diff --git a/customersim/.dockerignore b/customersim/.dockerignore
new file mode 100644
index 0000000..965fae0
--- /dev/null
+++ b/customersim/.dockerignore
@@ -0,0 +1,13 @@
+**/.git/
+**/.idea/
+*.iws
+
+
+**/__pycache__/
+*.py[cod]
+*$py.class
+
+*.so
+
+**/.venv*
+**/venv/
diff --git a/customersim/.environment.variables.sh b/customersim/.environment.variables.sh
new file mode 100644
index 0000000..efe92d8
--- /dev/null
+++ b/customersim/.environment.variables.sh
@@ -0,0 +1,8 @@
+export STORE_HEIGHT=10
+export STORE_WIDTH=6
+
+export CUSTOMERS_AVERAGE_IN_STORE=6
+export CUSTOMERS_LIST_FILE='customers.csv'
+
+export MQTT_HOST='127.0.0.1'
+export MQTT_NAME=test1
diff --git a/customersim/.s2i/environment b/customersim/.s2i/environment
deleted file mode 100644
index a585f44..0000000
--- a/customersim/.s2i/environment
+++ /dev/null
@@ -1 +0,0 @@
-APP_CONFIG=config.py
diff --git a/customersim/Dockerfile b/customersim/Dockerfile
new file mode 100644
index 0000000..a10fa56
--- /dev/null
+++ b/customersim/Dockerfile
@@ -0,0 +1,6 @@
+FROM tiangolo/uvicorn-gunicorn-fastapi:python3.8
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY ./app /app/app
diff --git a/customersim/README.md b/customersim/README.md
index 3aa0ba7..9320838 100644
--- a/customersim/README.md
+++ b/customersim/README.md
@@ -1,37 +1,182 @@
-# Flask Sample Application
+# Functionality 
+This service generates messages that simulate customer behaviour in a reatail shop:
+* customer entering the store
+* customer movement 
+* customer exiting the store
 
-This repository provides a sample Python web application implemented using the Flask web framework and hosted using ``gunicorn``. It is intended to be used to demonstrate deployment of Python web applications to OpenShift 3.
 
-## Implementation Notes
+## Table of contents
+* [Functionality](#functionality)
+* [Event payloads](#event-payloads)
+    * [customer/enter](#customerenter)
+    * [customer/move](#customermove)
+    * [customer/exit](#customerexit)
 
-This sample Python application relies on the support provided by the default S2I builder for deploying a WSGI application using the ``gunicorn`` WSGI server. The requirements which need to be satisfied for this to work are:
+* [Development](#development)
+  * [Dependencies](#dependencies)
+  * [Service configuration](#service-configuration)
+  * [Running the service](#running-the-service)
+  * [Testing with MQTT broker in docker](#testing-with-mqtt-broker-in-docker)
+  * [Testing without MQTT](#testing-without-mqtt)
+  * [Mock event endpoints](#mock-event-endpoints)
 
-* The WSGI application code file needs to be named ``wsgi.py``.
-* The WSGI application entry point within the code file needs to be named ``application``.
-* The ``gunicorn`` package must be listed in the ``requirements.txt`` file for ``pip``.
+* [Deployment](#deployment)
+  * [Docker image](#docker-image)
+  * [Connecting to a secured broker](#connecting-to-a-secured-broker)
 
-In addition, the ``.s2i/environment`` file has been created to allow environment variables to be set to override the behaviour of the default S2I builder for Python.
 
-* The environment variable ``APP_CONFIG`` has been set to declare the name of the config file for ``gunicorn``.
 
-## Deployment Steps
+## Event payloads
+The service assumes the following data will be provided with given event types.
 
-To deploy this sample Python web application from the OpenShift web console, you should select ``python:2.7``, ``python:3.3``, ``python:3.4`` or ``python:latest``, when using _Add to project_. Use of ``python:latest`` is the same as having selected the most up to date Python version available, which at this time is ``python:3.4``.
+This script generates the following MQTT messages
 
-The HTTPS URL of this code repository which should be supplied to the _Git Repository URL_ field when using _Add to project_ is:
+### customer/enter
 
-* https://github.com/OpenShiftDemos/os-sample-python.git
+```
+{ 
+  id: --ID representing customer--, 
+  ts: --timestamp of the entrance, in seconds since epoch-- 
+}
+```
+
+### customer/move
+
+```
+{ 
+ id: --ID representing customer--,
+ ts: --timestamp of the move, in seconds since epoch--,
+  x: --x coordinate of location sensor that fired--,
+  y: --y coordinate of location sensor that fired--
+}
+```
+
+### customer/exit
+
+```
+{ 
+ id: --ID representing customer--,
+ ts: --timestamp of the exit, in seconds since epoch--
+}
+```
+
+
+
+# Development
+
+## Dependencies
+
+Dependencies of the project are contained in [requirements.txt](requirements.txt) file. All the packages are publicly
+available.
+
+All the packages can be installed with:
+`pip install -f requirements.txt`
+
+## Service configuration
+
+The service reads the following **environment variables**:
+
+| Variable               | Description                          |  Default      |
+|------------------------|--------------------------------------|--------------:|
+| STORE_HEIGHT          |                                       | 10            |
+| STORE_WIDTH           |                                       | 6             |
+| CUSTOMERS_AVERAGE_IN_STORE |  					            | 6 		    |
+| CUSTOMERS_LIST_FILE   |                                       | customers.csv |
+| MQTT_HOST             |                                       | -             |
+| MQTT_PORT             |               	                    | 1883          |
+| MQTT_NAME             |                	                    | demoClient    |
+| ENTER_TOPIC           |                                       | customer/enter|
+| MOVE_TOPIC            |                                       | customer/move |
+| EXIT_TOPIC            |                                       | customer/exit |
+
+(Parameters with `-` in "Default" column are required.)
+
+Use [log_config.py](./app/utils/log_config.py) to **configure logging behaviour**. 
+By default, console and file handlers are used. The file appender writes to `messages.log`.
+
+
+## Running the service
 
-If using the ``oc`` command line tool instead of the OpenShift web console, to deploy this sample Python web application, you can run:
+For my development I created a project with dedicated virtual environment (Python 3.8, all the dependencies installed
+there).
+
+The code reads sensitive information (tokens, secrets) from environment variables. They need to be set accordingly in
+advance.
+`environment.variables.sh` can be used for that purpose. Then, in order to run the service the following commands can be
+used:
 
 ```
-oc new-app https://github.com/OpenShiftDemos/os-sample-python.git
+$ . .environment.variables.sh
+$ . venv/bin/activate
+(venv)$ uvicorn app.main:app --host 0.0.0.0 --reload --reload-dir app
 ```
+> Please, note `reload-dir` switch. Without it the reloader goes into an infinite loop because it detects log file changes (messages.log).
+
+## Testing with MQTT broker in docker
 
-In this case, because no language type was specified, OpenShift will determine the language by inspecting the code repository. Because the code repository contains a ``requirements.txt``, it will subsequently be interpreted as including a Python application. When such automatic detection is used, ``python:latest`` will be used.
+Quick way to **set up a simple MQTT broker** is to use Docker containers:
+```shell
+docker run -d --rm --name mosquitto -p 1883:1883 eclipse-mosquitto
+```
+or
+```shell
+docker run -it -p 1883:1883 --name mosquitto eclipse-mosquitto mosquitto -c /mosquitto-no-auth.conf
+```
 
-If needing to select a specific Python version when using ``oc new-app``, you should instead use the form:
+To **publish to a topic**:
 
+```shell
+docker exec mosquitto mosquitto_pub -h 127.0.0.1 -t test -m "test message"
 ```
-oc new-app python:2.7~https://github.com/OpenShiftDemos/os-sample-python.git
+
+To **subscribe to a topic**:
+```shell
+docker exec mosquitto mosquitto_sub -h 127.0.0.1 -t test
 ```
+
+### Testing without MQTT
+There is an environment variable, `TESTING_MOCK_MQTT`, that will create an MQTT client mock instead of trying to connect
+to a real MQTT broker. Instead of publishing the messages, they will be simply logged/printed out.
+
+This may be helpful for local development or testing.
+
+### Producing test messages
+
+```shell
+curl http://127.0.0.1:8000/produce_entry -d '{"id": "997", "ts": 192326400}'
+ ```
+
+```shell
+curl http://127.0.0.1:8000/produce_exit -d '{"id": "997", "ts": 192326400}'
+ ```
+
+```shell
+curl http://127.0.0.1:8000/produce_move -d '{"id": "997", "ts": 192326400, "x": 2, "y": 3}'
+ ```
+
+
+# Deployment
+
+## Docker image
+The docker image for the service is [Dockerfile](Dockerfile).
+It is based on FastAPI "official" image.
+See https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker
+for the details on configuring the container (http port, log level, etc.)
+
+In order to build the image use:
+```
+docker build -t customersim-service:0.0.1 .
+```
+
+> Set image name (`customersim-service`) and tag (`0.0.1`) according to
+> your needs.
+
+To run the service as a Docker container run:
+```
+docker run -d -e LOG_LEVEL="warning"  --name customersim-service customersim-service:0.0.1
+
+```
+
+## Connecting to a secured broker
+**TODO** Add info about setting user/password
+**TODO** Add info about using client certificates (TLS)
diff --git a/customersim/app/__init__.py b/customersim/app/__init__.py
new file mode 100644
index 0000000..c10988d
--- /dev/null
+++ b/customersim/app/__init__.py
@@ -0,0 +1,4 @@
+import logging
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
diff --git a/customersim/app/config.py b/customersim/app/config.py
new file mode 100644
index 0000000..07b59b7
--- /dev/null
+++ b/customersim/app/config.py
@@ -0,0 +1,34 @@
+import os
+import sys
+
+from app import logger
+
+
+def validate_and_crash(variable, message):
+    if not variable:
+        logger.error(message)
+        sys.exit(message)
+
+
+logger.info('Reading environment variables...')
+
+STORE_HEIGHT = int(os.getenv('STORE_HEIGHT', 10))
+STORE_WIDTH = int(os.getenv('STORE_WIDTH', 6))
+
+CUSTOMERS_AVERAGE_IN_STORE = int(os.getenv('CUSTOMERS_AVERAGE_IN_STORE', 6))
+CUSTOMERS_LIST_FILE = os.getenv('CUSTOMERS_LIST_FILE', 'customers.csv')
+
+MQTT_HOST = os.getenv('MQTT_HOST')
+MQTT_PORT = int(os.getenv('MQTT_PORT', 1883))
+MQTT_NAME = os.getenv('MQTT_NAME', 'demoClient')
+
+CUSTOMER_ENTER_TOPIC = os.getenv('ENTER_TOPIC', 'customer/enter')
+CUSTOMER_EXIT_TOPIC = os.getenv('EXIT_TOPIC', 'customer/exit')
+CUSTOMER_MOVE_TOPIC = os.getenv('MOVE_TOPIC', 'customer/move')
+
+TESTING_MOCK_MQTT = os.getenv('TESTING_MOCK_MQTT', 'false')
+TESTING_MOCK_MQTT = TESTING_MOCK_MQTT.lower() in ['1', 'yes', 'true']
+
+
+REQUIRED_PARAM_MESSAGE = 'Cannot read {} env variable. Please, make sure it is set before starting the service.'
+validate_and_crash(MQTT_HOST, REQUIRED_PARAM_MESSAGE.format('MQTT_HOST'))
diff --git a/customersim/app/domain_model.py b/customersim/app/domain_model.py
new file mode 100644
index 0000000..3f69561
--- /dev/null
+++ b/customersim/app/domain_model.py
@@ -0,0 +1,70 @@
+import datetime
+import random
+
+
+# Represents a "square" in the store at a particular location, and contain all valid moves from that location
+class Location:
+    def __init__(self, x: int, y: int, width: int, height: int):
+        self.x = x
+        self.y = y
+        # a list of all valid moves. Each move is a tuple of the form
+        # ("adjacent x location", "adjacent y location", "is this closer to the exit?")
+        self.validMoves = [(a, b, True if a <= self.x and b <= self.y else False) for a in
+                           range(max(self.x - 1, 0), min(self.x + 2, width)) for b in
+                           range(max(self.y - 1, 0), min(self.y + 2, height)) if not (a == self.x and b == self.y)]
+
+
+class Store:
+    def __init__(self, width: int, height: int):
+        self.height = height
+        self.width = width
+        self.locations = [[Location(x, y, width, height) for y in range(0, height)] for x in range(0, width)]
+
+
+class Customer:
+    def __init__(self, store, customer_id: str, name: str):
+        self.store = store
+        # customers enter and exit from the bottom left corner of the store
+        self.currentLocation = store.locations[0][0]
+        # the *average* amount of time this customer will spend on a square
+        self.meanDwellTime = random.uniform(1, 20)
+        # how consistently the customer spends that time. Higher means more inconsistent
+        self.consistency = random.uniform(1, 5)
+        self.nextMoveTime = self.get_next_move_time()
+        self.isExiting = False
+        # the time this customer will start to exit
+        self.exitTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600))
+        self.id = customer_id
+        self.name = name
+
+    def get_next_move_time(self):
+        # amount of time spent at a location is a random value picked from a gaussian distribution,
+        # with a mean equal to the customer's average dwell time and a standard deviation
+        # equal to the customer's consistency
+        return datetime.datetime.now() + datetime.timedelta(0, random.gauss(self.meanDwellTime, self.consistency))
+
+    def move(self):
+        # if the customer is exiting, only move to an adjacent location that is towards the exit.
+        # If they are already at the door, don't move
+        if self.isExiting:
+            if self.currentLocation.x == 0 and self.currentLocation.y == 0:
+                (newX, newY) = (0, 0)
+            else:
+                (newX, newY, isTowardsExit) = random.choice(
+                    [(x, y, e) for (x, y, e) in self.currentLocation.validMoves if e is True])
+        else:
+            # if the customer is not exiting, pick any adjacent location
+            (newX, newY, isTowardsExit) = random.choice(self.currentLocation.validMoves)
+
+        self.currentLocation = self.store.locations[newX][newY]
+
+    def tick(self):
+        if not self.isExiting and self.exitTime < datetime.datetime.now():
+            self.isExiting = True
+
+        if self.nextMoveTime < datetime.datetime.now():
+            self.nextMoveTime = self.get_next_move_time()
+            self.move()
+            return True
+
+        return False
diff --git a/customersim/app/events_model.py b/customersim/app/events_model.py
new file mode 100644
index 0000000..a4217f9
--- /dev/null
+++ b/customersim/app/events_model.py
@@ -0,0 +1,32 @@
+from pydantic import BaseModel
+
+
+class CustomerEnterEvent(BaseModel):
+    """
+    id: --ID representing customer--,
+    ts: --timestamp of the entrance, in seconds since epoch--
+    """
+    id: str
+    ts: int
+
+
+class CustomerExitEvent(BaseModel):
+    """
+    id: --ID representing customer--,
+    ts: --timestamp of the exit, in seconds since epoch--
+    """
+    id: str
+    ts: int
+
+
+class CustomerMoveEvent(BaseModel):
+    """
+    id: --ID representing customer--,
+    ts: --timestamp of the move, in seconds since epoch--,
+     x: --x coordinate of location sensor that fired--,
+     y: --y coordinate of location sensor that fired--
+    """
+    id: str
+    ts: int
+    x: int
+    y: int
diff --git a/customersim/app/log_config.py b/customersim/app/log_config.py
new file mode 100644
index 0000000..2648efd
--- /dev/null
+++ b/customersim/app/log_config.py
@@ -0,0 +1,25 @@
+import logging
+import os
+
+
+# TODO move to config
+LOG_FILENAME = "messages.log"
+LOG_FORMAT = "%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s]\t%(message)s"
+LOG_LEVEL = os.environ.get('LOG_LEVEL', 'INFO').upper()
+
+assert LOG_LEVEL in ['DEBUG', 'INFO', 'WARNING', 'ERROR']
+
+
+def configure_logger():
+    logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL)
+    # Basic console logger
+    logger = logging.getLogger("app")
+    # File logger
+    log_formatter = logging.Formatter(LOG_FORMAT)
+    file_handler = logging.FileHandler(LOG_FILENAME, encoding='UTF-8')
+    file_handler.setFormatter(log_formatter)
+    logger.addHandler(file_handler)
+    # Configuration done
+    logger.debug("Logger configured...")
+    return logger
+
diff --git a/customersim/app/main.py b/customersim/app/main.py
new file mode 100644
index 0000000..901794b
--- /dev/null
+++ b/customersim/app/main.py
@@ -0,0 +1,94 @@
+import asyncio
+
+from fastapi import FastAPI
+from fastapi.responses import PlainTextResponse
+
+from app import logger
+from app.config import CUSTOMER_ENTER_TOPIC, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC
+from app.events_model import CustomerMoveEvent, CustomerExitEvent, CustomerEnterEvent
+from app.log_config import configure_logger
+from app.mosquitto import get_mqtt_client
+from app.simulation_engine import CustomerSimulator
+from app.store_initializer import init_customer_list, init_store
+
+configure_logger()
+
+app = FastAPI()
+
+
+@app.on_event("startup")
+async def startup_event():
+    app.state.customer_list = init_customer_list()
+    app.state.store = init_store()
+
+    ####################
+    # init connections
+    app.state.mqttc = await get_mqtt_client()
+
+    ####################
+    # background tasks
+    customer_sim = CustomerSimulator(app.state.store, app.state.customer_list, app.state.mqttc)
+    asyncio.create_task(customer_sim.run())
+
+
+####################
+# web handlers
+logger.info('Defining web service handlers...')
+
+
+@app.get('/')
+async def root():
+    logger.debug('/')
+    return {'message': 'Hello World'}
+
+
+@app.get('/health')
+async def health() -> PlainTextResponse:
+    """
+    Service health check endpoint.
+    """
+    logger.info('verify health')
+    return PlainTextResponse('OK')
+
+
+class CustomerEnter(object):
+    pass
+
+
+@app.post('/produce_entry')
+async def produce_entry_event(event: CustomerEnterEvent) -> PlainTextResponse:
+    """
+    Test endpoint that forces publication of "customer/enter".
+    """
+    logger.info('produce_entry_event')
+    logger.debug(event)
+
+    message = event.json()
+    result = app.state.mqttc.publish(CUSTOMER_ENTER_TOPIC, message)
+    return PlainTextResponse(str(result))
+
+
+@app.post('/produce_move')
+async def produce_move_event(event: CustomerMoveEvent) -> PlainTextResponse:
+    """
+    Test endpoint that forces publication of "customer/move".
+    """
+    logger.info('produce_move_event')
+    logger.debug(event)
+
+    message = event.json()
+    result = app.state.mqttc.publish(CUSTOMER_MOVE_TOPIC, message)
+    return PlainTextResponse(str(result))
+
+
+@app.post('/produce_exit')
+async def produce_exit_event(event: CustomerExitEvent) -> PlainTextResponse:
+    """
+    Test endpoint that forces publication of "customer/exit".
+    """
+    logger.info('produce_exit_event')
+    logger.debug(event)
+
+    message = event.json()
+    result = app.state.mqttc.publish(CUSTOMER_EXIT_TOPIC, message)
+    return PlainTextResponse(str(result))
diff --git a/customersim/app/mosquitto.py b/customersim/app/mosquitto.py
new file mode 100644
index 0000000..6bb12a4
--- /dev/null
+++ b/customersim/app/mosquitto.py
@@ -0,0 +1,43 @@
+from paho.mqtt import client
+
+from app import logger
+from app.config import TESTING_MOCK_MQTT, MQTT_HOST, MQTT_PORT, MQTT_NAME
+
+if TESTING_MOCK_MQTT:
+    class MQTTClient:
+        def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str):
+            logger.info(f'simulating a client to {mqtt_host}')
+            self.mqtt_client_name = mqtt_client_name
+            self.mqtt_host = mqtt_host
+            self.mqtt_port = mqtt_port
+
+        def publish(self, topic, message):
+            logger.info(f'simulated publishing to {topic}. message: {message}')
+
+        async def connect(self):
+            pass
+else:
+    class MQTTClient:
+        def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str):
+
+            logger.info(f'Creating MQTT client {mqtt_host}, {mqtt_port}, {mqtt_client_name}')
+            self.mqtt_client_name = mqtt_client_name
+            self.mqtt_host = mqtt_host
+            self.mqtt_port = mqtt_port
+
+        def publish(self, topic, message):
+            logger.info(f' publishing to {topic}. message: {message}')
+            return self.mqttc.publish(topic, message)
+
+        async def connect(self):
+            logger.info("before connect")
+            self.mqttc = client.Client(self.mqtt_client_name)
+            logger.info(f'connect({self.mqtt_host}, {self.mqtt_port})')
+            self.mqttc.connect(self.mqtt_host, self.mqtt_port)
+            logger.info("after connect")
+
+
+async def get_mqtt_client():
+    mqttc = MQTTClient(MQTT_HOST, MQTT_PORT, MQTT_NAME)
+    await mqttc.connect()
+    return mqttc
diff --git a/customersim/app/simulation_engine.py b/customersim/app/simulation_engine.py
new file mode 100644
index 0000000..1aec42d
--- /dev/null
+++ b/customersim/app/simulation_engine.py
@@ -0,0 +1,81 @@
+import asyncio
+import json
+import datetime
+import random
+
+from app import logger
+from app.config import CUSTOMERS_AVERAGE_IN_STORE, CUSTOMER_ENTER_TOPIC, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC
+from app.domain_model import Store, Customer
+
+CUSTOMER_STATE_TEMPLATE = '{0} is Entering. MDT: {1:0.1f}, C: {2:0.1f}, E: {3}'
+
+
+class CustomerSimulator:
+    def __init__(self, store: Store, customer_list: list, publisher,
+                 next_entrance_time: datetime = datetime.datetime.now()):
+        self.customer_list = customer_list
+        self.store = store
+        self.customer_queue = []  # List of customers in the store
+        self.next_customer_entrance_time = next_entrance_time
+        self.is_running = True
+        self.mqttc = publisher
+
+    def manage_customer_movements(self, c):
+        if c.tick():
+            if c.isExiting and c.currentLocation.x == 0 and c.currentLocation.y == 0:
+                # remove the customer and signal the exit
+                timestamp_value = self.get_timestamp_value(datetime.datetime.now())
+                # TODO convert to pydantic model usage
+                msg = {'id': str(c.id), 'ts': timestamp_value}
+                # TODO simulation engine don't need to know details like topic name or message marshaling
+                # it should be encapsulated in event publisher
+                self.mqttc.publish(CUSTOMER_EXIT_TOPIC, json.dumps(msg))
+                logger.info(f'{c.name} is Exiting.')
+                self.customer_queue.remove(c)
+            else:
+                # signal move
+                timestamp_value = self.get_timestamp_value(datetime.datetime.now())
+                msg = {'id': str(c.id), 'ts': timestamp_value, 'x': c.currentLocation.x, 'y': c.currentLocation.y}
+                self.mqttc.publish(CUSTOMER_MOVE_TOPIC, json.dumps(msg))
+
+    async def run(self):
+        # Check if any customers are due to enter, move, or exit. Sleep for one second, then repeat
+        while self.is_running:
+            if self.next_customer_entrance_time < datetime.datetime.now():
+                # add the new customer, and signal the entrance
+                new_customer_prototype = random.choice(self.customer_list)
+                logger.info(f"new customer prototype {new_customer_prototype}")
+                new_customer = Customer(self.store, new_customer_prototype['customer_id'],
+                                        new_customer_prototype['name'])
+                self.customer_queue.append(new_customer)
+
+                timestamp = datetime.datetime.utcnow()
+                timestamp_value = self.get_timestamp_value(timestamp)
+
+                msg = {'id': str(new_customer.id), 'ts': timestamp_value}
+                self.mqttc.publish(CUSTOMER_ENTER_TOPIC, json.dumps(msg))
+
+                next_customer_entrance_time = self.get_new_entrance_time()
+
+                logger.info('inspecting customer: ----------')
+                logger.info(self.customer_state_dump(new_customer))
+                logger.info(f'Next Customer Entering at {next_customer_entrance_time}')
+
+            [self.manage_customer_movements(c) for c in self.customer_queue]
+            await asyncio.sleep(1)
+
+    @staticmethod
+    def get_timestamp_value(tmstmp: datetime):
+        # # For iso format use:
+        # timestamp_value = f'"{timestamp.isoformat()}"'
+        # For second since Epoch use:
+        timestamp_value = int(tmstmp.timestamp())
+        return timestamp_value
+
+    @staticmethod
+    def get_new_entrance_time():
+        return datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600 / CUSTOMERS_AVERAGE_IN_STORE))
+
+    @staticmethod
+    def customer_state_dump(c: Customer):
+        return CUSTOMER_STATE_TEMPLATE.format(c.name, c.meanDwellTime, c.consistency, c.exitTime)
diff --git a/customersim/app/store_initializer.py b/customersim/app/store_initializer.py
new file mode 100644
index 0000000..93bb0d7
--- /dev/null
+++ b/customersim/app/store_initializer.py
@@ -0,0 +1,21 @@
+import csv
+
+from app import logger
+from app.config import CUSTOMERS_LIST_FILE, STORE_WIDTH, STORE_HEIGHT
+from app.domain_model import Store
+
+
+def init_store():
+    return Store(STORE_WIDTH, STORE_HEIGHT)
+
+
+def init_customer_list(customer_file_path: str = CUSTOMERS_LIST_FILE):
+    try:
+        with open(customer_file_path, 'rt') as csvfile:
+            fake_reader = csv.DictReader(csvfile, delimiter=',')
+            customer_list = [row for row in fake_reader]
+    except IOError as e:
+        logger.error("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n")
+        exit(0)
+
+    return customer_list
diff --git a/customersim/config.cfg b/customersim/config.cfg
deleted file mode 100755
index 48d3a8c..0000000
--- a/customersim/config.cfg
+++ /dev/null
@@ -1,22 +0,0 @@
-[Store]                  
-# "Height" of the store (maximum Y value)
-height=10
-
-# "Width" of the store (maximum X value)
-width=6
-
-[Customers]
-# Average number of customers in the store
-averageCustomersInStore=6
-list=customers.dat
-
-[MQTT]
-# MQTT host
-host=172.30.106.185
-
-# MQTT port
-port=1883
-
-# Name of MQTT device
-name=demoQueue
-
diff --git a/customersim/config.py b/customersim/config.py
deleted file mode 100644
index cf9afbf..0000000
--- a/customersim/config.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-
-workers = int(os.environ.get('GUNICORN_PROCESSES', '3'))
-threads = int(os.environ.get('GUNICORN_THREADS', '1'))
-
-forwarded_allow_ips = '*'
-secure_scheme_headers = { 'X-Forwarded-Proto': 'https' }
diff --git a/customersim/customers.csv b/customersim/customers.csv
new file mode 100644
index 0000000..28fd4fc
--- /dev/null
+++ b/customersim/customers.csv
@@ -0,0 +1,1583 @@
+customer_id,name,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust
+1,Karen Montgomery,70+,Married,2,0,4,F,-1.75,463,99.22,1.0,-1832.94,78,103982.01,1048
+2,Tracey Newman,46-55,Married,2,0,1,F,-0.45,352,108.26,1.0,-189.97,4,45360.6,419
+3,Noah King,18-25,Married,2,0,2,M,-1.89,406,86.97,1.0,-1329.46,53,61312.59,707
+4,Jennifer West,46-55,Married,5,3,3,F,-0.08,125,138.34,1.0,-17.81,1,30434.3,220
+5,Joshua Willis,26-35,Single,3,2,3,M,-0.11,490,115.6,1.03,-90.83,2,91553.24,814
+6,Ricky Parker,46-55,Married,2,0,5,M,-0.63,429,102.43,1.0,-369.55,11,59716.13,583
+7,Lawrence Lawson,26-35,Married,3,1,3,M,-0.65,780,101.8,1.01,-687.47,17,107194.4,1066
+8,Mark Tate,26-35,Married,4,2,6,M,-3.72,719,126.24,1.25,-4899.4,192,166389.9,1643
+9,Karen Wyatt,26-35,Single,2,1,4,F,-0.77,405,90.97,1.05,-430.64,8,50672.81,585
+10,Christopher Gonzalez,46-55,Single,1,0,5,M,0.0,268,94.61,1.04,0.0,0,46455.82,509
+11,Marcus Smith,70+,Single,2,1,1,M,-1.6,282,116.04,1.11,-988.45,10,71594.7,683
+12,Thomas Morgan,46-55,Married,2,0,7,M,-0.96,436,119.82,1.03,-929.96,45,115504.13,993
+13,Sara Byrd,36-45,Single,1,0,2,F,-0.11,1192,83.27,1.03,-272.48,10,205750.82,2544
+14,Courtney Kemp,26-35,Married,2,0,6,F,-0.4,327,101.65,1.07,-492.72,27,126446.72,1328
+15,Pamela Knight,46-55,Married,2,0,6,F,-1.17,463,104.45,1.08,-967.07,39,86483.25,895
+16,Shannon Floyd,36-45,Married,2,0,1,F,0.0,328,136.27,1.08,0.0,0,72497.44,575
+17,Terry Smith,36-45,Single,1,0,5,M,-1.24,306,120.93,1.01,-708.13,5,69174.48,575
+18,Christina Schwartz,36-45,Married,2,0,5,F,0.0,315,82.29,1.0,0.0,0,41886.68,509
+19,Sharon Sanford,46-55,Single,1,0,3,F,-0.07,410,91.89,1.0,-90.82,4,127815.7,1391
+20,Susan Allen,36-45,Married,2,0,5,F,0.0,229,127.8,1.02,0.0,0,48181.07,385
+21,Christopher Ward,36-45,Married,2,0,4,M,-0.32,277,100.77,1.0,-120.75,3,38594.99,383
+22,Mrs. Wendy Santiago,36-45,Single,2,1,4,F,-7.84,329,130.09,1.1,-3334.06,154,55287.89,467
+23,Shawna Morales,46-55,Married,2,0,4,F,-0.15,467,104.15,1.11,-90.83,3,62072.75,661
+24,Nathan Park,70+,Single,1,0,4,M,-0.02,403,98.23,1.0,-12.47,1,63458.36,648
+25,Lindsay Roberts,46-55,Married,2,0,4,F,-0.09,96,107.29,1.07,-17.81,1,21028.23,210
+26,Collin Young,36-45,Married,3,1,3,M,-0.27,108,129.41,1.0,-29.68,1,14235.56,110
+27,Joshua Diaz,36-45,Married,2,0,8,M,-0.29,866,99.97,1.0,-334.71,14,116368.66,1167
+28,Angelica Wallace,46-55,Married,2,0,1,F,-0.11,1045,97.75,1.09,-231.52,7,200690.92,2242
+29,John Smith,70+,Single,3,2,1,M,0.0,232,91.09,1.0,0.0,0,47821.35,525
+30,Kelly Berry,70+,Single,1,0,5,F,-0.58,198,81.0,1.0,-325.92,18,45440.98,561
+31,William Garcia,36-45,Single,5,3,2,M,-0.72,190,66.47,1.03,-217.27,7,20139.08,313
+32,Michael Osborne,36-45,Single,1,0,9,M,-0.34,140,117.22,1.0,-71.24,1,24850.27,212
+33,Donna Smith,46-55,Married,5,3,9,F,-0.17,774,130.25,1.02,-169.2,3,132724.74,1040
+34,Brian Nicholson,46-55,Married,2,0,2,M,0.0,290,128.31,1.0,0.0,0,48887.4,381
+35,Robert Bates,18-25,Married,2,0,4,M,-0.82,170,107.59,1.05,-195.91,4,25712.86,250
+36,Michelle Stevenson,36-45,Married,2,0,4,F,-0.16,711,105.03,1.08,-158.86,8,105026.25,1081
+37,Anna Hamilton,36-45,Married,2,0,8,F,-0.04,276,97.55,1.0,-19.59,1,51801.68,531
+38,Thomas Morgan,46-55,Single,2,1,5,M,-0.02,697,110.31,1.02,-17.81,1,111413.62,1030
+39,Jennifer Allen,70+,Married,2,0,4,F,0.0,152,117.45,1.0,0.0,0,25956.71,221
+40,David Williams,56-70,Married,4,2,7,M,-0.59,696,106.64,1.0,-784.71,34,142365.91,1337
+41,Misty Ingram,46-55,Single,2,1,4,F,-0.29,295,94.35,1.05,-136.78,2,44626.62,495
+42,Allison Howell,26-35,Married,4,2,9,F,-0.72,983,107.26,1.15,-1199.48,45,179236.5,1916
+43,Tamara Rios,26-35,Single,1,0,5,F,-0.06,368,90.3,1.0,-37.4,2,55443.61,614
+44,Paul Anderson,36-45,Single,3,2,4,M,0.0,254,151.87,1.0,0.0,0,43890.5,289
+45,Shawn Hunt,46-55,Married,5,3,1,M,-2.88,728,105.31,1.03,-3849.98,163,140910.98,1373
+46,Karen Fuller,56-70,Married,2,0,5,F,-0.69,674,81.17,1.02,-768.49,24,90176.72,1137
+47,Melissa Anderson,26-35,Single,1,0,4,F,-0.02,828,98.48,1.0,-17.8,2,116401.41,1182
+48,Michael Scott,36-45,Married,2,0,3,M,-2.1,244,190.74,1.01,-810.35,12,73436.16,388
+49,Christine Newman,56-70,Married,2,0,7,F,-0.24,760,94.19,1.02,-391.46,2,152584.45,1648
+50,Terry Sutton,46-55,Married,3,1,4,M,0.0,431,109.45,1.09,0.0,0,65996.58,656
+51,Christina Lopez DDS,70+,Married,2,0,2,F,-0.53,259,75.6,1.0,-302.77,9,42942.81,568
+52,Jose Jones,36-45,Married,5,3,7,M,-0.42,858,100.29,1.0,-569.92,19,137394.07,1370
+53,Jessica Burns,56-70,Single,1,0,3,F,0.0,252,76.46,1.0,0.0,0,39682.81,519
+54,Stephen Hernandez,56-70,Single,1,0,3,M,-0.72,487,103.53,1.05,-532.52,23,76407.25,772
+55,Debra Sanchez,46-55,Married,2,0,5,F,-0.87,425,87.1,1.04,-456.83,11,45728.04,547
+56,Cindy Warner,18-25,Married,3,1,5,F,-0.06,799,91.86,1.0,-88.69,1,140914.43,1534
+57,Joe Williams,46-55,Married,2,0,6,M,0.0,208,96.0,1.04,0.0,0,23712.9,257
+58,Jeremy Russo,26-35,Single,1,0,4,M,-0.04,679,94.17,1.03,-41.85,2,102838.9,1125
+59,Edward Simmons,70+,Single,1,0,4,M,-0.07,523,113.24,1.01,-44.52,1,74965.42,666
+60,John Hart,36-45,Single,1,0,3,M,-0.57,1485,86.12,1.01,-1898.01,67,286593.68,3367
+61,Jason Bauer,46-55,Married,2,0,6,M,0.0,315,91.65,1.08,0.0,0,42708.98,501
+62,Steven Boyer,36-45,Married,3,1,1,M,0.0,252,88.86,1.01,0.0,0,24614.26,280
+63,Kathy Ramos,46-55,Single,1,0,2,F,-2.08,450,103.7,1.01,-2346.26,78,116973.33,1134
+64,Aaron Miller,46-55,Married,2,0,6,M,-1.1,775,103.82,1.06,-1206.09,17,113679.4,1161
+65,Daniel Allen,70+,Married,2,0,3,M,0.0,189,125.34,1.0,0.0,0,29454.17,235
+66,Nicholas Parker,46-55,Single,1,0,6,M,-0.01,681,96.39,1.06,-12.47,1,102173.46,1119
+67,Robert Kirk,36-45,Single,2,1,4,M,0.0,293,62.64,1.0,0.0,0,27747.96,443
+68,Robin Allen,26-35,Single,1,0,2,F,-0.32,941,82.8,1.01,-511.49,16,133560.54,1624
+69,Lindsey Johnson,46-55,Married,4,2,1,F,-0.38,335,97.62,1.08,-215.8,10,55839.46,620
+70,Megan Hernandez,26-35,Married,2,0,3,F,0.0,129,81.54,1.0,0.0,0,12068.23,148
+71,Garrett Cook,36-45,Married,5,3,4,M,-1.17,558,86.6,1.0,-1100.82,50,81229.51,942
+72,Danielle Johnson,26-35,Single,3,2,1,F,-0.16,884,71.6,1.0,-229.76,15,99884.29,1395
+73,Joseph Rodgers,36-45,Married,5,3,6,M,-0.1,439,104.17,1.02,-53.43,1,58127.59,571
+74,Brooke Wilson,26-35,Married,2,0,2,F,0.0,530,97.29,1.0,0.0,0,77055.52,792
+75,Amanda Powers,26-35,Single,1,0,4,F,-0.03,552,81.21,1.04,-26.71,1,63992.49,819
+76,Elizabeth Dawson,36-45,Married,2,0,4,F,-0.94,351,104.66,1.02,-400.35,15,44689.77,436
+77,Brandon Horton,56-70,Married,2,0,2,M,-0.23,136,104.38,1.19,-35.62,1,16387.1,187
+78,Mrs. Christina Valenzuela,36-45,Married,4,2,8,F,-0.48,1366,109.87,1.08,-1260.63,49,290936.29,2869
+79,Curtis Robles,26-35,Married,2,0,4,M,-0.05,488,96.9,1.02,-35.62,1,67734.62,715
+80,Nicholas Collier,36-45,Married,2,0,8,M,-1.02,838,129.26,1.08,-1705.95,50,217157.69,1814
+81,Douglas Carlson,36-45,Single,1,0,5,M,-1.57,99,101.86,1.0,-248.75,6,16094.07,158
+82,Miranda Williams,70+,Single,2,1,4,F,-0.44,484,80.74,1.01,-331.25,15,61039.57,763
+83,Dr. Gabriel Velez,46-55,Single,1,0,4,M,0.0,384,98.43,1.05,0.0,0,81205.81,865
+84,David West,46-55,Single,1,0,8,M,-0.54,326,122.88,1.0,-213.36,1,48169.8,392
+85,Mariah Murray,46-55,Married,3,1,3,F,-0.05,475,88.31,1.0,-32.65,2,53516.98,606
+86,Jeremy Knight,56-70,Married,3,1,3,M,-0.05,409,102.9,1.0,-26.71,1,55360.92,538
+87,Garrett Douglas,46-55,Single,2,1,4,M,-0.06,387,97.83,1.04,-35.62,1,62026.76,660
+88,John Perry,36-45,Single,1,0,4,M,-1.62,960,111.3,1.12,-2517.17,73,172846.55,1733
+89,Jacob Mcmillan,46-55,Single,1,0,3,M,-1.45,498,87.8,1.08,-970.34,49,58647.94,720
+90,Kelly Manning,46-55,Married,2,0,5,F,-0.46,888,107.83,1.03,-542.31,10,126272.5,1203
+91,Daniel Watson,36-45,Single,1,0,5,M,0.0,383,100.8,1.01,0.0,0,57052.5,569
+92,Brian Cross,46-55,Married,2,0,1,M,-0.27,417,109.59,1.0,-146.04,8,59508.78,543
+93,Alexandria Carpenter,56-70,Married,2,0,3,F,-0.21,750,110.3,1.01,-209.27,6,110076.58,1008
+94,Kelsey Stuart,46-55,Single,1,0,5,F,-0.05,555,88.76,1.0,-44.52,1,75181.31,847
+95,Jessica Gray,36-45,Single,2,1,4,F,-1.36,232,105.79,1.0,-356.19,12,27612.17,261
+96,Brian Tran,46-55,Single,1,0,6,M,-0.41,398,111.24,1.0,-216.4,8,58400.26,525
+97,Jessica Stewart,36-45,Single,1,0,6,F,-0.76,293,88.58,1.04,-375.94,10,43933.76,516
+98,Carolyn Adams,46-55,Married,2,0,3,F,-0.04,332,89.86,1.0,-20.07,1,49781.54,554
+99,Victoria Davis,26-35,Single,1,0,2,F,0.0,206,87.78,1.0,0.0,0,55828.0,636
+100,Taylor Mays,56-70,Married,2,0,7,M,-0.95,1011,90.91,1.04,-1821.35,58,174272.27,1994
+101,Leah Crawford,36-45,Single,2,1,4,F,0.0,370,117.04,1.0,0.0,0,55009.56,470
+102,Sarah Foster,46-55,Single,1,0,5,F,-0.25,237,118.3,1.0,-65.3,2,31349.23,265
+103,Brittany Reeves,46-55,Married,2,0,4,F,0.0,864,107.98,1.14,0.0,0,187337.79,1975
+104,Allen Horton,46-55,Single,1,0,4,M,-1.37,186,90.21,1.05,-348.17,14,23003.33,268
+105,Yvonne Hansen,36-45,Single,3,1,5,F,-0.25,296,113.68,1.01,-121.11,4,54794.34,486
+106,Sean Guerra,26-35,Married,5,3,3,M,-0.26,869,77.22,1.06,-347.1,14,101621.48,1391
+107,Kevin Newman,18-25,Married,2,0,2,M,-0.01,478,107.57,1.01,-8.9,1,64432.19,603
+108,Ann Allen,46-55,Single,1,0,5,F,-11.4,154,192.99,1.0,-3043.71,86,51529.28,267
+109,Danielle West,36-45,Married,2,0,8,F,0.0,178,90.92,1.02,0.0,0,19729.22,222
+110,Jordan Morris,18-25,Married,3,1,6,F,-0.04,424,90.75,1.03,-35.62,1,79137.98,896
+111,Emily Jones,26-35,Married,3,1,3,F,0.0,295,119.58,1.0,0.0,0,64213.63,537
+112,Joseph Sellers,46-55,Married,2,0,4,M,-0.26,701,86.62,1.01,-272.49,12,90950.58,1065
+113,Thomas Blankenship,56-70,Married,2,0,4,M,-0.93,309,92.4,1.13,-427.06,14,42505.39,520
+114,James Bishop,26-35,Married,3,1,5,M,0.0,227,110.83,1.0,0.0,0,45219.34,408
+115,Sean Thompson,36-45,Married,4,2,8,M,0.0,110,108.69,1.0,0.0,0,15651.35,144
+116,Curtis Wilson,26-35,Married,2,0,2,M,0.0,345,117.8,1.0,0.0,0,51123.96,434
+117,Benjamin Nelson,26-35,Single,1,0,9,M,-0.11,236,81.38,1.1,-35.62,2,26531.28,360
+118,Jennifer Cooper,26-35,Single,1,0,7,F,-0.17,270,92.92,1.0,-71.24,2,38190.54,411
+119,Kayla Wagner,46-55,Single,1,0,3,F,-0.31,467,116.58,1.12,-247.56,11,94200.17,901
+120,Theresa Thomas,46-55,Single,1,0,2,F,-0.31,304,86.02,1.05,-118.73,2,32772.56,399
+121,Belinda Freeman,26-35,Single,1,0,5,F,-0.73,165,71.17,1.0,-142.48,3,13949.05,196
+122,Tracey Rodriguez,46-55,Married,3,1,10,F,0.0,372,99.81,1.0,0.0,0,44314.42,444
+123,Blake Peterson,46-55,Married,2,0,4,M,-0.35,354,111.74,1.0,-276.06,13,87047.03,779
+124,Brenda Solis,46-55,Married,2,0,6,F,-0.16,377,110.51,1.24,-106.86,3,71721.55,803
+125,Kimberly Marsh,46-55,Married,2,0,6,F,0.0,282,111.55,1.02,0.0,0,35920.0,327
+126,Richard Rodriguez,46-55,Married,2,0,6,M,0.0,530,88.91,1.07,0.0,0,80192.83,968
+127,Kelly Cline,26-35,Married,2,0,5,F,-0.23,731,86.12,1.0,-292.27,14,107650.4,1256
+128,Leah Lane,36-45,Married,3,1,1,F,-0.27,1341,91.44,1.0,-605.18,12,204098.83,2232
+129,Jennifer Hammond,46-55,Married,2,0,6,F,0.0,132,100.65,1.0,0.0,0,15500.13,154
+130,Charles Green,26-35,Single,3,2,3,M,0.0,323,108.74,1.0,0.0,0,47085.34,435
+131,Brian Thompson,46-55,Married,2,0,5,M,-3.39,326,103.1,1.06,-2523.24,117,76813.06,786
+132,Patricia Chapman,46-55,Married,3,1,3,F,-2.47,584,113.68,1.06,-2174.63,41,100261.74,933
+133,Ashley Aguirre,36-45,Married,5,3,7,F,-0.16,467,94.33,1.0,-126.45,5,73389.73,778
+134,Dawn Brown,70+,Married,2,0,4,F,0.0,242,84.81,1.0,0.0,0,44692.52,527
+135,Ricardo Craig,36-45,Single,1,0,1,M,-0.16,614,97.87,1.01,-124.31,4,74673.18,770
+136,Annette Johnson,56-70,Single,2,1,1,F,-0.08,341,95.68,1.0,-55.21,3,65442.52,684
+137,George Williams,26-35,Single,1,0,5,M,-0.54,290,72.84,1.01,-231.53,8,31247.55,435
+138,Stephen Dean,36-45,Single,1,0,5,M,-0.08,222,113.26,1.0,-32.06,2,44283.2,391
+139,Denise Garcia,56-70,Married,2,0,9,F,-0.51,288,94.73,1.08,-206.6,9,38272.26,437
+140,Tara Green,46-55,Married,2,0,2,F,-0.09,725,115.38,1.13,-106.86,1,140882.46,1383
+141,John Case,56-70,Married,2,0,9,M,-0.03,897,120.18,1.01,-54.85,2,207434.32,1749
+142,Jasmine Johnson,46-55,Married,2,0,4,F,-0.22,348,83.89,1.08,-135.36,3,50671.48,655
+143,Eduardo Grant,70+,Single,1,0,2,M,-0.1,464,75.57,1.01,-64.12,4,50556.18,677
+144,Edward Williams,36-45,Married,2,0,4,M,-0.04,646,84.44,1.03,-53.43,1,121842.62,1491
+145,Michelle Thomas,46-55,Single,1,0,3,F,0.0,436,107.28,1.01,0.0,0,56751.89,534
+146,Paul Henry,46-55,Married,2,0,6,M,0.0,210,90.93,1.03,0.0,0,29096.32,330
+147,Michelle Salazar,36-45,Married,5,3,5,F,-0.04,227,52.98,1.0,-30.28,2,44129.64,835
+148,Mercedes Spencer,46-55,Married,2,0,4,F,-0.44,392,114.01,1.05,-204.82,4,53128.23,487
