diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..0b12613
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,11 @@
+{
+    "recommendations": [
+        "streetsidesoftware.code-spell-checker",
+        "vscode-icons-team.vscode-icons",
+        "dbaeumer.vscode-eslint",
+        "christian-kohler.path-intellisense",
+        "donjayamanne.python-extension-pack",
+        "Cameron.vscode-pytest",
+        "pamaron.pytest-runner"
+    ]
+}
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..8d24737
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+    "[python]": {
+        "editor.tabSize": 4
+    }
+}
\ No newline at end of file
diff --git a/FEEDBACK.md b/FEEDBACK.md
index 73f9010..62f11a0 100644
--- a/FEEDBACK.md
+++ b/FEEDBACK.md
@@ -2,44 +2,50 @@
 
 ## Team Name: <>
 
-* Team Member(s): <>, <>
+* Team Member(s): <Kyungchan Im>, <Gabriel Aracena>
 
 ## List of Required Software
 
 > Examples: `NodeJS version X` or `Java version Y`
 
-<>, <>, <>
+<Python 3.10>, <pipenv>
 
 ## Document and describe any enhancements included to help the judges properly grade your submission.
 	
 ### Step 1:
 
-<>
+<Data Integration and Processing>
 
 ### Step 2: 
 
-<>
+<Data Analysis Report>
+
+We analyzed the agents and claims per region to see if there is a balance between them
 
-### etc.
+Created a heatmap to analyze the area where most disaster happens
+
+Analyzed the cost of claim based on different attributes such as severity, total loss or partial loss, loss of life, and type of claims.
 
 ## How to Run
 
 > List any commands to run your project. For example:
 
 ```sh
-# To get most NodeJS apps to run locally
-npm install
+# Go to the terminal and locate to the directory,
+path/to/your/directory
+
+pipenv shell
+pipenv install # will install all packages needed
 
-npm start
-# OR
-npm run dev
-```
+Then run on your preferred IDE as a Jupyter Notebook. If you have VS Code, just click 'run all' on the top bar.
+Jupyter notebooks saves output and console so you don't even need to run if you don't want to.
 
 ## Known Issues or Bugs
 
+
 > If there's any known problems with running your app, please list them below.
 
-1. <>
+1. <Map is not generating the border for states where there are no claims for it. Program probably does not recognize as a state since claims: NULL>
 2. <>
 3. <>
 
diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..e48235f
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,19 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pytest = "==7.2.2"
+numpy = "*"
+pandas = "*"
+matplotlib = "*"
+seaborn = "*"
+flask = "==3.0.0"
+geopandas = "*"
+
+[dev-packages]
+ipykernel = "*"
+
+[requires]
+python_version = "3.10"
diff --git a/Pipfile.lock b/Pipfile.lock
new file mode 100644
index 0000000..b4c7188
--- /dev/null
+++ b/Pipfile.lock
@@ -0,0 +1,781 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "9245ebbe6baf660075e041723973012a1f511b80c14867e07df8a45a6954a0bf"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.10"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "attrs": {
+            "hashes": [
+                "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04",
+                "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==23.1.0"
+        },
+        "blinker": {
+            "hashes": [
+                "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9",
+                "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.7.0"
+        },
+        "certifi": {
+            "hashes": [
+                "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082",
+                "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==2023.7.22"
+        },
+        "click": {
+            "hashes": [
+                "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
+                "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==8.1.7"
+        },
+        "click-plugins": {
+            "hashes": [
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+                "sha256:5d262006d3222f5057fd81e1623d4443e41dcda5dc815c06b442aa3c02889fc8"
+            ],
+            "version": "==1.1.1"
+        },
+        "cligj": {
+            "hashes": [
+                "sha256:a4bc13d623356b373c2c27c53dbd9c68cae5d526270bfa71f6c6fa69669c6b27",
+                "sha256:c1ca117dbce1fe20a5809dc96f01e1c2840f6dcc939b3ddbb1111bf330ba82df"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' and python_version < '4'",
+            "version": "==0.7.2"
+        },
+        "colorama": {
+            "hashes": [
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+                "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"
+            ],
+            "markers": "sys_platform == 'win32'",
+            "version": "==0.4.6"
+        },
+        "contourpy": {
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+            ],
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+        },
+        "seaborn": {
+            "hashes": [
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+        },
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+            ],
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+        },
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+            "hashes": [
+                "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc",
+                "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"
+            ],
+            "markers": "python_version < '3.11'",
+            "version": "==2.0.1"
+        },
+        "tzdata": {
+            "hashes": [
+                "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a",
+                "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"
+            ],
+            "markers": "python_version >= '2'",
+            "version": "==2023.3"
+        },
+        "werkzeug": {
+            "hashes": [
+                "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc",
+                "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==3.0.1"
+        }
+    },
+    "develop": {}
+}
diff --git a/application.py b/application.py
new file mode 100644
index 0000000..544ad3b
--- /dev/null
+++ b/application.py
@@ -0,0 +1,148 @@
+# This is the implementation of our simple API to interact with the JSON data files
+# We enabled GET for every JSON. 1 to access all the data e.g: '/claims' (this can be used when displaying a list)
+# and another one to see one sepcific data e.g: 'claim/claim_id' (this can be used when searching for a data and looking for details)
+# We also created PUT and POST for disasters and claims, we thought it would make sense to enable the creation and update of those
+# One could use '/disasters' as a POST to create a new disaster and '/disaster/disaster_id' as a PUT to update an existing one
+
+from flask import Flask, jsonify, request
+import json
+
+app = Flask(__name__)
+
+# File paths
+AGENTS_FILEPATH = 'data/sfcc_2023_agents.json'
+CLAIM_HANDLERS_FILEPATH = 'data/sfcc_2023_claim_handlers.json'
+CLAIMS_FILEPATH = 'data/sfcc_2023_claims.json'
+DISASTERS_FILEPATH = 'data/sfcc_2023_disasters.json'
+
+# Utility function to load data from a given filepath
+def load_data(filepath):
+    with open(filepath, 'r') as file:
+        return json.load(file)
+
+# Utility function to save data to a given filepath
+def save_data(filepath, data):
+    with open(filepath, 'w') as file:
+        json.dump(data, file)
+
+@app.route('/claims', methods=['GET'])
+def get_claims():
+    return jsonify(load_data(CLAIMS_FILEPATH))
+
+@app.route('/claim/<int:claim_id>', methods=['GET'])
+def get_claim_by_id(claim_id):
+    claims = load_data(CLAIMS_FILEPATH)
+    claim = next((claim for claim in claims if claim["id"] == claim_id), None)
+    
+    if claim:
+        return jsonify(claim)
+    else:
+        return jsonify({"error": f"No claim found with ID {claim_id}"}), 404
+
+@app.route('/claims', methods=['POST'])
+def add_claim():
+    claims = load_data(CLAIMS_FILEPATH)
+    new_claim = request.json
+    claims.append(new_claim)
+    save_data(CLAIMS_FILEPATH, claims)
+    return jsonify({"message": "New claim added successfully!"})
+
+@app.route('/claims/<int:claim_id>', methods=['PUT'])
+def update_claim(claim_id):
+    claims = load_data(CLAIMS_FILEPATH)
+    claim_to_update = next((claim for claim in claims if claim["id"] == claim_id), None)
+    
+    if claim_to_update:
+        updated_data = request.json
+        for key, value in updated_data.items():
+            claim_to_update[key] = value
+        save_data(CLAIMS_FILEPATH, claims)
+        return jsonify({"message": f"Claim with ID {claim_id} updated successfully!"})
+    else:
+        return jsonify({"error": f"No claim found with ID {claim_id}"}), 404
+    
+@app.route('/claim_handlers', methods=['GET'])
+def get_claim_handlers():
+    return jsonify(load_data(CLAIM_HANDLERS_FILEPATH))
+
+@app.route('/claim_handler/<int:handler_id>', methods=['GET'])
+def get_claim_handler_by_id(handler_id):
+    claim_handlers = load_data(CLAIM_HANDLERS_FILEPATH)
+    handler = next((handler for handler in claim_handlers if handler["id"] == handler_id), None)
+    
+    if handler:
+        return jsonify(handler)
+    else:
+        return jsonify({"error": f"No claim handler found with ID {handler_id}"}), 404
+    
+@app.route('/agents', methods=['GET'])
+def get_agents():
+    return jsonify(load_data(AGENTS_FILEPATH))
+
+@app.route('/agent/<int:agent_id>', methods=['GET'])
+def get_agent_by_id(agent_id):
+    agents = load_data(AGENTS_FILEPATH)
+    agent = next((agent for agent in agents if agent["id"] == agent_id), None)
+    
+    if agent:
+        return jsonify(agent)
+    else:
+        return jsonify({"error": f"No agent found with ID {agent_id}"}), 404
+
+@app.route('/disasters', methods=['GET'])
+def get_disasters():
+    return jsonify(load_data(DISASTERS_FILEPATH))
+
+@app.route('/disaster/<int:disaster_id>', methods=['GET'])
+def get_disaster_by_id(disaster_id):
+    # Load existing disasters
+    disasters = load_data(DISASTERS_FILEPATH)
+    
+    # Find the disaster with the specified ID
+    disaster = next((disaster for disaster in disasters if disaster["id"] == disaster_id), None)
+    
+    if disaster:
+        return jsonify(disaster)
+    else:
+        return jsonify({"error": f"No disaster found with ID {disaster_id}"}), 404
+
+@app.route('/disasters', methods=['POST'])
+def add_disaster():
+    # Load existing disasters
+    disasters = load_data(DISASTERS_FILEPATH)
+    
+    # Retrieve the new disaster data from the request
+    new_disaster = request.json
+    
+    # Add the new disaster to the existing list
+    disasters.append(new_disaster)
+    
+    # Save the updated disasters list back to the file
+    save_data(DISASTERS_FILEPATH, disasters)
+    
+    return jsonify({"message": "New disaster added successfully!"})
+
+@app.route('/disasters/<int:disaster_id>', methods=['PUT'])
+def update_disaster(disaster_id):
+    # Load existing disasters
+    disasters = load_data(DISASTERS_FILEPATH)
+    
+    # Find the disaster to update
+    disaster_to_update = next((disaster for disaster in disasters if disaster["id"] == disaster_id), None)
+    
+    if disaster_to_update:
+        # Update the found disaster with data from the request
+        updated_data = request.json
+        for key, value in updated_data.items():
+            disaster_to_update[key] = value
+        
+        # Save the updated disasters list back to the file
+        save_data(DISASTERS_FILEPATH, disasters)
+        
+        return jsonify({"message": f"Disaster with ID {disaster_id} updated successfully!"})
+    else:
+        return jsonify({"error": f"No disaster found with ID {disaster_id}"}), 404
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/python/__pycache__/simple_data_tool.cpython-310.pyc b/python/__pycache__/simple_data_tool.cpython-310.pyc
new file mode 100644
index 0000000..53cac35
Binary files /dev/null and b/python/__pycache__/simple_data_tool.cpython-310.pyc differ
diff --git a/python/__pycache__/simple_data_tool.cpython-311.pyc b/python/__pycache__/simple_data_tool.cpython-311.pyc
new file mode 100644
index 0000000..45affe1
Binary files /dev/null and b/python/__pycache__/simple_data_tool.cpython-311.pyc differ
diff --git a/python/data/map/US_States.dbf b/python/data/map/US_States.dbf
new file mode 100644
index 0000000..f9bf260
Binary files /dev/null and b/python/data/map/US_States.dbf differ
diff --git a/python/data/map/US_States.prj b/python/data/map/US_States.prj
new file mode 100644
index 0000000..f45cbad
--- /dev/null
+++ b/python/data/map/US_States.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/python/data/map/US_States.sbn b/python/data/map/US_States.sbn
new file mode 100644
index 0000000..a40541d
Binary files /dev/null and b/python/data/map/US_States.sbn differ
diff --git a/python/data/map/US_States.sbx b/python/data/map/US_States.sbx
new file mode 100644
index 0000000..63e077d
Binary files /dev/null and b/python/data/map/US_States.sbx differ
diff --git a/python/data/map/US_States.shp b/python/data/map/US_States.shp
new file mode 100644
index 0000000..c1c3b4b
Binary files /dev/null and b/python/data/map/US_States.shp differ
diff --git a/python/data/map/US_States.shp.xml b/python/data/map/US_States.shp.xml
new file mode 100644
index 0000000..9dc2e2b
--- /dev/null
+++ b/python/data/map/US_States.shp.xml
@@ -0,0 +1,1763 @@
+<?xml version="1.0" encoding="ISO-8859-1"?>
+<!DOCTYPE metadata SYSTEM "http://www.fgdc.gov/metadata/fgdc-std-001-1998.dtd">
+<metadata>
+	<idinfo>
+		<citation>
+			<citeinfo>
+				<origin>U.S. Bureau of the Census (BOC)</origin>
+				<origin>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</origin>
+				<pubdate>2000</pubdate>
+				<title>U.S. State Boundaries</title>
+				<geoform>vector digital data</geoform>
+				<pubinfo>
+					<pubplace>Suitland, MD</pubplace>
+					<publish>U.S. Census Bureau (BOC)</publish>
+				</pubinfo>
+				<othercit>Compiled by the University of Tennessee
+Center for Transportation Research
+GIS GROUP from TIGER/Line machine readable files.</othercit>
+				<onlink>HSIP GOLD 2011</onlink>
+				<onlink>HSIP GOLD 2011</onlink>
+				<lworkcit>
+					<citeinfo>
+						<origin>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</origin>
+						<pubdate>2010</pubdate>
+						<title>National Transportation Atlas Databases (NTAD) 2010</title>
+						<pubinfo>
+							<pubplace>Washington DC</pubplace>
+							<publish>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</publish>
+						</pubinfo>
+						<onlink>http://www.bts.gov/programs/geographic_information_services/</onlink>
+					</citeinfo>
+				</lworkcit>
+				<ftname></ftname></citeinfo>
+		</citation>
+		<descript>
+			<abstract>The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2000 shapefile data gathered from ESRI's Geography Network.  The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages.  RITA/BTS Hydrographic data was integrated to create detailed shorelines.</abstract>
+			<purpose>The United States State Boundaries database is a geographic database of state political boundaries.  The database includes boundaries for all 50 states plus Puerto Rico and Washington D.C.</purpose>
+			<supplinf>The RITA/BTS State Boundary Database contains TIGER 2000 geometry and attributes. To find out more about TIGER/Line files and other Census TIGER data base derived data sets visit http://www.census.gov/geo/www/tiger.</supplinf>
