,Single,2,1,86927.56,3485.98,10,0,4,13,3,84 206 | 27146,1,0,1,0,0,1,0,1/6/23,Basic,Widow,0,2,53969.93,5628.38,8,6,5,13,9,20 207 | 90328,0,1,0,1,1,0,0,11/11/21,Basic,Single,2,2,89292.48,19014.2,8,2,1,8,20,29 208 | 80890,1,1,1,1,0,0,0,6/14/21,Basic,Divorced,1,2,67524.85,14901.53,1,1,0,4,20,21 209 | 51636,1,1,1,1,0,1,0,2/7/23,High School,Single,1,1,145477.19,5800.75,6,15,6,20,5,14 210 | 55230,0,0,0,0,1,1,0,5/22/24,Postgraduate,Married,2,1,123499.72,12421.49,10,4,7,15,3,52 211 | 27436,1,0,0,1,1,0,0,12/17/21,PhD,Separated,2,2,56878.19,19137.35,5,3,6,7,15,44 212 | 89840,1,0,0,0,0,1,0,12/21/22,High School,Single,1,1,58907.02,19417.83,0,7,8,20,12,51 213 | 39551,0,0,1,1,1,0,0,4/5/23,Postgraduate,Divorced,0,1,119428.68,846.5,6,13,0,17,17,0 214 | 89017,0,1,0,1,1,1,0,8/16/21,High School,Widow,1,2,122089.97,12199.05,10,11,7,3,3,97 215 | 22950,0,0,1,1,1,1,0,12/10/23,Basic,Married,1,0,128620.64,14978.96,6,3,4,19,6,24 216 | 99803,0,1,0,1,0,1,0,8/15/24,Postgraduate,Single,2,2,100657.33,15556.83,7,14,10,18,16,17 217 | 54167,1,0,0,1,1,0,0,4/2/25,High School,Married,0,1,108902.62,12320.14,5,12,6,19,7,35 218 | 67322,1,1,1,1,0,0,0,5/13/21,PhD,Divorced,2,2,133284.91,14381.55,8,6,3,6,19,91 219 | 58782,1,1,0,0,1,0,0,1/27/22,PhD,Widow,2,2,143120.84,14347.41,10,9,6,0,16,13 220 | 81791,0,1,0,0,0,0,0,4/19/21,High School,Divorced,2,1,47571.41,1709.64,4,6,8,1,11,57 221 | 25644,0,0,1,0,1,0,1,4/29/23,Postgraduate,Widow,2,1,145899.99,1549.14,4,6,9,14,7,81 222 | 18768,1,0,0,1,1,0,1,1/27/24,Basic,Married,0,2,91661.62,7855.37,6,8,8,8,4,20 223 | 34585,1,0,1,1,0,0,0,2/8/23,Graduate,Separated,0,2,103873.04,13406.86,7,11,1,10,16,28 224 | 12449,0,0,0,1,1,0,0,3/17/22,High School,Divorced,1,1,65228.92,19031.01,9,15,8,20,0,14 225 | 89005,1,1,0,0,0,1,0,11/9/22,Basic,Widow,1,2,116877.07,981.76,8,11,8,16,14,62 226 | 42480,0,1,0,1,0,0,0,7/24/24,PhD,Separated,0,2,37856.62,13571.53,3,13,1,6,17,89 227 | 58027,1,0,0,0,1,1,0,8/17/22,Basic,Single,0,1,96970.1,11711.76,3,8,6,12,14,80 228 | 58488,1,1,1,0,1,0,0,10/28/22,PhD,Single,2,2,35462.02,17040.9,5,3,4,9,19,11 229 | 60059,1,1,1,1,0,1,0,5/11/24,High School,Married,2,1,125612.45,17064.62,4,4,4,20,11,1 230 | 54718,0,0,0,0,1,0,0,12/15/23,Graduate,Married,1,2,132175.06,3277.87,2,7,9,8,6,14 231 | 40405,0,0,1,0,1,0,0,6/14/21,High School,Separated,1,1,72856.6,13590.26,3,3,4,19,2,24 232 | 24956,1,1,1,1,1,0,0,8/31/21,Graduate,Divorced,1,0,30579.83,5300.42,3,13,4,11,4,86 233 | 86085,1,1,1,1,1,1,0,2/13/22,Basic,Married,2,2,39836.84,5972.01,2,6,4,20,2,93 234 | 84891,0,0,0,1,0,0,0,2/17/23,PhD,Separated,1,2,123023.67,7633.46,3,9,2,14,16,65 235 | 24904,0,1,1,0,0,0,0,8/24/24,High School,Widow,0,0,97962.87,11533.34,2,15,5,1,7,97 236 | 72033,1,0,0,1,1,0,0,7/24/21,High School,Separated,1,1,86360.91,9365.55,0,1,10,10,3,90 237 | 97418,1,1,0,1,1,1,0,10/4/22,Basic,Separated,1,1,146720.89,4846.54,6,11,5,15,15,77 238 | 55191,0,0,1,0,0,0,0,7/28/21,Graduate,Married,0,0,48882.88,12239.4,1,7,6,10,3,61 239 | 61637,1,1,0,0,1,0,0,8/11/23,High School,Separated,2,0,46166.89,10496.53,9,4,0,5,8,23 240 | 77341,1,0,1,0,0,1,0,3/26/22,Postgraduate,Widow,0,0,129643.63,12216.35,9,5,9,16,8,21 241 | 70826,1,1,0,0,1,1,0,11/30/21,Basic,Married,2,2,112124.14,16617.03,9,3,2,15,19,76 242 | 73661,0,0,0,1,0,0,0,5/15/21,Postgraduate,Separated,1,1,134978.92,14104.03,6,7,7,12,11,75 243 | 33011,0,1,0,1,1,0,1,7/4/24,Basic,Divorced,2,2,79037.6,4185.91,0,3,8,0,12,83 244 | 28775,0,1,1,1,0,0,0,10/25/24,PhD,Divorced,1,2,80827.39,7630.06,4,12,3,13,17,100 245 | 92319,1,0,1,1,1,1,0,6/20/22,Postgraduate,Married,2,0,119737.7,9208.6,2,3,3,16,12,100 246 | 61139,1,0,0,1,0,0,0,2/13/23,Postgraduate,Separated,1,0,37689.65,4824.52,1,1,6,12,19,83 247 | 77540,1,0,0,1,1,0,0,5/16/23,PhD,Divorced,0,0,124075.13,10455.68,3,5,10,1,11,30 248 | 26774,0,0,0,1,1,1,0,8/6/23,Postgraduate,Separated,0,2,31843.98,14220.63,1,9,8,18,11,44 249 | 34735,1,0,0,1,1,0,0,7/13/23,Postgraduate,Separated,0,2,38686.32,6237.91,1,8,8,18,8,88 250 | 34135,1,1,1,0,1,1,0,2/12/24,PhD,Single,1,0,60938.37,7962.13,3,7,5,13,11,66 251 | 17652,0,1,0,1,0,1,0,1/17/23,Graduate,Single,2,0,87186.65,7393.99,9,8,2,6,15,94 252 | 81553,0,0,0,1,0,0,0,6/18/21,High School,Separated,1,0,123472.62,2837.63,1,15,9,0,17,5 253 | 59219,1,1,1,1,1,1,0,7/10/21,PhD,Divorced,1,0,120020.33,3000.8,8,13,7,17,1,64 254 | 26253,1,1,0,1,0,1,0,6/6/24,Basic,Single,2,0,75531.36,2464.58,5,8,2,17,8,30 255 | 57699,1,1,1,1,0,1,0,6/16/23,Postgraduate,Divorced,1,1,63555.54,4007.84,2,7,1,8,7,71 256 | 61791,1,1,0,0,0,1,0,9/21/24,High School,Widow,1,0,149467.41,17063.1,8,10,7,9,8,71 257 | 72454,0,1,0,0,0,0,0,7/17/23,Postgraduate,Single,1,2,70158.78,7538.76,8,8,5,0,13,15 258 | 67337,1,0,0,1,1,1,0,9/29/21,Postgraduate,Widow,0,1,103639.65,9543.17,2,5,10,10,13,80 259 | 59878,1,0,0,0,1,0,0,5/11/21,Graduate,Divorced,2,2,77454.64,17809.99,5,14,6,5,5,2 260 | 45387,0,0,1,0,0,0,0,1/31/23,Basic,Single,1,2,36459.05,15533.21,8,2,1,8,19,19 261 | 96459,0,1,1,0,0,1,0,1/14/22,High School,Married,2,0,33428.56,13439.21,5,15,7,17,8,70 262 | 62255,1,0,0,1,1,1,1,7/15/22,Postgraduate,Separated,0,0,83023.24,7632.77,4,4,4,0,0,66 263 | 32919,0,1,1,1,0,1,1,10/8/21,Postgraduate,Divorced,0,1,125755.77,12141.57,0,11,3,13,17,55 264 | 81773,1,0,0,1,1,0,0,10/19/21,Postgraduate,Married,2,2,126908.99,13850.49,4,8,6,6,2,23 265 | 80682,0,1,0,1,1,1,0,3/28/22,Postgraduate,Married,1,0,48257.92,19409.47,0,5,1,9,8,16 266 | 16423,1,0,0,0,0,0,0,2/21/22,Graduate,Widow,1,1,149755.61,9541.85,0,10,1,4,1,34 267 | 79901,1,0,1,0,0,1,0,9/6/22,Basic,Single,2,0,76175.75,19511.96,3,1,3,14,3,66 268 | 50946,0,1,1,1,1,0,0,11/2/24,Basic,Separated,2,0,60397.53,12951.14,3,11,0,6,3,30 269 | 87540,1,1,0,0,1,0,0,9/29/23,High School,Single,0,2,124017.03,9767.78,9,4,5,2,7,20 270 | 52004,1,0,1,0,0,1,0,4/7/22,PhD,Single,0,1,57920.96,19642.68,10,11,7,6,0,88 271 | 74882,1,1,0,1,0,1,0,6/28/23,PhD,Divorced,2,2,88811.04,11550.68,8,11,5,14,8,76 272 | 20213,0,0,0,1,1,1,0,5/15/21,Postgraduate,Single,2,2,75485.23,4117.92,0,11,9,7,10,92 273 | 31907,0,1,0,1,0,1,0,1/2/25,Graduate,Single,0,2,109066.92,1353.66,2,5,0,6,3,62 274 | 25990,0,0,0,1,1,1,0,7/9/23,High School,Separated,2,0,31570.33,18460.19,10,15,1,14,20,7 275 | 27713,1,1,1,0,1,0,0,8/27/21,Graduate,Single,0,2,41654.31,14640.85,8,12,3,6,16,84 276 | 57778,0,0,1,0,1,1,0,9/27/22,Postgraduate,Single,0,0,95995.86,10341.51,10,5,8,19,14,45 277 | 68713,1,1,1,1,1,1,0,12/12/21,PhD,Separated,0,1,121884.99,18559.86,4,11,7,14,12,0 278 | 43652,0,1,0,0,1,0,0,6/26/22,High School,Married,1,0,122582.77,18895.28,4,4,1,1,14,45 279 | 12946,0,0,1,1,0,0,0,3/27/25,Graduate,Single,2,1,98287.79,4740.03,1,10,7,5,15,88 280 | 60148,0,1,0,1,0,0,1,7/15/23,Graduate,Divorced,1,1,141629.53,2850.54,3,0,10,20,12,6 281 | 42056,1,0,1,0,1,0,0,11/29/23,Postgraduate,Widow,2,2,90567.52,4830.67,4,12,2,4,10,10 282 | 16009,1,0,1,1,0,1,0,1/30/25,High School,Married,1,2,53773.02,8208.72,9,6,8,19,16,31 283 | 28600,0,0,1,1,1,0,0,11/25/24,Postgraduate,Widow,1,1,52858.77,7218.24,10,0,4,5,11,78 284 | 93598,1,0,1,0,0,0,0,7/13/23,High School,Single,0,2,36544.56,19004.35,2,9,0,10,3,39 285 | 62925,1,1,0,1,0,0,0,6/8/21,PhD,Divorced,2,1,104765.04,19849.89,10,0,6,18,16,5 286 | 61120,1,0,0,1,0,1,0,8/29/24,Basic,Single,1,2,62557.25,17115.62,10,11,1,0,9,83 287 | 44430,1,0,1,0,1,1,0,2/22/22,PhD,Single,0,0,90078.1,9430.73,0,1,3,16,9,42 288 | 14413,1,1,0,0,1,1,0,6/10/21,Graduate,Married,2,1,127427.14,12702.95,6,0,8,8,7,19 289 | 37123,1,1,0,0,1,0,0,5/14/24,PhD,Married,1,1,139168.25,6921.05,8,4,3,20,4,74 290 | 39389,0,0,1,0,1,0,0,10/14/21,PhD,Married,1,0,96880.84,18997.73,1,5,5,4,0,97 291 | 54019,1,1,1,1,1,1,0,3/7/23,High School,Single,0,0,33143.79,19726.13,9,8,9,18,19,29 292 | 48504,0,0,0,1,0,1,0,9/17/23,PhD,Widow,2,2,74875.13,17116.52,7,15,2,5,6,28 293 | 48387,0,1,0,1,1,0,0,7/24/23,High School,Married,0,0,141252.38,19719.74,9,15,5,14,7,54 294 | 93823,1,0,1,1,1,0,0,10/29/23,Postgraduate,Widow,0,0,92244.32,8214.42,0,12,8,20,1,24 295 | 91379,1,1,1,1,1,1,0,2/9/25,Graduate,Separated,0,2,92036.36,3273.67,10,14,1,16,3,23 296 | 37819,0,1,1,1,1,0,0,9/15/21,PhD,Married,1,0,60009.88,13988.49,2,3,1,3,14,81 297 | 13580,1,0,1,0,1,1,0,10/10/24,High School,Divorced,2,2,133520.7,3661.42,2,5,0,13,9,39 298 | 47881,1,1,0,0,0,0,0,4/4/25,Graduate,Single,1,1,132649.33,11098.27,0,15,3,6,18,84 299 | 89375,1,1,1,1,0,0,0,10/4/23,PhD,Divorced,2,2,96905.84,16592.42,0,5,2,1,20,44 300 | 40223,0,0,0,1,1,0,0,8/8/24,Postgraduate,Married,0,2,64125.67,13388.03,9,5,7,18,11,4 301 | 20930,0,1,1,1,1,0,0,5/1/24,PhD,Single,1,1,46145.49,14045.08,4,10,8,13,16,26 302 | 28365,0,0,0,0,1,1,0,4/29/22,Graduate,Divorced,2,2,149402.92,7400.14,2,15,8,20,15,63 303 | 96025,0,0,1,0,0,0,0,6/15/21,Basic,Married,2,1,79684.94,7008.95,8,6,0,20,6,88 304 | 12320,1,1,0,1,1,1,0,8/30/23,Basic,Divorced,1,1,116927.6,12511.87,0,5,3,10,10,86 305 | 62240,1,1,0,0,1,0,0,8/16/23,PhD,Divorced,1,1,141334.8,1592.43,8,9,9,10,17,68 306 | 36031,0,0,1,1,0,0,0,3/4/23,Basic,Divorced,1,2,94112.78,3736.41,9,12,2,19,3,100 307 | 66315,1,1,0,1,1,0,0,2/26/24,High School,Divorced,1,1,55936.79,6395.21,1,11,0,6,2,56 308 | 48803,0,1,0,0,1,1,0,8/21/23,PhD,Divorced,1,0,122337.77,2415.91,8,4,3,1,14,49 309 | 36985,0,0,1,0,0,1,0,2/3/24,Basic,Single,2,2,119466.57,938.37,4,6,5,6,6,36 310 | 94753,1,1,1,0,1,0,0,5/5/22,Basic,Single,2,2,91687.28,4528.26,5,10,4,4,9,73 311 | 49295,0,0,0,1,1,1,0,6/17/23,Basic,Widow,2,0,42711.23,1549.1,6,12,2,19,18,80 312 | 62391,1,1,1,1,0,0,1,12/19/23,Postgraduate,Separated,0,0,127542.12,18433.94,7,3,7,9,20,90 313 | 88174,1,1,0,0,0,1,1,11/1/24,Postgraduate,Married,2,2,69639.35,17285.44,5,14,7,0,2,60 314 | 19830,0,1,1,1,0,1,0,11/18/21,PhD,Separated,0,2,42351.87,16442.28,1,10,8,20,2,38 315 | 13319,1,1,1,0,0,0,1,1/26/25,Postgraduate,Single,1,0,127669.88,12023.92,6,10,8,7,5,84 316 | 66932,0,1,0,1,1,0,0,3/14/22,High School,Divorced,1,0,101791.1,13867.42,9,12,5,15,20,84 317 | 52552,0,0,0,0,0,0,0,11/5/24,Postgraduate,Separated,1,1,103868.66,9219.62,3,10,4,18,9,55 318 | 10980,1,0,0,1,0,1,0,9/16/21,PhD,Single,0,2,97203.63,19584.58,1,9,0,7,13,89 319 | 72991,1,1,1,1,1,1,0,4/25/21,High School,Widow,0,0,96650.59,16944.89,5,13,2,9,4,17 320 | 56549,0,0,0,0,1,1,1,8/5/21,High School,Single,0,2,126712.99,17272.98,9,13,9,5,14,65 321 | 75405,1,1,1,1,0,0,0,4/28/21,Basic,Separated,1,2,58410,10283.71,6,11,0,1,18,61 322 | 74861,1,0,1,0,0,1,0,3/30/25,Basic,Separated,2,0,59427.76,12797.93,10,2,10,20,17,74 323 | 78432,1,0,1,0,1,1,0,1/21/23,Postgraduate,Separated,2,1,80521.93,19231.34,4,9,2,1,19,95 324 | 35713,1,1,0,1,0,0,0,10/8/21,Graduate,Separated,0,1,88171.22,2973.02,1,8,4,4,19,35 325 | 13803,1,1,0,0,0,0,0,9/17/22,Graduate,Married,1,2,58343.51,4025.78,8,1,5,2,4,76 326 | 95822,1,0,1,1,1,0,0,2/10/23,PhD,Divorced,1,2,74802.56,13926.03,2,11,9,15,2,2 327 | 49188,1,1,1,0,0,1,0,4/11/23,Graduate,Widow,1,2,70890.88,15979.45,3,4,5,20,18,29 328 | 95460,0,0,1,1,0,1,0,1/7/25,Basic,Widow,0,2,68598.59,10820.09,7,8,1,14,12,99 329 | 52114,0,1,0,1,0,1,0,9/29/22,Basic,Divorced,1,2,87906.37,3349.34,4,10,7,10,19,68 330 | 75998,0,0,0,1,0,0,0,3/9/22,PhD,Divorced,0,1,80758.96,16462.44,0,9,4,6,4,37 