+149,Mark Martin,26-35,Single,1,0,3,M,-0.06,659,82.1,1.03,-65.3,3,90230.33,1132
+150,Erin Bartlett,36-45,Married,2,0,4,F,-0.4,1493,91.72,1.04,-1094.17,36,248736.32,2817
+151,Paul Schultz,36-45,Married,5,3,12,M,-0.06,991,118.91,1.05,-100.62,6,201314.36,1780
+152,Pamela Walsh,36-45,Single,1,0,6,F,-0.81,310,98.47,1.0,-332.44,12,40175.1,408
+153,Carol Hobbs,46-55,Single,1,0,5,F,-1.77,740,67.92,1.0,-2410.36,86,92716.3,1365
+154,Debra Johnson,46-55,Married,5,3,6,F,-0.22,640,101.56,1.0,-224.4,7,101455.6,999
+155,Kimberly Banks,70+,Married,2,0,5,F,-0.22,718,106.88,1.11,-242.2,11,117675.63,1218
+156,Joseph Garcia,46-55,Married,3,1,7,M,0.0,158,91.26,1.11,0.0,0,16335.33,198
+157,Jason Kirby,26-35,Single,1,0,4,M,0.0,147,88.17,1.0,0.0,0,18515.96,211
+158,Bryan Henry,36-45,Married,4,2,8,M,-2.38,975,105.35,1.0,-3022.65,106,133902.58,1271
+159,Tony Blake,36-45,Married,3,1,1,M,-3.83,268,103.94,1.01,-1674.14,14,45421.61,441
+160,Scott Garrett,36-45,Married,2,0,9,M,-0.04,378,100.31,1.0,-17.81,1,47345.28,474
+161,Nicole Lee,46-55,Single,1,0,1,F,-0.54,1235,76.96,1.0,-1537.74,60,219958.63,2872
+162,Caleb Martin,26-35,Married,2,0,5,M,-0.99,702,117.55,1.01,-1990.73,64,236634.44,2040
+163,Thomas Mayo,26-35,Single,3,2,3,M,-0.02,369,75.14,1.0,-11.58,1,46963.12,628
+164,Madison Gonzalez,46-55,Married,2,0,5,F,-1.11,673,65.34,1.01,-1117.04,40,65997.64,1020
+165,Melanie Baker,46-55,Single,1,0,8,F,-0.17,171,118.17,1.16,-35.62,1,25052.5,245
+166,Dr. Michael Mitchell,46-55,Single,1,0,4,M,-0.16,718,95.06,1.0,-175.73,5,106559.85,1121
+167,Michael Casey,46-55,Married,2,0,4,M,-0.17,543,93.11,1.0,-189.86,10,102239.32,1098
+168,Evan Jones,46-55,Married,2,0,6,M,0.0,358,97.94,1.01,0.0,0,51615.54,534
+169,Brandy Peters,36-45,Married,2,0,5,F,-0.04,351,89.93,1.0,-19.59,1,42445.21,473
+170,Dana Cortez,36-45,Married,5,3,6,F,-0.08,301,105.52,1.02,-42.67,2,56031.17,542
+171,Richard Moreno,70+,Married,2,0,4,M,0.0,85,119.71,1.0,0.0,0,14365.53,120
+172,Michelle Cooper,26-35,Married,2,0,6,F,0.0,286,114.92,1.0,0.0,0,48497.74,422
+173,Julie Hill,26-35,Married,2,0,4,F,-0.01,441,90.78,1.03,-5.34,1,93772.47,1068
+174,Jennifer Alexander,36-45,Single,1,0,4,F,-0.02,582,96.18,1.05,-17.81,1,95407.08,1042
+175,Courtney Flynn,70+,Married,2,0,1,F,0.0,316,118.55,1.0,0.0,0,48366.74,408
+176,Michelle Frank,26-35,Married,2,0,4,F,-1.08,218,97.64,1.08,-333.06,25,30074.28,334
+177,Maureen Armstrong,46-55,Married,2,0,5,F,0.0,208,112.8,1.06,0.0,0,31246.76,295
+178,Ashley Hall,36-45,Single,1,0,5,F,-0.41,184,100.78,1.0,-114.87,5,28118.85,280
+179,John Edwards,46-55,Single,1,0,4,M,-1.52,462,112.09,1.04,-1113.8,42,81939.43,757
+180,Nicole Sanchez,26-35,Married,2,0,5,F,-0.43,545,121.35,1.0,-308.83,6,87006.78,717
+181,Linda Rice,36-45,Single,1,0,2,F,-0.21,509,97.02,1.04,-167.4,7,76161.72,818
+182,Bonnie Santiago,46-55,Single,1,0,2,F,0.0,115,134.07,1.0,0.0,0,19708.82,147
+183,Dana Gutierrez,26-35,Married,4,2,2,F,-0.04,481,90.64,1.03,-26.71,1,57737.32,656
+184,Joseph Gamble,26-35,Married,2,0,5,M,0.0,418,102.12,1.08,0.0,0,53814.79,571
+185,Kirsten Stone,46-55,Single,1,0,7,F,0.0,518,90.91,1.13,0.0,0,80632.95,1003
+186,Nicole Maxwell,26-35,Married,2,0,4,F,-0.01,652,78.5,1.0,-17.81,1,117276.26,1494
+187,Karen Adams,36-45,Married,2,0,7,F,-0.04,329,80.29,1.0,-17.81,1,39663.35,494
+188,Gary Bowman,70+,Married,2,0,6,M,-0.04,546,103.48,1.0,-35.62,1,83711.32,809
+189,Antonio Blankenship,26-35,Married,4,2,5,M,-0.88,716,94.65,1.0,-1206.92,36,130053.59,1377
+190,Todd Butler,46-55,Single,1,0,3,M,-0.22,323,93.98,1.0,-89.05,1,38251.42,407
+191,Patricia Brown,56-70,Married,2,0,1,F,-0.04,750,104.54,1.0,-46.3,3,110083.82,1053
+192,Danielle Casey,36-45,Married,4,2,4,F,-0.07,826,111.01,1.0,-90.84,4,153971.82,1387
+193,Ronnie Campbell,46-55,Married,3,1,4,M,0.0,287,114.9,1.01,0.0,0,42284.87,371
+194,Christian Sanchez,46-55,Single,1,0,3,M,-0.26,600,101.83,1.02,-235.98,8,91141.24,909
+195,Dennis Frederick,26-35,Married,2,0,5,M,-0.06,1218,96.12,1.06,-118.73,2,176671.35,1945
+196,Andrew Herrera,70+,Married,2,0,6,M,-0.84,335,114.27,1.06,-509.35,14,69247.58,642
+197,Christina Lopez,18-25,Married,4,2,4,F,-2.03,547,79.81,1.05,-1971.96,76,77579.45,1020
+198,George Lee,36-45,Single,1,0,3,M,0.0,215,90.61,1.02,0.0,0,57806.61,648
+199,Christopher Frost,46-55,Married,3,1,5,M,0.0,240,126.82,1.04,0.0,0,41091.1,336
+200,Vicki Hunt,70+,Single,1,0,4,F,-2.55,387,115.68,1.0,-1439.19,36,65241.99,564
+201,Shelia Clark,46-55,Married,3,1,6,F,0.0,381,73.89,1.0,0.0,0,52685.48,713
+202,Anita Gray,46-55,Married,2,0,6,F,-0.57,626,107.12,1.0,-626.07,39,117405.33,1096
+203,Megan Black,36-45,Married,5,3,5,F,-0.42,169,98.43,1.01,-124.67,2,29529.22,304
+204,Hunter Griffin,46-55,Single,4,3,4,M,-0.05,600,84.72,1.06,-41.85,2,75319.67,938
+205,Kevin Hicks,46-55,Married,2,0,7,M,-1.72,533,113.79,1.0,-1667.11,85,110372.74,970
+206,Laura Singh,36-45,Married,4,2,5,F,-0.06,465,106.15,1.01,-35.62,1,59869.59,567
+207,Patrick Sanders,46-55,Single,1,0,5,M,-0.49,892,115.44,1.08,-740.88,28,175582.84,1636
+208,Mr. Mike Johnson Jr.,46-55,Married,3,1,10,M,-0.08,1439,117.81,1.03,-231.53,6,333162.8,2902
+209,Dylan Fernandez,26-35,Single,1,0,6,M,0.0,366,80.42,1.0,0.0,0,53964.24,671
+210,Kathryn Mitchell,46-55,Married,2,0,6,F,-0.09,217,71.2,1.03,-50.76,3,38235.93,553
+211,Zachary Cross,56-70,Married,2,0,10,M,-0.07,217,101.18,1.0,-19.59,1,28230.09,279
+212,Elizabeth Ramirez MD,36-45,Married,5,3,8,F,-0.08,873,89.55,1.0,-80.14,2,94559.55,1056
+213,Douglas Glass,36-45,Married,3,1,2,M,0.0,209,79.16,1.0,0.0,0,24776.17,313
+214,Luis Ramirez,46-55,Married,2,0,1,M,-1.2,763,88.78,1.01,-1556.83,44,114787.63,1302
+215,Michael Clark,36-45,Single,1,0,9,M,0.0,304,119.01,1.0,0.0,0,56648.83,476
+216,Kristi Vargas,46-55,Single,1,0,5,F,-0.16,278,91.91,1.05,-71.24,2,40439.23,464
+217,Michael Owens,46-55,Married,2,0,12,M,-0.68,115,82.65,1.03,-159.75,11,19340.91,241
+218,Jeremy Carpenter,46-55,Married,3,1,5,M,0.0,246,76.88,1.0,0.0,0,27063.52,352
+219,Allison Campbell,70+,Married,2,0,3,F,-0.34,377,74.13,1.0,-187.89,6,40623.74,548
+220,Cynthia Santos,56-70,Single,1,0,5,F,-0.21,354,92.33,1.0,-84.78,3,37949.56,411
+221,Shannon Hernandez,46-55,Single,1,0,6,F,-0.15,175,129.2,1.0,-32.94,2,28423.71,220
+222,Alicia Smith,70+,Married,2,0,3,F,-0.18,801,85.59,1.0,-260.02,7,122143.72,1427
+223,Joseph Adams,70+,Married,2,0,5,M,0.0,335,97.34,1.0,0.0,0,65316.78,671
+224,Angie Johnson,46-55,Single,1,0,1,F,0.0,353,78.3,1.0,0.0,0,35391.44,452
+225,Karen Bailey,46-55,Married,4,2,5,F,0.0,252,115.53,1.21,0.0,0,38704.17,404
+226,Johnny Wood,46-55,Single,1,0,5,M,-0.29,587,137.29,1.01,-377.21,10,177518.67,1304
+227,John Gibbs,46-55,Married,2,0,9,M,-0.09,454,109.94,1.0,-45.42,3,57937.84,527
+228,Jessica Hammond,26-35,Married,5,3,4,F,-1.02,1104,101.42,1.0,-2032.05,86,201221.05,1984
+229,Amber Allen,18-25,Single,2,1,5,F,-0.32,262,107.64,1.05,-160.29,3,54682.02,533
+230,Melissa Crawford,36-45,Married,2,0,5,F,-0.13,1192,115.83,1.0,-276.76,6,253657.11,2190
+231,Lynn Cannon DDS,46-55,Single,1,0,5,F,-0.27,1071,75.15,1.0,-578.46,13,161114.84,2144
+232,Andrea Johnson,70+,Single,1,0,5,F,-0.15,364,98.55,1.02,-92.26,4,60707.49,628
+233,Mrs. Heather Jenkins,36-45,Married,2,0,8,F,-0.19,426,110.73,1.01,-106.86,2,63560.95,577
+234,Barbara Gibson,46-55,Married,3,1,10,F,-0.13,508,117.23,1.02,-114.57,4,100581.98,879
+235,Jennifer Henry,56-70,Married,2,0,5,F,-3.16,764,113.55,1.0,-4619.7,210,166009.0,1462
+236,Christopher Lamb,18-25,Married,2,0,4,M,-0.15,155,108.04,1.04,-35.62,1,25173.39,243
+237,Traci Bradley,36-45,Single,2,1,4,F,0.0,188,89.13,1.01,0.0,0,18805.61,214
+238,Carly Robinson,70+,Married,2,0,5,F,-0.16,504,85.62,1.1,-125.92,7,67214.24,862
+239,Joshua Porter,36-45,Married,3,1,6,M,-0.13,1409,102.16,1.0,-401.6,13,319031.88,3123
+240,Ronald Booth,46-55,Single,1,0,2,M,-0.48,615,146.5,1.02,-500.8,9,153385.98,1072
+241,Eric Flores,26-35,Married,5,3,11,M,0.0,511,72.02,1.0,0.0,0,112776.33,1566
+242,Philip Williams,46-55,Married,2,0,5,M,0.0,374,90.65,1.0,0.0,0,56748.56,626
+243,Teresa Cruz,56-70,Married,2,0,5,F,-0.63,494,137.96,1.04,-550.91,23,121268.21,913
+244,John Martinez,70+,Married,2,0,2,M,-0.11,666,95.88,1.0,-189.97,4,171623.86,1790
+245,Stephen Christian,26-35,Single,2,1,4,M,-0.64,456,110.64,1.0,-365.09,7,63172.92,571
+246,Robert Graham,46-55,Single,1,0,5,M,-0.39,420,100.46,1.03,-388.22,17,100059.2,1029
+247,Bianca Cruz,36-45,Married,2,0,5,F,0.0,445,125.11,1.04,0.0,0,66308.11,550
+248,Michael Jones,36-45,Single,1,0,3,M,-0.56,819,68.15,1.02,-858.08,39,104202.24,1555
+249,Valerie Wolf,26-35,Single,1,0,5,F,-1.04,566,131.73,1.0,-843.89,28,107095.86,813
+250,Elizabeth Fritz,36-45,Single,1,0,9,F,-0.77,1134,108.35,1.03,-1977.97,79,277043.37,2637
+251,Alexis Sims,46-55,Single,1,0,3,F,-0.27,238,113.41,1.01,-89.05,1,38106.74,339
+252,Deanna Knox,70+,Single,1,0,3,F,-0.19,209,121.79,1.0,-46.3,3,28986.97,238
+253,Joseph Schmidt,36-45,Married,3,1,12,M,-0.01,848,131.17,1.0,-12.47,1,223909.04,1707
+254,Erica Oconnell,70+,Single,1,0,3,F,-0.2,723,95.29,1.0,-186.82,6,91094.91,956
+255,Michael Saunders,26-35,Married,2,0,4,M,0.0,246,121.52,1.0,0.0,0,33417.86,275
+256,Brenda Thompson,26-35,Married,3,1,4,F,-0.49,171,94.61,1.04,-144.56,6,28192.49,311
+257,Matthew Spears,26-35,Married,5,3,5,M,-0.62,652,105.44,1.07,-708.53,23,119782.95,1212
+258,Kenneth Williamson,36-45,Single,1,0,8,M,-0.03,402,75.15,1.04,-17.8,2,38627.46,532
+259,Kelly Washington,36-45,Married,3,1,9,M,-0.27,1653,134.68,1.05,-743.74,6,370091.54,2892
+260,Daisy Jenkins,36-45,Married,5,3,5,F,-0.38,216,99.03,1.09,-117.54,3,30601.77,337
+261,Matthew Jones,56-70,Married,2,0,5,M,-1.19,93,158.07,1.0,-130.85,5,17387.23,110
+262,Brandy Holland,26-35,Married,2,0,3,F,-0.02,670,129.22,1.02,-19.59,1,150925.52,1194
+263,Aaron Richards,26-35,Single,1,0,5,M,-0.07,345,76.66,1.23,-35.62,1,41242.22,660
+264,Calvin Adams,46-55,Single,1,0,5,M,-0.66,321,147.74,1.0,-262.7,3,58799.62,398
+265,Ashley Wagner,18-25,Single,2,1,3,F,-2.65,713,92.47,1.02,-2723.87,109,95057.11,1049
+266,Stephen Kane,56-70,Married,5,3,4,M,-0.56,194,82.66,1.0,-145.69,3,21409.2,259
+267,Matthew Cherry,36-45,Married,2,0,1,M,-0.16,540,127.99,1.05,-117.37,6,95990.19,788
+268,Sarah Brooks,70+,Married,2,0,2,F,-1.06,437,82.83,1.0,-709.89,16,55495.9,672
+269,Larry Weber,46-55,Single,1,0,7,M,-0.02,371,90.4,1.13,-19.59,1,79104.01,990
+270,Russell Manning,36-45,Married,2,0,1,M,-0.1,391,89.35,1.02,-56.1,4,50928.12,584
+271,Timothy Hamilton,36-45,Married,3,1,4,M,-0.03,706,104.33,1.05,-31.16,2,126236.06,1266
+272,Jennifer Green,56-70,Single,1,0,6,F,-1.04,234,121.91,1.04,-286.74,10,33646.07,286
+273,Robert Yang,46-55,Single,1,0,5,M,0.0,448,101.72,1.0,0.0,0,57673.55,567
+274,Alicia Ray,46-55,Married,2,0,10,F,-2.09,746,101.62,1.11,-2051.65,79,99996.18,1088
+275,Michael Moore,46-55,Married,5,3,8,M,-0.03,660,86.76,1.0,-37.4,2,109573.46,1263
+276,Debra Richardson,46-55,Married,2,0,5,F,-0.76,597,97.5,1.15,-922.55,53,118950.83,1409
+277,Christina Gonzalez,18-25,Married,2,0,5,F,-0.07,658,76.31,1.0,-80.14,3,84095.62,1102
+278,Tony Wilson,70+,Married,2,0,6,M,-0.99,137,103.89,1.01,-158.15,3,16519.23,161
+279,William Torres,18-25,Single,1,0,3,M,-0.25,438,106.76,1.02,-160.29,3,68327.76,650
+280,Madison Williamson,26-35,Married,4,2,8,F,0.0,139,79.3,1.0,0.0,0,16652.88,210
+281,Diane Ali,36-45,Married,3,1,2,F,0.0,526,90.88,1.0,0.0,0,93058.67,1025
+282,Anna Powell,46-55,Married,3,1,6,F,0.0,322,113.98,1.0,0.0,0,47643.2,418
+283,Alex Joyce,26-35,Married,5,3,5,M,-1.4,684,125.88,1.03,-1582.34,47,142744.07,1165
+284,Tammie Sanchez,36-45,Married,2,0,5,F,-1.43,357,104.21,1.0,-785.21,27,57109.82,548
+285,Theresa Brown,46-55,Single,1,0,1,F,0.0,68,111.88,1.0,0.0,0,11635.7,104
+286,Michelle Fischer,46-55,Single,1,0,5,F,-0.03,155,98.28,1.0,-12.47,1,45109.41,459
+287,Kevin Kramer,26-35,Single,2,1,2,M,-0.76,328,88.54,1.05,-304.2,10,35239.21,417
+288,Samantha Flores,46-55,Married,2,0,3,F,-0.79,269,90.64,1.0,-395.02,13,45137.46,498
+289,Brett Carson,46-55,Married,2,0,6,M,-0.09,385,126.0,1.0,-55.21,3,77614.86,616
+290,Sarah Hernandez,70+,Married,2,0,3,F,0.0,214,110.86,1.01,0.0,0,33147.11,302
+291,Kristina Phillips,46-55,Married,2,0,12,F,0.0,167,82.44,1.0,0.0,0,33304.3,404
+292,Michelle Bailey,26-35,Single,2,1,1,F,0.0,226,105.83,1.02,0.0,0,30160.28,291
+293,Zachary Davis,56-70,Single,1,0,4,M,-0.51,915,94.12,1.0,-866.58,27,160282.6,1703
+294,Alex Martin,18-25,Single,2,1,5,M,-0.39,724,143.37,1.0,-425.66,9,155264.33,1087
+295,Rebecca Jones,56-70,Married,2,0,4,F,-2.79,293,85.89,1.02,-1091.22,26,33584.71,399
+296,Dennis Hubbard,18-25,Single,1,0,1,M,-0.4,97,133.92,1.0,-81.92,4,27185.71,203
+297,Jerry Sanchez,70+,Single,1,0,3,M,-0.65,132,76.42,1.06,-110.42,6,13068.45,182
+298,Jeffrey Franklin,70+,Single,1,0,7,M,-0.28,290,107.17,1.0,-89.05,4,34081.09,318
+299,John Allen,26-35,Married,3,1,6,M,-0.63,330,109.08,1.13,-446.23,14,77443.58,801
+300,Kenneth Mccarthy,70+,Married,2,0,5,M,-4.45,540,90.62,1.02,-3516.34,152,71587.39,804
+301,Robert Young,18-25,Married,2,0,2,M,-0.25,360,106.89,1.0,-106.5,1,44895.4,420
+302,Holly Johnson,36-45,Married,3,1,12,F,-0.43,602,103.13,1.0,-436.34,8,104780.38,1020
+303,Christy Johnson,46-55,Single,1,0,6,F,-1.39,931,108.83,1.0,-2193.59,90,171184.55,1579
+304,Ashley Gutierrez,70+,Married,3,1,4,F,-0.06,231,113.13,1.04,-26.71,1,53172.27,488
+305,Brian Bates,36-45,Married,5,3,4,M,-0.63,1087,129.02,1.0,-1131.8,35,231593.86,1795
+306,Maurice Clark,46-55,Single,1,0,5,M,0.0,347,91.46,1.08,0.0,0,51859.48,613
+307,Christina Williams,70+,Married,2,0,9,F,-0.4,330,89.82,1.0,-153.16,5,34759.82,387
+308,Julie Cantu,70+,Married,3,1,5,F,-0.06,316,102.93,1.0,-26.71,1,42716.18,415
+309,Michael Cherry,26-35,Single,1,0,5,M,-0.5,400,87.79,1.0,-260.02,7,45298.74,516
+310,Adam Cardenas,46-55,Married,2,0,1,M,-0.43,549,103.6,1.07,-316.06,10,75835.96,781
+311,Anthony Wright,26-35,Married,5,3,11,M,-0.75,397,95.46,1.0,-400.72,4,50880.84,533
+312,Vicki Dean,46-55,Married,2,0,10,F,0.0,303,97.17,1.0,0.0,0,35757.45,369
+313,Marcus Mcdonald,46-55,Single,1,0,4,M,-0.43,398,109.56,1.0,-222.62,4,56093.97,512
+314,Keith Shaw,26-35,Married,2,0,2,M,-0.4,440,101.05,1.0,-240.43,3,60933.44,606
+315,Pamela Smith,36-45,Single,1,0,3,F,-1.03,448,85.14,1.15,-802.69,19,66153.72,896
+316,Barbara Brooks,56-70,Single,1,0,9,F,-0.01,584,94.72,1.08,-8.9,1,84583.93,963
+317,Randall Bond MD,36-45,Married,5,3,6,M,-4.08,502,96.32,1.0,-3436.12,160,81098.05,842
+318,Ashley Phillips,46-55,Married,2,0,6,F,0.0,527,120.9,1.07,0.0,0,82938.83,734
+319,Marc Green,46-55,Married,2,0,5,M,-1.0,716,107.08,1.07,-1439.9,45,154191.78,1539
+320,Tina Lyons,56-70,Single,1,0,5,F,-0.46,480,105.33,1.02,-449.52,29,102805.11,1000
+321,Emma Price,36-45,Married,3,1,5,F,-0.17,414,112.13,1.0,-89.05,2,60099.82,536
+322,Maria Norman,46-55,Single,1,0,4,F,0.0,187,94.15,1.0,0.0,0,20901.34,222
+323,Veronica Turner,26-35,Married,2,0,3,F,-0.03,436,85.4,1.0,-17.81,1,50643.09,593
+324,Nancy Ellis,36-45,Single,1,0,6,F,-0.09,753,99.68,1.0,-94.39,3,102366.34,1028
+325,Stephanie Peterson,46-55,Single,1,0,5,F,-0.12,274,82.76,1.02,-47.37,1,32357.21,399
+326,Veronica Richardson,36-45,Single,3,1,5,F,0.0,180,124.41,1.0,0.0,0,26623.34,214
+327,Frank Nelson,36-45,Married,3,1,12,M,-0.4,1131,88.17,1.04,-727.0,15,159242.52,1886
+328,Catherine Ramirez,46-55,Married,2,0,8,F,-0.47,426,88.36,1.06,-400.72,7,74925.29,895
+329,Mark Pineda,46-55,Married,2,0,6,M,-1.62,604,169.19,1.0,-1841.55,27,192882.02,1142
+330,John Norris,36-45,Single,1,0,8,M,0.0,217,78.88,1.0,0.0,0,34705.62,440
+331,Jason Rogers,26-35,Married,3,1,5,M,0.0,350,87.58,1.0,0.0,0,37923.42,433
+332,Isaac Bradford,26-35,Married,5,3,4,M,-0.07,635,107.57,1.03,-55.21,2,83367.33,797
+333,Edward Nguyen,46-55,Married,2,0,6,M,-2.59,604,97.42,1.0,-2383.83,70,89529.02,919
+334,Victoria Sanders,26-35,Married,2,0,8,F,-1.18,198,108.73,1.0,-265.35,14,24463.21,225
+335,Kimberly Wallace,36-45,Married,5,3,4,F,-0.13,330,81.72,1.0,-73.02,3,45765.55,561
+336,Daniel Johnson,70+,Single,1,0,4,M,-0.74,374,91.62,1.07,-543.02,15,66972.11,784
+337,Christine Taylor,26-35,Single,1,0,5,F,-0.48,332,127.51,1.0,-213.72,3,56357.35,444
+338,Ashley Mckee,46-55,Single,1,0,5,F,-0.1,500,99.98,1.06,-65.89,3,66285.75,706
+339,Rebecca Lowe,46-55,Single,1,0,5,F,-0.08,276,112.4,1.0,-53.43,3,78228.97,696
+340,Marcus Aguirre,36-45,Married,4,2,2,M,-0.06,400,85.11,1.0,-29.68,1,44342.16,521
+341,Natalie Carson,36-45,Married,2,0,5,F,-0.34,254,129.12,1.0,-97.94,4,36926.97,287
+342,Christopher Moore,46-55,Single,1,0,9,M,-0.1,372,104.42,1.0,-59.37,2,60562.12,580
+343,Jacqueline Allen,46-55,Single,1,0,4,F,0.0,294,50.22,1.0,0.0,0,28525.24,568
+344,Lisa Key,46-55,Married,3,1,8,F,-0.16,381,79.73,1.0,-80.14,3,39945.73,502
+345,David Smith,46-55,Single,1,0,5,M,0.0,244,123.54,1.0,0.0,0,35827.07,290
+346,Matthew Russell,36-45,Married,2,0,9,M,-0.02,473,118.73,1.01,-13.36,1,80263.71,682
+347,Thomas Ross,26-35,Single,1,0,5,M,-0.04,259,124.25,1.01,-14.25,1,46220.58,374
+348,Meagan Daniel,26-35,Single,1,0,4,F,-0.18,405,91.08,1.03,-101.52,6,50275.0,571
+349,Jessica Richmond,36-45,Married,3,1,4,F,-0.04,217,96.88,1.0,-24.58,1,66268.78,684
+350,George Bailey,70+,Single,1,0,2,M,-0.14,397,97.57,1.01,-62.33,2,44100.34,455
+351,Stephanie Meyer,36-45,Married,3,1,1,F,-0.2,444,79.16,1.01,-160.3,7,62377.47,792
+352,Linda Walker,46-55,Married,2,0,5,F,-0.27,282,95.37,1.09,-164.92,9,57987.28,661
+353,James Hoffman,36-45,Single,2,1,6,M,-0.14,674,109.69,1.01,-128.24,5,101902.89,934
+354,Rhonda Wilson,18-25,Single,1,0,12,F,-0.03,253,112.4,1.0,-12.47,1,46756.5,416
+355,Gabriel Sanford,46-55,Married,2,0,6,M,-0.12,967,92.63,1.01,-189.05,6,150609.96,1644
+356,Todd Calhoun,36-45,Single,5,3,2,M,0.0,342,97.75,1.0,0.0,0,56598.12,579
+357,Erin Jones,18-25,Married,2,0,4,F,-0.05,894,105.59,1.06,-64.11,3,134525.24,1349
+358,Andrew Contreras,70+,Married,2,0,6,M,0.0,285,131.28,1.02,0.0,0,51069.83,396
+359,Michelle Foster,26-35,Married,3,1,3,F,-0.36,422,96.41,1.05,-191.99,4,50903.96,555
+360,Kristen Walters,46-55,Single,1,0,5,F,-0.17,256,75.59,1.05,-53.42,2,23809.56,331
+361,Sarah Taylor,18-25,Single,1,0,3,F,0.0,323,88.2,1.01,0.0,0,50540.89,578
+362,Taylor Romero,18-25,Single,1,0,5,M,-0.07,851,91.08,1.0,-108.64,2,136619.16,1500
+363,Kelly King,36-45,Married,3,1,12,F,-0.13,714,137.26,1.01,-154.58,5,164845.93,1217
+364,Sarah Martin,56-70,Single,1,0,2,F,-0.28,149,109.27,1.0,-44.52,1,17264.57,158
+365,Charles Gomez,70+,Married,2,0,8,M,0.0,271,99.47,1.0,0.0,0,34615.92,349
+366,James Perez,46-55,Married,2,0,6,M,-0.8,418,126.25,1.04,-523.6,10,82567.69,679
+367,Michael Todd,36-45,Married,3,1,6,M,-6.27,747,109.79,1.02,-7896.29,293,138229.78,1283
+368,Alexandra Morrison,36-45,Single,1,0,5,F,-0.04,1199,95.35,1.02,-103.3,4,241619.47,2581
+369,Donna Wells,56-70,Married,2,0,4,F,-0.02,527,112.44,1.01,-17.81,1,87814.42,789
+370,Rodney Mcfarland,46-55,Married,2,0,5,M,-0.51,554,114.58,1.0,-437.95,19,98651.77,861
+371,Patrick Mcgrath,26-35,Single,1,0,5,M,-0.02,447,115.09,1.01,-12.47,1,63298.54,554
+372,Patricia Vega,46-55,Married,2,0,7,F,-0.36,109,163.75,1.01,-73.01,3,33405.95,206
+373,Linda Simon,26-35,Married,3,1,4,F,-0.17,682,93.17,1.0,-169.2,3,90936.44,980
+374,Lisa Reed,46-55,Married,3,1,8,F,-0.08,528,148.98,1.02,-51.95,2,102202.48,702
+375,Justin White,36-45,Single,1,0,2,M,0.0,339,130.66,1.0,0.0,0,59581.64,456
+376,Alexandra Davis,46-55,Single,1,0,8,F,-0.4,146,130.55,1.17,-71.24,1,23237.97,209
+377,Kimberly Mckenzie,18-25,Married,3,1,4,F,-0.13,653,92.81,1.03,-139.8,5,102744.84,1145
+378,Mark White,36-45,Married,5,3,5,M,-0.78,988,96.46,1.08,-1380.24,48,170254.62,1902
+379,Amanda Martin,18-25,Single,1,0,1,F,-0.35,1347,120.46,1.01,-946.4,20,321875.91,2690
+380,Lisa Roberts,46-55,Married,2,0,7,F,-0.32,419,106.55,1.02,-169.2,3,56152.94,537
+381,Sara Allen,26-35,Married,3,1,4,F,-0.24,465,99.08,1.0,-140.52,4,58654.37,592
+382,James Sims,46-55,Married,3,1,2,M,0.0,267,142.02,1.0,0.0,0,61495.95,433
+383,Vickie King,18-25,Single,2,1,1,F,-0.1,646,88.31,1.0,-88.87,2,81331.58,921
+384,Rose Weber,36-45,Single,1,0,3,F,-0.64,372,93.35,1.02,-382.91,16,55634.34,606
+385,Samantha Brown,26-35,Single,1,0,3,F,-3.27,456,114.79,1.08,-2173.44,84,76220.79,716
+386,Roy Mcmillan,36-45,Married,4,2,7,M,0.0,681,98.93,1.04,0.0,0,107540.54,1132
+387,James Russell,46-55,Single,1,0,5,M,0.0,600,78.12,1.06,0.0,0,74679.53,1014
+388,Allison Peterson,26-35,Single,1,0,4,F,0.0,215,115.99,1.0,0.0,0,45467.77,392
+389,Michael Shelton,26-35,Married,4,2,6,M,-1.48,659,91.38,1.06,-1815.99,46,112393.84,1298
+390,Lauren Diaz,46-55,Single,1,0,4,F,0.0,543,99.87,1.01,0.0,0,71707.89,725
+391,Chad Gonzalez,46-55,Single,1,0,8,M,-0.35,602,76.79,1.04,-352.98,14,77172.1,1047
+392,Ryan Hall,26-35,Single,5,3,3,M,-0.27,488,107.88,1.0,-252.9,5,102812.77,953
+393,Julie Lawson DVM,46-55,Single,1,0,5,F,-1.2,303,83.72,1.01,-568.14,6,39683.09,481
+394,Thomas Carter,46-55,Single,3,1,5,M,-0.07,383,95.08,1.02,-44.52,1,64940.98,699
+395,Ariel Hill,36-45,Married,4,2,2,F,-0.36,291,85.7,1.0,-242.21,8,56903.16,664
+396,Brian Ferguson,70+,Married,3,1,8,M,-0.16,655,92.73,1.12,-186.42,7,107936.42,1301
+397,Amanda Castro,46-55,Single,1,0,4,F,-0.09,248,110.4,1.0,-41.86,3,53435.34,484
+398,John Gonzalez,36-45,Single,1,0,2,M,-0.52,341,132.72,1.0,-220.84,6,56138.86,424
+399,Bonnie Hawkins,46-55,Married,3,1,4,F,-0.23,412,118.67,1.04,-142.48,5,73931.75,650
+400,Angela Best,46-55,Single,1,0,5,F,-0.09,578,82.55,1.06,-91.72,3,82135.77,1050
+401,Michelle Johnson,36-45,Married,3,1,4,F,-1.86,529,96.42,1.21,-1646.65,72,85233.6,1067
+402,Angela Dunlap,70+,Married,3,1,7,F,-0.11,623,84.97,1.09,-187.0,4,145724.18,1864
+403,Brianna Lopez,46-55,Single,1,0,3,F,-1.62,510,92.95,1.04,-1025.62,43,58933.19,657
+404,Kimberly Lee,36-45,Single,1,0,3,F,-2.47,595,106.77,1.04,-2693.23,64,116381.28,1133
+405,Brenda Love,36-45,Married,2,0,6,F,0.0,306,120.32,1.0,0.0,0,50054.28,416
+406,Daniel Scott,26-35,Single,1,0,3,M,0.0,47,141.52,1.0,0.0,0,11745.84,83
+407,Christina Brown,36-45,Married,2,0,6,F,-0.13,1007,72.93,1.01,-259.67,10,147099.74,2035
+408,Mark Smith,46-55,Married,2,0,5,M,-0.22,396,95.25,1.0,-125.56,4,54767.12,576
+409,Shannon Lutz,56-70,Married,2,0,5,F,-0.22,622,101.93,1.11,-237.58,10,110803.34,1212
+410,Sarah Hall,26-35,Married,2,0,5,F,0.0,124,89.16,1.0,0.0,0,16226.61,182
+411,Lindsey Parker,46-55,Single,1,0,4,F,-0.12,472,84.96,1.03,-89.05,3,60576.18,734
+412,Willie Orr,46-55,Married,2,0,3,M,-0.32,1284,109.88,1.0,-762.26,18,262507.32,2389
+413,Kathryn Griffin,36-45,Single,1,0,1,F,-0.03,372,128.1,1.01,-17.81,1,66869.9,525
+414,Rebecca Price,70+,Married,2,0,6,F,-0.42,566,81.35,1.03,-471.6,15,90782.22,1149
+415,Stephen Garcia,26-35,Married,2,0,5,M,-0.37,404,87.45,1.06,-178.1,2,42238.17,510
+416,Douglas Irwin,36-45,Married,4,2,5,M,-1.63,609,113.97,1.09,-1410.82,46,98353.98,942
+417,Jessica Murphy,36-45,Married,2,0,3,F,-0.11,351,95.11,1.0,-60.55,3,50410.43,530
+418,Robert Mendoza,36-45,Single,4,2,1,M,-0.05,1021,97.45,1.07,-89.05,2,183490.62,2020
+419,Katie Galloway,36-45,Married,2,0,3,F,-0.25,362,103.39,1.0,-126.44,6,53040.48,513
+420,Manuel Leonard,26-35,Married,3,1,4,M,-0.04,634,106.85,1.0,-40.07,2,110055.11,1030
+421,Jared Aguilar,56-70,Married,2,0,5,M,-0.29,917,113.48,1.03,-414.25,11,164438.22,1497
+422,Caitlyn Adkins,46-55,Single,1,0,5,F,0.0,440,177.18,1.0,0.0,0,200925.82,1134
+423,Anthony Weber,46-55,Married,2,0,4,M,-0.21,149,136.68,1.0,-53.43,2,35263.55,258
+424,Gavin Gibson,46-55,Single,1,0,4,M,-1.37,525,97.26,1.01,-1050.44,17,74308.75,769
+425,Anthony Key,70+,Single,1,0,1,M,-0.13,87,102.87,1.0,-17.81,1,13990.3,136
+426,Sara Medina,26-35,Single,1,0,7,F,-0.03,827,132.58,1.01,-44.52,2,170900.26,1301
+427,Laurie Valencia,56-70,Married,3,1,4,F,-1.23,680,100.52,1.04,-1246.68,43,101925.23,1055
+428,Bradley King,36-45,Single,1,0,4,M,-0.25,333,121.94,1.15,-160.29,10,79020.03,745
+429,Laurie Grant,36-45,Married,2,0,3,F,-0.59,336,86.88,1.03,-296.83,6,43962.95,520
+430,Nicholas Kelley,70+,Married,2,0,6,M,-0.01,615,98.7,1.0,-8.9,1,89620.29,908
+431,Julie Smith,36-45,Married,2,0,5,F,-0.55,329,89.22,1.06,-413.79,25,66736.78,793
+432,John White DVM,46-55,Married,2,0,6,M,-0.19,910,95.15,1.0,-391.64,6,194286.63,2042
+433,Lisa Keller,46-55,Single,1,0,5,F,-1.53,869,125.19,1.0,-2137.34,53,175139.14,1399
+434,Sydney Brown,46-55,Single,1,0,5,F,-0.15,491,89.17,1.0,-124.67,2,74901.02,840
+435,Cynthia Beck,36-45,Single,1,0,5,F,-0.3,209,126.18,1.01,-160.29,5,66748.72,532
+436,Ross Rogers,70+,Married,2,0,8,M,-1.01,534,96.42,1.14,-822.45,28,78289.9,927
+437,Jessica Castro,26-35,Single,1,0,2,F,0.0,491,82.49,1.01,0.0,0,52874.32,649
+438,Gregory Williamson,36-45,Married,2,0,1,M,-0.06,569,94.46,1.01,-71.24,2,111274.5,1188
+439,Matthew Martin,36-45,Married,3,1,6,M,-0.5,388,97.07,1.0,-297.41,17,57465.9,592
+440,Diana Harding,36-45,Married,3,1,8,F,-0.12,497,129.84,1.0,-71.24,1,78165.67,602
+441,Mary Lyons,70+,Single,1,0,2,F,-0.09,523,124.27,1.06,-64.11,4,91217.75,776
+442,Steven Weiss,46-55,Married,5,3,6,M,0.0,370,107.61,1.0,0.0,0,60475.01,562
+443,Mark Figueroa,70+,Single,1,0,4,M,-0.19,298,103.87,1.05,-137.13,5,75303.79,758
+444,Linda Taylor,46-55,Married,2,0,2,F,-0.12,960,115.31,1.0,-163.85,6,163742.07,1420
+445,Sarah Williams,26-35,Married,2,0,5,F,0.0,164,106.03,1.0,0.0,0,41138.1,388
+446,Zachary Baker,36-45,Single,3,2,4,M,-0.05,697,94.16,1.05,-71.24,2,123449.87,1371
+447,April Rocha,36-45,Married,2,0,5,F,-0.41,1214,117.35,1.08,-862.7,36,246437.23,2260
+448,John Andrews,18-25,Married,3,1,5,M,-0.07,470,100.21,1.04,-44.52,2,63433.7,656
+449,Karen Potts,70+,Single,1,0,4,F,-0.12,252,83.96,1.01,-35.62,2,24683.65,298
+450,Matthew Thomas,46-55,Married,2,0,5,M,-0.32,839,120.83,1.12,-473.86,14,177618.34,1642
+451,Emily Marks,56-70,Married,2,0,1,F,-0.23,280,113.64,1.01,-98.04,2,47956.21,426
+452,Jon Payne,26-35,Married,2,0,5,M,-0.11,517,102.73,1.05,-141.76,6,138176.97,1418
+453,Patrick Rodriguez,70+,Single,2,1,2,M,-0.17,703,117.84,1.12,-233.31,7,162269.19,1542
+454,Justin Barnett,36-45,Married,2,0,5,M,0.0,199,110.5,1.0,0.0,0,35137.47,318
+455,Mary Flores,46-55,Single,2,1,5,F,0.0,613,110.32,1.02,0.0,0,79653.01,734
+456,Pam Baker,26-35,Married,5,3,7,F,-0.04,1252,123.26,1.12,-104.48,4,295583.04,2675
+457,Crystal Morrison,36-45,Married,4,2,7,F,0.0,421,106.65,1.0,0.0,0,56099.52,526
+458,Sarah Morales,46-55,Single,2,1,4,F,-0.15,723,87.54,1.0,-173.94,4,99091.73,1132
+459,Jason Graham,26-35,Married,2,0,4,M,-0.69,638,89.29,1.01,-640.71,26,83310.54,942
+460,John Moore,70+,Married,2,0,5,M,-0.53,915,113.17,1.01,-1097.09,33,232103.75,2081
+461,Clifford Rodriguez,56-70,Married,3,1,2,M,-0.86,352,93.82,1.0,-591.3,19,64733.24,690
+462,Jennifer Ellis,56-70,Married,2,0,2,F,-1.36,467,85.78,1.01,-829.8,23,52325.33,614
+463,William Roberts,46-55,Married,5,3,6,M,-3.25,182,91.56,1.02,-728.42,29,20509.29,228
+464,Daniel Holden,46-55,Married,5,3,3,M,-1.47,2040,89.27,1.04,-6063.05,220,369053.18,4314
+465,Blake Young,26-35,Married,3,1,4,M,0.0,89,193.96,1.0,0.0,0,35300.99,182
+466,Brandon Phillips,36-45,Married,2,0,5,M,-0.58,698,109.83,1.01,-654.5,24,123780.54,1140
+467,Victor Sanchez,46-55,Single,1,0,3,M,-0.2,554,74.05,1.0,-276.06,8,102852.06,1389
+468,Ashley Woodward,56-70,Married,2,0,4,F,-0.04,320,104.69,1.0,-17.81,1,41874.09,400
+469,Courtney Bradshaw,18-25,Single,1,0,4,F,-0.04,550,99.59,1.0,-44.52,1,109452.94,1099
+470,Christopher Rosario,18-25,Married,3,1,4,M,-0.02,896,98.51,1.1,-39.19,3,202740.39,2266
+471,Brandi Morgan,18-25,Married,3,1,5,F,0.0,252,104.65,1.0,0.0,0,31812.86,304
+472,Brandi Wilson,46-55,Married,2,0,5,F,0.0,97,120.93,1.0,0.0,0,23702.96,196
+473,Michelle Hammond,46-55,Married,2,0,4,F,-0.35,501,105.67,1.08,-215.74,6,65094.98,664
+474,Tammy Fitzgerald,56-70,Married,4,2,6,F,-0.31,501,88.61,1.02,-266.55,12,77268.3,888
+475,Timothy Vaughan,46-55,Married,2,0,6,M,-1.26,210,82.15,1.0,-713.65,9,46414.89,565
+476,Daryl Moore,36-45,Married,5,3,7,M,0.0,146,108.21,1.04,0.0,0,18827.8,181
+477,Stephanie Barnes,70+,Married,2,0,2,F,0.0,268,101.83,1.0,0.0,0,29836.31,293
+478,Nicole Kane,36-45,Married,5,3,7,F,-0.03,477,86.18,1.0,-17.81,1,56192.32,652
+479,Gerald Welch,46-55,Married,2,0,10,M,-0.03,771,90.9,1.0,-44.52,2,117618.64,1294
+480,Eric Duncan,70+,Married,3,1,5,M,-0.06,398,91.81,1.01,-35.62,2,53250.29,586
+481,Wendy Barker,36-45,Married,4,2,2,F,-0.71,724,60.82,1.01,-1322.03,30,113545.34,1880
+482,Ronald Cruz,26-35,Married,4,2,8,M,-0.04,274,84.16,1.01,-17.81,1,37365.42,449
+483,Scott Williams,70+,Married,2,0,8,M,-1.54,570,99.18,1.07,-1130.33,53,72798.18,784
+484,Dawn Tucker,70+,Married,2,0,8,F,-0.04,877,89.67,1.0,-62.33,2,156384.85,1744
+485,Natalie Perkins,56-70,Married,2,0,7,F,-0.36,196,97.35,1.0,-92.26,2,25115.84,258
+486,Amy Thomas,46-55,Single,3,1,1,F,-0.02,454,94.28,1.0,-11.13,1,57509.36,612
+487,Clinton Sims,26-35,Single,1,0,4,M,0.0,214,88.73,1.01,0.0,0,26175.27,298
+488,Richard Mcintyre,36-45,Married,2,0,6,M,0.0,543,96.05,1.11,0.0,0,65893.59,763
+489,Rose Mccarthy,70+,Married,2,0,2,F,-0.07,130,113.74,1.0,-14.25,1,23998.79,211
+490,Gina Alvarez,36-45,Single,1,0,5,F,-0.83,466,82.99,1.85,-623.35,15,62245.13,1387
+491,Kathryn Turner,26-35,Married,2,0,4,F,-0.43,327,111.32,1.03,-183.73,7,47312.91,436
+492,Tyler Mendoza,46-55,Married,2,0,4,M,0.0,159,128.97,1.0,0.0,0,27341.28,212
+493,Patricia Washington,36-45,Married,2,0,4,F,-0.46,187,128.25,1.0,-142.48,1,40014.67,313
+494,Angela Dean,26-35,Married,5,3,8,F,-0.8,839,111.66,1.05,-991.47,22,138574.38,1297
+495,Sharon Simmons,36-45,Married,2,0,8,F,-0.01,1090,103.69,1.04,-19.59,1,163311.42,1640
+496,Joshua Gallagher,26-35,Single,1,0,5,M,-0.09,427,96.91,1.0,-64.11,3,69195.51,715
+497,Samantha Casey,36-45,Married,5,3,5,F,-1.11,391,102.17,1.04,-539.9,21,49656.77,506
+498,Janet Whitehead,46-55,Married,2,0,9,F,-0.4,912,114.45,1.0,-644.37,20,182435.33,1594
+499,Carol Briggs,36-45,Married,2,0,5,F,-0.14,735,76.89,1.06,-215.49,8,117866.2,1620
+500,Rebecca Benson DDS,46-55,Single,1,0,4,F,0.0,348,79.55,1.0,0.0,0,35478.17,448
+501,Kendra Griffin,46-55,Single,1,0,5,F,-0.09,1021,83.38,1.0,-178.8,6,175019.74,2100
+502,Henry James,26-35,Married,4,2,6,M,-0.03,465,95.95,1.0,-17.81,1,64480.74,675
+503,Barbara West,46-55,Married,3,1,4,F,-0.38,310,95.98,1.04,-239.82,12,60276.3,651
+504,Charles Reyes,70+,Married,3,1,8,M,0.0,408,135.5,1.0,0.0,0,70322.66,519
+505,Robin Powers,56-70,Married,2,0,9,F,0.0,109,86.65,1.0,0.0,0,15076.44,174
+506,Brenda Obrien,46-55,Married,2,0,7,F,-0.22,257,105.27,1.09,-89.05,4,43581.02,453
+507,Michelle Bass,70+,Married,2,0,6,F,0.0,179,100.45,1.14,0.0,0,32445.94,369
+508,Kathryn Patrick,18-25,Married,2,0,6,F,0.0,545,134.99,1.0,0.0,0,155368.79,1151
+509,Kristina Hawkins,26-35,Single,1,0,3,F,-0.44,193,114.68,1.0,-219.78,3,57225.55,499
+510,Nicholas Thomas,26-35,Single,2,1,1,M,-1.0,1019,86.01,1.03,-1634.66,56,141234.55,1684
+511,Grant Thomas,46-55,Single,1,0,5,M,-0.14,377,105.48,1.01,-71.24,2,54956.13,527
+512,Debra Buchanan,46-55,Single,1,0,3,F,-0.31,217,166.42,1.05,-82.99,3,44932.79,284
+513,Tiffany Chen,70+,Married,2,0,4,F,-0.11,231,89.1,1.0,-35.62,2,27797.71,312
+514,Christopher Cooke,26-35,Married,2,0,6,M,-0.04,379,144.69,1.0,-17.81,1,69163.7,478
+515,Nicholas Werner,26-35,Married,2,0,4,M,-0.02,903,97.06,1.0,-53.43,2,251472.19,2591
+516,Brandon Mckenzie,70+,Married,2,0,4,M,-0.79,235,138.27,1.0,-249.34,2,43415.58,314
+517,Alyssa Swanson,26-35,Married,2,0,6,F,0.0,256,110.94,1.0,0.0,0,40935.73,369
+518,Matthew Foster,56-70,Married,5,3,4,M,-0.9,633,79.35,1.0,-1111.68,7,97517.87,1229
+519,John Cain,46-55,Married,2,0,5,M,-0.18,354,82.42,1.02,-142.48,7,65774.04,811
+520,Casey Marsh,36-45,Married,2,0,6,F,-0.64,688,103.4,1.01,-693.69,26,112702.84,1105
+521,Brittney Silva,56-70,Single,1,0,6,F,-0.07,448,90.11,1.06,-53.43,2,70646.71,834
+522,Crystal Wheeler,46-55,Married,3,1,6,F,0.0,369,87.68,1.05,0.0,0,44718.54,533
+523,Adriana Li,36-45,Single,3,1,5,F,-1.08,127,91.67,1.0,-262.68,16,22276.25,243
+524,Dawn Brown,26-35,Single,1,0,5,F,-0.38,493,110.52,1.0,-274.8,9,80456.72,731
+525,Jacob Reed,36-45,Married,3,1,9,M,-0.15,614,105.82,1.0,-171.69,4,122327.85,1156
+526,Deborah Johnson,18-25,Married,2,0,2,F,-0.78,754,108.97,1.0,-737.32,18,103633.25,953
+527,Ronald Castillo,36-45,Married,3,1,5,M,0.0,299,121.27,1.06,0.0,0,46566.02,406
+528,Julie Murphy,46-55,Married,2,0,10,F,-0.72,1109,117.85,1.09,-1249.53,32,204122.66,1887
+529,Joshua Ramos,18-25,Single,1,0,5,M,-1.95,250,67.5,1.0,-751.2,27,26055.99,386
+530,Diane Chambers,46-55,Single,3,2,5,F,-0.25,831,126.42,1.0,-387.36,6,196458.65,1554
+531,Cynthia Baker,36-45,Married,3,1,5,F,0.0,168,112.42,1.06,0.0,0,22484.32,212
+532,Cindy Wood,56-70,Married,2,0,1,F,0.0,249,110.35,1.0,0.0,0,42595.66,386
+533,Nathan Walls,36-45,Married,5,3,6,M,-0.06,983,137.23,1.0,-92.61,4,219300.47,1598
+534,Tina Morales,26-35,Married,5,3,5,F,-0.17,177,113.49,1.08,-38.29,2,24968.18,238
+535,Rachel Wood,36-45,Married,2,0,5,F,-0.08,693,79.22,1.06,-108.64,2,113521.47,1521
+536,Ariana Pierce,46-55,Married,2,0,11,F,-0.8,441,136.71,1.0,-595.29,27,101712.02,747
+537,Alyssa Mills,18-25,Single,1,0,5,F,-0.15,745,115.16,1.01,-178.1,5,133813.01,1175
+538,Matthew Spears,46-55,Married,2,0,6,M,0.0,570,105.12,1.0,0.0,0,92291.63,878
+539,Haley Anderson,46-55,Married,2,0,9,F,-0.05,214,76.5,1.0,-14.25,1,20808.28,273
+540,Brittany Cummings,26-35,Married,4,2,4,F,-0.33,267,132.62,1.0,-106.86,2,43101.62,325
+541,Jennifer Gray,26-35,Single,1,0,10,F,-0.04,523,173.78,1.08,-69.46,3,271093.03,1690
+542,Kathleen Pratt,46-55,Single,1,0,6,F,0.0,89,94.38,1.0,0.0,0,9721.61,103
+543,David Davis,26-35,Married,2,0,5,M,-0.26,510,121.83,1.0,-213.72,4,99411.04,816
+544,Christopher Diaz,56-70,Single,1,0,4,M,-0.16,432,101.88,1.03,-145.45,6,91485.66,927
+545,Shannon Williams,70+,Married,3,1,4,F,-0.01,445,100.54,1.0,-8.9,1,88475.16,880
+546,Karen Roach,26-35,Single,1,0,5,F,-0.1,682,100.15,1.05,-135.35,4,136299.31,1425
+547,Dustin Walker,46-55,Married,2,0,1,M,0.0,159,121.96,1.0,0.0,0,26953.01,221
+548,Mark Hughes,46-55,Married,2,0,5,M,-0.05,267,90.29,1.0,-19.59,1,34038.93,377
+549,Amber Olson,46-55,Single,1,0,2,F,-0.13,242,83.34,1.04,-35.62,1,23668.72,295
+550,Sandra Peterson,36-45,Single,1,0,2,F,-0.71,687,103.13,1.0,-868.22,20,126741.56,1229
+551,Mark Roberts,46-55,Single,1,0,6,M,-1.6,576,94.97,1.08,-1513.03,59,89558.63,1019
+552,Timothy Holloway,70+,Married,2,0,4,M,-0.6,786,119.81,1.08,-651.47,23,129871.16,1171
+553,Nicole Evans,36-45,Married,3,1,11,F,0.0,747,136.33,1.0,0.0,0,200818.57,1475
+554,Spencer Snyder,26-35,Single,1,0,4,M,-0.36,453,64.0,1.04,-320.58,5,56380.02,916
+555,Michael Campbell,46-55,Single,1,0,5,M,0.0,522,96.62,1.06,0.0,0,102221.05,1125
+556,Tina Hubbard,46-55,Single,1,0,5,F,-3.45,340,122.19,1.03,-1919.14,65,68062.56,574
+557,Christopher Griffith,26-35,Single,3,2,4,M,-0.02,742,86.91,1.01,-35.62,1,129411.09,1499
+558,Kathy Lopez,36-45,Single,2,1,5,F,-0.74,259,98.27,1.11,-252.46,4,33510.0,377
+559,Tabitha Maynard,36-45,Married,2,0,6,F,-0.09,649,71.19,1.0,-133.57,8,101665.5,1428
+560,George Church Jr.,46-55,Married,2,0,5,M,-0.43,657,97.41,1.03,-408.72,13,92344.92,975
+561,Eric Jones,36-45,Married,2,0,4,M,-0.27,684,127.42,1.1,-284.96,7,134681.94,1162
+562,James Gomez,46-55,Single,1,0,9,M,0.0,122,83.64,1.0,0.0,0,12378.21,148
+563,Joshua Obrien,26-35,Single,1,0,2,M,0.0,268,115.48,1.03,0.0,0,42958.84,382
+564,Kimberly Brock,36-45,Married,2,0,7,F,-0.54,438,120.23,1.08,-411.41,18,91252.32,817
+565,Hayley Jones,26-35,Married,3,1,9,F,-1.81,924,106.25,1.0,-3453.35,45,202715.62,1908
+566,Alison Garcia,26-35,Married,4,2,5,F,-2.21,991,102.65,1.07,-5873.98,219,272420.99,2830
+567,Christine Medina,18-25,Single,1,0,3,F,-0.2,696,105.57,1.0,-262.7,11,135346.15,1282
+568,Peter Rios,56-70,Single,1,0,4,M,-0.05,419,99.35,1.0,-35.62,1,73918.95,747
+569,Cynthia Anderson MD,46-55,Single,1,0,9,F,-0.02,457,130.25,1.06,-17.81,1,138850.84,1129
+570,Amber Foster,46-55,Single,1,0,4,F,-0.49,955,99.13,1.02,-644.71,24,129663.22,1337
+571,Hannah Rodriguez,56-70,Married,2,0,8,F,-0.02,662,114.21,1.1,-19.59,1,126999.48,1225
+572,Carolyn Martin,36-45,Married,2,0,4,F,-0.04,1145,104.44,1.1,-106.5,2,249094.2,2627
+573,Elizabeth Jones,36-45,Single,1,0,4,F,-0.06,482,86.97,1.0,-35.62,1,54183.06,624
+574,Steven Scott,36-45,Single,1,0,4,M,-0.13,302,136.96,1.0,-53.43,2,57387.68,419
+575,Renee Cherry,56-70,Married,2,0,1,F,-0.08,275,93.05,1.14,-83.7,5,96118.37,1173
+576,Martin Lee,26-35,Single,1,0,5,M,0.0,242,89.75,1.0,0.0,0,32308.98,360
+577,Lisa Barker,46-55,Married,3,1,7,F,-0.3,602,96.25,1.04,-287.57,7,93264.06,1010
+578,Brad Leblanc,46-55,Married,2,0,6,M,-0.49,1489,111.88,1.1,-1129.69,45,258554.78,2541
+579,Charles Gray,26-35,Married,2,0,8,M,-0.09,384,101.21,1.03,-53.43,2,63154.69,641
+580,Jeffrey Henderson,26-35,Married,3,1,6,M,-0.1,183,116.73,1.01,-29.68,1,35370.69,307
+581,Joseph Mason DDS,46-55,Single,1,0,2,M,0.0,102,60.7,1.0,0.0,0,14204.24,234
+582,Joseph May,70+,Married,2,0,4,M,0.0,401,90.17,1.05,0.0,0,45627.33,532
+583,William Cooper,36-45,Single,1,0,2,M,-0.29,635,74.46,1.0,-322.0,10,81977.89,1101
+584,Karen Brown,46-55,Single,1,0,4,F,-0.35,295,68.93,1.0,-163.5,1,31912.34,465
+585,April Valencia,46-55,Married,5,3,4,F,-0.52,534,95.59,1.01,-441.34,9,81155.53,861
+586,Dr. Steven Johnson MD,46-55,Single,1,0,4,M,-1.94,354,118.08,1.05,-975.26,13,59273.83,528
+587,Courtney Watkins,26-35,Married,2,0,4,F,-0.49,368,110.72,1.1,-236.87,7,53700.43,532
+588,Mitchell Wyatt,46-55,Married,2,0,5,M,0.0,257,117.11,1.0,0.0,0,52467.42,448
+589,Erica Munoz,56-70,Single,1,0,4,F,0.0,77,89.8,1.0,0.0,0,12661.71,141
+590,Regina David,36-45,Married,2,0,1,F,-0.04,552,91.03,1.11,-55.21,3,116881.96,1427
+591,Gloria Sexton,26-35,Married,5,3,7,F,-0.49,288,100.99,1.0,-147.82,6,30699.57,304
+592,Steven Alvarez,70+,Married,2,0,4,M,-0.07,578,95.61,1.04,-55.21,2,71321.88,775
+593,Kimberly Hodge,46-55,Single,2,1,3,F,-0.18,650,68.68,1.04,-233.3,7,88533.59,1342
+594,Kelly Doyle,46-55,Single,1,0,10,F,-0.98,574,111.42,1.01,-961.73,31,109187.26,991
+595,Crystal Yang,18-25,Married,2,0,2,F,-0.14,734,100.11,1.0,-162.07,5,119533.46,1194
+596,Tiffany Jones,46-55,Single,1,0,1,F,-0.32,1454,75.91,1.01,-852.8,29,200179.32,2659
+597,Alexandra Richardson,26-35,Single,1,0,5,F,0.0,192,78.43,1.24,0.0,0,18667.11,296
+598,Vincent Powell,70+,Married,2,0,6,M,-2.07,872,103.32,1.01,-2744.39,96,137102.95,1338
+599,Jenny Howard,36-45,Single,1,0,3,F,0.0,303,117.95,1.0,0.0,0,50130.63,425
+600,Elizabeth Hobbs,46-55,Married,2,0,6,F,-0.07,529,66.26,1.0,-80.15,4,71958.6,1091
+601,Alan Beard,70+,Married,2,0,6,M,-0.1,230,95.1,1.0,-35.62,1,33474.47,352
+602,Ricky Smith DDS,18-25,Married,2,0,11,M,0.0,351,117.11,1.04,0.0,0,49771.22,441
+603,Amanda Cook,36-45,Single,1,0,3,F,-0.13,776,86.79,1.0,-199.46,11,128970.14,1486
+604,Omar Crosby,46-55,Married,2,0,1,M,-0.14,562,83.92,1.03,-136.24,4,79051.04,967
+605,Eddie Simmons,36-45,Married,3,1,2,M,-0.06,272,100.01,1.17,-19.59,1,34804.11,407
+606,Kimberly Conley,18-25,Single,1,0,1,F,-0.06,460,74.93,1.0,-35.62,1,46457.67,620
+607,Thomas Daniels,70+,Single,1,0,4,M,-0.95,250,84.21,1.01,-572.05,22,50945.69,609
+608,Samantha Collier,26-35,Married,2,0,5,F,-0.8,588,86.75,1.08,-888.32,34,96815.64,1209
+609,Jasmine Flores,26-35,Married,2,0,1,F,-0.08,385,89.1,1.02,-44.52,1,49184.49,561
+610,Deborah Ayala,46-55,Single,1,0,5,F,-0.03,382,79.48,1.0,-13.36,1,38787.34,488
+611,Michael Taylor,36-45,Married,3,1,4,M,-0.3,497,106.51,1.0,-215.5,8,76263.55,716
+612,Michael Cohen,18-25,Single,1,0,1,M,0.0,570,74.19,1.01,0.0,0,76412.51,1041
+613,Christopher Hobbs,46-55,Married,2,0,5,M,-0.08,679,104.94,1.15,-105.08,5,143555.16,1577
+614,Annette Murray,36-45,Single,1,0,6,F,-0.05,554,85.56,1.02,-49.27,2,82480.33,982
+615,Michele Malone,36-45,Married,5,3,8,F,0.0,242,134.29,1.0,0.0,0,57342.02,427