+			<langdata Sync="TRUE">en</langdata></descript>
+		<timeperd>
+			<timeinfo>
+				<sngdate>
+					<caldate>2000</caldate>
+				</sngdate>
+			</timeinfo>
+			<current>publication date</current>
+		</timeperd>
+		<status>
+			<progress>Complete</progress>
+			<update>None planned</update>
+		</status>
+		<spdom>
+			<bounding>
+				<westbc>
+-179.168566</westbc>
+				<eastbc>
+179.748709</eastbc>
+				<northbc>
+71.389610</northbc>
+				<southbc>
+17.881241</southbc>
+			</bounding>
+			<lboundng><leftbc Sync="TRUE">-179.168566</leftbc><rightbc Sync="TRUE">179.748709</rightbc><bottombc Sync="TRUE">17.881241</bottombc><topbc Sync="TRUE">71.389610</topbc></lboundng></spdom>
+		<keywords>
+			<theme>
+				<themekt>None</themekt>
+				<themekey>state</themekey>
+				<themekey>polygon</themekey>
+				<themekey>line</themekey>
+				<themekey>boundary</themekey>
+			</theme>
+			<place>
+				<placekt>None</placekt>
+				<placekey>United States of America</placekey>
+				<placekey>USA</placekey>
+				<placekey>United States</placekey>
+				<placekey>US</placekey>
+			</place>
+			<stratum>
+				<stratkt>None</stratkt>
+				<stratkey>ground</stratkey>
+			</stratum>
+			<temporal>
+				<tempkt>None</tempkt>
+				<tempkey>2000</tempkey>
+				<tempkey>2006</tempkey>
+				<tempkey>2008</tempkey><tempkey>2009</tempkey><tempkey>2010</tempkey></temporal>
+		</keywords>
+		<accconst>None</accconst>
+		<useconst>Acknowledgement of the Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS) National Transportation Atlas Database (NTAD) 2010 would be appreciated in products derived from these data.</useconst>
+		<ptcontac>
+			<cntinfo>
+				<cntorgp>
+					<cntorg>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</cntorg>
+					<cntper>Office of Geospatial Information Systems</cntper>
+				</cntorgp>
+				<cntaddr>
+					<addrtype>mailing and physical address</addrtype>
+					<address>1200 New Jersey Ave., SE</address>
+					<city>Washington</city>
+					<state>DC</state>
+					<postal>20590</postal>
+					<country>USA</country>
+				</cntaddr>
+				<cntvoice>202-366-3282</cntvoice>
+				<cntemail>answers@bts.gov</cntemail>
+			</cntinfo>
+		</ptcontac>
+		<secinfo>
+			<secsys>none</secsys>
+			<secclass>Unclassified</secclass>
+		</secinfo>
+		<native>Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.1.0.722</native>
+		<crossref>
+			<citeinfo>
+				<origin>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</origin>
+				<origin>U.S. Bureau of the Census (BOC)</origin>
+				<pubdate>2010</pubdate>
+				<title>National Transportation Atlas Databases (NTAD) 2010</title>
+				<geoform>vector digital data</geoform>
+				<pubinfo>
+					<pubplace>Washington, DC</pubplace>
+					<publish>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</publish>
+				</pubinfo>
+				<onlink>http://www.bts.gov/programs/geographic_information_services/</onlink>
+				<lworkcit>
+					<citeinfo>
+						<pubinfo>
+						</pubinfo>
+					</citeinfo>
+				</lworkcit>
+			</citeinfo>
+		</crossref>
+		<natvform Sync="TRUE">Shapefile</natvform></idinfo>
+	<dataqual>
+		<attracc>
+			<attraccr>Attributes for the State Boundary data set were created using ARC/INFO, a Geographic Information System. All area and distance values were calculated to the nearest 50m.</attraccr>
+			<qattracc>
+				<attraccv>unknown</attraccv>
+			</qattracc>
+		</attracc>
+		<logic>All node-link and polygon topological relationships are created and verified using computer algorithms.  Unique entity identifiers were similarly verified by computer algorithm.</logic>
+		<complete>Completeness report not provided.</complete>
+		<posacc>
+			<horizpa>
+				<horizpar>Consistent with TIGER 2000</horizpar>
+				<qhorizpa>
+					<horizpav>unknown</horizpav>
+				</qhorizpa>
+			</horizpa>
+		</posacc>
+		<lineage>
+			<srcinfo>
+				<srccite>
+					<citeinfo>
+						<origin>University of Tennessee Center for Transportation Research GIS GROUP</origin>
+						<pubdate>2001</pubdate>
+						<title>Nationwide State Boundary Database with Detailed Shorelines</title>
+						<lworkcit>
+							<citeinfo>
+								<title>TIGER</title>
+							</citeinfo>
+						</lworkcit>
+					</citeinfo>
+				</srccite>
+				<srcscale>100,000</srcscale>
+				<typesrc>CD-ROM</typesrc>
+				<srccitea>state</srccitea>
+			</srcinfo>
+			<procstep>
+				<procdesc>1)  Downloaded zipped shapefiles from www.geographynetwork.com; 2)  Converted the Shapefiles to Coverages; 3)  Integrated Hydrographic Features into State Borders to create detailed shorelines.</procdesc>
+				<procdate>June 2000</procdate>
+				<proctime>3 Months</proctime>
+			</procstep>
+			<procstep>
+				<procdesc>When UTN processed the TIGER dataset for State, County and Hydrographic (line and polygons) it created a 3-mile buffer around Texas shorelines.  This buffer removed the island off the coast of Texas.  To correct this error, BTS used ARC/INFO to remove the 3-mile buffer to capture the islands along Texas.  To capture the islands the state dataset needed to be reselected, by selecting the code = 1.  This process created a few inaccurate polygons and they were removed.  To create the final datasets, the datasets were dissolved and regions created on STFIPS and County FIPS.</procdesc>
+				<procdate>March 2003</procdate>
+			</procstep>
+			<procstep><procdesc Sync="TRUE">Metadata imported.</procdesc><procdate Sync="TRUE">20101217</procdate><proctime Sync="TRUE">09333500</proctime></procstep></lineage>
+	</dataqual>
+	<spdoinfo>
+		<direct>Vector</direct>
+		<ptvctinf>
+			<sdtsterm Name="US_States">
+				<sdtstype>G-polygon</sdtstype>
+				<ptvctcnt>52</ptvctcnt>
+			</sdtsterm>
+			<esriterm Name="US_States"><efeatyp Sync="TRUE">Simple</efeatyp><efeageom Sync="TRUE">Polygon</efeageom><esritopo Sync="TRUE">FALSE</esritopo><efeacnt Sync="TRUE">52</efeacnt><spindex Sync="TRUE">TRUE</spindex><linrefer Sync="TRUE">FALSE</linrefer></esriterm></ptvctinf>
+	</spdoinfo>
+	<spref>
+		<horizsys>
+			<geograph>
+				<latres>0.000001</latres>
+				<longres>0.000001</longres>
+				<geogunit>Decimal degrees</geogunit>
+			</geograph>
+			<geodetic>
+				<horizdn>D_WGS_1984</horizdn>
+				<ellips>WGS_1984</ellips>
+				<semiaxis>6378137.000000</semiaxis>
+				<denflat>298.257224</denflat>
+			</geodetic>
+			<cordsysn><geogcsn Sync="TRUE">GCS_WGS_1984</geogcsn></cordsysn></horizsys>
+	</spref>
+	<eainfo>
+		<detailed Name="US_States">
+			<enttyp>
+				<enttypl>
+state</enttypl>
+				<enttypd>ESRI Shapefile of U.S. State Boundaries</enttypd>
+				<enttypds>ESRI and BTS</enttypds>
+				<enttypt Sync="TRUE">Feature Class</enttypt><enttypc Sync="TRUE">52</enttypc></enttyp>
+			<attr>
+				<attrlabl>FID</attrlabl>
+				<attrdef>Internal feature number.</attrdef>
+				<attrdefs>ESRI</attrdefs>
+				<attrdomv>
+					<rdom>
+						<rdommin>0</rdommin>
+						<rdommax>51</rdommax>
+					</rdom>
+					<udom Sync="TRUE">Sequential unique whole numbers that are automatically generated.</udom></attrdomv>
+				<attalias Sync="TRUE">FID</attalias><attrtype Sync="TRUE">OID</attrtype><attwidth Sync="TRUE">4</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale></attr>
+			<attr>
+				<attrlabl>Shape</attrlabl>
+				<attrdef>Feature geometry.</attrdef>
+				<attrdefs>ESRI</attrdefs>
+				<attrdomv>
+					<edom>
+						<edomv>Polygon</edomv>
+						<edomvd>A "polygon" is an area feature comprised of points and line segments.</edomvd>
+						<edomvds>ESRI and BTS</edomvds>
+					</edom>
+					<udom Sync="TRUE">Coordinates defining the features.</udom></attrdomv>
+				<attalias Sync="TRUE">Shape</attalias><attrtype Sync="TRUE">Geometry</attrtype><attwidth Sync="TRUE">0</attwidth><atprecis Sync="TRUE">0</atprecis><attscale Sync="TRUE">0</attscale></attr>
+			<attr>
+				<attrlabl>STFIPS</attrlabl>
+				<attrdef>State FIPS code</attrdef>
+				<attrdefs>NIST</attrdefs>
+				<attrdomv>
+					<codesetd>
+						<codesetn>Federal Information Processing Standard (FIPS), Publication 6-4</codesetn>
+						<codesets>National Institute for Standards and Technology (NIST)</codesets>
+					</codesetd>
+				</attrdomv>
+				<attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">2</attwidth><attalias Sync="TRUE">STFIPS</attalias></attr>
+			<attr>
+				<attrlabl>STATE</attrlabl>
+				<attrdef>State name</attrdef>
+				<attrdefs>NIST</attrdefs>
+				<attrdomv>
+					<udom>string </udom></attrdomv>
+				<attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">66</attwidth><attalias Sync="TRUE">STATE</attalias></attr>
+			<attr>
+				<attrlabl>STPOSTAL</attrlabl>
+				<attrdef>U.S. Postal Service two letter state abbreviation.</attrdef>
+				<attrdefs>USPS</attrdefs>
+				<attrdomv>
+					<codesetd>
+						<codesetn>Official List of State Postal Abbreviations</codesetn>
+						<codesets>U.S. Postal Service</codesets>
+					</codesetd>
+				</attrdomv>
+				<attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">2</attwidth><attalias Sync="TRUE">STPOSTAL</attalias></attr>
+			<attr>
+				<attrlabl>VERSION</attrlabl>
+				<attrdef>Version of the database.</attrdef>
+				<attrdefs>RITA/BTS</attrdefs>
+				<attrdomv>
+					<rdom>
+						<rdommin>00</rdommin>
+						<rdommax>99</rdommax>
+					</rdom>
+				</attrdomv>
+				<attrtype Sync="TRUE">String</attrtype><attwidth Sync="TRUE">2</attwidth><attalias Sync="TRUE">VERSION</attalias></attr>
+			<attr><attrlabl Sync="TRUE">DotRegion</attrlabl><attalias Sync="TRUE">DotRegion</attalias><attrtype Sync="TRUE">Number</attrtype><attwidth Sync="TRUE">4</attwidth></attr><attr><attrlabl Sync="TRUE">Shape_Leng</attrlabl><attalias Sync="TRUE">Shape_Leng</attalias><attrtype Sync="TRUE">Float</attrtype><attwidth Sync="TRUE">19</attwidth><atnumdec Sync="TRUE">11</atnumdec></attr><attr><attrlabl Sync="TRUE">Shape_Area</attrlabl><attalias Sync="TRUE">Shape_Area</attalias><attrtype Sync="TRUE">Float</attrtype><attwidth Sync="TRUE">19</attwidth><attrdef Sync="TRUE">Area of feature in internal units squared.</attrdef><attrdefs Sync="TRUE">ESRI</attrdefs><attrdomv><udom Sync="TRUE">Positive real numbers that are automatically generated.</udom></attrdomv><atnumdec Sync="TRUE">11</atnumdec></attr></detailed>
+	</eainfo>
+	<distinfo>
+		<distrib>
+			<cntinfo>
+				<cntorgp>
+					<cntorg>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</cntorg>
+					<cntper>Office of Geospatial Information Systems</cntper>
+				</cntorgp>
+				<cntaddr>
+					<addrtype>mailing and physical address</addrtype>
+					<address>1200 New Jersey Ave., SE</address>
+					<city>Washington</city>
+					<state>DC</state>
+					<postal>20590</postal>
+					<country>USA</country>
+				</cntaddr>
+				<cntvoice>202-366-3282</cntvoice>
+				<cntemail>answers@bts.gov</cntemail>
+			</cntinfo>
+		</distrib>
+		<resdesc>National Transportation Atlas Databases (NTAD) 2010</resdesc>
+		<distliab>None</distliab>
+		<stdorder>
+			<digform>
+				<digtinfo>
+					<formname>ESRI Shapefile</formname>
+					<formvern>2010</formvern>
+					<filedec>No compression applied.  However, if using digital transfer online option, note access instructions.</filedec>
+					<transize>43.235</transize>
+					<dssize Sync="TRUE">44.538</dssize></digtinfo>
+				<digtopt>
+					<onlinopt>
+						<computer>
+							<networka>
+							</networka>
+						</computer>
+						<accinstr>NTAD is available for download at www.bts.gov/programs/geographic_information_services.  The NTAD DVD can also be ordered from the online bookstore at www.bts.gov.</accinstr>
+					</onlinopt>
+					<offoptn>
+						<offmedia>DVD</offmedia>
+						<reccap>
+							<recden>650</recden>
+							<recdenu>megabytes</recdenu>
+						</reccap>
+						<recfmt>ISO 9660</recfmt>
+					</offoptn>
+				</digtopt>
+			</digform>
+			<fees>None</fees>
+			<ordering>Call 202-366-DATA, or E-mail (answers@bts.gov) RITA/BTS to request the National Transportation Atlas Databases (NTAD) 2010 DVD.  This and other RITA/BTS products may be ordered from the RITA/BTS Internet website http://www.bts.gov/programs/geographic_information_services/.</ordering>
+		</stdorder>
+		<techpreq>Software that can process GIS files.</techpreq>
+		<availabl>
+			<timeinfo>
+				<sngdate>
+					<caldate>2010</caldate>
+				</sngdate>
+			</timeinfo>
+		</availabl>
+	</distinfo>
+	<metainfo>
+		<metd>20100330</metd>
+		<metc>
+			<cntinfo>
+				<cntorgp>
+					<cntorg>Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)</cntorg>
+					<cntper>Office of Geospatial Information Systems</cntper>
+				</cntorgp>
+				<cntaddr>
+					<addrtype>mailing and physical address</addrtype>
+					<address>1200 New Jersey Ave., SE</address>
+					<city>Washington</city>
+					<state>DC</state>
+					<postal>20590</postal>
+				</cntaddr>
+				<cntvoice>202-366-3282</cntvoice>
+				<cntemail>answers@bts.gov</cntemail>
+			</cntinfo>
+		</metc>
+		<metstdn>FGDC Content Standards for Digital Geospatial Metadata</metstdn>
+		<metstdv>FGDC-STD-001-1998</metstdv>
+		<mettc>local time</mettc>
+		<metsi>
+			<metsc>Unclassified</metsc>
+		</metsi>
+		<langmeta Sync="TRUE">en</langmeta><metextns><onlink Sync="TRUE">http://www.esri.com/metadata/esriprof80.html</onlink><metprof Sync="TRUE">ESRI Metadata Profile</metprof></metextns></metainfo>
+	<dataIdInfo><envirDesc Sync="TRUE">Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.3.1.1850</envirDesc><dataLang><languageCode Sync="TRUE" value="en"></languageCode></dataLang><idCitation><resTitle>U.S. State Boundaries</resTitle><presForm><PresFormCd Sync="TRUE" value="005"></PresFormCd></presForm></idCitation><spatRpType><SpatRepTypCd Sync="TRUE" value="001"></SpatRepTypCd></spatRpType><dataExt><geoEle><GeoBndBox esriExtentType="native"><westBL Sync="TRUE">-179.168566</westBL><eastBL Sync="TRUE">179.748709</eastBL><northBL Sync="TRUE">71.38961</northBL><southBL Sync="TRUE">17.881241</southBL><exTypeCode Sync="TRUE">1</exTypeCode></GeoBndBox></geoEle></dataExt><geoBox esriExtentType="decdegrees"><westBL Sync="TRUE">-179.168566</westBL><eastBL Sync="TRUE">179.748709</eastBL><northBL Sync="TRUE">71.38961</northBL><southBL Sync="TRUE">17.881241</southBL><exTypeCode Sync="TRUE">1</exTypeCode></geoBox></dataIdInfo><mdLang><languageCode Sync="TRUE" value="en"></languageCode></mdLang><mdStanName Sync="TRUE">ISO 19115 Geographic Information - Metadata</mdStanName><mdStanVer Sync="TRUE">DIS_ESRI1.0</mdStanVer><mdChar><CharSetCd Sync="TRUE" value="004"></CharSetCd></mdChar><mdHrLv><ScopeCd Sync="TRUE" value="005"></ScopeCd></mdHrLv><mdHrLvName Sync="TRUE">dataset</mdHrLvName><distInfo><distributor><distorTran><onLineSrc><orDesc Sync="TRUE">002</orDesc><linkage>HSIP GOLD 2011</linkage><protocol Sync="TRUE">Local Area Network</protocol></onLineSrc><transSize Sync="TRUE">44.538</transSize></distorTran><distorFormat><formatName Sync="TRUE">Shapefile</formatName></distorFormat></distributor></distInfo><refSysInfo><RefSystem><refSysID><identCode Sync="TRUE">GCS_WGS_1984</identCode></refSysID></RefSystem></refSysInfo><spatRepInfo><VectSpatRep><topLvl><TopoLevCd Sync="TRUE" value="001"></TopoLevCd></topLvl><geometObjs Name="US_States"><geoObjTyp><GeoObjTypCd Sync="TRUE" value="001"></GeoObjTypCd></geoObjTyp><geoObjCnt Sync="TRUE">52</geoObjCnt></geometObjs></VectSpatRep></spatRepInfo><Esri><SyncDate>20110630</SyncDate><SyncTime>15210600</SyncTime><ModDate>20110630</ModDate><ModTime>15210600</ModTime><CreaDate>20101217</CreaDate><CreaTime>09333500</CreaTime><SyncOnce>FALSE</SyncOnce><MetaID>{A5A22055-4FC8-427B-B19B-407963E02867}</MetaID><DataProperties><lineage><Process ToolSource="C:\ArcGIS\ArcToolbox\Toolboxes\Conversion Tools.tbx\FeatureClassToFeatureClass" Date="20110621" Time="114421">FeatureClassToFeatureClass "C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States" "C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Shapes/Boundaries" US_States.shp # "STFIPS 'STATE FIPS CODE' true true false 2 Text 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,STFIPS,-1,-1;STATE 'STATE' true true false 66 Text 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,STATE,-1,-1;STPOSTAL 'STATE' true true false 2 Text 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,STPOSTAL,-1,-1;VERSION 'VERSION' true true false 2 Text 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,VERSION,-1,-1;DotRegion 'DEPARTMENT OF TRANSPORTATION REGION' true true false 2 Short 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,DotRegion,-1,-1;Shape_Leng 'Shape_Length' false true true 8 Double 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,Shape_Length,-1,-1;Shape_Area 'Shape_Area' false true true 8 Double 0 0 ,First,#,C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Infrastructure/Boundaries\Boundaries.gdb\US_States,Shape_Area,-1,-1" # "C:/Jeff/HSIP Gold 2011/Data/PublicAccess_Shapes/Boundaries\US_States.shp"</Process></lineage></DataProperties></Esri><mdDateSt Sync="TRUE">20110630</mdDateSt><Binary><Thumbnail><Data EsriPropertyType="Picture">Qk3uNwEAAAAAADYAAAAoAAAAyAAAAIUAAAABABgAAAAAALg3AQAAAAAAAAAAAAAAAAAAAAAA////