331 | 68155,0,0,1,0,1,1,0,9/24/24,High School,Widow,2,1,96631.72,12079.98,5,3,4,14,8,25 332 | 59143,1,1,0,0,1,0,0,3/16/23,Graduate,Separated,1,1,47010.34,16720.18,6,0,10,13,20,41 333 | 33373,1,0,1,0,1,1,0,3/20/24,PhD,Single,2,0,116733.2,14416.45,7,2,1,4,4,13 334 | 59480,0,0,0,1,1,1,0,5/27/22,Graduate,Single,1,1,95413.37,10731.25,10,12,2,17,2,26 335 | 51856,1,0,0,1,0,0,0,1/6/24,PhD,Single,0,2,131147.18,4897.64,0,1,3,7,4,17 336 | 59521,1,1,1,1,0,1,0,6/17/23,Postgraduate,Separated,0,0,54014.93,1202.11,3,15,0,11,2,74 337 | 81955,1,0,1,0,0,0,1,4/21/24,PhD,Divorced,1,2,74916.07,8074.19,3,8,5,6,10,33 338 | 79366,1,0,1,1,0,1,0,11/10/21,PhD,Widow,0,0,43523.22,8055.32,1,6,3,18,9,88 339 | 28596,0,1,1,1,0,0,0,9/18/22,Graduate,Married,2,0,67188.19,4720.26,2,10,3,15,12,92 340 | 47653,1,1,1,0,1,1,0,10/14/23,Graduate,Married,0,2,42871.39,505.28,0,1,0,8,0,38 341 | 96658,1,1,0,0,0,1,0,2/2/22,High School,Widow,2,1,39131.94,8641,0,9,0,3,9,18 342 | 21751,1,1,0,0,0,1,0,7/16/21,Basic,Single,2,0,84629.35,17785.58,8,8,4,0,18,61 343 | 72706,0,0,0,0,1,1,0,4/20/21,Postgraduate,Widow,1,1,51002.75,758.75,0,2,4,9,15,25 344 | 52792,1,0,1,0,0,1,0,3/11/25,Basic,Separated,1,2,144257.57,10026.29,3,1,4,11,2,76 345 | 74804,1,1,1,0,1,1,0,8/24/22,PhD,Married,1,2,106717.7,8436.53,9,12,5,14,11,98 346 | 90187,1,0,1,1,1,1,0,9/26/23,Graduate,Married,1,1,107455.7,9289.99,8,5,0,10,16,80 347 | 77495,0,1,0,0,0,1,0,3/13/25,Postgraduate,Single,2,2,35037.41,2524.4,0,8,8,5,7,87 348 | 28142,1,1,0,1,1,1,0,6/25/24,Postgraduate,Married,2,1,119979.16,13912.12,1,7,10,20,14,19 349 | 80151,0,0,0,1,0,0,0,9/27/21,Graduate,Married,1,1,129027.83,11068.35,1,6,0,0,0,37 350 | 66896,0,0,0,0,0,1,0,2/5/25,Basic,Widow,1,2,78360.1,14107.46,4,10,6,11,6,38 351 | 85765,1,1,0,1,0,1,0,5/24/22,Basic,Divorced,2,1,133340.98,7983.23,8,3,6,18,19,44 352 | 96454,1,0,1,0,1,0,0,6/29/23,PhD,Single,0,0,116913.44,14454.37,1,14,6,10,7,77 353 | 58535,1,0,1,1,1,0,0,11/26/21,Basic,Single,2,0,101211.49,13667.23,0,14,1,16,5,59 354 | 63040,1,1,0,1,0,1,0,12/19/22,Postgraduate,Divorced,0,0,64834.4,6962.6,1,4,1,6,15,62 355 | 43816,0,0,1,0,1,0,0,2/8/23,High School,Divorced,2,2,131464.61,4960.28,7,3,1,5,4,16 356 | 77180,0,1,1,1,1,0,0,6/10/21,Graduate,Separated,0,1,86838.96,5938.59,8,13,1,2,2,33 357 | 29557,1,1,1,0,0,0,0,12/14/23,Basic,Separated,0,2,53582.05,13367.31,8,11,8,8,15,8 358 | 82092,0,1,0,0,0,0,0,3/3/24,High School,Widow,0,1,37199.28,15867.7,1,8,9,16,0,38 359 | 20329,0,1,0,1,0,0,0,5/5/23,Postgraduate,Widow,0,0,50690.16,11284.13,10,2,2,4,16,18 360 | 68782,0,1,0,0,0,1,0,4/26/22,Graduate,Married,1,1,37160.76,8921.49,5,12,0,6,11,50 361 | 29018,1,0,0,1,1,0,0,9/23/23,PhD,Divorced,1,0,81928.98,1102.82,1,2,2,20,1,47 362 | 88272,1,1,1,1,1,1,0,10/17/22,PhD,Divorced,1,1,91372.8,8028.83,9,4,7,10,2,88 363 | 83137,1,1,0,1,1,1,0,2/23/24,PhD,Separated,0,1,141218.38,6821.2,3,6,5,9,6,42 364 | 17890,1,1,1,1,0,0,0,10/14/21,Graduate,Married,2,2,73378.43,11084.13,5,6,7,18,16,22 365 | 31458,0,1,0,0,1,0,0,2/2/24,Postgraduate,Divorced,0,1,88121.2,5779.44,9,14,8,10,4,32 366 | 14009,0,1,0,1,1,0,0,10/18/23,Graduate,Widow,1,2,100030.03,5903.29,9,13,3,8,1,54 367 | 49971,1,0,1,1,1,1,0,9/18/21,High School,Single,1,0,34924.15,1870.46,1,8,2,3,13,26 368 | 28259,0,0,0,0,1,1,0,3/15/23,High School,Married,0,2,102439.36,15158.67,8,11,3,4,0,70 369 | 31562,1,0,0,1,0,0,1,12/19/23,Basic,Separated,2,2,147440.7,5759.72,7,3,1,7,13,28 370 | 63919,0,1,0,0,0,1,0,9/21/23,High School,Divorced,1,2,133307.43,17067,7,6,3,3,2,52 371 | 61273,1,1,0,1,1,0,0,11/4/21,Graduate,Married,2,1,82398.03,17565.36,5,10,0,15,16,78 372 | 70501,1,1,0,1,0,0,0,9/16/21,Postgraduate,Married,1,0,56151.4,7388.68,4,7,4,7,12,76 373 | 97068,1,0,0,0,1,0,0,10/26/21,Graduate,Divorced,1,0,88914.9,14621.48,6,2,6,8,11,29 374 | 17281,1,1,1,1,0,0,0,4/18/22,PhD,Separated,2,2,127217.06,16995.1,6,11,0,7,2,90 375 | 43167,0,0,0,1,0,1,0,9/22/24,Graduate,Married,1,1,134521.05,4127.39,6,9,2,16,2,29 376 | 14549,0,0,0,1,1,1,0,2/8/23,High School,Single,0,2,115503.49,1813.95,1,8,2,17,3,99 377 | 59388,1,1,0,0,0,0,0,12/20/23,Graduate,Married,1,2,127596.69,10267.81,0,12,2,5,1,60 378 | 40376,0,0,0,1,1,1,0,1/26/22,High School,Single,2,0,49450.43,12317.21,0,2,6,4,0,36 379 | 70636,1,1,1,1,1,1,0,8/29/22,PhD,Widow,1,2,142424.41,3754.46,7,4,0,6,2,76 380 | 77308,1,1,0,1,1,1,0,2/3/22,Basic,Divorced,1,0,120037.36,4703.62,10,4,7,18,4,83 381 | 65853,1,1,0,0,0,0,1,8/8/23,Graduate,Widow,1,1,115546.83,7745.56,3,14,2,13,1,31 382 | 10712,0,0,1,1,0,1,0,2/24/25,Postgraduate,Widow,0,2,65274.12,16511.34,8,15,0,15,2,45 383 | 47267,1,0,1,0,0,0,0,8/22/22,PhD,Divorced,0,0,91140.8,12798.15,5,0,7,12,10,78 384 | 87812,1,1,1,1,1,0,0,12/31/23,PhD,Single,1,1,82008.98,10522.45,10,2,2,12,10,84 385 | 38751,1,1,0,0,0,0,0,12/18/22,Graduate,Widow,0,0,35281.61,736.24,2,6,6,18,6,53 386 | 43704,0,1,1,0,0,0,0,9/15/23,Basic,Divorced,2,1,124441.46,4772.34,2,15,1,2,14,43 387 | 52426,1,1,1,1,1,0,0,4/2/23,PhD,Separated,2,1,126742.68,15676.77,3,7,0,13,15,34 388 | 41384,1,1,1,1,0,1,0,6/3/22,Graduate,Single,0,2,56835.03,15777.79,3,14,2,3,17,64 389 | 86869,1,1,1,1,1,1,0,1/25/24,High School,Separated,1,2,37958.47,6622.16,0,13,6,13,4,53 390 | 39818,1,0,1,1,0,1,0,2/8/22,Postgraduate,Widow,2,0,78638.12,17064.28,4,7,9,5,10,20 391 | 44176,1,1,0,0,0,0,0,2/2/24,Basic,Married,2,1,89117.82,2300.14,10,4,0,0,15,22 392 | 97972,0,0,1,1,1,0,0,6/11/23,Graduate,Divorced,0,0,149557.86,2105.74,0,0,3,5,12,62 393 | 99977,0,1,1,0,1,0,0,8/11/24,PhD,Divorced,0,2,123129.02,10540.38,7,7,4,1,12,43 394 | 52421,0,1,1,1,1,1,0,12/29/21,Postgraduate,Widow,0,1,109950.64,4605.67,7,4,2,1,2,7 395 | 11452,0,0,0,1,0,0,0,10/12/23,Graduate,Divorced,2,1,99512.44,14513.49,6,13,1,20,17,93 396 | 48477,1,0,1,0,1,0,0,7/6/24,Graduate,Single,0,0,130934.56,16170.73,6,5,7,14,12,100 397 | 98752,1,1,0,1,1,1,1,4/10/24,PhD,Married,0,0,144514.75,8608.12,7,10,2,1,4,30 398 | 40201,1,1,0,0,1,0,0,7/27/24,Graduate,Divorced,2,1,70461.58,2539.65,8,6,3,4,10,87 399 | 22186,1,1,0,1,0,1,1,5/14/22,PhD,Separated,1,2,149614.81,9400.51,4,10,0,13,18,80 400 | 95598,0,1,1,1,0,1,0,6/3/24,High School,Widow,1,1,136981.58,17117.21,3,8,6,8,0,67 401 | 62372,0,0,0,1,0,1,0,7/12/21,Basic,Single,1,2,127730.96,11157.65,6,0,8,20,9,98 402 | 77227,0,1,0,1,0,0,1,7/24/23,Postgraduate,Separated,2,1,51268.41,13294,0,13,1,10,20,31 403 | 68304,1,0,0,0,1,0,0,4/8/23,Graduate,Widow,0,1,54491.2,3696.19,1,9,4,17,17,81 404 | 50206,0,0,0,0,0,0,0,10/16/23,Graduate,Married,0,1,70503.58,10259.03,8,14,8,3,15,85 405 | 80343,1,0,1,0,1,0,0,3/31/22,High School,Separated,1,2,32187.31,18320.66,1,2,2,4,10,20 406 | 69006,0,1,1,0,1,1,0,10/30/24,Postgraduate,Single,2,1,55483.2,4207.8,10,15,9,2,7,84 407 | 43898,1,1,1,1,1,0,0,2/23/23,Basic,Widow,0,0,98512.72,833.87,10,11,10,10,13,95 408 | 59486,0,1,1,0,0,0,0,8/30/21,PhD,Single,2,0,86811.58,11294.91,8,10,1,19,1,100 409 | 47233,1,0,1,1,1,0,0,3/3/23,Basic,Divorced,2,0,113508.81,16406.02,2,8,5,13,2,67 410 | 40819,1,0,1,1,1,1,0,1/18/22,Graduate,Divorced,0,2,41169.15,3179.78,8,3,5,3,3,33 411 | 92949,1,1,0,0,0,0,0,9/20/24,Graduate,Divorced,2,2,139556.79,2053.43,8,9,8,9,3,51 412 | 57388,0,1,1,0,1,1,0,8/4/23,High School,Single,0,2,30481.13,17882.35,0,15,5,12,17,13 413 | 90268,1,0,0,0,1,0,0,12/24/23,PhD,Single,2,0,86437.88,14710.6,2,5,10,16,7,52 414 | 61889,0,1,0,1,0,0,0,3/19/22,PhD,Widow,0,2,65124.76,3156.1,9,13,9,9,8,31 415 | 57525,0,1,1,1,0,0,0,5/12/23,High School,Divorced,2,0,99193.92,16381.7,9,3,10,7,4,91 416 | 13102,0,0,0,0,0,1,0,4/10/24,High School,Divorced,0,2,30338.12,4599,1,0,6,3,10,26 417 | 27484,1,0,0,0,1,1,1,8/7/23,Postgraduate,Separated,2,1,83209.41,4430.2,10,4,3,12,11,24 418 | 35721,0,1,1,1,1,0,0,8/11/21,Postgraduate,Widow,0,0,123767.17,16756.96,7,9,3,13,3,36 419 | 25894,0,1,1,0,1,0,0,8/1/24,PhD,Single,2,1,71741.16,13062.88,7,6,4,15,12,34 420 | 91287,0,1,0,1,0,0,0,10/19/22,Postgraduate,Widow,0,2,91163.73,18712.54,1,8,5,16,8,88 421 | 91382,0,1,1,1,0,0,0,4/6/24,Basic,Married,1,1,127165.22,15275.87,10,5,10,15,16,0 422 | 30385,0,0,1,1,1,0,0,8/7/24,High School,Single,2,1,116505.69,8784.55,4,6,10,12,19,72 423 | 83937,1,0,1,1,0,0,0,12/29/24,Graduate,Divorced,1,0,93898.32,10116.44,6,0,1,3,18,17 424 | 58794,0,1,0,0,1,1,0,11/3/21,Basic,Single,2,2,82953.74,3146.56,3,6,5,3,1,32 425 | 19126,0,1,0,1,1,0,0,10/1/22,High School,Separated,0,0,126649.9,15926.44,8,7,0,15,8,69 426 | 36337,0,0,1,0,1,1,0,9/28/21,PhD,Separated,0,2,63320.4,8828.56,5,11,7,17,5,3 427 | 58401,0,0,1,0,0,0,0,5/1/22,Basic,Married,0,0,53717.36,6252.5,0,14,3,19,4,4 428 | 18913,0,0,1,1,1,0,0,2/18/22,High School,Widow,1,0,50565.02,1185.03,2,13,1,2,5,27 429 | 65070,0,0,1,0,1,0,0,8/24/21,High School,Divorced,2,2,115646.68,17734.66,8,10,7,5,19,68 430 | 99372,0,1,1,0,1,1,0,4/28/23,PhD,Married,2,0,53552.36,6262.92,8,14,1,18,3,88 431 | 52705,0,0,0,0,1,1,1,8/22/24,PhD,Married,0,2,51575.11,17578.08,2,7,0,16,19,69 432 | 18081,0,0,0,1,0,1,0,7/25/24,PhD,Married,0,0,81326.48,17079.78,3,4,3,20,2,66 433 | 10880,0,1,0,1,0,1,0,12/11/23,Graduate,Divorced,0,0,121857.53,15710.93,10,0,0,2,10,21 434 | 93129,0,0,0,1,0,1,0,6/17/21,PhD,Divorced,0,2,119816.61,12294.98,2,11,5,12,3,64 435 | 62659,1,1,1,0,0,0,0,3/6/25,PhD,Separated,1,1,88936.7,18249.95,1,4,2,9,16,79 436 | 20950,0,1,1,1,0,1,0,4/16/24,Basic,Separated,0,0,109444.45,17772.96,6,11,10,10,2,46 437 | 35857,1,0,1,1,0,0,0,10/4/22,Graduate,Separated,0,1,74347.96,1455.46,4,5,9,12,1,70 438 | 93936,0,1,0,1,0,0,0,9/30/23,PhD,Separated,1,2,72609.25,19975.45,9,5,2,2,1,34 439 | 37805,0,0,1,1,0,0,0,11/25/22,Graduate,Married,2,1,89813.67,18652.55,8,6,6,6,16,82 440 | 71316,0,1,1,0,0,0,0,5/18/21,Postgraduate,Separated,2,1,117118.75,10673.61,10,9,6,9,5,2 441 | 46780,0,1,0,1,1,0,0,4/19/21,Graduate,Single,1,0,128644.43,6186.69,10,12,2,11,11,96 442 | 46887,1,0,0,1,0,1,0,6/28/21,High School,Divorced,0,2,32791.69,2149.83,6,9,6,8,9,65 443 | 31273,0,0,0,1,0,0,0,11/15/24,Graduate,Divorced,2,0,85881.92,16719.19,0,10,0,9,20,7 444 | 21078,1,0,0,0,0,1,0,1/28/25,PhD,Divorced,2,1,136017.63,2882.52,3,4,1,2,3,91 445 | 92658,1,1,0,0,0,0,0,4/20/24,High School,Widow,0,0,78599.34,6798.89,3,1,6,1,2,10 446 | 51728,1,1,1,0,1,1,0,6/26/24,PhD,Single,0,0,55044.43,1018.1,0,3,5,13,3,70 447 | 82524,0,0,0,1,1,1,0,6/29/22,Postgraduate,Divorced,2,1,128764.91,12123.41,6,0,9,6,12,47 448 | 26547,1,0,0,0,1,0,0,2/9/25,PhD,Separated,1,0,94707.52,4903.96,6,15,5,0,18,17 449 | 21562,1,0,0,0,0,0,0,4/4/24,Postgraduate,Divorced,1,1,80192.28,2298.38,0,9,2,16,17,52 450 | 55145,0,1,0,1,0,1,1,12/22/21,Postgraduate,Widow,2,0,92320.71,2371.71,2,13,0,1,6,7 451 | 43448,0,1,1,1,0,0,0,1/23/25,Postgraduate,Single,1,2,55542.41,19079.34,3,8,5,11,1,55 452 | 62766,0,1,0,1,1,1,0,1/10/23,High School,Separated,0,2,129766.98,6162.82,0,3,3,9,17,0 453 | 66730,1,0,1,1,0,1,0,5/31/24,PhD,Separated,0,1,35534.47,5058.08,5,3,0,3,11,57 454 | 93742,0,0,1,0,1,1,0,11/22/23,Basic,Widow,1,2,126878.98,8355.67,9,13,9,18,12,99 455 | 38101,1,1,0,1,0,1,0,6/8/21,PhD,Married,0,1,97840.78,6240.95,8,9,9,19,6,22 456 | 33087,0,0,0,0,1,0,0,5/16/21,Postgraduate,Widow,2,2,145275.36,1568.06,9,0,8,15,4,50 457 | 