+616,Joann Buchanan,46-55,Single,2,1,7,F,-0.07,548,122.78,1.0,-53.43,1,94296.18,768
+617,Madeline Gonzalez,56-70,Married,2,0,3,F,-0.05,256,83.52,1.0,-19.59,1,34157.68,410
+618,Susan French,26-35,Married,2,0,4,F,-0.34,316,102.95,1.0,-135.36,5,41282.86,401
+619,James Peterson,36-45,Single,2,1,4,M,-0.13,996,96.89,1.0,-258.24,6,196878.97,2032
+620,Melissa Barajas,26-35,Married,3,1,4,F,0.0,545,105.45,1.0,0.0,0,78873.61,748
+621,Michael Sanders,36-45,Married,2,0,5,M,-0.39,548,111.91,1.0,-391.64,14,111686.15,1001
+622,Jennifer Cruz,36-45,Married,4,2,5,F,-0.01,1125,110.54,1.01,-17.81,1,245076.25,2236
+623,Kristin Ward,46-55,Married,4,2,8,F,0.0,239,106.05,1.13,0.0,0,75509.0,804
+624,Jose Gordon,26-35,Married,5,3,11,M,-0.13,562,115.62,1.0,-108.64,3,93656.14,814
+625,Susan Humphrey,18-25,Married,2,0,4,F,0.0,394,125.07,1.0,0.0,0,58906.56,471
+626,Zachary Howard,36-45,Single,2,1,4,M,-2.0,1451,93.27,1.01,-5655.2,171,264033.32,2866
+627,Martin Saunders,46-55,Married,4,2,4,M,-1.11,1124,94.29,1.07,-3807.38,128,323317.55,3683
+628,Christopher Smith,46-55,Married,3,1,4,M,-1.13,572,96.96,1.0,-865.54,36,74269.49,766
+629,Jessica Silva DDS,36-45,Married,2,0,11,F,-0.48,184,133.44,1.0,-96.18,3,26820.66,201
+630,Zachary Reynolds,26-35,Single,1,0,4,M,0.0,237,110.15,1.0,0.0,0,44061.96,400
+631,Daniel Dudley,26-35,Married,2,0,5,M,-0.05,352,105.48,1.0,-19.59,1,44195.03,419
+632,Michael Wilson,46-55,Married,2,0,6,M,-2.58,842,103.43,1.08,-3639.12,143,145942.91,1525
+633,Victoria Pollard,26-35,Married,5,3,4,F,-0.18,789,93.28,1.01,-168.3,4,89358.24,969
+634,Jonathan Riley DDS,56-70,Married,5,3,5,M,-0.54,418,96.14,1.0,-336.6,12,59797.25,622
+635,Michelle Bradford,46-55,Single,2,1,7,F,-0.12,274,125.65,1.0,-35.62,1,38824.74,309
+636,Christopher Robinson,56-70,Married,2,0,3,M,-2.56,232,106.4,1.01,-979.55,11,40749.64,385
+637,Brandon Jones,46-55,Single,1,0,6,M,-0.04,587,100.91,1.0,-44.52,2,114635.43,1136
+638,Courtney Kelly,36-45,Single,1,0,4,F,-0.4,1048,99.13,1.03,-680.86,22,168912.76,1757
+639,Kathryn Robles,46-55,Married,2,0,7,F,-0.25,354,97.38,1.0,-113.09,3,43723.53,449
+640,Mr. Adrian Mitchell,46-55,Single,1,0,4,M,-0.09,388,84.91,1.01,-89.04,4,83556.33,996
+641,Ashley Austin,46-55,Single,1,0,5,F,-0.63,324,104.01,1.0,-330.68,18,54811.98,527
+642,Peter English,36-45,Single,1,0,3,M,-0.08,395,104.17,1.0,-44.52,1,55733.45,535
+643,Richard Walters,26-35,Single,1,0,2,M,0.0,281,112.55,1.17,0.0,0,58186.59,605
+644,Shelly Vargas,26-35,Married,3,1,9,F,-0.07,272,72.3,1.0,-44.52,3,46631.68,645
+645,Rebecca James,18-25,Married,2,0,6,F,0.0,358,75.54,1.0,0.0,0,35351.19,469
+646,Doris Harris,18-25,Married,2,0,4,F,0.0,313,75.59,1.1,0.0,0,34468.31,501
+647,Cassandra Benson,56-70,Married,2,0,1,F,-0.42,382,105.66,1.1,-215.5,7,54839.83,570
+648,Theresa Morris,36-45,Married,5,3,4,F,-0.04,461,84.46,1.05,-19.59,1,47041.67,586
+649,Diana Williams,46-55,Married,4,2,2,F,-0.23,816,95.31,1.02,-236.52,10,96068.62,1024
+650,Frank Ford,46-55,Single,1,0,5,M,-0.29,456,80.26,1.0,-309.89,15,86362.67,1080
+651,Martha Jackson,46-55,Single,2,1,4,F,0.0,327,125.57,1.01,0.0,0,50980.95,410
+652,Diana Baker,70+,Married,2,0,4,F,-0.26,567,87.17,1.0,-211.75,9,72006.39,826
+653,Phillip Williams,46-55,Married,5,3,4,M,-0.74,253,113.9,1.03,-258.77,9,39863.27,360
+654,Erica Bowers,46-55,Married,2,0,5,F,0.0,206,119.23,1.0,0.0,0,28853.33,242
+655,Janet Ramos,46-55,Married,2,0,3,F,-0.08,968,107.24,1.0,-131.44,2,180593.28,1692
+656,Connor Bradford,36-45,Married,5,3,5,M,-0.27,328,99.77,1.0,-124.67,2,46594.0,467
+657,Robert Reyes,26-35,Married,4,2,1,M,-0.75,734,108.96,1.02,-1037.77,36,150359.22,1403
+658,Diane Kent,46-55,Married,4,2,2,F,-0.06,614,80.16,1.0,-51.95,2,69581.0,868
+659,Timothy Casey,26-35,Single,1,0,3,M,-0.26,681,100.52,1.13,-562.44,22,213800.89,2399
+660,Maureen Robertson,36-45,Married,2,0,6,F,0.0,483,99.22,1.11,0.0,0,84335.94,940
+661,Gary Moore,26-35,Single,2,1,8,M,-0.49,426,126.28,1.01,-285.4,8,73875.36,589
+662,Dawn Galloway,26-35,Married,2,0,5,F,0.0,91,116.63,1.07,0.0,0,12829.01,118
+663,Tina Mckee,56-70,Single,1,0,1,F,0.0,320,60.09,1.0,0.0,0,33169.78,552
+664,Cheryl Rodriguez,26-35,Married,5,3,3,F,-0.24,599,126.76,1.0,-222.63,8,119664.5,944
+665,James Johnson,26-35,Single,1,0,7,M,-0.29,565,113.32,1.0,-382.91,5,150832.0,1331
+666,Tamara Hernandez,26-35,Single,1,0,4,F,-0.02,956,88.53,1.0,-35.62,1,132090.15,1492
+667,Kelli Larson,26-35,Married,2,0,6,F,-0.12,834,74.91,1.01,-175.13,7,112066.97,1512
+668,Steven Baker,26-35,Married,2,0,1,M,-0.02,636,83.88,1.12,-19.59,1,81361.82,1082
+669,Brian Smith,46-55,Single,2,1,3,M,0.0,442,111.0,1.05,0.0,0,75256.96,713
+670,Jesus Miranda,26-35,Married,2,0,5,M,-0.78,175,107.02,1.0,-170.98,5,23438.31,219
+671,Lindsay Hernandez DDS,70+,Married,2,0,4,F,-0.66,225,110.87,1.12,-201.26,9,34038.04,343
+672,Sandra Bailey,26-35,Married,2,0,5,F,0.0,448,76.59,1.18,0.0,0,52844.81,817
+673,Katelyn Rose,46-55,Single,2,1,4,F,-0.07,392,101.18,1.01,-35.62,1,50590.02,506
+674,Benjamin Smith MD,56-70,Married,2,0,7,M,0.0,259,94.62,1.01,0.0,0,55162.87,591
+675,Nicholas Harper,26-35,Married,2,0,5,M,0.0,199,89.9,1.01,0.0,0,29037.59,325
+676,Tiffany Hall,46-55,Married,2,0,5,F,0.0,589,116.33,1.0,0.0,0,121684.75,1046
+677,Amanda Robertson,36-45,Single,1,0,5,F,-0.03,322,96.53,1.0,-44.52,1,124625.38,1291
+678,Dana Thomas,46-55,Married,5,3,3,F,-0.06,500,89.47,1.0,-55.21,2,79184.72,889
+679,Mason Jenkins,36-45,Single,2,1,4,M,0.0,469,112.11,1.0,0.0,0,153370.43,1368
+680,Steven Quinn,26-35,Single,1,0,5,M,0.0,553,103.05,1.01,0.0,0,85221.47,834
+681,Diana Cooper,70+,Married,2,0,2,F,-0.26,245,80.7,1.0,-119.33,2,36962.32,458
+682,Richard Hernandez,56-70,Single,1,0,4,M,-1.04,529,102.49,1.0,-768.15,30,75845.95,740
+683,Jason King,36-45,Single,1,0,4,M,-0.11,747,96.32,1.0,-115.76,3,102962.41,1069
+684,Michael Bryant,36-45,Married,2,0,3,M,-0.81,391,81.81,1.0,-687.99,16,69129.66,845
+685,Philip Moore,36-45,Married,5,3,8,M,-0.04,346,91.68,1.04,-17.81,1,37314.36,422
+686,Jessica Rubio,26-35,Married,2,0,4,F,-0.05,355,111.84,1.0,-26.71,1,56031.01,501
+687,Anna Young,36-45,Married,2,0,5,F,-0.15,638,90.65,1.12,-146.04,5,86384.86,1068
+688,Carol Lambert,18-25,Single,2,1,1,F,-0.09,452,68.15,1.0,-56.99,2,41710.75,612
+689,Chelsea Chan,70+,Single,1,0,4,F,0.0,534,65.59,1.0,0.0,0,60082.37,917
+690,Gary Cruz,26-35,Married,2,0,5,M,-0.1,373,102.37,1.04,-55.21,2,54666.75,557
+691,Charles Barnes,46-55,Married,4,2,4,M,-0.09,712,103.09,1.02,-120.51,4,136494.81,1348
+692,Rebekah Sanchez,18-25,Single,1,0,5,F,-0.44,213,100.52,1.02,-117.54,6,26738.95,271
+693,Bryan Schultz,26-35,Single,2,1,4,M,-0.11,255,131.04,1.0,-35.62,1,44421.42,339
+694,Kara Davis,26-35,Married,3,1,8,F,-0.21,679,98.81,1.16,-229.74,11,110662.8,1297
+695,Jesus Christensen,56-70,Married,2,0,6,M,-0.27,1028,118.96,1.0,-479.08,25,208425.62,1755
+696,Alan Martinez,46-55,Single,1,0,4,M,0.0,297,58.15,1.01,0.0,0,34250.6,593
+697,Tiffany Rosales,36-45,Single,1,0,3,F,-0.21,176,127.11,1.0,-47.19,3,28599.08,225
+698,Lauren Moody,26-35,Married,3,1,4,F,-0.03,277,87.35,1.0,-17.81,1,51798.23,593
+699,Desiree Johnson,36-45,Married,2,0,3,F,-0.03,830,88.57,1.04,-35.62,1,102567.0,1207
+700,Nancy Rose,36-45,Married,3,1,11,F,-0.47,475,88.45,1.08,-280.8,8,52981.27,647
+701,Derrick Cox DVM,56-70,Married,2,0,4,M,0.0,147,64.82,1.01,0.0,0,26901.54,420
+702,Justin Navarro,46-55,Single,1,0,5,M,-0.03,396,87.51,1.03,-17.81,1,46553.03,546
+703,Mary Davis,46-55,Married,2,0,6,F,-0.06,587,112.37,1.18,-54.85,2,106076.88,1110
+704,Veronica Johnson,46-55,Married,5,3,6,F,-0.38,689,101.25,1.01,-362.43,12,97601.59,969
+705,Kelly Ross,36-45,Single,1,0,4,M,-0.03,780,131.26,1.09,-35.62,1,152134.06,1258
+706,Rebecca Fowler,56-70,Single,1,0,4,F,-1.81,739,96.58,1.05,-3284.92,115,175388.58,1903
+707,Nancy Nichols,18-25,Single,2,1,5,F,-0.19,305,119.5,1.0,-68.86,3,43618.18,365
+708,Rachel Burke,36-45,Married,3,1,5,F,-0.1,692,100.77,1.08,-124.67,3,124646.34,1336
+709,Dylan Rodriguez,70+,Married,2,0,3,M,-0.05,504,111.81,1.0,-35.61,2,87096.93,779
+710,Amy Vega,36-45,Married,2,0,6,F,-10.93,472,184.86,1.0,-6370.77,118,107773.53,583
+711,Jonathan Williams,26-35,Single,1,0,9,M,-0.03,1250,85.77,1.0,-113.0,3,357911.09,4173
+712,Melissa Cruz,46-55,Married,3,1,5,F,-1.01,554,111.9,1.01,-774.44,47,85824.73,775
+713,Paul Atkinson,46-55,Married,3,1,5,M,-0.08,349,98.38,1.0,-41.22,3,52045.25,530
+714,Aaron Cline,46-55,Single,1,0,3,M,-0.12,507,108.52,1.06,-89.05,1,80844.19,788
+715,Anthony Murphy,36-45,Married,2,0,9,M,-0.27,462,89.92,1.01,-171.86,9,57820.55,647
+716,Samantha Owen,36-45,Single,4,2,1,F,-0.09,368,98.44,1.01,-44.52,1,46364.86,475
+717,Jennifer Herrera,36-45,Single,1,0,2,F,-0.35,434,96.66,1.0,-181.66,5,50652.44,524
+718,Ashley Acosta,36-45,Married,2,0,11,F,-0.06,266,79.15,1.02,-26.71,1,36804.45,472
+719,Kari Smith,56-70,Married,2,0,2,F,-0.15,377,139.67,1.0,-89.05,3,82544.52,591
+720,Ryan Peters,18-25,Married,2,0,6,M,-0.05,425,77.9,1.0,-26.71,2,44950.41,577
+721,Sharon Mercado,26-35,Married,2,0,5,F,0.0,150,101.94,1.01,0.0,0,16105.89,160
+722,Jason Allison,26-35,Single,1,0,4,M,-0.09,370,107.05,1.0,-44.52,1,54169.35,506
+723,Jacob Ramos,46-55,Single,2,1,3,M,-0.49,233,111.76,1.0,-133.57,2,30399.81,272
+724,Jacob Hines,26-35,Married,3,1,6,M,-0.14,488,92.06,1.01,-89.05,1,56709.17,621
+725,Patricia Lamb,56-70,Married,3,1,3,F,-0.86,1011,94.42,1.0,-1155.86,26,127371.68,1350
+726,Robert Mercer,46-55,Single,3,1,5,M,-0.13,393,91.73,1.0,-71.24,3,48890.55,533
+727,Amy Kennedy,46-55,Single,1,0,5,F,-0.07,371,92.02,1.0,-35.62,1,45823.85,498
+728,Sarah Richards,46-55,Married,5,3,9,F,0.0,160,91.04,1.02,0.0,0,17389.29,194
+729,Terry Smith,46-55,Single,1,0,3,M,-0.45,725,107.27,1.0,-434.21,6,103514.3,965
+730,Kristen Andrews,26-35,Single,1,0,4,F,-0.16,222,106.41,1.0,-41.86,2,28198.48,265
+731,Mary Mcclure,70+,Single,1,0,4,F,0.0,111,126.15,1.02,0.0,0,41378.18,334
+732,Valerie Russell,36-45,Married,2,0,2,F,0.0,266,94.43,1.0,0.0,0,75261.24,797
+733,Jacob Bailey,70+,Single,1,0,4,M,-0.45,575,134.25,1.0,-567.24,10,169155.74,1260
+734,Misty Mason,36-45,Married,2,0,5,F,0.0,158,81.19,1.0,0.0,0,15425.81,190
+735,Jeffery Perkins,46-55,Married,2,0,5,M,-0.37,948,115.96,1.06,-860.18,29,272030.9,2476
+736,Elizabeth Daugherty,56-70,Married,2,0,4,F,-0.13,493,106.54,1.0,-89.05,1,72873.69,684
+737,Elijah Vasquez,36-45,Single,4,3,4,M,-0.81,688,87.17,1.02,-1308.58,57,140174.48,1637
+738,Christopher Howard,46-55,Single,1,0,1,M,0.0,261,129.45,1.0,0.0,0,51261.16,396
+739,Erik Norris,36-45,Married,5,3,7,M,0.0,180,90.46,1.0,0.0,0,24786.19,274
+740,Douglas Martin,46-55,Married,2,0,4,M,-0.04,425,88.39,1.0,-35.62,2,70708.9,800
+741,Yvonne Peterson,18-25,Single,1,0,3,F,0.0,406,130.87,1.0,0.0,0,75512.23,577
+742,Melissa Bailey,46-55,Married,2,0,2,F,-0.11,418,81.24,1.02,-75.69,3,54429.55,684
+743,Cathy Davis,46-55,Married,2,0,2,F,-0.04,523,102.27,1.07,-39.18,2,90104.05,939
+744,Kimberly Turner,70+,Single,1,0,4,F,0.0,599,77.34,1.01,0.0,0,77029.5,1004
+745,Holly Robinson MD,36-45,Single,1,0,1,F,-0.07,724,107.42,1.08,-64.12,3,104413.19,1049
+746,Jamie Jensen,26-35,Single,1,0,2,F,0.0,387,111.44,1.0,0.0,0,90151.59,809
+747,Tyler Payne,26-35,Single,1,0,5,M,0.0,204,123.39,1.04,0.0,0,32575.83,274
+748,Heather Reed,46-55,Married,3,1,8,F,-4.25,1351,105.32,1.11,-9978.66,345,247401.42,2604
+749,Laura Hart,36-45,Married,4,2,5,F,-0.11,747,111.81,1.03,-126.45,4,131715.45,1216
+750,Christopher Pena,18-25,Married,2,0,2,M,-0.11,549,96.67,1.0,-142.47,5,121128.2,1255
+751,Laura Clark,26-35,Single,1,0,5,F,-0.02,540,137.1,1.0,-10.69,1,91585.37,668
+752,Eric Riddle,26-35,Married,5,3,1,M,0.0,447,77.0,1.0,0.0,0,44891.67,583
+753,Kyle Gonzalez,26-35,Married,2,0,5,M,-0.2,504,121.8,1.12,-222.61,10,132393.74,1218
+754,Jenny Hernandez,46-55,Single,1,0,2,F,-0.32,539,115.13,1.06,-281.04,5,99705.4,922
+755,Tina Warren,36-45,Married,2,0,5,F,-0.74,869,111.18,1.03,-1000.2,12,150320.47,1397
+756,Cheryl Mitchell,56-70,Married,2,0,7,F,0.0,552,90.39,1.1,0.0,0,77460.73,946
+757,Eric Washington,46-55,Married,4,2,5,M,-0.37,526,85.89,1.01,-240.43,10,55745.61,654
+758,Valerie Moreno,70+,Single,1,0,4,F,0.0,363,88.68,1.03,0.0,0,42210.37,489
+759,John Watson,46-55,Married,2,0,6,M,0.0,141,108.3,1.01,0.0,0,16677.6,155
+760,Taylor Potter,46-55,Married,5,3,5,F,-0.04,589,127.55,1.03,-29.38,2,91327.67,739
+761,Sarah Smith,36-45,Single,1,0,4,F,-0.41,404,88.25,1.0,-277.48,1,60187.29,682
+762,Vickie Lang,46-55,Single,1,0,4,F,-0.09,614,66.13,1.0,-89.05,4,66458.97,1008
+763,Patrick Kelley,26-35,Married,2,0,4,M,-0.31,543,88.74,1.06,-224.4,8,64604.76,775
+764,Lindsay Carrillo,18-25,Single,2,1,1,F,0.0,263,118.98,1.0,0.0,0,50092.04,421
+765,Tracy Heath,46-55,Married,2,0,5,F,-0.42,628,98.05,1.0,-501.88,11,116489.21,1190
+766,Stephen Scott,36-45,Married,2,0,9,M,-2.09,670,114.96,1.04,-2191.29,77,120588.07,1096
+767,Linda Fisher,26-35,Married,3,1,6,F,-0.37,630,110.97,1.02,-429.27,18,129505.2,1188
+768,Lori Smith,36-45,Married,5,3,5,F,-0.51,315,106.85,1.0,-195.91,4,41031.18,384
+769,Brandi Castro,46-55,Married,2,0,5,F,-0.12,924,96.91,1.0,-192.35,9,149632.39,1544
+770,Steven Fitzgerald,36-45,Single,1,0,4,M,-1.15,139,84.09,1.0,-246.37,11,17994.69,214
+771,Donald Howell,36-45,Single,1,0,2,M,-0.14,342,150.63,1.11,-122.44,5,131200.37,964
+772,Cindy Chavez,26-35,Single,1,0,4,F,-0.3,895,96.4,1.0,-415.69,9,135242.3,1403
+773,Christopher Drake,36-45,Single,1,0,5,M,-0.17,358,87.14,1.03,-80.14,2,41739.81,491
+774,Richard Moore,46-55,Married,4,2,4,M,-1.02,466,93.9,1.06,-675.81,35,62348.58,702
+775,Christine Moore,18-25,Married,3,1,2,F,0.0,138,89.25,1.0,0.0,0,14904.12,167
+776,Angelica Perez,46-55,Single,1,0,2,F,-0.65,751,101.45,1.0,-805.74,26,125901.73,1241
+777,Patricia Brown,56-70,Married,2,0,2,F,-0.13,226,104.75,1.0,-35.62,2,29645.39,283
+778,Julie Nelson,46-55,Single,1,0,4,F,-0.03,453,70.9,1.0,-17.81,1,48427.54,683
+779,Alicia Walton,26-35,Single,3,2,4,F,-0.15,792,89.24,1.0,-177.2,8,107708.81,1207
+780,Edward Carlson,26-35,Single,1,0,7,M,-0.16,595,112.41,1.02,-139.8,4,96110.04,869
+781,Leslie Mcclain,46-55,Single,1,0,7,F,-2.3,963,131.12,1.09,-4447.69,34,253071.12,2102
+782,Alex Ball,56-70,Married,2,0,9,M,-0.26,1228,114.54,1.1,-604.91,32,261714.49,2515
+783,Jason Lucas,46-55,Married,2,0,6,M,-0.27,311,83.01,1.07,-118.7,3,36359.97,470
+784,Mitchell Williams,46-55,Single,2,1,4,M,-0.09,705,120.89,1.0,-92.61,4,118476.56,980
+785,Hannah Weiss,36-45,Married,3,1,5,F,0.0,494,89.77,1.0,0.0,0,93813.25,1045
+786,Allison Buckley,26-35,Single,2,1,8,F,-0.18,147,111.46,1.0,-35.62,2,22402.46,201
+787,Nicole Burton,46-55,Married,4,2,1,F,-1.13,669,93.04,1.0,-1312.56,63,107738.87,1158
+788,Timothy Hodges Jr.,36-45,Single,1,0,3,M,0.0,313,98.38,1.0,0.0,0,47812.52,486
+789,James Pena,46-55,Married,2,0,1,M,-0.12,615,122.73,1.04,-147.28,2,156726.52,1324
+790,Cathy Krueger,46-55,Married,2,0,5,F,-0.13,460,98.12,1.03,-90.83,2,67702.06,711
+791,Danielle Ramos,46-55,Single,1,0,6,F,-0.12,535,122.15,1.03,-97.95,3,98330.51,833
+792,Jennifer Silva,36-45,Married,2,0,4,F,-0.31,560,92.03,1.0,-398.23,6,116423.28,1270
+793,George Holder,46-55,Single,2,1,5,M,-0.45,869,118.12,1.02,-771.88,25,203161.94,1758
+794,Justin Dean,46-55,Single,1,0,5,M,0.0,66,125.97,1.02,0.0,0,11085.63,90
+795,Samantha Carter,46-55,Single,2,1,5,F,-0.22,429,110.61,1.04,-178.1,5,90919.14,852
+796,Thomas Wilson,46-55,Married,3,1,5,M,0.0,199,105.37,1.02,0.0,0,24868.24,241
+797,Victor Watson,46-55,Single,1,0,6,M,-0.13,288,110.31,1.01,-82.37,4,69051.57,633
+798,Joseph Rodriguez,46-55,Single,1,0,8,M,0.0,108,89.43,1.0,0.0,0,20925.79,234
+799,Daniel Moreno,70+,Single,1,0,5,M,0.0,892,112.95,1.0,0.0,0,226583.11,2009
+800,Jeremiah Holland,46-55,Married,2,0,3,M,-0.07,504,83.58,1.02,-65.89,3,80736.72,982
+801,Taylor Hernandez,26-35,Single,1,0,2,M,-0.24,672,122.33,1.0,-255.04,6,129787.75,1061
+802,Michael Davis,46-55,Single,1,0,2,M,-1.78,776,132.78,1.02,-1908.34,26,142212.22,1097
+803,Joy Chambers,18-25,Married,3,1,4,F,-1.43,266,93.23,1.0,-530.06,17,34493.43,370
+804,Austin Anderson,26-35,Married,3,1,2,M,0.0,180,108.61,1.0,0.0,0,23568.7,217
+805,Stephanie Johnson,46-55,Married,2,0,1,F,-0.03,499,84.52,1.0,-17.81,1,53753.48,636
+806,Tony Torres,46-55,Single,1,0,2,M,0.0,312,123.75,1.0,0.0,0,47519.57,384
+807,Jonathan Morris,46-55,Married,2,0,7,M,0.0,472,89.92,1.0,0.0,0,76071.37,846
+808,Brent Morgan,70+,Married,2,0,4,M,-0.21,1006,106.18,1.0,-402.51,20,203329.53,1918
+809,Valerie Collins,46-55,Single,1,0,2,F,-0.27,782,107.71,1.01,-321.17,8,127525.97,1193
+810,Lawrence Higgins,46-55,Single,1,0,5,M,0.0,730,79.89,1.0,0.0,0,104341.63,1309
+811,Monica Wilson,56-70,Single,1,0,7,F,-0.04,720,85.68,1.0,-53.42,3,113015.13,1319
+812,George Moore,46-55,Single,1,0,6,M,0.0,232,75.14,1.0,0.0,0,27125.63,361
+813,Paul Taylor,26-35,Single,3,2,3,M,-0.54,540,77.04,1.01,-661.93,25,94608.88,1236
+814,Rebecca Reyes,26-35,Married,5,3,3,F,-0.49,413,80.34,1.0,-384.69,19,62507.54,778
+815,Morgan Blackburn,46-55,Married,2,0,3,F,0.0,318,107.31,1.01,0.0,0,39384.08,371
+816,John Morris,26-35,Single,1,0,3,M,-0.04,237,68.26,1.0,-17.81,1,33651.44,493
+817,Jerome Jones,56-70,Married,2,0,4,M,-0.31,428,90.29,1.0,-238.35,10,68620.59,760
+818,Evan West,36-45,Married,5,3,3,M,0.0,72,109.58,1.0,0.0,0,12820.65,117
+819,James Smith,46-55,Married,3,1,6,M,0.0,408,55.12,1.0,0.0,0,45748.81,830
+820,John Valenzuela,36-45,Married,2,0,8,M,-0.48,687,100.49,1.0,-460.9,20,96469.66,960
+821,Melanie Barajas,70+,Married,2,0,3,F,-0.34,219,105.61,1.0,-140.7,6,44355.01,420
+822,Mrs. Amy Owens,36-45,Single,1,0,6,F,0.0,190,103.16,1.03,0.0,0,24654.29,246
+823,Matthew West,36-45,Married,4,2,2,M,-1.98,876,94.13,1.05,-2711.34,109,128678.87,1431
+824,Rose Garcia,46-55,Married,4,2,5,F,-1.13,224,96.22,1.05,-316.66,7,27037.33,296
+825,Eric Johnston Jr.,36-45,Married,5,3,6,M,-0.44,350,104.03,1.0,-199.12,2,47435.49,456
+826,Ryan Miller Jr.,26-35,Single,1,0,4,M,-0.42,738,117.73,1.09,-402.59,6,111726.39,1032
+827,Sylvia Rollins,26-35,Married,2,0,5,F,-0.24,120,95.3,1.0,-39.18,2,15724.61,165
+828,Amanda Johnson,18-25,Single,2,1,1,F,-0.05,651,102.33,1.04,-85.49,4,161985.41,1652
+829,Jonathan Turner,46-55,Single,1,0,1,M,-0.07,522,81.39,1.0,-47.73,1,56239.26,692
+830,Morgan Buck,46-55,Single,1,0,2,F,0.0,195,97.9,1.05,0.0,0,32501.34,349
+831,Andrew Mason,70+,Married,2,0,3,M,-1.42,769,110.65,1.04,-1515.45,73,117734.98,1108
+832,Kristen Monroe,36-45,Single,1,0,5,F,0.0,1297,132.23,1.15,0.0,0,248591.42,2155
+833,Natalie Martinez,56-70,Married,2,0,4,F,-0.27,473,109.55,1.0,-191.45,8,77671.08,709
+834,Charles Obrien,36-45,Single,1,0,3,M,-0.25,617,93.14,1.0,-260.02,5,98547.08,1058
+835,Helen Le,70+,Married,2,0,5,F,-0.22,510,118.42,1.01,-195.9,7,105159.28,899
+836,Benjamin Jones,36-45,Married,4,2,5,M,-1.0,191,104.74,1.0,-213.72,7,22413.41,214
+837,Brittney Harris,18-25,Married,2,0,4,F,-0.19,242,70.6,1.0,-89.05,1,32476.96,460
+838,Christina Zimmerman,46-55,Single,1,0,5,F,0.0,291,118.1,1.0,0.0,0,43341.81,367
+839,Elizabeth Tyler,46-55,Single,1,0,1,F,-0.14,763,97.32,1.05,-170.08,7,116097.68,1254
+840,Donald Morrow,70+,Married,2,0,6,M,-0.21,291,112.01,1.04,-79.78,2,43570.55,404
+841,Kendra Johnson,36-45,Married,2,0,6,F,0.0,277,99.12,1.0,0.0,0,42523.41,429
+842,John Conner,46-55,Married,4,2,8,M,-0.35,848,114.42,1.0,-504.72,13,164764.47,1440
+843,James Allen,46-55,Single,1,0,4,M,-0.77,593,95.26,1.0,-866.39,16,107260.66,1126
+844,Brittany Velasquez,46-55,Married,4,2,5,F,-0.41,1390,97.2,1.05,-997.72,31,233863.71,2529
+845,Mitchell Horton,56-70,Married,2,0,7,M,-4.11,303,132.04,1.0,-1847.19,53,59286.15,449
+846,Tracy Armstrong,36-45,Single,2,1,6,F,-0.06,240,115.04,1.0,-17.81,1,34512.94,300
+847,Sarah Williams,26-35,Single,2,1,4,F,-0.44,273,136.8,1.04,-151.38,4,47058.62,359
+848,Benjamin Fowler,26-35,Single,3,2,1,M,-0.35,554,95.97,1.01,-432.37,10,117663.97,1243
+849,Kaitlyn Combs,46-55,Single,1,0,5,F,0.0,385,74.2,1.0,0.0,0,37990.91,514
+850,Kyle Willis,46-55,Married,2,0,5,M,-0.38,64,102.92,1.0,-39.18,2,10497.35,102
+851,Brian Mitchell,36-45,Married,2,0,6,M,-0.06,526,101.69,1.1,-55.21,2,98641.03,1064
+852,Doris Adams,56-70,Married,2,0,6,F,-0.15,676,84.58,1.0,-174.54,4,97355.72,1155
+853,Brittany Steele,70+,Single,2,1,2,F,-0.44,352,102.48,1.03,-195.91,6,45708.2,458
+854,Karen Hopkins,46-55,Married,5,3,8,F,-0.03,287,97.98,1.0,-19.59,1,55260.91,564
+855,Jessica Martinez,56-70,Married,2,0,3,F,-2.02,140,114.3,1.06,-688.2,24,38976.03,360
+856,Anne Smith,26-35,Single,2,1,2,F,-0.18,375,110.42,1.0,-106.86,3,66580.55,604
+857,Eric Glenn,70+,Single,1,0,7,M,-2.47,374,115.63,1.0,-1162.1,26,54462.72,471
+858,Jonathan Wilson,70+,Single,1,0,7,M,0.0,542,85.84,1.0,0.0,0,84900.08,992
+859,Daniel Bradley,36-45,Married,3,1,6,M,-0.48,1302,105.54,1.08,-970.35,37,212666.08,2185
+860,Thomas Richardson,26-35,Single,1,0,1,M,0.0,238,117.58,1.0,0.0,0,60200.42,512
+861,Mr. Craig Bennett,70+,Married,2,0,3,M,-0.06,502,95.16,1.0,-80.14,2,126367.93,1328
+862,Kelsey Marsh,46-55,Married,2,0,6,F,0.0,379,114.35,1.0,0.0,0,63809.23,558
+863,Sarah Herman,56-70,Married,2,0,4,F,-0.12,656,102.14,1.0,-106.5,1,94068.78,924
+864,Joseph Hester,56-70,Single,1,0,5,M,-1.96,1067,103.86,1.13,-3471.48,156,184358.5,1999
+865,Regina Thomas,26-35,Single,1,0,7,F,-0.04,597,94.3,1.01,-71.24,2,156728.99,1674
+866,Andrew Jackson,36-45,Married,4,2,2,M,-0.09,995,93.82,1.1,-190.56,7,209780.88,2457
+867,Michael Johnson,46-55,Married,2,0,5,M,-0.04,378,125.51,1.0,-25.83,3,83837.97,668
+868,Sean Mcclain,36-45,Single,1,0,5,M,0.0,150,125.05,1.0,0.0,0,29511.05,236
+869,Teresa Nelson,56-70,Married,2,0,4,F,-0.02,589,104.84,1.0,-17.81,1,112181.99,1070
+870,Paul Lowe,18-25,Married,2,0,4,M,0.0,189,110.21,1.0,0.0,0,40004.58,363
+871,Tammy Hall,36-45,Married,3,1,7,F,-0.58,759,105.69,1.06,-840.32,33,153353.36,1544
+872,Dawn Cooper,70+,Married,2,0,7,F,-0.18,262,124.73,1.0,-63.22,2,44029.58,353
+873,Joseph Lambert,56-70,Married,4,2,6,M,-0.15,629,95.35,1.0,-123.78,7,78757.29,826
+874,Jennifer Hernandez,46-55,Married,2,0,5,F,-0.34,954,106.8,1.11,-481.15,31,149627.4,1559
+875,Denise Heath,46-55,Married,2,0,2,F,-0.98,351,93.68,1.04,-532.58,20,50682.96,565
+876,Michael Pacheco,36-45,Single,1,0,9,M,-0.28,426,129.11,1.0,-208.37,10,95281.85,738
+877,Dr. Kevin Gordon MD,46-55,Married,2,0,5,M,-0.46,374,113.12,1.0,-322.17,11,79185.16,700
+878,Jason Mejia,70+,Married,2,0,8,M,-0.72,1058,95.77,1.02,-1202.65,29,160122.06,1699
+879,Stephanie Reynolds,46-55,Single,1,0,4,F,-0.65,436,99.57,1.0,-732.52,37,111519.6,1123
+880,Sara Campbell,56-70,Single,1,0,2,F,-0.02,326,93.65,1.02,-8.9,1,52071.05,569
+881,Thomas Miller,46-55,Single,1,0,5,M,-1.18,293,131.3,1.21,-642.35,15,71429.55,658
+882,Randall Green,26-35,Married,3,1,9,M,-0.36,267,133.39,1.01,-108.64,3,39750.28,300
+883,Tara Olson,46-55,Single,1,0,3,F,-0.11,443,124.36,1.06,-56.99,2,65167.05,558
+884,Kenneth Taylor,26-35,Single,1,0,3,M,0.0,186,92.88,1.0,0.0,0,28234.27,304
+885,Lori Lowery,46-55,Single,1,0,3,F,0.0,500,118.05,1.0,0.0,0,101880.84,863
+886,Andres Cruz,46-55,Married,3,1,5,M,-0.16,339,102.6,1.0,-124.66,6,81154.34,791
+887,Kimberly Garcia,46-55,Single,1,0,5,F,-0.1,264,80.6,1.08,-42.21,2,32400.13,436
+888,Laurie Johnson,26-35,Married,3,1,4,F,-0.09,359,128.09,1.01,-42.74,3,60586.7,476
+889,Margaret Shah,36-45,Single,1,0,4,F,-1.84,891,93.37,1.01,-3207.64,126,162925.68,1764
+890,Emily Powell,26-35,Married,4,2,8,F,-0.25,824,112.7,1.0,-253.61,9,114394.96,1020
+891,Brandon Martinez,36-45,Married,2,0,5,M,-0.53,485,96.06,1.11,-538.74,19,96731.56,1115
+892,Brenda Gray,46-55,Single,1,0,2,F,-0.09,143,84.5,1.01,-26.71,1,24675.29,295
+893,Justin Wu,46-55,Married,5,3,8,M,0.0,94,97.72,1.0,0.0,0,10651.21,109
+894,Justin Day,18-25,Single,3,1,5,M,-1.19,546,104.33,1.0,-1161.2,42,101516.53,973
+895,Chelsey Campbell,46-55,Married,2,0,12,F,-0.08,312,100.73,1.02,-35.62,1,44322.64,447
+896,Cynthia Johnson,56-70,Married,2,0,4,F,-0.59,617,93.43,1.05,-472.49,11,74557.7,841
+897,Cindy Suarez,46-55,Single,1,0,4,F,-4.44,97,94.12,1.0,-639.36,32,13553.21,144
+898,Robert Murphy,36-45,Married,3,1,4,M,-0.36,1318,107.13,1.11,-1162.08,27,347968.46,3593
+899,Laura Reynolds,46-55,Married,3,1,2,F,0.0,273,114.94,1.0,0.0,0,57700.91,502
+900,Kerri Williams,56-70,Single,1,0,9,F,-0.02,748,96.29,1.0,-17.81,1,102168.5,1061
+901,James Friedman,26-35,Married,3,1,2,M,-1.79,979,89.81,1.0,-2661.66,107,133364.64,1485
+902,Alexandra Davis,46-55,Single,1,0,1,F,0.0,282,121.21,1.0,0.0,0,41090.53,339
+903,Marcus Scott,46-55,Married,3,1,3,M,-1.1,295,89.66,1.07,-432.78,16,35145.08,421
+904,Johnny Murphy,36-45,Married,2,0,9,M,0.0,169,126.9,1.01,0.0,0,46573.42,370
+905,Angel Prince,36-45,Married,2,0,1,F,-0.37,535,125.81,1.0,-293.86,5,99892.69,794
+906,Wendy White,46-55,Married,3,1,6,F,-2.49,327,103.62,1.01,-1326.95,40,55124.42,539
+907,Melissa Williams,46-55,Married,5,3,9,F,-1.89,71,127.91,1.0,-151.38,3,10232.46,80
+908,John White,56-70,Married,2,0,3,M,0.0,105,154.14,1.0,0.0,0,31906.65,208
+909,Michael Campbell,46-55,Married,5,3,8,M,-3.35,685,110.45,1.09,-3107.09,99,102383.61,1013
+910,Denise Nunez,46-55,Married,3,1,3,F,0.0,260,44.91,1.01,0.0,0,32563.23,730
+911,Richard Miller,46-55,Married,3,1,8,M,-0.38,2073,117.1,1.0,-1352.4,49,421577.36,3617
+912,James Brown,70+,Married,2,0,6,M,-1.11,356,102.56,1.07,-625.89,19,57841.33,604
+913,Julie Turner,18-25,Married,2,0,11,F,0.0,347,95.09,1.0,0.0,0,46595.02,490
+914,Matthew Lee,26-35,Single,1,0,9,M,-0.83,408,93.8,1.04,-438.29,16,49242.72,546
+915,Jenna Hawkins,46-55,Single,1,0,3,F,-0.03,505,113.18,1.0,-17.81,1,80017.64,707
+916,Robert Campos,56-70,Single,1,0,4,M,-1.05,439,116.73,1.03,-585.04,23,65019.74,574
+917,Erin Sanchez,36-45,Married,2,0,3,F,-0.08,568,84.12,1.08,-71.24,2,79657.15,1026
+918,Jeremiah Snyder,26-35,Married,2,0,5,M,-0.21,610,95.39,1.01,-216.93,5,97774.06,1032
+919,Sara Romero,36-45,Married,4,2,5,F,-4.4,360,108.19,1.14,-2554.65,13,62752.55,661
+920,Kristin Keller,18-25,Single,1,0,12,F,-0.13,414,105.12,1.01,-71.24,2,58759.78,564
+921,Sherri Lee,36-45,Married,4,2,5,F,-0.07,211,61.15,1.01,-35.62,1,31918.77,527
+922,Rebecca Salazar MD,70+,Married,2,0,6,F,-0.02,524,113.54,1.04,-17.81,1,101727.53,936
+923,Sharon Bean,46-55,Single,1,0,4,F,-0.12,255,108.41,1.05,-35.62,1,31981.1,309
+924,Tina Williams,36-45,Married,2,0,10,F,-0.02,627,110.45,1.0,-17.81,1,91120.86,825
+925,Samantha Tucker,36-45,Single,1,0,5,F,0.0,276,106.91,1.0,0.0,0,46933.63,439
+926,Richard Austin,26-35,Single,1,0,5,M,-0.2,709,110.94,1.06,-403.02,16,220207.61,2099
+927,Luis Mathis,46-55,Single,2,1,5,M,0.0,560,122.39,1.2,0.0,0,144907.44,1415
+928,Heather Rodriguez,46-55,Single,1,0,2,F,-0.4,1041,92.04,1.01,-768.66,22,177089.11,1945
+929,Lauren Alexander,18-25,Single,1,0,4,F,-0.14,460,87.81,1.03,-84.58,4,51366.98,600
+930,Kaitlyn Castro,18-25,Single,1,0,1,F,-0.06,775,84.57,1.03,-70.52,2,92180.87,1118
+931,Mark Davis,18-25,Married,2,0,4,M,-1.26,559,89.63,1.0,-933.83,56,66324.68,740
+932,Sarah Blevins,36-45,Single,1,0,5,F,-0.64,232,154.04,1.0,-195.73,6,47291.58,307
+933,Amy Nicholson,26-35,Married,2,0,5,F,-1.08,477,94.47,1.01,-715.35,17,62445.4,665
+934,Gabrielle Giles,46-55,Single,1,0,6,F,-0.13,1368,95.98,1.04,-390.93,13,300022.11,3253
+935,Jason Garcia,26-35,Single,2,1,1,M,-0.11,547,116.61,1.07,-89.05,1,97018.23,888
+936,Joseph Moore,70+,Married,2,0,4,M,-0.06,476,111.19,1.0,-60.55,3,114863.9,1033
+937,Allison Nixon,70+,Married,2,0,2,F,-0.35,223,91.99,1.0,-89.05,1,23641.03,257
+938,Brett Williams,56-70,Married,4,2,7,M,-0.42,157,108.0,1.0,-77.47,3,20087.37,186
+939,Raymond Lin,70+,Single,1,0,3,M,0.0,396,80.29,1.0,0.0,0,53716.44,669
+940,Pamela Stephens,18-25,Married,3,1,4,F,0.0,363,58.63,1.0,0.0,0,35940.71,613
+941,Thomas Sanders,56-70,Married,2,0,2,M,-0.45,224,100.69,1.01,-147.67,6,33127.65,332
+942,Christopher Torres,70+,Married,3,1,8,M,-0.03,655,109.57,1.13,-35.62,1,132905.05,1367
+943,Robert Ford,46-55,Married,2,0,5,M,-0.14,419,107.26,1.0,-81.92,3,61569.54,574
+944,Laura Bates,46-55,Single,1,0,3,F,-0.32,424,98.08,1.0,-276.06,10,83862.28,855
+945,Brianna Wilson,70+,Single,1,0,4,F,0.0,263,84.43,1.0,0.0,0,52937.33,627
+946,Samantha Pierce,46-55,Single,1,0,7,F,-1.05,150,118.43,1.12,-225.15,9,25461.89,240
+947,Catherine Stewart,46-55,Married,2,0,5,F,-0.17,478,131.23,1.13,-163.25,3,124147.03,1066
+948,Gary Wilson,46-55,Married,2,0,5,M,-0.11,962,66.6,1.03,-270.71,7,164044.66,2525
+949,Jerry Phillips,26-35,Single,1,0,4,M,-0.08,455,103.58,1.0,-64.11,2,78517.34,758
+950,Shelly Johnson,36-45,Married,5,3,4,F,-0.28,284,90.85,1.11,-89.05,3,29345.69,359
+951,Ashley Romero,46-55,Single,1,0,5,F,-0.46,715,115.26,1.0,-455.04,22,113989.02,989
+952,Carl Wagner,46-55,Married,4,2,5,M,-0.52,519,99.34,1.01,-365.63,7,69540.68,709
+953,Alison Stephens,46-55,Single,1,0,2,F,0.0,205,114.12,1.0,0.0,0,26817.69,236
+954,Todd Romero,26-35,Married,4,2,5,M,-0.21,439,79.89,1.22,-130.62,8,49054.31,751
+955,Lindsey George,46-55,Married,2,0,4,F,-2.96,340,105.35,1.07,-1497.65,48,53305.19,540
+956,Jessica Fernandez,46-55,Single,1,0,1,F,0.0,484,79.5,1.0,0.0,0,66622.78,838
+957,Ronald Wilson,26-35,Married,2,0,5,M,-0.06,1104,112.72,1.13,-142.48,3,249459.6,2498
+958,Edward Pratt,36-45,Single,1,0,8,M,-0.1,1146,96.71,1.03,-159.75,3,153960.61,1645
+959,Dylan Delgado,46-55,Single,1,0,5,M,-1.31,851,101.66,1.07,-2296.33,87,177907.36,1864
+960,Alfred Mitchell,46-55,Married,2,0,7,M,0.0,107,113.54,1.05,0.0,0,17938.64,166
+961,Rebecca Hensley,46-55,Married,5,3,6,F,-0.52,230,119.51,1.08,-188.73,8,43143.99,391
+962,Wesley Chavez,26-35,Married,3,1,6,M,-0.68,585,102.02,1.09,-925.42,42,138740.74,1489
+963,John Gordon,18-25,Single,1,0,4,M,-0.03,808,91.69,1.01,-53.43,2,141024.46,1547
+964,Megan Pierce,46-55,Single,1,0,5,F,-0.28,785,114.83,1.07,-329.12,10,133207.0,1245
+965,Joseph Moreno,18-25,Married,3,1,4,M,0.0,423,91.54,1.05,0.0,0,50162.64,578
+966,Joel Turner,46-55,Married,4,2,2,M,-0.02,535,77.83,1.0,-17.81,1,68027.54,874
+967,Keith Roberts,36-45,Single,1,0,5,M,-1.45,658,117.49,1.0,-1979.65,81,160022.32,1366
+968,Lisa Sharp,36-45,Married,2,0,9,F,-0.7,265,116.98,1.0,-267.15,3,44685.72,382
+969,Edward Evans,18-25,Married,3,1,6,M,0.0,446,96.53,1.03,0.0,0,63321.73,678
+970,Ruth Casey,46-55,Married,2,0,4,F,-0.29,534,109.5,1.01,-226.18,8,86397.85,796
+971,Paul Middleton,46-55,Married,5,3,6,M,-0.15,615,71.23,1.0,-160.3,9,75072.18,1057
+972,Francisco Walters,46-55,Married,2,0,9,M,-0.66,92,65.98,1.28,-159.94,5,16099.53,313
+973,Vincent Cruz,56-70,Married,2,0,4,M,-0.15,579,86.44,1.0,-142.48,6,82288.34,954
+974,Sally Smith,46-55,Single,1,0,2,F,-1.11,269,83.45,1.04,-417.34,17,31377.25,391
+975,Andrea Henson,36-45,Married,3,1,2,F,0.0,162,88.54,1.0,0.0,0,18594.09,210
+976,John Olsen,36-45,Single,1,0,1,M,-0.26,328,94.06,1.0,-110.42,5,40163.5,427
+977,Julia Taylor,46-55,Married,2,0,4,F,-0.94,585,76.19,1.0,-924.1,47,75273.91,988
+978,Adam Baldwin,26-35,Married,4,2,7,M,-0.19,536,96.26,1.12,-217.88,11,108965.32,1263
+979,Lisa Payne,46-55,Single,4,3,4,F,-0.05,529,135.55,1.0,-35.62,1,101118.3,746
+980,Jason Frey,46-55,Married,3,1,6,M,0.0,436,123.73,1.01,0.0,0,72753.68,595
+981,James Peters,36-45,Married,2,0,6,M,-0.21,638,132.82,1.06,-212.82,7,134674.89,1071
+982,Jeffrey Calderon,46-55,Single,1,0,2,M,-0.04,294,66.9,1.0,-19.59,1,35054.54,524
+983,Tracy Smith,56-70,Single,1,0,4,F,-0.37,513,119.64,1.0,-267.14,7,86617.84,724
+984,Peter Nichols,56-70,Married,3,1,7,M,-0.02,295,76.3,1.0,-8.9,1,31204.91,409
+985,Andrew White,36-45,Married,2,0,6,M,-0.38,242,110.93,1.04,-106.5,2,31505.18,296
+986,April West,46-55,Married,2,0,4,F,-0.2,277,89.45,1.01,-71.24,2,31396.53,353
+987,Ricky Joseph,26-35,Single,3,2,4,M,-0.11,628,96.59,1.0,-106.86,1,93404.46,971
+988,Shawn Moore,46-55,Married,2,0,2,M,-0.06,431,75.25,1.01,-37.4,2,50944.65,684
+989,Jenna Richardson,26-35,Married,2,0,2,F,-0.09,1057,112.57,1.08,-187.0,6,227385.4,2190
+990,Stephanie Evans,18-25,Single,2,1,5,F,-0.02,296,49.34,1.0,-14.25,1,28173.59,573
+991,David Beck,46-55,Married,3,1,1,M,-0.52,131,131.52,1.0,-71.24,1,18018.2,137
+992,James Reynolds,36-45,Single,1,0,6,M,0.0,364,98.19,1.01,0.0,0,48703.68,500
+993,Craig Mullins,46-55,Single,1,0,5,M,-0.03,555,133.25,1.06,-32.94,2,138842.33,1109
+994,David Brown,46-55,Single,1,0,9,M,-0.09,899,137.28,1.06,-107.75,4,159521.57,1227
+995,Steven Dougherty,36-45,Single,1,0,1,M,0.0,182,105.81,1.0,0.0,0,38619.32,365
+996,Kevin Cantrell,26-35,Single,1,0,5,M,-0.74,121,80.94,1.05,-101.52,4,11169.86,145
+997,Frank Lewis,36-45,Single,1,0,5,M,-0.16,687,104.89,1.0,-178.1,5,115377.83,1102
+998,Jeffrey Thomas,36-45,Married,2,0,8,M,-0.25,702,115.09,1.0,-323.95,15,146623.77,1274
+999,Karen Chavez,26-35,Single,1,0,3,F,-1.25,423,95.77,1.05,-828.45,31,63594.41,695
+1000,Anne Fox,70+,Single,1,0,2,F,-0.15,179,127.97,1.08,-35.62,2,29946.05,252
+1001,Raymond Paul,46-55,Married,2,0,4,M,-0.42,320,103.99,1.0,-170.07,9,41805.57,402
+1002,Chase Duarte,46-55,Married,2,0,4,M,-0.22,351,94.02,1.02,-89.05,1,38924.4,424
+1003,Jeremy Johnson,46-55,Single,1,0,5,M,-0.09,545,118.64,1.08,-89.05,2,111399.74,1012
+1004,Amber Brown,46-55,Married,2,0,6,F,-0.07,1196,99.66,1.02,-129.42,6,180885.05,1855
+1005,Raven Vega,26-35,Married,2,0,4,F,-0.09,913,75.32,1.0,-142.48,2,125099.06,1661
+1006,Jennifer Jones,36-45,Married,2,0,7,F,-0.09,374,94.31,1.0,-35.62,1,39234.86,416
+1007,Carol Reyes MD,36-45,Married,3,1,9,F,-0.03,917,108.3,1.0,-39.18,2,140360.3,1296
+1008,Jo Price,36-45,Married,2,0,4,F,0.0,100,77.53,1.0,0.0,0,15428.28,199
+1009,Alicia Austin,26-35,Single,2,1,4,F,-5.39,255,78.73,1.0,-1520.64,70,22200.82,282
+1010,Angela Wong,46-55,Married,2,0,6,F,-1.15,112,119.18,1.0,-418.54,20,43263.49,363
+1011,Deborah Martinez,46-55,Married,5,3,8,F,-0.42,1134,109.11,1.1,-1618.52,70,421369.11,4253
+1012,Christina Shaffer,70+,Married,2,0,3,F,-0.15,617,108.23,1.0,-158.51,4,116780.7,1079
+1013,Sara Johnson,18-25,Single,1,0,4,F,-0.42,193,105.15,1.0,-92.62,6,22922.56,219
+1014,Daniel King,36-45,Single,1,0,3,M,0.0,512,96.65,1.0,0.0,0,90073.64,934
+1015,Andrew Scott,36-45,Married,2,0,6,M,-3.2,405,112.93,1.1,-2135.95,102,75437.48,735
+1016,Mr. Allen Mcknight,36-45,Married,2,0,8,M,-0.08,412,111.38,1.14,-44.52,2,62486.71,639
+1017,Scott Smith DVM,18-25,Single,3,1,5,M,-0.17,1113,87.85,1.05,-307.22,11,160686.26,1915
+1018,Jeffrey Lewis,46-55,Married,2,0,6,M,-0.2,399,98.9,1.0,-104.18,7,52416.29,530
+1019,Nancy Wright,36-45,Married,2,0,4,F,-0.02,435,103.3,1.03,-17.81,1,74166.4,739
+1020,Jacob Davis,46-55,Single,1,0,3,M,0.0,161,99.29,1.0,0.0,0,24127.03,243
+1021,Hannah Andersen,26-35,Single,1,0,4,F,-0.12,209,111.59,1.11,-35.62,1,32250.62,321
+1022,Anna Smith,70+,Married,2,0,4,F,-0.55,423,141.87,1.0,-577.75,24,147828.33,1042
+1023,Martha Briggs,18-25,Single,4,3,1,F,-0.15,1230,75.15,1.0,-398.94,11,205622.55,2738
+1024,Linda Jimenez,26-35,Married,2,0,3,F,0.0,222,111.05,1.07,0.0,0,39977.67,384
+1025,Jerry Schultz,46-55,Married,2,0,5,M,-1.54,1051,98.53,1.02,-2984.9,138,191434.34,1989
+1026,Tara Campbell,26-35,Married,5,3,4,F,-0.11,614,84.95,1.0,-135.34,7,107629.22,1268
+1027,Mr. Anthony Simon,46-55,Single,2,1,5,M,0.0,407,82.45,1.0,0.0,0,51285.96,622
+1028,Christina Noble,56-70,Married,2,0,4,F,-0.12,469,122.65,1.08,-94.4,4,95055.68,838
+1029,Jacob Beasley,26-35,Single,2,1,2,M,-0.29,251,78.12,1.0,-131.2,7,34996.98,449
+1030,Lisa Brown,36-45,Married,2,0,4,F,0.0,110,187.18,1.0,0.0,0,37997.8,203
+1031,Julie Davila,36-45,Single,1,0,2,F,-2.02,552,92.43,1.09,-1632.78,47,74681.98,880
+1032,Melanie Love,36-45,Married,3,1,6,F,-0.12,235,116.12,1.0,-35.62,1,35882.3,309
+1033,Elizabeth Wilson,36-45,Single,1,0,1,F,-0.21,1596,96.43,1.06,-589.15,15,271549.46,2985
+1034,Amber Dickson,46-55,Married,2,0,4,F,0.0,398,88.02,1.0,0.0,0,48232.84,548
+1035,Brian Hall,46-55,Married,2,0,5,M,-0.23,319,98.02,1.01,-89.05,1,37249.03,383
+1036,John Taylor,36-45,Married,2,0,9,M,-0.08,871,115.38,1.0,-124.67,2,179880.33,1559
+1037,Robert Salazar,46-55,Single,1,0,5,M,0.0,121,68.54,1.39,0.0,0,12405.47,252
+1038,Jordan Anderson,46-55,Single,1,0,4,F,0.0,112,142.98,1.01,0.0,0,27738.26,195
+1039,Trevor Anderson,36-45,Single,1,0,6,M,-0.37,157,104.18,1.0,-67.08,3,18855.91,181
+1040,Ricky French,46-55,Married,2,0,1,M,0.0,316,110.35,1.07,0.0,0,40386.41,390
+1041,Sarah Hughes,70+,Single,1,0,4,F,-0.03,306,92.92,1.0,-12.47,1,34658.19,373
+1042,Ian Duarte,70+,Married,2,0,6,M,-0.48,297,82.11,1.0,-172.16,3,29641.51,361
+1043,Nichole Dunlap,36-45,Married,5,3,8,F,-0.37,718,99.41,1.05,-382.54,10,103285.09,1095
+1044,Leslie Myers,36-45,Single,1,0,2,F,-0.98,729,103.84,1.01,-1333.7,42,141633.72,1374
+1045,Maria Parker,36-45,Married,2,0,6,F,-0.08,591,94.56,1.04,-76.58,3,90398.24,997
+1046,Colton Barnes,46-55,Married,2,0,6,M,-0.2,1093,123.4,1.0,-473.15,16,298616.63,2420
+1047,Jeremiah Davenport,46-55,Single,1,0,6,M,-0.06,483,110.15,1.0,-35.62,1,70495.85,642
+1048,Sonya Adkins,26-35,Married,2,0,5,F,0.0,322,104.49,1.04,0.0,0,53289.48,528
+1049,Kenneth Jordan,70+,Married,3,1,8,M,-0.25,225,142.24,1.01,-71.24,3,41106.26,292
+1050,Robert Johnson,26-35,Married,2,0,5,M,-0.19,216,101.08,1.0,-44.52,2,23957.11,237
+1051,Dr. Stephen Williams Jr.,46-55,Married,2,0,2,M,-0.32,177,82.89,1.01,-81.93,3,21303.96,260
+1052,Lauren Mcconnell,26-35,Single,1,0,4,F,-4.62,522,114.88,1.0,-3979.87,98,99027.94,862
+1053,John Young,46-55,Single,1,0,5,M,-0.29,208,164.25,1.08,-89.05,1,50918.77,335
+1054,Robert Lara,36-45,Single,1,0,5,M,0.0,415,75.74,1.01,0.0,0,39084.33,520
+1055,Kenneth Mcdaniel,26-35,Married,3,1,4,M,-0.12,996,99.22,1.02,-215.5,8,171458.68,1754
+1056,Christian Davis,36-45,Single,1,0,2,M,-0.86,646,84.66,1.02,-1405.86,46,139018.76,1678
+1057,Eddie Perez,36-45,Single,1,0,3,M,-0.11,240,86.42,1.03,-35.62,1,27482.68,326
+1058,Chelsea Smith,46-55,Married,3,1,5,F,0.0,428,111.93,1.0,0.0,0,68613.16,613
+1059,Jane Lopez,46-55,Married,2,0,5,F,-0.1,468,88.01,1.0,-73.02,3,63897.48,726
+1060,Lisa Schmidt,36-45,Married,2,0,2,F,0.0,398,72.96,1.0,0.0,0,39690.91,546
+1061,Ronnie Ruiz,36-45,Single,1,0,3,M,-0.85,818,103.47,1.0,-1231.29,48,150545.63,1455
+1062,Mark Marshall,46-55,Single,1,0,3,M,-0.11,392,109.91,1.0,-68.12,4,66276.55,603
+1063,Ryan Curtis,26-35,Married,3,1,4,M,-1.22,348,97.93,1.02,-603.38,25,48571.58,508
+1064,Ryan Simmons,36-45,Married,2,0,9,M,-0.26,950,115.0,1.03,-548.84,12,243574.25,2189
+1065,Heather Bryant,46-55,Single,1,0,5,F,-0.26,398,87.72,1.01,-144.26,5,48332.26,555
+1066,Samantha Cannon,46-55,Married,3,1,1,F,-0.39,142,122.36,1.0,-106.85,3,33650.22,275
+1067,Kelly Patton,46-55,Single,1,0,5,F,-0.22,533,114.02,1.01,-209.27,11,110597.4,981
+1068,Elizabeth Duarte,46-55,Married,2,0,2,F,-0.55,935,91.12,1.03,-1079.86,48,177419.11,2015
+1069,Kristine Cook,46-55,Married,2,0,5,F,0.0,135,202.68,1.01,0.0,0,54318.08,272
+1070,Stephanie Walsh,56-70,Single,1,0,6,F,-3.34,594,112.44,1.1,-3646.57,131,122668.62,1202
+1071,Richard Bennett,36-45,Single,1,0,2,M,-0.14,656,95.51,1.0,-103.3,4,72207.66,757
+1072,Joseph Price,70+,Married,2,0,2,M,-0.42,412,79.97,1.01,-218.12,7,41822.23,529
+1073,John Castro,36-45,Single,1,0,5,M,-0.55,582,92.82,1.0,-422.08,18,70913.74,764
+1074,Jon Watts,26-35,Single,2,1,2,M,0.0,171,117.15,1.0,0.0,0,23547.43,201
+1075,Vincent Anthony,36-45,Married,4,2,7,M,-0.05,303,125.75,1.03,-21.38,2,50804.6,418
+1076,Brenda Hernandez,18-25,Single,1,0,12,F,0.0,608,98.15,1.03,0.0,0,167927.65,1766
+1077,Elizabeth Smith,70+,Married,2,0,4,F,0.0,297,87.81,1.0,0.0,0,49610.63,566
+1078,Alicia Bush,26-35,Married,2,0,4,F,-0.4,116,91.35,1.0,-89.05,1,20188.28,221
+1079,Barbara Patterson,46-55,Married,2,0,1,F,0.0,317,97.39,1.0,0.0,0,64080.26,658
+1080,Christine Rogers,70+,Single,1,0,2,F,-0.1,288,126.83,1.01,-35.62,1,45786.64,365
+1081,Kevin Caldwell,56-70,Single,1,0,4,M,-0.03,351,88.27,1.09,-17.81,1,47668.48,590
+1082,Jacob Solis,56-70,Single,1,0,4,M,-0.58,331,115.65,1.15,-301.86,16,60370.67,599