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diff --git a/python/data/map/US_States.shx b/python/data/map/US_States.shx
new file mode 100644
index 0000000..0cc3b6d
Binary files /dev/null and b/python/data/map/US_States.shx differ
diff --git a/python/data/sfcc_2023_agents.json b/python/data/sfcc_2023_agents.json
new file mode 100644
index 0000000..645e104
--- /dev/null
+++ b/python/data/sfcc_2023_agents.json
@@ -0,0 +1,1002 @@
+[
+    {
+        "id": 1,
+        "first_name": "Catha",
+        "last_name": "Abrahmer",
+        "state": "Minnesota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 10
+    },
+    {
+        "id": 2,
+        "first_name": "Yetta",
+        "last_name": "Eason",
+        "state": "Oregon",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Korean",
+        "years_active": 12
+    },
+    {
+        "id": 3,
+        "first_name": "Janeta",
+        "last_name": "D'Avaux",
+        "state": "Connecticut",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 47
+    },
+    {
+        "id": 4,
+        "first_name": "Kalie",
+        "last_name": "Tomkins",
+        "state": "Virginia",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 33
+    },
+    {
+        "id": 5,
+        "first_name": "Teddy",
+        "last_name": "Dennitts",
+        "state": "Illinois",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Chinese",
+        "years_active": 48
+    },
+    {
+        "id": 6,
+        "first_name": "Xylina",
+        "last_name": "MacMurray",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 17
+    },
+    {
+        "id": 7,
+        "first_name": "Walker",
+        "last_name": "Nosworthy",
+        "state": "Tennessee",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 39
+    },
+    {
+        "id": 8,
+        "first_name": "Kip",
+        "last_name": "Scuffham",
+        "state": "Maine",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "French",
+        "years_active": 35
+    },
+    {
+        "id": 9,
+        "first_name": "Winifred",
+        "last_name": "Duddy",
+        "state": "North Dakota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 32
+    },
+    {
+        "id": 10,
+        "first_name": "Layney",
+        "last_name": "Drewry",
+        "state": "Maryland",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 25
+    },
+    {
+        "id": 11,
+        "first_name": "Beatrice",
+        "last_name": "Sprowles",
+        "state": "Arizona",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 7
+    },
+    {
+        "id": 12,
+        "first_name": "Candida",
+        "last_name": "Dungay",
+        "state": "South Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Chinese",
+        "years_active": 43
+    },
+    {
+        "id": 13,
+        "first_name": "Whitby",
+        "last_name": "Awin",
+        "state": "Pennsylvania",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 1
+    },
+    {
+        "id": 14,
+        "first_name": "Buck",
+        "last_name": "Withringten",
+        "state": "Alabama",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 16
+    },
+    {
+        "id": 15,
+        "first_name": "Ladonna",
+        "last_name": "Earengey",
+        "state": "Indiana",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 36
+    },
+    {
+        "id": 16,
+        "first_name": "Guglielmo",
+        "last_name": "Lamboll",
+        "state": "Vermont",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 4
+    },
+    {
+        "id": 17,
+        "first_name": "Gardner",
+        "last_name": "Nasey",
+        "state": "Idaho",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 21
+    },
+    {
+        "id": 18,
+        "first_name": "Georgina",
+        "last_name": "Lovegrove",
+        "state": "California",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 11
+    },
+    {
+        "id": 19,
+        "first_name": "Xaviera",
+        "last_name": "Darell",
+        "state": "Massachusetts",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 10
+    },
+    {
+        "id": 20,
+        "first_name": "Dick",
+        "last_name": "Andras",
+        "state": "Ohio",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 14
+    },
+    {
+        "id": 21,
+        "first_name": "Luise",
+        "last_name": "Reeken",
+        "state": "North Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 14
+    },
+    {
+        "id": 22,
+        "first_name": "Gale",
+        "last_name": "Cantua",
+        "state": "Arkansas",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 2
+    },
+    {
+        "id": 23,
+        "first_name": "Gracie",
+        "last_name": "Polland",
+        "state": "Washington",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 29
+    },
+    {
+        "id": 24,
+        "first_name": "Connie",
+        "last_name": "Loyley",
+        "state": "Ohio",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 33
+    },
+    {
+        "id": 25,
+        "first_name": "Laryssa",
+        "last_name": "Affleck",
+        "state": "South Dakota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 7
+    },
+    {
+        "id": 26,
+        "first_name": "Erina",
+        "last_name": "Coan",
+        "state": "North Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 43
+    },
+    {
+        "id": 27,
+        "first_name": "Evered",
+        "last_name": "Sergent",
+        "state": "Utah",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 50
+    },
+    {
+        "id": 28,
+        "first_name": "Cassandry",
+        "last_name": "Curston",
+        "state": "New Mexico",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 49
+    },
+    {
+        "id": 29,
+        "first_name": "Cirstoforo",
+        "last_name": "Yurov",
+        "state": "Washington",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 34
+    },
+    {
+        "id": 30,
+        "first_name": "Hewe",
+        "last_name": "Matveiko",
+        "state": "Missouri",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 25
+    },
+    {
+        "id": 31,
+        "first_name": "Nevil",
+        "last_name": "O' Hern",
+        "state": "Alabama",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "French",
+        "years_active": 19
+    },
+    {
+        "id": 32,
+        "first_name": "Cynthea",
+        "last_name": "Pitkins",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 16
+    },
+    {
+        "id": 33,
+        "first_name": "Irita",
+        "last_name": "Segebrecht",
+        "state": "South Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 38
+    },
+    {
+        "id": 34,
+        "first_name": "Loraine",
+        "last_name": "Turmel",
+        "state": "Iowa",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 21
+    },
+    {
+        "id": 35,
+        "first_name": "Doris",
+        "last_name": "Illem",
+        "state": "Oklahoma",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 5
+    },
+    {
+        "id": 36,
+        "first_name": "Maritsa",
+        "last_name": "Hulme",
+        "state": "Michigan",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 28
+    },
+    {
+        "id": 37,
+        "first_name": "Hagen",
+        "last_name": "Baxter",
+        "state": "Oregon",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 44
+    },
+    {
+        "id": 38,
+        "first_name": "Patty",
+        "last_name": "Saxelby",
+        "state": "Hawaii",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 4
+    },
+    {
+        "id": 39,
+        "first_name": "Hans",
+        "last_name": "Hardway",
+        "state": "Georgia",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 47
+    },
+    {
+        "id": 40,
+        "first_name": "Lind",
+        "last_name": "Loughton",
+        "state": "Rhode Island",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 6
+    },
+    {
+        "id": 41,
+        "first_name": "Cynthie",
+        "last_name": "Gytesham",
+        "state": "North Dakota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 36
+    },
+    {
+        "id": 42,
+        "first_name": "Dougie",
+        "last_name": "Faulconer",
+        "state": "Utah",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 15
+    },
+    {
+        "id": 43,
+        "first_name": "Briano",
+        "last_name": "Lowell",
+        "state": "Missouri",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 36
+    },
+    {
+        "id": 44,
+        "first_name": "Eli",
+        "last_name": "Ginity",
+        "state": "Nevada",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 7
+    },
+    {
+        "id": 45,
+        "first_name": "Wendell",
+        "last_name": "Tolputt",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 50
+    },
+    {
+        "id": 46,
+        "first_name": "Briggs",
+        "last_name": "Snasdell",
+        "state": "Minnesota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 6
+    },
+    {
+        "id": 47,
+        "first_name": "Peadar",
+        "last_name": "D'Hooge",
+        "state": "Idaho",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 17
+    },
+    {
+        "id": 48,
+        "first_name": "Babbette",
+        "last_name": "Tower",
+        "state": "Rhode Island",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 45
+    },
+    {
+        "id": 49,
+        "first_name": "Tedda",
+        "last_name": "Highwood",
+        "state": "Kansas",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 41
+    },
+    {
+        "id": 50,
+        "first_name": "Davina",
+        "last_name": "Totton",
+        "state": "Oregon",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Korean",
+        "years_active": 35
+    },
+    {
+        "id": 51,
+        "first_name": "Blondelle",
+        "last_name": "MacDearmaid",
+        "state": "South Dakota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 31
+    },
+    {
+        "id": 52,
+        "first_name": "Vinnie",
+        "last_name": "Chree",
+        "state": "South Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 43
+    },
+    {
+        "id": 53,
+        "first_name": "Boniface",
+        "last_name": "Antwis",
+        "state": "Rhode Island",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 25
+    },
+    {
+        "id": 54,
+        "first_name": "Merrill",
+        "last_name": "Pallin",
+        "state": "New Hampshire",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 40
+    },
+    {
+        "id": 55,
+        "first_name": "Brent",
+        "last_name": "Alywen",
+        "state": "New Mexico",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 13
+    },
+    {
+        "id": 56,
+        "first_name": "Vinnie",
+        "last_name": "Lindemann",
+        "state": "District of Columbia",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 45
+    },
+    {
+        "id": 57,
+        "first_name": "Pooh",
+        "last_name": "Wilce",
+        "state": "Louisiana",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 19
+    },
+    {
+        "id": 58,
+        "first_name": "Kaiser",
+        "last_name": "Toffolini",
+        "state": "Ohio",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Chinese",
+        "years_active": 21
+    },
+    {
+        "id": 59,
+        "first_name": "Clark",
+        "last_name": "Tussaine",
+        "state": "Washington",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 1
+    },
+    {
+        "id": 60,
+        "first_name": "Annissa",
+        "last_name": "Moxted",
+        "state": "Utah",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 26
+    },
+    {
+        "id": 61,
+        "first_name": "Imelda",
+        "last_name": "McMenamie",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 6
+    },
+    {
+        "id": 62,
+        "first_name": "Melanie",
+        "last_name": "Blunsom",
+        "state": "Arizona",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 49
+    },
+    {
+        "id": 63,
+        "first_name": "Sarge",
+        "last_name": "Iredale",
+        "state": "Alaska",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 6
+    },
+    {
+        "id": 64,
+        "first_name": "Hanson",
+        "last_name": "Buxam",
+        "state": "Nevada",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 35
+    },
+    {
+        "id": 65,
+        "first_name": "Nettle",
+        "last_name": "Apps",
+        "state": "Hawaii",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 19
+    },
+    {
+        "id": 66,
+        "first_name": "Joellen",
+        "last_name": "Chapiro",
+        "state": "Nebraska",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "French",
+        "years_active": 25
+    },
+    {
+        "id": 67,
+        "first_name": "Smith",
+        "last_name": "Budden",
+        "state": "Hawaii",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Chinese",
+        "years_active": 7
+    },
+    {
+        "id": 68,
+        "first_name": "Roz",
+        "last_name": "Sygroves",
+        "state": "Alaska",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 42
+    },
+    {
+        "id": 69,
+        "first_name": "Emylee",
+        "last_name": "Simonetto",
+        "state": "Wyoming",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 24
+    },
+    {
+        "id": 70,
+        "first_name": "Trev",
+        "last_name": "Balint",
+        "state": "District of Columbia",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 14
+    },
+    {
+        "id": 71,
+        "first_name": "Karrah",
+        "last_name": "Gartenfeld",
+        "state": "Mississippi",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "French",
+        "years_active": 25
+    },
+    {
+        "id": 72,
+        "first_name": "Newton",