94670,0,1,1,0,1,0,0,11/8/21,Graduate,Separated,2,0,92399.25,4797.75,10,11,10,7,16,17 458 | 72238,0,1,0,1,1,0,1,10/31/22,PhD,Separated,1,1,33592.79,16189.41,2,12,9,11,17,86 459 | 60265,0,1,1,0,1,1,0,7/15/22,Basic,Married,0,2,87831.49,8806.1,10,4,3,16,10,73 460 | 52893,1,0,1,0,1,1,0,2/17/22,High School,Separated,1,2,62628.17,18030.38,0,5,7,7,0,0 461 | 10124,1,1,1,0,0,0,0,11/20/24,Postgraduate,Separated,0,0,39745.69,4269.99,9,10,0,1,2,48 462 | 71590,0,1,0,1,0,1,0,4/2/25,Postgraduate,Married,2,2,53507.94,11858.02,7,13,10,4,1,93 463 | 15860,1,0,1,1,0,1,0,8/11/21,PhD,Divorced,1,0,36981.84,18887.15,2,12,6,4,19,17 464 | 89523,1,0,0,1,1,1,1,3/24/24,High School,Separated,2,2,38015.29,15665.93,0,12,8,17,8,8 465 | 27820,0,0,0,1,0,0,1,8/30/23,Graduate,Divorced,2,0,54314.23,18358.48,6,3,7,17,2,59 466 | 38792,0,0,0,0,0,0,0,12/9/24,Basic,Married,1,0,129020.08,19601.11,5,4,7,15,17,73 467 | 42301,1,1,0,0,0,1,0,3/30/23,PhD,Divorced,1,1,101106.6,14656.74,2,10,0,4,12,37 468 | 20917,0,0,1,1,1,0,0,9/8/22,High School,Divorced,0,1,86447.76,2611.51,2,14,7,16,5,23 469 | 53373,0,0,0,0,1,0,0,11/7/22,High School,Divorced,2,1,50594.35,11149.44,7,14,3,13,7,69 470 | 22532,0,0,0,1,1,0,0,11/19/22,PhD,Widow,2,0,89139.13,14204.98,8,13,6,3,14,61 471 | 12608,0,1,1,0,1,0,0,10/20/24,Basic,Divorced,0,1,94874.48,2144.87,2,5,3,20,11,41 472 | 83512,0,0,1,1,0,1,0,11/25/23,Postgraduate,Single,2,1,78592.16,3431.64,1,5,1,18,4,41 473 | 23529,1,1,0,1,0,1,0,9/30/24,PhD,Divorced,2,0,144370.43,2974.04,0,10,0,19,7,84 474 | 59651,1,1,1,0,1,1,0,5/15/22,PhD,Separated,2,2,37922.07,2138.28,5,14,10,17,16,58 475 | 12098,0,1,0,1,0,0,0,11/26/22,Postgraduate,Widow,1,1,53518.65,15497.59,10,5,0,19,14,35 476 | 84413,1,0,0,1,1,1,0,12/14/23,High School,Separated,2,1,90696.66,14511.52,2,4,5,16,18,25 477 | 56532,1,0,1,0,1,0,0,8/16/23,High School,Divorced,0,2,62319.27,1083.01,5,12,6,19,10,87 478 | 58133,1,0,0,0,0,1,0,2/22/23,High School,Divorced,1,1,60144.69,18717.5,6,14,9,12,6,17 479 | 55235,1,1,0,0,0,1,0,3/12/22,High School,Married,2,2,142144.36,7133.63,2,8,1,10,5,68 480 | 40055,1,1,0,1,1,0,1,11/28/21,High School,Widow,2,0,134200.12,4834.27,8,11,1,7,16,13 481 | 78955,1,1,1,0,1,1,1,2/4/24,PhD,Divorced,0,2,68452.97,10011.48,10,9,4,7,0,24 482 | 98307,1,0,0,0,1,1,0,2/5/24,High School,Separated,2,1,99837.24,5748.11,0,2,5,6,12,39 483 | 38079,0,1,1,1,0,1,0,10/21/22,High School,Single,0,2,50821.16,12577.9,3,13,7,16,3,30 484 | 54476,1,1,1,0,0,1,1,11/14/21,High School,Single,1,0,34223.06,7400.26,7,12,9,16,3,89 485 | 17112,1,0,0,1,0,1,0,10/24/22,High School,Separated,2,2,132774.41,7292.17,2,4,5,17,13,53 486 | 71423,0,1,1,1,1,1,0,6/27/24,High School,Married,1,2,70546.86,7077.06,10,8,8,20,10,93 487 | 59964,1,1,1,1,0,0,0,4/13/23,PhD,Single,0,2,81372.87,6979.75,2,0,0,1,4,39 488 | 96040,1,0,0,0,0,0,0,10/17/24,Basic,Divorced,1,0,55001.27,6817.54,7,1,8,6,20,81 489 | 44330,1,0,0,1,1,1,0,7/18/21,PhD,Separated,1,1,100372.02,19904.46,7,4,7,7,13,57 490 | 43775,1,1,1,0,0,0,0,6/23/21,PhD,Married,1,1,55098.1,19194.49,8,6,2,17,3,94 491 | 75163,0,0,0,0,0,0,0,12/25/24,Basic,Divorced,2,0,37078.14,14896.78,1,7,6,8,15,20 492 | 41381,0,1,1,0,0,1,0,10/30/24,Basic,Widow,0,0,137982.2,9861.55,5,8,5,17,20,34 493 | 58262,1,0,1,0,0,1,0,11/15/23,Basic,Separated,2,2,84428.79,18043.2,8,7,1,12,1,70 494 | 11640,0,1,0,1,0,0,0,10/14/24,High School,Married,1,2,117396.41,18553,10,5,0,10,5,22 495 | 93859,0,0,1,0,0,1,0,5/25/22,PhD,Single,2,1,58352.65,11237.85,3,5,5,20,11,89 496 | 83269,0,0,1,1,1,1,0,12/31/24,Postgraduate,Widow,0,0,126989.02,6151.93,0,10,4,8,18,99 497 | 64849,1,0,0,1,1,0,0,5/14/22,High School,Widow,0,0,36652.5,5306.54,9,14,8,15,3,27 498 | 41851,1,1,0,0,0,0,0,12/22/22,Basic,Married,1,1,117504.37,14659.13,0,6,2,1,1,33 499 | 96418,1,1,1,1,0,1,0,12/5/23,Graduate,Married,0,2,94198.75,18523.9,0,6,0,8,9,30 500 | 98804,1,0,1,0,1,0,0,12/19/23,High School,Married,2,2,137824.77,17418.05,0,4,10,18,5,99 501 | 85102,1,0,0,1,0,1,0,5/2/23,PhD,Married,0,2,128789.9,18277.26,9,13,7,2,6,87 502 | 92866,1,0,0,0,1,0,0,9/30/24,High School,Single,0,0,44169.51,13892.33,7,13,7,4,1,14 503 | 88600,0,0,0,1,1,0,0,4/2/22,High School,Single,0,2,116600.48,9206.57,5,14,3,6,19,95 504 | 57987,0,0,1,0,1,1,0,8/8/22,PhD,Single,2,0,84641.19,17599.01,4,3,2,9,5,12 505 | 41164,0,1,0,1,0,1,0,5/10/22,PhD,Divorced,0,0,101558.79,7823.75,0,6,5,11,12,8 506 | 49921,0,0,1,0,1,0,0,8/6/24,High School,Widow,0,0,63055.43,8765.31,5,10,8,11,12,39 507 | 74097,0,0,0,1,0,0,0,11/22/22,Postgraduate,Single,1,0,73418.71,3658.1,3,15,4,1,5,76 508 | 16188,0,1,0,1,1,0,0,12/23/24,High School,Married,1,2,108011.31,18071.84,5,10,4,5,20,82 509 | 50228,0,0,1,0,0,0,0,11/18/24,PhD,Divorced,1,1,97731.39,15484.04,7,11,6,9,19,8 510 | 78755,1,1,1,1,1,1,0,12/12/23,High School,Divorced,1,0,46423.1,16296.71,7,13,0,3,1,71 511 | 59017,0,0,0,0,0,0,0,8/5/21,Postgraduate,Separated,0,0,83919.09,5885.31,1,15,2,13,16,5 512 | 50337,0,0,1,1,0,0,0,10/3/24,Basic,Divorced,2,0,85614.64,4311.97,0,13,7,3,7,6 513 | 83753,1,0,1,0,0,1,0,3/4/25,PhD,Divorced,1,1,53939.34,8312.03,1,6,0,0,18,55 514 | 55138,0,1,1,1,1,1,0,8/8/21,Postgraduate,Single,0,0,97350.94,7008.37,10,6,9,12,5,56 515 | 44543,1,1,1,1,0,0,0,11/30/23,Basic,Separated,0,0,113937.27,9762.57,0,1,4,12,13,54 516 | 19197,0,1,1,0,0,0,0,2/10/24,PhD,Widow,2,1,44918.81,5420.17,0,8,9,7,17,98 517 | 10920,0,1,0,1,0,0,0,2/23/22,High School,Single,1,2,103061.91,1239.01,9,9,10,19,15,50 518 | 97894,1,0,0,0,1,1,0,3/22/23,Postgraduate,Widow,2,2,119988.81,2490.08,6,3,2,6,15,36 519 | 16075,0,1,1,1,0,0,0,4/16/23,Graduate,Widow,0,0,118436.16,19357.3,10,9,9,18,3,78 520 | 20742,1,0,0,1,0,1,0,4/4/22,High School,Single,0,1,36117.7,9484.46,8,5,1,2,13,7 521 | 25803,0,0,1,0,0,0,0,1/21/22,PhD,Separated,2,2,90534.81,19864.5,9,14,2,3,2,56 522 | 88108,0,1,0,0,1,1,0,7/11/24,High School,Married,0,1,143616.38,17727.11,8,7,9,14,16,47 523 | 15923,1,0,0,0,0,1,0,7/4/22,Postgraduate,Widow,1,1,109990.08,16661.81,2,6,6,18,2,38 524 | 72416,1,0,0,0,1,1,0,4/11/25,PhD,Widow,2,2,48052.44,14661.01,6,15,10,20,1,34 525 | 84622,0,1,0,0,1,0,0,10/16/23,Postgraduate,Single,1,1,35282.54,18041.95,10,7,2,15,9,86 526 | 81811,0,1,1,1,0,1,0,6/30/23,Postgraduate,Married,1,2,35420.11,12292.56,6,9,9,12,12,69 527 | 65346,0,1,1,0,1,1,0,8/13/21,High School,Single,1,0,48353.91,7091.35,6,4,2,11,18,97 528 | 43967,0,1,1,0,0,0,0,3/25/24,Postgraduate,Separated,2,2,57354.25,1301.21,6,4,4,12,15,19 529 | 22472,1,0,1,0,0,0,0,9/17/24,High School,Single,2,2,59794.22,13393.03,9,5,3,13,3,29 530 | 63924,0,1,1,0,1,0,1,3/7/23,Basic,Divorced,1,2,91325.23,8311.86,0,5,5,8,9,63 531 | 43078,0,1,0,0,1,1,0,8/9/21,High School,Divorced,1,0,124523.76,8619.62,1,15,7,2,16,16 532 | 40274,1,0,0,1,1,0,0,12/23/21,Basic,Widow,1,1,92036.59,19907.89,3,10,9,5,12,80 533 | 55767,1,1,1,0,1,1,0,6/16/21,PhD,Widow,2,1,112642.7,17401.28,5,10,9,8,10,84 534 | 67736,0,0,0,0,0,0,0,12/3/21,Postgraduate,Divorced,0,2,141449.8,17838.5,0,11,1,2,15,10 535 | 94590,1,0,0,1,0,1,0,6/20/22,Postgraduate,Married,0,2,99512.26,6889.25,9,1,6,15,0,6 536 | 91952,0,1,0,1,1,0,0,5/23/22,Postgraduate,Widow,2,1,124262.06,14940.92,8,13,6,15,13,6 537 | 22256,1,1,1,0,0,1,0,11/30/21,Graduate,Widow,1,1,94502.15,1069.09,7,9,5,7,6,100 538 | 78636,0,1,0,0,1,1,0,2/23/23,Postgraduate,Married,2,0,122450.96,7061.89,4,1,1,12,20,42 539 | 45203,1,0,0,0,1,0,0,3/23/25,Basic,Married,0,1,51725.27,15359.71,7,13,7,15,9,51 540 | 14779,0,1,0,1,0,0,0,5/21/21,Graduate,Single,0,1,139286.27,17109.84,4,12,0,0,17,1 541 | 38851,0,1,1,1,1,0,0,7/8/23,Basic,Married,2,2,135897.43,7488.12,0,8,7,4,13,33 542 | 21147,0,1,0,0,0,0,0,7/27/24,PhD,Divorced,0,2,121166.66,9835.08,3,13,5,18,12,96 543 | 53593,0,1,1,0,1,1,1,9/5/22,High School,Divorced,0,0,110609.9,14046.96,3,10,4,11,8,6 544 | 29482,0,1,1,1,1,1,0,11/27/22,Graduate,Married,0,0,43368.44,18917.05,2,14,4,17,19,97 545 | 44447,1,0,1,1,1,0,0,8/2/24,Graduate,Married,1,0,33684.61,17418.39,8,12,10,5,2,60 546 | 25654,1,0,0,0,1,0,0,8/31/22,Graduate,Divorced,2,2,53323.43,8038.95,3,10,8,16,4,34 547 | 21947,0,1,0,1,0,0,0,6/5/23,Basic,Separated,0,2,134710.44,9090.56,8,9,8,2,13,44 548 | 25399,0,1,1,1,1,1,0,9/28/21,Basic,Separated,0,2,135480.98,13924.46,7,6,7,17,3,1 549 | 84547,1,0,0,0,1,1,0,1/16/24,Postgraduate,Separated,2,2,81070.19,11933.35,8,4,9,1,9,40 550 | 38921,1,0,1,0,1,1,0,10/13/22,Graduate,Widow,0,0,127226.86,17306.03,9,12,4,5,8,16 551 | 30142,0,0,1,0,0,0,0,9/18/23,Basic,Married,0,2,109143.72,10249.4,6,5,9,6,5,66 552 | 35269,0,0,0,0,0,0,0,10/31/24,Basic,Married,0,0,136675.64,817.41,8,12,7,13,12,14 553 | 77893,0,0,1,1,0,0,0,2/25/25,Graduate,Married,1,1,125623.15,6130.9,3,9,5,20,7,82 554 | 33994,0,0,1,1,1,0,0,6/23/22,Graduate,Widow,2,1,129846.9,15907.06,0,0,3,0,14,96 555 | 71018,1,0,0,1,1,1,1,12/28/22,PhD,Widow,1,0,69768.89,18107.32,0,13,6,12,13,34 556 | 62102,0,0,0,1,1,1,0,3/20/22,Basic,Married,1,0,126931.54,11062.25,1,12,9,18,8,98 557 | 77731,1,0,0,1,0,1,0,1/29/25,High School,Divorced,2,0,75350.48,14649.5,9,12,2,12,19,58 558 | 78905,0,0,1,0,1,1,0,4/25/24,Basic,Divorced,0,1,126342.09,8860.71,5,14,1,18,14,48 559 | 13638,0,0,0,0,0,0,0,4/7/22,Graduate,Widow,2,2,99277.47,16705.22,2,12,0,9,2,70 560 | 31161,0,1,0,0,1,1,0,12/8/21,Graduate,Single,1,1,84830.18,14268.29,10,4,7,9,12,77 561 | 71873,0,0,0,0,1,0,0,6/20/21,PhD,Widow,0,1,61952.54,3673.02,9,15,5,16,18,9 562 | 12744,1,0,1,1,1,0,0,9/12/24,PhD,Divorced,2,1,74741.81,16752.06,0,10,6,16,4,0 563 | 49269,0,0,1,1,0,0,1,3/16/25,Basic,Single,2,1,56346.88,14230.47,5,13,4,1,14,21 564 | 97647,0,0,1,1,0,0,1,4/17/23,PhD,Divorced,2,1,30606.82,2234.71,8,5,2,8,14,30 565 | 93253,0,0,1,0,1,1,0,11/28/22,Basic,Single,1,2,106977.65,15184.63,0,2,1,18,11,0 566 | 57849,0,1,0,0,1,0,0,4/29/23,Basic,Divorced,0,1,36388.81,11605.48,3,1,0,14,2,55 567 | 34542,1,0,0,1,1,0,0,2/19/25,PhD,Divorced,2,2,69267.46,6214.19,10,4,9,16,14,52 568 | 24254,0,1,1,0,1,1,1,6/3/23,Basic,Widow,2,0,145120.34,15964.11,8,4,7,6,9,68 569 | 18795,0,0,0,0,1,1,0,1/26/24,Graduate,Separated,2,2,78393.8,13849.75,1,14,4,6,11,56 570 | 80330,1,0,1,0,1,0,0,2/17/24,Graduate,Separated,1,0,119439.83,9232.19,9,7,4,9,17,64 571 | 82767,0,1,0,1,0,1,1,11/17/21,Graduate,Widow,0,2,35691.94,7012.85,7,14,4,1,12,46 572 | 97534,1,1,1,0,1,1,0,9/3/21,PhD,Married,2,0,101287.99,11046.42,5,10,1,1,15,80 573 | 26349,1,0,0,0,0,0,1,11/9/24,Postgraduate,Separated,1,0,102123.24,13890.22,2,10,4,14,12,6 574 | 15210,0,0,0,1,1,0,0,7/13/22,Graduate,Single,2,0,149785.31,17912.89,10,4,2,14,10,100 575 | 12107,1,0,0,0,1,0,0,1/23/23,Basic,Divorced,0,2,141660.17,18176.86,5,8,7,20,19,43 576 | 87983,0,1,1,0,1,0,0,1/4/24,High School,Married,1,2,96089.34,18649.63,9,11,10,0,7,85 577 | 35899,0,0,0,1,1,0,1,2/8/23,Basic,Married,2,2,44222.1,5909.97,2,1,3,13,1,64 578 | 40719,1,0,0,1,0,0,1,9/25/22,Graduate,Separated,2,0,145181.32,7503.9,3,11,10,18,10,18 579 | 13727,0,1,1,0,0,0,0,6/16/22,Postgraduate,Separated,2,0,107295.04,8256.78,6,1,1,17,12,91 580 | 20550,1,1,1,0,0,1,0,2/14/25,High School,Married,2,0,98433.12,6135.03,9,11,3,0,0,50 581 | 21762,1,1,1,0,1,1,0,2/16/22,High School,Divorced,0,0,89187.63,19714.68,5,8,2,12,5,36 582 | 