+1083,Aaron Branch,26-35,Married,4,2,2,M,-0.71,653,85.81,1.0,-720.87,24,87610.29,1021
+1084,Kelly Torres,26-35,Married,2,0,4,M,-0.02,811,85.12,1.0,-27.61,3,133559.26,1573
+1085,Paul Rivera,46-55,Single,1,0,4,M,-0.35,450,79.16,1.06,-248.97,8,56912.61,763
+1086,Krystal Turner,26-35,Married,3,1,4,F,-3.78,506,93.65,1.04,-3061.8,92,75948.49,846
+1087,Tina Barajas,46-55,Single,1,0,4,F,-0.03,486,111.03,1.06,-17.81,1,72499.53,689
+1088,Loretta Beasley,36-45,Married,5,3,5,F,-0.26,279,102.37,1.0,-89.05,1,35216.02,344
+1089,Tyrone Alexander,36-45,Married,5,3,4,M,-0.04,723,81.7,1.0,-47.49,2,89627.15,1097
+1090,Stephanie Harris,70+,Married,2,0,6,F,0.0,204,94.27,1.0,0.0,0,22152.92,235
+1091,Johnny Gonzalez,46-55,Married,5,3,8,M,-0.42,1376,92.92,1.11,-1086.74,32,239362.16,2854
+1092,Jacob Cooper,46-55,Single,1,0,10,M,-0.08,307,145.77,1.0,-35.62,1,66764.46,458
+1093,Danielle Sherman,46-55,Married,4,2,6,F,-0.55,945,114.63,1.03,-746.53,33,155433.93,1403
+1094,Christina Watkins,46-55,Single,1,0,4,F,-0.05,529,94.77,1.01,-35.62,1,68612.92,731
+1095,Timothy Byrd,36-45,Single,1,0,2,M,-0.05,1518,87.68,1.0,-108.64,4,206410.02,2359
+1096,Morgan Rodriguez,46-55,Married,2,0,2,F,0.0,199,87.24,1.01,0.0,0,24514.86,283
+1097,Aaron Smith,26-35,Married,3,1,7,M,-0.05,564,136.57,1.01,-47.2,3,127285.82,938
+1098,Jordan Jensen,26-35,Married,2,0,4,M,-0.05,329,95.44,1.0,-35.62,1,65946.41,691
+1099,Kelly Evans,46-55,Single,1,0,5,F,-1.1,324,125.28,1.1,-525.38,23,60009.45,529
+1100,Nichole Lawson,46-55,Single,1,0,5,F,-0.59,645,98.27,1.0,-593.07,23,98069.27,998
+1101,Jessica Garza,46-55,Single,1,0,6,F,-0.34,492,85.82,1.0,-286.39,7,73206.51,853
+1102,Miranda Arroyo,56-70,Married,2,0,5,F,0.0,138,127.76,1.0,0.0,0,20186.87,158
+1103,Marilyn Morgan,26-35,Married,2,0,4,F,-0.16,819,81.02,1.14,-293.86,8,149963.89,2113
+1104,Anne Rogers,70+,Married,2,0,6,F,-0.14,588,87.3,1.01,-118.44,8,74990.16,871
+1105,Ashley White,46-55,Married,2,0,9,F,-0.06,455,119.72,1.07,-35.62,1,69077.26,615
+1106,Eduardo Aguilar,26-35,Married,2,0,3,M,-0.3,457,93.27,1.03,-213.72,5,66033.63,730
+1107,Mary Mcknight,46-55,Single,1,0,5,F,0.0,272,126.32,1.0,0.0,0,44591.82,353
+1108,Amy Mendez,26-35,Married,5,3,4,F,0.0,285,84.03,1.0,0.0,0,28569.5,340
+1109,Darius Cook,36-45,Married,2,0,6,M,-0.13,702,123.71,1.04,-178.1,5,175664.84,1482
+1110,Yesenia Baldwin,70+,Married,2,0,6,F,0.0,183,103.03,1.0,0.0,0,36471.21,354
+1111,Danielle Fletcher,46-55,Single,2,1,7,F,-1.56,305,101.45,1.02,-745.63,25,48592.78,490
+1112,Andres Smith,36-45,Single,3,2,5,M,-0.2,346,85.17,1.05,-133.57,2,56809.35,698
+1113,Melanie Hopkins,26-35,Married,2,0,4,F,-0.26,400,80.92,1.0,-142.48,4,44022.1,546
+1114,Kenneth Barry,46-55,Single,1,0,5,M,-0.26,591,95.99,1.0,-225.0,13,83412.64,870
+1115,Tammie Cochran,46-55,Married,2,0,3,F,-1.28,1208,89.86,1.01,-3315.5,98,233018.22,2623
+1116,Rebecca James,18-25,Single,1,0,4,F,0.0,314,92.42,1.02,0.0,0,46392.75,511
+1117,Holly Estrada MD,70+,Single,1,0,1,F,-0.26,610,111.1,1.0,-241.31,13,102212.17,920
+1118,Anthony Howell,56-70,Married,2,0,4,M,-0.61,438,92.12,1.0,-346.4,10,52045.72,565
+1119,Sierra King,36-45,Married,2,0,10,F,-0.35,403,112.97,1.26,-161.89,4,52982.39,590
+1120,Brandon Moran,46-55,Married,2,0,9,M,-0.03,508,111.59,1.0,-26.71,1,90837.21,814
+1121,Craig Barnes,18-25,Married,3,1,5,M,-0.1,1010,143.09,1.0,-222.26,8,323822.41,2263
+1122,Brian Lane,70+,Married,2,0,4,M,-0.36,897,112.94,1.09,-520.5,14,163870.05,1584
+1123,Danielle Stanley MD,36-45,Married,2,0,8,F,0.0,553,91.61,1.0,0.0,0,80799.12,885
+1124,Cassidy Jones,46-55,Married,2,0,1,F,-0.02,949,102.09,1.01,-35.62,1,152317.03,1505
+1125,Marcus Smith,26-35,Single,1,0,5,M,-0.24,207,118.81,1.0,-71.24,2,35880.54,302
+1126,Kathleen Howard,26-35,Single,1,0,5,F,-0.37,295,85.71,1.0,-144.26,5,33596.54,392
+1127,Jason Perkins,36-45,Single,1,0,3,M,-0.03,339,138.17,1.06,-17.81,1,78343.32,600
+1128,David Douglas,46-55,Single,1,0,5,M,-0.28,302,121.49,1.02,-106.86,2,45678.63,385
+1129,Brittany Wiggins,70+,Married,2,0,9,F,-0.66,607,127.33,1.0,-476.95,5,91552.56,721
+1130,Lisa Acosta,46-55,Married,2,0,3,F,-0.31,497,93.99,1.04,-198.4,5,59306.21,656
+1131,Kyle Hunt,18-25,Single,5,3,1,M,-1.36,707,90.57,1.05,-1226.73,38,81515.56,943
+1132,Kaitlyn Bowman,46-55,Married,2,0,5,F,-0.13,205,100.57,1.0,-35.62,2,27353.89,272
+1133,Bryan Aguirre MD,46-55,Married,2,0,9,M,0.0,540,96.86,1.01,0.0,0,84171.31,875
+1134,Brandon Bowman,26-35,Married,4,2,6,M,-0.04,525,106.08,1.02,-31.16,2,77334.73,740
+1135,Michael Madden,56-70,Married,2,0,4,M,-0.02,801,85.52,1.0,-17.81,1,83554.71,978
+1136,Madeline Wilson,46-55,Married,2,0,6,F,-2.0,957,98.73,1.07,-4036.85,137,199340.79,2151
+1137,Michelle Smith,36-45,Married,3,1,5,F,-0.17,374,104.19,1.0,-102.4,3,61368.24,589
+1138,Matthew Stewart,36-45,Single,1,0,9,M,0.0,246,124.02,1.06,0.0,0,45516.46,389
+1139,Teresa Tyler,56-70,Married,4,2,7,F,0.0,413,69.74,1.0,0.0,0,39614.35,568
+1140,Jennifer Taylor,26-35,Single,1,0,4,F,-0.15,722,116.31,1.0,-182.25,9,144694.52,1246
+1141,Phillip Turner,70+,Married,2,0,6,M,-0.41,448,120.38,1.07,-386.48,10,113153.54,1003
+1142,Jill Escobar,56-70,Single,1,0,6,F,-1.93,511,96.01,1.1,-1602.84,71,79689.22,909
+1143,Jonathon Barnes,46-55,Married,2,0,6,M,-0.18,250,84.36,1.0,-64.11,3,30114.98,357
+1144,Mark Bridges,26-35,Married,5,3,3,M,-1.15,432,105.44,1.0,-648.28,26,59466.92,565
+1145,Jennifer Murphy,46-55,Married,5,3,8,F,-0.41,560,108.66,1.02,-382.91,10,102362.12,960
+1146,Kelly Osborne,36-45,Married,2,0,5,F,-0.31,942,99.36,1.06,-609.99,15,198426.03,2121
+1147,Shelby Martin,46-55,Married,3,1,3,F,0.0,357,110.19,1.04,0.0,0,58950.7,555
+1148,Shane Hopkins,18-25,Single,1,0,12,M,-1.17,477,149.31,1.04,-873.74,19,111533.88,780
+1149,Sheryl Horton,46-55,Single,1,0,6,F,-0.3,193,59.73,1.0,-178.1,6,34941.06,585
+1150,James Weeks,36-45,Married,3,1,1,M,-0.23,70,74.49,1.0,-19.59,1,6480.43,87
+1151,William Harrington,46-55,Single,2,1,4,M,-0.2,220,97.43,1.0,-74.8,5,36048.76,370
+1152,Alexis Martin,46-55,Married,3,1,10,F,-0.17,1195,107.89,1.0,-474.28,13,292823.13,2717
+1153,Christopher Strickland,46-55,Single,1,0,10,M,-0.22,259,119.26,1.01,-89.05,1,48537.32,412
+1154,Angela Garza,46-55,Married,3,1,6,F,-0.07,840,121.32,1.02,-129.12,6,229904.66,1936
+1155,Julie Frazier,46-55,Married,5,3,1,F,-1.16,381,89.57,1.0,-670.18,13,51950.05,580
+1156,Isabella Gonzalez,70+,Married,2,0,6,F,-0.03,761,102.08,1.11,-30.28,2,106672.49,1160
+1157,Andrea Hall,18-25,Married,3,1,5,F,-0.21,1402,105.29,1.11,-425.16,15,210580.78,2220
+1158,Rhonda Long,36-45,Married,3,1,6,F,0.0,788,108.68,1.06,0.0,0,140201.64,1370
+1159,Stephen Thompson,36-45,Married,2,0,6,M,-1.74,824,93.01,1.0,-1928.11,67,103237.7,1112
+1160,Amanda Morrow,36-45,Married,2,0,4,F,-0.08,295,94.07,1.05,-34.35,2,38476.45,430
+1161,Natalie Hines,26-35,Single,1,0,9,F,-1.39,314,114.38,1.0,-760.47,28,62567.49,547
+1162,Kevin King,70+,Married,2,0,4,M,-0.01,243,121.44,1.0,-4.45,1,36431.43,300
+1163,Jessica Aguilar,46-55,Single,2,1,7,F,-0.52,150,99.05,1.04,-92.26,3,17532.22,184
+1164,Lindsey Campbell,26-35,Married,4,2,2,F,-0.43,354,89.61,1.0,-191.46,4,39698.58,443
+1165,Richard Webb,36-45,Married,5,3,5,M,-0.75,313,106.56,1.07,-335.7,10,47630.73,477
+1166,Jill Rivera,46-55,Married,5,3,8,F,-0.13,1083,123.6,1.04,-283.47,13,271916.85,2297
+1167,Omar Williams,46-55,Single,1,0,5,M,-0.07,585,103.99,1.0,-97.95,4,150676.87,1449
+1168,Joshua Boyer,18-25,Married,2,0,4,M,-1.28,639,91.73,1.01,-1084.98,40,77878.3,858
+1169,Kathryn Becker,46-55,Married,2,0,5,F,-0.45,429,129.8,1.05,-275.75,10,79696.82,646
+1170,Linda Green,36-45,Single,1,0,2,F,-0.12,850,84.01,1.06,-151.38,3,106861.26,1350
+1171,Kimberly Brooks,46-55,Married,2,0,3,F,0.0,302,115.15,1.0,0.0,0,42490.9,369
+1172,Kristina Parks,70+,Married,2,0,4,F,-0.2,203,71.66,1.0,-189.98,8,68002.28,949
+1173,Brittney Peterson,46-55,Single,1,0,7,F,-0.09,203,106.7,1.09,-35.62,1,42467.42,433
+1174,Brittany Dixon,26-35,Married,2,0,6,F,-0.15,509,92.19,1.01,-97.95,5,61767.6,674
+1175,Sara Little,70+,Married,2,0,6,F,-1.45,76,113.72,1.0,-178.1,2,13987.26,123
+1176,Amy Malone,70+,Single,1,0,2,F,-1.57,284,110.34,1.02,-572.92,18,40275.69,374
+1177,Alfred Lowery,26-35,Married,3,1,4,M,-0.33,226,118.6,1.0,-89.04,4,31548.39,266
+1178,Kurt Valdez,36-45,Married,3,1,4,M,-0.11,696,90.08,1.02,-128.23,3,107739.85,1222
+1179,Michael Leach,36-45,Married,2,0,7,M,-0.4,421,103.79,1.0,-207.46,11,53556.62,516
+1180,Shawn Jones,70+,Married,2,0,5,M,-2.36,74,204.43,1.0,-195.91,2,16967.64,83
+1181,Amber Ramirez,46-55,Married,2,0,5,F,0.0,528,100.04,1.01,0.0,0,67930.35,686
+1182,Kevin Liu,26-35,Married,3,1,3,M,-0.68,989,128.39,1.0,-1341.92,59,252546.08,1969
+1183,Christopher Taylor,46-55,Married,3,1,3,M,-0.05,672,127.01,1.0,-53.43,3,131327.46,1034
+1184,Tamara Mason,46-55,Married,3,1,4,F,-0.21,1874,100.36,1.0,-532.21,16,260236.54,2594
+1185,Barbara Barrett,36-45,Married,2,0,4,F,-0.15,271,121.66,1.01,-47.49,2,38445.76,320
+1186,Robert Garcia,46-55,Single,1,0,10,M,-0.02,721,119.54,1.04,-26.71,1,144877.87,1261
+1187,Robert Gray,26-35,Single,1,0,5,M,-0.63,909,79.15,1.05,-1014.4,35,128373.55,1706
+1188,Cole Hernandez,36-45,Married,2,0,5,M,0.0,198,83.37,1.12,0.0,0,25426.51,342
+1189,Danielle Harvey,46-55,Single,1,0,5,F,-0.36,342,83.96,1.0,-227.08,4,53316.04,635
+1190,Michele Stewart,56-70,Single,1,0,6,F,0.0,248,133.71,1.0,0.0,0,104295.08,780
+1191,Jordan Reynolds,46-55,Married,5,3,4,M,-0.16,491,93.24,1.0,-106.86,2,63215.93,680
+1192,Ashley Gutierrez,70+,Single,1,0,2,F,-0.2,938,93.91,1.0,-343.72,10,159740.01,1701
+1193,David Fowler,56-70,Married,2,0,4,M,-0.69,418,119.93,1.04,-378.76,19,65964.02,571
+1194,Julie Ramirez,36-45,Married,5,3,4,F,-1.05,526,90.59,1.05,-888.7,17,76910.8,888
+1195,Teresa Hunter,56-70,Married,2,0,5,F,-0.03,221,141.15,1.0,-8.9,1,39099.25,277
+1196,Michael Terry,36-45,Married,2,0,4,M,-0.84,150,89.03,1.04,-180.78,8,19051.39,223
+1197,Meredith Johnson,46-55,Single,1,0,4,F,-0.07,358,105.44,1.03,-35.62,1,54724.31,532
+1198,Michelle Allen,18-25,Single,1,0,4,F,-0.89,494,105.36,1.11,-669.63,27,79443.14,835
+1199,Robert Mcdonald,36-45,Single,1,0,9,M,-0.4,850,99.98,1.01,-549.62,8,136968.68,1380
+1200,Dr. Kari Walker PhD,36-45,Married,5,3,5,F,0.0,452,101.08,1.04,0.0,0,65092.54,671
+1201,Jasmine Ward,18-25,Single,1,0,1,F,0.0,667,101.6,1.0,0.0,0,89406.31,880
+1202,Ashley Rogers,36-45,Single,3,1,6,F,-0.99,338,85.21,1.03,-540.82,24,46526.21,565
+1203,Savannah Walter,36-45,Single,1,0,3,F,-0.02,522,87.55,1.0,-15.27,1,86232.99,985
+1204,Shannon Hernandez,46-55,Single,1,0,5,F,-0.08,457,107.0,1.0,-48.09,2,64305.65,601
+1205,Allison Bishop,46-55,Single,1,0,5,F,-0.05,284,103.21,1.0,-23.15,2,46959.6,455
+1206,Heather Clark,36-45,Married,3,1,12,F,-0.01,262,134.7,1.16,-3.92,1,47413.9,410
+1207,Megan Wagner,36-45,Single,4,3,4,F,-0.23,350,91.96,1.06,-106.5,1,41748.47,479
+1208,Kyle Adkins,56-70,Married,2,0,9,M,-3.84,493,121.62,1.13,-5535.66,203,175137.19,1626
+1209,Melissa Alexander,36-45,Married,4,2,7,F,-0.39,587,83.51,1.05,-415.56,8,88689.14,1111
+1210,Robert Murray,46-55,Married,2,0,5,M,-0.35,848,84.91,1.0,-614.44,23,148174.55,1751
+1211,Dr. Betty Taylor,36-45,Married,2,0,5,F,0.0,353,99.39,1.07,0.0,0,52078.36,563
+1212,Christopher Dunn,26-35,Married,2,0,6,M,-0.16,446,77.67,1.0,-124.67,5,61436.2,791
+1213,Tracy Stevens,36-45,Single,3,1,5,M,0.0,125,69.39,1.01,0.0,0,14641.56,213
+1214,Mark Robinson,46-55,Single,1,0,6,M,0.0,300,128.87,1.02,0.0,0,44847.28,354
+1215,Laurie Booth,36-45,Married,3,1,6,F,-0.08,738,102.72,1.02,-99.73,5,125519.31,1250
+1216,Katie Terry,26-35,Married,4,2,8,F,-0.69,752,121.92,1.08,-1034.73,38,183970.73,1624
+1217,Michelle Reynolds,26-35,Single,1,0,6,F,0.0,331,112.45,1.01,0.0,0,68370.9,613
+1218,Melissa Hill,36-45,Married,2,0,5,F,0.0,401,138.2,1.0,0.0,0,135159.68,980
+1219,Brittany Gillespie,36-45,Single,1,0,5,F,-0.41,269,75.21,1.0,-156.72,7,28431.14,378
+1220,Roberto Davis,46-55,Single,1,0,1,M,0.0,191,103.95,1.0,0.0,0,25363.51,244
+1221,Stephanie Bennett,46-55,Single,1,0,2,F,-0.75,111,87.41,1.0,-89.05,3,10401.32,119
+1222,Russell Mathews,46-55,Single,1,0,5,M,-0.03,397,91.23,1.0,-14.25,1,49809.5,546
+1223,Victor Jones,36-45,Married,2,0,5,M,0.0,169,106.81,1.01,0.0,0,31400.7,297
+1224,Tyler Flores,26-35,Single,1,0,4,M,-0.45,717,101.41,1.03,-630.15,27,141774.87,1443
+1225,Jamie Harris,46-55,Married,3,1,6,F,-0.26,301,110.52,1.0,-101.77,4,42883.22,388
+1226,Dwayne Valdez,46-55,Single,1,0,8,M,-0.58,652,104.89,1.1,-612.37,23,110553.58,1160
+1227,Jonathan Vasquez,46-55,Married,2,0,5,M,-0.13,292,89.9,1.0,-47.73,1,33531.16,373
+1228,Bryan Woods,70+,Married,2,0,3,M,-0.04,459,100.71,1.03,-26.71,3,61229.19,624
+1229,Amanda Stewart,26-35,Single,1,0,2,F,-0.92,411,108.34,1.0,-530.74,19,62297.3,576
+1230,Rebecca Farley,70+,Single,1,0,1,F,-0.46,416,78.75,1.0,-255.76,3,44102.31,560
+1231,Donald Adkins,26-35,Married,3,1,5,M,-0.89,445,99.86,1.22,-567.41,19,63514.08,774
+1232,Ronnie May MD,70+,Single,1,0,3,M,-0.13,766,87.31,1.0,-144.26,5,100314.44,1151
+1233,Dennis Perez,46-55,Single,1,0,3,M,0.0,248,78.74,1.0,0.0,0,39054.61,496
+1234,Susan Brown,46-55,Single,1,0,1,F,-0.23,275,86.38,1.0,-120.4,2,46038.76,533
+1235,Norman Vaughn,46-55,Married,5,3,1,M,-0.01,201,87.15,1.0,-3.56,1,23095.58,266
+1236,Joshua Roth,46-55,Single,1,0,5,M,-0.07,590,105.37,1.1,-73.03,5,110318.04,1152
+1237,Sean Lee,46-55,Single,1,0,4,M,-0.03,500,79.47,1.0,-17.81,1,53724.66,678
+1238,Jessica Harris,46-55,Single,1,0,5,F,-0.27,538,117.53,1.05,-311.31,6,137157.67,1225
+1239,Lindsey Rodriguez,36-45,Single,1,0,6,F,-1.61,1114,136.89,1.05,-3748.52,163,318531.62,2432
+1240,Jon Graham,26-35,Married,5,3,5,M,-0.2,603,95.5,1.0,-190.38,7,89862.13,941
+1241,Colin Campbell,46-55,Single,1,0,1,M,-0.12,247,88.38,1.04,-44.52,2,33494.31,395
+1242,Lisa White,46-55,Married,3,1,9,F,-2.7,134,119.71,1.03,-487.99,10,21667.37,187
+1243,Susan Gardner DVM,56-70,Married,2,0,5,F,-0.11,573,114.71,1.02,-89.05,1,94059.43,838
+1244,Allison Baker,46-55,Single,1,0,2,F,-0.03,143,89.13,1.0,-6.24,1,17291.43,194
+1245,Bridget Lamb,26-35,Single,1,0,7,F,0.0,328,70.68,1.07,0.0,0,59299.91,901
+1246,Edward Dunn,26-35,Married,5,3,5,M,-0.52,791,119.62,1.0,-703.66,26,161487.69,1350
+1247,Diana Murphy,56-70,Married,4,2,7,F,-0.18,491,77.3,1.0,-115.76,5,49860.08,645
+1248,Sarah Petersen,36-45,Married,4,2,8,F,-1.9,637,122.85,1.09,-1627.7,38,105407.55,937
+1249,Rebecca Williams,70+,Single,1,0,2,F,-0.81,292,85.56,1.03,-580.54,21,61002.33,737
+1250,Jaime Greene,36-45,Married,5,3,8,F,-1.82,409,99.26,1.11,-1252.31,60,68192.36,765
+1251,Brian Smith,70+,Single,1,0,1,M,-0.25,288,80.67,1.04,-89.04,5,28476.1,366
+1252,Francisco Harvey,26-35,Single,3,2,3,M,-0.16,1414,117.78,1.02,-376.96,11,286085.8,2482
+1253,Joanna White,46-55,Married,2,0,11,F,-0.03,318,148.73,1.0,-12.47,1,72431.35,487
+1254,Howard Frazier,26-35,Single,1,0,5,M,-0.08,499,123.91,1.03,-55.21,2,90327.51,749
+1255,William Bradley,46-55,Married,2,0,6,M,0.0,312,55.61,1.0,0.0,0,59886.93,1077
+1256,Beverly Solis,36-45,Single,1,0,4,F,-0.08,317,88.82,1.02,-35.62,1,39878.8,460
+1257,Judith Chen,46-55,Single,1,0,5,F,-1.76,738,106.12,1.0,-3774.69,172,227305.14,2142
+1258,Marcus Lawson,46-55,Single,1,0,1,M,-0.94,434,98.07,1.02,-697.25,24,72670.09,754
+1259,Kimberly Martinez,46-55,Married,3,1,5,F,-0.33,717,115.99,1.08,-407.85,17,145225.36,1358
+1260,Cory Thompson,46-55,Single,1,0,5,M,-0.79,360,107.7,1.0,-369.56,19,50080.68,465
+1261,Pamela Brown,46-55,Married,2,0,5,F,-0.53,330,137.48,1.02,-210.16,6,54168.86,402
+1262,Charles Gonzalez,46-55,Single,1,0,6,M,0.0,164,90.86,1.0,0.0,0,26168.07,288
+1263,Paula Coffey,46-55,Married,2,0,3,F,-0.35,896,90.04,1.04,-618.72,18,157745.3,1830
+1264,Paul Garcia,18-25,Single,1,0,3,M,-0.1,361,83.19,1.0,-39.18,2,34025.15,409
+1265,Marcus Lloyd,70+,Single,1,0,7,M,0.0,274,83.32,1.0,0.0,0,31660.61,380
+1266,James Levy,46-55,Single,1,0,4,M,-0.82,468,127.39,1.0,-846.28,38,131719.27,1034
+1267,Kimberly Miller,46-55,Single,1,0,5,F,-0.13,339,70.24,1.0,-80.14,2,41932.53,597
+1268,Daniel Valenzuela,46-55,Married,2,0,6,M,-0.03,767,78.55,1.07,-35.62,1,98265.36,1338
+1269,Wendy Boone,46-55,Single,1,0,5,F,-0.05,408,96.57,1.0,-26.71,1,54272.55,562
+1270,Patricia Maldonado,46-55,Single,1,0,2,F,-0.73,1080,97.34,1.0,-1593.57,40,212502.35,2188
+1271,Steven Armstrong,36-45,Married,5,3,7,M,-0.02,823,103.12,1.0,-35.62,1,158909.8,1541
+1272,Preston Bell,46-55,Single,1,0,4,M,-0.14,165,113.05,1.0,-35.62,1,28375.75,251
+1273,Natalie Davis,26-35,Single,2,1,8,F,-1.45,530,125.44,1.05,-1110.38,31,95837.62,801
+1274,Tammy Shelton,46-55,Married,2,0,9,F,-0.04,356,92.0,1.0,-17.81,1,43146.28,469
+1275,Tammy Reed,36-45,Married,2,0,8,F,-0.15,195,135.37,1.0,-35.62,1,33166.11,245
+1276,Christina Price,36-45,Single,1,0,6,F,-0.7,446,96.63,1.01,-381.38,21,52855.71,551
+1277,Timothy King,70+,Married,2,0,3,M,0.0,104,153.49,1.1,0.0,0,21796.06,156
+1278,Casey Moore,46-55,Single,1,0,1,F,-0.24,237,123.37,1.07,-75.16,4,38736.7,337
+1279,David Hanson,70+,Married,2,0,5,M,-0.23,368,71.94,1.08,-117.19,2,37190.74,556
+1280,Cheryl Caldwell,36-45,Married,5,3,8,F,-0.27,229,78.95,1.0,-83.71,3,24632.85,312
+1281,Casey Alexander,18-25,Single,1,0,5,M,-0.07,340,119.19,1.01,-28.14,1,50896.07,431
+1282,Cheryl Gonzalez,46-55,Single,2,1,4,F,0.0,259,95.08,1.0,0.0,0,35845.88,377
+1283,Paula Cain,36-45,Married,5,3,5,F,-0.1,221,105.83,1.01,-26.71,1,29631.11,284
+1284,Jared King,36-45,Single,2,1,4,M,-0.08,537,129.76,1.03,-71.24,2,112113.2,887
+1285,Cody Bradley MD,56-70,Married,2,0,7,M,-0.87,423,125.88,1.01,-605.61,26,87359.86,701
+1286,Elizabeth Rodriguez,36-45,Married,5,3,2,F,-0.04,276,78.93,1.0,-17.81,1,31808.69,405
+1287,Donna Perez,36-45,Married,2,0,3,F,0.0,202,99.05,1.0,0.0,0,24068.19,243
+1288,Steven Stephens,56-70,Married,2,0,4,M,0.0,123,136.85,1.03,0.0,0,18200.7,137
+1289,Danny Williams,18-25,Single,1,0,1,M,-0.22,687,92.34,1.07,-195.91,5,81901.65,949
+1290,Austin White,36-45,Married,5,3,3,M,0.0,587,114.18,1.12,0.0,0,89060.43,873
+1291,Felicia Thomas,46-55,Married,2,0,2,F,-0.1,538,107.27,1.06,-103.3,7,110376.29,1091
+1292,Jordan Herrera,46-55,Married,2,0,2,M,-0.05,415,102.46,1.02,-35.62,2,80632.68,800
+1293,Kimberly Martinez,26-35,Single,1,0,6,F,-0.03,242,121.69,1.02,-12.47,1,45024.12,378
+1294,Sydney Jones,70+,Married,2,0,4,F,-0.55,319,116.22,1.0,-270.36,4,57063.85,491
+1295,Katelyn Mullins,36-45,Married,2,0,4,F,-0.44,415,84.74,1.08,-239.18,8,46098.52,589
+1296,Robert Lyons,26-35,Single,2,1,8,M,-0.13,461,123.04,1.04,-120.66,7,111839.33,943
+1297,Brandon Rogers,46-55,Married,2,0,12,M,0.0,283,101.11,1.0,0.0,0,35691.2,353
+1298,Lisa Thompson MD,26-35,Married,2,0,3,F,-0.36,786,85.82,1.01,-480.86,22,115422.0,1358
+1299,Ricardo Kennedy,56-70,Married,2,0,6,M,-0.14,263,94.59,1.0,-55.21,2,37930.26,401
+1300,Ryan Fowler,18-25,Single,1,0,5,M,-0.18,686,117.4,1.01,-160.28,5,106250.8,910
+1301,James Garrett,26-35,Married,3,1,3,M,-0.52,419,105.61,1.0,-302.77,12,61150.7,579
+1302,April Merritt,56-70,Married,2,0,2,F,-0.45,335,123.22,1.01,-206.85,11,56187.45,461
+1303,Jacob Miller,56-70,Married,2,0,5,M,-0.56,472,85.7,1.01,-365.99,14,56133.77,662
+1304,Emily Martinez,36-45,Single,1,0,2,F,-0.01,326,100.05,1.0,-5.34,1,38218.66,382
+1305,Theresa Barnes,46-55,Single,1,0,5,F,-0.43,715,95.56,1.02,-469.29,10,103108.51,1099
+1306,Pamela Davis,36-45,Married,2,0,3,F,-0.03,237,109.74,1.0,-8.9,1,34350.05,313
+1307,Kenneth Peters,36-45,Married,2,0,8,M,-0.3,162,83.67,1.0,-65.89,4,18407.07,221
+1308,Jason Henderson,26-35,Married,3,1,3,M,-0.25,512,101.44,1.05,-192.35,6,77095.46,798
+1309,Vincent Bennett,26-35,Married,3,1,7,M,-0.41,745,93.47,1.08,-708.84,23,162732.27,1877
+1310,Joseph Blevins,56-70,Married,3,1,4,M,-0.18,244,167.52,1.1,-57.88,3,55113.24,362
+1311,Kevin Watkins,70+,Married,2,0,3,M,0.0,488,115.62,1.0,0.0,0,96770.69,839
+1312,Patrick Gonzalez,56-70,Single,1,0,3,M,-0.19,485,84.85,1.01,-295.65,11,133388.93,1590
+1313,Cynthia Kelly,26-35,Married,2,0,4,F,0.0,137,113.71,1.0,0.0,0,19557.52,172
+1314,Jamie Simpson,46-55,Married,3,1,1,F,-0.27,473,95.34,1.11,-196.8,6,70262.81,820
+1315,Tina Hill,26-35,Single,1,0,3,F,-1.13,653,112.75,1.06,-1377.55,58,137671.22,1294
+1316,Terri Armstrong,56-70,Married,2,0,8,F,-0.08,367,121.99,1.0,-35.62,1,53189.52,438
+1317,Jay Smith,36-45,Married,2,0,4,M,-0.28,1164,103.77,1.03,-581.72,25,214704.6,2141
+1318,Tina Ayala,46-55,Married,2,0,3,F,-0.07,419,103.67,1.0,-48.97,2,78060.16,755
+1319,Christopher Smith,46-55,Single,1,0,7,M,-0.24,872,97.49,1.07,-355.01,11,145949.43,1607
+1320,Whitney Young,26-35,Single,1,0,4,F,-0.17,565,81.39,1.0,-173.94,4,85375.69,1049
+1321,Diana Garner,36-45,Married,3,1,1,F,-2.55,366,90.34,1.07,-1548.37,58,54835.72,648
+1322,Brad Howard,46-55,Married,2,0,5,M,0.0,353,89.57,1.0,0.0,0,40398.29,451
+1323,Mr. David Harris Jr.,46-55,Married,2,0,5,M,-0.14,561,66.75,1.02,-128.23,5,63080.75,960
+1324,Johnathan Gibson,36-45,Married,5,3,2,M,-0.27,572,120.83,1.16,-272.43,6,123854.01,1193
+1325,Richard Cummings,26-35,Married,2,0,5,M,-0.13,101,109.25,1.0,-17.22,2,14858.11,136
+1326,Andrew Clark MD,70+,Single,1,0,2,M,0.0,52,79.21,1.16,0.0,0,6257.57,92
+1327,Theodore Baxter,70+,Married,2,0,4,M,-0.71,359,91.07,1.01,-401.79,13,51818.83,574
+1328,Michael Anthony,56-70,Married,2,0,10,M,-0.09,279,115.73,1.0,-39.18,2,50112.88,433
+1329,Tara Stone,36-45,Married,5,3,6,F,0.0,376,99.12,1.0,0.0,0,60464.96,610
+1330,Hannah Walls,46-55,Married,3,1,4,F,-0.16,273,91.13,1.0,-62.33,3,34446.29,379
+1331,Aaron Clark,36-45,Married,2,0,8,M,-0.05,225,93.16,1.0,-26.71,1,50400.75,541
+1332,Haley Thornton,36-45,Married,2,0,8,F,-0.44,1254,122.0,1.03,-848.82,9,233147.45,1972
+1333,Elaine Chase,46-55,Married,2,0,6,F,-8.39,336,125.32,1.05,-3928.63,132,58648.95,491
+1334,Ruth Miller,46-55,Married,5,3,4,F,-0.61,632,97.05,1.01,-527.88,17,83757.24,873
+1335,Keith Allen,46-55,Married,3,1,10,M,-1.35,375,85.13,1.01,-887.18,37,56018.38,663
+1336,Allison Alvarado,36-45,Married,5,3,9,F,-2.31,1089,107.46,1.15,-5023.1,172,233194.4,2503
+1337,Paul Walker,36-45,Married,5,3,6,M,-0.14,1587,100.76,1.04,-397.14,15,283041.99,2920
+1338,Destiny Hughes,46-55,Married,2,0,4,F,-1.5,221,75.95,1.0,-494.76,11,24988.93,329
+1339,Jeremy Martinez,56-70,Single,2,1,4,M,-1.19,599,111.17,1.09,-939.64,19,88048.23,865
+1340,Erin Ramirez,46-55,Married,2,0,6,F,-0.5,323,118.03,1.0,-341.35,10,80024.57,678
+1341,Melissa Elliott,26-35,Married,4,2,5,F,-1.42,681,75.74,1.14,-2073.35,79,110583.67,1662
+1342,Brittany Joyce,46-55,Single,2,1,5,F,-0.27,282,121.5,1.08,-89.05,2,40216.69,356
+1343,David Humphrey PhD,26-35,Single,2,1,4,M,-0.1,810,88.3,1.05,-125.47,5,112057.7,1336
+1344,Melissa Allen,26-35,Single,1,0,5,F,-2.41,576,122.18,1.01,-1705.08,66,86505.33,714
+1345,Nicholas Scott,46-55,Single,3,1,5,M,-0.13,565,102.09,1.0,-115.76,6,88208.84,864
+1346,Brian House,56-70,Married,2,0,7,M,0.0,624,128.61,1.04,0.0,0,132473.31,1070
+1347,Antonio Griffith,36-45,Single,1,0,4,M,-0.27,1015,108.19,1.0,-447.92,15,178946.27,1654
+1348,Amy Pope,36-45,Married,2,0,4,F,0.0,473,86.99,1.07,0.0,0,55673.44,682
+1349,Crystal Rogers,46-55,Single,1,0,5,F,-1.43,362,102.12,1.0,-607.3,19,43501.18,426
+1350,Wesley Mccoy,46-55,Married,2,0,1,M,-0.19,646,101.81,1.02,-203.04,7,107006.82,1076
+1351,Cindy Parker,46-55,Married,5,3,6,F,-0.17,484,122.96,1.0,-115.4,3,83735.33,681
+1352,Diana Flores,46-55,Single,1,0,2,F,-0.01,750,98.79,1.0,-14.25,1,146108.82,1479
+1353,Michelle Shannon,46-55,Married,2,0,6,F,-0.22,252,130.02,1.01,-63.05,1,37314.46,290
+1354,Kathryn Turner,36-45,Single,1,0,4,F,-0.17,182,130.45,1.02,-35.62,1,26741.68,209
+1355,Gregory Potts,46-55,Married,3,1,2,M,0.0,282,118.25,1.0,0.0,0,44816.27,379
+1356,John Thomas,26-35,Married,3,1,6,M,-0.21,622,85.82,1.01,-222.62,9,91398.14,1080
+1357,Sarah Brown,56-70,Married,4,2,4,F,-0.43,342,85.68,1.0,-328.58,16,65032.34,761
+1358,Jennifer Bailey,26-35,Married,2,0,2,F,-1.2,159,151.16,1.24,-320.58,5,40358.58,331
+1359,Marcus Neal,46-55,Married,2,0,4,M,-0.66,279,108.01,1.04,-243.28,4,39964.7,386
+1360,Eric Elliott,46-55,Married,2,0,5,M,-0.33,660,77.2,1.05,-481.77,24,113253.47,1536
+1361,Renee Deleon,26-35,Single,1,0,5,F,-1.86,348,74.53,1.14,-997.03,43,39949.64,610
+1362,David Baxter,46-55,Married,2,0,5,M,0.0,197,91.43,1.03,0.0,0,28708.83,324
+1363,Jordan Robinson,56-70,Married,2,0,9,M,0.0,86,104.62,1.33,0.0,0,10984.98,140
+1364,Sara Simon,46-55,Single,1,0,6,F,-6.64,567,185.74,1.1,-7898.5,98,221033.28,1314
+1365,Courtney Mayer,56-70,Single,1,0,4,F,-0.9,384,84.04,1.0,-452.02,6,42103.36,501
+1366,Joshua Davila,36-45,Married,5,3,6,M,0.0,362,84.3,1.0,0.0,0,48895.13,580
+1367,Kristy Lloyd,36-45,Single,1,0,5,F,-1.05,576,92.34,1.0,-1378.28,43,120686.45,1307
+1368,Scott Petersen,46-55,Married,2,0,5,M,-0.23,824,94.15,1.01,-376.98,8,151494.81,1622
+1369,Brandi Thompson,36-45,Married,2,0,8,F,-0.93,498,101.67,1.0,-815.32,41,89162.18,881
+1370,Mark Smith,26-35,Married,2,0,4,M,-0.14,315,120.16,1.01,-53.43,2,44337.56,372
+1371,Taylor Lamb,26-35,Married,3,1,6,F,-0.31,555,86.85,1.07,-250.76,4,70869.59,871
+1372,Daniel Thomas,46-55,Single,1,0,5,M,-0.09,718,89.7,1.02,-97.95,3,97328.79,1111
+1373,Corey Wilson,46-55,Married,3,1,4,M,0.0,198,123.46,1.02,0.0,0,27407.47,227
+1374,Ebony Herrera,56-70,Married,3,1,7,F,0.0,143,128.97,1.0,0.0,0,19604.05,152
+1375,Jeffery Powell,46-55,Married,2,0,6,M,-0.06,339,91.34,1.0,-29.68,1,43751.62,479
+1376,Jeffrey Cruz,70+,Married,2,0,3,M,-0.33,295,104.39,1.16,-134.76,4,42801.17,476
+1377,Elijah Conley,26-35,Single,1,0,5,M,0.0,699,116.14,1.06,0.0,0,110099.81,1007
+1378,Crystal Frank,26-35,Married,4,2,8,F,-0.49,915,119.89,1.02,-828.13,26,201536.88,1720
+1379,Keith Blanchard,36-45,Single,1,0,3,M,-0.07,456,125.22,1.0,-54.33,4,97044.98,775
+1380,Shelley Olson,46-55,Married,2,0,1,F,-0.19,231,153.12,1.0,-65.3,2,51908.7,339
+1381,Mary Taylor,70+,Single,2,1,4,F,0.0,383,94.63,1.02,0.0,0,54698.66,592
+1382,Roy Lane,36-45,Married,4,2,5,M,-0.34,961,92.44,1.12,-535.48,12,146149.06,1764
+1383,Elizabeth Rodriguez,26-35,Married,2,0,5,F,-0.05,1438,116.44,1.02,-90.83,2,232754.09,2039
+1384,Jeremy Sims,56-70,Single,1,0,3,M,-0.18,742,105.2,1.05,-234.14,4,135494.46,1356
+1385,Amanda Melton,36-45,Single,1,0,3,F,-0.73,713,91.61,1.03,-983.1,41,123765.43,1393
+1386,Mr. David Bryan,18-25,Married,3,1,5,M,-0.41,590,84.52,1.06,-562.78,18,116552.16,1461
+1387,Stephanie Haney,46-55,Single,1,0,1,F,-0.67,1104,86.49,1.05,-1415.86,41,183797.39,2228
+1388,Jenny Mcdaniel,46-55,Married,2,0,5,F,0.0,474,161.38,1.03,0.0,0,102796.18,657
+1389,Gary Walker,36-45,Married,2,0,5,M,-0.18,533,65.12,1.02,-146.92,4,52744.62,824
+1390,Jorge Lara,26-35,Married,2,0,5,M,-0.13,344,97.93,1.01,-71.24,2,53372.18,553
+1391,Charles Munoz,26-35,Single,1,0,5,M,0.0,979,80.85,1.0,0.0,0,109149.37,1353
+1392,Jared Perez,36-45,Single,3,2,5,M,-0.07,1170,101.12,1.01,-155.83,6,229548.14,2285
+1393,Patrick Morales,26-35,Married,5,3,4,M,0.0,751,101.24,1.12,0.0,0,106100.2,1172
+1394,Stanley Graham,46-55,Single,1,0,4,M,0.0,322,96.22,1.0,0.0,0,35600.16,370
+1395,Phillip Herrera,56-70,Single,1,0,3,M,-0.11,258,109.58,1.04,-35.62,1,35394.05,337
+1396,Julie Guerra,46-55,Married,2,0,2,F,-0.14,659,101.97,1.0,-138.92,5,98299.52,966
+1397,Daniel Scott,46-55,Single,1,0,5,M,0.0,322,107.1,1.0,0.0,0,51623.2,482
+1398,Jeffrey Rodriguez,36-45,Single,1,0,2,M,-0.71,749,102.31,1.04,-669.65,16,97089.33,984
+1399,Brooke Patel,70+,Single,1,0,4,F,0.0,442,89.73,1.0,0.0,0,61197.01,682
+1400,Brianna Cole,46-55,Single,1,0,2,F,-1.22,437,110.73,1.01,-757.76,34,68985.68,629
+1401,Matthew Perez,36-45,Single,1,0,5,M,-0.06,352,89.68,1.0,-35.62,1,56495.34,630
+1402,Debbie Dean,18-25,Single,1,0,12,F,0.0,586,114.82,1.0,0.0,0,112413.46,979
+1403,Erica Ortiz,70+,Single,1,0,5,F,0.0,471,110.57,1.02,0.0,0,72758.19,671
+1404,James Carpenter,36-45,Married,5,3,5,M,-0.44,1024,98.32,1.07,-807.66,29,181195.39,1964
+1405,Javier Davis,56-70,Single,1,0,4,M,-0.01,683,123.96,1.06,-12.47,1,122848.21,1047
+1406,Gabrielle Elliott,46-55,Single,1,0,5,F,0.0,209,100.09,1.0,0.0,0,23220.8,232
+1407,James Cline,36-45,Married,2,0,11,M,-1.96,613,110.51,1.01,-1544.95,52,87082.01,794
+1408,Joanne Randall,26-35,Single,1,0,9,F,-0.44,1033,99.63,1.07,-696.37,18,157110.37,1688
+1409,Charles Cross,70+,Married,2,0,1,M,-0.08,567,119.41,1.06,-83.71,4,122399.87,1085
+1410,Cathy Garcia,46-55,Married,3,1,10,F,-0.03,422,135.51,1.03,-14.25,1,69923.14,529
+1411,Scott Robertson,26-35,Single,1,0,9,M,-0.22,495,93.77,1.07,-150.31,4,65356.71,744
+1412,Holly Gordon,46-55,Single,1,0,5,F,-0.82,502,100.62,1.05,-751.28,35,92669.13,965
+1413,Brian Hamilton,46-55,Married,2,0,5,M,-0.48,363,79.93,1.0,-216.57,3,36368.68,455
+1414,Gabriella Rivas,46-55,Single,1,0,2,F,-0.42,270,100.42,1.01,-138.63,8,32938.16,332
+1415,Gwendolyn Murphy,36-45,Single,1,0,2,F,-0.69,579,104.91,1.0,-712.58,13,108899.89,1040
+1416,William Perry,36-45,Married,3,1,4,M,-0.16,297,86.53,1.01,-56.99,3,31495.19,366
+1417,Monica Henry,46-55,Married,2,0,5,F,-0.13,309,111.26,1.0,-71.24,2,61306.78,551
+1418,Christopher Charles,36-45,Married,3,1,8,M,-1.35,640,80.16,1.01,-1808.05,7,107419.88,1347
+1419,Jessica Frazier,46-55,Married,4,2,4,F,-0.02,487,81.41,1.01,-19.59,1,63903.72,795
+1420,Aaron Leon,70+,Married,2,0,5,M,-0.09,343,126.36,1.0,-35.62,1,52816.53,418
+1421,Timothy Hill,36-45,Single,1,0,6,M,0.0,161,103.23,1.0,0.0,0,17239.23,167
+1422,Timothy Cole,36-45,Single,1,0,6,M,-1.08,269,101.94,1.06,-387.9,6,36697.07,380
+1423,John Grant,46-55,Married,5,3,9,M,-0.19,288,100.16,1.0,-71.24,1,38461.79,384
+1424,Amber Mcgee,46-55,Married,4,2,5,F,-0.46,1009,89.97,1.05,-748.31,25,146650.32,1719
+1425,Antonio Rodriguez,18-25,Single,1,0,4,M,0.0,251,104.19,1.05,0.0,0,29588.84,298
+1426,Monique Higgins,46-55,Single,1,0,5,F,-0.33,219,115.58,1.0,-87.27,6,30167.22,261
+1427,Chris Bass,46-55,Single,1,0,3,M,-0.07,576,95.08,1.11,-85.49,4,124173.79,1450
+1428,Jennifer Burnett,36-45,Married,2,0,5,F,-0.08,922,107.68,1.08,-174.54,7,233350.82,2345
+1429,Jose Gibbs,26-35,Married,2,0,4,M,-0.22,204,98.14,1.02,-71.24,1,31601.37,328
+1430,Melissa Jackson,70+,Single,1,0,2,F,-0.05,921,85.41,1.0,-71.24,1,120602.43,1414
+1431,Andre Blackwell,46-55,Married,2,0,12,M,-0.1,1351,129.62,1.0,-233.31,9,316005.54,2443
+1432,Michele Edwards,26-35,Single,1,0,4,F,0.0,960,114.59,1.02,0.0,0,180360.78,1607
+1433,Angela Davis,56-70,Married,2,0,4,F,-0.4,264,100.82,1.0,-190.56,10,47686.02,473
+1434,Ashley Davis,26-35,Married,2,0,4,F,-0.09,898,75.01,1.0,-137.13,4,117017.63,1563
+1435,Michelle Garcia,56-70,Single,2,1,4,F,-0.09,209,102.49,1.0,-26.71,1,31670.25,309
+1436,Brandon Peterson,46-55,Married,2,0,9,M,-0.09,579,131.5,1.0,-65.3,2,98623.31,750
+1437,Matthew Sparks,26-35,Married,5,3,4,M,-1.13,356,89.95,1.0,-528.6,20,41914.54,466
+1438,Teresa Martin,36-45,Married,5,3,4,F,-0.05,639,80.99,1.0,-62.33,2,95321.49,1177
+1439,Carolyn Middleton,46-55,Single,1,0,6,F,0.0,149,149.18,1.02,0.0,0,24017.46,164
+1440,Heather Gregory,26-35,Married,2,0,5,F,-0.45,158,85.53,1.0,-89.05,2,16764.55,196
+1441,Cheryl Johnson,36-45,Married,3,1,2,F,-1.23,960,129.4,1.02,-2311.12,75,243015.89,1914
+1442,Kurt Harris,36-45,Married,3,1,12,M,-0.07,289,114.11,1.0,-35.62,1,54203.18,475
+1443,Edwin Sanders,56-70,Single,1,0,10,M,-0.6,157,88.03,1.0,-121.11,6,17781.84,202
+1444,James Hoffman,46-55,Married,2,0,12,M,-0.48,1680,121.41,1.02,-1367.8,13,342980.16,2870
+1445,Brian Oconnor,26-35,Married,4,2,8,M,0.0,141,123.6,1.04,0.0,0,20146.02,169
+1446,Denise Hernandez,18-25,Single,2,1,5,F,-0.18,351,106.56,1.01,-81.92,4,47526.32,449
+1447,Sarah Whitehead,26-35,Married,3,1,1,F,-0.19,343,112.19,1.0,-76.58,3,45660.12,407
+1448,Veronica Alvarez,36-45,Single,1,0,5,F,0.0,222,120.6,1.04,0.0,0,29788.93,256
+1449,Jeffery Ellison,46-55,Single,4,2,3,M,-0.08,1241,85.22,1.04,-167.41,5,174794.57,2124
+1450,Mariah Walker,36-45,Single,1,0,4,F,-0.12,1081,97.22,1.01,-226.19,4,181420.41,1880
+1451,Rhonda Lara,36-45,Married,2,0,8,F,-0.06,1385,92.61,1.0,-152.81,3,248194.38,2680
+1452,Karen Hanson,70+,Married,2,0,5,F,-0.61,361,104.8,1.0,-317.9,18,54704.18,522
+1453,Madeline Strickland,46-55,Married,2,0,5,F,-0.07,610,99.42,1.0,-64.12,4,94344.86,952
+1454,Debra Campbell MD,46-55,Married,3,1,3,F,-0.22,503,111.55,1.03,-194.13,6,98829.5,912
+1455,Mary Russo,36-45,Married,3,1,4,F,-0.23,358,82.35,1.01,-115.76,4,41502.75,508
+1456,Crystal Drake,46-55,Single,1,0,4,F,-0.17,244,151.57,1.01,-53.43,2,46834.38,312
+1457,Bobby Johnston,46-55,Single,1,0,5,M,-0.87,694,115.24,1.01,-1069.44,46,141517.23,1237
+1458,Michelle Day,46-55,Married,3,1,6,F,-0.36,628,94.98,1.04,-424.05,12,111980.79,1230
+1459,Kelly Brown,46-55,Single,2,1,5,F,-0.1,288,79.28,1.0,-40.25,2,32742.12,413
+1460,Pamela Williams,46-55,Married,2,0,12,F,-0.11,1034,108.94,1.0,-224.05,6,215258.23,1976
+1461,Thomas Fletcher,46-55,Married,2,0,8,M,-0.41,486,87.19,1.0,-424.75,17,90682.28,1043
+1462,Emily Gomez,56-70,Married,4,2,6,F,0.0,146,124.91,1.01,0.0,0,20235.99,164
+1463,Julie Franklin,36-45,Married,5,3,5,F,-0.53,1356,94.08,1.02,-1476.08,58,263427.89,2845
+1464,Anna Alexander,18-25,Single,1,0,1,F,-0.03,1630,99.32,1.02,-71.24,2,260113.05,2667
+1465,Joseph Green,56-70,Married,2,0,3,M,0.0,233,83.79,1.0,0.0,0,27649.63,330
+1466,Alejandra Vasquez,46-55,Single,1,0,10,F,-0.11,2088,130.45,1.07,-393.89,16,449140.38,3672
+1467,April Mueller,36-45,Married,2,0,5,F,-2.77,361,108.22,1.01,-1309.07,53,51190.17,476
+1468,Taylor Johns,46-55,Single,1,0,3,F,-1.15,403,70.92,1.02,-565.64,5,34822.13,499
+1469,Michael Morgan,46-55,Married,2,0,12,M,-0.62,1037,145.12,1.01,-1114.18,8,260483.14,1811
+1470,Ashley Galvan,36-45,Single,3,1,5,F,-0.28,572,100.61,1.02,-218.17,6,78171.13,790
+1471,Daniel Acosta,46-55,Single,1,0,5,M,-3.15,679,118.77,1.12,-2758.89,95,104158.82,980
+1472,Jorge Williams,36-45,Married,5,3,4,M,-1.14,797,83.67,1.07,-1531.84,64,112111.94,1429
+1473,Marissa Mercado,36-45,Single,1,0,5,F,-0.03,1198,111.55,1.0,-62.33,2,257009.56,2304
+1474,Kristen Reese,46-55,Married,2,0,1,F,-0.23,409,79.45,1.0,-133.57,2,46477.43,585
+1475,Leslie Shaw,70+,Single,3,2,1,F,-0.06,1229,68.56,1.0,-253.49,9,270468.17,3945
+1476,Jeffrey Mahoney,36-45,Single,1,0,5,M,-0.7,364,100.93,1.03,-302.4,7,43700.98,445
+1477,Annette Gates,46-55,Single,1,0,5,F,-0.06,926,79.14,1.0,-113.27,7,150911.69,1911
+1478,Thomas Gonzalez,56-70,Single,1,0,7,M,-1.36,291,115.73,1.38,-731.7,41,62376.54,744
+1479,Kathy Johnson,18-25,Single,1,0,5,F,-0.42,666,95.17,1.07,-404.29,15,90792.79,1022
+1480,Erica Andrade,18-25,Single,1,0,5,F,-0.78,419,102.65,1.01,-415.69,8,54607.83,539
+1481,William Garcia,56-70,Married,5,3,4,M,-0.09,644,102.68,1.01,-75.99,3,87794.71,862
+1482,Joel Sanchez,36-45,Married,2,0,6,M,-0.15,133,102.24,1.0,-35.62,1,24945.94,244
+1483,Annette Murphy,46-55,Married,2,0,4,F,-0.34,274,124.62,1.05,-115.76,5,42370.44,358
+1484,Cody Gomez,56-70,Single,1,0,10,M,-0.18,296,97.28,1.03,-67.68,4,36384.21,384
+1485,Jennifer Kelly,46-55,Married,2,0,6,F,-0.34,1200,105.36,1.07,-986.91,28,307332.44,3116
+1486,Andrew Parker,18-25,Married,2,0,4,M,0.0,434,105.77,1.08,0.0,0,84090.34,859
+1487,Cory Hobbs,36-45,Married,4,2,5,M,-0.2,503,84.77,1.0,-154.23,4,65527.38,773
+1488,Brandon Schwartz,46-55,Single,1,0,5,M,-0.13,156,118.4,1.0,-35.62,1,31257.55,264
+1489,Jonathan King,46-55,Married,2,0,3,M,-0.4,327,85.42,1.06,-225.23,10,48004.42,596
+1490,Mrs. Heather Simpson,26-35,Single,1,0,5,F,-0.2,903,91.93,1.13,-320.58,4,145707.14,1796
+1491,Pamela Newman,26-35,Married,2,0,4,F,0.0,355,115.91,1.0,0.0,0,104902.36,905
+1492,David Roberts,46-55,Married,2,0,3,M,-0.04,925,99.26,1.0,-71.24,2,200311.72,2018
+1493,Michele Foster,46-55,Single,1,0,6,F,-0.78,803,93.29,1.06,-991.31,26,118391.35,1350
+1494,Laura Williamson,46-55,Married,2,0,1,F,-0.06,789,87.13,1.0,-74.8,4,111266.07,1281
+1495,Brittany Mason,46-55,Single,1,0,3,F,-0.16,316,105.72,1.12,-71.24,2,45778.06,484
+1496,Brittany Wheeler,26-35,Married,2,0,1,F,-3.27,601,87.2,1.06,-3249.86,152,86674.49,1055
+1497,Jonathan Fisher,70+,Married,2,0,2,M,0.0,261,110.29,1.0,0.0,0,43013.92,390
+1498,Stacy Roberts,46-55,Married,5,3,6,F,-0.25,509,98.67,1.01,-162.07,4,64233.03,657
+1499,Dwayne Tucker,26-35,Single,3,2,1,M,-0.21,648,82.0,1.02,-193.77,7,77159.92,960
+1500,Ashlee Rodriguez,46-55,Married,2,0,9,F,-1.06,689,123.26,1.01,-954.01,41,110687.12,905
+1501,Amy Snyder,46-55,Single,1,0,5,F,-2.12,273,108.03,1.0,-771.16,31,39215.1,363
+1502,Craig Frost,46-55,Single,1,0,4,M,-0.57,408,106.27,1.04,-284.6,5,52604.13,516
+1503,Brianna Hernandez,36-45,Married,4,2,8,F,0.0,210,67.0,1.02,0.0,0,21505.56,326
+1504,Cheryl Padilla,26-35,Married,5,3,5,F,-0.29,756,113.25,1.01,-381.85,8,147454.23,1310
+1505,David Gaines,26-35,Married,2,0,5,M,0.0,173,116.44,1.06,0.0,0,23753.24,216
+1506,David Calderon,46-55,Single,1,0,5,M,-1.56,495,68.4,1.1,-1296.88,73,56769.14,912
+1507,Dr. David Simpson,46-55,Single,1,0,5,M,-0.44,313,67.08,1.03,-276.05,12,41993.96,642
+1508,Jonathon Larson,70+,Single,1,0,3,M,0.0,163,91.78,1.0,0.0,0,19457.7,212
+1509,Daniel Lee,70+,Married,2,0,4,M,-0.01,706,96.43,1.06,-8.9,1,121690.23,1336
+1510,Robert Carpenter,46-55,Single,1,0,5,M,-0.22,155,133.87,1.01,-35.62,1,21418.89,161
+1511,Krista Parrish,70+,Married,2,0,8,F,0.0,284,99.76,1.0,0.0,0,31822.74,319
+1512,Amanda Hernandez,26-35,Married,2,0,5,F,-0.06,200,103.86,1.0,-13.36,1,23471.27,226
+1513,Hector Martinez,36-45,Married,2,0,8,M,-2.67,75,95.48,1.1,-320.58,4,11457.73,132
+1514,Michelle Duncan,36-45,Single,1,0,6,F,-0.2,351,123.51,1.0,-103.3,4,63976.53,518
+1515,Cassandra Lee,46-55,Single,1,0,4,F,0.0,101,104.31,1.61,0.0,0,15750.84,243
+1516,Ricky Campos,46-55,Single,1,0,2,M,-0.08,704,82.06,1.0,-100.91,4,107176.67,1306
+1517,Mary Brown,26-35,Married,2,0,3,F,-0.49,290,103.27,1.0,-224.41,12,47506.03,460
+1518,Dustin Bowman,36-45,Married,3,1,3,M,-0.88,1194,91.06,1.07,-1949.98,86,202604.51,2383
+1519,Jillian Harvey,26-35,Single,1,0,2,F,-0.06,364,99.37,1.05,-35.62,1,58630.03,621
+1520,Patrick Martin DDS,26-35,Married,3,1,8,M,-0.22,463,127.9,1.06,-137.14,5,79044.45,657
+1521,Douglas Rodriguez,46-55,Married,2,0,7,M,-0.65,576,88.1,1.06,-676.42,20,92059.7,1110
+1522,Jennifer Allen,56-70,Married,2,0,6,F,-0.48,358,121.59,1.0,-237.76,6,60431.66,497
+1523,Kevin Sandoval,70+,Married,2,0,1,M,0.0,356,119.45,1.0,0.0,0,55185.01,462
+1524,Diana Herrera,36-45,Married,2,0,5,F,-0.19,1202,106.7,1.0,-491.2,16,273251.48,2561
+1525,Andrea Carroll,36-45,Single,1,0,2,F,0.0,219,88.42,1.0,0.0,0,41113.19,465
+1526,Joshua Dickerson,36-45,Single,1,0,3,M,0.0,450,130.61,1.01,0.0,0,86853.72,674
+1527,Laura Phillips,46-55,Married,2,0,10,F,0.0,188,98.48,1.02,0.0,0,25112.3,259
+1528,Terri Williams,46-55,Married,3,1,2,F,0.0,454,86.42,1.01,0.0,0,77345.55,906
+1529,Kyle Alvarado,46-55,Married,5,3,9,M,-1.27,1068,101.82,1.0,-2139.42,55,172067.37,1690
+1530,Glenda Simon,70+,Married,2,0,6,F,-0.63,456,86.59,1.01,-499.84,14,68230.22,792
+1531,Jordan Richmond,18-25,Single,5,3,1,M,0.0,625,103.67,1.0,0.0,0,98282.8,948
+1532,Mary Moreno,26-35,Single,1,0,9,F,-0.58,522,126.21,1.13,-404.28,12,88092.71,789
+1533,John Bullock,18-25,Single,1,0,1,M,-0.34,198,89.2,1.09,-148.54,2,38625.41,473
+1534,Jimmy Fox,36-45,Married,3,1,5,M,-0.61,848,96.3,1.06,-1053.84,41,165355.5,1825
+1535,Alison Wolf,46-55,Married,2,0,5,F,-0.31,238,95.25,1.0,-80.14,2,24955.02,262
+1536,Jonathan Fitzpatrick,46-55,Married,2,0,3,M,-0.29,376,89.87,1.02,-144.26,4,45202.1,513
+1537,Roger Miller,18-25,Single,1,0,4,M,-0.23,926,84.92,1.02,-354.06,10,130689.9,1567
+1538,Maria Edwards,36-45,Married,5,3,8,F,-0.14,590,119.76,1.0,-109.34,6,95809.9,800
+1539,Jordan Taylor,56-70,Married,5,3,4,M,0.0,309,108.98,1.13,0.0,0,43590.12,451
+1540,Hannah Douglas,70+,Single,1,0,5,F,0.0,112,86.35,1.0,0.0,0,12693.02,147
+1541,Jeremiah Holmes,70+,Married,3,1,8,M,-0.46,234,96.59,1.0,-142.48,1,30039.05,311