+        "last_name": "Orro",
+        "state": "Kentucky",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 40
+    },
+    {
+        "id": 73,
+        "first_name": "Letta",
+        "last_name": "Fury",
+        "state": "Massachusetts",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 15
+    },
+    {
+        "id": 74,
+        "first_name": "Ashien",
+        "last_name": "McGeachey",
+        "state": "Massachusetts",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Chinese",
+        "years_active": 46
+    },
+    {
+        "id": 75,
+        "first_name": "Calypso",
+        "last_name": "Yuryev",
+        "state": "Alaska",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 16
+    },
+    {
+        "id": 76,
+        "first_name": "Rhys",
+        "last_name": "Kobelt",
+        "state": "California",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 34
+    },
+    {
+        "id": 77,
+        "first_name": "Gaspard",
+        "last_name": "Pegden",
+        "state": "Michigan",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 35
+    },
+    {
+        "id": 78,
+        "first_name": "Lory",
+        "last_name": "de Juares",
+        "state": "Alabama",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 27
+    },
+    {
+        "id": 79,
+        "first_name": "Nils",
+        "last_name": "Brashier",
+        "state": "Texas",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 18
+    },
+    {
+        "id": 80,
+        "first_name": "Idalia",
+        "last_name": "Farleigh",
+        "state": "North Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 16
+    },
+    {
+        "id": 81,
+        "first_name": "Vi",
+        "last_name": "Barkus",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 33
+    },
+    {
+        "id": 82,
+        "first_name": "Darcee",
+        "last_name": "Timewell",
+        "state": "Arizona",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 26
+    },
+    {
+        "id": 83,
+        "first_name": "Rozanna",
+        "last_name": "Heakins",
+        "state": "Minnesota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "French",
+        "years_active": 20
+    },
+    {
+        "id": 84,
+        "first_name": "Jemima",
+        "last_name": "Rosling",
+        "state": "California",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Korean",
+        "years_active": 37
+    },
+    {
+        "id": 85,
+        "first_name": "Fancie",
+        "last_name": "Kilner",
+        "state": "Nebraska",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 6
+    },
+    {
+        "id": 86,
+        "first_name": "Gabrila",
+        "last_name": "Ridgedell",
+        "state": "California",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": "Arabic",
+        "years_active": 32
+    },
+    {
+        "id": 87,
+        "first_name": "Curcio",
+        "last_name": "Musgrave",
+        "state": "Colorado",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 19
+    },
+    {
+        "id": 88,
+        "first_name": "Honey",
+        "last_name": "Duffill",
+        "state": "Pennsylvania",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 26
+    },
+    {
+        "id": 89,
+        "first_name": "Harley",
+        "last_name": "Vittel",
+        "state": "Arizona",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 20
+    },
+    {
+        "id": 90,
+        "first_name": "Blake",
+        "last_name": "Penddreth",
+        "state": "New Jersey",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 36
+    },
+    {
+        "id": 91,
+        "first_name": "Starlene",
+        "last_name": "Boscott",
+        "state": "Illinois",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": "Italian",
+        "years_active": 34
+    },
+    {
+        "id": 92,
+        "first_name": "Orsa",
+        "last_name": "Bellamy",
+        "state": "Florida",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Spanish",
+        "years_active": 38
+    },
+    {
+        "id": 93,
+        "first_name": "Philis",
+        "last_name": "Bover",
+        "state": "Rhode Island",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 25
+    },
+    {
+        "id": 94,
+        "first_name": "Kathe",
+        "last_name": "Tipler",
+        "state": "New York",
+        "region": "Northeast",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 47
+    },
+    {
+        "id": 95,
+        "first_name": "Durand",
+        "last_name": "Ellwood",
+        "state": "Alaska",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 45
+    },
+    {
+        "id": 96,
+        "first_name": "Brana",
+        "last_name": "Drinan",
+        "state": "South Dakota",
+        "region": "Midwest",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 42
+    },
+    {
+        "id": 97,
+        "first_name": "Wiatt",
+        "last_name": "Kitchenman",
+        "state": "North Carolina",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Korean",
+        "years_active": 40
+    },
+    {
+        "id": 98,
+        "first_name": "Lionello",
+        "last_name": "Krauze",
+        "state": "Mississippi",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "German",
+        "years_active": 48
+    },
+    {
+        "id": 99,
+        "first_name": "Louella",
+        "last_name": "Evangelinos",
+        "state": "Georgia",
+        "region": "South",
+        "primary_language": "English",
+        "secondary_language": "Korean",
+        "years_active": 6
+    },
+    {
+        "id": 100,
+        "first_name": "Christa",
+        "last_name": "McPartling",
+        "state": "California",
+        "region": "West",
+        "primary_language": "English",
+        "secondary_language": null,
+        "years_active": 39
+    }
+]
diff --git a/python/data/sfcc_2023_claim_handlers.json b/python/data/sfcc_2023_claim_handlers.json
new file mode 100644
index 0000000..6d951b5
--- /dev/null
+++ b/python/data/sfcc_2023_claim_handlers.json
@@ -0,0 +1,782 @@
+[
+    {
+        "first_name": "Barnabe",
+        "last_name": "Clynman",
+        "id": 1
+    },
+    {
+        "first_name": "Valdemar",
+        "last_name": "Size",
+        "id": 2
+    },
+    {
+        "first_name": "Edithe",
+        "last_name": "Bleakley",
+        "id": 3
+    },
+    {
+        "first_name": "Sonja",
+        "last_name": "Diamant",
+        "id": 4
+    },
+    {
+        "first_name": "Elsey",
+        "last_name": "Sreenan",
+        "id": 5
+    },
+    {
+        "first_name": "Wilmer",
+        "last_name": "Sykes",
+        "id": 6
+    },
+    {
+        "first_name": "Wang",
+        "last_name": "California",
+        "id": 7
+    },
+    {
+        "first_name": "Shadow",
+        "last_name": "Cabane",
+        "id": 8
+    },
+    {
+        "first_name": "Deborah",
+        "last_name": "Longfellow",
+        "id": 9
+    },
+    {
+        "first_name": "Albertina",
+        "last_name": "McCrorie",
+        "id": 10
+    },
+    {
+        "first_name": "Trude",
+        "last_name": "Vakhonin",
+        "id": 11
+    },
+    {
+        "first_name": "Corabel",
+        "last_name": "Greeding",
+        "id": 12
+    },
+    {
+        "first_name": "Farra",
+        "last_name": "Pickett",
+        "id": 13
+    },
+    {
+        "first_name": "Sal",
+        "last_name": "Plott",
+        "id": 14
+    },
+    {
+        "first_name": "Eddi",
+        "last_name": "Cicchitello",
+        "id": 15
+    },
+    {
+        "first_name": "Jonas",
+        "last_name": "East",
+        "id": 16
+    },
+    {
+        "first_name": "Emiline",
+        "last_name": "Moultrie",
+        "id": 17
+    },
+    {
+        "first_name": "Laverna",
+        "last_name": "Rignold",
+        "id": 18
+    },
+    {
+        "first_name": "Ludovico",
+        "last_name": "Hebborn",
+        "id": 19
+    },
+    {
+        "first_name": "Des",
+        "last_name": "Tubritt",
+        "id": 20
+    },
+    {
+        "first_name": "Fritz",
+        "last_name": "Angove",
+        "id": 21
+    },
+    {
+        "first_name": "Persis",
+        "last_name": "Dinesen",
+        "id": 22
+    },
+    {
+        "first_name": "Christian",
+        "last_name": "Kelshaw",
+        "id": 23
+    },
+    {
+        "first_name": "Bartholomeo",
+        "last_name": "McBlain",
+        "id": 24
+    },
+    {
+        "first_name": "Lorrie",
+        "last_name": "Lightbourn",
+        "id": 25
+    },
+    {
+        "first_name": "Bobbi",
+        "last_name": "Adriani",
+        "id": 26
+    },
+    {
+        "first_name": "Heriberto",
+        "last_name": "Carlesi",
+        "id": 27
+    },
+    {
+        "first_name": "Stearn",
+        "last_name": "Quilligan",
+        "id": 28
+    },
+    {
+        "first_name": "Amalle",
+        "last_name": "Vanyutin",
+        "id": 29
+    },
+    {
+        "first_name": "Wilden",
+        "last_name": "Stratley",
+        "id": 30
+    },
+    {
+        "first_name": "Netty",
+        "last_name": "Motion",
+        "id": 31
+    },
+    {
+        "first_name": "Garvin",
+        "last_name": "Cotte",
+        "id": 32
+    },
+    {
+        "first_name": "Ashby",
+        "last_name": "Woolbrook",
+        "id": 33
+    },
+    {
+        "first_name": "Modesty",
+        "last_name": "Doldon",
+        "id": 34
+    },
+    {
+        "first_name": "Maurene",
+        "last_name": "Hesbrook",
+        "id": 35
+    },
+    {
+        "first_name": "Cindee",
+        "last_name": "Mogey",
+        "id": 36
+    },
+    {
+        "first_name": "Korie",
+        "last_name": "Donwell",
+        "id": 37
+    },
+    {
+        "first_name": "Casi",
+        "last_name": "Miebes",
+        "id": 38
+    },
+    {
+        "first_name": "Alvira",
+        "last_name": "Loxdale",
+        "id": 39
+    },
+    {
+        "first_name": "Ellis",
+        "last_name": "Cadagan",
+        "id": 40
+    },
+    {
+        "first_name": "Jarib",
+        "last_name": "Brownsett",
+        "id": 41
+    },
+    {
+        "first_name": "Esdras",
+        "last_name": "Gullis",
+        "id": 42
+    },
+    {
+        "first_name": "Jordain",
+        "last_name": "Gaine",
+        "id": 43
+    },
+    {
+        "first_name": "Kingsly",
+        "last_name": "Kelemen",
+        "id": 44
+    },
+    {
+        "first_name": "Sallee",
+        "last_name": "Ashworth",
+        "id": 45
+    },
+    {
+        "first_name": "Erika",
+        "last_name": "Willmont",
+        "id": 46
+    },
+    {
+        "first_name": "Rinaldo",
+        "last_name": "Standbrook",
+        "id": 47
+    },
+    {
+        "first_name": "Abbey",
+        "last_name": "Scholer",
+        "id": 48
+    },
+    {
+        "first_name": "Colver",
+        "last_name": "Ilyenko",
+        "id": 49
+    },
+    {
+        "first_name": "Phillipp",
+        "last_name": "Klulik",
+        "id": 50
+    },
+    {
+        "first_name": "Rancell",
+        "last_name": "Salvidge",
+        "id": 51
+    },
+    {
+        "first_name": "Lesli",
+        "last_name": "Sidry",
+        "id": 52
+    },
+    {
+        "first_name": "Ellette",
+        "last_name": "Connikie",
+        "id": 53
+    },
+    {
+        "first_name": "Olivie",
+        "last_name": "Easterbrook",
+        "id": 54
+    },
+    {
+        "first_name": "Idell",
+        "last_name": "McVicker",
+        "id": 55
+    },
+    {
+        "first_name": "Tonie",
+        "last_name": "Roos",
+        "id": 56
+    },
+    {
+        "first_name": "Herby",
+        "last_name": "Duffit",
+        "id": 57
+    },
+    {
+        "first_name": "Lila",
+        "last_name": "Byrd",
+        "id": 58
+    },
+    {
+        "first_name": "Shayna",
+        "last_name": "McCluskey",
+        "id": 59
+    },
+    {
+        "first_name": "Ellie",
+        "last_name": "Bottomer",
+        "id": 60
+    },
+    {
+        "first_name": "Gardie",
+        "last_name": "Lamputt",
+        "id": 61
+    },
+    {
+        "first_name": "Angelina",
+        "last_name": "Incogna",
+        "id": 62
+    },
+    {
+        "first_name": "Berget",
+        "last_name": "Nussen",
+        "id": 63
+    },
+    {
+        "first_name": "Alley",
+        "last_name": "Wrighton",
+        "id": 64
+    },
+    {
+        "first_name": "Dione",
+        "last_name": "Shire",
+        "id": 65
+    },
+    {
+        "first_name": "Ashly",
+        "last_name": "Connichie",
+        "id": 66
+    },
+    {
+        "first_name": "Ossie",
+        "last_name": "Besson",
+        "id": 67
+    },
+    {
+        "first_name": "Luce",
+        "last_name": "Jackling",
+        "id": 68
+    },
+    {
+        "first_name": "Juliann",
+        "last_name": "Wickham",
+        "id": 69
+    },
+    {
+        "first_name": "Lillian",
+        "last_name": "Lansdale",
+        "id": 70
+    },
+    {
+        "first_name": "Cornie",
+        "last_name": "Tindall",
+        "id": 71
+    },
+    {
+        "first_name": "Dina",
+        "last_name": "Alster",
+        "id": 72
+    },
+    {
+        "first_name": "Normand",
+        "last_name": "Mein",
+        "id": 73
+    },
+    {
+        "first_name": "Wildon",
+        "last_name": "Rizzillo",
+        "id": 74
+    },
+    {
+        "first_name": "Hailee",
+        "last_name": "Jeroch",
+        "id": 75
+    },
+    {
+        "first_name": "Corissa",
+        "last_name": "Newarte",
+        "id": 76
+    },
+    {
+        "first_name": "Rancell",
+        "last_name": "Chrispin",
+        "id": 77
+    },
+    {
+        "first_name": "Kariotta",
+        "last_name": "Zammett",
+        "id": 78
+    },
+    {
+        "first_name": "Tammy",
+        "last_name": "Jeannet",
+        "id": 79
+    },
+    {
+        "first_name": "Osbourne",
+        "last_name": "Heinert",
+        "id": 80
+    },
+    {
+        "first_name": "Marcia",
+        "last_name": "Kaes",
+        "id": 81
+    },
+    {
+        "first_name": "Curry",
+        "last_name": "MacCracken",
+        "id": 82
+    },
+    {
+        "first_name": "Duke",
+        "last_name": "Ramalhete",
+        "id": 83
+    },
+    {
+        "first_name": "Domeniga",
+        "last_name": "Cutajar",
+        "id": 84
+    },
+    {
+        "first_name": "Blaire",
+        "last_name": "Kolyagin",
+        "id": 85
+    },
+    {
+        "first_name": "Kerry",
+        "last_name": "Glassard",
+        "id": 86
+    },
+    {
+        "first_name": "Adolphe",
+        "last_name": "Snook",
+        "id": 87
+    },
+    {
+        "first_name": "Byrom",
+        "last_name": "Kaygill",
+        "id": 88
+    },
+    {
+        "first_name": "Neddie",
+        "last_name": "Klimaszewski",
+        "id": 89
+    },
+    {
+        "first_name": "Neal",
+        "last_name": "Marzelle",
+        "id": 90
+    },
+    {
+        "first_name": "Laureen",
+        "last_name": "Ezzell",
+        "id": 91
+    },
+    {
+        "first_name": "Davine",
+        "last_name": "Druce",
+        "id": 92
+    },
+    {
+        "first_name": "Carny",
+        "last_name": "Baird",
+        "id": 93
+    },
+    {
+        "first_name": "Thadeus",
+        "last_name": "Alcido",
+        "id": 94
+    },
+    {
+        "first_name": "Berry",
+        "last_name": "Chanter",
+        "id": 95
+    },
+    {
+        "first_name": "Kamillah",