77944,0,1,1,0,0,0,0,8/21/22,PhD,Married,0,0,66925.83,17192.22,1,0,0,18,1,82 583 | 39640,1,0,1,0,0,1,0,12/9/24,High School,Single,0,0,40606.35,8155.35,3,4,9,0,9,86 584 | 77186,1,1,1,1,1,0,0,3/2/25,PhD,Separated,1,1,50098.45,1405.31,3,13,10,5,0,88 585 | 60644,0,1,1,1,0,0,0,8/7/22,Postgraduate,Single,2,2,45129.88,18192.95,1,2,5,5,7,91 586 | 55069,0,0,0,1,0,0,0,7/1/22,PhD,Married,0,1,138828.52,10726.44,1,15,0,11,18,12 587 | 57238,1,0,0,1,0,1,0,9/10/24,PhD,Single,2,2,135686.95,11595.01,7,15,2,3,12,71 588 | 66758,0,0,1,0,1,0,0,9/13/22,Postgraduate,Separated,0,2,139033.78,9852.04,2,8,5,9,1,84 589 | 82938,0,0,0,1,0,0,0,7/17/22,PhD,Married,2,1,94398.55,12692.81,3,4,10,4,1,32 590 | 24029,0,1,1,1,1,0,0,11/29/23,PhD,Single,0,1,146872.57,19152.89,9,6,0,4,20,94 591 | 16322,0,0,0,0,0,1,1,7/22/23,High School,Married,0,0,116032.74,10211.53,2,3,3,11,6,5 592 | 89478,0,0,1,1,0,1,0,7/10/23,High School,Single,2,0,71332.12,15964.42,3,14,7,14,11,50 593 | 63043,0,0,0,0,0,0,1,10/29/21,Basic,Widow,2,2,147393.62,1943.54,4,12,9,1,5,74 594 | 15353,0,0,0,1,0,1,0,10/11/24,Postgraduate,Widow,0,0,98098.69,16128.44,3,3,6,15,0,64 595 | 92216,0,1,1,1,1,1,0,11/7/24,Basic,Widow,0,2,77280.15,3482.95,9,12,5,11,1,38 596 | 73541,1,1,1,0,1,1,0,5/15/21,High School,Divorced,2,0,116073.57,11567.28,5,0,4,20,19,48 597 | 15719,0,1,0,0,0,1,0,7/26/21,Basic,Single,1,0,60687.17,15823.69,10,4,5,20,17,89 598 | 64416,1,1,0,0,0,1,1,10/20/22,PhD,Separated,0,2,104732.96,8447.5,10,7,4,2,19,73 599 | 57394,1,1,0,0,1,0,0,5/4/22,Graduate,Widow,0,1,110005.25,7611.07,3,7,3,8,6,4 600 | 37665,0,0,0,1,1,1,0,5/16/23,Graduate,Divorced,1,2,76848.52,13951.66,10,8,7,10,14,51 601 | 22691,1,1,0,1,0,1,0,5/1/21,Postgraduate,Divorced,1,1,141743.2,12095.62,8,12,1,13,6,35 602 | 48896,0,0,0,1,1,1,0,5/28/22,Basic,Separated,1,2,140047.8,6473.82,1,10,2,12,19,18 603 | 54451,1,1,1,1,1,1,0,6/29/24,High School,Single,1,1,46405.06,7674.88,9,3,4,5,12,15 604 | 85862,1,0,1,0,0,0,1,6/6/24,Basic,Divorced,1,1,55618.92,16287.07,7,13,3,14,7,13 605 | 65914,0,0,0,1,1,0,0,8/21/22,Basic,Single,1,2,95877.94,2301.37,8,8,5,5,6,46 606 | 11390,1,0,1,1,1,1,0,1/30/24,Postgraduate,Single,0,1,53279.47,19315.03,10,0,8,13,10,94 607 | 40037,0,0,1,1,1,0,0,8/9/24,Postgraduate,Divorced,1,0,53397.5,566.4,4,1,10,1,20,89 608 | 39952,1,0,1,0,0,1,0,7/9/22,PhD,Single,0,0,140492.98,9934.98,10,5,2,2,9,23 609 | 35873,1,1,0,0,1,0,0,12/3/21,High School,Separated,2,0,122308.73,4743.53,6,2,7,5,1,25 610 | 54914,0,0,1,1,1,0,0,7/13/21,Postgraduate,Married,2,0,102111.97,4136.5,10,15,3,11,4,92 611 | 21177,1,1,0,0,0,0,0,10/15/21,Postgraduate,Married,2,2,52812.49,10683.14,9,5,0,9,11,60 612 | 55358,0,1,0,0,0,1,0,7/18/24,PhD,Divorced,0,1,77651.52,18194.26,8,14,0,16,5,49 613 | 81658,1,0,0,0,1,0,0,10/9/24,Postgraduate,Single,0,1,41973.2,13073.72,8,7,8,17,20,23 614 | 77548,0,1,1,1,1,1,0,11/26/21,Graduate,Widow,1,0,47417.83,19603.72,3,4,2,5,16,2 615 | 47617,0,1,1,0,1,0,0,11/24/22,High School,Married,2,1,78603.29,7852.03,9,10,3,11,20,6 616 | 52390,1,0,1,0,0,0,0,5/18/21,Graduate,Widow,2,1,63093.57,16639.63,0,15,10,10,7,71 617 | 82945,1,0,1,0,0,0,0,4/29/22,Postgraduate,Married,1,2,36879.83,15798.63,10,12,3,18,15,84 618 | 78569,1,1,0,1,1,1,0,7/28/23,Graduate,Divorced,2,0,125913.86,18767.28,10,2,5,2,13,92 619 | 16016,1,0,0,1,1,1,0,6/19/22,PhD,Single,2,2,75117.03,19702.94,9,10,7,17,15,45 620 | 88842,0,0,1,0,0,0,0,7/30/21,Postgraduate,Married,2,0,38534.48,17404.11,7,3,2,2,4,39 621 | 13611,0,0,0,1,1,1,0,1/14/24,Postgraduate,Widow,0,2,144586.93,9754.05,1,5,6,15,15,29 622 | 59715,0,0,0,1,1,0,0,8/12/21,Graduate,Separated,2,2,104596.49,13150.59,6,11,3,20,6,80 623 | 12054,1,0,0,0,1,1,0,7/7/21,Basic,Separated,2,2,124276.53,12920.22,2,7,1,4,1,20 624 | 87063,0,0,1,0,1,1,0,8/5/23,Graduate,Divorced,2,2,84731.38,18472.39,7,0,0,0,4,53 625 | 42534,1,1,1,1,1,1,0,10/27/24,PhD,Widow,2,1,140802.87,17753.86,1,13,3,12,11,54 626 | 76429,0,1,1,0,0,1,0,8/15/24,Graduate,Married,1,0,34516.21,14912.74,4,13,9,5,14,56 627 | 62141,0,0,1,0,1,0,0,4/6/22,Basic,Divorced,2,1,116281.9,3995.3,5,3,2,16,5,59 628 | 24411,1,0,1,1,0,0,0,1/21/25,PhD,Divorced,2,2,131992.67,16889.67,5,12,5,9,13,84 629 | 80144,1,0,1,1,1,1,0,6/12/22,Graduate,Divorced,0,1,103438.41,8212.68,9,7,9,7,8,35 630 | 20034,1,1,0,1,1,1,0,1/3/23,PhD,Married,0,0,35729.31,6899.67,6,4,9,15,6,73 631 | 36882,1,0,0,0,0,0,1,6/1/24,PhD,Widow,0,0,112952.63,2745.3,7,1,7,10,9,66 632 | 59778,1,0,1,1,1,1,0,9/12/22,PhD,Married,0,0,59332.06,1336.76,4,13,4,12,2,18 633 | 18589,0,0,0,0,1,0,0,5/8/23,Basic,Married,2,1,69998.21,8172.9,5,12,3,10,18,43 634 | 40962,1,0,1,0,0,0,0,8/5/21,Graduate,Separated,1,0,61124.99,6881.49,6,7,1,2,7,69 635 | 63350,1,0,0,1,0,0,0,6/17/22,High School,Married,0,0,53388.34,9421.63,2,8,6,6,20,13 636 | 48727,0,1,0,1,1,1,0,7/14/22,PhD,Single,0,2,124976.37,19777.24,10,10,2,18,8,48 637 | 66781,0,0,0,1,1,1,0,11/20/23,Graduate,Married,0,1,54940.72,6711.47,2,7,8,11,1,20 638 | 17876,0,1,1,1,1,0,0,10/8/22,Graduate,Married,0,0,46017.19,19883.32,9,7,0,4,0,17 639 | 73072,1,1,0,0,0,0,0,3/21/22,High School,Married,1,1,86213.52,1820.56,1,13,7,11,0,56 640 | 99519,1,0,1,1,1,0,0,9/2/21,Basic,Married,1,2,74423.41,1592.98,2,7,9,17,8,62 641 | 67335,0,0,0,0,1,0,0,10/19/22,PhD,Divorced,1,0,143585.76,16091.32,0,2,7,5,5,84 642 | 30680,0,1,0,1,0,1,0,5/11/21,Postgraduate,Divorced,2,2,85508.39,15635.66,1,15,10,20,15,41 643 | 67286,1,1,0,0,1,0,0,10/23/24,PhD,Married,2,1,48490.34,13713.95,10,4,5,16,2,23 644 | 99266,0,1,0,1,1,1,0,12/7/23,Graduate,Divorced,2,2,118739.71,18969.51,7,7,10,6,14,73 645 | 59513,0,0,0,0,0,0,1,3/3/23,Basic,Separated,1,2,35035.64,14089.34,8,13,9,1,2,58 646 | 88480,0,1,1,0,0,0,0,6/23/23,PhD,Separated,2,1,113333.39,3974.95,5,3,7,3,5,22 647 | 54426,0,1,1,1,0,0,0,12/8/23,High School,Married,0,1,132858.6,3418.24,1,12,2,12,10,87 648 | 59133,1,1,0,0,0,0,0,7/1/22,Graduate,Divorced,1,1,135265.09,16753.52,4,15,8,19,18,42 649 | 61418,0,1,1,0,0,0,0,8/3/24,PhD,Widow,2,2,71923.3,12435.51,5,6,10,14,15,32 650 | 42433,0,1,1,0,0,1,0,7/24/23,Graduate,Married,1,1,86899.84,15998.41,2,11,9,4,0,53 651 | 34402,0,1,1,0,0,0,0,7/11/22,Graduate,Separated,2,0,48109.13,6073.83,1,10,6,6,1,19 652 | 17696,1,1,1,0,1,0,0,8/11/24,High School,Widow,0,2,116726.38,13449.57,2,1,6,12,2,42 653 | 75590,1,0,1,0,0,0,0,10/24/22,High School,Separated,0,0,62713.36,10921.44,3,5,1,20,2,22 654 | 30268,0,0,0,0,1,1,0,10/5/23,Basic,Divorced,2,0,134367.62,11097.08,8,15,6,9,0,13 655 | 74172,0,1,1,0,1,1,1,5/6/23,High School,Single,0,1,51319.36,18597.34,7,14,10,16,15,31 656 | 67519,1,1,1,1,1,1,0,5/29/23,Basic,Separated,1,2,45516.14,6275.79,2,3,9,4,13,18 657 | 32285,0,0,0,0,0,1,0,11/25/22,Basic,Divorced,0,2,60352.28,4730.68,1,9,7,16,18,98 658 | 64010,0,0,1,1,0,1,0,2/12/22,PhD,Separated,1,2,112878.56,2382.43,2,6,1,17,0,7 659 | 95752,0,1,1,1,0,0,0,7/10/21,Basic,Single,1,0,119759.49,11015.21,3,12,3,3,0,41 660 | 95218,0,1,0,1,0,0,0,6/28/21,Basic,Divorced,2,2,49420.66,8649.63,9,5,10,18,19,7 661 | 89850,0,1,1,1,0,1,0,6/26/24,High School,Married,0,2,149153.21,4532.78,8,12,10,6,6,61 662 | 30225,1,0,1,0,1,0,0,4/3/25,Basic,Married,1,2,66564.34,15650.94,0,2,3,4,16,20 663 | 31272,0,0,0,0,0,1,0,9/22/23,Postgraduate,Separated,2,0,93402.6,6844.65,0,12,7,15,12,12 664 | 84026,0,1,1,0,0,0,0,6/16/22,Graduate,Married,0,2,45292.28,12330.63,6,3,7,16,3,45 665 | 80174,1,0,1,1,1,0,0,10/9/24,Basic,Married,2,1,75224.45,14070.66,6,12,2,19,11,73 666 | 65145,0,0,1,1,0,1,0,2/11/23,PhD,Separated,0,1,69138.04,12245.71,10,13,10,20,0,97 667 | 24827,1,1,0,0,1,1,0,12/9/21,High School,Separated,1,1,100831.88,19941.81,1,14,9,19,12,62 668 | 52887,0,0,1,1,1,1,0,3/15/24,PhD,Separated,1,1,63095.95,19095.36,3,9,1,13,14,31 669 | 22077,1,1,1,0,1,0,1,9/12/24,Graduate,Separated,2,0,61826.53,6662.72,9,5,10,20,16,50 670 | 88756,0,1,0,0,0,0,0,2/7/23,Postgraduate,Single,1,1,64785.36,12235.67,0,0,8,5,8,68 671 | 21451,1,1,0,0,0,0,0,4/17/23,Basic,Divorced,1,1,110010.87,11046.71,3,15,4,3,17,44 672 | 35573,1,0,0,0,1,1,1,5/29/21,Basic,Single,0,0,111558.85,14543.98,10,12,1,18,19,45 673 | 16724,0,0,1,1,1,0,0,1/28/23,PhD,Married,1,2,109795.97,17995.45,4,13,7,18,1,64 674 | 23027,1,0,0,1,0,1,0,8/29/22,Basic,Single,0,2,120014.43,8840.67,1,1,3,8,9,38 675 | 42753,1,0,0,1,1,1,0,9/27/22,Postgraduate,Separated,1,1,49780.16,18829.97,6,4,2,3,10,44 676 | 43294,1,1,0,1,1,0,0,8/10/21,High School,Single,2,0,69814.23,4701.37,10,9,7,0,8,63 677 | 52972,1,1,1,1,1,1,0,6/28/21,High School,Separated,1,1,50486.69,17038.01,3,7,5,0,15,10 678 | 64765,0,1,1,1,0,0,0,1/22/24,PhD,Single,0,2,59896.42,2990.65,2,2,1,15,7,99 679 | 82533,0,1,1,0,1,1,0,9/9/21,PhD,Married,2,1,96682.21,14391.13,5,10,2,5,18,85 680 | 23579,1,0,1,0,1,0,0,10/19/24,PhD,Divorced,1,1,103793.73,9248.22,0,10,0,16,20,6 681 | 83265,1,1,0,1,1,1,0,11/10/23,Postgraduate,Married,1,2,55778.87,15561.36,4,14,8,9,3,59 682 | 86730,0,1,1,1,0,1,0,6/28/24,PhD,Married,0,1,57764.16,10455.63,0,7,3,3,16,27 683 | 27486,1,0,0,0,0,0,0,2/17/25,Basic,Separated,1,2,94193.75,3914.11,8,2,5,5,15,69 684 | 62295,1,0,0,1,1,0,0,9/3/23,Basic,Divorced,0,0,83420.44,6461.73,5,6,5,7,15,100 685 | 88027,0,0,0,1,1,1,0,9/10/22,Graduate,Widow,1,0,118226.48,9132.52,6,5,9,12,18,8 686 | 57023,1,0,0,1,0,1,0,5/19/21,Postgraduate,Divorced,1,0,103150.97,11714.75,0,1,1,1,17,76 687 | 39679,1,1,0,0,0,1,0,8/26/24,Basic,Separated,2,1,109628.53,6164.93,4,5,10,9,12,35 688 | 92795,1,1,1,1,0,1,0,3/12/22,Postgraduate,Divorced,2,2,98521.76,15207.56,2,3,9,1,5,18 689 | 77129,0,1,1,0,0,1,0,7/9/24,High School,Separated,1,0,85454.65,18561.49,9,9,4,10,3,34 690 | 32374,0,1,1,0,1,0,0,4/9/23,High School,Single,0,2,90333.3,15163.72,8,3,1,18,7,78 691 | 65009,1,0,1,1,0,0,0,9/25/24,PhD,Separated,2,0,130007.34,9183.25,9,1,5,10,4,71 692 | 33873,1,1,0,0,1,0,0,1/18/24,Graduate,Separated,2,0,120545.3,9561.25,0,10,3,4,12,2 693 | 78229,0,0,0,0,0,0,1,1/22/25,PhD,Married,2,1,66884.85,13103.21,5,11,1,7,13,5 694 | 74628,0,1,0,0,0,1,0,7/22/24,High School,Divorced,0,2,85324.62,8308.99,5,15,3,12,12,17 695 | 18797,1,0,0,1,1,0,0,3/4/24,High School,Separated,0,2,30853.73,7010.34,5,13,1,11,20,37 696 | 52099,1,0,0,1,1,1,0,9/5/23,Postgraduate,Separated,0,1,71606.08,17605.8,1,12,4,7,12,86 697 | 94370,0,0,0,1,1,0,0,4/15/24,High School,Separated,2,1,149612.22,13178.06,6,0,5,7,9,22 698 | 82033,0,1,1,1,0,1,0,9/12/23,Basic,Separated,2,1,145067.96,6804.93,3,10,8,20,9,52 699 | 25618,1,0,1,0,0,0,0,7/4/22,PhD,Single,0,1,147615.51,661.31,2,15,6,6,13,17 700 | 79673,0,1,1,1,1,0,0,1/12/23,PhD,Married,2,1,125309.63,18568.02,9,13,9,1,13,63 701 | 20676,1,1,0,0,0,1,1,7/19/24,PhD,Widow,1,1,120527.86,7336.8,3,10,8,14,4,62 702 | 87827,0,1,1,0,0,1,0,10/4/24,PhD,Single,0,1,130697.25,16504.84,0,6,4,6,18,64 703 | 62242,0,1,0,1,1,1,1,6/6/21,High School,Married,0,1,64075.76,9760.48,0,8,1,7,0,77 704 | 25247,0,0,1,0,1,0,0,8/7/22,High School,Widow,0,1,61740.88,3753.75,0,8,1,20,5,29 705 | 22255,0,0,1,1,1,0,0,9/10/24,Graduate,Single,0,0,114873.13,10147.58,9,8,1,18,6,20 706 | 29974,1,1,0,0,0,1,0,5/8/22,Graduate,Single,2,0,47303.81,5902.43,7,0,9,17,15,19 707 | 71454,1,1,1,0,0,0,0,4/15/24,Postgraduate,Divorced,0,0,64753.51,5667.79,10,6,2,13,3,13 708 | 