+1542,Melissa Lindsey,26-35,Married,3,1,5,F,-4.25,200,97.8,1.0,-1029.21,45,23667.42,242
+1543,Virginia Thompson,36-45,Married,5,3,5,F,-0.42,347,101.03,1.13,-273.92,10,65973.28,740
+1544,Julie Davis,46-55,Single,1,0,4,F,-0.55,1143,86.37,1.02,-893.43,27,140343.78,1657
+1545,Angela Ward,36-45,Married,2,0,8,F,-0.47,649,102.52,1.17,-505.51,28,110828.02,1262
+1546,Eric Johnson,36-45,Married,2,0,3,M,0.0,628,152.88,1.0,0.0,0,183920.5,1203
+1547,Vanessa Blevins,46-55,Married,2,0,5,F,-3.57,953,127.41,1.0,-6733.7,227,240038.7,1886
+1548,Brianna Barnett,56-70,Married,2,0,4,F,-2.92,460,90.45,1.08,-1711.71,61,53006.19,633
+1549,Teresa Lee,70+,Single,1,0,1,F,-2.87,856,110.79,1.01,-4354.98,197,168074.68,1533
+1550,Jessica Miller,46-55,Married,2,0,9,F,0.0,433,92.16,1.0,0.0,0,59353.22,645
+1551,Roger Baird,46-55,Single,1,0,5,M,-0.39,515,128.05,1.0,-258.23,10,85792.63,670
+1552,Jeffrey Nichols,46-55,Single,2,1,3,M,-0.27,460,136.95,1.0,-174.52,8,89700.97,655
+1553,Taylor Wells,46-55,Married,2,0,5,F,0.0,254,79.75,1.0,0.0,0,29665.85,372
+1554,Tonya Dominguez,46-55,Married,2,0,7,F,-1.93,320,92.36,1.01,-797.9,33,38144.06,416
+1555,Brian Luna,26-35,Single,1,0,7,M,-0.57,2432,94.82,1.02,-2594.37,75,428777.76,4628
+1556,Matthew Mason,36-45,Married,3,1,6,M,-1.07,855,110.03,1.0,-1624.9,77,167244.01,1523
+1557,Kenneth Gomez,46-55,Married,2,0,12,M,0.0,326,124.3,1.0,0.0,0,53447.04,430
+1558,Jordan Terry,36-45,Married,3,1,6,M,-1.67,1407,95.39,1.0,-4548.92,164,260142.02,2737
+1559,Dr. Lynn Ruiz DDS,46-55,Married,3,1,3,F,-0.47,494,97.9,1.0,-354.39,17,73229.48,748
+1560,Nancy Harris,36-45,Married,2,0,6,F,-4.39,137,96.55,1.02,-935.54,33,20565.24,218
+1561,Anthony Robertson,46-55,Single,1,0,5,M,0.0,166,111.86,1.0,0.0,0,19799.92,177
+1562,Sheila Smith,36-45,Single,2,1,4,F,-2.61,110,105.87,1.0,-468.99,17,19056.39,180
+1563,Monica Quinn,46-55,Single,1,0,5,F,-1.24,507,125.02,1.0,-1073.06,18,108267.7,870
+1564,Brandon Collier,36-45,Single,1,0,6,M,-0.23,163,112.32,1.1,-44.52,1,21565.86,212
+1565,James Gardner,26-35,Single,1,0,4,M,-0.06,448,86.66,1.0,-51.05,3,78429.31,905
+1566,Daniel Williams,26-35,Married,2,0,9,M,-0.45,1214,96.35,1.03,-1187.02,31,256394.87,2729
+1567,Dennis Hill,46-55,Single,1,0,5,M,-0.68,581,96.52,1.09,-551.78,30,78661.13,892
+1568,Richard Stein,36-45,Married,2,0,8,M,-0.16,487,87.11,1.0,-106.86,2,59062.29,678
+1569,Kevin Ortiz,46-55,Single,1,0,6,M,-0.07,57,58.66,1.0,-8.9,1,7391.78,126
+1570,Jordan Jones,36-45,Married,3,1,4,M,-0.06,795,106.79,1.06,-71.24,2,126762.78,1256
+1571,Zachary Brock,36-45,Single,1,0,5,M,0.0,341,95.79,1.02,0.0,0,38700.09,413
+1572,Traci Banks,36-45,Single,1,0,3,F,-0.16,648,76.1,1.01,-135.36,4,64075.33,851
+1573,Daisy Roberts,46-55,Single,1,0,4,F,-0.63,647,108.02,1.04,-745.27,26,127568.1,1230
+1574,Tracy Hayes,36-45,Married,2,0,5,F,-4.29,1689,99.46,1.02,-10415.74,430,241697.0,2469
+1575,Madison Coffey,46-55,Married,2,0,1,F,-0.11,315,84.11,1.01,-53.43,2,41636.19,502
+1576,Brianna Cox,26-35,Married,5,3,1,F,0.0,397,96.39,1.03,0.0,0,66217.37,710
+1577,Matthew Boyle,36-45,Married,2,0,5,M,-0.28,252,123.52,1.03,-80.14,2,35080.7,293
+1578,Jennifer Pruitt,46-55,Married,3,1,6,F,-0.78,481,92.87,1.03,-613.86,29,72719.29,810
+1579,John Murphy,46-55,Single,1,0,4,M,-0.13,639,114.29,1.0,-145.45,6,132804.28,1162
+1580,Miss Katrina Hamilton MD,26-35,Married,2,0,5,F,0.0,422,112.76,1.01,0.0,0,59651.72,534
+1581,Kristine Williams,26-35,Married,3,1,1,F,0.0,390,89.82,1.07,0.0,0,45356.97,541
+1582,Matthew Stevens,46-55,Single,3,2,5,M,-2.43,681,112.42,1.05,-2064.13,61,95556.72,895
diff --git a/customersim/requirements.txt b/customersim/requirements.txt
index 31aacf2..778524e 100644
--- a/customersim/requirements.txt
+++ b/customersim/requirements.txt
@@ -1,3 +1,5 @@
-gunicorn
-Flask
-paho-mqtt
+paho-mqtt~=1.5.1
+fastapi==0.63
+setuptools~=39.2.0
+uvicorn[standard]==0.13.4
+pydantic~=1.8.1
diff --git a/customersim/tools/customer_info.csv b/customersim/tools/customer_info.csv
new file mode 100644
index 0000000..426a462
--- /dev/null
+++ b/customersim/tools/customer_info.csv
@@ -0,0 +1,1583 @@
+customer_id,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust
+1,70+,Married,2,0,4,F,-1.75,463,99.22,1.0,-1832.94,78,103982.01,1048
+2,46-55,Married,2,0,1,F,-0.45,352,108.26,1.0,-189.97,4,45360.6,419
+3,18-25,Married,2,0,2,M,-1.89,406,86.97,1.0,-1329.46,53,61312.59,707
+4,46-55,Married,5,3,3,F,-0.08,125,138.34,1.0,-17.81,1,30434.3,220
+5,26-35,Single,3,2,3,M,-0.11,490,115.6,1.03,-90.83,2,91553.24,814
+6,46-55,Married,2,0,5,M,-0.63,429,102.43,1.0,-369.55,11,59716.13,583
+7,26-35,Married,3,1,3,M,-0.65,780,101.8,1.01,-687.47,17,107194.4,1066
+8,26-35,Married,4,2,6,M,-3.72,719,126.24,1.25,-4899.4,192,166389.9,1643
+9,26-35,Single,2,1,4,F,-0.77,405,90.97,1.05,-430.64,8,50672.81,585
+10,46-55,Single,1,0,5,M,0.0,268,94.61,1.04,0.0,0,46455.82,509
+11,70+,Single,2,1,1,M,-1.6,282,116.04,1.11,-988.45,10,71594.7,683
+12,46-55,Married,2,0,7,M,-0.96,436,119.82,1.03,-929.96,45,115504.13,993
+13,36-45,Single,1,0,2,F,-0.11,1192,83.27,1.03,-272.48,10,205750.82,2544
+14,26-35,Married,2,0,6,F,-0.4,327,101.65,1.07,-492.72,27,126446.72,1328
+15,46-55,Married,2,0,6,F,-1.17,463,104.45,1.08,-967.07,39,86483.25,895
+16,36-45,Married,2,0,1,F,0.0,328,136.27,1.08,0.0,0,72497.44,575
+17,36-45,Single,1,0,5,M,-1.24,306,120.93,1.01,-708.13,5,69174.48,575
+18,36-45,Married,2,0,5,F,0.0,315,82.29,1.0,0.0,0,41886.68,509
+19,46-55,Single,1,0,3,F,-0.07,410,91.89,1.0,-90.82,4,127815.7,1391
+20,36-45,Married,2,0,5,F,0.0,229,127.8,1.02,0.0,0,48181.07,385
+21,36-45,Married,2,0,4,M,-0.32,277,100.77,1.0,-120.75,3,38594.99,383
+22,36-45,Single,2,1,4,F,-7.84,329,130.09,1.1,-3334.06,154,55287.89,467
+23,46-55,Married,2,0,4,F,-0.15,467,104.15,1.11,-90.83,3,62072.75,661
+24,70+,Single,1,0,4,M,-0.02,403,98.23,1.0,-12.47,1,63458.36,648
+25,46-55,Married,2,0,4,F,-0.09,96,107.29,1.07,-17.81,1,21028.23,210
+26,36-45,Married,3,1,3,M,-0.27,108,129.41,1.0,-29.68,1,14235.56,110
+27,36-45,Married,2,0,8,M,-0.29,866,99.97,1.0,-334.71,14,116368.66,1167
+28,46-55,Married,2,0,1,F,-0.11,1045,97.75,1.09,-231.52,7,200690.92,2242
+29,70+,Single,3,2,1,M,0.0,232,91.09,1.0,0.0,0,47821.35,525
+30,70+,Single,1,0,5,F,-0.58,198,81.0,1.0,-325.92,18,45440.98,561
+31,36-45,Single,5,3,2,M,-0.72,190,66.47,1.03,-217.27,7,20139.08,313
+32,36-45,Single,1,0,9,M,-0.34,140,117.22,1.0,-71.24,1,24850.27,212
+33,46-55,Married,5,3,9,F,-0.17,774,130.25,1.02,-169.2,3,132724.74,1040
+34,46-55,Married,2,0,2,M,0.0,290,128.31,1.0,0.0,0,48887.4,381
+35,18-25,Married,2,0,4,M,-0.82,170,107.59,1.05,-195.91,4,25712.86,250
+36,36-45,Married,2,0,4,F,-0.16,711,105.03,1.08,-158.86,8,105026.25,1081
+37,36-45,Married,2,0,8,F,-0.04,276,97.55,1.0,-19.59,1,51801.68,531
+38,46-55,Single,2,1,5,M,-0.02,697,110.31,1.02,-17.81,1,111413.62,1030
+39,70+,Married,2,0,4,F,0.0,152,117.45,1.0,0.0,0,25956.71,221
+40,56-70,Married,4,2,7,M,-0.59,696,106.64,1.0,-784.71,34,142365.91,1337
+41,46-55,Single,2,1,4,F,-0.29,295,94.35,1.05,-136.78,2,44626.62,495
+42,26-35,Married,4,2,9,F,-0.72,983,107.26,1.15,-1199.48,45,179236.5,1916
+43,26-35,Single,1,0,5,F,-0.06,368,90.3,1.0,-37.4,2,55443.61,614
+44,36-45,Single,3,2,4,M,0.0,254,151.87,1.0,0.0,0,43890.5,289
+45,46-55,Married,5,3,1,M,-2.88,728,105.31,1.03,-3849.98,163,140910.98,1373
+46,56-70,Married,2,0,5,F,-0.69,674,81.17,1.02,-768.49,24,90176.72,1137
+47,26-35,Single,1,0,4,F,-0.02,828,98.48,1.0,-17.8,2,116401.41,1182
+48,36-45,Married,2,0,3,M,-2.1,244,190.74,1.01,-810.35,12,73436.16,388
+49,56-70,Married,2,0,7,F,-0.24,760,94.19,1.02,-391.46,2,152584.45,1648
+50,46-55,Married,3,1,4,M,0.0,431,109.45,1.09,0.0,0,65996.58,656
+51,70+,Married,2,0,2,F,-0.53,259,75.6,1.0,-302.77,9,42942.81,568
+52,36-45,Married,5,3,7,M,-0.42,858,100.29,1.0,-569.92,19,137394.07,1370
+53,56-70,Single,1,0,3,F,0.0,252,76.46,1.0,0.0,0,39682.81,519
+54,56-70,Single,1,0,3,M,-0.72,487,103.53,1.05,-532.52,23,76407.25,772
+55,46-55,Married,2,0,5,F,-0.87,425,87.1,1.04,-456.83,11,45728.04,547
+56,18-25,Married,3,1,5,F,-0.06,799,91.86,1.0,-88.69,1,140914.43,1534
+57,46-55,Married,2,0,6,M,0.0,208,96.0,1.04,0.0,0,23712.9,257
+58,26-35,Single,1,0,4,M,-0.04,679,94.17,1.03,-41.85,2,102838.9,1125
+59,70+,Single,1,0,4,M,-0.07,523,113.24,1.01,-44.52,1,74965.42,666
+60,36-45,Single,1,0,3,M,-0.57,1485,86.12,1.01,-1898.01,67,286593.68,3367
+61,46-55,Married,2,0,6,M,0.0,315,91.65,1.08,0.0,0,42708.98,501
+62,36-45,Married,3,1,1,M,0.0,252,88.86,1.01,0.0,0,24614.26,280
+63,46-55,Single,1,0,2,F,-2.08,450,103.7,1.01,-2346.26,78,116973.33,1134
+64,46-55,Married,2,0,6,M,-1.1,775,103.82,1.06,-1206.09,17,113679.4,1161
+65,70+,Married,2,0,3,M,0.0,189,125.34,1.0,0.0,0,29454.17,235
+66,46-55,Single,1,0,6,M,-0.01,681,96.39,1.06,-12.47,1,102173.46,1119
+67,36-45,Single,2,1,4,M,0.0,293,62.64,1.0,0.0,0,27747.96,443
+68,26-35,Single,1,0,2,F,-0.32,941,82.8,1.01,-511.49,16,133560.54,1624
+69,46-55,Married,4,2,1,F,-0.38,335,97.62,1.08,-215.8,10,55839.46,620
+70,26-35,Married,2,0,3,F,0.0,129,81.54,1.0,0.0,0,12068.23,148
+71,36-45,Married,5,3,4,M,-1.17,558,86.6,1.0,-1100.82,50,81229.51,942
+72,26-35,Single,3,2,1,F,-0.16,884,71.6,1.0,-229.76,15,99884.29,1395
+73,36-45,Married,5,3,6,M,-0.1,439,104.17,1.02,-53.43,1,58127.59,571
+74,26-35,Married,2,0,2,F,0.0,530,97.29,1.0,0.0,0,77055.52,792
+75,26-35,Single,1,0,4,F,-0.03,552,81.21,1.04,-26.71,1,63992.49,819
+76,36-45,Married,2,0,4,F,-0.94,351,104.66,1.02,-400.35,15,44689.77,436
+77,56-70,Married,2,0,2,M,-0.23,136,104.38,1.19,-35.62,1,16387.1,187
+78,36-45,Married,4,2,8,F,-0.48,1366,109.87,1.08,-1260.63,49,290936.29,2869
+79,26-35,Married,2,0,4,M,-0.05,488,96.9,1.02,-35.62,1,67734.62,715
+80,36-45,Married,2,0,8,M,-1.02,838,129.26,1.08,-1705.95,50,217157.69,1814
+81,36-45,Single,1,0,5,M,-1.57,99,101.86,1.0,-248.75,6,16094.07,158
+82,70+,Single,2,1,4,F,-0.44,484,80.74,1.01,-331.25,15,61039.57,763
+83,46-55,Single,1,0,4,M,0.0,384,98.43,1.05,0.0,0,81205.81,865
+84,46-55,Single,1,0,8,M,-0.54,326,122.88,1.0,-213.36,1,48169.8,392
+85,46-55,Married,3,1,3,F,-0.05,475,88.31,1.0,-32.65,2,53516.98,606
+86,56-70,Married,3,1,3,M,-0.05,409,102.9,1.0,-26.71,1,55360.92,538
+87,46-55,Single,2,1,4,M,-0.06,387,97.83,1.04,-35.62,1,62026.76,660
+88,36-45,Single,1,0,4,M,-1.62,960,111.3,1.12,-2517.17,73,172846.55,1733
+89,46-55,Single,1,0,3,M,-1.45,498,87.8,1.08,-970.34,49,58647.94,720
+90,46-55,Married,2,0,5,F,-0.46,888,107.83,1.03,-542.31,10,126272.5,1203
+91,36-45,Single,1,0,5,M,0.0,383,100.8,1.01,0.0,0,57052.5,569
+92,46-55,Married,2,0,1,M,-0.27,417,109.59,1.0,-146.04,8,59508.78,543
+93,56-70,Married,2,0,3,F,-0.21,750,110.3,1.01,-209.27,6,110076.58,1008
+94,46-55,Single,1,0,5,F,-0.05,555,88.76,1.0,-44.52,1,75181.31,847
+95,36-45,Single,2,1,4,F,-1.36,232,105.79,1.0,-356.19,12,27612.17,261
+96,46-55,Single,1,0,6,M,-0.41,398,111.24,1.0,-216.4,8,58400.26,525
+97,36-45,Single,1,0,6,F,-0.76,293,88.58,1.04,-375.94,10,43933.76,516
+98,46-55,Married,2,0,3,F,-0.04,332,89.86,1.0,-20.07,1,49781.54,554
+99,26-35,Single,1,0,2,F,0.0,206,87.78,1.0,0.0,0,55828.0,636
+100,56-70,Married,2,0,7,M,-0.95,1011,90.91,1.04,-1821.35,58,174272.27,1994
+101,36-45,Single,2,1,4,F,0.0,370,117.04,1.0,0.0,0,55009.56,470
+102,46-55,Single,1,0,5,F,-0.25,237,118.3,1.0,-65.3,2,31349.23,265
+103,46-55,Married,2,0,4,F,0.0,864,107.98,1.14,0.0,0,187337.79,1975
+104,46-55,Single,1,0,4,M,-1.37,186,90.21,1.05,-348.17,14,23003.33,268
+105,36-45,Single,3,1,5,F,-0.25,296,113.68,1.01,-121.11,4,54794.34,486
+106,26-35,Married,5,3,3,M,-0.26,869,77.22,1.06,-347.1,14,101621.48,1391
+107,18-25,Married,2,0,2,M,-0.01,478,107.57,1.01,-8.9,1,64432.19,603
+108,46-55,Single,1,0,5,F,-11.4,154,192.99,1.0,-3043.71,86,51529.28,267
+109,36-45,Married,2,0,8,F,0.0,178,90.92,1.02,0.0,0,19729.22,222
+110,18-25,Married,3,1,6,F,-0.04,424,90.75,1.03,-35.62,1,79137.98,896
+111,26-35,Married,3,1,3,F,0.0,295,119.58,1.0,0.0,0,64213.63,537
+112,46-55,Married,2,0,4,M,-0.26,701,86.62,1.01,-272.49,12,90950.58,1065
+113,56-70,Married,2,0,4,M,-0.93,309,92.4,1.13,-427.06,14,42505.39,520
+114,26-35,Married,3,1,5,M,0.0,227,110.83,1.0,0.0,0,45219.34,408
+115,36-45,Married,4,2,8,M,0.0,110,108.69,1.0,0.0,0,15651.35,144
+116,26-35,Married,2,0,2,M,0.0,345,117.8,1.0,0.0,0,51123.96,434
+117,26-35,Single,1,0,9,M,-0.11,236,81.38,1.1,-35.62,2,26531.28,360
+118,26-35,Single,1,0,7,F,-0.17,270,92.92,1.0,-71.24,2,38190.54,411
+119,46-55,Single,1,0,3,F,-0.31,467,116.58,1.12,-247.56,11,94200.17,901
+120,46-55,Single,1,0,2,F,-0.31,304,86.02,1.05,-118.73,2,32772.56,399
+121,26-35,Single,1,0,5,F,-0.73,165,71.17,1.0,-142.48,3,13949.05,196
+122,46-55,Married,3,1,10,F,0.0,372,99.81,1.0,0.0,0,44314.42,444
+123,46-55,Married,2,0,4,M,-0.35,354,111.74,1.0,-276.06,13,87047.03,779
+124,46-55,Married,2,0,6,F,-0.16,377,110.51,1.24,-106.86,3,71721.55,803
+125,46-55,Married,2,0,6,F,0.0,282,111.55,1.02,0.0,0,35920.0,327
+126,46-55,Married,2,0,6,M,0.0,530,88.91,1.07,0.0,0,80192.83,968
+127,26-35,Married,2,0,5,F,-0.23,731,86.12,1.0,-292.27,14,107650.4,1256
+128,36-45,Married,3,1,1,F,-0.27,1341,91.44,1.0,-605.18,12,204098.83,2232
+129,46-55,Married,2,0,6,F,0.0,132,100.65,1.0,0.0,0,15500.13,154
+130,26-35,Single,3,2,3,M,0.0,323,108.74,1.0,0.0,0,47085.34,435
+131,46-55,Married,2,0,5,M,-3.39,326,103.1,1.06,-2523.24,117,76813.06,786
+132,46-55,Married,3,1,3,F,-2.47,584,113.68,1.06,-2174.63,41,100261.74,933
+133,36-45,Married,5,3,7,F,-0.16,467,94.33,1.0,-126.45,5,73389.73,778
+134,70+,Married,2,0,4,F,0.0,242,84.81,1.0,0.0,0,44692.52,527
+135,36-45,Single,1,0,1,M,-0.16,614,97.87,1.01,-124.31,4,74673.18,770
+136,56-70,Single,2,1,1,F,-0.08,341,95.68,1.0,-55.21,3,65442.52,684
+137,26-35,Single,1,0,5,M,-0.54,290,72.84,1.01,-231.53,8,31247.55,435
+138,36-45,Single,1,0,5,M,-0.08,222,113.26,1.0,-32.06,2,44283.2,391
+139,56-70,Married,2,0,9,F,-0.51,288,94.73,1.08,-206.6,9,38272.26,437
+140,46-55,Married,2,0,2,F,-0.09,725,115.38,1.13,-106.86,1,140882.46,1383
+141,56-70,Married,2,0,9,M,-0.03,897,120.18,1.01,-54.85,2,207434.32,1749
+142,46-55,Married,2,0,4,F,-0.22,348,83.89,1.08,-135.36,3,50671.48,655
+143,70+,Single,1,0,2,M,-0.1,464,75.57,1.01,-64.12,4,50556.18,677
+144,36-45,Married,2,0,4,M,-0.04,646,84.44,1.03,-53.43,1,121842.62,1491
+145,46-55,Single,1,0,3,F,0.0,436,107.28,1.01,0.0,0,56751.89,534
+146,46-55,Married,2,0,6,M,0.0,210,90.93,1.03,0.0,0,29096.32,330
+147,36-45,Married,5,3,5,F,-0.04,227,52.98,1.0,-30.28,2,44129.64,835
+148,46-55,Married,2,0,4,F,-0.44,392,114.01,1.05,-204.82,4,53128.23,487
+149,26-35,Single,1,0,3,M,-0.06,659,82.1,1.03,-65.3,3,90230.33,1132
+150,36-45,Married,2,0,4,F,-0.4,1493,91.72,1.04,-1094.17,36,248736.32,2817
+151,36-45,Married,5,3,12,M,-0.06,991,118.91,1.05,-100.62,6,201314.36,1780
+152,36-45,Single,1,0,6,F,-0.81,310,98.47,1.0,-332.44,12,40175.1,408
+153,46-55,Single,1,0,5,F,-1.77,740,67.92,1.0,-2410.36,86,92716.3,1365
+154,46-55,Married,5,3,6,F,-0.22,640,101.56,1.0,-224.4,7,101455.6,999
+155,70+,Married,2,0,5,F,-0.22,718,106.88,1.11,-242.2,11,117675.63,1218
+156,46-55,Married,3,1,7,M,0.0,158,91.26,1.11,0.0,0,16335.33,198
+157,26-35,Single,1,0,4,M,0.0,147,88.17,1.0,0.0,0,18515.96,211
+158,36-45,Married,4,2,8,M,-2.38,975,105.35,1.0,-3022.65,106,133902.58,1271
+159,36-45,Married,3,1,1,M,-3.83,268,103.94,1.01,-1674.14,14,45421.61,441
+160,36-45,Married,2,0,9,M,-0.04,378,100.31,1.0,-17.81,1,47345.28,474
+161,46-55,Single,1,0,1,F,-0.54,1235,76.96,1.0,-1537.74,60,219958.63,2872
+162,26-35,Married,2,0,5,M,-0.99,702,117.55,1.01,-1990.73,64,236634.44,2040
+163,26-35,Single,3,2,3,M,-0.02,369,75.14,1.0,-11.58,1,46963.12,628
+164,46-55,Married,2,0,5,F,-1.11,673,65.34,1.01,-1117.04,40,65997.64,1020
+165,46-55,Single,1,0,8,F,-0.17,171,118.17,1.16,-35.62,1,25052.5,245
+166,46-55,Single,1,0,4,M,-0.16,718,95.06,1.0,-175.73,5,106559.85,1121
+167,46-55,Married,2,0,4,M,-0.17,543,93.11,1.0,-189.86,10,102239.32,1098
+168,46-55,Married,2,0,6,M,0.0,358,97.94,1.01,0.0,0,51615.54,534
+169,36-45,Married,2,0,5,F,-0.04,351,89.93,1.0,-19.59,1,42445.21,473
+170,36-45,Married,5,3,6,F,-0.08,301,105.52,1.02,-42.67,2,56031.17,542
+171,70+,Married,2,0,4,M,0.0,85,119.71,1.0,0.0,0,14365.53,120
+172,26-35,Married,2,0,6,F,0.0,286,114.92,1.0,0.0,0,48497.74,422
+173,26-35,Married,2,0,4,F,-0.01,441,90.78,1.03,-5.34,1,93772.47,1068
+174,36-45,Single,1,0,4,F,-0.02,582,96.18,1.05,-17.81,1,95407.08,1042
+175,70+,Married,2,0,1,F,0.0,316,118.55,1.0,0.0,0,48366.74,408
+176,26-35,Married,2,0,4,F,-1.08,218,97.64,1.08,-333.06,25,30074.28,334
+177,46-55,Married,2,0,5,F,0.0,208,112.8,1.06,0.0,0,31246.76,295
+178,36-45,Single,1,0,5,F,-0.41,184,100.78,1.0,-114.87,5,28118.85,280
+179,46-55,Single,1,0,4,M,-1.52,462,112.09,1.04,-1113.8,42,81939.43,757
+180,26-35,Married,2,0,5,F,-0.43,545,121.35,1.0,-308.83,6,87006.78,717
+181,36-45,Single,1,0,2,F,-0.21,509,97.02,1.04,-167.4,7,76161.72,818
+182,46-55,Single,1,0,2,F,0.0,115,134.07,1.0,0.0,0,19708.82,147
+183,26-35,Married,4,2,2,F,-0.04,481,90.64,1.03,-26.71,1,57737.32,656
+184,26-35,Married,2,0,5,M,0.0,418,102.12,1.08,0.0,0,53814.79,571
+185,46-55,Single,1,0,7,F,0.0,518,90.91,1.13,0.0,0,80632.95,1003
+186,26-35,Married,2,0,4,F,-0.01,652,78.5,1.0,-17.81,1,117276.26,1494
+187,36-45,Married,2,0,7,F,-0.04,329,80.29,1.0,-17.81,1,39663.35,494
+188,70+,Married,2,0,6,M,-0.04,546,103.48,1.0,-35.62,1,83711.32,809
+189,26-35,Married,4,2,5,M,-0.88,716,94.65,1.0,-1206.92,36,130053.59,1377
+190,46-55,Single,1,0,3,M,-0.22,323,93.98,1.0,-89.05,1,38251.42,407
+191,56-70,Married,2,0,1,F,-0.04,750,104.54,1.0,-46.3,3,110083.82,1053
+192,36-45,Married,4,2,4,F,-0.07,826,111.01,1.0,-90.84,4,153971.82,1387
+193,46-55,Married,3,1,4,M,0.0,287,114.9,1.01,0.0,0,42284.87,371
+194,46-55,Single,1,0,3,M,-0.26,600,101.83,1.02,-235.98,8,91141.24,909
+195,26-35,Married,2,0,5,M,-0.06,1218,96.12,1.06,-118.73,2,176671.35,1945
+196,70+,Married,2,0,6,M,-0.84,335,114.27,1.06,-509.35,14,69247.58,642
+197,18-25,Married,4,2,4,F,-2.03,547,79.81,1.05,-1971.96,76,77579.45,1020
+198,36-45,Single,1,0,3,M,0.0,215,90.61,1.02,0.0,0,57806.61,648
+199,46-55,Married,3,1,5,M,0.0,240,126.82,1.04,0.0,0,41091.1,336
+200,70+,Single,1,0,4,F,-2.55,387,115.68,1.0,-1439.19,36,65241.99,564
+201,46-55,Married,3,1,6,F,0.0,381,73.89,1.0,0.0,0,52685.48,713
+202,46-55,Married,2,0,6,F,-0.57,626,107.12,1.0,-626.07,39,117405.33,1096
+203,36-45,Married,5,3,5,F,-0.42,169,98.43,1.01,-124.67,2,29529.22,304
+204,46-55,Single,4,3,4,M,-0.05,600,84.72,1.06,-41.85,2,75319.67,938
+205,46-55,Married,2,0,7,M,-1.72,533,113.79,1.0,-1667.11,85,110372.74,970
+206,36-45,Married,4,2,5,F,-0.06,465,106.15,1.01,-35.62,1,59869.59,567
+207,46-55,Single,1,0,5,M,-0.49,892,115.44,1.08,-740.88,28,175582.84,1636
+208,46-55,Married,3,1,10,M,-0.08,1439,117.81,1.03,-231.53,6,333162.8,2902
+209,26-35,Single,1,0,6,M,0.0,366,80.42,1.0,0.0,0,53964.24,671
+210,46-55,Married,2,0,6,F,-0.09,217,71.2,1.03,-50.76,3,38235.93,553
+211,56-70,Married,2,0,10,M,-0.07,217,101.18,1.0,-19.59,1,28230.09,279
+212,36-45,Married,5,3,8,F,-0.08,873,89.55,1.0,-80.14,2,94559.55,1056
+213,36-45,Married,3,1,2,M,0.0,209,79.16,1.0,0.0,0,24776.17,313
+214,46-55,Married,2,0,1,M,-1.2,763,88.78,1.01,-1556.83,44,114787.63,1302
+215,36-45,Single,1,0,9,M,0.0,304,119.01,1.0,0.0,0,56648.83,476
+216,46-55,Single,1,0,5,F,-0.16,278,91.91,1.05,-71.24,2,40439.23,464
+217,46-55,Married,2,0,12,M,-0.68,115,82.65,1.03,-159.75,11,19340.91,241
+218,46-55,Married,3,1,5,M,0.0,246,76.88,1.0,0.0,0,27063.52,352
+219,70+,Married,2,0,3,F,-0.34,377,74.13,1.0,-187.89,6,40623.74,548
+220,56-70,Single,1,0,5,F,-0.21,354,92.33,1.0,-84.78,3,37949.56,411
+221,46-55,Single,1,0,6,F,-0.15,175,129.2,1.0,-32.94,2,28423.71,220
+222,70+,Married,2,0,3,F,-0.18,801,85.59,1.0,-260.02,7,122143.72,1427
+223,70+,Married,2,0,5,M,0.0,335,97.34,1.0,0.0,0,65316.78,671
+224,46-55,Single,1,0,1,F,0.0,353,78.3,1.0,0.0,0,35391.44,452
+225,46-55,Married,4,2,5,F,0.0,252,115.53,1.21,0.0,0,38704.17,404
+226,46-55,Single,1,0,5,M,-0.29,587,137.29,1.01,-377.21,10,177518.67,1304
+227,46-55,Married,2,0,9,M,-0.09,454,109.94,1.0,-45.42,3,57937.84,527
+228,26-35,Married,5,3,4,F,-1.02,1104,101.42,1.0,-2032.05,86,201221.05,1984
+229,18-25,Single,2,1,5,F,-0.32,262,107.64,1.05,-160.29,3,54682.02,533
+230,36-45,Married,2,0,5,F,-0.13,1192,115.83,1.0,-276.76,6,253657.11,2190
+231,46-55,Single,1,0,5,F,-0.27,1071,75.15,1.0,-578.46,13,161114.84,2144
+232,70+,Single,1,0,5,F,-0.15,364,98.55,1.02,-92.26,4,60707.49,628
+233,36-45,Married,2,0,8,F,-0.19,426,110.73,1.01,-106.86,2,63560.95,577
+234,46-55,Married,3,1,10,F,-0.13,508,117.23,1.02,-114.57,4,100581.98,879
+235,56-70,Married,2,0,5,F,-3.16,764,113.55,1.0,-4619.7,210,166009.0,1462
+236,18-25,Married,2,0,4,M,-0.15,155,108.04,1.04,-35.62,1,25173.39,243
+237,36-45,Single,2,1,4,F,0.0,188,89.13,1.01,0.0,0,18805.61,214
+238,70+,Married,2,0,5,F,-0.16,504,85.62,1.1,-125.92,7,67214.24,862
+239,36-45,Married,3,1,6,M,-0.13,1409,102.16,1.0,-401.6,13,319031.88,3123
+240,46-55,Single,1,0,2,M,-0.48,615,146.5,1.02,-500.8,9,153385.98,1072
+241,26-35,Married,5,3,11,M,0.0,511,72.02,1.0,0.0,0,112776.33,1566
+242,46-55,Married,2,0,5,M,0.0,374,90.65,1.0,0.0,0,56748.56,626
+243,56-70,Married,2,0,5,F,-0.63,494,137.96,1.04,-550.91,23,121268.21,913
+244,70+,Married,2,0,2,M,-0.11,666,95.88,1.0,-189.97,4,171623.86,1790
+245,26-35,Single,2,1,4,M,-0.64,456,110.64,1.0,-365.09,7,63172.92,571
+246,46-55,Single,1,0,5,M,-0.39,420,100.46,1.03,-388.22,17,100059.2,1029
+247,36-45,Married,2,0,5,F,0.0,445,125.11,1.04,0.0,0,66308.11,550
+248,36-45,Single,1,0,3,M,-0.56,819,68.15,1.02,-858.08,39,104202.24,1555
+249,26-35,Single,1,0,5,F,-1.04,566,131.73,1.0,-843.89,28,107095.86,813
+250,36-45,Single,1,0,9,F,-0.77,1134,108.35,1.03,-1977.97,79,277043.37,2637
+251,46-55,Single,1,0,3,F,-0.27,238,113.41,1.01,-89.05,1,38106.74,339
+252,70+,Single,1,0,3,F,-0.19,209,121.79,1.0,-46.3,3,28986.97,238
+253,36-45,Married,3,1,12,M,-0.01,848,131.17,1.0,-12.47,1,223909.04,1707
+254,70+,Single,1,0,3,F,-0.2,723,95.29,1.0,-186.82,6,91094.91,956
+255,26-35,Married,2,0,4,M,0.0,246,121.52,1.0,0.0,0,33417.86,275
+256,26-35,Married,3,1,4,F,-0.49,171,94.61,1.04,-144.56,6,28192.49,311
+257,26-35,Married,5,3,5,M,-0.62,652,105.44,1.07,-708.53,23,119782.95,1212
+258,36-45,Single,1,0,8,M,-0.03,402,75.15,1.04,-17.8,2,38627.46,532
+259,36-45,Married,3,1,9,M,-0.27,1653,134.68,1.05,-743.74,6,370091.54,2892
+260,36-45,Married,5,3,5,F,-0.38,216,99.03,1.09,-117.54,3,30601.77,337
+261,56-70,Married,2,0,5,M,-1.19,93,158.07,1.0,-130.85,5,17387.23,110
+262,26-35,Married,2,0,3,F,-0.02,670,129.22,1.02,-19.59,1,150925.52,1194
+263,26-35,Single,1,0,5,M,-0.07,345,76.66,1.23,-35.62,1,41242.22,660
+264,46-55,Single,1,0,5,M,-0.66,321,147.74,1.0,-262.7,3,58799.62,398
+265,18-25,Single,2,1,3,F,-2.65,713,92.47,1.02,-2723.87,109,95057.11,1049
+266,56-70,Married,5,3,4,M,-0.56,194,82.66,1.0,-145.69,3,21409.2,259
+267,36-45,Married,2,0,1,M,-0.16,540,127.99,1.05,-117.37,6,95990.19,788
+268,70+,Married,2,0,2,F,-1.06,437,82.83,1.0,-709.89,16,55495.9,672
+269,46-55,Single,1,0,7,M,-0.02,371,90.4,1.13,-19.59,1,79104.01,990
+270,36-45,Married,2,0,1,M,-0.1,391,89.35,1.02,-56.1,4,50928.12,584
+271,36-45,Married,3,1,4,M,-0.03,706,104.33,1.05,-31.16,2,126236.06,1266
+272,56-70,Single,1,0,6,F,-1.04,234,121.91,1.04,-286.74,10,33646.07,286
+273,46-55,Single,1,0,5,M,0.0,448,101.72,1.0,0.0,0,57673.55,567
+274,46-55,Married,2,0,10,F,-2.09,746,101.62,1.11,-2051.65,79,99996.18,1088
+275,46-55,Married,5,3,8,M,-0.03,660,86.76,1.0,-37.4,2,109573.46,1263
+276,46-55,Married,2,0,5,F,-0.76,597,97.5,1.15,-922.55,53,118950.83,1409
+277,18-25,Married,2,0,5,F,-0.07,658,76.31,1.0,-80.14,3,84095.62,1102
+278,70+,Married,2,0,6,M,-0.99,137,103.89,1.01,-158.15,3,16519.23,161
+279,18-25,Single,1,0,3,M,-0.25,438,106.76,1.02,-160.29,3,68327.76,650
+280,26-35,Married,4,2,8,F,0.0,139,79.3,1.0,0.0,0,16652.88,210
+281,36-45,Married,3,1,2,F,0.0,526,90.88,1.0,0.0,0,93058.67,1025
+282,46-55,Married,3,1,6,F,0.0,322,113.98,1.0,0.0,0,47643.2,418
+283,26-35,Married,5,3,5,M,-1.4,684,125.88,1.03,-1582.34,47,142744.07,1165
+284,36-45,Married,2,0,5,F,-1.43,357,104.21,1.0,-785.21,27,57109.82,548
+285,46-55,Single,1,0,1,F,0.0,68,111.88,1.0,0.0,0,11635.7,104
+286,46-55,Single,1,0,5,F,-0.03,155,98.28,1.0,-12.47,1,45109.41,459
+287,26-35,Single,2,1,2,M,-0.76,328,88.54,1.05,-304.2,10,35239.21,417
+288,46-55,Married,2,0,3,F,-0.79,269,90.64,1.0,-395.02,13,45137.46,498
+289,46-55,Married,2,0,6,M,-0.09,385,126.0,1.0,-55.21,3,77614.86,616
+290,70+,Married,2,0,3,F,0.0,214,110.86,1.01,0.0,0,33147.11,302
+291,46-55,Married,2,0,12,F,0.0,167,82.44,1.0,0.0,0,33304.3,404
+292,26-35,Single,2,1,1,F,0.0,226,105.83,1.02,0.0,0,30160.28,291
+293,56-70,Single,1,0,4,M,-0.51,915,94.12,1.0,-866.58,27,160282.6,1703
+294,18-25,Single,2,1,5,M,-0.39,724,143.37,1.0,-425.66,9,155264.33,1087
+295,56-70,Married,2,0,4,F,-2.79,293,85.89,1.02,-1091.22,26,33584.71,399
+296,18-25,Single,1,0,1,M,-0.4,97,133.92,1.0,-81.92,4,27185.71,203
+297,70+,Single,1,0,3,M,-0.65,132,76.42,1.06,-110.42,6,13068.45,182
+298,70+,Single,1,0,7,M,-0.28,290,107.17,1.0,-89.05,4,34081.09,318
+299,26-35,Married,3,1,6,M,-0.63,330,109.08,1.13,-446.23,14,77443.58,801
+300,70+,Married,2,0,5,M,-4.45,540,90.62,1.02,-3516.34,152,71587.39,804
+301,18-25,Married,2,0,2,M,-0.25,360,106.89,1.0,-106.5,1,44895.4,420
+302,36-45,Married,3,1,12,F,-0.43,602,103.13,1.0,-436.34,8,104780.38,1020
+303,46-55,Single,1,0,6,F,-1.39,931,108.83,1.0,-2193.59,90,171184.55,1579
+304,70+,Married,3,1,4,F,-0.06,231,113.13,1.04,-26.71,1,53172.27,488
+305,36-45,Married,5,3,4,M,-0.63,1087,129.02,1.0,-1131.8,35,231593.86,1795
+306,46-55,Single,1,0,5,M,0.0,347,91.46,1.08,0.0,0,51859.48,613
+307,70+,Married,2,0,9,F,-0.4,330,89.82,1.0,-153.16,5,34759.82,387
+308,70+,Married,3,1,5,F,-0.06,316,102.93,1.0,-26.71,1,42716.18,415
+309,26-35,Single,1,0,5,M,-0.5,400,87.79,1.0,-260.02,7,45298.74,516
+310,46-55,Married,2,0,1,M,-0.43,549,103.6,1.07,-316.06,10,75835.96,781
+311,26-35,Married,5,3,11,M,-0.75,397,95.46,1.0,-400.72,4,50880.84,533
+312,46-55,Married,2,0,10,F,0.0,303,97.17,1.0,0.0,0,35757.45,369
+313,46-55,Single,1,0,4,M,-0.43,398,109.56,1.0,-222.62,4,56093.97,512
+314,26-35,Married,2,0,2,M,-0.4,440,101.05,1.0,-240.43,3,60933.44,606
+315,36-45,Single,1,0,3,F,-1.03,448,85.14,1.15,-802.69,19,66153.72,896
+316,56-70,Single,1,0,9,F,-0.01,584,94.72,1.08,-8.9,1,84583.93,963
+317,36-45,Married,5,3,6,M,-4.08,502,96.32,1.0,-3436.12,160,81098.05,842
+318,46-55,Married,2,0,6,F,0.0,527,120.9,1.07,0.0,0,82938.83,734
+319,46-55,Married,2,0,5,M,-1.0,716,107.08,1.07,-1439.9,45,154191.78,1539
+320,56-70,Single,1,0,5,F,-0.46,480,105.33,1.02,-449.52,29,102805.11,1000
+321,36-45,Married,3,1,5,F,-0.17,414,112.13,1.0,-89.05,2,60099.82,536
+322,46-55,Single,1,0,4,F,0.0,187,94.15,1.0,0.0,0,20901.34,222
+323,26-35,Married,2,0,3,F,-0.03,436,85.4,1.0,-17.81,1,50643.09,593
+324,36-45,Single,1,0,6,F,-0.09,753,99.68,1.0,-94.39,3,102366.34,1028
+325,46-55,Single,1,0,5,F,-0.12,274,82.76,1.02,-47.37,1,32357.21,399
+326,36-45,Single,3,1,5,F,0.0,180,124.41,1.0,0.0,0,26623.34,214
+327,36-45,Married,3,1,12,M,-0.4,1131,88.17,1.04,-727.0,15,159242.52,1886
+328,46-55,Married,2,0,8,F,-0.47,426,88.36,1.06,-400.72,7,74925.29,895
+329,46-55,Married,2,0,6,M,-1.62,604,169.19,1.0,-1841.55,27,192882.02,1142
+330,36-45,Single,1,0,8,M,0.0,217,78.88,1.0,0.0,0,34705.62,440
+331,26-35,Married,3,1,5,M,0.0,350,87.58,1.0,0.0,0,37923.42,433
+332,26-35,Married,5,3,4,M,-0.07,635,107.57,1.03,-55.21,2,83367.33,797
+333,46-55,Married,2,0,6,M,-2.59,604,97.42,1.0,-2383.83,70,89529.02,919
+334,26-35,Married,2,0,8,F,-1.18,198,108.73,1.0,-265.35,14,24463.21,225
+335,36-45,Married,5,3,4,F,-0.13,330,81.72,1.0,-73.02,3,45765.55,561
+336,70+,Single,1,0,4,M,-0.74,374,91.62,1.07,-543.02,15,66972.11,784
+337,26-35,Single,1,0,5,F,-0.48,332,127.51,1.0,-213.72,3,56357.35,444
+338,46-55,Single,1,0,5,F,-0.1,500,99.98,1.06,-65.89,3,66285.75,706
+339,46-55,Single,1,0,5,F,-0.08,276,112.4,1.0,-53.43,3,78228.97,696
+340,36-45,Married,4,2,2,M,-0.06,400,85.11,1.0,-29.68,1,44342.16,521
+341,36-45,Married,2,0,5,F,-0.34,254,129.12,1.0,-97.94,4,36926.97,287
+342,46-55,Single,1,0,9,M,-0.1,372,104.42,1.0,-59.37,2,60562.12,580
+343,46-55,Single,1,0,4,F,0.0,294,50.22,1.0,0.0,0,28525.24,568
+344,46-55,Married,3,1,8,F,-0.16,381,79.73,1.0,-80.14,3,39945.73,502
+345,46-55,Single,1,0,5,M,0.0,244,123.54,1.0,0.0,0,35827.07,290
+346,36-45,Married,2,0,9,M,-0.02,473,118.73,1.01,-13.36,1,80263.71,682
+347,26-35,Single,1,0,5,M,-0.04,259,124.25,1.01,-14.25,1,46220.58,374
+348,26-35,Single,1,0,4,F,-0.18,405,91.08,1.03,-101.52,6,50275.0,571
+349,36-45,Married,3,1,4,F,-0.04,217,96.88,1.0,-24.58,1,66268.78,684
+350,70+,Single,1,0,2,M,-0.14,397,97.57,1.01,-62.33,2,44100.34,455
+351,36-45,Married,3,1,1,F,-0.2,444,79.16,1.01,-160.3,7,62377.47,792
+352,46-55,Married,2,0,5,F,-0.27,282,95.37,1.09,-164.92,9,57987.28,661
+353,36-45,Single,2,1,6,M,-0.14,674,109.69,1.01,-128.24,5,101902.89,934
+354,18-25,Single,1,0,12,F,-0.03,253,112.4,1.0,-12.47,1,46756.5,416
+355,46-55,Married,2,0,6,M,-0.12,967,92.63,1.01,-189.05,6,150609.96,1644
+356,36-45,Single,5,3,2,M,0.0,342,97.75,1.0,0.0,0,56598.12,579
+357,18-25,Married,2,0,4,F,-0.05,894,105.59,1.06,-64.11,3,134525.24,1349
+358,70+,Married,2,0,6,M,0.0,285,131.28,1.02,0.0,0,51069.83,396
+359,26-35,Married,3,1,3,F,-0.36,422,96.41,1.05,-191.99,4,50903.96,555
+360,46-55,Single,1,0,5,F,-0.17,256,75.59,1.05,-53.42,2,23809.56,331
+361,18-25,Single,1,0,3,F,0.0,323,88.2,1.01,0.0,0,50540.89,578
+362,18-25,Single,1,0,5,M,-0.07,851,91.08,1.0,-108.64,2,136619.16,1500
+363,36-45,Married,3,1,12,F,-0.13,714,137.26,1.01,-154.58,5,164845.93,1217
+364,56-70,Single,1,0,2,F,-0.28,149,109.27,1.0,-44.52,1,17264.57,158
+365,70+,Married,2,0,8,M,0.0,271,99.47,1.0,0.0,0,34615.92,349
+366,46-55,Married,2,0,6,M,-0.8,418,126.25,1.04,-523.6,10,82567.69,679
+367,36-45,Married,3,1,6,M,-6.27,747,109.79,1.02,-7896.29,293,138229.78,1283
+368,36-45,Single,1,0,5,F,-0.04,1199,95.35,1.02,-103.3,4,241619.47,2581
+369,56-70,Married,2,0,4,F,-0.02,527,112.44,1.01,-17.81,1,87814.42,789
+370,46-55,Married,2,0,5,M,-0.51,554,114.58,1.0,-437.95,19,98651.77,861
+371,26-35,Single,1,0,5,M,-0.02,447,115.09,1.01,-12.47,1,63298.54,554
+372,46-55,Married,2,0,7,F,-0.36,109,163.75,1.01,-73.01,3,33405.95,206
+373,26-35,Married,3,1,4,F,-0.17,682,93.17,1.0,-169.2,3,90936.44,980
+374,46-55,Married,3,1,8,F,-0.08,528,148.98,1.02,-51.95,2,102202.48,702
+375,36-45,Single,1,0,2,M,0.0,339,130.66,1.0,0.0,0,59581.64,456
+376,46-55,Single,1,0,8,F,-0.4,146,130.55,1.17,-71.24,1,23237.97,209
+377,18-25,Married,3,1,4,F,-0.13,653,92.81,1.03,-139.8,5,102744.84,1145
+378,36-45,Married,5,3,5,M,-0.78,988,96.46,1.08,-1380.24,48,170254.62,1902
+379,18-25,Single,1,0,1,F,-0.35,1347,120.46,1.01,-946.4,20,321875.91,2690
+380,46-55,Married,2,0,7,F,-0.32,419,106.55,1.02,-169.2,3,56152.94,537
+381,26-35,Married,3,1,4,F,-0.24,465,99.08,1.0,-140.52,4,58654.37,592
+382,46-55,Married,3,1,2,M,0.0,267,142.02,1.0,0.0,0,61495.95,433
+383,18-25,Single,2,1,1,F,-0.1,646,88.31,1.0,-88.87,2,81331.58,921
+384,36-45,Single,1,0,3,F,-0.64,372,93.35,1.02,-382.91,16,55634.34,606
+385,26-35,Single,1,0,3,F,-3.27,456,114.79,1.08,-2173.44,84,76220.79,716
+386,36-45,Married,4,2,7,M,0.0,681,98.93,1.04,0.0,0,107540.54,1132
+387,46-55,Single,1,0,5,M,0.0,600,78.12,1.06,0.0,0,74679.53,1014
+388,26-35,Single,1,0,4,F,0.0,215,115.99,1.0,0.0,0,45467.77,392
+389,26-35,Married,4,2,6,M,-1.48,659,91.38,1.06,-1815.99,46,112393.84,1298
+390,46-55,Single,1,0,4,F,0.0,543,99.87,1.01,0.0,0,71707.89,725
+391,46-55,Single,1,0,8,M,-0.35,602,76.79,1.04,-352.98,14,77172.1,1047
+392,26-35,Single,5,3,3,M,-0.27,488,107.88,1.0,-252.9,5,102812.77,953
+393,46-55,Single,1,0,5,F,-1.2,303,83.72,1.01,-568.14,6,39683.09,481
+394,46-55,Single,3,1,5,M,-0.07,383,95.08,1.02,-44.52,1,64940.98,699
+395,36-45,Married,4,2,2,F,-0.36,291,85.7,1.0,-242.21,8,56903.16,664
+396,70+,Married,3,1,8,M,-0.16,655,92.73,1.12,-186.42,7,107936.42,1301
+397,46-55,Single,1,0,4,F,-0.09,248,110.4,1.0,-41.86,3,53435.34,484
+398,36-45,Single,1,0,2,M,-0.52,341,132.72,1.0,-220.84,6,56138.86,424
+399,46-55,Married,3,1,4,F,-0.23,412,118.67,1.04,-142.48,5,73931.75,650
+400,46-55,Single,1,0,5,F,-0.09,578,82.55,1.06,-91.72,3,82135.77,1050
+401,36-45,Married,3,1,4,F,-1.86,529,96.42,1.21,-1646.65,72,85233.6,1067
+402,70+,Married,3,1,7,F,-0.11,623,84.97,1.09,-187.0,4,145724.18,1864
+403,46-55,Single,1,0,3,F,-1.62,510,92.95,1.04,-1025.62,43,58933.19,657
+404,36-45,Single,1,0,3,F,-2.47,595,106.77,1.04,-2693.23,64,116381.28,1133
+405,36-45,Married,2,0,6,F,0.0,306,120.32,1.0,0.0,0,50054.28,416
+406,26-35,Single,1,0,3,M,0.0,47,141.52,1.0,0.0,0,11745.84,83
+407,36-45,Married,2,0,6,F,-0.13,1007,72.93,1.01,-259.67,10,147099.74,2035
+408,46-55,Married,2,0,5,M,-0.22,396,95.25,1.0,-125.56,4,54767.12,576
+409,56-70,Married,2,0,5,F,-0.22,622,101.93,1.11,-237.58,10,110803.34,1212
+410,26-35,Married,2,0,5,F,0.0,124,89.16,1.0,0.0,0,16226.61,182
+411,46-55,Single,1,0,4,F,-0.12,472,84.96,1.03,-89.05,3,60576.18,734
+412,46-55,Married,2,0,3,M,-0.32,1284,109.88,1.0,-762.26,18,262507.32,2389
+413,36-45,Single,1,0,1,F,-0.03,372,128.1,1.01,-17.81,1,66869.9,525
+414,70+,Married,2,0,6,F,-0.42,566,81.35,1.03,-471.6,15,90782.22,1149
+415,26-35,Married,2,0,5,M,-0.37,404,87.45,1.06,-178.1,2,42238.17,510
+416,36-45,Married,4,2,5,M,-1.63,609,113.97,1.09,-1410.82,46,98353.98,942
+417,36-45,Married,2,0,3,F,-0.11,351,95.11,1.0,-60.55,3,50410.43,530
+418,36-45,Single,4,2,1,M,-0.05,1021,97.45,1.07,-89.05,2,183490.62,2020
+419,36-45,Married,2,0,3,F,-0.25,362,103.39,1.0,-126.44,6,53040.48,513
+420,26-35,Married,3,1,4,M,-0.04,634,106.85,1.0,-40.07,2,110055.11,1030
+421,56-70,Married,2,0,5,M,-0.29,917,113.48,1.03,-414.25,11,164438.22,1497
+422,46-55,Single,1,0,5,F,0.0,440,177.18,1.0,0.0,0,200925.82,1134
+423,46-55,Married,2,0,4,M,-0.21,149,136.68,1.0,-53.43,2,35263.55,258
+424,46-55,Single,1,0,4,M,-1.37,525,97.26,1.01,-1050.44,17,74308.75,769
+425,70+,Single,1,0,1,M,-0.13,87,102.87,1.0,-17.81,1,13990.3,136
+426,26-35,Single,1,0,7,F,-0.03,827,132.58,1.01,-44.52,2,170900.26,1301
+427,56-70,Married,3,1,4,F,-1.23,680,100.52,1.04,-1246.68,43,101925.23,1055
+428,36-45,Single,1,0,4,M,-0.25,333,121.94,1.15,-160.29,10,79020.03,745
+429,36-45,Married,2,0,3,F,-0.59,336,86.88,1.03,-296.83,6,43962.95,520
+430,70+,Married,2,0,6,M,-0.01,615,98.7,1.0,-8.9,1,89620.29,908
+431,36-45,Married,2,0,5,F,-0.55,329,89.22,1.06,-413.79,25,66736.78,793
+432,46-55,Married,2,0,6,M,-0.19,910,95.15,1.0,-391.64,6,194286.63,2042
+433,46-55,Single,1,0,5,F,-1.53,869,125.19,1.0,-2137.34,53,175139.14,1399
+434,46-55,Single,1,0,5,F,-0.15,491,89.17,1.0,-124.67,2,74901.02,840
+435,36-45,Single,1,0,5,F,-0.3,209,126.18,1.01,-160.29,5,66748.72,532
+436,70+,Married,2,0,8,M,-1.01,534,96.42,1.14,-822.45,28,78289.9,927
+437,26-35,Single,1,0,2,F,0.0,491,82.49,1.01,0.0,0,52874.32,649
+438,36-45,Married,2,0,1,M,-0.06,569,94.46,1.01,-71.24,2,111274.5,1188
+439,36-45,Married,3,1,6,M,-0.5,388,97.07,1.0,-297.41,17,57465.9,592
+440,36-45,Married,3,1,8,F,-0.12,497,129.84,1.0,-71.24,1,78165.67,602
+441,70+,Single,1,0,2,F,-0.09,523,124.27,1.06,-64.11,4,91217.75,776
+442,46-55,Married,5,3,6,M,0.0,370,107.61,1.0,0.0,0,60475.01,562
+443,70+,Single,1,0,4,M,-0.19,298,103.87,1.05,-137.13,5,75303.79,758
+444,46-55,Married,2,0,2,F,-0.12,960,115.31,1.0,-163.85,6,163742.07,1420
+445,26-35,Married,2,0,5,F,0.0,164,106.03,1.0,0.0,0,41138.1,388
+446,36-45,Single,3,2,4,M,-0.05,697,94.16,1.05,-71.24,2,123449.87,1371
+447,36-45,Married,2,0,5,F,-0.41,1214,117.35,1.08,-862.7,36,246437.23,2260
+448,18-25,Married,3,1,5,M,-0.07,470,100.21,1.04,-44.52,2,63433.7,656
+449,70+,Single,1,0,4,F,-0.12,252,83.96,1.01,-35.62,2,24683.65,298
+450,46-55,Married,2,0,5,M,-0.32,839,120.83,1.12,-473.86,14,177618.34,1642
+451,56-70,Married,2,0,1,F,-0.23,280,113.64,1.01,-98.04,2,47956.21,426
+452,26-35,Married,2,0,5,M,-0.11,517,102.73,1.05,-141.76,6,138176.97,1418
+453,70+,Single,2,1,2,M,-0.17,703,117.84,1.12,-233.31,7,162269.19,1542
+454,36-45,Married,2,0,5,M,0.0,199,110.5,1.0,0.0,0,35137.47,318
+455,46-55,Single,2,1,5,F,0.0,613,110.32,1.02,0.0,0,79653.01,734
+456,26-35,Married,5,3,7,F,-0.04,1252,123.26,1.12,-104.48,4,295583.04,2675
+457,36-45,Married,4,2,7,F,0.0,421,106.65,1.0,0.0,0,56099.52,526
+458,46-55,Single,2,1,4,F,-0.15,723,87.54,1.0,-173.94,4,99091.73,1132
+459,26-35,Married,2,0,4,M,-0.69,638,89.29,1.01,-640.71,26,83310.54,942
+460,70+,Married,2,0,5,M,-0.53,915,113.17,1.01,-1097.09,33,232103.75,2081
+461,56-70,Married,3,1,2,M,-0.86,352,93.82,1.0,-591.3,19,64733.24,690
+462,56-70,Married,2,0,2,F,-1.36,467,85.78,1.01,-829.8,23,52325.33,614
+463,46-55,Married,5,3,6,M,-3.25,182,91.56,1.02,-728.42,29,20509.29,228
+464,46-55,Married,5,3,3,M,-1.47,2040,89.27,1.04,-6063.05,220,369053.18,4314
+465,26-35,Married,3,1,4,M,0.0,89,193.96,1.0,0.0,0,35300.99,182
+466,36-45,Married,2,0,5,M,-0.58,698,109.83,1.01,-654.5,24,123780.54,1140
+467,46-55,Single,1,0,3,M,-0.2,554,74.05,1.0,-276.06,8,102852.06,1389
+468,56-70,Married,2,0,4,F,-0.04,320,104.69,1.0,-17.81,1,41874.09,400
+469,18-25,Single,1,0,4,F,-0.04,550,99.59,1.0,-44.52,1,109452.94,1099
+470,18-25,Married,3,1,4,M,-0.02,896,98.51,1.1,-39.19,3,202740.39,2266
+471,18-25,Married,3,1,5,F,0.0,252,104.65,1.0,0.0,0,31812.86,304
+472,46-55,Married,2,0,5,F,0.0,97,120.93,1.0,0.0,0,23702.96,196
+473,46-55,Married,2,0,4,F,-0.35,501,105.67,1.08,-215.74,6,65094.98,664
+474,56-70,Married,4,2,6,F,-0.31,501,88.61,1.02,-266.55,12,77268.3,888
+475,46-55,Married,2,0,6,M,-1.26,210,82.15,1.0,-713.65,9,46414.89,565
+476,36-45,Married,5,3,7,M,0.0,146,108.21,1.04,0.0,0,18827.8,181
+477,70+,Married,2,0,2,F,0.0,268,101.83,1.0,0.0,0,29836.31,293
+478,36-45,Married,5,3,7,F,-0.03,477,86.18,1.0,-17.81,1,56192.32,652
+479,46-55,Married,2,0,10,M,-0.03,771,90.9,1.0,-44.52,2,117618.64,1294
+480,70+,Married,3,1,5,M,-0.06,398,91.81,1.01,-35.62,2,53250.29,586
+481,36-45,Married,4,2,2,F,-0.71,724,60.82,1.01,-1322.03,30,113545.34,1880
+482,26-35,Married,4,2,8,M,-0.04,274,84.16,1.01,-17.81,1,37365.42,449
+483,70+,Married,2,0,8,M,-1.54,570,99.18,1.07,-1130.33,53,72798.18,784
+484,70+,Married,2,0,8,F,-0.04,877,89.67,1.0,-62.33,2,156384.85,1744
+485,56-70,Married,2,0,7,F,-0.36,196,97.35,1.0,-92.26,2,25115.84,258
+486,46-55,Single,3,1,1,F,-0.02,454,94.28,1.0,-11.13,1,57509.36,612
+487,26-35,Single,1,0,4,M,0.0,214,88.73,1.01,0.0,0,26175.27,298
+488,36-45,Married,2,0,6,M,0.0,543,96.05,1.11,0.0,0,65893.59,763
+489,70+,Married,2,0,2,F,-0.07,130,113.74,1.0,-14.25,1,23998.79,211
+490,36-45,Single,1,0,5,F,-0.83,466,82.99,1.85,-623.35,15,62245.13,1387
+491,26-35,Married,2,0,4,F,-0.43,327,111.32,1.03,-183.73,7,47312.91,436
+492,46-55,Married,2,0,4,M,0.0,159,128.97,1.0,0.0,0,27341.28,212
+493,36-45,Married,2,0,4,F,-0.46,187,128.25,1.0,-142.48,1,40014.67,313
+494,26-35,Married,5,3,8,F,-0.8,839,111.66,1.05,-991.47,22,138574.38,1297
+495,36-45,Married,2,0,8,F,-0.01,1090,103.69,1.04,-19.59,1,163311.42,1640
+496,26-35,Single,1,0,5,M,-0.09,427,96.91,1.0,-64.11,3,69195.51,715
+497,36-45,Married,5,3,5,F,-1.11,391,102.17,1.04,-539.9,21,49656.77,506
+498,46-55,Married,2,0,9,F,-0.4,912,114.45,1.0,-644.37,20,182435.33,1594
+499,36-45,Married,2,0,5,F,-0.14,735,76.89,1.06,-215.49,8,117866.2,1620
+500,46-55,Single,1,0,4,F,0.0,348,79.55,1.0,0.0,0,35478.17,448
+501,46-55,Single,1,0,5,F,-0.09,1021,83.38,1.0,-178.8,6,175019.74,2100
+502,26-35,Married,4,2,6,M,-0.03,465,95.95,1.0,-17.81,1,64480.74,675
+503,46-55,Married,3,1,4,F,-0.38,310,95.98,1.04,-239.82,12,60276.3,651
+504,70+,Married,3,1,8,M,0.0,408,135.5,1.0,0.0,0,70322.66,519
+505,56-70,Married,2,0,9,F,0.0,109,86.65,1.0,0.0,0,15076.44,174