+        "last_name": "Mott",
+        "id": 96
+    },
+    {
+        "first_name": "Godfree",
+        "last_name": "Cains",
+        "id": 97
+    },
+    {
+        "first_name": "Rubia",
+        "last_name": "Blundan",
+        "id": 98
+    },
+    {
+        "first_name": "Bart",
+        "last_name": "Godain",
+        "id": 99
+    },
+    {
+        "first_name": "Carlina",
+        "last_name": "Chamberlayne",
+        "id": 100
+    },
+    {
+        "first_name": "Alaric",
+        "last_name": "Dils",
+        "id": 101
+    },
+    {
+        "first_name": "Tracy",
+        "last_name": "Bamell",
+        "id": 102
+    },
+    {
+        "first_name": "Laural",
+        "last_name": "Melly",
+        "id": 103
+    },
+    {
+        "first_name": "Bary",
+        "last_name": "Ableson",
+        "id": 104
+    },
+    {
+        "first_name": "Joell",
+        "last_name": "M'Chirrie",
+        "id": 105
+    },
+    {
+        "first_name": "Danni",
+        "last_name": "Vaines",
+        "id": 106
+    },
+    {
+        "first_name": "Emelia",
+        "last_name": "Bullough",
+        "id": 107
+    },
+    {
+        "first_name": "Cinnamon",
+        "last_name": "Flounders",
+        "id": 108
+    },
+    {
+        "first_name": "Farica",
+        "last_name": "Soaper",
+        "id": 109
+    },
+    {
+        "first_name": "Peggy",
+        "last_name": "Lemerie",
+        "id": 110
+    },
+    {
+        "first_name": "Craig",
+        "last_name": "Greenslade",
+        "id": 111
+    },
+    {
+        "first_name": "Zitella",
+        "last_name": "Bachman",
+        "id": 112
+    },
+    {
+        "first_name": "Willa",
+        "last_name": "Schlag",
+        "id": 113
+    },
+    {
+        "first_name": "Leonerd",
+        "last_name": "Dewes",
+        "id": 114
+    },
+    {
+        "first_name": "Jacquelin",
+        "last_name": "Willavize",
+        "id": 115
+    },
+    {
+        "first_name": "Amble",
+        "last_name": "Brahams",
+        "id": 116
+    },
+    {
+        "first_name": "Rona",
+        "last_name": "McGlew",
+        "id": 117
+    },
+    {
+        "first_name": "Libbi",
+        "last_name": "Cargenven",
+        "id": 118
+    },
+    {
+        "first_name": "Ina",
+        "last_name": "Le",
+        "id": 119
+    },
+    {
+        "first_name": "Jamie",
+        "last_name": "Skittle",
+        "id": 120
+    },
+    {
+        "first_name": "Stephani",
+        "last_name": "Battle",
+        "id": 121
+    },
+    {
+        "first_name": "Rogers",
+        "last_name": "Ciccetti",
+        "id": 122
+    },
+    {
+        "first_name": "Calv",
+        "last_name": "Drinkhill",
+        "id": 123
+    },
+    {
+        "first_name": "Timothea",
+        "last_name": "Sprulls",
+        "id": 124
+    },
+    {
+        "first_name": "Darb",
+        "last_name": "Seston",
+        "id": 125
+    },
+    {
+        "first_name": "Jessee",
+        "last_name": "Wale",
+        "id": 126
+    },
+    {
+        "first_name": "Eve",
+        "last_name": "Reith",
+        "id": 127
+    },
+    {
+        "first_name": "Rolland",
+        "last_name": "Currier",
+        "id": 128
+    },
+    {
+        "first_name": "Hayes",
+        "last_name": "Halshaw",
+        "id": 129
+    },
+    {
+        "first_name": "Pieter",
+        "last_name": "Ventris",
+        "id": 130
+    },
+    {
+        "first_name": "Dalia",
+        "last_name": "Blades",
+        "id": 131
+    },
+    {
+        "first_name": "Sabine",
+        "last_name": "Fader",
+        "id": 132
+    },
+    {
+        "first_name": "Steward",
+        "last_name": "Rogez",
+        "id": 133
+    },
+    {
+        "first_name": "Vanya",
+        "last_name": "Cicchinelli",
+        "id": 134
+    },
+    {
+        "first_name": "Rene",
+        "last_name": "Rockhill",
+        "id": 135
+    },
+    {
+        "first_name": "Marji",
+        "last_name": "Coulbeck",
+        "id": 136
+    },
+    {
+        "first_name": "Nicolle",
+        "last_name": "Strain",
+        "id": 137
+    },
+    {
+        "first_name": "Xaviera",
+        "last_name": "Swash",
+        "id": 138
+    },
+    {
+        "first_name": "Selle",
+        "last_name": "Johnston",
+        "id": 139
+    },
+    {
+        "first_name": "Caressa",
+        "last_name": "Bavridge",
+        "id": 140
+    },
+    {
+        "first_name": "Benedict",
+        "last_name": "Robichon",
+        "id": 141
+    },
+    {
+        "first_name": "Rogerio",
+        "last_name": "Armitage",
+        "id": 142
+    },
+    {
+        "first_name": "Otto",
+        "last_name": "Nelius",
+        "id": 143
+    },
+    {
+        "first_name": "Bing",
+        "last_name": "Ferrai",
+        "id": 144
+    },
+    {
+        "first_name": "Avril",
+        "last_name": "Wagner",
+        "id": 145
+    },
+    {
+        "first_name": "Devondra",
+        "last_name": "Cutforth",
+        "id": 146
+    },
+    {
+        "first_name": "Giustino",
+        "last_name": "Minchella",
+        "id": 147
+    },
+    {
+        "first_name": "Dore",
+        "last_name": "McIlvaney",
+        "id": 148
+    },
+    {
+        "first_name": "Elene",
+        "last_name": "Lukacs",
+        "id": 149
+    },
+    {
+        "first_name": "Leicester",
+        "last_name": "Mattedi",
+        "id": 150
+    },
+    {
+        "first_name": "Blayne",
+        "last_name": "Wordsworth",
+        "id": 151
+    },
+    {
+        "first_name": "Bidget",
+        "last_name": "Royle",
+        "id": 152
+    },
+    {
+        "first_name": "Heidie",
+        "last_name": "Cossem",
+        "id": 153
+    },
+    {
+        "first_name": "Seward",
+        "last_name": "Greenhead",
+        "id": 154
+    },
+    {
+        "first_name": "Ariana",
+        "last_name": "Duigan",
+        "id": 155
+    },
+    {
+        "first_name": "Rollin",
+        "last_name": "Dainton",
+        "id": 156
+    }
+]
\ No newline at end of file
diff --git a/python/data/sfcc_2023_claims.json b/python/data/sfcc_2023_claims.json
new file mode 100644
index 0000000..6c4e089
--- /dev/null
+++ b/python/data/sfcc_2023_claims.json
@@ -0,0 +1,12002 @@
+[
+    {
+        "id": 1,
+        "disaster_id": 15,
+        "status": "Received",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 4,
+        "estimate_cost": 3580.02,
+        "agent_assigned_id": 97,
+        "claim_handler_assigned_id": 50
+    },
+    {
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+        "disaster_id": 24,
+        "status": "In Review",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 6,
+        "estimate_cost": 1741.32,
+        "agent_assigned_id": 36,
+        "claim_handler_assigned_id": 92
+    },
+    {
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+        "disaster_id": 31,
+        "status": "Received",
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+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 4,
+        "estimate_cost": 15224.3,
+        "agent_assigned_id": 94,
+        "claim_handler_assigned_id": 133
+    },
+    {
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+        "status": "Received",
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+        "loss_of_life": true,
+        "type": "Auto",
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+        "estimate_cost": 6542.46,
+        "agent_assigned_id": 92,
+        "claim_handler_assigned_id": 39
+    },
+    {
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+        "status": "Closed",
+        "total_loss": false,
+        "loss_of_life": false,
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+        "estimate_cost": 979.81,
+        "agent_assigned_id": 86,
+        "claim_handler_assigned_id": 103
+    },
+    {
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+        "disaster_id": 77,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 9,
+        "estimate_cost": 200430.52,
+        "agent_assigned_id": 98,
+        "claim_handler_assigned_id": 19
+    },
+    {
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+        "disaster_id": 31,
+        "status": "Received",
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+        "loss_of_life": false,
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+        "estimate_cost": 1362.17,
+        "agent_assigned_id": 94,
+        "claim_handler_assigned_id": 156
+    },
+    {
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+        "disaster_id": 17,
+        "status": "Closed",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Home",
+        "severity_rating": 8,
+        "estimate_cost": 438400.51,
+        "agent_assigned_id": 3,
+        "claim_handler_assigned_id": 112
+    },
+    {
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+        "disaster_id": 20,
+        "status": "In Review",
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+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 4,
+        "estimate_cost": 1326.11,
+        "agent_assigned_id": 94,
+        "claim_handler_assigned_id": 35
+    },
+    {
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+        "status": "In Review",
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+        "loss_of_life": true,
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+        "estimate_cost": 437.48,
+        "agent_assigned_id": 100,
+        "claim_handler_assigned_id": 49
+    },
+    {
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+        "status": "In Review",
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+        "loss_of_life": false,
+        "type": "Auto",
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+        "estimate_cost": 8797.07,
+        "agent_assigned_id": 100,
+        "claim_handler_assigned_id": 68
+    },
+    {
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+        "status": "Closed",
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+        "estimate_cost": 2918.5,
+        "agent_assigned_id": 83,
+        "claim_handler_assigned_id": 150
+    },
+    {
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+        "estimate_cost": 23890.38,
+        "agent_assigned_id": 26,
+        "claim_handler_assigned_id": 19
+    },
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+        "estimate_cost": 1681.48,
+        "agent_assigned_id": 79,
+        "claim_handler_assigned_id": 117
+    },
+    {
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+        "estimate_cost": 993.53,
+        "agent_assigned_id": 85,
+        "claim_handler_assigned_id": 59
+    },
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+        "estimate_cost": 39400.46,
+        "agent_assigned_id": 88,
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+    },
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+        "estimate_cost": 140820.81,
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+        "claim_handler_assigned_id": 11
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+        "claim_handler_assigned_id": 118
+    },
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+        "estimate_cost": 11272.93,
+        "agent_assigned_id": 79,
+        "claim_handler_assigned_id": 28
+    },
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+        "status": "Received",
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+        "estimate_cost": 81160.19,
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+        "claim_handler_assigned_id": 108
+    },
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+        "status": "In Review",
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+        "type": "Life",
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+        "estimate_cost": 28912.89,
+        "agent_assigned_id": 4,
+        "claim_handler_assigned_id": 11
+    },
+    {
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+        "claim_handler_assigned_id": 152
+    },
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+        "claim_handler_assigned_id": 11
+    },
+    {
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+        "estimate_cost": 1256.87,
+        "agent_assigned_id": 70,
+        "claim_handler_assigned_id": 27
+    },
+    {
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+        "claim_handler_assigned_id": 31
+    },
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+        "estimate_cost": 8764.87,
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+        "claim_handler_assigned_id": 148
+    },
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+        "estimate_cost": 95060.58,
+        "agent_assigned_id": 86,
+        "claim_handler_assigned_id": 19
+    },
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+        "status": "Received",
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+        "estimate_cost": 72510.72,
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+    },
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+        "status": "Closed",
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+        "type": "Life",
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+        "estimate_cost": 860000.96,
+        "agent_assigned_id": 15,
+        "claim_handler_assigned_id": 34
+    },
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+        "estimate_cost": 440.44,
+        "agent_assigned_id": 92,
+        "claim_handler_assigned_id": 17
+    },
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+        "estimate_cost": 420540.13,
+        "agent_assigned_id": 24,
+        "claim_handler_assigned_id": 85
+    },
+    {
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+        "type": "Auto",
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+        "estimate_cost": 972.58,
+        "agent_assigned_id": 57,
+        "claim_handler_assigned_id": 143
+    },
+    {
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+        "status": "Received",
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+        "estimate_cost": 186210.41,
+        "agent_assigned_id": 79,
+        "claim_handler_assigned_id": 114
+    },
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+    },
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+    },
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+    },
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+    },
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+        "estimate_cost": 76840.43,
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+        "claim_handler_assigned_id": 57
+    },
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+        "estimate_cost": 748080.1,
+        "agent_assigned_id": 27,
+        "claim_handler_assigned_id": 142
+    },
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+        "estimate_cost": 6508.15,
+        "agent_assigned_id": 86,
+        "claim_handler_assigned_id": 41
+    },
+    {
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+        "status": "Closed",
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+        "claim_handler_assigned_id": 140
+    },
+    {
+        "id": 980,
+        "disaster_id": 99,
+        "status": "In Review",
+        "total_loss": true,
+        "loss_of_life": false,
+        "type": "Home",
+        "severity_rating": 5,
+        "estimate_cost": 19410.9,