26367,0,0,0,1,1,1,0,2/23/22,Postgraduate,Married,0,1,31439.9,11150.03,7,5,0,4,1,4 709 | 84677,1,0,1,1,0,0,0,2/8/25,High School,Widow,2,2,33741.27,7109.8,7,10,0,1,3,63 710 | 89114,0,0,1,0,0,1,0,4/23/24,Postgraduate,Single,0,0,108521.82,3847.47,1,3,3,17,12,72 711 | 90928,0,1,0,1,1,0,0,5/10/21,Basic,Divorced,1,2,122816.88,14997.89,1,15,1,18,5,70 712 | 93602,0,1,0,1,1,1,0,11/14/21,Basic,Married,0,1,65176.29,15837.11,2,5,5,4,2,82 713 | 66374,1,0,0,1,0,0,1,6/10/24,Graduate,Single,0,2,139663.49,13454.22,0,8,4,14,15,23 714 | 21803,1,0,0,0,1,1,0,3/15/25,High School,Married,1,1,39468.34,9579.56,4,0,10,4,8,44 715 | 18586,1,1,0,1,0,0,0,9/30/23,PhD,Single,1,2,99773.06,16911.08,2,13,8,12,9,93 716 | 36053,1,1,1,1,1,0,0,11/18/22,Postgraduate,Single,0,2,93062.27,6583.25,4,0,8,7,14,84 717 | 22743,0,0,0,1,1,0,0,3/12/24,Graduate,Single,0,1,98553.67,14658.94,8,6,6,19,20,16 718 | 81525,0,1,0,1,1,1,0,2/2/22,Basic,Separated,0,0,56880.17,19326.55,6,10,4,13,5,18 719 | 12727,0,0,0,0,0,1,0,12/4/23,High School,Single,1,1,30178.81,4558.68,3,0,0,0,0,70 720 | 76893,1,0,1,1,1,0,0,8/30/21,PhD,Single,0,2,92531.07,15478.3,9,2,1,8,3,51 721 | 45025,0,0,0,1,0,0,0,6/12/23,Postgraduate,Married,1,2,129800.3,13628.46,9,14,9,1,12,1 722 | 48801,1,1,0,1,1,1,0,3/23/25,Basic,Single,0,2,127912.28,19462.6,8,8,10,7,7,55 723 | 51260,0,0,0,1,0,0,0,1/26/23,Basic,Divorced,0,2,102495.49,13092.35,4,10,8,4,18,39 724 | 16340,0,0,0,1,0,0,0,5/22/24,Graduate,Married,1,1,79333.9,9206.32,6,8,4,18,8,51 725 | 86340,1,1,0,0,0,0,0,8/14/22,Graduate,Single,0,2,122398.92,18249.87,8,4,2,20,7,65 726 | 81504,1,1,0,0,1,0,0,11/29/24,Graduate,Separated,0,1,61449.59,6832.85,2,14,3,11,20,24 727 | 87519,1,1,0,0,1,1,0,3/11/25,Graduate,Single,0,2,41364.15,15546,2,10,1,17,3,87 728 | 24562,1,0,0,1,1,0,0,3/16/22,Postgraduate,Single,0,0,85757.79,3871.41,0,8,6,13,0,6 729 | 70770,0,0,0,0,1,0,0,10/26/21,High School,Married,0,2,118200.97,18541.99,6,2,5,2,4,3 730 | 18224,1,1,0,1,1,1,0,4/25/23,High School,Divorced,1,1,117048.73,19427.58,4,5,2,14,13,28 731 | 92627,1,1,0,0,0,1,0,8/9/22,Graduate,Widow,2,2,125825.95,19669.19,4,15,2,8,7,44 732 | 88435,1,0,1,1,0,0,1,12/14/24,Basic,Married,1,2,144325.69,8187.7,0,12,6,12,11,89 733 | 57828,1,0,0,1,0,1,0,11/3/24,Postgraduate,Separated,1,2,132751.11,5756.47,9,9,7,14,2,84 734 | 82503,0,0,0,1,0,0,0,12/16/23,Basic,Separated,1,2,97875.04,19871.72,2,4,3,10,8,26 735 | 86925,1,0,1,1,1,0,1,3/17/22,High School,Single,1,2,81938.84,5429.17,5,5,5,8,18,100 736 | 43471,0,0,1,1,1,0,0,3/31/22,Basic,Separated,1,0,95604.24,2066,2,14,3,0,13,29 737 | 91292,1,0,1,1,0,0,0,11/23/21,Graduate,Separated,1,2,32783.89,8400.46,4,0,5,3,3,5 738 | 54216,0,0,0,1,1,0,0,9/28/24,Graduate,Single,2,0,59656.15,1071.5,6,1,4,19,16,54 739 | 10886,0,0,1,0,0,1,0,7/13/23,Basic,Married,0,0,149940.3,4432.07,9,14,8,18,19,77 740 | 47175,0,0,0,1,0,0,0,11/12/24,Postgraduate,Single,0,0,120025.49,10179.45,1,14,10,17,13,5 741 | 71837,0,1,0,1,0,1,0,1/5/25,Graduate,Widow,2,1,134080.28,18464.66,10,3,1,6,14,43 742 | 49206,0,1,0,0,0,1,1,4/25/21,PhD,Separated,2,1,92710.35,567.53,2,14,9,1,7,59 743 | 14285,0,1,0,1,0,0,0,10/8/23,Postgraduate,Married,0,0,111221.53,17642.75,9,12,4,15,15,99 744 | 17037,0,0,1,1,0,0,0,1/24/22,Postgraduate,Separated,0,0,51847.34,6092.52,0,11,5,14,16,4 745 | 41780,0,0,1,1,0,0,0,8/1/24,Postgraduate,Married,0,0,76884.37,1242,6,12,8,11,14,33 746 | 93318,1,1,0,1,1,1,0,5/21/24,PhD,Single,0,2,131964.54,18330.62,5,4,1,10,1,94 747 | 77815,0,1,1,0,1,0,0,5/15/21,Basic,Married,0,1,35570.48,18849.83,10,8,2,9,6,89 748 | 58834,0,0,0,1,1,0,0,1/2/25,Basic,Divorced,0,2,81010.37,16282.69,1,0,5,7,1,18 749 | 76165,0,1,0,1,1,1,0,6/26/22,PhD,Widow,2,1,78513.59,15986.66,3,10,3,9,17,11 750 | 90881,0,0,1,0,0,1,1,12/12/24,Postgraduate,Divorced,0,1,90667.72,16263.5,6,3,5,15,7,70 751 | 13861,1,1,0,0,0,1,1,10/14/21,Graduate,Single,0,0,101073.04,15419,7,3,5,11,8,19 752 | 34856,1,0,1,0,0,1,0,11/20/24,Graduate,Divorced,1,2,131109.1,7711.33,4,13,7,14,4,4 753 | 54315,1,0,1,0,0,1,0,7/9/23,PhD,Widow,0,1,76112.89,5639.7,10,15,8,20,7,61 754 | 93002,0,0,0,1,1,1,1,8/5/24,PhD,Single,0,1,65580.82,1921.47,4,10,7,5,12,6 755 | 19887,0,0,1,1,1,1,1,5/3/23,Postgraduate,Married,2,2,130068.66,19942.12,7,13,6,8,4,1 756 | 33865,1,1,0,0,1,0,0,8/15/24,High School,Divorced,0,2,95818.78,18456.51,7,13,2,11,19,16 757 | 76682,0,1,1,0,1,0,0,7/11/24,PhD,Separated,1,2,111530.82,3939.12,5,11,3,8,0,44 758 | 62990,0,0,1,0,1,0,1,5/22/22,Graduate,Separated,1,1,144185.37,15641.98,5,6,10,20,10,52 759 | 82085,1,0,1,1,1,1,0,2/23/23,High School,Widow,0,2,114535.64,1038.61,8,8,2,5,15,26 760 | 22106,1,0,1,0,0,0,0,12/7/23,High School,Married,1,0,42770.02,1962.72,7,11,10,15,13,77 761 | 70667,0,1,0,1,0,0,0,2/11/25,High School,Separated,2,1,43682.41,5729.99,8,12,9,17,7,4 762 | 19451,1,0,0,1,0,0,0,12/7/23,High School,Married,0,0,119625.28,19900.59,9,0,1,8,5,86 763 | 30254,1,1,1,0,0,1,0,12/3/24,Basic,Widow,1,0,140590.94,16221.36,1,12,3,14,1,43 764 | 97363,0,0,1,0,0,0,0,11/6/23,Postgraduate,Married,1,1,75208.21,4865.6,1,14,2,18,8,100 765 | 52670,1,1,0,0,0,0,0,12/26/24,Graduate,Divorced,0,0,135717.17,12507.75,9,10,1,19,7,94 766 | 97737,0,0,1,0,1,0,0,11/25/23,Basic,Married,2,0,128669.57,4833.07,10,1,4,18,13,7 767 | 41951,0,1,1,1,1,1,0,5/13/22,Postgraduate,Married,2,2,66446.69,19555.99,9,15,4,19,20,75 768 | 68158,1,0,1,0,1,1,0,9/19/23,Postgraduate,Widow,1,1,131578.5,923.16,0,0,10,3,18,21 769 | 56201,0,0,0,0,1,1,0,10/29/23,High School,Separated,0,1,61085.77,11662.24,0,7,2,5,11,24 770 | 32071,0,0,0,0,0,0,0,3/27/24,Basic,Married,1,2,126643.28,7209.02,0,3,9,7,10,36 771 | 24701,1,0,0,1,1,0,0,1/2/22,High School,Married,0,1,131976.23,8185.23,2,13,2,0,17,3 772 | 92037,1,0,0,0,1,1,0,1/10/23,PhD,Single,1,1,120162.82,8160.03,9,4,0,3,4,31 773 | 32511,1,1,0,0,1,1,0,7/19/22,Graduate,Divorced,0,0,138769.73,7986.65,9,8,10,2,18,26 774 | 45703,1,1,0,0,0,1,0,10/28/21,High School,Single,1,1,57854.3,8315.25,6,8,7,10,4,85 775 | 82376,1,1,1,0,0,0,0,7/15/24,Postgraduate,Single,1,0,33173.96,11636.1,2,8,5,4,8,7 776 | 87483,0,0,0,0,1,0,0,2/12/24,Graduate,Single,1,0,62508.69,14536.01,2,3,9,20,3,38 777 | 58804,0,0,1,1,1,0,0,6/3/23,PhD,Married,1,2,118949.88,17269.41,2,2,8,17,7,90 778 | 74278,1,1,0,0,0,1,0,7/28/21,Postgraduate,Widow,0,1,89125.12,7952.97,9,6,3,7,5,75 779 | 87480,1,1,0,1,0,0,0,5/20/22,PhD,Divorced,1,1,36879.61,10635.42,10,11,8,16,6,94 780 | 82016,0,1,1,1,0,1,0,5/24/21,Basic,Separated,1,1,87870.6,13340.01,2,11,4,6,20,40 781 | 80230,1,0,1,0,0,0,0,6/22/24,Graduate,Single,0,2,52002.12,14722.05,10,12,2,9,14,22 782 | 13300,1,0,1,0,0,0,0,3/8/22,High School,Married,0,1,40462.74,11609.77,2,0,0,19,20,53 783 | 18394,1,0,1,0,0,1,0,9/5/21,Graduate,Divorced,0,2,73356.78,4287,1,15,4,20,1,2 784 | 51332,0,0,0,0,0,0,0,10/18/22,High School,Separated,2,0,46136.25,6308.63,7,2,1,18,7,48 785 | 55338,0,0,1,0,0,1,0,1/30/22,Basic,Divorced,1,1,78027.99,18507.64,6,1,7,13,4,50 786 | 61159,1,0,1,0,0,1,0,5/22/22,High School,Married,1,1,40815.81,16610.17,10,4,3,3,6,25 787 | 81804,1,0,0,1,1,0,1,10/12/22,Graduate,Married,0,2,41897.01,7304.84,4,3,3,14,5,59 788 | 76256,1,1,1,1,1,0,0,11/27/23,High School,Divorced,2,1,51607.29,19506.22,4,13,1,15,10,43 789 | 73941,0,1,0,0,1,0,0,11/7/22,High School,Single,0,1,97243.43,9183.28,4,8,1,15,5,28 790 | 20291,1,1,1,1,0,1,0,8/6/23,Postgraduate,Separated,0,0,73072.25,6221.73,8,11,3,12,4,53 791 | 30681,0,1,1,0,0,1,0,2/22/24,Graduate,Single,2,0,147510.34,562.65,5,7,8,10,20,40 792 | 36647,1,1,0,0,1,1,0,8/28/22,PhD,Separated,0,0,99786.13,4797.3,8,2,4,13,1,39 793 | 92538,1,1,0,1,0,1,0,1/22/22,PhD,Divorced,1,0,84844.37,8225.5,6,2,8,14,20,44 794 | 87982,1,1,1,1,0,1,0,1/12/23,Postgraduate,Separated,0,1,109094.59,17704.23,1,11,8,19,18,4 795 | 87803,0,0,1,1,0,0,0,3/27/22,PhD,Divorced,1,0,113700.52,13171.34,8,12,0,9,7,46 796 | 30913,1,1,0,1,0,0,0,10/17/22,Postgraduate,Divorced,1,2,91103.53,4619.84,6,15,10,14,6,2 797 | 92501,1,1,0,0,0,0,0,4/21/23,Postgraduate,Separated,2,1,123132.18,12479.01,8,14,8,20,16,55 798 | 51441,1,0,1,0,1,1,0,4/8/22,PhD,Widow,0,1,111568.48,3798.62,8,15,6,0,10,81 799 | 57124,1,0,1,0,0,0,0,1/7/25,Basic,Divorced,2,2,80710.49,14153.1,1,12,10,14,0,52 800 | 67069,1,0,0,0,0,1,0,11/26/23,PhD,Single,1,0,143220.65,11814.22,2,2,10,18,17,20 801 | 87843,1,0,0,1,1,0,0,3/26/22,High School,Widow,0,1,118504.53,18480.07,8,13,4,19,8,21 802 | 32302,1,1,0,0,0,0,1,11/5/24,Basic,Married,1,1,112450.55,12974.81,0,3,4,11,18,78 803 | 92265,1,1,0,1,0,0,0,11/28/23,PhD,Widow,0,0,74143.73,19926.57,8,5,4,7,17,65 804 | 55297,1,0,0,0,0,1,0,10/11/23,Basic,Widow,1,2,133203.89,608.82,10,12,10,8,7,86 805 | 61511,0,1,0,0,1,1,0,11/26/21,PhD,Married,2,2,41178.43,528.72,7,1,1,15,1,76 806 | 91038,0,1,1,0,1,0,0,6/9/23,PhD,Separated,0,2,106874.63,17390.29,1,14,2,12,4,67 807 | 77095,0,1,0,1,1,1,0,9/9/24,Basic,Single,1,0,91960.82,13365.54,5,5,10,20,14,80 808 | 52085,1,0,0,1,0,1,1,8/10/21,Basic,Separated,1,0,50578.86,9708.83,6,4,0,17,1,67 809 | 13182,0,0,0,0,1,0,0,6/11/21,High School,Widow,0,2,138162.59,5658.97,4,6,5,19,12,80 810 | 57486,0,1,1,1,0,0,0,6/13/24,Graduate,Married,1,0,108915.53,7791.73,6,0,9,2,4,28 811 | 46353,1,1,0,0,0,1,0,9/22/22,Postgraduate,Single,0,2,75206.7,15340.38,0,4,4,7,12,40 812 | 20308,0,1,0,1,1,0,0,4/10/22,Postgraduate,Divorced,2,0,113989.48,16395.47,5,8,10,0,7,51 813 | 74139,1,0,0,1,1,1,0,10/14/21,Graduate,Divorced,2,1,79333.66,19981.72,2,7,6,1,12,51 814 | 57563,1,1,1,0,0,1,0,3/14/24,PhD,Widow,2,0,46186.61,1484.71,4,5,9,16,12,23 815 | 22820,0,1,0,0,1,1,0,3/18/23,Postgraduate,Separated,2,1,51230.49,17904.54,6,4,9,19,17,31 816 | 74415,0,1,1,1,1,1,0,9/22/24,High School,Divorced,1,0,66132.95,5317.65,8,5,0,16,8,77 817 | 79886,0,0,1,0,0,0,0,1/15/25,High School,Widow,0,0,30530.28,1796.72,4,1,10,14,12,18 818 | 38126,0,1,0,0,1,0,0,3/23/25,Basic,Single,0,2,127383.29,10166.27,2,15,2,1,18,78 819 | 24499,1,1,1,1,0,0,0,10/19/24,Basic,Divorced,2,2,107875.74,1584.42,2,7,5,7,0,72 820 | 84087,1,1,1,0,1,1,0,12/21/22,PhD,Separated,0,2,148528.48,7715.42,8,4,0,2,15,84 821 | 35355,1,0,0,0,1,1,0,1/10/25,PhD,Married,0,0,54135.34,758.81,4,4,10,17,20,32 822 | 78113,0,1,0,0,0,0,0,4/24/21,Graduate,Separated,1,2,31728.06,6229.23,10,9,1,5,10,72 823 | 20632,1,0,0,0,0,1,0,4/20/22,Postgraduate,Widow,1,0,149249.34,8051.23,2,10,1,6,9,2 824 | 26420,0,1,0,1,1,1,0,7/25/22,High School,Single,2,0,91264.01,8445.61,2,6,10,19,14,90 825 | 64377,0,0,1,1,0,0,0,8/9/22,Postgraduate,Single,1,1,97108.85,11243.16,1,7,3,3,2,60 826 | 23722,1,0,1,1,1,1,0,9/14/24,Postgraduate,Single,2,0,148504.6,15753.58,10,4,0,7,1,19 827 | 63151,1,1,1,1,0,1,0,6/12/21,High School,Married,2,0,55105.7,752.71,7,15,8,15,3,76 828 | 79628,0,1,1,0,1,1,0,8/15/21,Postgraduate,Single,0,1,63816.47,16909.79,7,8,5,3,9,40 829 | 48442,0,1,1,0,1,1,0,3/31/24,Graduate,Separated,2,0,147971.78,6801.21,6,1,1,16,11,13 830 | 41209,0,0,1,1,1,0,0,2/3/22,PhD,Divorced,0,1,85193.64,6093.56,6,15,0,20,11,7 831 | 76117,1,0,0,0,0,0,0,2/15/25,Graduate,Single,0,1,68404.59,19084.88,0,7,10,9,11,48 832 | 73622,0,0,0,0,0,1,0,10/23/22,Graduate,Married,1,1,84248.44,7110.53,5,10,7,16,20,17 833 | 18449,1,0,0,0,0,1,1,6/2/22,High