+506,46-55,Married,2,0,7,F,-0.22,257,105.27,1.09,-89.05,4,43581.02,453
+507,70+,Married,2,0,6,F,0.0,179,100.45,1.14,0.0,0,32445.94,369
+508,18-25,Married,2,0,6,F,0.0,545,134.99,1.0,0.0,0,155368.79,1151
+509,26-35,Single,1,0,3,F,-0.44,193,114.68,1.0,-219.78,3,57225.55,499
+510,26-35,Single,2,1,1,M,-1.0,1019,86.01,1.03,-1634.66,56,141234.55,1684
+511,46-55,Single,1,0,5,M,-0.14,377,105.48,1.01,-71.24,2,54956.13,527
+512,46-55,Single,1,0,3,F,-0.31,217,166.42,1.05,-82.99,3,44932.79,284
+513,70+,Married,2,0,4,F,-0.11,231,89.1,1.0,-35.62,2,27797.71,312
+514,26-35,Married,2,0,6,M,-0.04,379,144.69,1.0,-17.81,1,69163.7,478
+515,26-35,Married,2,0,4,M,-0.02,903,97.06,1.0,-53.43,2,251472.19,2591
+516,70+,Married,2,0,4,M,-0.79,235,138.27,1.0,-249.34,2,43415.58,314
+517,26-35,Married,2,0,6,F,0.0,256,110.94,1.0,0.0,0,40935.73,369
+518,56-70,Married,5,3,4,M,-0.9,633,79.35,1.0,-1111.68,7,97517.87,1229
+519,46-55,Married,2,0,5,M,-0.18,354,82.42,1.02,-142.48,7,65774.04,811
+520,36-45,Married,2,0,6,F,-0.64,688,103.4,1.01,-693.69,26,112702.84,1105
+521,56-70,Single,1,0,6,F,-0.07,448,90.11,1.06,-53.43,2,70646.71,834
+522,46-55,Married,3,1,6,F,0.0,369,87.68,1.05,0.0,0,44718.54,533
+523,36-45,Single,3,1,5,F,-1.08,127,91.67,1.0,-262.68,16,22276.25,243
+524,26-35,Single,1,0,5,F,-0.38,493,110.52,1.0,-274.8,9,80456.72,731
+525,36-45,Married,3,1,9,M,-0.15,614,105.82,1.0,-171.69,4,122327.85,1156
+526,18-25,Married,2,0,2,F,-0.78,754,108.97,1.0,-737.32,18,103633.25,953
+527,36-45,Married,3,1,5,M,0.0,299,121.27,1.06,0.0,0,46566.02,406
+528,46-55,Married,2,0,10,F,-0.72,1109,117.85,1.09,-1249.53,32,204122.66,1887
+529,18-25,Single,1,0,5,M,-1.95,250,67.5,1.0,-751.2,27,26055.99,386
+530,46-55,Single,3,2,5,F,-0.25,831,126.42,1.0,-387.36,6,196458.65,1554
+531,36-45,Married,3,1,5,F,0.0,168,112.42,1.06,0.0,0,22484.32,212
+532,56-70,Married,2,0,1,F,0.0,249,110.35,1.0,0.0,0,42595.66,386
+533,36-45,Married,5,3,6,M,-0.06,983,137.23,1.0,-92.61,4,219300.47,1598
+534,26-35,Married,5,3,5,F,-0.17,177,113.49,1.08,-38.29,2,24968.18,238
+535,36-45,Married,2,0,5,F,-0.08,693,79.22,1.06,-108.64,2,113521.47,1521
+536,46-55,Married,2,0,11,F,-0.8,441,136.71,1.0,-595.29,27,101712.02,747
+537,18-25,Single,1,0,5,F,-0.15,745,115.16,1.01,-178.1,5,133813.01,1175
+538,46-55,Married,2,0,6,M,0.0,570,105.12,1.0,0.0,0,92291.63,878
+539,46-55,Married,2,0,9,F,-0.05,214,76.5,1.0,-14.25,1,20808.28,273
+540,26-35,Married,4,2,4,F,-0.33,267,132.62,1.0,-106.86,2,43101.62,325
+541,26-35,Single,1,0,10,F,-0.04,523,173.78,1.08,-69.46,3,271093.03,1690
+542,46-55,Single,1,0,6,F,0.0,89,94.38,1.0,0.0,0,9721.61,103
+543,26-35,Married,2,0,5,M,-0.26,510,121.83,1.0,-213.72,4,99411.04,816
+544,56-70,Single,1,0,4,M,-0.16,432,101.88,1.03,-145.45,6,91485.66,927
+545,70+,Married,3,1,4,F,-0.01,445,100.54,1.0,-8.9,1,88475.16,880
+546,26-35,Single,1,0,5,F,-0.1,682,100.15,1.05,-135.35,4,136299.31,1425
+547,46-55,Married,2,0,1,M,0.0,159,121.96,1.0,0.0,0,26953.01,221
+548,46-55,Married,2,0,5,M,-0.05,267,90.29,1.0,-19.59,1,34038.93,377
+549,46-55,Single,1,0,2,F,-0.13,242,83.34,1.04,-35.62,1,23668.72,295
+550,36-45,Single,1,0,2,F,-0.71,687,103.13,1.0,-868.22,20,126741.56,1229
+551,46-55,Single,1,0,6,M,-1.6,576,94.97,1.08,-1513.03,59,89558.63,1019
+552,70+,Married,2,0,4,M,-0.6,786,119.81,1.08,-651.47,23,129871.16,1171
+553,36-45,Married,3,1,11,F,0.0,747,136.33,1.0,0.0,0,200818.57,1475
+554,26-35,Single,1,0,4,M,-0.36,453,64.0,1.04,-320.58,5,56380.02,916
+555,46-55,Single,1,0,5,M,0.0,522,96.62,1.06,0.0,0,102221.05,1125
+556,46-55,Single,1,0,5,F,-3.45,340,122.19,1.03,-1919.14,65,68062.56,574
+557,26-35,Single,3,2,4,M,-0.02,742,86.91,1.01,-35.62,1,129411.09,1499
+558,36-45,Single,2,1,5,F,-0.74,259,98.27,1.11,-252.46,4,33510.0,377
+559,36-45,Married,2,0,6,F,-0.09,649,71.19,1.0,-133.57,8,101665.5,1428
+560,46-55,Married,2,0,5,M,-0.43,657,97.41,1.03,-408.72,13,92344.92,975
+561,36-45,Married,2,0,4,M,-0.27,684,127.42,1.1,-284.96,7,134681.94,1162
+562,46-55,Single,1,0,9,M,0.0,122,83.64,1.0,0.0,0,12378.21,148
+563,26-35,Single,1,0,2,M,0.0,268,115.48,1.03,0.0,0,42958.84,382
+564,36-45,Married,2,0,7,F,-0.54,438,120.23,1.08,-411.41,18,91252.32,817
+565,26-35,Married,3,1,9,F,-1.81,924,106.25,1.0,-3453.35,45,202715.62,1908
+566,26-35,Married,4,2,5,F,-2.21,991,102.65,1.07,-5873.98,219,272420.99,2830
+567,18-25,Single,1,0,3,F,-0.2,696,105.57,1.0,-262.7,11,135346.15,1282
+568,56-70,Single,1,0,4,M,-0.05,419,99.35,1.0,-35.62,1,73918.95,747
+569,46-55,Single,1,0,9,F,-0.02,457,130.25,1.06,-17.81,1,138850.84,1129
+570,46-55,Single,1,0,4,F,-0.49,955,99.13,1.02,-644.71,24,129663.22,1337
+571,56-70,Married,2,0,8,F,-0.02,662,114.21,1.1,-19.59,1,126999.48,1225
+572,36-45,Married,2,0,4,F,-0.04,1145,104.44,1.1,-106.5,2,249094.2,2627
+573,36-45,Single,1,0,4,F,-0.06,482,86.97,1.0,-35.62,1,54183.06,624
+574,36-45,Single,1,0,4,M,-0.13,302,136.96,1.0,-53.43,2,57387.68,419
+575,56-70,Married,2,0,1,F,-0.08,275,93.05,1.14,-83.7,5,96118.37,1173
+576,26-35,Single,1,0,5,M,0.0,242,89.75,1.0,0.0,0,32308.98,360
+577,46-55,Married,3,1,7,F,-0.3,602,96.25,1.04,-287.57,7,93264.06,1010
+578,46-55,Married,2,0,6,M,-0.49,1489,111.88,1.1,-1129.69,45,258554.78,2541
+579,26-35,Married,2,0,8,M,-0.09,384,101.21,1.03,-53.43,2,63154.69,641
+580,26-35,Married,3,1,6,M,-0.1,183,116.73,1.01,-29.68,1,35370.69,307
+581,46-55,Single,1,0,2,M,0.0,102,60.7,1.0,0.0,0,14204.24,234
+582,70+,Married,2,0,4,M,0.0,401,90.17,1.05,0.0,0,45627.33,532
+583,36-45,Single,1,0,2,M,-0.29,635,74.46,1.0,-322.0,10,81977.89,1101
+584,46-55,Single,1,0,4,F,-0.35,295,68.93,1.0,-163.5,1,31912.34,465
+585,46-55,Married,5,3,4,F,-0.52,534,95.59,1.01,-441.34,9,81155.53,861
+586,46-55,Single,1,0,4,M,-1.94,354,118.08,1.05,-975.26,13,59273.83,528
+587,26-35,Married,2,0,4,F,-0.49,368,110.72,1.1,-236.87,7,53700.43,532
+588,46-55,Married,2,0,5,M,0.0,257,117.11,1.0,0.0,0,52467.42,448
+589,56-70,Single,1,0,4,F,0.0,77,89.8,1.0,0.0,0,12661.71,141
+590,36-45,Married,2,0,1,F,-0.04,552,91.03,1.11,-55.21,3,116881.96,1427
+591,26-35,Married,5,3,7,F,-0.49,288,100.99,1.0,-147.82,6,30699.57,304
+592,70+,Married,2,0,4,M,-0.07,578,95.61,1.04,-55.21,2,71321.88,775
+593,46-55,Single,2,1,3,F,-0.18,650,68.68,1.04,-233.3,7,88533.59,1342
+594,46-55,Single,1,0,10,F,-0.98,574,111.42,1.01,-961.73,31,109187.26,991
+595,18-25,Married,2,0,2,F,-0.14,734,100.11,1.0,-162.07,5,119533.46,1194
+596,46-55,Single,1,0,1,F,-0.32,1454,75.91,1.01,-852.8,29,200179.32,2659
+597,26-35,Single,1,0,5,F,0.0,192,78.43,1.24,0.0,0,18667.11,296
+598,70+,Married,2,0,6,M,-2.07,872,103.32,1.01,-2744.39,96,137102.95,1338
+599,36-45,Single,1,0,3,F,0.0,303,117.95,1.0,0.0,0,50130.63,425
+600,46-55,Married,2,0,6,F,-0.07,529,66.26,1.0,-80.15,4,71958.6,1091
+601,70+,Married,2,0,6,M,-0.1,230,95.1,1.0,-35.62,1,33474.47,352
+602,18-25,Married,2,0,11,M,0.0,351,117.11,1.04,0.0,0,49771.22,441
+603,36-45,Single,1,0,3,F,-0.13,776,86.79,1.0,-199.46,11,128970.14,1486
+604,46-55,Married,2,0,1,M,-0.14,562,83.92,1.03,-136.24,4,79051.04,967
+605,36-45,Married,3,1,2,M,-0.06,272,100.01,1.17,-19.59,1,34804.11,407
+606,18-25,Single,1,0,1,F,-0.06,460,74.93,1.0,-35.62,1,46457.67,620
+607,70+,Single,1,0,4,M,-0.95,250,84.21,1.01,-572.05,22,50945.69,609
+608,26-35,Married,2,0,5,F,-0.8,588,86.75,1.08,-888.32,34,96815.64,1209
+609,26-35,Married,2,0,1,F,-0.08,385,89.1,1.02,-44.52,1,49184.49,561
+610,46-55,Single,1,0,5,F,-0.03,382,79.48,1.0,-13.36,1,38787.34,488
+611,36-45,Married,3,1,4,M,-0.3,497,106.51,1.0,-215.5,8,76263.55,716
+612,18-25,Single,1,0,1,M,0.0,570,74.19,1.01,0.0,0,76412.51,1041
+613,46-55,Married,2,0,5,M,-0.08,679,104.94,1.15,-105.08,5,143555.16,1577
+614,36-45,Single,1,0,6,F,-0.05,554,85.56,1.02,-49.27,2,82480.33,982
+615,36-45,Married,5,3,8,F,0.0,242,134.29,1.0,0.0,0,57342.02,427
+616,46-55,Single,2,1,7,F,-0.07,548,122.78,1.0,-53.43,1,94296.18,768
+617,56-70,Married,2,0,3,F,-0.05,256,83.52,1.0,-19.59,1,34157.68,410
+618,26-35,Married,2,0,4,F,-0.34,316,102.95,1.0,-135.36,5,41282.86,401
+619,36-45,Single,2,1,4,M,-0.13,996,96.89,1.0,-258.24,6,196878.97,2032
+620,26-35,Married,3,1,4,F,0.0,545,105.45,1.0,0.0,0,78873.61,748
+621,36-45,Married,2,0,5,M,-0.39,548,111.91,1.0,-391.64,14,111686.15,1001
+622,36-45,Married,4,2,5,F,-0.01,1125,110.54,1.01,-17.81,1,245076.25,2236
+623,46-55,Married,4,2,8,F,0.0,239,106.05,1.13,0.0,0,75509.0,804
+624,26-35,Married,5,3,11,M,-0.13,562,115.62,1.0,-108.64,3,93656.14,814
+625,18-25,Married,2,0,4,F,0.0,394,125.07,1.0,0.0,0,58906.56,471
+626,36-45,Single,2,1,4,M,-2.0,1451,93.27,1.01,-5655.2,171,264033.32,2866
+627,46-55,Married,4,2,4,M,-1.11,1124,94.29,1.07,-3807.38,128,323317.55,3683
+628,46-55,Married,3,1,4,M,-1.13,572,96.96,1.0,-865.54,36,74269.49,766
+629,36-45,Married,2,0,11,F,-0.48,184,133.44,1.0,-96.18,3,26820.66,201
+630,26-35,Single,1,0,4,M,0.0,237,110.15,1.0,0.0,0,44061.96,400
+631,26-35,Married,2,0,5,M,-0.05,352,105.48,1.0,-19.59,1,44195.03,419
+632,46-55,Married,2,0,6,M,-2.58,842,103.43,1.08,-3639.12,143,145942.91,1525
+633,26-35,Married,5,3,4,F,-0.18,789,93.28,1.01,-168.3,4,89358.24,969
+634,56-70,Married,5,3,5,M,-0.54,418,96.14,1.0,-336.6,12,59797.25,622
+635,46-55,Single,2,1,7,F,-0.12,274,125.65,1.0,-35.62,1,38824.74,309
+636,56-70,Married,2,0,3,M,-2.56,232,106.4,1.01,-979.55,11,40749.64,385
+637,46-55,Single,1,0,6,M,-0.04,587,100.91,1.0,-44.52,2,114635.43,1136
+638,36-45,Single,1,0,4,F,-0.4,1048,99.13,1.03,-680.86,22,168912.76,1757
+639,46-55,Married,2,0,7,F,-0.25,354,97.38,1.0,-113.09,3,43723.53,449
+640,46-55,Single,1,0,4,M,-0.09,388,84.91,1.01,-89.04,4,83556.33,996
+641,46-55,Single,1,0,5,F,-0.63,324,104.01,1.0,-330.68,18,54811.98,527
+642,36-45,Single,1,0,3,M,-0.08,395,104.17,1.0,-44.52,1,55733.45,535
+643,26-35,Single,1,0,2,M,0.0,281,112.55,1.17,0.0,0,58186.59,605
+644,26-35,Married,3,1,9,F,-0.07,272,72.3,1.0,-44.52,3,46631.68,645
+645,18-25,Married,2,0,6,F,0.0,358,75.54,1.0,0.0,0,35351.19,469
+646,18-25,Married,2,0,4,F,0.0,313,75.59,1.1,0.0,0,34468.31,501
+647,56-70,Married,2,0,1,F,-0.42,382,105.66,1.1,-215.5,7,54839.83,570
+648,36-45,Married,5,3,4,F,-0.04,461,84.46,1.05,-19.59,1,47041.67,586
+649,46-55,Married,4,2,2,F,-0.23,816,95.31,1.02,-236.52,10,96068.62,1024
+650,46-55,Single,1,0,5,M,-0.29,456,80.26,1.0,-309.89,15,86362.67,1080
+651,46-55,Single,2,1,4,F,0.0,327,125.57,1.01,0.0,0,50980.95,410
+652,70+,Married,2,0,4,F,-0.26,567,87.17,1.0,-211.75,9,72006.39,826
+653,46-55,Married,5,3,4,M,-0.74,253,113.9,1.03,-258.77,9,39863.27,360
+654,46-55,Married,2,0,5,F,0.0,206,119.23,1.0,0.0,0,28853.33,242
+655,46-55,Married,2,0,3,F,-0.08,968,107.24,1.0,-131.44,2,180593.28,1692
+656,36-45,Married,5,3,5,M,-0.27,328,99.77,1.0,-124.67,2,46594.0,467
+657,26-35,Married,4,2,1,M,-0.75,734,108.96,1.02,-1037.77,36,150359.22,1403
+658,46-55,Married,4,2,2,F,-0.06,614,80.16,1.0,-51.95,2,69581.0,868
+659,26-35,Single,1,0,3,M,-0.26,681,100.52,1.13,-562.44,22,213800.89,2399
+660,36-45,Married,2,0,6,F,0.0,483,99.22,1.11,0.0,0,84335.94,940
+661,26-35,Single,2,1,8,M,-0.49,426,126.28,1.01,-285.4,8,73875.36,589
+662,26-35,Married,2,0,5,F,0.0,91,116.63,1.07,0.0,0,12829.01,118
+663,56-70,Single,1,0,1,F,0.0,320,60.09,1.0,0.0,0,33169.78,552
+664,26-35,Married,5,3,3,F,-0.24,599,126.76,1.0,-222.63,8,119664.5,944
+665,26-35,Single,1,0,7,M,-0.29,565,113.32,1.0,-382.91,5,150832.0,1331
+666,26-35,Single,1,0,4,F,-0.02,956,88.53,1.0,-35.62,1,132090.15,1492
+667,26-35,Married,2,0,6,F,-0.12,834,74.91,1.01,-175.13,7,112066.97,1512
+668,26-35,Married,2,0,1,M,-0.02,636,83.88,1.12,-19.59,1,81361.82,1082
+669,46-55,Single,2,1,3,M,0.0,442,111.0,1.05,0.0,0,75256.96,713
+670,26-35,Married,2,0,5,M,-0.78,175,107.02,1.0,-170.98,5,23438.31,219
+671,70+,Married,2,0,4,F,-0.66,225,110.87,1.12,-201.26,9,34038.04,343
+672,26-35,Married,2,0,5,F,0.0,448,76.59,1.18,0.0,0,52844.81,817
+673,46-55,Single,2,1,4,F,-0.07,392,101.18,1.01,-35.62,1,50590.02,506
+674,56-70,Married,2,0,7,M,0.0,259,94.62,1.01,0.0,0,55162.87,591
+675,26-35,Married,2,0,5,M,0.0,199,89.9,1.01,0.0,0,29037.59,325
+676,46-55,Married,2,0,5,F,0.0,589,116.33,1.0,0.0,0,121684.75,1046
+677,36-45,Single,1,0,5,F,-0.03,322,96.53,1.0,-44.52,1,124625.38,1291
+678,46-55,Married,5,3,3,F,-0.06,500,89.47,1.0,-55.21,2,79184.72,889
+679,36-45,Single,2,1,4,M,0.0,469,112.11,1.0,0.0,0,153370.43,1368
+680,26-35,Single,1,0,5,M,0.0,553,103.05,1.01,0.0,0,85221.47,834
+681,70+,Married,2,0,2,F,-0.26,245,80.7,1.0,-119.33,2,36962.32,458
+682,56-70,Single,1,0,4,M,-1.04,529,102.49,1.0,-768.15,30,75845.95,740
+683,36-45,Single,1,0,4,M,-0.11,747,96.32,1.0,-115.76,3,102962.41,1069
+684,36-45,Married,2,0,3,M,-0.81,391,81.81,1.0,-687.99,16,69129.66,845
+685,36-45,Married,5,3,8,M,-0.04,346,91.68,1.04,-17.81,1,37314.36,422
+686,26-35,Married,2,0,4,F,-0.05,355,111.84,1.0,-26.71,1,56031.01,501
+687,36-45,Married,2,0,5,F,-0.15,638,90.65,1.12,-146.04,5,86384.86,1068
+688,18-25,Single,2,1,1,F,-0.09,452,68.15,1.0,-56.99,2,41710.75,612
+689,70+,Single,1,0,4,F,0.0,534,65.59,1.0,0.0,0,60082.37,917
+690,26-35,Married,2,0,5,M,-0.1,373,102.37,1.04,-55.21,2,54666.75,557
+691,46-55,Married,4,2,4,M,-0.09,712,103.09,1.02,-120.51,4,136494.81,1348
+692,18-25,Single,1,0,5,F,-0.44,213,100.52,1.02,-117.54,6,26738.95,271
+693,26-35,Single,2,1,4,M,-0.11,255,131.04,1.0,-35.62,1,44421.42,339
+694,26-35,Married,3,1,8,F,-0.21,679,98.81,1.16,-229.74,11,110662.8,1297
+695,56-70,Married,2,0,6,M,-0.27,1028,118.96,1.0,-479.08,25,208425.62,1755
+696,46-55,Single,1,0,4,M,0.0,297,58.15,1.01,0.0,0,34250.6,593
+697,36-45,Single,1,0,3,F,-0.21,176,127.11,1.0,-47.19,3,28599.08,225
+698,26-35,Married,3,1,4,F,-0.03,277,87.35,1.0,-17.81,1,51798.23,593
+699,36-45,Married,2,0,3,F,-0.03,830,88.57,1.04,-35.62,1,102567.0,1207
+700,36-45,Married,3,1,11,F,-0.47,475,88.45,1.08,-280.8,8,52981.27,647
+701,56-70,Married,2,0,4,M,0.0,147,64.82,1.01,0.0,0,26901.54,420
+702,46-55,Single,1,0,5,M,-0.03,396,87.51,1.03,-17.81,1,46553.03,546
+703,46-55,Married,2,0,6,F,-0.06,587,112.37,1.18,-54.85,2,106076.88,1110
+704,46-55,Married,5,3,6,F,-0.38,689,101.25,1.01,-362.43,12,97601.59,969
+705,36-45,Single,1,0,4,M,-0.03,780,131.26,1.09,-35.62,1,152134.06,1258
+706,56-70,Single,1,0,4,F,-1.81,739,96.58,1.05,-3284.92,115,175388.58,1903
+707,18-25,Single,2,1,5,F,-0.19,305,119.5,1.0,-68.86,3,43618.18,365
+708,36-45,Married,3,1,5,F,-0.1,692,100.77,1.08,-124.67,3,124646.34,1336
+709,70+,Married,2,0,3,M,-0.05,504,111.81,1.0,-35.61,2,87096.93,779
+710,36-45,Married,2,0,6,F,-10.93,472,184.86,1.0,-6370.77,118,107773.53,583
+711,26-35,Single,1,0,9,M,-0.03,1250,85.77,1.0,-113.0,3,357911.09,4173
+712,46-55,Married,3,1,5,F,-1.01,554,111.9,1.01,-774.44,47,85824.73,775
+713,46-55,Married,3,1,5,M,-0.08,349,98.38,1.0,-41.22,3,52045.25,530
+714,46-55,Single,1,0,3,M,-0.12,507,108.52,1.06,-89.05,1,80844.19,788
+715,36-45,Married,2,0,9,M,-0.27,462,89.92,1.01,-171.86,9,57820.55,647
+716,36-45,Single,4,2,1,F,-0.09,368,98.44,1.01,-44.52,1,46364.86,475
+717,36-45,Single,1,0,2,F,-0.35,434,96.66,1.0,-181.66,5,50652.44,524
+718,36-45,Married,2,0,11,F,-0.06,266,79.15,1.02,-26.71,1,36804.45,472
+719,56-70,Married,2,0,2,F,-0.15,377,139.67,1.0,-89.05,3,82544.52,591
+720,18-25,Married,2,0,6,M,-0.05,425,77.9,1.0,-26.71,2,44950.41,577
+721,26-35,Married,2,0,5,F,0.0,150,101.94,1.01,0.0,0,16105.89,160
+722,26-35,Single,1,0,4,M,-0.09,370,107.05,1.0,-44.52,1,54169.35,506
+723,46-55,Single,2,1,3,M,-0.49,233,111.76,1.0,-133.57,2,30399.81,272
+724,26-35,Married,3,1,6,M,-0.14,488,92.06,1.01,-89.05,1,56709.17,621
+725,56-70,Married,3,1,3,F,-0.86,1011,94.42,1.0,-1155.86,26,127371.68,1350
+726,46-55,Single,3,1,5,M,-0.13,393,91.73,1.0,-71.24,3,48890.55,533
+727,46-55,Single,1,0,5,F,-0.07,371,92.02,1.0,-35.62,1,45823.85,498
+728,46-55,Married,5,3,9,F,0.0,160,91.04,1.02,0.0,0,17389.29,194
+729,46-55,Single,1,0,3,M,-0.45,725,107.27,1.0,-434.21,6,103514.3,965
+730,26-35,Single,1,0,4,F,-0.16,222,106.41,1.0,-41.86,2,28198.48,265
+731,70+,Single,1,0,4,F,0.0,111,126.15,1.02,0.0,0,41378.18,334
+732,36-45,Married,2,0,2,F,0.0,266,94.43,1.0,0.0,0,75261.24,797
+733,70+,Single,1,0,4,M,-0.45,575,134.25,1.0,-567.24,10,169155.74,1260
+734,36-45,Married,2,0,5,F,0.0,158,81.19,1.0,0.0,0,15425.81,190
+735,46-55,Married,2,0,5,M,-0.37,948,115.96,1.06,-860.18,29,272030.9,2476
+736,56-70,Married,2,0,4,F,-0.13,493,106.54,1.0,-89.05,1,72873.69,684
+737,36-45,Single,4,3,4,M,-0.81,688,87.17,1.02,-1308.58,57,140174.48,1637
+738,46-55,Single,1,0,1,M,0.0,261,129.45,1.0,0.0,0,51261.16,396
+739,36-45,Married,5,3,7,M,0.0,180,90.46,1.0,0.0,0,24786.19,274
+740,46-55,Married,2,0,4,M,-0.04,425,88.39,1.0,-35.62,2,70708.9,800
+741,18-25,Single,1,0,3,F,0.0,406,130.87,1.0,0.0,0,75512.23,577
+742,46-55,Married,2,0,2,F,-0.11,418,81.24,1.02,-75.69,3,54429.55,684
+743,46-55,Married,2,0,2,F,-0.04,523,102.27,1.07,-39.18,2,90104.05,939
+744,70+,Single,1,0,4,F,0.0,599,77.34,1.01,0.0,0,77029.5,1004
+745,36-45,Single,1,0,1,F,-0.07,724,107.42,1.08,-64.12,3,104413.19,1049
+746,26-35,Single,1,0,2,F,0.0,387,111.44,1.0,0.0,0,90151.59,809
+747,26-35,Single,1,0,5,M,0.0,204,123.39,1.04,0.0,0,32575.83,274
+748,46-55,Married,3,1,8,F,-4.25,1351,105.32,1.11,-9978.66,345,247401.42,2604
+749,36-45,Married,4,2,5,F,-0.11,747,111.81,1.03,-126.45,4,131715.45,1216
+750,18-25,Married,2,0,2,M,-0.11,549,96.67,1.0,-142.47,5,121128.2,1255
+751,26-35,Single,1,0,5,F,-0.02,540,137.1,1.0,-10.69,1,91585.37,668
+752,26-35,Married,5,3,1,M,0.0,447,77.0,1.0,0.0,0,44891.67,583
+753,26-35,Married,2,0,5,M,-0.2,504,121.8,1.12,-222.61,10,132393.74,1218
+754,46-55,Single,1,0,2,F,-0.32,539,115.13,1.06,-281.04,5,99705.4,922
+755,36-45,Married,2,0,5,F,-0.74,869,111.18,1.03,-1000.2,12,150320.47,1397
+756,56-70,Married,2,0,7,F,0.0,552,90.39,1.1,0.0,0,77460.73,946
+757,46-55,Married,4,2,5,M,-0.37,526,85.89,1.01,-240.43,10,55745.61,654
+758,70+,Single,1,0,4,F,0.0,363,88.68,1.03,0.0,0,42210.37,489
+759,46-55,Married,2,0,6,M,0.0,141,108.3,1.01,0.0,0,16677.6,155
+760,46-55,Married,5,3,5,F,-0.04,589,127.55,1.03,-29.38,2,91327.67,739
+761,36-45,Single,1,0,4,F,-0.41,404,88.25,1.0,-277.48,1,60187.29,682
+762,46-55,Single,1,0,4,F,-0.09,614,66.13,1.0,-89.05,4,66458.97,1008
+763,26-35,Married,2,0,4,M,-0.31,543,88.74,1.06,-224.4,8,64604.76,775
+764,18-25,Single,2,1,1,F,0.0,263,118.98,1.0,0.0,0,50092.04,421
+765,46-55,Married,2,0,5,F,-0.42,628,98.05,1.0,-501.88,11,116489.21,1190
+766,36-45,Married,2,0,9,M,-2.09,670,114.96,1.04,-2191.29,77,120588.07,1096
+767,26-35,Married,3,1,6,F,-0.37,630,110.97,1.02,-429.27,18,129505.2,1188
+768,36-45,Married,5,3,5,F,-0.51,315,106.85,1.0,-195.91,4,41031.18,384
+769,46-55,Married,2,0,5,F,-0.12,924,96.91,1.0,-192.35,9,149632.39,1544
+770,36-45,Single,1,0,4,M,-1.15,139,84.09,1.0,-246.37,11,17994.69,214
+771,36-45,Single,1,0,2,M,-0.14,342,150.63,1.11,-122.44,5,131200.37,964
+772,26-35,Single,1,0,4,F,-0.3,895,96.4,1.0,-415.69,9,135242.3,1403
+773,36-45,Single,1,0,5,M,-0.17,358,87.14,1.03,-80.14,2,41739.81,491
+774,46-55,Married,4,2,4,M,-1.02,466,93.9,1.06,-675.81,35,62348.58,702
+775,18-25,Married,3,1,2,F,0.0,138,89.25,1.0,0.0,0,14904.12,167
+776,46-55,Single,1,0,2,F,-0.65,751,101.45,1.0,-805.74,26,125901.73,1241
+777,56-70,Married,2,0,2,F,-0.13,226,104.75,1.0,-35.62,2,29645.39,283
+778,46-55,Single,1,0,4,F,-0.03,453,70.9,1.0,-17.81,1,48427.54,683
+779,26-35,Single,3,2,4,F,-0.15,792,89.24,1.0,-177.2,8,107708.81,1207
+780,26-35,Single,1,0,7,M,-0.16,595,112.41,1.02,-139.8,4,96110.04,869
+781,46-55,Single,1,0,7,F,-2.3,963,131.12,1.09,-4447.69,34,253071.12,2102
+782,56-70,Married,2,0,9,M,-0.26,1228,114.54,1.1,-604.91,32,261714.49,2515
+783,46-55,Married,2,0,6,M,-0.27,311,83.01,1.07,-118.7,3,36359.97,470
+784,46-55,Single,2,1,4,M,-0.09,705,120.89,1.0,-92.61,4,118476.56,980
+785,36-45,Married,3,1,5,F,0.0,494,89.77,1.0,0.0,0,93813.25,1045
+786,26-35,Single,2,1,8,F,-0.18,147,111.46,1.0,-35.62,2,22402.46,201
+787,46-55,Married,4,2,1,F,-1.13,669,93.04,1.0,-1312.56,63,107738.87,1158
+788,36-45,Single,1,0,3,M,0.0,313,98.38,1.0,0.0,0,47812.52,486
+789,46-55,Married,2,0,1,M,-0.12,615,122.73,1.04,-147.28,2,156726.52,1324
+790,46-55,Married,2,0,5,F,-0.13,460,98.12,1.03,-90.83,2,67702.06,711
+791,46-55,Single,1,0,6,F,-0.12,535,122.15,1.03,-97.95,3,98330.51,833
+792,36-45,Married,2,0,4,F,-0.31,560,92.03,1.0,-398.23,6,116423.28,1270
+793,46-55,Single,2,1,5,M,-0.45,869,118.12,1.02,-771.88,25,203161.94,1758
+794,46-55,Single,1,0,5,M,0.0,66,125.97,1.02,0.0,0,11085.63,90
+795,46-55,Single,2,1,5,F,-0.22,429,110.61,1.04,-178.1,5,90919.14,852
+796,46-55,Married,3,1,5,M,0.0,199,105.37,1.02,0.0,0,24868.24,241
+797,46-55,Single,1,0,6,M,-0.13,288,110.31,1.01,-82.37,4,69051.57,633
+798,46-55,Single,1,0,8,M,0.0,108,89.43,1.0,0.0,0,20925.79,234
+799,70+,Single,1,0,5,M,0.0,892,112.95,1.0,0.0,0,226583.11,2009
+800,46-55,Married,2,0,3,M,-0.07,504,83.58,1.02,-65.89,3,80736.72,982
+801,26-35,Single,1,0,2,M,-0.24,672,122.33,1.0,-255.04,6,129787.75,1061
+802,46-55,Single,1,0,2,M,-1.78,776,132.78,1.02,-1908.34,26,142212.22,1097
+803,18-25,Married,3,1,4,F,-1.43,266,93.23,1.0,-530.06,17,34493.43,370
+804,26-35,Married,3,1,2,M,0.0,180,108.61,1.0,0.0,0,23568.7,217
+805,46-55,Married,2,0,1,F,-0.03,499,84.52,1.0,-17.81,1,53753.48,636
+806,46-55,Single,1,0,2,M,0.0,312,123.75,1.0,0.0,0,47519.57,384
+807,46-55,Married,2,0,7,M,0.0,472,89.92,1.0,0.0,0,76071.37,846
+808,70+,Married,2,0,4,M,-0.21,1006,106.18,1.0,-402.51,20,203329.53,1918
+809,46-55,Single,1,0,2,F,-0.27,782,107.71,1.01,-321.17,8,127525.97,1193
+810,46-55,Single,1,0,5,M,0.0,730,79.89,1.0,0.0,0,104341.63,1309
+811,56-70,Single,1,0,7,F,-0.04,720,85.68,1.0,-53.42,3,113015.13,1319
+812,46-55,Single,1,0,6,M,0.0,232,75.14,1.0,0.0,0,27125.63,361
+813,26-35,Single,3,2,3,M,-0.54,540,77.04,1.01,-661.93,25,94608.88,1236
+814,26-35,Married,5,3,3,F,-0.49,413,80.34,1.0,-384.69,19,62507.54,778
+815,46-55,Married,2,0,3,F,0.0,318,107.31,1.01,0.0,0,39384.08,371
+816,26-35,Single,1,0,3,M,-0.04,237,68.26,1.0,-17.81,1,33651.44,493
+817,56-70,Married,2,0,4,M,-0.31,428,90.29,1.0,-238.35,10,68620.59,760
+818,36-45,Married,5,3,3,M,0.0,72,109.58,1.0,0.0,0,12820.65,117
+819,46-55,Married,3,1,6,M,0.0,408,55.12,1.0,0.0,0,45748.81,830
+820,36-45,Married,2,0,8,M,-0.48,687,100.49,1.0,-460.9,20,96469.66,960
+821,70+,Married,2,0,3,F,-0.34,219,105.61,1.0,-140.7,6,44355.01,420
+822,36-45,Single,1,0,6,F,0.0,190,103.16,1.03,0.0,0,24654.29,246
+823,36-45,Married,4,2,2,M,-1.98,876,94.13,1.05,-2711.34,109,128678.87,1431
+824,46-55,Married,4,2,5,F,-1.13,224,96.22,1.05,-316.66,7,27037.33,296
+825,36-45,Married,5,3,6,M,-0.44,350,104.03,1.0,-199.12,2,47435.49,456
+826,26-35,Single,1,0,4,M,-0.42,738,117.73,1.09,-402.59,6,111726.39,1032
+827,26-35,Married,2,0,5,F,-0.24,120,95.3,1.0,-39.18,2,15724.61,165
+828,18-25,Single,2,1,1,F,-0.05,651,102.33,1.04,-85.49,4,161985.41,1652
+829,46-55,Single,1,0,1,M,-0.07,522,81.39,1.0,-47.73,1,56239.26,692
+830,46-55,Single,1,0,2,F,0.0,195,97.9,1.05,0.0,0,32501.34,349
+831,70+,Married,2,0,3,M,-1.42,769,110.65,1.04,-1515.45,73,117734.98,1108
+832,36-45,Single,1,0,5,F,0.0,1297,132.23,1.15,0.0,0,248591.42,2155
+833,56-70,Married,2,0,4,F,-0.27,473,109.55,1.0,-191.45,8,77671.08,709
+834,36-45,Single,1,0,3,M,-0.25,617,93.14,1.0,-260.02,5,98547.08,1058
+835,70+,Married,2,0,5,F,-0.22,510,118.42,1.01,-195.9,7,105159.28,899
+836,36-45,Married,4,2,5,M,-1.0,191,104.74,1.0,-213.72,7,22413.41,214
+837,18-25,Married,2,0,4,F,-0.19,242,70.6,1.0,-89.05,1,32476.96,460
+838,46-55,Single,1,0,5,F,0.0,291,118.1,1.0,0.0,0,43341.81,367
+839,46-55,Single,1,0,1,F,-0.14,763,97.32,1.05,-170.08,7,116097.68,1254
+840,70+,Married,2,0,6,M,-0.21,291,112.01,1.04,-79.78,2,43570.55,404
+841,36-45,Married,2,0,6,F,0.0,277,99.12,1.0,0.0,0,42523.41,429
+842,46-55,Married,4,2,8,M,-0.35,848,114.42,1.0,-504.72,13,164764.47,1440
+843,46-55,Single,1,0,4,M,-0.77,593,95.26,1.0,-866.39,16,107260.66,1126
+844,46-55,Married,4,2,5,F,-0.41,1390,97.2,1.05,-997.72,31,233863.71,2529
+845,56-70,Married,2,0,7,M,-4.11,303,132.04,1.0,-1847.19,53,59286.15,449
+846,36-45,Single,2,1,6,F,-0.06,240,115.04,1.0,-17.81,1,34512.94,300
+847,26-35,Single,2,1,4,F,-0.44,273,136.8,1.04,-151.38,4,47058.62,359
+848,26-35,Single,3,2,1,M,-0.35,554,95.97,1.01,-432.37,10,117663.97,1243
+849,46-55,Single,1,0,5,F,0.0,385,74.2,1.0,0.0,0,37990.91,514
+850,46-55,Married,2,0,5,M,-0.38,64,102.92,1.0,-39.18,2,10497.35,102
+851,36-45,Married,2,0,6,M,-0.06,526,101.69,1.1,-55.21,2,98641.03,1064
+852,56-70,Married,2,0,6,F,-0.15,676,84.58,1.0,-174.54,4,97355.72,1155
+853,70+,Single,2,1,2,F,-0.44,352,102.48,1.03,-195.91,6,45708.2,458
+854,46-55,Married,5,3,8,F,-0.03,287,97.98,1.0,-19.59,1,55260.91,564
+855,56-70,Married,2,0,3,F,-2.02,140,114.3,1.06,-688.2,24,38976.03,360
+856,26-35,Single,2,1,2,F,-0.18,375,110.42,1.0,-106.86,3,66580.55,604
+857,70+,Single,1,0,7,M,-2.47,374,115.63,1.0,-1162.1,26,54462.72,471
+858,70+,Single,1,0,7,M,0.0,542,85.84,1.0,0.0,0,84900.08,992
+859,36-45,Married,3,1,6,M,-0.48,1302,105.54,1.08,-970.35,37,212666.08,2185
+860,26-35,Single,1,0,1,M,0.0,238,117.58,1.0,0.0,0,60200.42,512
+861,70+,Married,2,0,3,M,-0.06,502,95.16,1.0,-80.14,2,126367.93,1328
+862,46-55,Married,2,0,6,F,0.0,379,114.35,1.0,0.0,0,63809.23,558
+863,56-70,Married,2,0,4,F,-0.12,656,102.14,1.0,-106.5,1,94068.78,924
+864,56-70,Single,1,0,5,M,-1.96,1067,103.86,1.13,-3471.48,156,184358.5,1999
+865,26-35,Single,1,0,7,F,-0.04,597,94.3,1.01,-71.24,2,156728.99,1674
+866,36-45,Married,4,2,2,M,-0.09,995,93.82,1.1,-190.56,7,209780.88,2457
+867,46-55,Married,2,0,5,M,-0.04,378,125.51,1.0,-25.83,3,83837.97,668
+868,36-45,Single,1,0,5,M,0.0,150,125.05,1.0,0.0,0,29511.05,236
+869,56-70,Married,2,0,4,F,-0.02,589,104.84,1.0,-17.81,1,112181.99,1070
+870,18-25,Married,2,0,4,M,0.0,189,110.21,1.0,0.0,0,40004.58,363
+871,36-45,Married,3,1,7,F,-0.58,759,105.69,1.06,-840.32,33,153353.36,1544
+872,70+,Married,2,0,7,F,-0.18,262,124.73,1.0,-63.22,2,44029.58,353
+873,56-70,Married,4,2,6,M,-0.15,629,95.35,1.0,-123.78,7,78757.29,826
+874,46-55,Married,2,0,5,F,-0.34,954,106.8,1.11,-481.15,31,149627.4,1559
+875,46-55,Married,2,0,2,F,-0.98,351,93.68,1.04,-532.58,20,50682.96,565
+876,36-45,Single,1,0,9,M,-0.28,426,129.11,1.0,-208.37,10,95281.85,738
+877,46-55,Married,2,0,5,M,-0.46,374,113.12,1.0,-322.17,11,79185.16,700
+878,70+,Married,2,0,8,M,-0.72,1058,95.77,1.02,-1202.65,29,160122.06,1699
+879,46-55,Single,1,0,4,F,-0.65,436,99.57,1.0,-732.52,37,111519.6,1123
+880,56-70,Single,1,0,2,F,-0.02,326,93.65,1.02,-8.9,1,52071.05,569
+881,46-55,Single,1,0,5,M,-1.18,293,131.3,1.21,-642.35,15,71429.55,658
+882,26-35,Married,3,1,9,M,-0.36,267,133.39,1.01,-108.64,3,39750.28,300
+883,46-55,Single,1,0,3,F,-0.11,443,124.36,1.06,-56.99,2,65167.05,558
+884,26-35,Single,1,0,3,M,0.0,186,92.88,1.0,0.0,0,28234.27,304
+885,46-55,Single,1,0,3,F,0.0,500,118.05,1.0,0.0,0,101880.84,863
+886,46-55,Married,3,1,5,M,-0.16,339,102.6,1.0,-124.66,6,81154.34,791
+887,46-55,Single,1,0,5,F,-0.1,264,80.6,1.08,-42.21,2,32400.13,436
+888,26-35,Married,3,1,4,F,-0.09,359,128.09,1.01,-42.74,3,60586.7,476
+889,36-45,Single,1,0,4,F,-1.84,891,93.37,1.01,-3207.64,126,162925.68,1764
+890,26-35,Married,4,2,8,F,-0.25,824,112.7,1.0,-253.61,9,114394.96,1020
+891,36-45,Married,2,0,5,M,-0.53,485,96.06,1.11,-538.74,19,96731.56,1115
+892,46-55,Single,1,0,2,F,-0.09,143,84.5,1.01,-26.71,1,24675.29,295
+893,46-55,Married,5,3,8,M,0.0,94,97.72,1.0,0.0,0,10651.21,109
+894,18-25,Single,3,1,5,M,-1.19,546,104.33,1.0,-1161.2,42,101516.53,973
+895,46-55,Married,2,0,12,F,-0.08,312,100.73,1.02,-35.62,1,44322.64,447
+896,56-70,Married,2,0,4,F,-0.59,617,93.43,1.05,-472.49,11,74557.7,841
+897,46-55,Single,1,0,4,F,-4.44,97,94.12,1.0,-639.36,32,13553.21,144
+898,36-45,Married,3,1,4,M,-0.36,1318,107.13,1.11,-1162.08,27,347968.46,3593
+899,46-55,Married,3,1,2,F,0.0,273,114.94,1.0,0.0,0,57700.91,502
+900,56-70,Single,1,0,9,F,-0.02,748,96.29,1.0,-17.81,1,102168.5,1061
+901,26-35,Married,3,1,2,M,-1.79,979,89.81,1.0,-2661.66,107,133364.64,1485
+902,46-55,Single,1,0,1,F,0.0,282,121.21,1.0,0.0,0,41090.53,339
+903,46-55,Married,3,1,3,M,-1.1,295,89.66,1.07,-432.78,16,35145.08,421
+904,36-45,Married,2,0,9,M,0.0,169,126.9,1.01,0.0,0,46573.42,370
+905,36-45,Married,2,0,1,F,-0.37,535,125.81,1.0,-293.86,5,99892.69,794
+906,46-55,Married,3,1,6,F,-2.49,327,103.62,1.01,-1326.95,40,55124.42,539
+907,46-55,Married,5,3,9,F,-1.89,71,127.91,1.0,-151.38,3,10232.46,80
+908,56-70,Married,2,0,3,M,0.0,105,154.14,1.0,0.0,0,31906.65,208
+909,46-55,Married,5,3,8,M,-3.35,685,110.45,1.09,-3107.09,99,102383.61,1013
+910,46-55,Married,3,1,3,F,0.0,260,44.91,1.01,0.0,0,32563.23,730
+911,46-55,Married,3,1,8,M,-0.38,2073,117.1,1.0,-1352.4,49,421577.36,3617
+912,70+,Married,2,0,6,M,-1.11,356,102.56,1.07,-625.89,19,57841.33,604
+913,18-25,Married,2,0,11,F,0.0,347,95.09,1.0,0.0,0,46595.02,490
+914,26-35,Single,1,0,9,M,-0.83,408,93.8,1.04,-438.29,16,49242.72,546
+915,46-55,Single,1,0,3,F,-0.03,505,113.18,1.0,-17.81,1,80017.64,707
+916,56-70,Single,1,0,4,M,-1.05,439,116.73,1.03,-585.04,23,65019.74,574
+917,36-45,Married,2,0,3,F,-0.08,568,84.12,1.08,-71.24,2,79657.15,1026
+918,26-35,Married,2,0,5,M,-0.21,610,95.39,1.01,-216.93,5,97774.06,1032
+919,36-45,Married,4,2,5,F,-4.4,360,108.19,1.14,-2554.65,13,62752.55,661
+920,18-25,Single,1,0,12,F,-0.13,414,105.12,1.01,-71.24,2,58759.78,564
+921,36-45,Married,4,2,5,F,-0.07,211,61.15,1.01,-35.62,1,31918.77,527
+922,70+,Married,2,0,6,F,-0.02,524,113.54,1.04,-17.81,1,101727.53,936
+923,46-55,Single,1,0,4,F,-0.12,255,108.41,1.05,-35.62,1,31981.1,309
+924,36-45,Married,2,0,10,F,-0.02,627,110.45,1.0,-17.81,1,91120.86,825
+925,36-45,Single,1,0,5,F,0.0,276,106.91,1.0,0.0,0,46933.63,439
+926,26-35,Single,1,0,5,M,-0.2,709,110.94,1.06,-403.02,16,220207.61,2099
+927,46-55,Single,2,1,5,M,0.0,560,122.39,1.2,0.0,0,144907.44,1415
+928,46-55,Single,1,0,2,F,-0.4,1041,92.04,1.01,-768.66,22,177089.11,1945
+929,18-25,Single,1,0,4,F,-0.14,460,87.81,1.03,-84.58,4,51366.98,600
+930,18-25,Single,1,0,1,F,-0.06,775,84.57,1.03,-70.52,2,92180.87,1118
+931,18-25,Married,2,0,4,M,-1.26,559,89.63,1.0,-933.83,56,66324.68,740
+932,36-45,Single,1,0,5,F,-0.64,232,154.04,1.0,-195.73,6,47291.58,307
+933,26-35,Married,2,0,5,F,-1.08,477,94.47,1.01,-715.35,17,62445.4,665
+934,46-55,Single,1,0,6,F,-0.13,1368,95.98,1.04,-390.93,13,300022.11,3253
+935,26-35,Single,2,1,1,M,-0.11,547,116.61,1.07,-89.05,1,97018.23,888
+936,70+,Married,2,0,4,M,-0.06,476,111.19,1.0,-60.55,3,114863.9,1033
+937,70+,Married,2,0,2,F,-0.35,223,91.99,1.0,-89.05,1,23641.03,257
+938,56-70,Married,4,2,7,M,-0.42,157,108.0,1.0,-77.47,3,20087.37,186
+939,70+,Single,1,0,3,M,0.0,396,80.29,1.0,0.0,0,53716.44,669
+940,18-25,Married,3,1,4,F,0.0,363,58.63,1.0,0.0,0,35940.71,613
+941,56-70,Married,2,0,2,M,-0.45,224,100.69,1.01,-147.67,6,33127.65,332
+942,70+,Married,3,1,8,M,-0.03,655,109.57,1.13,-35.62,1,132905.05,1367
+943,46-55,Married,2,0,5,M,-0.14,419,107.26,1.0,-81.92,3,61569.54,574
+944,46-55,Single,1,0,3,F,-0.32,424,98.08,1.0,-276.06,10,83862.28,855
+945,70+,Single,1,0,4,F,0.0,263,84.43,1.0,0.0,0,52937.33,627
+946,46-55,Single,1,0,7,F,-1.05,150,118.43,1.12,-225.15,9,25461.89,240
+947,46-55,Married,2,0,5,F,-0.17,478,131.23,1.13,-163.25,3,124147.03,1066
+948,46-55,Married,2,0,5,M,-0.11,962,66.6,1.03,-270.71,7,164044.66,2525
+949,26-35,Single,1,0,4,M,-0.08,455,103.58,1.0,-64.11,2,78517.34,758
+950,36-45,Married,5,3,4,F,-0.28,284,90.85,1.11,-89.05,3,29345.69,359
+951,46-55,Single,1,0,5,F,-0.46,715,115.26,1.0,-455.04,22,113989.02,989
+952,46-55,Married,4,2,5,M,-0.52,519,99.34,1.01,-365.63,7,69540.68,709
+953,46-55,Single,1,0,2,F,0.0,205,114.12,1.0,0.0,0,26817.69,236
+954,26-35,Married,4,2,5,M,-0.21,439,79.89,1.22,-130.62,8,49054.31,751
+955,46-55,Married,2,0,4,F,-2.96,340,105.35,1.07,-1497.65,48,53305.19,540
+956,46-55,Single,1,0,1,F,0.0,484,79.5,1.0,0.0,0,66622.78,838
+957,26-35,Married,2,0,5,M,-0.06,1104,112.72,1.13,-142.48,3,249459.6,2498
+958,36-45,Single,1,0,8,M,-0.1,1146,96.71,1.03,-159.75,3,153960.61,1645
+959,46-55,Single,1,0,5,M,-1.31,851,101.66,1.07,-2296.33,87,177907.36,1864
+960,46-55,Married,2,0,7,M,0.0,107,113.54,1.05,0.0,0,17938.64,166
+961,46-55,Married,5,3,6,F,-0.52,230,119.51,1.08,-188.73,8,43143.99,391
+962,26-35,Married,3,1,6,M,-0.68,585,102.02,1.09,-925.42,42,138740.74,1489
+963,18-25,Single,1,0,4,M,-0.03,808,91.69,1.01,-53.43,2,141024.46,1547
+964,46-55,Single,1,0,5,F,-0.28,785,114.83,1.07,-329.12,10,133207.0,1245
+965,18-25,Married,3,1,4,M,0.0,423,91.54,1.05,0.0,0,50162.64,578
+966,46-55,Married,4,2,2,M,-0.02,535,77.83,1.0,-17.81,1,68027.54,874
+967,36-45,Single,1,0,5,M,-1.45,658,117.49,1.0,-1979.65,81,160022.32,1366
+968,36-45,Married,2,0,9,F,-0.7,265,116.98,1.0,-267.15,3,44685.72,382
+969,18-25,Married,3,1,6,M,0.0,446,96.53,1.03,0.0,0,63321.73,678
+970,46-55,Married,2,0,4,F,-0.29,534,109.5,1.01,-226.18,8,86397.85,796
+971,46-55,Married,5,3,6,M,-0.15,615,71.23,1.0,-160.3,9,75072.18,1057
+972,46-55,Married,2,0,9,M,-0.66,92,65.98,1.28,-159.94,5,16099.53,313
+973,56-70,Married,2,0,4,M,-0.15,579,86.44,1.0,-142.48,6,82288.34,954
+974,46-55,Single,1,0,2,F,-1.11,269,83.45,1.04,-417.34,17,31377.25,391
+975,36-45,Married,3,1,2,F,0.0,162,88.54,1.0,0.0,0,18594.09,210
+976,36-45,Single,1,0,1,M,-0.26,328,94.06,1.0,-110.42,5,40163.5,427
+977,46-55,Married,2,0,4,F,-0.94,585,76.19,1.0,-924.1,47,75273.91,988
+978,26-35,Married,4,2,7,M,-0.19,536,96.26,1.12,-217.88,11,108965.32,1263
+979,46-55,Single,4,3,4,F,-0.05,529,135.55,1.0,-35.62,1,101118.3,746
+980,46-55,Married,3,1,6,M,0.0,436,123.73,1.01,0.0,0,72753.68,595
+981,36-45,Married,2,0,6,M,-0.21,638,132.82,1.06,-212.82,7,134674.89,1071
+982,46-55,Single,1,0,2,M,-0.04,294,66.9,1.0,-19.59,1,35054.54,524
+983,56-70,Single,1,0,4,F,-0.37,513,119.64,1.0,-267.14,7,86617.84,724
+984,56-70,Married,3,1,7,M,-0.02,295,76.3,1.0,-8.9,1,31204.91,409
+985,36-45,Married,2,0,6,M,-0.38,242,110.93,1.04,-106.5,2,31505.18,296
+986,46-55,Married,2,0,4,F,-0.2,277,89.45,1.01,-71.24,2,31396.53,353
+987,26-35,Single,3,2,4,M,-0.11,628,96.59,1.0,-106.86,1,93404.46,971
+988,46-55,Married,2,0,2,M,-0.06,431,75.25,1.01,-37.4,2,50944.65,684
+989,26-35,Married,2,0,2,F,-0.09,1057,112.57,1.08,-187.0,6,227385.4,2190
+990,18-25,Single,2,1,5,F,-0.02,296,49.34,1.0,-14.25,1,28173.59,573
+991,46-55,Married,3,1,1,M,-0.52,131,131.52,1.0,-71.24,1,18018.2,137
+992,36-45,Single,1,0,6,M,0.0,364,98.19,1.01,0.0,0,48703.68,500
+993,46-55,Single,1,0,5,M,-0.03,555,133.25,1.06,-32.94,2,138842.33,1109
+994,46-55,Single,1,0,9,M,-0.09,899,137.28,1.06,-107.75,4,159521.57,1227
+995,36-45,Single,1,0,1,M,0.0,182,105.81,1.0,0.0,0,38619.32,365
+996,26-35,Single,1,0,5,M,-0.74,121,80.94,1.05,-101.52,4,11169.86,145
+997,36-45,Single,1,0,5,M,-0.16,687,104.89,1.0,-178.1,5,115377.83,1102
+998,36-45,Married,2,0,8,M,-0.25,702,115.09,1.0,-323.95,15,146623.77,1274
+999,26-35,Single,1,0,3,F,-1.25,423,95.77,1.05,-828.45,31,63594.41,695
+1000,70+,Single,1,0,2,F,-0.15,179,127.97,1.08,-35.62,2,29946.05,252
+1001,46-55,Married,2,0,4,M,-0.42,320,103.99,1.0,-170.07,9,41805.57,402
+1002,46-55,Married,2,0,4,M,-0.22,351,94.02,1.02,-89.05,1,38924.4,424
+1003,46-55,Single,1,0,5,M,-0.09,545,118.64,1.08,-89.05,2,111399.74,1012
+1004,46-55,Married,2,0,6,F,-0.07,1196,99.66,1.02,-129.42,6,180885.05,1855
+1005,26-35,Married,2,0,4,F,-0.09,913,75.32,1.0,-142.48,2,125099.06,1661
+1006,36-45,Married,2,0,7,F,-0.09,374,94.31,1.0,-35.62,1,39234.86,416
+1007,36-45,Married,3,1,9,F,-0.03,917,108.3,1.0,-39.18,2,140360.3,1296
+1008,36-45,Married,2,0,4,F,0.0,100,77.53,1.0,0.0,0,15428.28,199
+1009,26-35,Single,2,1,4,F,-5.39,255,78.73,1.0,-1520.64,70,22200.82,282
+1010,46-55,Married,2,0,6,F,-1.15,112,119.18,1.0,-418.54,20,43263.49,363
+1011,46-55,Married,5,3,8,F,-0.42,1134,109.11,1.1,-1618.52,70,421369.11,4253
+1012,70+,Married,2,0,3,F,-0.15,617,108.23,1.0,-158.51,4,116780.7,1079
+1013,18-25,Single,1,0,4,F,-0.42,193,105.15,1.0,-92.62,6,22922.56,219
+1014,36-45,Single,1,0,3,M,0.0,512,96.65,1.0,0.0,0,90073.64,934
+1015,36-45,Married,2,0,6,M,-3.2,405,112.93,1.1,-2135.95,102,75437.48,735
+1016,36-45,Married,2,0,8,M,-0.08,412,111.38,1.14,-44.52,2,62486.71,639
+1017,18-25,Single,3,1,5,M,-0.17,1113,87.85,1.05,-307.22,11,160686.26,1915
+1018,46-55,Married,2,0,6,M,-0.2,399,98.9,1.0,-104.18,7,52416.29,530
+1019,36-45,Married,2,0,4,F,-0.02,435,103.3,1.03,-17.81,1,74166.4,739
+1020,46-55,Single,1,0,3,M,0.0,161,99.29,1.0,0.0,0,24127.03,243
+1021,26-35,Single,1,0,4,F,-0.12,209,111.59,1.11,-35.62,1,32250.62,321
+1022,70+,Married,2,0,4,F,-0.55,423,141.87,1.0,-577.75,24,147828.33,1042
+1023,18-25,Single,4,3,1,F,-0.15,1230,75.15,1.0,-398.94,11,205622.55,2738
+1024,26-35,Married,2,0,3,F,0.0,222,111.05,1.07,0.0,0,39977.67,384
+1025,46-55,Married,2,0,5,M,-1.54,1051,98.53,1.02,-2984.9,138,191434.34,1989
+1026,26-35,Married,5,3,4,F,-0.11,614,84.95,1.0,-135.34,7,107629.22,1268
+1027,46-55,Single,2,1,5,M,0.0,407,82.45,1.0,0.0,0,51285.96,622
+1028,56-70,Married,2,0,4,F,-0.12,469,122.65,1.08,-94.4,4,95055.68,838
+1029,26-35,Single,2,1,2,M,-0.29,251,78.12,1.0,-131.2,7,34996.98,449
+1030,36-45,Married,2,0,4,F,0.0,110,187.18,1.0,0.0,0,37997.8,203
+1031,36-45,Single,1,0,2,F,-2.02,552,92.43,1.09,-1632.78,47,74681.98,880
+1032,36-45,Married,3,1,6,F,-0.12,235,116.12,1.0,-35.62,1,35882.3,309
+1033,36-45,Single,1,0,1,F,-0.21,1596,96.43,1.06,-589.15,15,271549.46,2985
+1034,46-55,Married,2,0,4,F,0.0,398,88.02,1.0,0.0,0,48232.84,548
+1035,46-55,Married,2,0,5,M,-0.23,319,98.02,1.01,-89.05,1,37249.03,383
+1036,36-45,Married,2,0,9,M,-0.08,871,115.38,1.0,-124.67,2,179880.33,1559
+1037,46-55,Single,1,0,5,M,0.0,121,68.54,1.39,0.0,0,12405.47,252
+1038,46-55,Single,1,0,4,F,0.0,112,142.98,1.01,0.0,0,27738.26,195
+1039,36-45,Single,1,0,6,M,-0.37,157,104.18,1.0,-67.08,3,18855.91,181
+1040,46-55,Married,2,0,1,M,0.0,316,110.35,1.07,0.0,0,40386.41,390
+1041,70+,Single,1,0,4,F,-0.03,306,92.92,1.0,-12.47,1,34658.19,373
+1042,70+,Married,2,0,6,M,-0.48,297,82.11,1.0,-172.16,3,29641.51,361
+1043,36-45,Married,5,3,8,F,-0.37,718,99.41,1.05,-382.54,10,103285.09,1095
+1044,36-45,Single,1,0,2,F,-0.98,729,103.84,1.01,-1333.7,42,141633.72,1374
+1045,36-45,Married,2,0,6,F,-0.08,591,94.56,1.04,-76.58,3,90398.24,997
+1046,46-55,Married,2,0,6,M,-0.2,1093,123.4,1.0,-473.15,16,298616.63,2420
+1047,46-55,Single,1,0,6,M,-0.06,483,110.15,1.0,-35.62,1,70495.85,642
+1048,26-35,Married,2,0,5,F,0.0,322,104.49,1.04,0.0,0,53289.48,528
+1049,70+,Married,3,1,8,M,-0.25,225,142.24,1.01,-71.24,3,41106.26,292
+1050,26-35,Married,2,0,5,M,-0.19,216,101.08,1.0,-44.52,2,23957.11,237
+1051,46-55,Married,2,0,2,M,-0.32,177,82.89,1.01,-81.93,3,21303.96,260
+1052,26-35,Single,1,0,4,F,-4.62,522,114.88,1.0,-3979.87,98,99027.94,862
+1053,46-55,Single,1,0,5,M,-0.29,208,164.25,1.08,-89.05,1,50918.77,335
+1054,36-45,Single,1,0,5,M,0.0,415,75.74,1.01,0.0,0,39084.33,520
+1055,26-35,Married,3,1,4,M,-0.12,996,99.22,1.02,-215.5,8,171458.68,1754
+1056,36-45,Single,1,0,2,M,-0.86,646,84.66,1.02,-1405.86,46,139018.76,1678
+1057,36-45,Single,1,0,3,M,-0.11,240,86.42,1.03,-35.62,1,27482.68,326
+1058,46-55,Married,3,1,5,F,0.0,428,111.93,1.0,0.0,0,68613.16,613
+1059,46-55,Married,2,0,5,F,-0.1,468,88.01,1.0,-73.02,3,63897.48,726
+1060,36-45,Married,2,0,2,F,0.0,398,72.96,1.0,0.0,0,39690.91,546
+1061,36-45,Single,1,0,3,M,-0.85,818,103.47,1.0,-1231.29,48,150545.63,1455
+1062,46-55,Single,1,0,3,M,-0.11,392,109.91,1.0,-68.12,4,66276.55,603
+1063,26-35,Married,3,1,4,M,-1.22,348,97.93,1.02,-603.38,25,48571.58,508
+1064,36-45,Married,2,0,9,M,-0.26,950,115.0,1.03,-548.84,12,243574.25,2189
+1065,46-55,Single,1,0,5,F,-0.26,398,87.72,1.01,-144.26,5,48332.26,555
+1066,46-55,Married,3,1,1,F,-0.39,142,122.36,1.0,-106.85,3,33650.22,275
+1067,46-55,Single,1,0,5,F,-0.22,533,114.02,1.01,-209.27,11,110597.4,981