+        "agent_assigned_id": 7,
+        "claim_handler_assigned_id": 95
+    },
+    {
+        "id": 981,
+        "disaster_id": 42,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 5,
+        "estimate_cost": 43560.01,
+        "agent_assigned_id": 87,
+        "claim_handler_assigned_id": 40
+    },
+    {
+        "id": 982,
+        "disaster_id": 52,
+        "status": "Received",
+        "total_loss": false,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 1,
+        "estimate_cost": 584.99,
+        "agent_assigned_id": 5,
+        "claim_handler_assigned_id": 48
+    },
+    {
+        "id": 983,
+        "disaster_id": 94,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": false,
+        "type": "Home",
+        "severity_rating": 4,
+        "estimate_cost": 20256.5,
+        "agent_assigned_id": 15,
+        "claim_handler_assigned_id": 136
+    },
+    {
+        "id": 984,
+        "disaster_id": 3,
+        "status": "Closed",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 3,
+        "estimate_cost": 2400.82,
+        "agent_assigned_id": 4,
+        "claim_handler_assigned_id": 53
+    },
+    {
+        "id": 985,
+        "disaster_id": 63,
+        "status": "In Review",
+        "total_loss": false,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 2,
+        "estimate_cost": 969.22,
+        "agent_assigned_id": 100,
+        "claim_handler_assigned_id": 122
+    },
+    {
+        "id": 986,
+        "disaster_id": 99,
+        "status": "In Review",
+        "total_loss": true,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 9,
+        "estimate_cost": 63981.35,
+        "agent_assigned_id": 7,
+        "claim_handler_assigned_id": 47
+    },
+    {
+        "id": 987,
+        "disaster_id": 27,
+        "status": "In Review",
+        "total_loss": false,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 5,
+        "estimate_cost": 985.83,
+        "agent_assigned_id": 1,
+        "claim_handler_assigned_id": 125
+    },
+    {
+        "id": 988,
+        "disaster_id": 93,
+        "status": "In Review",
+        "total_loss": false,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 4,
+        "estimate_cost": 3833.72,
+        "agent_assigned_id": 43,
+        "claim_handler_assigned_id": 15
+    },
+    {
+        "id": 989,
+        "disaster_id": 68,
+        "status": "Received",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 6,
+        "estimate_cost": 2935.22,
+        "agent_assigned_id": 10,
+        "claim_handler_assigned_id": 67
+    },
+    {
+        "id": 990,
+        "disaster_id": 95,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Home",
+        "severity_rating": 2,
+        "estimate_cost": 17720.19,
+        "agent_assigned_id": 84,
+        "claim_handler_assigned_id": 66
+    },
+    {
+        "id": 991,
+        "disaster_id": 34,
+        "status": "Closed",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 10,
+        "estimate_cost": 913300.2,
+        "agent_assigned_id": 24,
+        "claim_handler_assigned_id": 129
+    },
+    {
+        "id": 992,
+        "disaster_id": 12,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Home",
+        "severity_rating": 3,
+        "estimate_cost": 79980.03,
+        "agent_assigned_id": 92,
+        "claim_handler_assigned_id": 92
+    },
+    {
+        "id": 993,
+        "disaster_id": 42,
+        "status": "Closed",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Auto",
+        "severity_rating": 6,
+        "estimate_cost": 4862.2,
+        "agent_assigned_id": 87,
+        "claim_handler_assigned_id": 110
+    },
+    {
+        "id": 994,
+        "disaster_id": 82,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Home",
+        "severity_rating": 5,
+        "estimate_cost": 177150.91,
+        "agent_assigned_id": 79,
+        "claim_handler_assigned_id": 88
+    },
+    {
+        "id": 995,
+        "disaster_id": 15,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 9,
+        "estimate_cost": 248670.91,
+        "agent_assigned_id": 21,
+        "claim_handler_assigned_id": 140
+    },
+    {
+        "id": 996,
+        "disaster_id": 58,
+        "status": "Received",
+        "total_loss": false,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 4,
+        "estimate_cost": 1662.24,
+        "agent_assigned_id": 83,
+        "claim_handler_assigned_id": 114
+    },
+    {
+        "id": 997,
+        "disaster_id": 95,
+        "status": "In Review",
+        "total_loss": false,
+        "loss_of_life": false,
+        "type": "Home",
+        "severity_rating": 6,
+        "estimate_cost": 2078.24,
+        "agent_assigned_id": 84,
+        "claim_handler_assigned_id": 80
+    },
+    {
+        "id": 998,
+        "disaster_id": 66,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 2,
+        "estimate_cost": 17420.91,
+        "agent_assigned_id": 94,
+        "claim_handler_assigned_id": 113
+    },
+    {
+        "id": 999,
+        "disaster_id": 17,
+        "status": "Closed",
+        "total_loss": true,
+        "loss_of_life": true,
+        "type": "Auto",
+        "severity_rating": 10,
+        "estimate_cost": 484100.78,
+        "agent_assigned_id": 3,
+        "claim_handler_assigned_id": 85
+    },
+    {
+        "id": 1000,
+        "disaster_id": 9,
+        "status": "Received",
+        "total_loss": true,
+        "loss_of_life": false,
+        "type": "Life",
+        "severity_rating": 6,
+        "estimate_cost": 42330.53,
+        "agent_assigned_id": 94,
+        "claim_handler_assigned_id": 156
+    }
+]
\ No newline at end of file
diff --git a/python/data/sfcc_2023_disasters.json b/python/data/sfcc_2023_disasters.json
new file mode 100644
index 0000000..eb8f6ac
--- /dev/null
+++ b/python/data/sfcc_2023_disasters.json
@@ -0,0 +1,1302 @@
+[
+    {
+        "id": 1,
+        "type": "Flood",
+        "state": "Alaska",
+        "name": "Alaska Flood",
+        "description": "Fusce consequat. Nulla nisl. Nunc nisl.\n\nDuis bibendum, felis sed interdum venenatis, turpis enim blandit mi, in porttitor pede justo eu massa. Donec dapibus. Duis at velit eu est congue elementum.\n\nIn hac habitasse platea dictumst. Morbi vestibulum, velit id pretium iaculis, diam erat fermentum justo, nec condimentum neque sapien placerat ante. Nulla justo.",
+        "start_date": "2023-06-13",
+        "end_date": "2023-06-20",
+        "declared_date": "2023-06-17",
+        "lat": 58.3271,
+        "long": -134.4742,
+        "radius_miles": 168
+    },
+    {
+        "id": 2,
+        "type": "Wildfire",
+        "state": "Texas",
+        "name": "Texas Wildfire",
+        "description": "Duis consequat dui nec nisi volutpat eleifend. Donec ut dolor. Morbi vel lectus in quam fringilla rhoncus.\n\nMauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.",
+        "start_date": "2023-04-11",
+        "end_date": "2023-04-19",
+        "declared_date": "2023-04-12",
+        "lat": 33.5693,
+        "long": -101.8904,
+        "radius_miles": 235
+    },
+    {
+        "id": 3,
+        "type": "Tornado",
+        "state": "Virginia",
+        "name": "Virginia Tornado",
+        "description": "Nullam sit amet turpis elementum ligula vehicula consequat. Morbi a ipsum. Integer a nibh.\n\nIn quis justo. Maecenas rhoncus aliquam lacus. Morbi quis tortor id nulla ultrices aliquet.\n\nMaecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.",
+        "start_date": "2023-04-29",
+        "end_date": "2023-05-07",
+        "declared_date": "2023-05-06",
+        "lat": 38.9776,
+        "long": -77.386,
+        "radius_miles": 273
+    },
+    {
+        "id": 4,
+        "type": "Earthquake",
+        "state": "Connecticut",
+        "name": "Connecticut Earthquake",
+        "description": "Fusce consequat. Nulla nisl. Nunc nisl.",
+        "start_date": "2023-04-24",
+        "end_date": "2023-05-09",
+        "declared_date": "2023-04-28",
+        "lat": 41.3657,
+        "long": -72.9275,
+        "radius_miles": 120
+    },
+    {
+        "id": 5,
+        "type": "Winter Storm",
+        "state": "Missouri",
+        "name": "Missouri Winter Storm",
+        "description": "Suspendisse potenti. In eleifend quam a odio. In hac habitasse platea dictumst.",
+        "start_date": "2023-03-11",
+        "end_date": "2023-03-21",
+        "declared_date": "2023-03-14",
+        "lat": 39.0663,
+        "long": -94.5674,
+        "radius_miles": 155
+    },
+    {
+        "id": 6,
+        "type": "Tornado",
+        "state": "California",
+        "name": "California Tornado",
+        "description": "Mauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.\n\nNullam sit amet turpis elementum ligula vehicula consequat. Morbi a ipsum. Integer a nibh.",
+        "start_date": "2022-11-15",
+        "end_date": "2022-11-19",
+        "declared_date": "2022-11-20",
+        "lat": 38.025,
+        "long": -121.2972,
+        "radius_miles": 142
+    },
+    {
+        "id": 7,
+        "type": "Thunderstorm",
+        "state": "Massachusetts",
+        "name": "Massachusetts Thunderstorm",
+        "description": "Sed ante. Vivamus tortor. Duis mattis egestas metus.\n\nAenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.\n\nQuisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.",
+        "start_date": "2023-05-19",
+        "end_date": "2023-06-06",
+        "declared_date": "2023-05-26",
+        "lat": 41.6913,
+        "long": -70.9355,
+        "radius_miles": 266
+    },
+    {
+        "id": 8,
+        "type": "Earthquake",
+        "state": "Tennessee",
+        "name": "Tennessee Earthquake",
+        "description": "Morbi porttitor lorem id ligula. Suspendisse ornare consequat lectus. In est risus, auctor sed, tristique in, tempus sit amet, sem.",
+        "start_date": "2023-04-13",
+        "end_date": "2023-04-21",
+        "declared_date": "2023-04-20",
+        "lat": 35.6124,
+        "long": -88.8412,
+        "radius_miles": 147
+    },
+    {
+        "id": 9,
+        "type": "Thunderstorm",
+        "state": "New York",
+        "name": "New York Thunderstorm",
+        "description": "Curabitur gravida nisi at nibh. In hac habitasse platea dictumst. Aliquam augue quam, sollicitudin vitae, consectetuer eget, rutrum at, lorem.",
+        "start_date": "2022-11-04",
+        "end_date": "2022-11-15",
+        "declared_date": "2022-11-06",
+        "lat": 40.7536,
+        "long": -73.8226,
+        "radius_miles": 48
+    },
+    {
+        "id": 10,
+        "type": "Winter Storm",
+        "state": "Nebraska",
+        "name": "Nebraska Winter Storm",
+        "description": "Maecenas ut massa quis augue luctus tincidunt. Nulla mollis molestie lorem. Quisque ut erat.\n\nCurabitur gravida nisi at nibh. In hac habitasse platea dictumst. Aliquam augue quam, sollicitudin vitae, consectetuer eget, rutrum at, lorem.",
+        "start_date": "2022-11-28",
+        "end_date": "2022-12-03",
+        "declared_date": "2022-11-30",
+        "lat": 40.8529,
+        "long": -96.7943,
+        "radius_miles": 239
+    },
+    {
+        "id": 11,
+        "type": "Wildfire",
+        "state": "Kansas",
+        "name": "Kansas Wildfire",
+        "description": "Mauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.",
+        "start_date": "2022-11-12",
+        "end_date": "2022-11-28",
+        "declared_date": "2022-11-17",
+        "lat": 38.8999,
+        "long": -94.832,
+        "radius_miles": 169
+    },
+    {
+        "id": 12,
+        "type": "Landslide",
+        "state": "Florida",
+        "name": "Florida Landslide",
+        "description": "Donec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2022-12-01",
+        "end_date": "2022-12-07",
+        "declared_date": "2022-12-03",
+        "lat": 28.5559,
+        "long": -81.3535,
+        "radius_miles": 102
+    },
+    {
+        "id": 13,
+        "type": "Hurricane",
+        "state": "Texas",
+        "name": "Texas Hurricane Bliss",
+        "description": "Proin interdum mauris non ligula pellentesque ultrices. Phasellus id sapien in sapien iaculis congue. Vivamus metus arcu, adipiscing molestie, hendrerit at, vulputate vitae, nisl.\n\nAenean lectus. Pellentesque eget nunc. Donec quis orci eget orci vehicula condimentum.\n\nCurabitur in libero ut massa volutpat convallis. Morbi odio odio, elementum eu, interdum eu, tincidunt in, leo. Maecenas pulvinar lobortis est.",
+        "start_date": "2023-03-29",
+        "end_date": "2023-04-12",
+        "declared_date": "2023-04-05",
+        "lat": 30.43,
+        "long": -97.8326,
+        "radius_miles": 186
+    },
+    {
+        "id": 14,
+        "type": "Flood",
+        "state": "Florida",
+        "name": "Florida Flood",
+        "description": "Aliquam quis turpis eget elit sodales scelerisque. Mauris sit amet eros. Suspendisse accumsan tortor quis turpis.\n\nSed ante. Vivamus tortor. Duis mattis egestas metus.\n\nAenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.",
+        "start_date": "2023-04-05",
+        "end_date": "2023-04-18",
+        "declared_date": "2023-04-12",
+        "lat": 30.4793,
+        "long": -84.3462,
+        "radius_miles": 110
+    },
+    {
+        "id": 15,
+        "type": "Wildfire",
+        "state": "North Carolina",
+        "name": "North Carolina Wildfire",
+        "description": "In sagittis dui vel nisl. Duis ac nibh. Fusce lacus purus, aliquet at, feugiat non, pretium quis, lectus.\n\nSuspendisse potenti. In eleifend quam a odio. In hac habitasse platea dictumst.\n\nMaecenas ut massa quis augue luctus tincidunt. Nulla mollis molestie lorem. Quisque ut erat.",
+        "start_date": "2023-03-28",
+        "end_date": "2023-04-04",
+        "declared_date": "2023-03-30",
+        "lat": 36.1824,
+        "long": -79.806,
+        "radius_miles": 36
+    },
+    {
+        "id": 16,
+        "type": "Flood",
+        "state": "New York",
+        "name": "New York Flood",
+        "description": "Duis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.\n\nDonec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2023-02-27",
+        "end_date": "2023-03-03",
+        "declared_date": "2023-02-28",
+        "lat": 42.8161,
+        "long": -73.9398,
+        "radius_miles": 182
+    },
+    {
+        "id": 17,
+        "type": "Thunderstorm",
+        "state": "Connecticut",
+        "name": "Connecticut Thunderstorm",
+        "description": "Donec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.\n\nDuis bibendum. Morbi non quam nec dui luctus rutrum. Nulla tellus.\n\nIn sagittis dui vel nisl. Duis ac nibh. Fusce lacus purus, aliquet at, feugiat non, pretium quis, lectus.",
+        "start_date": "2023-04-05",
+        "end_date": "2023-04-19",
+        "declared_date": "2023-04-08",
+        "lat": 41.3089,
+        "long": -73.3637,
+        "radius_miles": 57
+    },
+    {
+        "id": 18,
+        "type": "Wildfire",
+        "state": "Georgia",
+        "name": "Georgia Wildfire",
+        "description": "Proin eu mi. Nulla ac enim. In tempor, turpis nec euismod scelerisque, quam turpis adipiscing lorem, vitae mattis nibh ligula nec sem.\n\nDuis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.\n\nDonec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2023-03-08",
+        "end_date": "2023-03-20",
+        "declared_date": "2023-03-14",
+        "lat": 31.55,