School,Widow,0,1,43212.2,10744.03,10,12,2,16,20,48 834 | 42698,1,1,1,1,0,0,0,8/20/22,Basic,Married,2,2,71345.11,19874.74,7,14,2,13,16,12 835 | 86834,0,0,0,0,1,0,0,5/4/24,High School,Married,0,2,120440.14,519.04,6,5,5,7,18,32 836 | 92887,0,1,1,1,1,1,0,12/9/24,PhD,Separated,0,1,94652.67,10831.82,3,2,4,0,1,29 837 | 66069,1,1,0,0,0,0,0,1/16/23,Postgraduate,Widow,0,1,110128.55,17486.03,0,9,7,18,17,48 838 | 19022,1,1,0,1,1,0,0,5/28/22,High School,Divorced,0,2,80139.15,17083.46,6,10,6,9,20,26 839 | 48083,1,1,1,0,0,0,0,10/16/21,High School,Divorced,2,1,51251.2,12160.71,10,9,6,14,6,50 840 | 40605,0,0,1,0,0,0,0,2/7/23,Graduate,Widow,0,0,86290.74,15012.29,2,0,3,6,12,39 841 | 29757,0,1,1,1,0,0,0,10/21/24,High School,Divorced,2,2,56708.36,19945.9,0,7,1,1,3,49 842 | 75818,1,0,1,1,0,0,0,4/27/24,Graduate,Divorced,1,0,64812.51,551.88,10,10,7,19,12,19 843 | 30086,0,1,0,0,0,1,0,12/9/24,Basic,Single,1,2,147891.48,8584.15,0,1,2,12,0,37 844 | 47026,1,0,0,1,1,1,0,11/11/23,PhD,Single,2,1,136944.96,3114.9,4,8,1,0,13,49 845 | 95639,1,1,1,1,0,0,1,11/8/24,Postgraduate,Divorced,0,0,139341.03,5839.21,3,6,5,8,8,1 846 | 55993,1,0,0,0,1,0,0,5/13/23,Graduate,Single,0,0,109346.79,997.92,5,0,6,13,14,12 847 | 32311,1,1,0,0,0,0,0,6/30/23,Basic,Divorced,1,2,121242.77,7458.22,1,5,9,17,5,37 848 | 51731,0,0,0,0,0,1,0,2/17/25,High School,Single,2,2,133143.12,13761.27,2,3,7,12,17,0 849 | 81443,0,0,1,1,0,1,1,8/28/22,Graduate,Single,1,2,57356.77,14392.88,4,2,3,19,9,30 850 | 34775,0,0,0,1,0,0,0,5/29/22,PhD,Widow,0,0,97338.46,10363.65,4,9,0,7,15,82 851 | 56298,1,1,1,0,1,0,0,8/6/21,PhD,Divorced,2,2,32298.92,4882.91,0,13,10,8,3,36 852 | 64347,1,0,0,0,1,0,0,1/28/24,Basic,Divorced,2,2,132592.91,13385.42,5,0,5,3,17,16 853 | 78174,0,1,1,0,0,0,0,12/25/22,Postgraduate,Divorced,1,1,86462.43,13972.01,9,3,1,15,3,62 854 | 65626,1,0,0,0,1,1,0,7/16/23,PhD,Divorced,2,1,69546.47,18701.91,3,15,4,8,19,48 855 | 62610,1,1,0,1,0,0,0,4/11/23,Basic,Divorced,1,1,42424.43,630.26,1,0,3,13,16,76 856 | 42020,0,0,1,0,1,0,0,11/22/24,PhD,Married,1,0,78258.46,3166.28,9,10,7,14,11,16 857 | 17132,1,1,1,0,0,0,0,10/20/21,Postgraduate,Widow,2,0,85191.99,3414.66,10,2,3,10,7,62 858 | 92425,1,1,0,0,0,0,0,6/25/24,PhD,Divorced,0,2,74003.39,14939.7,6,1,8,20,17,44 859 | 34522,0,0,1,0,1,0,0,4/6/25,Graduate,Separated,2,1,130292.1,6439.32,5,8,7,6,20,57 860 | 99601,1,0,1,0,1,1,0,10/19/21,PhD,Divorced,1,1,147199.49,7059.64,7,14,3,19,12,12 861 | 66595,0,0,1,0,1,1,0,11/20/21,Postgraduate,Separated,2,2,41588.52,1497.98,0,9,1,15,10,54 862 | 54704,1,0,0,1,0,0,0,11/19/24,PhD,Separated,1,2,84470.8,1832.91,6,3,6,10,16,59 863 | 28682,0,0,1,0,1,0,0,10/3/22,Graduate,Separated,1,1,149607.43,18385.38,6,14,1,4,12,41 864 | 71811,1,0,0,0,0,0,0,11/11/22,PhD,Separated,2,2,91066.94,18280.03,2,8,9,18,2,79 865 | 99348,1,0,1,0,1,1,0,8/16/22,High School,Widow,2,0,37405.4,15998.62,3,14,6,6,3,67 866 | 84683,1,1,1,1,0,1,0,1/27/24,Postgraduate,Separated,1,1,95692.39,18478.34,1,8,4,0,0,7 867 | 94383,1,0,1,1,1,0,0,11/23/24,Basic,Single,2,1,67481.83,15622.28,7,15,2,18,13,67 868 | 50017,0,0,1,1,0,1,1,9/16/23,Graduate,Divorced,2,2,88250.81,16545.25,9,4,5,3,20,76 869 | 30255,1,0,1,1,1,1,0,11/13/23,Graduate,Separated,1,2,114269.57,18830.4,3,15,1,1,10,24 870 | 41026,0,1,0,1,1,1,0,6/19/21,Graduate,Married,2,1,30242.7,12885.99,7,9,3,0,8,7 871 | 46369,0,1,1,0,0,1,0,2/6/23,Basic,Widow,2,1,131606.69,9564.54,2,15,0,14,3,19 872 | 40986,1,1,0,1,1,0,0,3/5/25,Graduate,Single,1,1,51413.75,14077.86,2,0,4,6,16,70 873 | 79198,0,1,1,1,1,0,1,5/12/21,High School,Widow,0,0,118589.11,6584.21,1,12,10,7,18,8 874 | 49614,1,1,1,1,1,0,0,8/8/23,Basic,Divorced,1,1,148659.52,10491.27,10,1,10,6,13,79 875 | 26305,0,1,0,1,1,0,0,7/22/23,High School,Widow,0,0,60883.96,14180.65,7,7,1,4,4,98 876 | 50359,0,1,1,1,1,1,1,10/1/24,Graduate,Single,1,0,118306.02,3985.82,2,3,6,18,8,28 877 | 17340,1,0,0,1,0,1,0,3/29/25,Basic,Divorced,2,0,58771.25,19356.82,0,10,7,7,5,14 878 | 47612,0,1,1,1,1,1,0,12/25/24,Graduate,Married,1,0,67836.34,1088.36,0,11,10,11,15,1 879 | 12447,1,0,1,0,1,1,0,9/2/22,Graduate,Divorced,0,0,122692.82,7201.62,10,13,10,5,10,71 880 | 16989,1,0,1,0,1,1,0,3/4/22,Postgraduate,Married,1,2,71500.62,7640.99,7,14,5,2,18,25 881 | 12398,1,1,1,0,0,0,0,5/23/24,High School,Divorced,1,1,93595.25,887.93,1,15,6,5,3,25 882 | 56168,1,1,0,1,1,1,0,2/28/22,High School,Widow,1,0,48140.21,3252.18,10,8,7,1,16,12 883 | 85067,0,0,1,0,0,1,0,3/21/24,PhD,Separated,1,0,122927.26,11333.35,10,8,3,14,9,70 884 | 94493,0,0,1,0,1,0,0,7/23/23,Basic,Separated,0,1,91798.21,16225.36,9,0,5,20,16,5 885 | 15779,0,1,0,0,0,1,0,6/13/24,Basic,Divorced,0,2,130661.86,2689.97,0,10,6,7,10,82 886 | 66515,1,0,0,0,0,0,0,3/3/24,Postgraduate,Separated,1,1,32644.85,3173.39,10,2,2,9,13,57 887 | 95363,1,1,0,0,0,1,0,6/28/21,High School,Widow,0,0,84844.46,16270.15,9,8,9,10,19,9 888 | 20484,0,1,0,0,0,0,0,7/22/21,Graduate,Widow,1,1,146234.2,7402.06,1,13,6,11,15,65 889 | 57870,0,0,0,1,1,0,0,8/24/21,PhD,Separated,2,1,64884.21,2458.33,4,11,0,16,12,18 890 | 47651,1,0,0,0,0,0,0,6/19/24,High School,Divorced,2,2,140095.1,8018.19,1,5,6,3,6,85 891 | 26409,1,1,1,1,1,1,0,10/20/22,Basic,Divorced,2,0,99193.05,6589.7,0,7,5,8,20,35 892 | 96130,0,1,1,0,0,0,0,6/2/23,Postgraduate,Divorced,1,2,53537.34,7723.89,9,13,2,14,13,88 893 | 58317,0,0,0,1,0,0,0,6/3/22,Postgraduate,Divorced,0,1,44731.9,4514.28,9,11,5,20,15,71 894 | 96378,0,0,0,0,0,1,0,1/31/22,Basic,Widow,0,2,65544.07,17417.17,0,4,6,18,9,34 895 | 35352,1,0,1,0,1,0,1,7/15/24,Graduate,Married,2,0,33678.09,4730.77,3,3,8,13,13,59 896 | 32889,0,1,1,1,1,1,0,4/11/22,High School,Single,2,0,39536.77,17513.41,4,0,8,7,16,32 897 | 33747,0,1,0,1,0,0,0,10/6/24,Postgraduate,Widow,2,1,124301.77,15182.24,10,7,3,9,4,17 898 | 22944,1,0,0,0,0,1,0,4/2/23,Postgraduate,Widow,1,0,84718.56,5418.96,7,10,0,5,3,84 899 | 48004,1,0,0,0,1,1,0,1/26/25,Basic,Married,2,1,52752.53,4990.71,6,15,8,16,9,22 900 | 62356,0,1,1,1,0,1,0,12/24/24,PhD,Divorced,1,1,36717.32,7974.72,3,5,8,10,7,16 901 | 87278,0,0,1,1,1,0,0,2/17/24,Postgraduate,Separated,1,1,119535.65,9179.56,7,2,8,18,16,100 902 | 41743,1,0,1,0,0,1,0,5/13/21,Graduate,Divorced,2,0,114511.58,1760.54,8,10,7,5,7,80 903 | 94442,1,0,0,1,1,0,0,11/9/21,PhD,Widow,0,1,110962.05,10351.04,9,12,5,9,18,79 904 | 11368,1,1,0,1,0,0,0,8/31/21,PhD,Single,0,0,41690.07,5482.69,8,13,10,13,19,7 905 | 25243,1,0,1,0,1,1,0,1/7/22,Graduate,Single,1,2,122066.28,15342.49,9,12,7,3,20,89 906 | 10373,0,0,0,1,1,1,1,10/19/21,Graduate,Married,2,0,88052.5,13631.06,10,9,9,12,13,44 907 | 78559,0,1,0,0,1,0,0,2/10/22,PhD,Widow,0,2,69826.49,4010.81,3,8,3,7,10,29 908 | 38221,1,1,1,1,1,1,0,3/17/22,Graduate,Married,1,1,93843.9,9901.08,0,6,10,2,0,38 909 | 77230,1,0,1,0,1,1,0,2/2/22,High School,Separated,0,1,40240.69,13377.06,9,1,7,8,12,50 910 | 99143,1,1,1,1,0,0,0,1/3/23,High School,Divorced,0,1,76805.78,10258.81,3,1,3,4,11,96 911 | 75312,0,1,1,0,1,1,0,6/28/21,Postgraduate,Widow,0,1,117515.41,9753.7,8,2,0,15,5,66 912 | 39256,0,1,1,1,1,1,0,9/10/24,Basic,Married,2,1,40580.1,12383.17,10,6,10,12,7,13 913 | 31064,1,0,1,1,1,1,0,3/2/23,PhD,Single,2,1,37635.97,12300.54,5,12,6,6,0,7 914 | 89596,1,0,1,0,0,0,0,11/23/21,Basic,Widow,1,0,83439.98,9515.25,10,1,4,16,3,11 915 | 28144,0,1,0,1,0,1,0,10/31/22,High School,Widow,2,0,121850.6,12116.09,9,12,7,14,15,89 916 | 35662,0,1,1,1,0,1,0,5/27/23,High School,Separated,2,1,103078.09,12225.77,1,10,4,17,5,61 917 | 39535,0,0,0,0,0,0,0,7/18/24,High School,Widow,2,2,53404.3,16399.73,6,0,2,2,7,55 918 | 75062,1,0,1,1,1,0,0,1/30/24,PhD,Single,2,1,50733.8,12695.38,5,2,9,11,13,96 919 | 41646,0,1,1,1,1,0,0,7/24/24,Graduate,Divorced,0,2,95615.46,11349.26,10,0,6,6,0,100 920 | 85255,0,0,0,0,0,1,1,4/21/21,PhD,Widow,2,2,89742.36,7804,3,11,9,20,13,38 921 | 67350,1,0,1,1,1,1,0,6/16/22,Graduate,Divorced,0,1,112133.8,17988.89,3,3,0,3,17,45 922 | 44267,0,0,1,1,1,1,0,10/28/24,Graduate,Single,2,2,70610.21,2666.73,0,10,8,0,3,0 923 | 98712,1,1,1,1,1,1,0,11/5/22,PhD,Married,1,2,127358.29,14732.52,6,12,3,12,20,37 924 | 76419,1,1,0,0,0,1,0,9/15/23,Postgraduate,Divorced,2,2,79010.86,4962.98,7,4,2,13,5,64 925 | 12630,0,0,1,0,0,0,0,1/24/24,High School,Separated,2,1,59384.85,19745.39,0,10,2,3,19,37 926 | 76743,0,1,0,1,1,0,0,10/13/23,Basic,Separated,1,2,97640.71,3694.99,1,8,5,18,15,57 927 | 78364,0,1,1,0,1,0,0,2/14/23,Basic,Widow,1,1,147238.03,5020.99,6,11,3,19,14,82 928 | 45991,1,1,1,1,1,1,0,1/31/25,Basic,Separated,2,1,145102.77,5646.95,9,12,1,13,7,72 929 | 29649,1,0,1,1,1,1,0,4/20/22,Graduate,Married,1,2,48937.31,3261.26,8,8,8,14,14,47 930 | 15448,1,0,0,1,1,1,0,6/3/22,PhD,Married,0,2,52487.89,12095.65,10,0,0,10,18,1 931 | 91477,1,1,1,0,1,0,0,11/15/23,PhD,Divorced,1,0,68702.37,11561.86,9,7,4,9,5,37 932 | 57891,1,0,1,0,0,0,0,9/23/21,High School,Single,1,1,58409.46,7751.26,3,11,9,15,10,74 933 | 99640,0,1,0,0,0,1,1,4/8/22,PhD,Widow,2,1,121668.86,6841.72,7,4,7,6,14,54 934 | 70908,0,0,1,1,0,0,0,10/9/24,Postgraduate,Widow,0,2,89942.21,11602.96,7,10,5,0,1,87 935 | 34637,1,1,1,0,1,0,0,6/11/24,High School,Separated,2,2,33264.3,2092.95,7,7,2,4,12,45 936 | 36493,0,1,0,1,1,1,0,5/23/23,Graduate,Separated,1,2,39409.13,7348.96,7,3,1,14,14,40 937 | 60121,0,1,0,1,1,0,0,4/20/22,PhD,Separated,1,2,34115.88,10367.4,8,9,0,15,10,91 938 | 42096,1,0,0,1,0,1,0,5/18/24,Postgraduate,Married,1,2,56031.01,12574.67,5,1,7,0,11,10 939 | 16946,0,0,1,1,1,0,0,8/4/24,High School,Separated,0,1,76575.2,5225.47,7,0,2,15,16,42 940 | 30406,0,1,0,0,1,0,0,7/19/22,High School,Widow,0,1,58960.06,17480.52,6,6,9,1,16,47 941 | 16933,0,0,1,1,0,1,0,7/13/24,PhD,Single,1,1,49425.98,10946.74,6,10,10,13,1,51 942 | 31240,1,1,1,1,1,1,0,6/27/22,Graduate,Divorced,0,0,68214.17,15028.48,10,14,7,4,11,1 943 | 15428,1,0,0,1,0,0,0,12/9/22,Graduate,Divorced,2,1,48149.84,7180.94,8,9,5,15,20,19 944 | 35529,0,0,0,1,0,0,0,7/2/21,Basic,Divorced,0,1,118217.08,18758.23,4,7,1,12,12,1 945 | 40272,0,1,0,0,1,1,0,1/4/24,Postgraduate,Single,0,2,64143.93,14240.25,7,11,0,1,1,39 946 | 94441,1,1,0,0,0,1,0,5/4/23,PhD,Separated,2,0,141222.34,7996.33,9,9,9,7,10,55 947 | 29025,1,0,0,1,0,0,0,12/26/24,Postgraduate,Married,0,1,73538.4,15538.7,1,15,0,16,7,30 948 | 55505,1,0,1,0,1,1,0,11/24/24,PhD,Widow,1,2,59734.17,12345.64,0,1,4,11,6,75 949 | 40418,0,1,1,1,0,1,0,8/22/23,Basic,Separated,1,0,118648.85,1493.13,9,14,3,2,15,1 950 | 39542,0,1,1,0,0,0,0,5/9/23,PhD,Divorced,1,2,43029.82,4022.35,1,11,0,3,12,32 951 | 38596,1,1,1,1,0,1,0,7/31/24,Basic,Married,1,0,80604.45,2981,6,14,6,5,5,100 952 | 55218,1,1,1,1,1,1,0,10/22/24,Basic,Separated,2,2,104027.43,9301.37,7,7,10,7,2,97 953 | 48927,1,1,0,1,0,1,0,1/15/22,Basic,Divorced,1,1,40507.62,8639.16,0,8,4,3,11,23 954 | 73694,1,1,1,1,0,1,0,10/20/23,Graduate,Divorced,1,0,109183.16,16865.95,1,7,7,17,1,32 955 | 85580,1,1,0,0,0,0,0,8/7/22,PhD,Widow,1,2,49773.74,18029.61,7,13,0,15,9,84 956 | 56027,0,1,1,1,1,0,0,12/3/23,Postgraduate,Single,0,1,52953.81,3520.99,5,10,6,2,17,25 957 | 10379,0,1,0,0,0,1,1,8/29/24,Graduate,Divorced,0,0,99076.74,7355.66,2,11,2,15,10,0 958 | 32492,1,1,0,1,0,0,0,10/12/24,Postgraduate,Single,0,1,52577.68,6483.63,10,5,10,19,6,62 959 | 72317,0,0,0,1,1,0,0,8/14/21,High