+1068,46-55,Married,2,0,2,F,-0.55,935,91.12,1.03,-1079.86,48,177419.11,2015
+1069,46-55,Married,2,0,5,F,0.0,135,202.68,1.01,0.0,0,54318.08,272
+1070,56-70,Single,1,0,6,F,-3.34,594,112.44,1.1,-3646.57,131,122668.62,1202
+1071,36-45,Single,1,0,2,M,-0.14,656,95.51,1.0,-103.3,4,72207.66,757
+1072,70+,Married,2,0,2,M,-0.42,412,79.97,1.01,-218.12,7,41822.23,529
+1073,36-45,Single,1,0,5,M,-0.55,582,92.82,1.0,-422.08,18,70913.74,764
+1074,26-35,Single,2,1,2,M,0.0,171,117.15,1.0,0.0,0,23547.43,201
+1075,36-45,Married,4,2,7,M,-0.05,303,125.75,1.03,-21.38,2,50804.6,418
+1076,18-25,Single,1,0,12,F,0.0,608,98.15,1.03,0.0,0,167927.65,1766
+1077,70+,Married,2,0,4,F,0.0,297,87.81,1.0,0.0,0,49610.63,566
+1078,26-35,Married,2,0,4,F,-0.4,116,91.35,1.0,-89.05,1,20188.28,221
+1079,46-55,Married,2,0,1,F,0.0,317,97.39,1.0,0.0,0,64080.26,658
+1080,70+,Single,1,0,2,F,-0.1,288,126.83,1.01,-35.62,1,45786.64,365
+1081,56-70,Single,1,0,4,M,-0.03,351,88.27,1.09,-17.81,1,47668.48,590
+1082,56-70,Single,1,0,4,M,-0.58,331,115.65,1.15,-301.86,16,60370.67,599
+1083,26-35,Married,4,2,2,M,-0.71,653,85.81,1.0,-720.87,24,87610.29,1021
+1084,26-35,Married,2,0,4,M,-0.02,811,85.12,1.0,-27.61,3,133559.26,1573
+1085,46-55,Single,1,0,4,M,-0.35,450,79.16,1.06,-248.97,8,56912.61,763
+1086,26-35,Married,3,1,4,F,-3.78,506,93.65,1.04,-3061.8,92,75948.49,846
+1087,46-55,Single,1,0,4,F,-0.03,486,111.03,1.06,-17.81,1,72499.53,689
+1088,36-45,Married,5,3,5,F,-0.26,279,102.37,1.0,-89.05,1,35216.02,344
+1089,36-45,Married,5,3,4,M,-0.04,723,81.7,1.0,-47.49,2,89627.15,1097
+1090,70+,Married,2,0,6,F,0.0,204,94.27,1.0,0.0,0,22152.92,235
+1091,46-55,Married,5,3,8,M,-0.42,1376,92.92,1.11,-1086.74,32,239362.16,2854
+1092,46-55,Single,1,0,10,M,-0.08,307,145.77,1.0,-35.62,1,66764.46,458
+1093,46-55,Married,4,2,6,F,-0.55,945,114.63,1.03,-746.53,33,155433.93,1403
+1094,46-55,Single,1,0,4,F,-0.05,529,94.77,1.01,-35.62,1,68612.92,731
+1095,36-45,Single,1,0,2,M,-0.05,1518,87.68,1.0,-108.64,4,206410.02,2359
+1096,46-55,Married,2,0,2,F,0.0,199,87.24,1.01,0.0,0,24514.86,283
+1097,26-35,Married,3,1,7,M,-0.05,564,136.57,1.01,-47.2,3,127285.82,938
+1098,26-35,Married,2,0,4,M,-0.05,329,95.44,1.0,-35.62,1,65946.41,691
+1099,46-55,Single,1,0,5,F,-1.1,324,125.28,1.1,-525.38,23,60009.45,529
+1100,46-55,Single,1,0,5,F,-0.59,645,98.27,1.0,-593.07,23,98069.27,998
+1101,46-55,Single,1,0,6,F,-0.34,492,85.82,1.0,-286.39,7,73206.51,853
+1102,56-70,Married,2,0,5,F,0.0,138,127.76,1.0,0.0,0,20186.87,158
+1103,26-35,Married,2,0,4,F,-0.16,819,81.02,1.14,-293.86,8,149963.89,2113
+1104,70+,Married,2,0,6,F,-0.14,588,87.3,1.01,-118.44,8,74990.16,871
+1105,46-55,Married,2,0,9,F,-0.06,455,119.72,1.07,-35.62,1,69077.26,615
+1106,26-35,Married,2,0,3,M,-0.3,457,93.27,1.03,-213.72,5,66033.63,730
+1107,46-55,Single,1,0,5,F,0.0,272,126.32,1.0,0.0,0,44591.82,353
+1108,26-35,Married,5,3,4,F,0.0,285,84.03,1.0,0.0,0,28569.5,340
+1109,36-45,Married,2,0,6,M,-0.13,702,123.71,1.04,-178.1,5,175664.84,1482
+1110,70+,Married,2,0,6,F,0.0,183,103.03,1.0,0.0,0,36471.21,354
+1111,46-55,Single,2,1,7,F,-1.56,305,101.45,1.02,-745.63,25,48592.78,490
+1112,36-45,Single,3,2,5,M,-0.2,346,85.17,1.05,-133.57,2,56809.35,698
+1113,26-35,Married,2,0,4,F,-0.26,400,80.92,1.0,-142.48,4,44022.1,546
+1114,46-55,Single,1,0,5,M,-0.26,591,95.99,1.0,-225.0,13,83412.64,870
+1115,46-55,Married,2,0,3,F,-1.28,1208,89.86,1.01,-3315.5,98,233018.22,2623
+1116,18-25,Single,1,0,4,F,0.0,314,92.42,1.02,0.0,0,46392.75,511
+1117,70+,Single,1,0,1,F,-0.26,610,111.1,1.0,-241.31,13,102212.17,920
+1118,56-70,Married,2,0,4,M,-0.61,438,92.12,1.0,-346.4,10,52045.72,565
+1119,36-45,Married,2,0,10,F,-0.35,403,112.97,1.26,-161.89,4,52982.39,590
+1120,46-55,Married,2,0,9,M,-0.03,508,111.59,1.0,-26.71,1,90837.21,814
+1121,18-25,Married,3,1,5,M,-0.1,1010,143.09,1.0,-222.26,8,323822.41,2263
+1122,70+,Married,2,0,4,M,-0.36,897,112.94,1.09,-520.5,14,163870.05,1584
+1123,36-45,Married,2,0,8,F,0.0,553,91.61,1.0,0.0,0,80799.12,885
+1124,46-55,Married,2,0,1,F,-0.02,949,102.09,1.01,-35.62,1,152317.03,1505
+1125,26-35,Single,1,0,5,M,-0.24,207,118.81,1.0,-71.24,2,35880.54,302
+1126,26-35,Single,1,0,5,F,-0.37,295,85.71,1.0,-144.26,5,33596.54,392
+1127,36-45,Single,1,0,3,M,-0.03,339,138.17,1.06,-17.81,1,78343.32,600
+1128,46-55,Single,1,0,5,M,-0.28,302,121.49,1.02,-106.86,2,45678.63,385
+1129,70+,Married,2,0,9,F,-0.66,607,127.33,1.0,-476.95,5,91552.56,721
+1130,46-55,Married,2,0,3,F,-0.31,497,93.99,1.04,-198.4,5,59306.21,656
+1131,18-25,Single,5,3,1,M,-1.36,707,90.57,1.05,-1226.73,38,81515.56,943
+1132,46-55,Married,2,0,5,F,-0.13,205,100.57,1.0,-35.62,2,27353.89,272
+1133,46-55,Married,2,0,9,M,0.0,540,96.86,1.01,0.0,0,84171.31,875
+1134,26-35,Married,4,2,6,M,-0.04,525,106.08,1.02,-31.16,2,77334.73,740
+1135,56-70,Married,2,0,4,M,-0.02,801,85.52,1.0,-17.81,1,83554.71,978
+1136,46-55,Married,2,0,6,F,-2.0,957,98.73,1.07,-4036.85,137,199340.79,2151
+1137,36-45,Married,3,1,5,F,-0.17,374,104.19,1.0,-102.4,3,61368.24,589
+1138,36-45,Single,1,0,9,M,0.0,246,124.02,1.06,0.0,0,45516.46,389
+1139,56-70,Married,4,2,7,F,0.0,413,69.74,1.0,0.0,0,39614.35,568
+1140,26-35,Single,1,0,4,F,-0.15,722,116.31,1.0,-182.25,9,144694.52,1246
+1141,70+,Married,2,0,6,M,-0.41,448,120.38,1.07,-386.48,10,113153.54,1003
+1142,56-70,Single,1,0,6,F,-1.93,511,96.01,1.1,-1602.84,71,79689.22,909
+1143,46-55,Married,2,0,6,M,-0.18,250,84.36,1.0,-64.11,3,30114.98,357
+1144,26-35,Married,5,3,3,M,-1.15,432,105.44,1.0,-648.28,26,59466.92,565
+1145,46-55,Married,5,3,8,F,-0.41,560,108.66,1.02,-382.91,10,102362.12,960
+1146,36-45,Married,2,0,5,F,-0.31,942,99.36,1.06,-609.99,15,198426.03,2121
+1147,46-55,Married,3,1,3,F,0.0,357,110.19,1.04,0.0,0,58950.7,555
+1148,18-25,Single,1,0,12,M,-1.17,477,149.31,1.04,-873.74,19,111533.88,780
+1149,46-55,Single,1,0,6,F,-0.3,193,59.73,1.0,-178.1,6,34941.06,585
+1150,36-45,Married,3,1,1,M,-0.23,70,74.49,1.0,-19.59,1,6480.43,87
+1151,46-55,Single,2,1,4,M,-0.2,220,97.43,1.0,-74.8,5,36048.76,370
+1152,46-55,Married,3,1,10,F,-0.17,1195,107.89,1.0,-474.28,13,292823.13,2717
+1153,46-55,Single,1,0,10,M,-0.22,259,119.26,1.01,-89.05,1,48537.32,412
+1154,46-55,Married,3,1,6,F,-0.07,840,121.32,1.02,-129.12,6,229904.66,1936
+1155,46-55,Married,5,3,1,F,-1.16,381,89.57,1.0,-670.18,13,51950.05,580
+1156,70+,Married,2,0,6,F,-0.03,761,102.08,1.11,-30.28,2,106672.49,1160
+1157,18-25,Married,3,1,5,F,-0.21,1402,105.29,1.11,-425.16,15,210580.78,2220
+1158,36-45,Married,3,1,6,F,0.0,788,108.68,1.06,0.0,0,140201.64,1370
+1159,36-45,Married,2,0,6,M,-1.74,824,93.01,1.0,-1928.11,67,103237.7,1112
+1160,36-45,Married,2,0,4,F,-0.08,295,94.07,1.05,-34.35,2,38476.45,430
+1161,26-35,Single,1,0,9,F,-1.39,314,114.38,1.0,-760.47,28,62567.49,547
+1162,70+,Married,2,0,4,M,-0.01,243,121.44,1.0,-4.45,1,36431.43,300
+1163,46-55,Single,2,1,7,F,-0.52,150,99.05,1.04,-92.26,3,17532.22,184
+1164,26-35,Married,4,2,2,F,-0.43,354,89.61,1.0,-191.46,4,39698.58,443
+1165,36-45,Married,5,3,5,M,-0.75,313,106.56,1.07,-335.7,10,47630.73,477
+1166,46-55,Married,5,3,8,F,-0.13,1083,123.6,1.04,-283.47,13,271916.85,2297
+1167,46-55,Single,1,0,5,M,-0.07,585,103.99,1.0,-97.95,4,150676.87,1449
+1168,18-25,Married,2,0,4,M,-1.28,639,91.73,1.01,-1084.98,40,77878.3,858
+1169,46-55,Married,2,0,5,F,-0.45,429,129.8,1.05,-275.75,10,79696.82,646
+1170,36-45,Single,1,0,2,F,-0.12,850,84.01,1.06,-151.38,3,106861.26,1350
+1171,46-55,Married,2,0,3,F,0.0,302,115.15,1.0,0.0,0,42490.9,369
+1172,70+,Married,2,0,4,F,-0.2,203,71.66,1.0,-189.98,8,68002.28,949
+1173,46-55,Single,1,0,7,F,-0.09,203,106.7,1.09,-35.62,1,42467.42,433
+1174,26-35,Married,2,0,6,F,-0.15,509,92.19,1.01,-97.95,5,61767.6,674
+1175,70+,Married,2,0,6,F,-1.45,76,113.72,1.0,-178.1,2,13987.26,123
+1176,70+,Single,1,0,2,F,-1.57,284,110.34,1.02,-572.92,18,40275.69,374
+1177,26-35,Married,3,1,4,M,-0.33,226,118.6,1.0,-89.04,4,31548.39,266
+1178,36-45,Married,3,1,4,M,-0.11,696,90.08,1.02,-128.23,3,107739.85,1222
+1179,36-45,Married,2,0,7,M,-0.4,421,103.79,1.0,-207.46,11,53556.62,516
+1180,70+,Married,2,0,5,M,-2.36,74,204.43,1.0,-195.91,2,16967.64,83
+1181,46-55,Married,2,0,5,F,0.0,528,100.04,1.01,0.0,0,67930.35,686
+1182,26-35,Married,3,1,3,M,-0.68,989,128.39,1.0,-1341.92,59,252546.08,1969
+1183,46-55,Married,3,1,3,M,-0.05,672,127.01,1.0,-53.43,3,131327.46,1034
+1184,46-55,Married,3,1,4,F,-0.21,1874,100.36,1.0,-532.21,16,260236.54,2594
+1185,36-45,Married,2,0,4,F,-0.15,271,121.66,1.01,-47.49,2,38445.76,320
+1186,46-55,Single,1,0,10,M,-0.02,721,119.54,1.04,-26.71,1,144877.87,1261
+1187,26-35,Single,1,0,5,M,-0.63,909,79.15,1.05,-1014.4,35,128373.55,1706
+1188,36-45,Married,2,0,5,M,0.0,198,83.37,1.12,0.0,0,25426.51,342
+1189,46-55,Single,1,0,5,F,-0.36,342,83.96,1.0,-227.08,4,53316.04,635
+1190,56-70,Single,1,0,6,F,0.0,248,133.71,1.0,0.0,0,104295.08,780
+1191,46-55,Married,5,3,4,M,-0.16,491,93.24,1.0,-106.86,2,63215.93,680
+1192,70+,Single,1,0,2,F,-0.2,938,93.91,1.0,-343.72,10,159740.01,1701
+1193,56-70,Married,2,0,4,M,-0.69,418,119.93,1.04,-378.76,19,65964.02,571
+1194,36-45,Married,5,3,4,F,-1.05,526,90.59,1.05,-888.7,17,76910.8,888
+1195,56-70,Married,2,0,5,F,-0.03,221,141.15,1.0,-8.9,1,39099.25,277
+1196,36-45,Married,2,0,4,M,-0.84,150,89.03,1.04,-180.78,8,19051.39,223
+1197,46-55,Single,1,0,4,F,-0.07,358,105.44,1.03,-35.62,1,54724.31,532
+1198,18-25,Single,1,0,4,F,-0.89,494,105.36,1.11,-669.63,27,79443.14,835
+1199,36-45,Single,1,0,9,M,-0.4,850,99.98,1.01,-549.62,8,136968.68,1380
+1200,36-45,Married,5,3,5,F,0.0,452,101.08,1.04,0.0,0,65092.54,671
+1201,18-25,Single,1,0,1,F,0.0,667,101.6,1.0,0.0,0,89406.31,880
+1202,36-45,Single,3,1,6,F,-0.99,338,85.21,1.03,-540.82,24,46526.21,565
+1203,36-45,Single,1,0,3,F,-0.02,522,87.55,1.0,-15.27,1,86232.99,985
+1204,46-55,Single,1,0,5,F,-0.08,457,107.0,1.0,-48.09,2,64305.65,601
+1205,46-55,Single,1,0,5,F,-0.05,284,103.21,1.0,-23.15,2,46959.6,455
+1206,36-45,Married,3,1,12,F,-0.01,262,134.7,1.16,-3.92,1,47413.9,410
+1207,36-45,Single,4,3,4,F,-0.23,350,91.96,1.06,-106.5,1,41748.47,479
+1208,56-70,Married,2,0,9,M,-3.84,493,121.62,1.13,-5535.66,203,175137.19,1626
+1209,36-45,Married,4,2,7,F,-0.39,587,83.51,1.05,-415.56,8,88689.14,1111
+1210,46-55,Married,2,0,5,M,-0.35,848,84.91,1.0,-614.44,23,148174.55,1751
+1211,36-45,Married,2,0,5,F,0.0,353,99.39,1.07,0.0,0,52078.36,563
+1212,26-35,Married,2,0,6,M,-0.16,446,77.67,1.0,-124.67,5,61436.2,791
+1213,36-45,Single,3,1,5,M,0.0,125,69.39,1.01,0.0,0,14641.56,213
+1214,46-55,Single,1,0,6,M,0.0,300,128.87,1.02,0.0,0,44847.28,354
+1215,36-45,Married,3,1,6,F,-0.08,738,102.72,1.02,-99.73,5,125519.31,1250
+1216,26-35,Married,4,2,8,F,-0.69,752,121.92,1.08,-1034.73,38,183970.73,1624
+1217,26-35,Single,1,0,6,F,0.0,331,112.45,1.01,0.0,0,68370.9,613
+1218,36-45,Married,2,0,5,F,0.0,401,138.2,1.0,0.0,0,135159.68,980
+1219,36-45,Single,1,0,5,F,-0.41,269,75.21,1.0,-156.72,7,28431.14,378
+1220,46-55,Single,1,0,1,M,0.0,191,103.95,1.0,0.0,0,25363.51,244
+1221,46-55,Single,1,0,2,F,-0.75,111,87.41,1.0,-89.05,3,10401.32,119
+1222,46-55,Single,1,0,5,M,-0.03,397,91.23,1.0,-14.25,1,49809.5,546
+1223,36-45,Married,2,0,5,M,0.0,169,106.81,1.01,0.0,0,31400.7,297
+1224,26-35,Single,1,0,4,M,-0.45,717,101.41,1.03,-630.15,27,141774.87,1443
+1225,46-55,Married,3,1,6,F,-0.26,301,110.52,1.0,-101.77,4,42883.22,388
+1226,46-55,Single,1,0,8,M,-0.58,652,104.89,1.1,-612.37,23,110553.58,1160
+1227,46-55,Married,2,0,5,M,-0.13,292,89.9,1.0,-47.73,1,33531.16,373
+1228,70+,Married,2,0,3,M,-0.04,459,100.71,1.03,-26.71,3,61229.19,624
+1229,26-35,Single,1,0,2,F,-0.92,411,108.34,1.0,-530.74,19,62297.3,576
+1230,70+,Single,1,0,1,F,-0.46,416,78.75,1.0,-255.76,3,44102.31,560
+1231,26-35,Married,3,1,5,M,-0.89,445,99.86,1.22,-567.41,19,63514.08,774
+1232,70+,Single,1,0,3,M,-0.13,766,87.31,1.0,-144.26,5,100314.44,1151
+1233,46-55,Single,1,0,3,M,0.0,248,78.74,1.0,0.0,0,39054.61,496
+1234,46-55,Single,1,0,1,F,-0.23,275,86.38,1.0,-120.4,2,46038.76,533
+1235,46-55,Married,5,3,1,M,-0.01,201,87.15,1.0,-3.56,1,23095.58,266
+1236,46-55,Single,1,0,5,M,-0.07,590,105.37,1.1,-73.03,5,110318.04,1152
+1237,46-55,Single,1,0,4,M,-0.03,500,79.47,1.0,-17.81,1,53724.66,678
+1238,46-55,Single,1,0,5,F,-0.27,538,117.53,1.05,-311.31,6,137157.67,1225
+1239,36-45,Single,1,0,6,F,-1.61,1114,136.89,1.05,-3748.52,163,318531.62,2432
+1240,26-35,Married,5,3,5,M,-0.2,603,95.5,1.0,-190.38,7,89862.13,941
+1241,46-55,Single,1,0,1,M,-0.12,247,88.38,1.04,-44.52,2,33494.31,395
+1242,46-55,Married,3,1,9,F,-2.7,134,119.71,1.03,-487.99,10,21667.37,187
+1243,56-70,Married,2,0,5,F,-0.11,573,114.71,1.02,-89.05,1,94059.43,838
+1244,46-55,Single,1,0,2,F,-0.03,143,89.13,1.0,-6.24,1,17291.43,194
+1245,26-35,Single,1,0,7,F,0.0,328,70.68,1.07,0.0,0,59299.91,901
+1246,26-35,Married,5,3,5,M,-0.52,791,119.62,1.0,-703.66,26,161487.69,1350
+1247,56-70,Married,4,2,7,F,-0.18,491,77.3,1.0,-115.76,5,49860.08,645
+1248,36-45,Married,4,2,8,F,-1.9,637,122.85,1.09,-1627.7,38,105407.55,937
+1249,70+,Single,1,0,2,F,-0.81,292,85.56,1.03,-580.54,21,61002.33,737
+1250,36-45,Married,5,3,8,F,-1.82,409,99.26,1.11,-1252.31,60,68192.36,765
+1251,70+,Single,1,0,1,M,-0.25,288,80.67,1.04,-89.04,5,28476.1,366
+1252,26-35,Single,3,2,3,M,-0.16,1414,117.78,1.02,-376.96,11,286085.8,2482
+1253,46-55,Married,2,0,11,F,-0.03,318,148.73,1.0,-12.47,1,72431.35,487
+1254,26-35,Single,1,0,5,M,-0.08,499,123.91,1.03,-55.21,2,90327.51,749
+1255,46-55,Married,2,0,6,M,0.0,312,55.61,1.0,0.0,0,59886.93,1077
+1256,36-45,Single,1,0,4,F,-0.08,317,88.82,1.02,-35.62,1,39878.8,460
+1257,46-55,Single,1,0,5,F,-1.76,738,106.12,1.0,-3774.69,172,227305.14,2142
+1258,46-55,Single,1,0,1,M,-0.94,434,98.07,1.02,-697.25,24,72670.09,754
+1259,46-55,Married,3,1,5,F,-0.33,717,115.99,1.08,-407.85,17,145225.36,1358
+1260,46-55,Single,1,0,5,M,-0.79,360,107.7,1.0,-369.56,19,50080.68,465
+1261,46-55,Married,2,0,5,F,-0.53,330,137.48,1.02,-210.16,6,54168.86,402
+1262,46-55,Single,1,0,6,M,0.0,164,90.86,1.0,0.0,0,26168.07,288
+1263,46-55,Married,2,0,3,F,-0.35,896,90.04,1.04,-618.72,18,157745.3,1830
+1264,18-25,Single,1,0,3,M,-0.1,361,83.19,1.0,-39.18,2,34025.15,409
+1265,70+,Single,1,0,7,M,0.0,274,83.32,1.0,0.0,0,31660.61,380
+1266,46-55,Single,1,0,4,M,-0.82,468,127.39,1.0,-846.28,38,131719.27,1034
+1267,46-55,Single,1,0,5,F,-0.13,339,70.24,1.0,-80.14,2,41932.53,597
+1268,46-55,Married,2,0,6,M,-0.03,767,78.55,1.07,-35.62,1,98265.36,1338
+1269,46-55,Single,1,0,5,F,-0.05,408,96.57,1.0,-26.71,1,54272.55,562
+1270,46-55,Single,1,0,2,F,-0.73,1080,97.34,1.0,-1593.57,40,212502.35,2188
+1271,36-45,Married,5,3,7,M,-0.02,823,103.12,1.0,-35.62,1,158909.8,1541
+1272,46-55,Single,1,0,4,M,-0.14,165,113.05,1.0,-35.62,1,28375.75,251
+1273,26-35,Single,2,1,8,F,-1.45,530,125.44,1.05,-1110.38,31,95837.62,801
+1274,46-55,Married,2,0,9,F,-0.04,356,92.0,1.0,-17.81,1,43146.28,469
+1275,36-45,Married,2,0,8,F,-0.15,195,135.37,1.0,-35.62,1,33166.11,245
+1276,36-45,Single,1,0,6,F,-0.7,446,96.63,1.01,-381.38,21,52855.71,551
+1277,70+,Married,2,0,3,M,0.0,104,153.49,1.1,0.0,0,21796.06,156
+1278,46-55,Single,1,0,1,F,-0.24,237,123.37,1.07,-75.16,4,38736.7,337
+1279,70+,Married,2,0,5,M,-0.23,368,71.94,1.08,-117.19,2,37190.74,556
+1280,36-45,Married,5,3,8,F,-0.27,229,78.95,1.0,-83.71,3,24632.85,312
+1281,18-25,Single,1,0,5,M,-0.07,340,119.19,1.01,-28.14,1,50896.07,431
+1282,46-55,Single,2,1,4,F,0.0,259,95.08,1.0,0.0,0,35845.88,377
+1283,36-45,Married,5,3,5,F,-0.1,221,105.83,1.01,-26.71,1,29631.11,284
+1284,36-45,Single,2,1,4,M,-0.08,537,129.76,1.03,-71.24,2,112113.2,887
+1285,56-70,Married,2,0,7,M,-0.87,423,125.88,1.01,-605.61,26,87359.86,701
+1286,36-45,Married,5,3,2,F,-0.04,276,78.93,1.0,-17.81,1,31808.69,405
+1287,36-45,Married,2,0,3,F,0.0,202,99.05,1.0,0.0,0,24068.19,243
+1288,56-70,Married,2,0,4,M,0.0,123,136.85,1.03,0.0,0,18200.7,137
+1289,18-25,Single,1,0,1,M,-0.22,687,92.34,1.07,-195.91,5,81901.65,949
+1290,36-45,Married,5,3,3,M,0.0,587,114.18,1.12,0.0,0,89060.43,873
+1291,46-55,Married,2,0,2,F,-0.1,538,107.27,1.06,-103.3,7,110376.29,1091
+1292,46-55,Married,2,0,2,M,-0.05,415,102.46,1.02,-35.62,2,80632.68,800
+1293,26-35,Single,1,0,6,F,-0.03,242,121.69,1.02,-12.47,1,45024.12,378
+1294,70+,Married,2,0,4,F,-0.55,319,116.22,1.0,-270.36,4,57063.85,491
+1295,36-45,Married,2,0,4,F,-0.44,415,84.74,1.08,-239.18,8,46098.52,589
+1296,26-35,Single,2,1,8,M,-0.13,461,123.04,1.04,-120.66,7,111839.33,943
+1297,46-55,Married,2,0,12,M,0.0,283,101.11,1.0,0.0,0,35691.2,353
+1298,26-35,Married,2,0,3,F,-0.36,786,85.82,1.01,-480.86,22,115422.0,1358
+1299,56-70,Married,2,0,6,M,-0.14,263,94.59,1.0,-55.21,2,37930.26,401
+1300,18-25,Single,1,0,5,M,-0.18,686,117.4,1.01,-160.28,5,106250.8,910
+1301,26-35,Married,3,1,3,M,-0.52,419,105.61,1.0,-302.77,12,61150.7,579
+1302,56-70,Married,2,0,2,F,-0.45,335,123.22,1.01,-206.85,11,56187.45,461
+1303,56-70,Married,2,0,5,M,-0.56,472,85.7,1.01,-365.99,14,56133.77,662
+1304,36-45,Single,1,0,2,F,-0.01,326,100.05,1.0,-5.34,1,38218.66,382
+1305,46-55,Single,1,0,5,F,-0.43,715,95.56,1.02,-469.29,10,103108.51,1099
+1306,36-45,Married,2,0,3,F,-0.03,237,109.74,1.0,-8.9,1,34350.05,313
+1307,36-45,Married,2,0,8,M,-0.3,162,83.67,1.0,-65.89,4,18407.07,221
+1308,26-35,Married,3,1,3,M,-0.25,512,101.44,1.05,-192.35,6,77095.46,798
+1309,26-35,Married,3,1,7,M,-0.41,745,93.47,1.08,-708.84,23,162732.27,1877
+1310,56-70,Married,3,1,4,M,-0.18,244,167.52,1.1,-57.88,3,55113.24,362
+1311,70+,Married,2,0,3,M,0.0,488,115.62,1.0,0.0,0,96770.69,839
+1312,56-70,Single,1,0,3,M,-0.19,485,84.85,1.01,-295.65,11,133388.93,1590
+1313,26-35,Married,2,0,4,F,0.0,137,113.71,1.0,0.0,0,19557.52,172
+1314,46-55,Married,3,1,1,F,-0.27,473,95.34,1.11,-196.8,6,70262.81,820
+1315,26-35,Single,1,0,3,F,-1.13,653,112.75,1.06,-1377.55,58,137671.22,1294
+1316,56-70,Married,2,0,8,F,-0.08,367,121.99,1.0,-35.62,1,53189.52,438
+1317,36-45,Married,2,0,4,M,-0.28,1164,103.77,1.03,-581.72,25,214704.6,2141
+1318,46-55,Married,2,0,3,F,-0.07,419,103.67,1.0,-48.97,2,78060.16,755
+1319,46-55,Single,1,0,7,M,-0.24,872,97.49,1.07,-355.01,11,145949.43,1607
+1320,26-35,Single,1,0,4,F,-0.17,565,81.39,1.0,-173.94,4,85375.69,1049
+1321,36-45,Married,3,1,1,F,-2.55,366,90.34,1.07,-1548.37,58,54835.72,648
+1322,46-55,Married,2,0,5,M,0.0,353,89.57,1.0,0.0,0,40398.29,451
+1323,46-55,Married,2,0,5,M,-0.14,561,66.75,1.02,-128.23,5,63080.75,960
+1324,36-45,Married,5,3,2,M,-0.27,572,120.83,1.16,-272.43,6,123854.01,1193
+1325,26-35,Married,2,0,5,M,-0.13,101,109.25,1.0,-17.22,2,14858.11,136
+1326,70+,Single,1,0,2,M,0.0,52,79.21,1.16,0.0,0,6257.57,92
+1327,70+,Married,2,0,4,M,-0.71,359,91.07,1.01,-401.79,13,51818.83,574
+1328,56-70,Married,2,0,10,M,-0.09,279,115.73,1.0,-39.18,2,50112.88,433
+1329,36-45,Married,5,3,6,F,0.0,376,99.12,1.0,0.0,0,60464.96,610
+1330,46-55,Married,3,1,4,F,-0.16,273,91.13,1.0,-62.33,3,34446.29,379
+1331,36-45,Married,2,0,8,M,-0.05,225,93.16,1.0,-26.71,1,50400.75,541
+1332,36-45,Married,2,0,8,F,-0.44,1254,122.0,1.03,-848.82,9,233147.45,1972
+1333,46-55,Married,2,0,6,F,-8.39,336,125.32,1.05,-3928.63,132,58648.95,491
+1334,46-55,Married,5,3,4,F,-0.61,632,97.05,1.01,-527.88,17,83757.24,873
+1335,46-55,Married,3,1,10,M,-1.35,375,85.13,1.01,-887.18,37,56018.38,663
+1336,36-45,Married,5,3,9,F,-2.31,1089,107.46,1.15,-5023.1,172,233194.4,2503
+1337,36-45,Married,5,3,6,M,-0.14,1587,100.76,1.04,-397.14,15,283041.99,2920
+1338,46-55,Married,2,0,4,F,-1.5,221,75.95,1.0,-494.76,11,24988.93,329
+1339,56-70,Single,2,1,4,M,-1.19,599,111.17,1.09,-939.64,19,88048.23,865
+1340,46-55,Married,2,0,6,F,-0.5,323,118.03,1.0,-341.35,10,80024.57,678
+1341,26-35,Married,4,2,5,F,-1.42,681,75.74,1.14,-2073.35,79,110583.67,1662
+1342,46-55,Single,2,1,5,F,-0.27,282,121.5,1.08,-89.05,2,40216.69,356
+1343,26-35,Single,2,1,4,M,-0.1,810,88.3,1.05,-125.47,5,112057.7,1336
+1344,26-35,Single,1,0,5,F,-2.41,576,122.18,1.01,-1705.08,66,86505.33,714
+1345,46-55,Single,3,1,5,M,-0.13,565,102.09,1.0,-115.76,6,88208.84,864
+1346,56-70,Married,2,0,7,M,0.0,624,128.61,1.04,0.0,0,132473.31,1070
+1347,36-45,Single,1,0,4,M,-0.27,1015,108.19,1.0,-447.92,15,178946.27,1654
+1348,36-45,Married,2,0,4,F,0.0,473,86.99,1.07,0.0,0,55673.44,682
+1349,46-55,Single,1,0,5,F,-1.43,362,102.12,1.0,-607.3,19,43501.18,426
+1350,46-55,Married,2,0,1,M,-0.19,646,101.81,1.02,-203.04,7,107006.82,1076
+1351,46-55,Married,5,3,6,F,-0.17,484,122.96,1.0,-115.4,3,83735.33,681
+1352,46-55,Single,1,0,2,F,-0.01,750,98.79,1.0,-14.25,1,146108.82,1479
+1353,46-55,Married,2,0,6,F,-0.22,252,130.02,1.01,-63.05,1,37314.46,290
+1354,36-45,Single,1,0,4,F,-0.17,182,130.45,1.02,-35.62,1,26741.68,209
+1355,46-55,Married,3,1,2,M,0.0,282,118.25,1.0,0.0,0,44816.27,379
+1356,26-35,Married,3,1,6,M,-0.21,622,85.82,1.01,-222.62,9,91398.14,1080
+1357,56-70,Married,4,2,4,F,-0.43,342,85.68,1.0,-328.58,16,65032.34,761
+1358,26-35,Married,2,0,2,F,-1.2,159,151.16,1.24,-320.58,5,40358.58,331
+1359,46-55,Married,2,0,4,M,-0.66,279,108.01,1.04,-243.28,4,39964.7,386
+1360,46-55,Married,2,0,5,M,-0.33,660,77.2,1.05,-481.77,24,113253.47,1536
+1361,26-35,Single,1,0,5,F,-1.86,348,74.53,1.14,-997.03,43,39949.64,610
+1362,46-55,Married,2,0,5,M,0.0,197,91.43,1.03,0.0,0,28708.83,324
+1363,56-70,Married,2,0,9,M,0.0,86,104.62,1.33,0.0,0,10984.98,140
+1364,46-55,Single,1,0,6,F,-6.64,567,185.74,1.1,-7898.5,98,221033.28,1314
+1365,56-70,Single,1,0,4,F,-0.9,384,84.04,1.0,-452.02,6,42103.36,501
+1366,36-45,Married,5,3,6,M,0.0,362,84.3,1.0,0.0,0,48895.13,580
+1367,36-45,Single,1,0,5,F,-1.05,576,92.34,1.0,-1378.28,43,120686.45,1307
+1368,46-55,Married,2,0,5,M,-0.23,824,94.15,1.01,-376.98,8,151494.81,1622
+1369,36-45,Married,2,0,8,F,-0.93,498,101.67,1.0,-815.32,41,89162.18,881
+1370,26-35,Married,2,0,4,M,-0.14,315,120.16,1.01,-53.43,2,44337.56,372
+1371,26-35,Married,3,1,6,F,-0.31,555,86.85,1.07,-250.76,4,70869.59,871
+1372,46-55,Single,1,0,5,M,-0.09,718,89.7,1.02,-97.95,3,97328.79,1111
+1373,46-55,Married,3,1,4,M,0.0,198,123.46,1.02,0.0,0,27407.47,227
+1374,56-70,Married,3,1,7,F,0.0,143,128.97,1.0,0.0,0,19604.05,152
+1375,46-55,Married,2,0,6,M,-0.06,339,91.34,1.0,-29.68,1,43751.62,479
+1376,70+,Married,2,0,3,M,-0.33,295,104.39,1.16,-134.76,4,42801.17,476
+1377,26-35,Single,1,0,5,M,0.0,699,116.14,1.06,0.0,0,110099.81,1007
+1378,26-35,Married,4,2,8,F,-0.49,915,119.89,1.02,-828.13,26,201536.88,1720
+1379,36-45,Single,1,0,3,M,-0.07,456,125.22,1.0,-54.33,4,97044.98,775
+1380,46-55,Married,2,0,1,F,-0.19,231,153.12,1.0,-65.3,2,51908.7,339
+1381,70+,Single,2,1,4,F,0.0,383,94.63,1.02,0.0,0,54698.66,592
+1382,36-45,Married,4,2,5,M,-0.34,961,92.44,1.12,-535.48,12,146149.06,1764
+1383,26-35,Married,2,0,5,F,-0.05,1438,116.44,1.02,-90.83,2,232754.09,2039
+1384,56-70,Single,1,0,3,M,-0.18,742,105.2,1.05,-234.14,4,135494.46,1356
+1385,36-45,Single,1,0,3,F,-0.73,713,91.61,1.03,-983.1,41,123765.43,1393
+1386,18-25,Married,3,1,5,M,-0.41,590,84.52,1.06,-562.78,18,116552.16,1461
+1387,46-55,Single,1,0,1,F,-0.67,1104,86.49,1.05,-1415.86,41,183797.39,2228
+1388,46-55,Married,2,0,5,F,0.0,474,161.38,1.03,0.0,0,102796.18,657
+1389,36-45,Married,2,0,5,M,-0.18,533,65.12,1.02,-146.92,4,52744.62,824
+1390,26-35,Married,2,0,5,M,-0.13,344,97.93,1.01,-71.24,2,53372.18,553
+1391,26-35,Single,1,0,5,M,0.0,979,80.85,1.0,0.0,0,109149.37,1353
+1392,36-45,Single,3,2,5,M,-0.07,1170,101.12,1.01,-155.83,6,229548.14,2285
+1393,26-35,Married,5,3,4,M,0.0,751,101.24,1.12,0.0,0,106100.2,1172
+1394,46-55,Single,1,0,4,M,0.0,322,96.22,1.0,0.0,0,35600.16,370
+1395,56-70,Single,1,0,3,M,-0.11,258,109.58,1.04,-35.62,1,35394.05,337
+1396,46-55,Married,2,0,2,F,-0.14,659,101.97,1.0,-138.92,5,98299.52,966
+1397,46-55,Single,1,0,5,M,0.0,322,107.1,1.0,0.0,0,51623.2,482
+1398,36-45,Single,1,0,2,M,-0.71,749,102.31,1.04,-669.65,16,97089.33,984
+1399,70+,Single,1,0,4,F,0.0,442,89.73,1.0,0.0,0,61197.01,682
+1400,46-55,Single,1,0,2,F,-1.22,437,110.73,1.01,-757.76,34,68985.68,629
+1401,36-45,Single,1,0,5,M,-0.06,352,89.68,1.0,-35.62,1,56495.34,630
+1402,18-25,Single,1,0,12,F,0.0,586,114.82,1.0,0.0,0,112413.46,979
+1403,70+,Single,1,0,5,F,0.0,471,110.57,1.02,0.0,0,72758.19,671
+1404,36-45,Married,5,3,5,M,-0.44,1024,98.32,1.07,-807.66,29,181195.39,1964
+1405,56-70,Single,1,0,4,M,-0.01,683,123.96,1.06,-12.47,1,122848.21,1047
+1406,46-55,Single,1,0,5,F,0.0,209,100.09,1.0,0.0,0,23220.8,232
+1407,36-45,Married,2,0,11,M,-1.96,613,110.51,1.01,-1544.95,52,87082.01,794
+1408,26-35,Single,1,0,9,F,-0.44,1033,99.63,1.07,-696.37,18,157110.37,1688
+1409,70+,Married,2,0,1,M,-0.08,567,119.41,1.06,-83.71,4,122399.87,1085
+1410,46-55,Married,3,1,10,F,-0.03,422,135.51,1.03,-14.25,1,69923.14,529
+1411,26-35,Single,1,0,9,M,-0.22,495,93.77,1.07,-150.31,4,65356.71,744
+1412,46-55,Single,1,0,5,F,-0.82,502,100.62,1.05,-751.28,35,92669.13,965
+1413,46-55,Married,2,0,5,M,-0.48,363,79.93,1.0,-216.57,3,36368.68,455
+1414,46-55,Single,1,0,2,F,-0.42,270,100.42,1.01,-138.63,8,32938.16,332
+1415,36-45,Single,1,0,2,F,-0.69,579,104.91,1.0,-712.58,13,108899.89,1040
+1416,36-45,Married,3,1,4,M,-0.16,297,86.53,1.01,-56.99,3,31495.19,366
+1417,46-55,Married,2,0,5,F,-0.13,309,111.26,1.0,-71.24,2,61306.78,551
+1418,36-45,Married,3,1,8,M,-1.35,640,80.16,1.01,-1808.05,7,107419.88,1347
+1419,46-55,Married,4,2,4,F,-0.02,487,81.41,1.01,-19.59,1,63903.72,795
+1420,70+,Married,2,0,5,M,-0.09,343,126.36,1.0,-35.62,1,52816.53,418
+1421,36-45,Single,1,0,6,M,0.0,161,103.23,1.0,0.0,0,17239.23,167
+1422,36-45,Single,1,0,6,M,-1.08,269,101.94,1.06,-387.9,6,36697.07,380
+1423,46-55,Married,5,3,9,M,-0.19,288,100.16,1.0,-71.24,1,38461.79,384
+1424,46-55,Married,4,2,5,F,-0.46,1009,89.97,1.05,-748.31,25,146650.32,1719
+1425,18-25,Single,1,0,4,M,0.0,251,104.19,1.05,0.0,0,29588.84,298
+1426,46-55,Single,1,0,5,F,-0.33,219,115.58,1.0,-87.27,6,30167.22,261
+1427,46-55,Single,1,0,3,M,-0.07,576,95.08,1.11,-85.49,4,124173.79,1450
+1428,36-45,Married,2,0,5,F,-0.08,922,107.68,1.08,-174.54,7,233350.82,2345
+1429,26-35,Married,2,0,4,M,-0.22,204,98.14,1.02,-71.24,1,31601.37,328
+1430,70+,Single,1,0,2,F,-0.05,921,85.41,1.0,-71.24,1,120602.43,1414
+1431,46-55,Married,2,0,12,M,-0.1,1351,129.62,1.0,-233.31,9,316005.54,2443
+1432,26-35,Single,1,0,4,F,0.0,960,114.59,1.02,0.0,0,180360.78,1607
+1433,56-70,Married,2,0,4,F,-0.4,264,100.82,1.0,-190.56,10,47686.02,473
+1434,26-35,Married,2,0,4,F,-0.09,898,75.01,1.0,-137.13,4,117017.63,1563
+1435,56-70,Single,2,1,4,F,-0.09,209,102.49,1.0,-26.71,1,31670.25,309
+1436,46-55,Married,2,0,9,M,-0.09,579,131.5,1.0,-65.3,2,98623.31,750
+1437,26-35,Married,5,3,4,M,-1.13,356,89.95,1.0,-528.6,20,41914.54,466
+1438,36-45,Married,5,3,4,F,-0.05,639,80.99,1.0,-62.33,2,95321.49,1177
+1439,46-55,Single,1,0,6,F,0.0,149,149.18,1.02,0.0,0,24017.46,164
+1440,26-35,Married,2,0,5,F,-0.45,158,85.53,1.0,-89.05,2,16764.55,196
+1441,36-45,Married,3,1,2,F,-1.23,960,129.4,1.02,-2311.12,75,243015.89,1914
+1442,36-45,Married,3,1,12,M,-0.07,289,114.11,1.0,-35.62,1,54203.18,475
+1443,56-70,Single,1,0,10,M,-0.6,157,88.03,1.0,-121.11,6,17781.84,202
+1444,46-55,Married,2,0,12,M,-0.48,1680,121.41,1.02,-1367.8,13,342980.16,2870
+1445,26-35,Married,4,2,8,M,0.0,141,123.6,1.04,0.0,0,20146.02,169
+1446,18-25,Single,2,1,5,F,-0.18,351,106.56,1.01,-81.92,4,47526.32,449
+1447,26-35,Married,3,1,1,F,-0.19,343,112.19,1.0,-76.58,3,45660.12,407
+1448,36-45,Single,1,0,5,F,0.0,222,120.6,1.04,0.0,0,29788.93,256
+1449,46-55,Single,4,2,3,M,-0.08,1241,85.22,1.04,-167.41,5,174794.57,2124
+1450,36-45,Single,1,0,4,F,-0.12,1081,97.22,1.01,-226.19,4,181420.41,1880
+1451,36-45,Married,2,0,8,F,-0.06,1385,92.61,1.0,-152.81,3,248194.38,2680
+1452,70+,Married,2,0,5,F,-0.61,361,104.8,1.0,-317.9,18,54704.18,522
+1453,46-55,Married,2,0,5,F,-0.07,610,99.42,1.0,-64.12,4,94344.86,952
+1454,46-55,Married,3,1,3,F,-0.22,503,111.55,1.03,-194.13,6,98829.5,912
+1455,36-45,Married,3,1,4,F,-0.23,358,82.35,1.01,-115.76,4,41502.75,508
+1456,46-55,Single,1,0,4,F,-0.17,244,151.57,1.01,-53.43,2,46834.38,312
+1457,46-55,Single,1,0,5,M,-0.87,694,115.24,1.01,-1069.44,46,141517.23,1237
+1458,46-55,Married,3,1,6,F,-0.36,628,94.98,1.04,-424.05,12,111980.79,1230
+1459,46-55,Single,2,1,5,F,-0.1,288,79.28,1.0,-40.25,2,32742.12,413
+1460,46-55,Married,2,0,12,F,-0.11,1034,108.94,1.0,-224.05,6,215258.23,1976
+1461,46-55,Married,2,0,8,M,-0.41,486,87.19,1.0,-424.75,17,90682.28,1043
+1462,56-70,Married,4,2,6,F,0.0,146,124.91,1.01,0.0,0,20235.99,164
+1463,36-45,Married,5,3,5,F,-0.53,1356,94.08,1.02,-1476.08,58,263427.89,2845
+1464,18-25,Single,1,0,1,F,-0.03,1630,99.32,1.02,-71.24,2,260113.05,2667
+1465,56-70,Married,2,0,3,M,0.0,233,83.79,1.0,0.0,0,27649.63,330
+1466,46-55,Single,1,0,10,F,-0.11,2088,130.45,1.07,-393.89,16,449140.38,3672
+1467,36-45,Married,2,0,5,F,-2.77,361,108.22,1.01,-1309.07,53,51190.17,476
+1468,46-55,Single,1,0,3,F,-1.15,403,70.92,1.02,-565.64,5,34822.13,499
+1469,46-55,Married,2,0,12,M,-0.62,1037,145.12,1.01,-1114.18,8,260483.14,1811
+1470,36-45,Single,3,1,5,F,-0.28,572,100.61,1.02,-218.17,6,78171.13,790
+1471,46-55,Single,1,0,5,M,-3.15,679,118.77,1.12,-2758.89,95,104158.82,980
+1472,36-45,Married,5,3,4,M,-1.14,797,83.67,1.07,-1531.84,64,112111.94,1429
+1473,36-45,Single,1,0,5,F,-0.03,1198,111.55,1.0,-62.33,2,257009.56,2304
+1474,46-55,Married,2,0,1,F,-0.23,409,79.45,1.0,-133.57,2,46477.43,585
+1475,70+,Single,3,2,1,F,-0.06,1229,68.56,1.0,-253.49,9,270468.17,3945
+1476,36-45,Single,1,0,5,M,-0.7,364,100.93,1.03,-302.4,7,43700.98,445
+1477,46-55,Single,1,0,5,F,-0.06,926,79.14,1.0,-113.27,7,150911.69,1911
+1478,56-70,Single,1,0,7,M,-1.36,291,115.73,1.38,-731.7,41,62376.54,744
+1479,18-25,Single,1,0,5,F,-0.42,666,95.17,1.07,-404.29,15,90792.79,1022
+1480,18-25,Single,1,0,5,F,-0.78,419,102.65,1.01,-415.69,8,54607.83,539
+1481,56-70,Married,5,3,4,M,-0.09,644,102.68,1.01,-75.99,3,87794.71,862
+1482,36-45,Married,2,0,6,M,-0.15,133,102.24,1.0,-35.62,1,24945.94,244
+1483,46-55,Married,2,0,4,F,-0.34,274,124.62,1.05,-115.76,5,42370.44,358
+1484,56-70,Single,1,0,10,M,-0.18,296,97.28,1.03,-67.68,4,36384.21,384
+1485,46-55,Married,2,0,6,F,-0.34,1200,105.36,1.07,-986.91,28,307332.44,3116
+1486,18-25,Married,2,0,4,M,0.0,434,105.77,1.08,0.0,0,84090.34,859
+1487,36-45,Married,4,2,5,M,-0.2,503,84.77,1.0,-154.23,4,65527.38,773
+1488,46-55,Single,1,0,5,M,-0.13,156,118.4,1.0,-35.62,1,31257.55,264
+1489,46-55,Married,2,0,3,M,-0.4,327,85.42,1.06,-225.23,10,48004.42,596
+1490,26-35,Single,1,0,5,F,-0.2,903,91.93,1.13,-320.58,4,145707.14,1796
+1491,26-35,Married,2,0,4,F,0.0,355,115.91,1.0,0.0,0,104902.36,905
+1492,46-55,Married,2,0,3,M,-0.04,925,99.26,1.0,-71.24,2,200311.72,2018
+1493,46-55,Single,1,0,6,F,-0.78,803,93.29,1.06,-991.31,26,118391.35,1350
+1494,46-55,Married,2,0,1,F,-0.06,789,87.13,1.0,-74.8,4,111266.07,1281
+1495,46-55,Single,1,0,3,F,-0.16,316,105.72,1.12,-71.24,2,45778.06,484
+1496,26-35,Married,2,0,1,F,-3.27,601,87.2,1.06,-3249.86,152,86674.49,1055
+1497,70+,Married,2,0,2,M,0.0,261,110.29,1.0,0.0,0,43013.92,390
+1498,46-55,Married,5,3,6,F,-0.25,509,98.67,1.01,-162.07,4,64233.03,657
+1499,26-35,Single,3,2,1,M,-0.21,648,82.0,1.02,-193.77,7,77159.92,960
+1500,46-55,Married,2,0,9,F,-1.06,689,123.26,1.01,-954.01,41,110687.12,905
+1501,46-55,Single,1,0,5,F,-2.12,273,108.03,1.0,-771.16,31,39215.1,363
+1502,46-55,Single,1,0,4,M,-0.57,408,106.27,1.04,-284.6,5,52604.13,516
+1503,36-45,Married,4,2,8,F,0.0,210,67.0,1.02,0.0,0,21505.56,326
+1504,26-35,Married,5,3,5,F,-0.29,756,113.25,1.01,-381.85,8,147454.23,1310
+1505,26-35,Married,2,0,5,M,0.0,173,116.44,1.06,0.0,0,23753.24,216
+1506,46-55,Single,1,0,5,M,-1.56,495,68.4,1.1,-1296.88,73,56769.14,912
+1507,46-55,Single,1,0,5,M,-0.44,313,67.08,1.03,-276.05,12,41993.96,642
+1508,70+,Single,1,0,3,M,0.0,163,91.78,1.0,0.0,0,19457.7,212
+1509,70+,Married,2,0,4,M,-0.01,706,96.43,1.06,-8.9,1,121690.23,1336
+1510,46-55,Single,1,0,5,M,-0.22,155,133.87,1.01,-35.62,1,21418.89,161
+1511,70+,Married,2,0,8,F,0.0,284,99.76,1.0,0.0,0,31822.74,319
+1512,26-35,Married,2,0,5,F,-0.06,200,103.86,1.0,-13.36,1,23471.27,226
+1513,36-45,Married,2,0,8,M,-2.67,75,95.48,1.1,-320.58,4,11457.73,132
+1514,36-45,Single,1,0,6,F,-0.2,351,123.51,1.0,-103.3,4,63976.53,518
+1515,46-55,Single,1,0,4,F,0.0,101,104.31,1.61,0.0,0,15750.84,243
+1516,46-55,Single,1,0,2,M,-0.08,704,82.06,1.0,-100.91,4,107176.67,1306
+1517,26-35,Married,2,0,3,F,-0.49,290,103.27,1.0,-224.41,12,47506.03,460
+1518,36-45,Married,3,1,3,M,-0.88,1194,91.06,1.07,-1949.98,86,202604.51,2383
+1519,26-35,Single,1,0,2,F,-0.06,364,99.37,1.05,-35.62,1,58630.03,621
+1520,26-35,Married,3,1,8,M,-0.22,463,127.9,1.06,-137.14,5,79044.45,657
+1521,46-55,Married,2,0,7,M,-0.65,576,88.1,1.06,-676.42,20,92059.7,1110
+1522,56-70,Married,2,0,6,F,-0.48,358,121.59,1.0,-237.76,6,60431.66,497
+1523,70+,Married,2,0,1,M,0.0,356,119.45,1.0,0.0,0,55185.01,462
+1524,36-45,Married,2,0,5,F,-0.19,1202,106.7,1.0,-491.2,16,273251.48,2561
+1525,36-45,Single,1,0,2,F,0.0,219,88.42,1.0,0.0,0,41113.19,465
+1526,36-45,Single,1,0,3,M,0.0,450,130.61,1.01,0.0,0,86853.72,674
+1527,46-55,Married,2,0,10,F,0.0,188,98.48,1.02,0.0,0,25112.3,259
+1528,46-55,Married,3,1,2,F,0.0,454,86.42,1.01,0.0,0,77345.55,906
+1529,46-55,Married,5,3,9,M,-1.27,1068,101.82,1.0,-2139.42,55,172067.37,1690
+1530,70+,Married,2,0,6,F,-0.63,456,86.59,1.01,-499.84,14,68230.22,792
+1531,18-25,Single,5,3,1,M,0.0,625,103.67,1.0,0.0,0,98282.8,948
+1532,26-35,Single,1,0,9,F,-0.58,522,126.21,1.13,-404.28,12,88092.71,789
+1533,18-25,Single,1,0,1,M,-0.34,198,89.2,1.09,-148.54,2,38625.41,473
+1534,36-45,Married,3,1,5,M,-0.61,848,96.3,1.06,-1053.84,41,165355.5,1825
+1535,46-55,Married,2,0,5,F,-0.31,238,95.25,1.0,-80.14,2,24955.02,262
+1536,46-55,Married,2,0,3,M,-0.29,376,89.87,1.02,-144.26,4,45202.1,513
+1537,18-25,Single,1,0,4,M,-0.23,926,84.92,1.02,-354.06,10,130689.9,1567
+1538,36-45,Married,5,3,8,F,-0.14,590,119.76,1.0,-109.34,6,95809.9,800
+1539,56-70,Married,5,3,4,M,0.0,309,108.98,1.13,0.0,0,43590.12,451
+1540,70+,Single,1,0,5,F,0.0,112,86.35,1.0,0.0,0,12693.02,147
+1541,70+,Married,3,1,8,M,-0.46,234,96.59,1.0,-142.48,1,30039.05,311
+1542,26-35,Married,3,1,5,F,-4.25,200,97.8,1.0,-1029.21,45,23667.42,242
+1543,36-45,Married,5,3,5,F,-0.42,347,101.03,1.13,-273.92,10,65973.28,740
+1544,46-55,Single,1,0,4,F,-0.55,1143,86.37,1.02,-893.43,27,140343.78,1657
+1545,36-45,Married,2,0,8,F,-0.47,649,102.52,1.17,-505.51,28,110828.02,1262
+1546,36-45,Married,2,0,3,M,0.0,628,152.88,1.0,0.0,0,183920.5,1203
+1547,46-55,Married,2,0,5,F,-3.57,953,127.41,1.0,-6733.7,227,240038.7,1886
+1548,56-70,Married,2,0,4,F,-2.92,460,90.45,1.08,-1711.71,61,53006.19,633
+1549,70+,Single,1,0,1,F,-2.87,856,110.79,1.01,-4354.98,197,168074.68,1533
+1550,46-55,Married,2,0,9,F,0.0,433,92.16,1.0,0.0,0,59353.22,645
+1551,46-55,Single,1,0,5,M,-0.39,515,128.05,1.0,-258.23,10,85792.63,670
+1552,46-55,Single,2,1,3,M,-0.27,460,136.95,1.0,-174.52,8,89700.97,655
+1553,46-55,Married,2,0,5,F,0.0,254,79.75,1.0,0.0,0,29665.85,372
+1554,46-55,Married,2,0,7,F,-1.93,320,92.36,1.01,-797.9,33,38144.06,416
+1555,26-35,Single,1,0,7,M,-0.57,2432,94.82,1.02,-2594.37,75,428777.76,4628
+1556,36-45,Married,3,1,6,M,-1.07,855,110.03,1.0,-1624.9,77,167244.01,1523
+1557,46-55,Married,2,0,12,M,0.0,326,124.3,1.0,0.0,0,53447.04,430
+1558,36-45,Married,3,1,6,M,-1.67,1407,95.39,1.0,-4548.92,164,260142.02,2737
+1559,46-55,Married,3,1,3,F,-0.47,494,97.9,1.0,-354.39,17,73229.48,748
+1560,36-45,Married,2,0,6,F,-4.39,137,96.55,1.02,-935.54,33,20565.24,218
+1561,46-55,Single,1,0,5,M,0.0,166,111.86,1.0,0.0,0,19799.92,177
+1562,36-45,Single,2,1,4,F,-2.61,110,105.87,1.0,-468.99,17,19056.39,180
+1563,46-55,Single,1,0,5,F,-1.24,507,125.02,1.0,-1073.06,18,108267.7,870
+1564,36-45,Single,1,0,6,M,-0.23,163,112.32,1.1,-44.52,1,21565.86,212
+1565,26-35,Single,1,0,4,M,-0.06,448,86.66,1.0,-51.05,3,78429.31,905
+1566,26-35,Married,2,0,9,M,-0.45,1214,96.35,1.03,-1187.02,31,256394.87,2729
+1567,46-55,Single,1,0,5,M,-0.68,581,96.52,1.09,-551.78,30,78661.13,892
+1568,36-45,Married,2,0,8,M,-0.16,487,87.11,1.0,-106.86,2,59062.29,678
+1569,46-55,Single,1,0,6,M,-0.07,57,58.66,1.0,-8.9,1,7391.78,126
+1570,36-45,Married,3,1,4,M,-0.06,795,106.79,1.06,-71.24,2,126762.78,1256
+1571,36-45,Single,1,0,5,M,0.0,341,95.79,1.02,0.0,0,38700.09,413
+1572,36-45,Single,1,0,3,F,-0.16,648,76.1,1.01,-135.36,4,64075.33,851
+1573,46-55,Single,1,0,4,F,-0.63,647,108.02,1.04,-745.27,26,127568.1,1230
+1574,36-45,Married,2,0,5,F,-4.29,1689,99.46,1.02,-10415.74,430,241697.0,2469
+1575,46-55,Married,2,0,1,F,-0.11,315,84.11,1.01,-53.43,2,41636.19,502
+1576,26-35,Married,5,3,1,F,0.0,397,96.39,1.03,0.0,0,66217.37,710
+1577,36-45,Married,2,0,5,M,-0.28,252,123.52,1.03,-80.14,2,35080.7,293
+1578,46-55,Married,3,1,6,F,-0.78,481,92.87,1.03,-613.86,29,72719.29,810
+1579,46-55,Single,1,0,4,M,-0.13,639,114.29,1.0,-145.45,6,132804.28,1162
+1580,26-35,Married,2,0,5,F,0.0,422,112.76,1.01,0.0,0,59651.72,534
+1581,26-35,Married,3,1,1,F,0.0,390,89.82,1.07,0.0,0,45356.97,541
+1582,46-55,Single,3,2,5,M,-2.43,681,112.42,1.05,-2064.13,61,95556.72,895
diff --git a/customersim/tools/fake_customers.py b/customersim/tools/fake_customers.py
new file mode 100644
index 0000000..26d859e
--- /dev/null
+++ b/customersim/tools/fake_customers.py
@@ -0,0 +1,27 @@
+import csv
+from faker import Faker
+
+
+fake = Faker()
+fake_data_file = 'customer_info.csv'
+output_file = 'customers.csv'
+
+customers = []
+try:
+    with open(fake_data_file, 'rt') as csvfile:
+        fake_reader = csv.DictReader(csvfile, delimiter=',')
+        # customer_list = [row for row in fake_reader]
+        for row in fake_reader:
+            row['name'] = fake.name_male() if row['gender'] == 'M' else fake.name_female()
+            customers.append(row)
+except IOError as e:
+    print("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n")
+    exit(0)
+
+
+with open(output_file, 'w', newline='') as csvfile:
+    fieldnames = 'customer_id,name,age_range,marital_status,family_size,no_of_children,income_bracket,gender,mean_discount_used_by_cust,unique_items_bought_by_cust,mean_selling_price_paid_by_cust,mean_quantity_bought_by_cust,total_discount_used_by_cust,total_coupons_used_by_cust,total_price_paid_by_cust,total_quantity_bought_by_cust'.split(sep=',')
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+    writer.writeheader()
+    writer.writerows(customers)
\ No newline at end of file
diff --git a/customersim/wsgi.py b/customersim/wsgi.py
deleted file mode 100644
index 692a85f..0000000
--- a/customersim/wsgi.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from flask import Flask
-import random, time, datetime, uuid, json, configparser, os
-import paho.mqtt.client as mqtt
-import csv
-application = Flask(__name__)
-
-@application.route("/")
-
-
-# This script generates the following MQTT messages
-#
-# 1. customer/enter
-#
-#    { 
-#      id: --ID representing customer--,
-#      ts: --timestamp of the entrance, in seconds since epoch--
-#    }
-#
-# 2. customer/move
-#   
-#    { 
-#      id: --ID representing customer--,
-#      ts: --timestamp of the move, in seconds since epoch--,
-#       x: --x coordinate of location sensor that fired--,
-#       y: --y coordinate of location sensor that fired--
-#    }
-#
-# 2. customer/exit
-#
-#    { 
-#      id: --ID representing customer--,
-#      ts: --timestamp of the exit, in seconds since epoch--
-#    }
-#
-
-# Represents a "square" in the store at a particular location, and contain all valid moves from that location
-class Location:
-   def __init__(self,x,y,width, height):
-      self.x = x
-      self.y = y
-      # a list of all valid moves. Each move is a tuple of the form ("adjacent x location", "adjacent y loaction", "is this closer to the exit?")