+        "long": -84.0612,
+        "radius_miles": 31
+    },
+    {
+        "id": 19,
+        "type": "Earthquake",
+        "state": "Georgia",
+        "name": "Georgia Earthquake",
+        "description": "Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.\n\nPraesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.",
+        "start_date": "2023-04-01",
+        "end_date": "2023-04-11",
+        "declared_date": "2023-04-02",
+        "lat": 32.491,
+        "long": -84.8741,
+        "radius_miles": 79
+    },
+    {
+        "id": 20,
+        "type": "Landslide",
+        "state": "New York",
+        "name": "New York Landslide",
+        "description": "Sed ante. Vivamus tortor. Duis mattis egestas metus.\n\nAenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.\n\nQuisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.",
+        "start_date": "2023-01-01",
+        "end_date": "2023-01-18",
+        "declared_date": "2023-01-03",
+        "lat": 40.6407,
+        "long": -73.9028,
+        "radius_miles": 164
+    },
+    {
+        "id": 21,
+        "type": "Winter Storm",
+        "state": "Minnesota",
+        "name": "Minnesota Winter Storm",
+        "description": "Quisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.\n\nPhasellus in felis. Donec semper sapien a libero. Nam dui.",
+        "start_date": "2023-01-02",
+        "end_date": "2023-01-19",
+        "declared_date": "2023-01-07",
+        "lat": 45.0059,
+        "long": -93.1059,
+        "radius_miles": 185
+    },
+    {
+        "id": 22,
+        "type": "Hurricane",
+        "state": "District of Columbia",
+        "name": "District of Columbia Hurricane Carolina",
+        "description": "Morbi porttitor lorem id ligula. Suspendisse ornare consequat lectus. In est risus, auctor sed, tristique in, tempus sit amet, sem.\n\nFusce consequat. Nulla nisl. Nunc nisl.",
+        "start_date": "2023-06-17",
+        "end_date": "2023-06-22",
+        "declared_date": "2023-06-24",
+        "lat": 38.8933,
+        "long": -77.0146,
+        "radius_miles": 269
+    },
+    {
+        "id": 23,
+        "type": "Winter Storm",
+        "state": "Florida",
+        "name": "Florida Winter Storm",
+        "description": "Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.",
+        "start_date": "2023-01-27",
+        "end_date": "2023-02-14",
+        "declared_date": "2023-01-30",
+        "lat": 30.6143,
+        "long": -87.2758,
+        "radius_miles": 121
+    },
+    {
+        "id": 24,
+        "type": "Earthquake",
+        "state": "Michigan",
+        "name": "Michigan Earthquake",
+        "description": "Aliquam quis turpis eget elit sodales scelerisque. Mauris sit amet eros. Suspendisse accumsan tortor quis turpis.\n\nSed ante. Vivamus tortor. Duis mattis egestas metus.",
+        "start_date": "2022-10-27",
+        "end_date": "2022-11-11",
+        "declared_date": "2022-11-03",
+        "lat": 42.3749,
+        "long": -83.1087,
+        "radius_miles": 97
+    },
+    {
+        "id": 25,
+        "type": "Landslide",
+        "state": "Texas",
+        "name": "Texas Landslide",
+        "description": "In quis justo. Maecenas rhoncus aliquam lacus. Morbi quis tortor id nulla ultrices aliquet.\n\nMaecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.",
+        "start_date": "2022-10-14",
+        "end_date": "2022-11-02",
+        "declared_date": "2022-10-17",
+        "lat": 29.4189,
+        "long": -98.6895,
+        "radius_miles": 130
+    },
+    {
+        "id": 26,
+        "type": "Thunderstorm",
+        "state": "Indiana",
+        "name": "Indiana Thunderstorm",
+        "description": "In congue. Etiam justo. Etiam pretium iaculis justo.\n\nIn hac habitasse platea dictumst. Etiam faucibus cursus urna. Ut tellus.\n\nNulla ut erat id mauris vulputate elementum. Nullam varius. Nulla facilisi.",
+        "start_date": "2023-04-27",
+        "end_date": "2023-05-18",
+        "declared_date": "2023-04-29",
+        "lat": 39.4336,
+        "long": -87.4101,
+        "radius_miles": 103
+    },
+    {
+        "id": 27,
+        "type": "Winter Storm",
+        "state": "Minnesota",
+        "name": "Minnesota Winter Storm",
+        "description": "Maecenas ut massa quis augue luctus tincidunt. Nulla mollis molestie lorem. Quisque ut erat.",
+        "start_date": "2022-11-16",
+        "end_date": "2022-11-22",
+        "declared_date": "2022-11-23",
+        "lat": 45.2009,
+        "long": -93.8881,
+        "radius_miles": 23
+    },
+    {
+        "id": 28,
+        "type": "Tornado",
+        "state": "Oklahoma",
+        "name": "Oklahoma Tornado",
+        "description": "Praesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.\n\nCras mi pede, malesuada in, imperdiet et, commodo vulputate, justo. In blandit ultrices enim. Lorem ipsum dolor sit amet, consectetuer adipiscing elit.",
+        "start_date": "2023-03-30",
+        "end_date": "2023-04-11",
+        "declared_date": "2023-04-03",
+        "lat": 35.411,
+        "long": -97.4388,
+        "radius_miles": 101
+    },
+    {
+        "id": 29,
+        "type": "Earthquake",
+        "state": "Pennsylvania",
+        "name": "Pennsylvania Earthquake",
+        "description": "Praesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.\n\nCras mi pede, malesuada in, imperdiet et, commodo vulputate, justo. In blandit ultrices enim. Lorem ipsum dolor sit amet, consectetuer adipiscing elit.",
+        "start_date": "2022-12-09",
+        "end_date": "2022-12-30",
+        "declared_date": "2022-12-14",
+        "lat": 42.1109,
+        "long": -80.1534,
+        "radius_miles": 125
+    },
+    {
+        "id": 30,
+        "type": "Thunderstorm",
+        "state": "California",
+        "name": "California Thunderstorm",
+        "description": "Sed sagittis. Nam congue, risus semper porta volutpat, quam pede lobortis ligula, sit amet eleifend pede libero quis orci. Nullam molestie nibh in lectus.\n\nPellentesque at nulla. Suspendisse potenti. Cras in purus eu magna vulputate luctus.",
+        "start_date": "2023-05-24",
+        "end_date": "2023-06-06",
+        "declared_date": "2023-05-29",
+        "lat": 34.4453,
+        "long": -119.8025,
+        "radius_miles": 282
+    },
+    {
+        "id": 31,
+        "type": "Landslide",
+        "state": "New York",
+        "name": "New York Landslide",
+        "description": "Proin interdum mauris non ligula pellentesque ultrices. Phasellus id sapien in sapien iaculis congue. Vivamus metus arcu, adipiscing molestie, hendrerit at, vulputate vitae, nisl.\n\nAenean lectus. Pellentesque eget nunc. Donec quis orci eget orci vehicula condimentum.",
+        "start_date": "2022-10-19",
+        "end_date": "2022-10-31",
+        "declared_date": "2022-10-20",
+        "lat": 43.1577,
+        "long": -77.608,
+        "radius_miles": 119
+    },
+    {
+        "id": 32,
+        "type": "Winter Storm",
+        "state": "Massachusetts",
+        "name": "Massachusetts Winter Storm",
+        "description": "Nulla ut erat id mauris vulputate elementum. Nullam varius. Nulla facilisi.",
+        "start_date": "2023-01-12",
+        "end_date": "2023-01-28",
+        "declared_date": "2023-01-18",
+        "lat": 42.3253,
+        "long": -71.1122,
+        "radius_miles": 79
+    },
+    {
+        "id": 33,
+        "type": "Wildfire",
+        "state": "Texas",
+        "name": "Texas Wildfire",
+        "description": "Nulla ut erat id mauris vulputate elementum. Nullam varius. Nulla facilisi.",
+        "start_date": "2022-11-08",
+        "end_date": "2022-11-15",
+        "declared_date": "2022-11-11",
+        "lat": 32.7673,
+        "long": -96.7776,
+        "radius_miles": 255
+    },
+    {
+        "id": 34,
+        "type": "Flood",
+        "state": "Ohio",
+        "name": "Ohio Flood",
+        "description": "Phasellus sit amet erat. Nulla tempus. Vivamus in felis eu sapien cursus vestibulum.",
+        "start_date": "2023-01-25",
+        "end_date": "2023-01-30",
+        "declared_date": "2023-01-29",
+        "lat": 41.6767,
+        "long": -83.5573,
+        "radius_miles": 142
+    },
+    {
+        "id": 35,
+        "type": "Landslide",
+        "state": "Nebraska",
+        "name": "Nebraska Landslide",
+        "description": "Praesent blandit. Nam nulla. Integer pede justo, lacinia eget, tincidunt eget, tempus vel, pede.\n\nMorbi porttitor lorem id ligula. Suspendisse ornare consequat lectus. In est risus, auctor sed, tristique in, tempus sit amet, sem.",
+        "start_date": "2023-04-08",
+        "end_date": "2023-04-16",
+        "declared_date": "2023-04-10",
+        "lat": 41.2338,
+        "long": -96.0495,
+        "radius_miles": 7
+    },
+    {
+        "id": 36,
+        "type": "Thunderstorm",
+        "state": "Tennessee",
+        "name": "Tennessee Thunderstorm",
+        "description": "Maecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.",
+        "start_date": "2023-04-27",
+        "end_date": "2023-05-13",
+        "declared_date": "2023-04-30",
+        "lat": 35.9918,
+        "long": -83.8496,
+        "radius_miles": 53
+    },
+    {
+        "id": 37,
+        "type": "Winter Storm",
+        "state": "Texas",
+        "name": "Texas Winter Storm",
+        "description": "Vestibulum ac est lacinia nisi venenatis tristique. Fusce congue, diam id ornare imperdiet, sapien urna pretium nisl, ut volutpat sapien arcu sed augue. Aliquam erat volutpat.\n\nIn congue. Etiam justo. Etiam pretium iaculis justo.\n\nIn hac habitasse platea dictumst. Etiam faucibus cursus urna. Ut tellus.",
+        "start_date": "2022-10-27",
+        "end_date": "2022-11-01",
+        "declared_date": "2022-11-02",
+        "lat": 31.8767,
+        "long": -102.4375,
+        "radius_miles": 193
+    },
+    {
+        "id": 38,
+        "type": "Winter Storm",
+        "state": "Ohio",
+        "name": "Ohio Winter Storm",
+        "description": "In hac habitasse platea dictumst. Morbi vestibulum, velit id pretium iaculis, diam erat fermentum justo, nec condimentum neque sapien placerat ante. Nulla justo.",
+        "start_date": "2023-02-10",
+        "end_date": "2023-03-03",
+        "declared_date": "2023-02-16",
+        "lat": 41.5008,
+        "long": -81.5516,
+        "radius_miles": 59
+    },
+    {
+        "id": 39,
+        "type": "Thunderstorm",
+        "state": "Arizona",
+        "name": "Arizona Thunderstorm",
+        "description": "In congue. Etiam justo. Etiam pretium iaculis justo.",
+        "start_date": "2022-10-07",
+        "end_date": "2022-10-23",
+        "declared_date": "2022-10-14",
+        "lat": 33.2765,
+        "long": -112.1872,
+        "radius_miles": 191
+    },
+    {
+        "id": 40,
+        "type": "Thunderstorm",
+        "state": "Florida",
+        "name": "Florida Thunderstorm",
+        "description": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Proin risus. Praesent lectus.\n\nVestibulum quam sapien, varius ut, blandit non, interdum in, ante. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Duis faucibus accumsan odio. Curabitur convallis.\n\nDuis consequat dui nec nisi volutpat eleifend. Donec ut dolor. Morbi vel lectus in quam fringilla rhoncus.",
+        "start_date": "2022-10-16",
+        "end_date": "2022-10-25",
+        "declared_date": "2022-10-18",
+        "lat": 27.872,
+        "long": -82.4388,
+        "radius_miles": 29
+    },
+    {
+        "id": 41,
+        "type": "Flood",
+        "state": "Kansas",
+        "name": "Kansas Flood",
+        "description": "Donec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2023-01-29",
+        "end_date": "2023-02-03",
+        "declared_date": "2023-01-30",
+        "lat": 38.9613,
+        "long": -94.8222,
+        "radius_miles": 244
+    },
+    {
+        "id": 42,
+        "type": "Wildfire",
+        "state": "Colorado",
+        "name": "Colorado Wildfire",
+        "description": "Curabitur gravida nisi at nibh. In hac habitasse platea dictumst. Aliquam augue quam, sollicitudin vitae, consectetuer eget, rutrum at, lorem.\n\nInteger tincidunt ante vel ipsum. Praesent blandit lacinia erat. Vestibulum sed magna at nunc commodo placerat.\n\nPraesent blandit. Nam nulla. Integer pede justo, lacinia eget, tincidunt eget, tempus vel, pede.",
+        "start_date": "2023-05-11",
+        "end_date": "2023-05-29",
+        "declared_date": "2023-05-17",
+        "lat": 39.7388,
+        "long": -104.4083,
+        "radius_miles": 31
+    },
+    {
+        "id": 43,
+        "type": "Flood",
+        "state": "Michigan",
+        "name": "Michigan Flood",
+        "description": "Pellentesque at nulla. Suspendisse potenti. Cras in purus eu magna vulputate luctus.\n\nCum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.",
+        "start_date": "2022-12-09",
+        "end_date": "2022-12-30",
+        "declared_date": "2022-12-15",
+        "lat": 43.0349,
+        "long": -83.6887,
+        "radius_miles": 199
+    },
+    {
+        "id": 44,
+        "type": "Tornado",
+        "state": "California",
+        "name": "California Tornado",
+        "description": "Nulla ut erat id mauris vulputate elementum. Nullam varius. Nulla facilisi.\n\nCras non velit nec nisi vulputate nonummy. Maecenas tincidunt lacus at velit. Vivamus vel nulla eget eros elementum pellentesque.\n\nQuisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.",
+        "start_date": "2022-10-14",
+        "end_date": "2022-10-25",
+        "declared_date": "2022-10-21",
+        "lat": 33.0169,
+        "long": -116.846,
+        "radius_miles": 268
+    },
+    {
+        "id": 45,
+        "type": "Wildfire",
+        "state": "Tennessee",
+        "name": "Tennessee Wildfire",
+        "description": "Maecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.\n\nNullam porttitor lacus at turpis. Donec posuere metus vitae ipsum. Aliquam non mauris.",
+        "start_date": "2022-10-14",
+        "end_date": "2022-10-27",
+        "declared_date": "2022-10-20",
+        "lat": 35.9918,
+        "long": -83.8496,
+        "radius_miles": 144
+    },
+    {
+        "id": 46,
+        "type": "Landslide",
+        "state": "Florida",
+        "name": "Florida Landslide",
+        "description": "Mauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.\n\nNullam sit amet turpis elementum ligula vehicula consequat. Morbi a ipsum. Integer a nibh.\n\nIn quis justo. Maecenas rhoncus aliquam lacus. Morbi quis tortor id nulla ultrices aliquet.",
+        "start_date": "2022-11-19",
+        "end_date": "2022-12-01",
+        "declared_date": "2022-11-22",
+        "lat": 28.1147,
+        "long": -82.3678,
+        "radius_miles": 96
+    },
+    {
+        "id": 47,
+        "type": "Winter Storm",
+        "state": "California",
+        "name": "California Winter Storm",
+        "description": "Fusce consequat. Nulla nisl. Nunc nisl.\n\nDuis bibendum, felis sed interdum venenatis, turpis enim blandit mi, in porttitor pede justo eu massa. Donec dapibus. Duis at velit eu est congue elementum.\n\nIn hac habitasse platea dictumst. Morbi vestibulum, velit id pretium iaculis, diam erat fermentum justo, nec condimentum neque sapien placerat ante. Nulla justo.",
+        "start_date": "2022-11-15",
+        "end_date": "2022-11-28",
+        "declared_date": "2022-11-17",
+        "lat": 34.0606,