School,Married,1,0,83859.04,18359.86,6,14,4,13,19,42 960 | 84742,0,1,1,1,1,1,0,10/8/21,High School,Separated,1,1,102851.64,4280.71,1,6,6,15,11,81 961 | 34348,1,0,1,1,1,0,1,10/25/24,Graduate,Married,1,2,80723.05,9063.23,8,6,2,1,6,9 962 | 53174,1,0,0,0,1,1,0,9/8/22,Graduate,Married,1,0,139816.68,11314.27,8,7,3,6,3,83 963 | 95848,1,0,0,0,1,1,0,8/15/24,Basic,Married,0,1,46220.64,1637.26,1,7,8,16,19,82 964 | 59006,1,0,0,0,1,1,0,3/24/22,High School,Separated,1,2,139090.63,7509.85,3,8,1,10,18,10 965 | 27088,0,1,1,0,1,0,0,7/13/24,Postgraduate,Divorced,0,1,77661.37,19036.2,6,5,2,9,15,70 966 | 37831,0,0,1,0,0,1,1,1/10/23,Basic,Married,1,0,144656.17,2944.77,0,9,0,2,10,61 967 | 72605,1,0,1,0,0,0,0,4/27/24,PhD,Widow,2,1,52072.26,19142.31,8,12,6,11,1,68 968 | 27853,0,1,0,1,0,0,0,3/26/25,Postgraduate,Married,1,0,36470.03,13875.12,7,2,4,6,18,2 969 | 38306,0,0,0,0,0,1,0,3/3/23,Postgraduate,Married,1,0,87885.1,12833.58,10,9,7,9,16,41 970 | 34901,1,0,0,0,0,1,0,8/9/23,Basic,Divorced,0,0,101526.5,16610.5,2,4,9,14,5,98 971 | 89783,1,1,0,0,1,0,0,5/24/22,Graduate,Widow,1,1,58166.94,2163.69,8,12,4,11,19,34 972 | 50637,1,0,0,1,0,1,0,2/18/23,High School,Widow,1,2,130300.41,5222.55,5,11,10,20,11,82 973 | 85518,0,1,1,0,1,1,0,9/22/23,Postgraduate,Widow,0,2,99726.86,14769.04,1,14,2,3,20,9 974 | 19836,0,1,0,1,0,0,0,12/27/24,Graduate,Widow,2,1,61757.05,2752.87,1,9,6,0,2,79 975 | 70336,0,0,1,0,0,1,0,6/4/21,High School,Widow,0,2,98045.63,2006.61,6,8,2,12,12,29 976 | 73381,0,0,1,0,1,0,1,8/11/22,High School,Married,0,0,37656.79,2171.44,7,3,9,18,9,40 977 | 71147,1,1,0,1,0,1,0,1/18/24,Basic,Separated,2,0,30681.56,16685.89,5,13,7,7,20,45 978 | 37017,0,1,0,1,1,0,0,1/18/24,Postgraduate,Married,1,0,61647.95,5176.26,0,4,2,16,12,61 979 | 32021,0,0,1,1,1,1,0,4/10/23,Graduate,Separated,0,0,73959.89,9192.15,7,9,1,9,18,5 980 | 34768,1,1,0,1,1,0,0,5/28/24,Postgraduate,Separated,2,1,113291.78,13714.95,5,5,6,3,10,67 981 | 84072,1,0,1,0,0,1,1,11/4/23,Graduate,Single,0,0,60966.99,14604.72,1,5,4,16,9,46 982 | 18189,1,1,1,0,0,0,0,6/26/22,Postgraduate,Divorced,0,2,136962.63,510.65,6,12,9,10,9,40 983 | 97404,0,1,1,0,0,1,0,11/2/23,Basic,Single,1,1,135951.94,2242.04,0,12,1,6,20,4 984 | 51419,0,0,0,1,0,0,0,9/30/22,PhD,Single,1,0,33553.61,5265.62,5,15,9,12,11,54 985 | 79264,1,0,0,0,0,1,0,3/19/25,High School,Widow,2,0,115152.92,17526.4,8,4,4,2,3,53 986 | 13393,1,0,1,0,1,0,0,8/14/22,Graduate,Widow,1,1,109065.39,18881.26,2,8,8,4,4,29 987 | 68471,0,1,1,0,0,0,0,5/7/24,PhD,Divorced,1,0,56917.9,16230.53,9,11,1,20,10,78 988 | 46017,0,0,1,1,1,1,0,6/21/21,Postgraduate,Divorced,1,1,68970.21,15344.37,3,3,6,8,18,40 989 | 82587,0,1,0,1,1,0,0,2/11/22,High School,Widow,2,0,141761.8,13016.28,4,13,3,20,3,96 990 | 22931,0,1,0,1,1,1,0,5/5/23,Postgraduate,Single,0,2,126087.39,10774.32,0,8,1,10,20,35 991 | 75050,1,1,1,1,0,0,0,1/26/23,High School,Divorced,2,2,132786.25,9641.55,6,15,0,3,13,64 992 | 54958,1,0,1,0,0,1,0,7/13/23,Graduate,Single,1,0,125419.53,4678.2,0,2,1,16,10,79 993 | 46048,1,1,1,0,1,0,0,9/25/21,Graduate,Divorced,0,2,72012.08,18473.16,2,2,8,8,14,93 994 | 31436,0,1,1,0,1,0,0,2/1/22,PhD,Separated,2,0,64562.41,8807.41,10,3,1,19,11,34 995 | 45202,0,1,0,1,0,0,0,2/12/22,High School,Single,0,0,145203.2,16522.76,7,10,9,4,17,19 996 | 36890,0,0,0,0,0,1,0,4/20/21,Postgraduate,Married,1,0,128947.68,14314.65,3,11,6,3,13,22 997 | 55579,1,0,1,0,0,1,0,6/29/24,Postgraduate,Widow,0,0,54186.01,13185.88,3,15,4,5,3,86 998 | 97399,1,0,0,1,0,0,0,1/11/23,High School,Widow,1,0,74353.91,10409.61,1,9,2,10,1,84 999 | 53878,1,0,1,0,0,1,0,1/3/24,PhD,Widow,1,2,126087.2,14269.6,3,12,4,5,15,26 1000 | 51635,1,1,1,0,0,0,0,4/21/23,Basic,Married,2,0,40208.07,6752.08,1,2,3,20,6,6 1001 | 61760,0,0,1,1,0,1,1,12/4/22,Basic,Married,1,1,146921.52,5938.93,8,9,1,5,6,17 1002 | 17646,0,0,0,0,0,1,0,4/2/24,PhD,Divorced,2,1,37108.14,15478.65,9,9,3,18,3,31 1003 | 71718,1,0,1,1,0,1,0,2/13/23,Basic,Married,0,1,127890.93,14941.77,2,4,6,20,1,66 1004 | 40069,0,0,1,0,1,0,0,5/10/23,Basic,Widow,2,1,62394.82,12172.08,8,7,8,0,6,56 1005 | 11152,1,1,0,0,0,0,0,9/15/24,Postgraduate,Single,2,1,98983.55,8215.93,4,11,5,9,0,56 1006 | 40994,1,0,0,1,0,1,0,10/29/23,PhD,Separated,0,0,140066.33,11259.51,1,3,8,0,17,22 1007 | 89559,0,1,0,0,1,1,0,4/27/23,PhD,Married,1,2,88359.32,7405.08,7,2,5,13,9,12 1008 | 71989,1,0,0,0,1,0,0,3/22/23,Postgraduate,Widow,0,2,136820.02,14113.04,3,7,1,3,18,100 1009 | 78579,0,1,0,0,1,1,0,1/17/23,Basic,Separated,0,1,54667.4,2724.73,3,8,10,1,7,62 1010 | 39611,1,0,1,0,0,1,0,12/23/23,High School,Single,1,0,127290.05,11823.84,0,0,4,11,16,16 1011 | 10788,0,1,0,0,1,0,0,7/15/22,Postgraduate,Divorced,0,1,81791.89,5765.32,7,7,2,11,10,7 1012 | 45731,1,1,0,0,1,0,0,4/22/22,Postgraduate,Separated,2,0,45526.27,16381.68,2,1,9,11,15,70 1013 | 65975,0,0,1,1,0,0,0,5/16/24,Postgraduate,Separated,2,1,63321.52,8990.99,7,2,7,8,9,23 1014 | 56574,0,1,1,0,1,0,1,10/17/23,Graduate,Divorced,1,1,125707.74,5971.86,9,5,0,3,12,82 1015 | 40139,1,1,0,0,0,0,0,5/10/24,Graduate,Single,1,0,71893.75,12768.36,0,12,10,15,0,73 1016 | 68050,0,1,1,1,1,1,0,12/7/24,Basic,Single,0,0,136735.2,1229.5,4,13,4,11,8,79 1017 | 97196,1,1,0,1,1,1,0,9/22/22,Postgraduate,Single,1,2,99715.45,3268.4,4,12,6,4,10,56 1018 | 82018,1,0,0,0,0,1,0,12/11/23,Basic,Single,2,0,50476.6,17902.8,6,11,2,11,1,71 1019 | 18979,0,1,1,0,1,0,0,9/8/21,Basic,Widow,0,1,60806.74,12823.09,10,8,7,15,12,24 1020 | 61311,0,0,0,0,0,0,0,3/23/25,Postgraduate,Widow,0,0,73466.58,19286.47,1,13,8,8,9,35 1021 | 78216,1,1,1,1,0,1,0,7/11/24,Basic,Separated,0,0,91018.62,15566.58,1,7,7,20,4,34 1022 | 34424,1,1,0,1,1,0,0,1/5/24,Graduate,Divorced,2,2,149094.41,890.3,0,9,6,10,17,32 1023 | 71631,1,0,0,1,1,0,0,9/28/22,Postgraduate,Married,1,1,109929.52,13634.68,7,13,4,18,15,26 1024 | 72218,1,0,1,0,1,0,0,4/30/22,PhD,Single,2,1,44714.32,2972.54,8,14,4,14,13,58 1025 | 83381,1,1,1,0,0,0,0,5/9/23,Graduate,Married,1,1,45141.55,11748.88,10,6,1,14,13,74 1026 | 37408,1,0,1,1,1,1,0,11/9/23,Basic,Divorced,2,1,118039.08,4644.86,9,13,7,1,9,35 1027 | 83583,1,1,1,0,1,0,0,6/18/22,Postgraduate,Widow,0,0,146311.6,19922.43,0,2,5,11,9,32 1028 | 34865,1,0,1,1,1,0,0,8/4/24,High School,Widow,1,1,142055.13,11216.7,5,10,5,20,15,49 1029 | 40277,1,0,0,1,1,0,0,3/9/22,Graduate,Single,2,1,144241.71,15716.77,10,14,2,15,15,16 1030 | 13789,0,1,1,1,1,0,1,3/28/23,PhD,Separated,1,0,118245.73,13079.72,6,12,0,7,13,54 1031 | 87168,0,0,1,1,1,1,0,11/3/22,PhD,Divorced,0,2,73243.05,1604.34,7,12,1,13,11,28 1032 | 30797,1,1,1,1,0,1,0,1/1/23,Postgraduate,Divorced,2,0,100370.28,8097.03,10,13,9,10,0,87 1033 | 35385,1,0,1,0,1,0,0,5/2/21,PhD,Single,1,1,103565.89,10838.17,7,4,3,1,15,85 1034 | 56421,0,0,0,0,0,1,0,1/4/25,PhD,Single,0,0,143633.02,9866.34,3,5,6,18,5,97 1035 | 59970,1,1,0,0,0,1,0,3/3/25,Graduate,Separated,2,0,44158.03,14038.17,9,9,6,8,1,31 1036 | 68845,0,0,0,1,0,1,0,8/5/23,Graduate,Separated,1,0,147274.17,13243.73,0,6,3,18,1,47 1037 | 54931,1,1,1,1,0,0,0,11/14/21,Graduate,Married,2,2,54936.98,9892.59,10,9,2,16,16,38 1038 | 51847,1,0,1,1,0,1,0,11/29/22,Graduate,Single,2,0,31167.44,19632.05,2,14,7,4,2,58 1039 | 97659,0,0,1,0,1,0,1,1/23/22,Graduate,Divorced,0,0,80942.23,12539.09,2,8,10,20,9,86 1040 | 72608,0,1,0,1,1,1,0,9/26/23,High School,Separated,2,2,98327.47,5733.82,9,2,2,16,13,56 1041 | 95614,0,1,1,1,1,1,1,11/6/22,Basic,Single,1,0,83173.63,14246.72,2,12,9,12,10,23 1042 | 92169,1,0,1,1,0,0,0,5/8/23,Basic,Married,0,1,32422.53,2011.11,0,14,1,18,2,17 1043 | 26554,1,0,0,1,0,0,0,5/11/22,Postgraduate,Separated,0,1,104438.86,4829.34,5,10,0,2,18,26 1044 | 10773,1,0,0,0,0,0,0,8/1/24,PhD,Divorced,1,1,83150.44,4892.34,1,7,7,1,1,92 1045 | 77497,0,1,0,0,1,0,0,9/10/21,Postgraduate,Divorced,1,2,83115.85,18888.97,10,6,2,0,4,2 1046 | 39352,1,0,0,0,0,0,0,7/19/21,PhD,Widow,1,1,129804.25,15823.99,7,8,4,16,5,22 1047 | 76260,1,0,1,0,0,0,1,9/4/23,Graduate,Separated,1,0,33754.76,11782.89,6,13,6,14,13,22 1048 | 69284,1,0,0,1,1,0,1,8/3/24,Basic,Divorced,2,1,44761.06,8593.03,9,10,4,10,20,64 1049 | 51009,1,1,1,1,1,1,0,5/10/24,Basic,Married,2,2,84024.39,7219.16,4,5,7,16,18,27 1050 | 34764,0,1,0,0,0,1,0,7/25/22,Postgraduate,Divorced,1,2,94115.38,6145.86,7,9,9,9,13,88 1051 | 59100,1,1,1,0,0,0,0,5/18/21,High School,Single,1,0,76169.58,6863.99,9,9,5,6,17,70 1052 | 36276,0,0,0,0,1,1,0,10/20/24,High School,Divorced,1,1,91763.41,3657.27,2,13,5,15,11,38 1053 | 51373,0,0,0,0,0,0,0,5/6/23,Postgraduate,Widow,0,2,39358.63,7625.8,7,15,0,3,9,11 1054 | 33469,0,0,1,0,1,0,0,5/26/24,Graduate,Married,2,1,49254.96,1929.55,5,14,6,10,1,73 1055 | 79136,0,1,0,1,1,0,0,10/16/21,Postgraduate,Separated,0,0,134610.4,7727.54,0,2,0,0,20,74 1056 | 56863,1,1,0,0,0,0,0,9/17/21,High School,Married,0,2,75008.44,8241.11,4,4,7,20,13,80 1057 | 19724,0,0,0,1,0,0,0,10/26/24,Postgraduate,Widow,2,0,113397.14,2458.93,4,7,1,1,14,43 1058 | 37996,0,1,0,1,0,1,0,4/21/22,High School,Widow,2,1,46083.38,4804.45,6,8,6,3,9,25 1059 | 77765,0,0,1,1,1,0,0,7/18/21,PhD,Divorced,1,0,72691.68,6045.53,7,8,3,17,18,37 1060 | 76967,1,1,0,1,0,1,0,7/28/24,PhD,Widow,2,2,143112.85,11092.4,2,9,5,11,9,3 1061 | 27213,1,0,0,0,0,0,0,10/19/24,PhD,Separated,1,0,43141.89,16678.19,7,14,8,10,8,47 1062 | 66092,0,0,1,1,1,1,1,6/28/23,Postgraduate,Separated,0,0,58429.74,7098.87,3,0,0,20,4,83 1063 | 40308,1,1,0,1,0,1,0,4/9/24,High School,Widow,1,2,119561.33,2557.46,5,5,4,11,16,92 1064 | 77813,1,0,0,0,0,0,0,5/3/23,Graduate,Single,1,0,110044.24,7614.37,3,13,2,9,18,80 1065 | 96739,0,0,1,0,1,1,0,11/13/21,Graduate,Married,2,1,136308.68,9774.14,7,2,3,10,19,27 1066 | 75673,1,0,0,1,1,0,0,5/12/22,High School,Widow,0,2,80943.63,5972.11,10,6,1,13,2,98 1067 | 11441,0,0,0,1,1,1,0,9/17/23,Postgraduate,Widow,1,0,67738.93,8241.83,7,4,0,18,14,95 1068 | 49018,1,0,1,1,0,0,1,3/31/22,PhD,Divorced,2,1,92158.47,11524.78,4,9,1,13,9,73 1069 | 96238,0,0,0,0,0,1,0,2/26/24,PhD,Divorced,1,0,50417.78,15096.1,2,3,8,1,12,91 1070 | 12220,0,0,1,0,0,0,0,3/15/24,Basic,Separated,1,0,47353.68,13353.91,6,11,6,2,6,88 1071 | 42276,0,1,0,1,0,1,0,7/3/23,Graduate,Divorced,1,0,33558.6,9552.01,4,10,10,14,15,60 1072 | 37642,1,0,1,1,1,0,0,7/2/22,Basic,Widow,0,0,120932.19,17770.2,3,7,6,6,1,42 1073 | 58779,1,1,1,1,1,0,0,7/3/24,Basic,Married,1,0,84778.03,19775.88,6,13,0,12,18,82 1074 | 43767,1,0,1,0,1,0,1,6/28/22,Postgraduate,Separated,2,1,74694.27,17260.52,3,4,6,8,15,34 1075 | 42278,0,1,0,0,1,0,0,4/21/23,Graduate,Married,2,1,92446.35,10538.55,3,10,9,12,1,0 1076 | 23082,0,0,0,0,1,0,0,7/18/23,High School,Separated,2,0,101090.02,14363.03,9,4,7,2,18,12 1077 | 70312,0,0,1,0,0,1,0,1/30/22,Basic,Separated,0,1,118442.58,9403.7,0,3,8,1,16,55 1078 | 46134,1,1,0,0,1,0,0,12/24/22,Basic,Divorced,0,2,92199.73,4884.49,8,3,6,13,10,95 1079 | 91077,0,1,1,0,0,0,0,2/1/25,PhD,Widow,1,2,59517.25,5166.14,2,15,10,13,5,100 1080 | 62873,1,1,0,1,1,1,0,5/15/21,PhD,Married,1,1,120414.58,8765.24,5,3,6,6,19,14 1081 | 32138,1,0,1,0,1,1,0,1/17/25,High School,Widow,0,2,41160.22,14892.26,3,9,1,7,10,3 1082 | 19093,0,0,1,1,0,1,0,8/28/23,PhD,Widow,2,2,82917.38,9194.11,10,9,6,10,9,90 1083 | 77372,1,0,0,1,0,0,0,2/10/22,High School,Divorced,1,2,95196.1,10047.36,7,8,0,11,1,27 1084 | 94042,0,1,0,0,1,1,0,5/18/22,PhD,Widow,1,2,80174.75,11471.81,6,7,8,15,8,37 1085 | 86722,1,1,0,0,1,0,0,7/18/23,High School,Separated,2,2,59868.56,17346.43,1,0,6,11,14,17 1086 | 75653,1,1,1,1,1,0,1,1/31/25,Basic,Widow,1,2,35910.97,5108.06,9,7,3,12,14,87 1087 | 11853,1,1,1,1,0,0,0,1/30/25,PhD,Divorced,0,2,86294.42,2879.07,1,5,0,0,6,85 1088 | 89664,1,0,0,1,0,1,0,12/27/21,Basic,Widow,0,2,117587.01,3136.59,0,6,1,0,13,50 1089 | 17862,1,1,1,1,0,1,0,6/1/24,PhD,Widow,2,2,104663.07,10756.08,8,9,5,16,20,90 1090 | 58882,1,1,1,1,0,0,0,11/13/21,PhD,Single,1,2,131114.08,8626.36,10,1,6,18,5,28 1091 | 16877,1,0,0,0,0,1,0,3/7/25,Postgraduate,Widow,2,1,95039.83,13375.48,9,9,2,0,0,29 1092 | 75735,0,1,1,0,0,0,0,4/26/22,High School,Divorced,0,0,62709.75,3783.79,7,13,5,3,7,27 1093 | 51562,1,0,1,1,1,1,0,11/22/22,PhD,Widow,2,0,149259.04,11028.87,10,5,9,15,1,38 1094 | 90749,1,0,1,1,1,0,0,7/28/21,PhD,Widow,1,1,75209.99,2713.92,10,6,5,9,20,41 1095 | 27263,1,0,1,0,0,1,0,8/11/24,PhD,Married,2,1,97016.68,16781.35,9,11,3,2,5,6 1096 | 31548,0,1,0,1,0,0,0,8/29/23,Graduate,Single,2,1,33841.49,8386.84,4,5,3,15,7,27 1097 | 32461,0,0,1,0,1,0,0,10/4/23,Postgraduate,Single,2,2,137832.54,17486.86,6,5,1,15,15,77 1098 | 12997,1,0,0,0,0,0,0,2/18/23,Postgraduate,Divorced,2,0,39708.86,7142.73,1,15,5,0,15,87 1099 | 59860,1,0,0,0,0,1,0,7/5/23,Graduate,Separated,1,0,104678.06,8685.05,10,10,1,19,12,50 1100 | 39089,0,1,0,1,1,1,0,7/2/22,Postgraduate,Divorced,2,2,138208.51,8953.06,8,13,2,6,14,65 1101 | 43315,1,1,1,0,1,0,0,6/5/23,PhD,Separated,2,0,132512.38,13286.77,9,2,7,12,15,0 1102 | 15040,1,0,0,0,0,1,0,8/19/23,Postgraduate,Married,2,1,66730.09,4069.25,8,9,3,0,0,28 1103 | 11815,1,0,1,1,1,1,0,10/6/24,Graduate,Separated,0,2,121535.59,11040.05,8,9,9,20,16,62 1104 | 61348,1,0,0,0,0,0,0,5/2/24,PhD,Widow,2,1,33076.05,13858.47,7,11,5,15,17,47 1105 | 30397,1,1,1,1,1,0,0,4/10/23,High School,Divorced,2,1,64544.34,852.28,8,3,3,4,4,22 1106 | 81995,1,1,1,0,1,0,0,7/6/24,Postgraduate,Widow,1,0,113065.73,7782.32,0,8,5,11,20,82 1107 | 87238,1,0,0,1,0,1,0,5/31/22,High School,Divorced,1,1,81416.47,4427.98,8,4,10,16,1,61 1108 | 11880,0,0,0,0,1,1,0,6/7/21,PhD,Widow,1,0,105475.35,13277.23,3,12,9,15,19,31 1109 | 11165,0,0,0,0,0,1,0,9/22/23,PhD,Separated,0,2,116366.54,8271.87,9,4,5,16,3,57 1110 | 12290,0,1,0,1,1,0,0,10/9/22,High School,Divorced,0,1,126992.27,12882.11,10,7,7,17,15,29 1111 | 20907,1,0,0,0,1,1,1,4/14/22,PhD,Widow,0,2,53636.46,4567.93,4,5,9,19,11,7 1112 | 30237,1,1,0,0,1,1,0,8/15/22,Basic,Single,1,1,82844.02,14949.55,7,13,7,7,19,61 1113 | 54007,0,0,0,1,1,1,1,10/22/21,PhD,Separated,2,0,56064.98,7550.24,7,8,0,13,5,32 1114 | 77792,0,1,0,0,0,0,0,1/15/25,PhD,Widow,1,0,76743.86,2619.19,8,8,9,3,19,68 1115 | 10573,1,1,0,0,0,1,0,12/1/22,High School,Divorced,1,0,61729.04,8357.64,3,11,7,4,12,84 1116 | 89531,1,0,0,1,0,1,0,4/20/24,PhD,Divorced,2,0,33241.96,12627.36,9,13,6,18,5,100 1117 | 84384,1,1,0,1,0,1,0,4/27/23,Graduate,Separated,1,2,56818.65,14556.28,7,11,4,7,9,41 1118 | 98310,0,1,1,1,0,0,0,3/25/23,PhD,Separated,2,1,84782.95,3415.23,2,13,4,12,2,96 1119 | 51465,1,0,0,0,0,0,0,9/18/23,High School,Widow,1,2,37186.11,17013,6,15,6,18,7,68 1120 | 89195,0,1,0,1,1,0,0,12/2/23,Postgraduate,Separated,2,0,44041.92,8979.34,1,4,0,15,10,0 1121 | 85100,0,0,1,0,1,1,0,7/13/21,Graduate,Separated,0,2,137243.95,3557.13,2,8,4,10,11,55 1122 | 50669,1,0,0,0,0,0,0,2/4/25,PhD,Separated,1,1,122014.7,4195.95,9,11,10,2,14,51 1123 | 25039,0,0,0,0,0,1,0,3/23/24,Graduate,Separated,0,1,31591.24,14891.16,10,13,9,11,2,1 1124 | 93132,1,0,1,0,0,0,0,9/11/21,Postgraduate,Divorced,0,2,126830.54,13138.49,1,14,3,0,12,73 1125 | 22044,0,0,0,1,1,0,0,7/25/23,PhD,Separated,2,2,136655.66,8232.21,8,7,0,20,5,70 1126 | 28331,1,0,1,0,0,1,0,3/3/24,Postgraduate,Single,2,0,119285.66,11633.65,2,13,0,8,17,45 1127 | 76977,1,1,1,0,1,1,1,7/21/21,PhD,Married,2,1,72385.06,6503.11,1,5,7,20,1,87 1128 | 61680,1,1,1,1,1,0,0,7/3/23,Postgraduate,Married,0,0,132948.09,18568.69,0,11,6,0,18,83 1129 | 45578,1,1,1,0,1,0,0,2/27/24,High School,Married,1,0,80045.98,12753.32,6,13,0,11,0,44 1130 | 12540,0,1,1,1,1,1,0,4/6/25,PhD,Married,1,2,113945.42,5348.67,1,9,7,8,7,59 1131 | 84324,0,0,0,1,0,0,0,10/18/24,PhD,Widow,0,2,44969.93,3192.67,6,8,7,2,9,23 1132 | 85449,1,0,0,0,0,0,0,7/26/24,PhD,Single,0,1,107764.19,15357.33,2,7,7,14,6,80 1133 | 35353,0,0,0,0,0,0,1,9/5/24,Graduate,Married,0,1,94081.34,15481.02,0,6,4,12,6,25 1134 | 80669,0,1,1,0,1,1,0,7/13/23,PhD,Widow,1,2,34088.69,13338.75,5,1,0,1,11,71 1135 | 62162,1,0,0,1,1,1,0,2/2/25,Basic,Single,1,1,56504.58,11098.22,5,14,8,3,17,81 1136 | 71167,0,1,1,1,0,1,0,2/2/23,PhD,Single,1,2,120923.12,12874.74,1,9,4,13,13,11 1137 | 49463,1,1,0,1,0,0,0,4/5/23,Postgraduate,Divorced,1,0,139283.93,8122.33,8,11,4,10,10,30 1138 | 66061,1,0,1,1,1,1,0,12/18/21,PhD,Divorced,2,2,35466,13422.78,4,9,2,7,17,13 1139 | 86528,1,0,0,1,1,1,0,7/9/23,Graduate,Separated,2,0,93179.1,8145.56,4,12,2,17,16,29 1140 | 32549,1,0,1,0,1,0,0,6/22/23,Graduate,Single,2,0,39267.9,19579.78,5,13,1,3,14,0 1141 | 55473,0,1,1,1,0,1,0,5/17/22,PhD,Married,1,0,119777.58,4919.92,9,3,7,12,2,78 1142 | 88364,1,0,0,1,1,1,0,12/10/24,Basic,Married,2,2,120153.11,14076.25,10,15,4,0,16,57 1143 | 73007,1,1,1,0,1,0,0,9/26/22,High School,Separated,2,1,42522.48,9998.51,6,10,7,15,18,73 1144 | 44704,0,1,0,0,1,1,0,3/30/24,Basic,Single,2,0,101678.43,5583.67,5,2,9,4,9,13 1145 | 31428,1,0,1,0,1,1,0,11/27/22,Postgraduate,Single,2,1,35754.56,16723.91,10,12,4,19,3,38 1146 | 37512,1,1,0,0,1,1,0,11/11/23,Basic,Single,1,1,103562.68,5883.74,10,3,6,9,2,44 1147 | 26910,1,0,0,1,1,1,0,11/16/21,PhD,Single,0,1,38604.28,10082.25,1,0,9,0,15,14 1148 | 96547,1,1,0,1,1,0,0,3/17/23,Graduate,Single,1,2,132858.16,13204.39,5,6,8,15,6,49 1149 | 90714,0,1,0,1,1,0,0,2/1/23,PhD,Single,2,1,58949.25,10648.98,1,12,8,9,16,17 1150 | 76157,1,0,0,0,0,1,1,10/1/23,Graduate,Widow,1,1,70846.22,5104,10,0,6,0,8,45 1151 | 81785,0,0,0,1,0,0,0,3/16/25,High School,Widow,2,1,58699.08,19896.38,7,5,2,6,15,28 1152 | 63652,1,1,0,1,0,0,1,3/28/22,Postgraduate,Single,1,2,35768.77,9554.38,5,6,5,4,5,53 1153 | 60679,1,1,0,0,1,0,1,9/10/24,PhD,Widow,0,1,131216.28,2382.72,10,11,9,18,13,37 1154 | 47059,0,1,0,1,0,0,0,8/7/22,PhD,Widow,0,1,60631.12,715.89,5,2,4,20,7,14 1155 | 90375,1,1,0,0,0,0,0,2/22/22,Postgraduate,Divorced,2,0,146815.35,19571.72,2,11,4,5,13,5 1156 | 78392,0,1,0,0,0,0,0,1/14/23,High School,Divorced,1,2,139585.03,4158.72,10,10,9,9,11,71 1157 | 81922,0,0,1,0,1,1,0,12/12/23,Graduate,Married,2,1,104720.59,9439.2,6,11,5,3,5,65 1158 | 87859,1,1,0,0,1,1,0,4/3/23,Graduate,Widow,0,0,39634.12,563.43,2,4,10,2,7,28 1159 | 12683,1,1,0,1,0,0,0,10/30/23,PhD,Married,1,1,111107.71,15277.94,7,9,9,16,16,91 1160 | 93731,0,1,0,0,1,0,0,9/2/22,Graduate,Married,0,2,132854.17,18795.57,3,10,2,2,11,8 1161 | 49475,1,0,1,1,1,0,1,4/17/23,Postgraduate,Separated,2,2,106838.44,16279.2,2,8,7,3,11,60 1162 | 15619,1,1,0,0,0,0,0,6/11/24,High School,Single,0,0,136818.57,14456.96,3,10,5,3,5,12 1163 | 19406,1,0,0,0,0,1,1,11/13/24,Basic,Divorced,0,0,79114.1,4998.22,2,13,2,3,9,33 1164 | 93846,1,0,1,0,1,0,0,11/30/23,Basic,Separated,1,1,95227.29,9091.52,8,1,1,3,20,45 1165 | 87372,0,0,0,1,1,0,0,2/10/25,High School,Widow,0,1,72639.73,5739.99,9,1,3,6,12,94 1166 | 68508,0,0,0,1,1,0,0,5/25/23,High School,Married,0,2,95783.21,7403.14,5,10,2,14,17,93 1167 | 17705,1,1,0,1,0,0,0,3/31/24,Graduate,Widow,1,0,148493.49,11873.95,0,13,8,19,2,76 1168 | 42220,1,0,1,1,1,1,0,9/19/24,PhD,Separated,0,2,92953.19,2610.59,9,3,10,18,9,71 1169 | 64817,1,1,1,0,0,0,1,2/22/25,Basic,Separated,2,2,31053.53,3269.39,1,11,5,3,15,41 1170 | 88862,1,0,1,0,1,0,0,1/6/23,Postgraduate,Single,0,2,123021.51,12066.21,6,13,6,16,13,68 1171 | 49259,1,1,0,0,0,1,0,12/3/24,Basic,Single,2,2,113286.28,7413.36,1,9,9,8,14,26 1172 | 89424,1,1,1,0,0,0,0,3/23/24,Postgraduate,Widow,0,2,35422.58,4495.69,0,4,7,10,18,53 1173 | 57575,0,1,1,1,0,1,0,10/29/23,Graduate,Married,1,0,52600.86,5604.01,4,10,2,3,5,44 1174 | 19845,1,0,1,0,0,0,0,9/18/21,Graduate,Single,2,2,134736.18,14550.91,9,3,5,3,9,95 1175 | 71633,0,1,0,1,0,1,0,9/27/21,Basic,Married,2,1,87503.88,5818.79,2,12,3,13,8,9 1176 | 13944,0,0,1,1,1,0,0,7/6/21,Graduate,Married,2,2,118776.16,3976.96,5,6,9,12,0,37 1177 | 12072,0,1,0,0,1,1,0,10/25/21,Graduate,Married,1,2,58474.84,11638.36,6,10,10,5,14,55 1178 | 59404,0,0,0,0,0,0,0,3/13/25,Postgraduate,Widow,0,1,136046.63,15697.2,8,1,9,15,0,20 1179 | 18159,0,1,0,1,0,1,0,6/19/21,Basic,Divorced,2,0,38976.14,7160.75,3,7,1,14,1,17 1180 | 46278,1,0,0,1,1,0,0,8/14/22,Basic,Divorced,2,0,86125.39,9832.66,5,14,2,11,6,59 1181 | 46889,0,1,0,0,0,1,0,11/2/21,High School,Single,0,0,140995.11,4134.04,6,13,4,14,8,8 1182 | 21499,0,0,0,0,1,0,0,8/16/24,PhD,Married,2,1,148430.82,15135.95,4,5,2,13,20,55 1183 | 41888,0,0,1,1,0,0,0,12/3/21,PhD,Single,0,2,145581.47,18902.7,9,3,3,2,11,48 1184 | 33986,1,0,1,1,1,0,0,7/25/21,Graduate,Widow,2,0,69444.04,15064.33,4,10,2,5,16,96 1185 | 42181,0,0,0,1,0,1,0,9/15/22,Graduate,Married,2,2,83622.51,12440.62,3,8,9,13,11,56 1186 | 69047,1,1,0,0,1,0,0,4/28/24,PhD,Widow,0,2,76507.46,627.06,6,9,1,4,6,88 1187 | 47565,0,1,1,0,0,1,0,3/8/25,Postgraduate,Separated,0,0,136021.86,9324.64,5,0,1,12,6,18 1188 | 88289,1,0,0,1,0,1,0,7/8/24,Basic,Divorced,0,1,143825.33,12756.44,3,13,4,5,14,19 1189 | 69733,0,0,1,1,1,1,0,6/15/22,Postgraduate,Widow,2,2,143326.21,9880.69,6,15,10,9,12,61 1190 | 92720,0,1,1,0,0,0,0,5/26/24,High School,Married,0,1,103805.89,11438.38,8,4,1,14,0,21 1191 | 83211,1,1,0,1,0,1,0,6/23/21,Graduate,Separated,0,2,72220.14,6509.81,7,11,6,3,13,94 1192 | 64357,0,0,1,1,0,1,0,4/30/21,Postgraduate,Separated,0,0,146482.98,9439.18,5,12,1,9,2,96 1193 | 12820,0,1,1,0,1,0,0,3/25/23,High School,Married,2,1,144382.07,9310.01,9,1,8,16,10,5 1194 | 56039,1,0,0,0,0,1,0,8/5/24,Postgraduate,Single,0,0,35911.94,9640.67,0,4,3,16,20,46 1195 | 47490,1,0,0,0,0,1,0,1/13/23,High School,Single,0,0,74603.39,11292.26,3,7,9,17,19,86 1196 | 73120,1,0,0,0,0,1,0,4/5/24,Postgraduate,Married,2,2,84927.83,3568.01,8,14,6,14,17,20 1197 | 19983,0,1,0,1,0,0,0,3/5/22,Postgraduate,Widow,1,0,66550.81,13882.83,8,12,5,14,8,25 1198 | 39238,0,1,0,0,1,0,0,11/18/21,PhD,Divorced,2,2,105148.92,11958.34,7,4,0,5,18,65 1199 | 99545,0,0,0,0,0,1,0,7/22/22,High School,Separated,0,1,137811.43,11794.45,8,3,1,20,14,75 1200 | 99027,1,0,0,0,1,0,1,5/23/21,Graduate,Separated,1,0,134161.03,6016.01,2,14,1,18,5,73 1201 | 69118,0,0,1,1,0,1,0,2/26/25,Postgraduate,Separated,0,2,145387.62,16273.75,4,1,8,16,10,14 1202 | 62497,1,0,0,0,0,0,0,5/2/21,Graduate,Widow,2,2,65310.07,1234.35,7,13,2,5,13,93 1203 | 82324,1,1,0,1,0,1,1,6/5/24,PhD,Married,2,1,40040.41,8916.95,5,15,8,2,3,52 1204 | 70993,1,1,1,1,1,1,0,6/29/24,High School,Married,2,2,37044.07,18083.4,0,5,9,20,3,43 1205 | 10782,1,0,0,1,0,0,0,7/31/24,Basic,Single,1,1,35477.04,4230.75,5,7,5,20,18,50 1206 | 63982,0,0,0,1,0,0,0,6/1/24,High School,Divorced,1,0,127454.77,8282.63,6,12,0,4,14,12 1207 | 24521,1,0,1,1,1,1,1,9/19/23,PhD,Single,2,0,131250.1,10869.81,4,15,10,14,1,44 --------------------------------------------------------------------------------