-      self.validMoves = [(a,b, True if a <= self.x and b <= self.y else False) for a in range(max(self.x - 1, 0), min(self.x + 2,width)) for b in range(max(self.y -1, 0), min(self.y + 2, height)) if not(a == self.x and b == self.y)]
-
-class Store:
-   def __init__(self, width, height):
-      self.height = height
-      self.width = width
-      self.locations = [[Location(x,y, width, height) for y in range(0,height)] for x in range(0, width)]
-
-class Customer:
-   def __init__(self, store, id, name):
-      self.store = store
-      self.currentLocation = store.locations[0][0]  # customers enter and exit from the bottom left corner of the store
-
-      self.meanDwellTime = random.uniform(1, 20)    # the *average* amount of time this customer will spend on a square
-      self.consistancy = random.uniform(1,5)        # how consistantly the customer spends that time. Higher means more inconsistant
-      self.nextMoveTime = self.getNextMoveTime()   
-      self.isExiting = False
-      self.exitTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600))  # the time this customer will start to exit
-      self.id = id
-      self.name = name
-
-   def getNextMoveTime(self):
-      # amount of time spent at a location is a random value picked from a guassian distribution, with a mean equal to the customer's 
-      # average dwell time and a standard devivation equal to the customer's consistancy
-      return (datetime.datetime.now() + datetime.timedelta(0, random.gauss(self.meanDwellTime, self.consistancy)))
-
-   def move(self):
-      # if the customer is exiting, only move to an adjacent location that is towards the exit. If they are already at the door, don't move
-      if self.isExiting:
-         if self.currentLocation.x == 0 and self.currentLocation.y == 0:
-            (newX, newY) = (0,0)
-         else:
-            (newX, newY, isTowardsExit) = random.choice([(x,y,e) for (x,y,e) in self.currentLocation.validMoves if e is True])
-      else:
-      # if the customer is not exiting, pick any adjacent location
-         (newX, newY, isTowardsExit) = random.choice(self.currentLocation.validMoves)
-      
-      self.currentLocation = self.store.locations[newX][newY]
-
-   def tick(self):
-      if self.isExiting == False and self.exitTime < datetime.datetime.now():
-         self.isExiting = True
-      
-      if self.nextMoveTime < datetime.datetime.now():
-         self.nextMoveTime = self.getNextMoveTime()
-         self.move()
-         return True
-
-      return False
-
-def main():
-  
-   # configuration information
-   config = configparser.ConfigParser()
-   config.read('config.cfg')
-   mqttHost = config.get('MQTT', 'host')
-   mqttPort = config.get('MQTT', 'port')
-   mqttClientName = config.get('MQTT', 'name')
-   width = config.getint('Store', 'width')
-   height = config.getint('Store', 'height')
-   averageCustomersInStore = config.getint('Customers', 'averageCustomersInStore')
-   fakeDataFile = config.get('Customers', 'list')
-
-   mqttc = mqtt.Client(mqttClientName)
-   mqttc.connect(mqttHost, mqttPort)
-  
-   # load customer list
-   try:
-      with open(fakeDataFile, 'rb') as csvfile:
-         fakereader = csv.reader(csvfile, delimiter='|')
-         customerList = [row for row in fakereader]
-   except IOError as e:
-      print("Whoops....can't find the fake data file.\nTry generating the fake data file and try again\n")
-      exit(0)
-
-   myStore = Store(width, height)
-   customerQueue = []   # List of customers in the store
-   nextCustomerEntranceTime = datetime.datetime.now()
-
-   def manageCustomerMovements(c):
-      if c.tick():
-         if c.isExiting and c.currentLocation.x == 0 and c.currentLocation.y == 0:
-            # remove the customer and signal the exit
-            msg = {'id': str(c.id), 'ts': int(datetime.datetime.now().strftime("%s"))}
-            mqttc.publish("customer/exit", json.dumps(msg))
-            print('%s is Exiting.') % (c.name)
-            customerQueue.remove(c)
-         else:
-            # signal move
-            msg = {'id': str(c.id), 'ts': int(datetime.datetime.now().strftime("%s")), 'x': c.currentLocation.x, 'y': c.currentLocation.y}
-            mqttc.publish("customer/move", json.dumps(msg))
-
-   # Check if any customers are due to entier, move, or exit. Sleep for one second, then repeat
-   while True:
-      if nextCustomerEntranceTime < datetime.datetime.now():
-         # add the new customer, and signal the entrance
-         newCustomerPrototype = random.choice(customerList)
-         newCustomer = Customer(myStore, newCustomerPrototype[0], newCustomerPrototype[1])
-         customerQueue.append(newCustomer)
-
-         msg = {'id': str(newCustomer.id), 'ts': int(datetime.datetime.now().strftime("%s"))}
-         mqttc.publish("customer/enter", json.dumps(msg))
-
-         nextCustomerEntranceTime = datetime.datetime.now() + datetime.timedelta(0, random.uniform(1, 600 / averageCustomersInStore))
-         print('%s is Entering. MDT: %0.1f, C: %0.1f, E: %s') % (newCustomer.name, newCustomer.meanDwellTime, newCustomer.consistancy, newCustomer.exitTime)
-         print('Next Customer Entering at %s.') % (nextCustomerEntranceTime)
-
-      [manageCustomerMovements(c) for c in customerQueue]  
-      time.sleep(1)
-
-if __name__=='__main__':
-   main()
-
diff --git a/scenario-player/.environment.variables.sh b/scenario-player/.environment.variables.sh
new file mode 100644
index 0000000..1f3a3eb
--- /dev/null
+++ b/scenario-player/.environment.variables.sh
@@ -0,0 +1,8 @@
+export STORE_HEIGHT=10
+export STORE_WIDTH=6
+export CUSTOMERS_AVERAGE_IN_STORE=6
+export CUSTOMERS_LIST_FILE='customers.csv'
+export MQTT_HOST='127.0.0.1'
+export MQTT_NAME=test1
+
+export LOG_LEVEL=INFO
\ No newline at end of file
diff --git a/scenario-player/README.md b/scenario-player/README.md
new file mode 100644
index 0000000..baff830
--- /dev/null
+++ b/scenario-player/README.md
@@ -0,0 +1,154 @@
+# Project description
+
+This project was a part of broader demo. That broader demo analyzed customers movement in a retail store, determined
+their behaviour (for example: "customer stopped in men's clothes department") and use Machine Learning to model for
+purchase/product recommendation. The customer location was determined by movement sensors placed in the store.
+
+This service customer behaviour in a retail shop:
+
+* customer entering the store
+* customer movement
+* customer exiting the store
+
+by generating proper MQTT messages.
+
+# Usage
+
+This is a web service (implemented with FastAPI). By default, it works on port 8000.
+(See [instructions](./development.md#running-the-service) for details on configuring and running the service.)
+
+When starting, it does the following:
+
+* connects to MQTT server
+* waits for and registers new user movement scenarios (HTTP POST to `/scenario` endpoint)
+* creates a background task (ran every second) that checks if there is something to be sent (if it is time for giving an
+  update on particular customer)
+
+### Main simulator loop
+The main loop executes every second and tries to locate events (in the timeline) that should be 
+"replayed". If there are any, proper messages are constructed and published (via. `Publisher` object).
+
+> Please, note, that in the current implementation, the main simulator loop replays
+> events from the timeline for **current timestamp** (current date/time).
+
+### Scenario definitions
+
+Scenario is a list of locations for given customer in certain moments: 
+```
+{
+  "customer": {
+    "customer_id": "3"
+  },
+  "path": [
+    {
+      "type": "ENTER",
+      "location": {"x": 200, "y": 10},
+      "timestamp": "2021-04-11T10:01:53.955760"
+    },
+    {
+      "type": "MOVE",
+      "location": {"x": 320, "y": 150},
+      "timestamp": "2021-04-11T10:13:49.614897"
+    },
+    ...
+    {
+      "type": "EXIT",
+      "location": {"x": 200, "y": 10},
+      "timestamp": "2021-04-11T12:31:17.437862"
+    }
+  ]
+}
+```
+
+The service can register a scenario:
+```shell
+curl -X 'POST' \
+  'http://localhost:8000/scenario' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{"customer":{"customer_id":"1"},"path":[{"type":"ENTER","location":{"x":935,"y":50},"timestamp":1618323771180},{"type":"MOVE","location":{"x":588.9128630705394,"y":454.08039288409145},"timestamp":1618323772180},{"type":"EXIT","location":{"x":1075,"y":50},"timestamp":1618323773180}]}'
+```
+
+After receiving the request, the service all adds all scenario steps (from `path`) to the current timeline
+with timestamps as defined in the payload.
+
+As the main loop uses current timestamp for "locating" the events on the timeline, 
+it is possible that registered events won't ever be published. 
+In case the timestamp of a given event _is in the past_, it will be never be retrieved and processed.
+
+
+For the user convenience, it is possible to reuse a scenario definition that refers to the past.
+`recalculate_time=true` parameter for `/scenario` request can be used here:
+```
+curl -X 'POST' \
+  'http://localhost:8000/scenario?recalculate_time=true' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{"customer":{"customer_id":"1"},"path":[{"type":"ENTER","location":{"x":935,"y":50},"timestamp":1618323771180},{"type":"MOVE","location":{"x":588.9128630705394,"y":454.08039288409145},"timestamp":1618323772180},{"type":"EXIT","location":{"x":1075,"y":50},"timestamp":1618323773180}]}'
+```
+
+In this case, scenario will start with current time and step timestamps will be recalculated appropriately
+(they will be "refreshed").
+
+
+## Messages payloads
+
+### **customer/exit** Channel
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | ...|
+| ts | integer | Timestamp of the event, in seconds since epoch | ...|
+
+Example of payload:
+
+```json
+{
+  "id": "127",
+  "ts": 192322800
+}
+```
+
+### **customer/exit** Channel
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | ...|
+| ts | integer | Timestamp (unix time) | ...|
+
+Example of payload:
+
+```json
+{
+  "id": "127",
+  "ts": 192322800
+}
+```
+
+### **customer/move** Channel
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | ...|
+| ts | integer | Timestamp (unix time) | ...|
+| x | integer | coordinate x | ...|
+| y | integer | coordinate y | ...|
+
+Example of payload:
+
+```json
+{
+  "id": "127",
+  "ts": 192322800,
+  "x": 0,
+  "y": 0
+}
+```
+
+# Running the service
+See [instructions](./development.md#running-the-service) for details on configuring and running the service locally.
+
+# Development information
+See [development.md](./development.md) for information about configuring the service, how to run test and run the
+service.
+
diff --git a/scenario-player/app/__init__.py b/scenario-player/app/__init__.py
new file mode 100644
index 0000000..c10988d
--- /dev/null
+++ b/scenario-player/app/__init__.py
@@ -0,0 +1,4 @@
+import logging
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
diff --git a/scenario-player/app/backend/__init__.py b/scenario-player/app/backend/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/scenario-player/app/backend/base.py b/scenario-player/app/backend/base.py
new file mode 100644
index 0000000..e946dc5
--- /dev/null
+++ b/scenario-player/app/backend/base.py
@@ -0,0 +1,43 @@
+from typing import List, Tuple
+
+from app.scenario.scenario_model import Scenario, Step
+
+
+class BaseTimelineBackend:
+    def __init__(self):
+        """
+        Initialize internal fields.
+        """
+        pass
+
+    async def initialize(self):
+        """
+        Initialize the backend (for example, connect to the DB, etc.)
+        :return:
+        """
+        pass
+
+    async def store_scenario(self, scenario: Scenario):
+        """
+        Persist timeline metadata.
+        :param scenario:
+        :return:
+        """
+        raise NotImplementedError
+
+    async def add_to_timeline(self, customer_id: str, step: Step):
+        """
+        Store info about given step for a given customer.
+        :param customer_id:
+        :param step:
+        :return:
+        """
+        raise NotImplementedError
+
+    async def get_events(self, unix_time: int) -> List[Tuple[str, Step]]:
+        """
+
+        :param unix_time:
+        :return: list of tuples (customer_id, step)
+        """
+        raise NotImplementedError
diff --git a/scenario-player/app/backend/priority_queue.py b/scenario-player/app/backend/priority_queue.py
new file mode 100644
index 0000000..d8ffd6b
--- /dev/null
+++ b/scenario-player/app/backend/priority_queue.py
@@ -0,0 +1,56 @@
+from queue import PriorityQueue
+from typing import List, Tuple
+
+from app import logger
+from app.scenario.scenario_model import Scenario, Step
+
+
+class PQueueTimelineBackend:
+    def __init__(self):
+        logger.info('Initializing PriorityQueue backend...')
+        self.timeline = PriorityQueue()
+        self.scenarios = {}
+
+    async def initialize(self):
+        pass
+
+    async def store_scenario(self, scenario: Scenario):
+        logger.info('store_scenario')
+        scenario_key = f'{scenario.customer.customer_id}'
+        self.scenarios.update({scenario_key: scenario})
+        return scenario_key
+
+    async def add_to_timeline(self, customer_id: str, step: Step):
+        logger.info(f'add_to_timeline: {customer_id} {step} ')
+        self.timeline.put((int(step.timestamp.timestamp()), (customer_id, step)))
+        return True
+
+    # XXX TODO probably wrong return type definition (Tuple[int, Tuple[str, Step])
+    def peek(self) -> Tuple[str, Step]:
+        return self.timeline.queue[0] if len(self.timeline.queue) > 0 else None
+
+    async def get_events(self, unix_time: int, include_earlier: bool = False) -> List[Tuple[str, Step]]:
+        logger.debug(f'get events for timestamp {unix_time}')
+        earliest_element = self.peek()
+        logger.debug(f'earliest_element: {earliest_element}')
+
+        result = []
+        # proceed, if the next element in the queue is "old enough" for our query
+        if earliest_element and earliest_element[0] <= unix_time:
+            # consume elements until we reach desired timestamp
+            while True:
+                element = self.timeline.get_nowait()
+                print(element)
+                print(type(element))
+                if element[0] < unix_time:
+                    if include_earlier:
+                        result.append(element[1])
+                else:
+                    result.append(element[1])
+
+                earliest_element = self.peek()
+                # if there is no elements left or if the next element is to new, break
+                if not earliest_element or earliest_element[0] > unix_time:
+                    break
+
+        return result
diff --git a/scenario-player/app/backend/redis.py b/scenario-player/app/backend/redis.py
new file mode 100644
index 0000000..b2af4a4
--- /dev/null
+++ b/scenario-player/app/backend/redis.py
@@ -0,0 +1,124 @@
+from datetime import datetime
+from typing import List, Tuple
+
+import aioredis
+
+from app import logger
+from app.backend.base import BaseTimelineBackend
+from app.scenario.scenario_model import Scenario, Step, Location
+
+TIMELINE_KEY = f'TIMELINE:CURRENT'
+SCENARIO_KEY = 'SCENARIO'
+
+
+class RedisTimelineBackend(BaseTimelineBackend):
+
+    def __init__(self, connection_url: str = 'redis://localhost', database: int = 0, redis_password: str = None):
+        """
+        Initialize internal fields.
+        """
+        super().__init__()
+        self.connection_url = connection_url
+        self.database = database
+        self.redis_password = redis_password
+        self.redis = None
+
+    async def initialize(self):
+        """
+        Initialize the backend (for example, connect to the DB, etc.)
+        :return:
+        """
+        logger.info("initializing redis backend...")
+        logger.info(f'{self.connection_url}, {self.database},{self.redis_password}')
+        try:
+            self.redis = await aioredis.create_redis_pool(address=self.connection_url, db=self.database,
+                                                          password=self.redis_password,
+                                                          encoding='utf-8')
+        except Exception as e:
+            logger.error(f"Error while talking to redis: {e}")
+            # raise
+
+    ###
+    # scenario related
+    #
+
+    @staticmethod
+    def marshall_step(p: Step):
+        return f'{p.timestamp}|{p.type}|{p.location.x}|{p.location.y}'
+
+    def serialize_steps(self, steps: List[Step]):
+        return [self.marshall_step(x) for x in steps]
+
+    async def store_scenario(self, scenario: Scenario, namespace: str = SCENARIO_KEY):
+        logger.info('store_scenario')
+        result = None
+        try:
+            scenario_key = f'{namespace}:{scenario.customer.customer_id}'
+            values = self.serialize_steps(scenario.path)
+            length = len(values)
+
+            if length >= 1:
+                logger.info(f'Sending multiple values ... {values}')
+                result = await self.redis.rpush(scenario_key, *values)
+
+                result = scenario_key
+        except Exception as e:
+            logger.error(f"Error while talking to redis: {e}")
+
+        return result
+
+    ###
+    # timeline related
+    #
+
+    @staticmethod
+    def marshall_event(client_id: str, p: Step):
+        return f'{client_id}|{p.location.x}|{p.location.y}|{p.type}|{p.timestamp}'
+
+    @staticmethod
+    def unmarshall_event(s: str):
+        logger.debug(f'unmarshal event {s}')
+        parts = s.split(sep='|')
+
+        client_id = parts[0]
+        loc = Location(x=int(parts[1]), y=int(parts[2]))
+
+        return client_id, Step(location=loc, type=parts[3],
+                               timestamp=datetime.strptime(parts[4], '%Y-%m-%d %H:%M:%S.%f'))
+
+    async def add_to_timeline(self, customer_id: str, step: Step):
+        """
+        Store info about given step for a given customer.
+        :param customer_id:
+        :param step:
+        :return:
+        """
+        logger.info(f'add_to_timeline: {customer_id} {step} ')
+
+        result = False
+
+        try:
+            event_representation = self.marshall_event(customer_id, step)
+            logger.debug(f'marshalled event_representation: {event_representation}')
+            result = await self.redis.zadd(TIMELINE_KEY, int(step.timestamp.timestamp()), event_representation)
+        except Exception as e:
+            logger.error(f"Error while talking to redis: {e}")
+
+        return result
+
+    async def get_events(self, unix_time: int) -> List[Tuple[str, Step]]:
+        logger.debug(f'get events from redis for timestamp {unix_time}')
+        """
+        Get events definitions for a given point in time
+        """
+        result = []
+
+        try:
+            events = await self.redis.zrangebyscore(TIMELINE_KEY, min=unix_time, max=unix_time)
+            result = [self.unmarshall_event(e) for e in events]
+            mop = await self.redis.zremrangebyscore(TIMELINE_KEY, min=unix_time, max=unix_time)
+        except Exception as e:
+            logger.error(f"Error while talking to redis: {e}")
+            logger.error(type(e))
+
+        return result
diff --git a/scenario-player/app/config.py b/scenario-player/app/config.py
new file mode 100644
index 0000000..07b59b7
--- /dev/null
+++ b/scenario-player/app/config.py
@@ -0,0 +1,34 @@
+import os
+import sys
+
+from app import logger
+
+
+def validate_and_crash(variable, message):
+    if not variable:
+        logger.error(message)
+        sys.exit(message)
+
+
+logger.info('Reading environment variables...')
+
+STORE_HEIGHT = int(os.getenv('STORE_HEIGHT', 10))
+STORE_WIDTH = int(os.getenv('STORE_WIDTH', 6))
+
+CUSTOMERS_AVERAGE_IN_STORE = int(os.getenv('CUSTOMERS_AVERAGE_IN_STORE', 6))
+CUSTOMERS_LIST_FILE = os.getenv('CUSTOMERS_LIST_FILE', 'customers.csv')
+
+MQTT_HOST = os.getenv('MQTT_HOST')
+MQTT_PORT = int(os.getenv('MQTT_PORT', 1883))
+MQTT_NAME = os.getenv('MQTT_NAME', 'demoClient')
+
+CUSTOMER_ENTER_TOPIC = os.getenv('ENTER_TOPIC', 'customer/enter')
+CUSTOMER_EXIT_TOPIC = os.getenv('EXIT_TOPIC', 'customer/exit')
+CUSTOMER_MOVE_TOPIC = os.getenv('MOVE_TOPIC', 'customer/move')
+
+TESTING_MOCK_MQTT = os.getenv('TESTING_MOCK_MQTT', 'false')
+TESTING_MOCK_MQTT = TESTING_MOCK_MQTT.lower() in ['1', 'yes', 'true']
+
+
+REQUIRED_PARAM_MESSAGE = 'Cannot read {} env variable. Please, make sure it is set before starting the service.'
+validate_and_crash(MQTT_HOST, REQUIRED_PARAM_MESSAGE.format('MQTT_HOST'))
diff --git a/scenario-player/app/controller.py b/scenario-player/app/controller.py
new file mode 100644
index 0000000..21852b6
--- /dev/null
+++ b/scenario-player/app/controller.py
@@ -0,0 +1,52 @@
+from datetime import datetime, timezone
+
+from app import logger
+from app.backend.base import BaseTimelineBackend
+from app.scenario.scenario_deployer import ScenarioDeployer
+from app.scenario.scenario_model import Scenario
+from app.scenario.scenario_producer import ScenarioProducer
+from app.simulator.simulation_engine import CustomerSimulator
+
+
+class TimelineController:
+    def __init__(self, backend: BaseTimelineBackend, scenario_producer: ScenarioProducer,
+                 scenario_deployer: ScenarioDeployer, autostart=True):
+        self.backend = backend
+        self.scenario_producer = scenario_producer
+        self.scenario_deployer = scenario_deployer
+        self.autostart = autostart
+
+    async def accept_scenario_draft(self, scenario: Scenario):
+        logger.info("Converting draft to scenario...")
+
+        result = self.scenario_producer.expand(scenario)
+
+        await self.backend.store_scenario(result)
+        if self.autostart:
+            await self.deploy_scenario(result)
+
+        return result.json(exclude_none=True)
+
+    async def deploy_scenario(self, scenario: Scenario, recalculate_time: bool = False):
+        logger.info('Deploying scenario...')
+
+        if recalculate_time:
+            logger.debug('Recalculating scenario time...')
+            new_start = datetime.now(timezone.utc)
+            scenario = self.scenario_deployer.recalculate_time(scenario, new_start)
+        # persist the scenario
+        await self.backend.store_scenario(scenario)
+
+        # add scenario steps to the timeline
+        result = await self.scenario_deployer.deploy_scenario(scenario)
+        return result
+
+    async def get_current_state(self, timestamp: datetime):
+        logger.info(f'get_current_state({timestamp}):')
+        epoch = int(timestamp.timestamp())
+        events_for_users = await self.backend.get_events(epoch)
+        logger.debug(f'events: {events_for_users}')
+
+        customer_states = [CustomerSimulator.create_customer_state(efo[0], efo[1]) for efo in events_for_users]
+
+        return customer_states
diff --git a/scenario-player/app/log_config.py b/scenario-player/app/log_config.py
new file mode 100644
index 0000000..f7836eb
--- /dev/null
+++ b/scenario-player/app/log_config.py
@@ -0,0 +1,24 @@
+import logging
+import os
+
+
+# TODO move to config
+LOG_FILENAME = "messages.log"
+LOG_FORMAT = "%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s]\t%(message)s"
+LOG_LEVEL = os.environ.get('LOG_LEVEL', 'DEBUG').upper()
+
+assert LOG_LEVEL in ['DEBUG', 'INFO', 'WARNING', 'ERROR']
+
+
+def configure_logger():
+    logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL)
+    # Basic console logger
+    logger = logging.getLogger("app")
+    # File logger
+    log_formatter = logging.Formatter(LOG_FORMAT)
+    file_handler = logging.FileHandler(LOG_FILENAME, encoding='UTF-8')
+    file_handler.setFormatter(log_formatter)
+    logger.addHandler(file_handler)
+    # Configuration done
+    logger.debug("Logger configured...")
+    return logger
diff --git a/scenario-player/app/main.py b/scenario-player/app/main.py
new file mode 100644
index 0000000..6dbce6f
--- /dev/null
+++ b/scenario-player/app/main.py
@@ -0,0 +1,135 @@
+import asyncio
+from datetime import datetime
+from typing import List
+
+import uvicorn
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.responses import PlainTextResponse
+
+from app import logger
+from app.backend.priority_queue import PQueueTimelineBackend
+from app.controller import TimelineController
+from app.log_config import configure_logger
+from app.publisher.mqtt_publisher import MQTTEventPublisher
+from app.scenario.scenario_deployer import ScenarioDeployer
+from app.scenario.scenario_model import Scenario, CustomerState
+from app.scenario.scenario_producer import ScenarioProducer
+from app.simulator.simulation_engine import CustomerSimulator
+
+configure_logger()
+
+app = FastAPI()
+
+# For bigger scale and volume, use Redis backend
+USE_REDIS_BACKEND = False
+
+
+async def init_backend():
+    if USE_REDIS_BACKEND:
+        # XXX TODO add error handling
+        from app.backend.redis import RedisTimelineBackend
+        backend = RedisTimelineBackend('redis://127.0.0.1:6379', database=0, redis_password='redis123')
+    else:
+        backend = PQueueTimelineBackend()
+
+    await backend.initialize()
+    return backend
+
+
+@app.on_event("startup")
+async def startup_event():
+    app.state.backend = await init_backend()
+    app.state.scenario_producer = ScenarioProducer()
+    app.state.scenario_deployer = ScenarioDeployer(app.state.backend)
+    app.state.timeline_controller = TimelineController(app.state.backend, app.state.scenario_producer,
+                                                       app.state.scenario_deployer)
+
+    # app.state.event_publisher = LoggerEventPublisher()
+    app.state.event_publisher = MQTTEventPublisher(app)
+    await app.state.event_publisher.initialize()
+
+    # ####################
+    # # background tasks
+    customer_sim = CustomerSimulator(app.state.backend, app.state.event_publisher)
+
+    asyncio.create_task(customer_sim.run())
+
+
+####################
+# web handlers
+logger.info('Defining web service handlers...')
+
+
+@app.get('/')
+async def root():
+    logger.debug('/')
+    return {'message': 'Hello World'}
+
+
+@app.get('/health')
+async def health() -> PlainTextResponse:
+    """
+    Service health check endpoint.
+    """
+    logger.info('verify health')
+    return PlainTextResponse('OK')
+
+
+#
+# Timeline probably should be created on the app startup.
+# Only modification to the timeline should be adding scenarios and rewinding current time of timeline.
+
+@app.post('/scenario_draft')
+async def accept_scenario_draft(payload: Scenario) -> PlainTextResponse:
+    """
+    """
+    logger.info('accept_scenario')
+    logger.debug(payload)
+
+    message = payload.json(exclude_none=True)
+    logger.debug(f'Received {message} payload')
+
+    result = await app.state.timeline_controller.accept_scenario_draft(payload)
+
+    if not result:
+        raise HTTPException(status_code=404, detail='Problem storing scenario.')
+
+    # result = 'Scenario created'
+    return PlainTextResponse(result)
+
+
+RECALCULATE_DESCRIPTION = 'if set to True, scenario will start with current time ' \
+                          '(and step timestamps will be recalculated appropriately)'
+
+
+@app.post('/scenario')
+async def deploy_scenario(payload: Scenario,
+                          recalculate_time: bool = Query(default=False,
+                                                         description=RECALCULATE_DESCRIPTION)) -> PlainTextResponse:
+    """
+    Accepts a scenario definition and adds it to the current timeline.
+    """
+    logger.info('deploy_scenario')
+    logger.debug(f'recalculate_time: {recalculate_time}')
+    logger.debug(payload)
+
+    message = payload.json(exclude_none=True)
+    logger.debug(f'Received {message} payload')
+
+    result = await app.state.timeline_controller.deploy_scenario(payload, recalculate_time)
+
+    if not result:
+        raise HTTPException(status_code=404, detail='Problem storing scenario.')
+
+    return PlainTextResponse(str(result))
+
+
+@app.get('/state')
+async def get_current_state(timestamp: datetime) -> List[CustomerState]:
+    logger.info(f'get_current_state as for {timestamp}')
+    return await app.state.timeline_controller.get_current_state(timestamp)
+
+
+# For debugging
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/scenario-player/app/publisher/__init__.py b/scenario-player/app/publisher/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/scenario-player/app/publisher/base.py b/scenario-player/app/publisher/base.py
new file mode 100644
index 0000000..04aeb4c
--- /dev/null
+++ b/scenario-player/app/publisher/base.py
@@ -0,0 +1,28 @@
+from app import logger
+from app.scenario.scenario_model import CustomerState
+
+
+class BaseEventPublisher:
+
+    async def initialize(self):
+        """
+        Initialize the backend (for example, connect to the DB, etc.)
+        :return:
+        """
+        raise NotImplemented
+
+    def prepare_payload(self, customer_state: CustomerState):
+        return str(customer_state)
+
+    async def publish_state(self, customer_state: CustomerState):
+        raise NotImplemented
+
+
+class LoggerEventPublisher(BaseEventPublisher):
+
+    async def initialize(self):
+        pass
+
+    async def publish_state(self, customer_state: CustomerState):
+        message = self.prepare_payload(customer_state)
+        logger.info(f'Publishing {message}')
diff --git a/scenario-player/app/publisher/mqtt_model.py b/scenario-player/app/publisher/mqtt_model.py
new file mode 100644
index 0000000..e6733b8
--- /dev/null
+++ b/scenario-player/app/publisher/mqtt_model.py
@@ -0,0 +1,31 @@
+from pydantic import BaseModel, Field
+
+
+class CustomerEnterEvent(BaseModel):
+    """
+    Object representing the event of customer entering the store.
+    """
+    id: str = Field(description='ID representing customer')
+    ts: int = Field(description='timestamp of the entrance, in seconds since epoch')
+
+
+class CustomerExitEvent(BaseModel):
+    """
+    id: --ID representing customer--,
+    ts: --timestamp of the exit, in seconds since epoch--
+    """
+    id: str
+    ts: int
+
+
+class CustomerMoveEvent(BaseModel):
+    """
+    id: --ID representing customer--,
+    ts: --timestamp of the move, in seconds since epoch--,
+     x: --x coordinate of location sensor that fired--,
+     y: --y coordinate of location sensor that fired--
+    """
+    id: str
+    ts: int
+    x: int
+    y: int
diff --git a/scenario-player/app/publisher/mqtt_publisher.py b/scenario-player/app/publisher/mqtt_publisher.py
new file mode 100644
index 0000000..bffe7e4
--- /dev/null
+++ b/scenario-player/app/publisher/mqtt_publisher.py
@@ -0,0 +1,116 @@
+from fastapi import FastAPI
+from fastapi_mqtt import FastMQTT, MQTTConfig
+
+from app import logger
+from app.config import TESTING_MOCK_MQTT, MQTT_HOST, MQTT_PORT, MQTT_NAME, CUSTOMER_EXIT_TOPIC, CUSTOMER_MOVE_TOPIC, \
+    CUSTOMER_ENTER_TOPIC
+from app.publisher.base import BaseEventPublisher
+from app.publisher.mqtt_model import CustomerMoveEvent, CustomerEnterEvent, CustomerExitEvent
+from app.scenario.scenario_model import CustomerState, STEP_TYPE_ENTER, STEP_TYPE_MOVE, STEP_TYPE_EXIT
+
+MAP = {
+    STEP_TYPE_ENTER: CUSTOMER_ENTER_TOPIC,
+    STEP_TYPE_MOVE: CUSTOMER_MOVE_TOPIC,
+    STEP_TYPE_EXIT: CUSTOMER_EXIT_TOPIC
+}
+
+if TESTING_MOCK_MQTT:
+    class MQTTClient:
+        def __init__(self, mqtt_host: str, mqtt_port: int, mqtt_client_name: str, app: FastAPI):
+            logger.info(f'simulating a client to {mqtt_host}')
+            self.mqtt_client_name = mqtt_client_name
+            self.mqtt_host = mqtt_host
+            self.mqtt_port = mqtt_port
+
+        def publish(self, topic, message):
+            logger.info(f'simulated publishing to {topic}. message: {message}')
+
+        async def connect(self):
+            pass
+else:
+    class MQTTClient:
+        def __init__(self, app: FastAPI, mqtt_host: str, mqtt_port: int, mqtt_client_name: str):
+            logger.info(f'Creating MQTT client {mqtt_host}, {mqtt_port}, {mqtt_client_name}')
+            self.mqtt_client_name = mqtt_client_name
+            self.mqtt_host = mqtt_host
+            self.mqtt_port = mqtt_port
+            self.app = app
+
+            # TODO XXX there is now way to pass client name...
+            mqtt_config = MQTTConfig(host=self.mqtt_host, port=self.mqtt_port)
+            self.fast_mqtt = FastMQTT(config=mqtt_config)
+
+        def publish(self, topic, message):
+            logger.info(f' publishing to {topic}. message: {message}')
+            return self.fast_mqtt.publish(topic, message)
+
+        async def connect(self):
+            logger.info("before connect")
+
+            self.fast_mqtt.init_app(self.app)
+            logger.info("after connect")
+
+
+class MQTTEventMarshaller(object):
+    @staticmethod
+    def construct_entry_message(cs: CustomerState):
+        message = CustomerEnterEvent(id=cs.customer_description.customer_id, ts=int(cs.timestamp.timestamp()))
+        return message.json()
+
+    @staticmethod
+    def construct_move_message(cs: CustomerState):
+        ts = int(cs.timestamp.timestamp())
+        message = CustomerMoveEvent(id=cs.customer_description.customer_id, ts=ts, x=cs.location.x, y=cs.location.y)
+        return message.json()
+
+    @staticmethod
+    def construct_exit_message(cs: CustomerState):
+        message = CustomerExitEvent(id=cs.customer_description.customer_id, ts=int(cs.timestamp.timestamp()))
+        return message.json()
+
+
+class MQTTEventPublisher(BaseEventPublisher):
+    def __init__(self, app: FastAPI, mqtt_host=MQTT_HOST, mqtt_port=MQTT_PORT, mqtt_client_name=MQTT_NAME):
+        logger.info(f'Initializing MQTT client {mqtt_host}')
+        self.app = app
+        self.client = MQTTClient(app, mqtt_host, mqtt_port, mqtt_client_name)
+
+    async def initialize(self):
+        logger.info('Initializing MQTT connection')
+        self.client.fast_mqtt.user_connect_handler = MQTTEventPublisher.on_connect
+        self.client.fast_mqtt.client.on_disconnect = MQTTEventPublisher.on_disconnect
+
+        await self.client.connect()
+
+    @staticmethod
+    def on_connect(client, flags, rc, properties):
+        logger.warning(f'Connected: , {client}, {flags}, {rc}, {properties}')
+
+    @staticmethod
+    def on_disconnect(client, packet):
+        logger.warning(f'Disconnected: {client}, {packet}')
+
+    @staticmethod
+    def get_topic_for_event_type(event_type):
+        return MAP[event_type]
+
+    def prepare_payload(self, customer_state: CustomerState):
+
+        if customer_state.status == STEP_TYPE_ENTER:
+            message = MQTTEventMarshaller.construct_entry_message(customer_state)
+        elif customer_state.status == STEP_TYPE_MOVE:
+            message = MQTTEventMarshaller.construct_move_message(customer_state)
+        elif customer_state.status == STEP_TYPE_EXIT:
+            message = MQTTEventMarshaller.construct_exit_message(customer_state)
+        else:
+            raise RuntimeError(f'Unknown message type ({customer_state.status})')
+
+        return message
+
+    async def publish_state(self, customer_state: CustomerState):
+        logger.debug('publish_state')
+        logger.debug(customer_state)
+        topic = self.get_topic_for_event_type(customer_state.status)
+        logger.warn(f'Publishing {customer_state} to {topic} topic')
+        message = self.prepare_payload(customer_state)
+        self.client.publish(topic, message)
diff --git a/scenario-player/app/scenario/__init__.py b/scenario-player/app/scenario/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/scenario-player/app/scenario/scenario_deployer.py b/scenario-player/app/scenario/scenario_deployer.py
new file mode 100644
index 0000000..2292ba3
--- /dev/null
+++ b/scenario-player/app/scenario/scenario_deployer.py
@@ -0,0 +1,39 @@
+from datetime import datetime
+
+from app import logger
+from app.backend.base import BaseTimelineBackend
+from app.scenario.scenario_model import Scenario
+
+
+class ScenarioDeployer(object):
+    def __init__(self, backend: BaseTimelineBackend):
+        logger.info(f'Initializing ScenarioDeployer')
+        self.backend = backend
+
+    @staticmethod
+    def recalculate_time(scenario: Scenario, start_time: datetime):
+        logger.info(f'recalculate_time {scenario} (start from {start_time}')
+
+        new_scenario = Scenario(customer=scenario.customer, path=[])
+
+        first_timestamp = scenario.path[0].timestamp
+        time_delta = start_time - first_timestamp
+
+        # go trough draft steps
+        for s in scenario.path:
+            step = s.copy()
+            step.timestamp = s.timestamp + time_delta
+
+            # add the step to the new scenario
+            new_scenario.path.append(step)
+
+        return new_scenario
+
+    async def deploy_scenario(self, scenario: Scenario):
+        logger.info(f'deploy_scenario {scenario}')
+
+        # go trough draft steps
+        for step in scenario.path:
+            await self.backend.add_to_timeline(scenario.customer.customer_id, step)
+
+        return True
diff --git a/scenario-player/app/scenario/scenario_model.py b/scenario-player/app/scenario/scenario_model.py
new file mode 100644
index 0000000..5be424c
--- /dev/null
+++ b/scenario-player/app/scenario/scenario_model.py
@@ -0,0 +1,53 @@
+from datetime import datetime
+from typing import Optional, List
+
+from pydantic import BaseModel, Field
+
+STEP_TYPE_MOVE = 'MOVE'
+STEP_TYPE_FOCUS = 'FOCUS'
+STEP_TYPE_ENTER = 'ENTER'
+STEP_TYPE_EXIT = 'EXIT'
+
+
+class Location(BaseModel):
+    """
+    Object representing location of the customer in the store.
+    """
+    x: int = Field(description='coordinate X')
+    y: int = Field(description='coordinate Y')
+
+
+class Step(BaseModel):
+    type: str # TODO: introduce enum/Literal
+    location: Location
+    timestamp: Optional[datetime]
+
+
+class CustomerDescription(BaseModel):
+    customer_id: str
+    # name: Optional[str]
+    # gender: Optional[str]
+    # age_bucket: Optional[str]
+    # preferred_vendors: Optional[List[str]]
+
+
+class Scenario(BaseModel):
+    customer: CustomerDescription
+    path: Optional[List[Step]]
+
+
+class CustomerState(BaseModel):
+    customer_description: CustomerDescription
+    location: Location
+    status: Optional[str] = STEP_TYPE_MOVE
+    timestamp: Optional[datetime]
+
+
+class State(BaseModel):
+    customer_states: Optional[List[CustomerState]]
+
+
+class Timeline(BaseModel):
+    name: str
+    from_timestamp: int
+    to_timestamp: int
diff --git a/scenario-player/app/scenario/scenario_producer.py b/scenario-player/app/scenario/scenario_producer.py
new file mode 100644
index 0000000..44db2a1
--- /dev/null
+++ b/scenario-player/app/scenario/scenario_producer.py
@@ -0,0 +1,84 @@
+from datetime import datetime, timedelta, timezone
+import math
+
+from app import logger
+from app.scenario.scenario_model import Scenario, Step, Location, STEP_TYPE_MOVE, STEP_TYPE_FOCUS
+
+CUSTOMER_AVERAGE_PACE = 0.03  # seconds per space unit
+# XXX TODO: the speed per unit should be determined by the map size...
+
+SCENARIO_AUTOSTART_DELAY = 10
+
+
+class ScenarioProducer(object):
+    def __init__(self, store_map=None):
+        logger.info(f'Initializing ScenarioProducer with map {store_map}')
+        self.map = store_map
+
+    def expand(self, scenario_draft: Scenario, start_timestamp=None):
+        logger.info(f'Expanding scenario {scenario_draft}')
+        if not start_timestamp:
+            start_timestamp = datetime.now(timezone.utc)
+            if SCENARIO_AUTOSTART_DELAY:
+                start_timestamp = start_timestamp + timedelta(seconds=SCENARIO_AUTOSTART_DELAY)
+
+        # create new scenario
+        scenario = Scenario(customer=scenario_draft.customer, path=[])
+
+        last_step = None
+
+        # go trough draft's steps
+        for i, s in enumerate(scenario_draft.path):
+            step = s.copy(update={'type': STEP_TYPE_MOVE})
+            step.timestamp = self.compute_timestamp(last_step, s) if last_step else start_timestamp
+
+            # add the step to the new scenario
+            scenario.path.append(step)
+
+            # remember last step
+            last_step = step
+
+            if s.type.upper() == STEP_TYPE_FOCUS:
+                additional_steps = self.generate_additional_steps(last_step, count=5, period=30)
+                scenario.path.extend(additional_steps)
+                last_step = additional_steps[-1]
+
+        return scenario
+
+    def compute_timestamp(self, previous_step: Step, current_step: Step,
+                          customer_pace: float = CUSTOMER_AVERAGE_PACE) -> datetime:
+        logger.debug(f'compute_timestamp {previous_step}, {current_step}')
+        distance = self.get_distance(previous_step.location, current_step.location)
+        elapsed_time = distance * customer_pace
+        return previous_step.timestamp + timedelta(seconds=elapsed_time)
+
+    def get_distance(self, l1: Location, l2: Location):
+        # In the future, use map and path finding to find distance
+        # for now use cartesian distance
+
+        return math.dist([l1.x, l1.y], [l2.x, l2.y])
+
+    def generate_additional_steps(self, previous_step: Step, count: int, period: float):
+        """
+        :param previous_step:
+        :param count: how many steps to produce
+        :param period: [seconds] the period to fill with the steps (time from the previous step to the last step)
+        :return:
+        """
+        assert count >= 1
+
+        time_delta = period / (count - 1)
+
+        result = []
+        previous_timestamp = previous_step.timestamp
+        previous_location = previous_step.location
+        for i in range(0, count):
+            # TODO: add some movement around the same place (for example move left then right, then left
+            new_location = previous_location
+            new_timestamp = previous_timestamp + timedelta(seconds=time_delta)
+
+            result.append(Step(type=STEP_TYPE_MOVE, location=new_location, timestamp=new_timestamp))
+            previous_timestamp = new_timestamp
+            previous_location = new_location
+
+        return result
diff --git a/scenario-player/app/simulator/__init__.py b/scenario-player/app/simulator/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/scenario-player/app/simulator/simulation_engine.py b/scenario-player/app/simulator/simulation_engine.py
new file mode 100644
index 0000000..4b36b6d
--- /dev/null
+++ b/scenario-player/app/simulator/simulation_engine.py
@@ -0,0 +1,46 @@
+import asyncio
+from datetime import datetime, timezone
+
+from app import logger
+from app.backend.base import BaseTimelineBackend
+from app.publisher.base import BaseEventPublisher
+from app.scenario.scenario_model import CustomerDescription, CustomerState, Step
+
+CUSTOMER_STATE_TEMPLATE = '{0} is Entering. MDT: {1:0.1f}, C: {2:0.1f}, E: {3}'
+
+
+class CustomerSimulator:
+    def __init__(self, backend: BaseTimelineBackend, event_publisher: BaseEventPublisher,
+                 start_time: datetime = datetime.now(timezone.utc)):
+        self.tick_time = start_time
+        self.last_tick_time: int = 0
+        self.backend = backend
+        self.event_publisher = event_publisher
+        self.is_running = True
+
+    @staticmethod
+    def create_customer_state(customer_id, step: Step):
+        customer_description = CustomerDescription(customer_id=customer_id)
+        return CustomerState(customer_description=customer_description, location=step.location,
+                             timestamp=step.timestamp, status=step.type)
+
+    async def run(self):
+        logger.info('Starting simulator loop')
+        while self.is_running:
+            logger.debug('another pass of simulator loop...')
+            tmpstmp = int(datetime.now(timezone.utc).timestamp())
+
+            # TODO get_events returns the events and REMOVES them from the queue, maybe change name to consume_events
+            # TODO maybe it should be: get events from the last tick 'till now
+            events_for_users = await self.backend.get_events(tmpstmp)
+            logger.debug(f'events: {events_for_users}')
+
+            customer_states = [self.create_customer_state(efo[0], efo[1]) for efo in events_for_users]
+            for state in customer_states:
+                await self.event_publisher.publish_state(state)
+
+            # remember last tick time
+            self.last_tick_time = tmpstmp
+
+            # go to sleep
+            await asyncio.sleep(1)
diff --git a/scenario-player/development.md b/scenario-player/development.md
new file mode 100644
index 0000000..b41b4c2
--- /dev/null
+++ b/scenario-player/development.md
@@ -0,0 +1,141 @@
+# Functionality 
+This service generates messages that simulate customer behaviour in a retail shop:
+* customer entering the store
+* customer movement 
+* customer exiting the store
+
+
+# Table of contents
+* [Functionality](#functionality)
+
+* [Development](#development)
+  * [Dependencies](#dependencies)
+  * [Service configuration](#service-configuration)
+  * [Running the service](#running-the-service)
+  * [Testing with MQTT broker in docker](#testing-with-mqtt-broker-in-docker)
+  * [Testing without MQTT](#testing-without-mqtt)
+  * [Mock event endpoints](#mock-event-endpoints)
+
+* [Deployment](#deployment)
+  * [Docker image](#docker-image)
+  * [Connecting to a secured broker](#connecting-to-a-secured-broker)
+  
+
+# Development
+
+## Dependencies
+
+Dependencies of the project are contained in [requirements.txt](requirements.txt) file. All the packages are publicly
+available.
+
+All the packages can be installed with:
+`pip install -f requirements.txt`
+
+## Service configuration
+
+The service reads the following **environment variables**:
+
+| Variable               | Description                          |  Default      |
+|------------------------|--------------------------------------|--------------:|
+| STORE_HEIGHT          |                                       | 10            |
+| STORE_WIDTH           |                                       | 6             |
+| CUSTOMERS_AVERAGE_IN_STORE |  					            | 6 		    |
+| CUSTOMERS_LIST_FILE   |                                       | customers.csv |
+| MQTT_HOST             |                                       | -             |
+| MQTT_PORT             |               	                    | 1883          |
+| MQTT_NAME             |                	                    | demoClient    |
+| ENTER_TOPIC           |                                       | customer/enter|
+| MOVE_TOPIC            |                                       | customer/move |
+| EXIT_TOPIC            |                                       | customer/exit |
+
+(Parameters with `-` in "Default" column are required.)
+
+Use [log_config.py](./app/utils/log_config.py) to **configure logging behaviour**. 
+By default, console and file handlers are used. The file appender writes to `messages.log`.
+
+
+## Running the service
+
+For my development I created a project with dedicated virtual environment (Python 3.8, all the dependencies installed
+there).
+
+The code reads sensitive information (tokens, secrets) from environment variables. They need to be set accordingly in
+advance.
+`environment.variables.sh` can be used for that purpose. Then, in order to run the service the following commands can be
+used:
+
+```
+$ . .environment.variables.sh
+$ . venv/bin/activate
+(venv)$ uvicorn app.main:app --host 0.0.0.0 --reload --reload-dir app
+```
+> Please, note `reload-dir` switch. Without it the reloader goes into an infinite loop because it detects log file changes (messages.log).
+
+## Testing with MQTT broker in docker
+
+Quick way to **set up a simple MQTT broker** is to use Docker containers:
+```shell
+docker run -d --rm --name mosquitto -p 1883:1883 eclipse-mosquitto
+```
+or
+```shell
+docker run -it -p 1883:1883 --name mosquitto eclipse-mosquitto mosquitto -c /mosquitto-no-auth.conf
+```
+
+To **publish to a topic**:
+
+```shell
+docker exec mosquitto mosquitto_pub -h 127.0.0.1 -t test -m "test message"
+```
+
+To **subscribe to a topic**:
+```shell
+docker exec mosquitto mosquitto_sub -h 127.0.0.1 -t test
+```
+
+### Testing without MQTT
+There is an environment variable, `TESTING_MOCK_MQTT`, that will create an MQTT client mock instead of trying to connect
+to a real MQTT broker. Instead of publishing the messages, they will be simply logged/printed out.
+
+This may be helpful for local development or testing.
+
+### Producing test messages
+
+```shell
+curl http://127.0.0.1:8000/produce_entry -d '{"id": "997", "ts": 192326400}'
+ ```
+
+```shell
+curl http://127.0.0.1:8000/produce_exit -d '{"id": "997", "ts": 192326400}'
+ ```
+
+```shell
+curl http://127.0.0.1:8000/produce_move -d '{"id": "997", "ts": 192326400, "x": 2, "y": 3}'
+ ```
+
+
+# Deployment
+
+## Docker image
+The docker image for the service is [Dockerfile](Dockerfile).
+It is based on FastAPI "official" image.
+See https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker
+for the details on configuring the container (http port, log level, etc.)
+
+In order to build the image use:
+```
+docker build -t customersim-service:0.0.1 .
+```
+
+> Set image name (`customersim-service`) and tag (`0.0.1`) according to
+> your needs.
+
+To run the service as a Docker container run:
+```
+docker run -d -e LOG_LEVEL="warning"  --name customersim-service customersim-service:0.0.1
+
+```
+
+## Connecting to a secured broker
+**TODO** Add info about setting user/password
+**TODO** Add info about using client certificates (TLS)
diff --git a/scenario-player/docs/asyncapi.md b/scenario-player/docs/asyncapi.md
new file mode 100644
index 0000000..36508fb
--- /dev/null
+++ b/scenario-player/docs/asyncapi.md
@@ -0,0 +1,97 @@
+# Customer movement simulator 0.1.0 documentation
+
+This service is responsible for simulating customer movement around retail store.
+## Table of Contents
+
+* [Servers](#servers)
+* [Channels](#channels)
+
+## Servers
+
+### **development** Server
+
+| URL | Protocol | Description |
+|---|---|---|
+| localhost | mqtt | Development mosquitto broker |
+
+## Channels
+
+### **customer/enter** Channel
+
+#### `publish` Operation
+
+##### Message
+
+###### Payload
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | _Any_ |
+| ts | integer | Timestamp (unix time) | _Any_ |
+
+> Examples of payload _(generated)_
+
+```json
+{
+  "id": "string",
+  "ts": 0
+}
+```
+
+
+
+
+### **customer/exit** Channel
+
+#### `publish` Operation
+
+##### Message
+
+###### Payload
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | _Any_ |
+| ts | integer | Timestamp (unix time) | _Any_ |
+
+> Examples of payload _(generated)_
+
+```json
+{
+  "id": "string",
+  "ts": 0
+}
+```
+
+
+
+
+### **customer/move** Channel
+
+#### `publish` Operation
+
+##### Message
+
+###### Payload
+
+| Name | Type | Description | Accepted values |
+|---|---|---|---|
+| id | string | ID of the customer | _Any_ |
+| ts | integer | Timestamp (unix time) | _Any_ |
+| x | integer | coordinate x | _Any_ |
+| y | integer | coordinate y | _Any_ |
+
+> Examples of payload _(generated)_
+
+```json
+{
+  "id": "string",
+  "ts": 0,
+  "x": 0,
+  "y": 0
+}
+```
+
+
+
+
diff --git a/scenario-player/docs/asyncapi.yml b/scenario-player/docs/asyncapi.yml
new file mode 100644
index 0000000..e98a0bb
--- /dev/null
+++ b/scenario-player/docs/asyncapi.yml
@@ -0,0 +1,61 @@
+asyncapi: 2.0.0
+info:
+  title: Customer movement simulator
+  version: 0.1.0
+  description: This service is responsible for simulating customer movement around retail store.
+servers:
+  development:
+    url: localhost
+    protocol: mqtt
+    description: Development mosquitto broker
+channels:
+  customer/enter:
+    publish:
+      message:
+        $ref: '#/components/messages/CustomerEnter'
+  customer/exit:
+    publish:
+      message:
+        $ref: '#/components/messages/CustomerEnter'
+  customer/move:
+    publish:
+      message:
+        $ref: '#/components/messages/CustomerMove'
+components:
+  messages:
+    CustomerEnter:
+      payload:
+        type: object
+        properties:
+          id:
+            type: string
+            description: ID of the customer
+          ts:
+            type: integer
+            description: Timestamp (unix time)
+    CustomerExit:
+      payload:
+        type: object
+        properties:
+          id:
+            type: string
+            description: ID of the customer
+          ts:
+            type: integer
+            description: Timestamp (unix time)
+    CustomerMove:
+      payload:
+        type: object
+        properties:
+          id:
+            type: string
+            description: ID of the customer
+          ts:
+            type: integer
+            description: Timestamp (unix time)
+          x:
+            type: integer
+            description: coordinate x
+          y:
+            type: integer
+            description: coordinate y
diff --git a/scenario-player/requirements.txt b/scenario-player/requirements.txt
new file mode 100644
index 0000000..ecbfc2b
--- /dev/null
+++ b/scenario-player/requirements.txt
@@ -0,0 +1,7 @@
+setuptools~=39.2.0
+fastapi==0.63
+uvicorn[standard]==0.13.4
+pydantic~=1.8.1
+aioredis~=1.3.1
+pip~=21.0.1
+fastapi-mqtt~=0.3.0
diff --git a/scenario-player/testing/feed.drafts.sh b/scenario-player/testing/feed.drafts.sh
new file mode 100644
index 0000000..4fb7b79
--- /dev/null
+++ b/scenario-player/testing/feed.drafts.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+for payload in $(jq '.[]' -c test_drafts.json); do
+  curl -X 'POST' -H 'accept: application/json' -H 'Content-Type: application/json' -d $payload 'http://localhost:8000/scenario_draft'
+  sleep $(( RANDOM % 5 ))
+done
diff --git a/scenario-player/testing/feed.scenarios.sh b/scenario-player/testing/feed.scenarios.sh
new file mode 100644
index 0000000..557bd53
--- /dev/null
+++ b/scenario-player/testing/feed.scenarios.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+for payload in $(jq '.[]' -c test.scenarios.json); do
+  curl -X 'POST' -H 'accept: application/json' -H 'Content-Type: application/json' -d $payload 'http://localhost:8000/scenario?recalculate_time=true'
+  sleep $(( RANDOM % 5 ))
+done
diff --git a/scenario-player/testing/sample.scenario.json b/scenario-player/testing/sample.scenario.json
new file mode 100644
index 0000000..06089c3
--- /dev/null
+++ b/scenario-player/testing/sample.scenario.json
@@ -0,0 +1,103 @@
+{
+  "customer":{
+    "customer_id":"3"
+  },
+  "path":[
+    {
+      "type":"MOVE",
+      "location":{
+        "x":100,
+        "y":10
+      },
+      "timestamp":"2021-01-01T00:00:00.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":354,
+        "y":222
+      },
+      "timestamp":"2021-01-01T00:00:02.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":650,
+        "y":375
+      },
+      "timestamp":"2021-01-01T00:00:04.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":138,
+        "y":450
+      },
+      "timestamp":"2021-01-01T00:00:06.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:08.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:10.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:12.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:14.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:16.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":568,
+        "y":794
+      },
+      "timestamp":"2021-01-01T00:00:18.000000"
+    },
+    {
+      "type":"MOVE",
+      "location":{
+        "x":879,
+        "y":743
+      },
+      "timestamp":"2021-01-01T00:00:20.000000"
+    },
+   {
+      "type":"EXIT",
+      "location":{
+        "x":1237,
+        "y":168
+      },
+      "timestamp":"2021-01-01T00:00:24.000000"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/scenario-player/testing/test.scenarios.json b/scenario-player/testing/test.scenarios.json
new file mode 100644
index 0000000..b7f8937
--- /dev/null
+++ b/scenario-player/testing/test.scenarios.json
@@ -0,0 +1,5 @@
+[
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diff --git a/scenario-player/testing/test_drafts.json b/scenario-player/testing/test_drafts.json
new file mode 100644
index 0000000..9b99281
--- /dev/null
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