+        "long": -118.3027,
+        "radius_miles": 120
+    },
+    {
+        "id": 48,
+        "type": "Flood",
+        "state": "Illinois",
+        "name": "Illinois Flood",
+        "description": "Nulla ut erat id mauris vulputate elementum. Nullam varius. Nulla facilisi.\n\nCras non velit nec nisi vulputate nonummy. Maecenas tincidunt lacus at velit. Vivamus vel nulla eget eros elementum pellentesque.\n\nQuisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.",
+        "start_date": "2022-12-29",
+        "end_date": "2023-01-08",
+        "declared_date": "2023-01-05",
+        "lat": 42.0144,
+        "long": -88.0935,
+        "radius_miles": 170
+    },
+    {
+        "id": 49,
+        "type": "Wildfire",
+        "state": "Indiana",
+        "name": "Indiana Wildfire",
+        "description": "Fusce consequat. Nulla nisl. Nunc nisl.\n\nDuis bibendum, felis sed interdum venenatis, turpis enim blandit mi, in porttitor pede justo eu massa. Donec dapibus. Duis at velit eu est congue elementum.",
+        "start_date": "2023-03-10",
+        "end_date": "2023-03-16",
+        "declared_date": "2023-03-12",
+        "lat": 40.0938,
+        "long": -85.6578,
+        "radius_miles": 44
+    },
+    {
+        "id": 50,
+        "type": "Tornado",
+        "state": "Maine",
+        "name": "Maine Tornado",
+        "description": "Mauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.\n\nNullam sit amet turpis elementum ligula vehicula consequat. Morbi a ipsum. Integer a nibh.",
+        "start_date": "2022-11-14",
+        "end_date": "2022-11-17",
+        "declared_date": "2022-11-20",
+        "lat": 43.6783,
+        "long": -70.1987,
+        "radius_miles": 96
+    },
+    {
+        "id": 51,
+        "type": "Wildfire",
+        "state": "Georgia",
+        "name": "Georgia Wildfire",
+        "description": "Morbi porttitor lorem id ligula. Suspendisse ornare consequat lectus. In est risus, auctor sed, tristique in, tempus sit amet, sem.\n\nFusce consequat. Nulla nisl. Nunc nisl.\n\nDuis bibendum, felis sed interdum venenatis, turpis enim blandit mi, in porttitor pede justo eu massa. Donec dapibus. Duis at velit eu est congue elementum.",
+        "start_date": "2023-02-07",
+        "end_date": "2023-02-27",
+        "declared_date": "2023-02-12",
+        "lat": 33.9845,
+        "long": -84.1529,
+        "radius_miles": 42
+    },
+    {
+        "id": 52,
+        "type": "Earthquake",
+        "state": "Illinois",
+        "name": "Illinois Earthquake",
+        "description": "Duis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.",
+        "start_date": "2023-02-16",
+        "end_date": "2023-03-02",
+        "declared_date": "2023-02-22",
+        "lat": 39.778,
+        "long": -89.6466,
+        "radius_miles": 213
+    },
+    {
+        "id": 53,
+        "type": "Winter Storm",
+        "state": "New York",
+        "name": "New York Winter Storm",
+        "description": "Duis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.\n\nDonec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2023-03-17",
+        "end_date": "2023-03-26",
+        "declared_date": "2023-03-21",
+        "lat": 43.1216,
+        "long": -77.7311,
+        "radius_miles": 252
+    },
+    {
+        "id": 54,
+        "type": "Earthquake",
+        "state": "Iowa",
+        "name": "Iowa Earthquake",
+        "description": "Maecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.\n\nNullam porttitor lacus at turpis. Donec posuere metus vitae ipsum. Aliquam non mauris.",
+        "start_date": "2022-11-10",
+        "end_date": "2022-11-22",
+        "declared_date": "2022-11-16",
+        "lat": 42.4011,
+        "long": -96.372,
+        "radius_miles": 192
+    },
+    {
+        "id": 55,
+        "type": "Landslide",
+        "state": "Texas",
+        "name": "Texas Landslide",
+        "description": "Aenean lectus. Pellentesque eget nunc. Donec quis orci eget orci vehicula condimentum.\n\nCurabitur in libero ut massa volutpat convallis. Morbi odio odio, elementum eu, interdum eu, tincidunt in, leo. Maecenas pulvinar lobortis est.",
+        "start_date": "2023-02-19",
+        "end_date": "2023-02-26",
+        "declared_date": "2023-02-24",
+        "lat": 31.8693,
+        "long": -102.0317,
+        "radius_miles": 268
+    },
+    {
+        "id": 56,
+        "type": "Landslide",
+        "state": "Texas",
+        "name": "Texas Landslide",
+        "description": "Aliquam quis turpis eget elit sodales scelerisque. Mauris sit amet eros. Suspendisse accumsan tortor quis turpis.\n\nSed ante. Vivamus tortor. Duis mattis egestas metus.",
+        "start_date": "2023-01-08",
+        "end_date": "2023-01-29",
+        "declared_date": "2023-01-10",
+        "lat": 30.0167,
+        "long": -95.447,
+        "radius_miles": 17
+    },
+    {
+        "id": 57,
+        "type": "Hurricane",
+        "state": "Arizona",
+        "name": "Arizona Hurricane Waylen",
+        "description": "Curabitur in libero ut massa volutpat convallis. Morbi odio odio, elementum eu, interdum eu, tincidunt in, leo. Maecenas pulvinar lobortis est.\n\nPhasellus sit amet erat. Nulla tempus. Vivamus in felis eu sapien cursus vestibulum.",
+        "start_date": "2023-03-09",
+        "end_date": "2023-03-19",
+        "declared_date": "2023-03-10",
+        "lat": 31.9701,
+        "long": -111.8907,
+        "radius_miles": 60
+    },
+    {
+        "id": 58,
+        "type": "Earthquake",
+        "state": "Minnesota",
+        "name": "Minnesota Earthquake",
+        "description": "Integer tincidunt ante vel ipsum. Praesent blandit lacinia erat. Vestibulum sed magna at nunc commodo placerat.\n\nPraesent blandit. Nam nulla. Integer pede justo, lacinia eget, tincidunt eget, tempus vel, pede.",
+        "start_date": "2023-06-23",
+        "end_date": "2023-07-04",
+        "declared_date": "2023-06-24",
+        "lat": 45.0159,
+        "long": -93.4719,
+        "radius_miles": 256
+    },
+    {
+        "id": 59,
+        "type": "Thunderstorm",
+        "state": "Colorado",
+        "name": "Colorado Thunderstorm",
+        "description": "Suspendisse potenti. In eleifend quam a odio. In hac habitasse platea dictumst.\n\nMaecenas ut massa quis augue luctus tincidunt. Nulla mollis molestie lorem. Quisque ut erat.\n\nCurabitur gravida nisi at nibh. In hac habitasse platea dictumst. Aliquam augue quam, sollicitudin vitae, consectetuer eget, rutrum at, lorem.",
+        "start_date": "2023-05-20",
+        "end_date": "2023-06-07",
+        "declared_date": "2023-05-27",
+        "lat": 38.8247,
+        "long": -104.562,
+        "radius_miles": 174
+    },
+    {
+        "id": 60,
+        "type": "Tornado",
+        "state": "New York",
+        "name": "New York Tornado",
+        "description": "Duis consequat dui nec nisi volutpat eleifend. Donec ut dolor. Morbi vel lectus in quam fringilla rhoncus.",
+        "start_date": "2022-11-15",
+        "end_date": "2022-11-25",
+        "declared_date": "2022-11-18",
+        "lat": 43.286,
+        "long": -77.6843,
+        "radius_miles": 156
+    },
+    {
+        "id": 61,
+        "type": "Tornado",
+        "state": "Louisiana",
+        "name": "Louisiana Tornado",
+        "description": "Morbi porttitor lorem id ligula. Suspendisse ornare consequat lectus. In est risus, auctor sed, tristique in, tempus sit amet, sem.",
+        "start_date": "2023-03-02",
+        "end_date": "2023-03-22",
+        "declared_date": "2023-03-06",
+        "lat": 30.3633,
+        "long": -91.0919,
+        "radius_miles": 30
+    },
+    {
+        "id": 62,
+        "type": "Tornado",
+        "state": "New Mexico",
+        "name": "New Mexico Tornado",
+        "description": "Cras mi pede, malesuada in, imperdiet et, commodo vulputate, justo. In blandit ultrices enim. Lorem ipsum dolor sit amet, consectetuer adipiscing elit.\n\nProin interdum mauris non ligula pellentesque ultrices. Phasellus id sapien in sapien iaculis congue. Vivamus metus arcu, adipiscing molestie, hendrerit at, vulputate vitae, nisl.",
+        "start_date": "2022-10-01",
+        "end_date": "2022-10-13",
+        "declared_date": "2022-10-06",
+        "lat": 35.6219,
+        "long": -105.8688,
+        "radius_miles": 72
+    },
+    {
+        "id": 63,
+        "type": "Earthquake",
+        "state": "California",
+        "name": "California Earthquake",
+        "description": "Integer tincidunt ante vel ipsum. Praesent blandit lacinia erat. Vestibulum sed magna at nunc commodo placerat.\n\nPraesent blandit. Nam nulla. Integer pede justo, lacinia eget, tincidunt eget, tempus vel, pede.",
+        "start_date": "2022-11-07",
+        "end_date": "2022-11-26",
+        "declared_date": "2022-11-14",
+        "lat": 38.3774,
+        "long": -121.4444,
+        "radius_miles": 87
+    },
+    {
+        "id": 64,
+        "type": "Landslide",
+        "state": "Texas",
+        "name": "Texas Landslide",
+        "description": "Proin leo odio, porttitor id, consequat in, consequat ut, nulla. Sed accumsan felis. Ut at dolor quis odio consequat varius.\n\nInteger ac leo. Pellentesque ultrices mattis odio. Donec vitae nisi.",
+        "start_date": "2022-11-29",
+        "end_date": "2022-12-18",
+        "declared_date": "2022-12-01",
+        "lat": 32.7714,
+        "long": -97.2915,
+        "radius_miles": 14
+    },
+    {
+        "id": 65,
+        "type": "Thunderstorm",
+        "state": "Alabama",
+        "name": "Alabama Thunderstorm",
+        "description": "Aenean lectus. Pellentesque eget nunc. Donec quis orci eget orci vehicula condimentum.",
+        "start_date": "2022-12-19",
+        "end_date": "2023-01-01",
+        "declared_date": "2022-12-21",
+        "lat": 33.9352,
+        "long": -85.9576,
+        "radius_miles": 81
+    },
+    {
+        "id": 66,
+        "type": "Thunderstorm",
+        "state": "New York",
+        "name": "New York Thunderstorm",
+        "description": "Suspendisse potenti. In eleifend quam a odio. In hac habitasse platea dictumst.",
+        "start_date": "2023-04-21",
+        "end_date": "2023-04-24",
+        "declared_date": "2023-04-25",
+        "lat": 40.8085,
+        "long": -73.9198,
+        "radius_miles": 49
+    },
+    {
+        "id": 67,
+        "type": "Flood",
+        "state": "Pennsylvania",
+        "name": "Pennsylvania Flood",
+        "description": "Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.",
+        "start_date": "2022-10-28",
+        "end_date": "2022-11-02",
+        "declared_date": "2022-10-29",
+        "lat": 40.4344,
+        "long": -80.0248,
+        "radius_miles": 64
+    },
+    {
+        "id": 68,
+        "type": "Landslide",
+        "state": "Maryland",
+        "name": "Maryland Landslide",
+        "description": "In quis justo. Maecenas rhoncus aliquam lacus. Morbi quis tortor id nulla ultrices aliquet.\n\nMaecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.",
+        "start_date": "2023-02-22",
+        "end_date": "2023-03-02",
+        "declared_date": "2023-02-28",
+        "lat": 39.5207,
+        "long": -77.9162,
+        "radius_miles": 93
+    },
+    {
+        "id": 69,
+        "type": "Earthquake",
+        "state": "Georgia",
+        "name": "Georgia Earthquake",
+        "description": "Vestibulum quam sapien, varius ut, blandit non, interdum in, ante. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Duis faucibus accumsan odio. Curabitur convallis.\n\nDuis consequat dui nec nisi volutpat eleifend. Donec ut dolor. Morbi vel lectus in quam fringilla rhoncus.",
+        "start_date": "2022-10-18",
+        "end_date": "2022-10-30",
+        "declared_date": "2022-10-21",
+        "lat": 33.9464,
+        "long": -83.9912,
+        "radius_miles": 39
+    },
+    {
+        "id": 70,
+        "type": "Hurricane",
+        "state": "Ohio",
+        "name": "Ohio Hurricane Darnell",
+        "description": "Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.\n\nPraesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.",
+        "start_date": "2023-03-29",
+        "end_date": "2023-04-18",
+        "declared_date": "2023-04-02",
+        "lat": 41.6782,
+        "long": -83.4972,
+        "radius_miles": 34
+    },
+    {
+        "id": 71,
+        "type": "Thunderstorm",
+        "state": "Utah",
+        "name": "Utah Thunderstorm",
+        "description": "Aenean lectus. Pellentesque eget nunc. Donec quis orci eget orci vehicula condimentum.\n\nCurabitur in libero ut massa volutpat convallis. Morbi odio odio, elementum eu, interdum eu, tincidunt in, leo. Maecenas pulvinar lobortis est.\n\nPhasellus sit amet erat. Nulla tempus. Vivamus in felis eu sapien cursus vestibulum.",
+        "start_date": "2023-04-03",
+        "end_date": "2023-04-10",
+        "declared_date": "2023-04-10",
+        "lat": 40.7713,
+        "long": -111.9309,
+        "radius_miles": 39
+    },
+    {
+        "id": 72,
+        "type": "Wildfire",
+        "state": "Iowa",
+        "name": "Iowa Wildfire",
+        "description": "Duis consequat dui nec nisi volutpat eleifend. Donec ut dolor. Morbi vel lectus in quam fringilla rhoncus.",
+        "start_date": "2023-06-17",
+        "end_date": "2023-06-30",
+        "declared_date": "2023-06-21",
+        "lat": 41.6727,
+        "long": -93.5722,
+        "radius_miles": 132
+    },
+    {
+        "id": 73,
+        "type": "Flood",
+        "state": "Michigan",
+        "name": "Michigan Flood",
+        "description": "Maecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.\n\nNullam porttitor lacus at turpis. Donec posuere metus vitae ipsum. Aliquam non mauris.\n\nMorbi non lectus. Aliquam sit amet diam in magna bibendum imperdiet. Nullam orci pede, venenatis non, sodales sed, tincidunt eu, felis.",
+        "start_date": "2023-05-28",
+        "end_date": "2023-06-17",
+        "declared_date": "2023-05-29",
+        "lat": 43.1791,
+        "long": -86.1989,
+        "radius_miles": 170
+    },
+    {
+        "id": 74,
+        "type": "Flood",
+        "state": "North Carolina",
+        "name": "North Carolina Flood",
+        "description": "Nullam porttitor lacus at turpis. Donec posuere metus vitae ipsum. Aliquam non mauris.\n\nMorbi non lectus. Aliquam sit amet diam in magna bibendum imperdiet. Nullam orci pede, venenatis non, sodales sed, tincidunt eu, felis.",
+        "start_date": "2022-12-01",
+        "end_date": "2022-12-19",
+        "declared_date": "2022-12-08",
+        "lat": 35.1316,
+        "long": -80.8577,
+        "radius_miles": 168
+    },
+    {
+        "id": 75,
+        "type": "Flood",
+        "state": "Alabama",
+        "name": "Alabama Flood",
+        "description": "Etiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.\n\nPraesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.",
+        "start_date": "2023-06-15",
+        "end_date": "2023-06-24",
+        "declared_date": "2023-06-20",
+        "lat": 32.2334,
+        "long": -86.2085,
+        "radius_miles": 259
+    },
+    {
+        "id": 76,
+        "type": "Winter Storm",
+        "state": "Ohio",
+        "name": "Ohio Winter Storm",
+        "description": "Proin eu mi. Nulla ac enim. In tempor, turpis nec euismod scelerisque, quam turpis adipiscing lorem, vitae mattis nibh ligula nec sem.",
+        "start_date": "2023-03-21",
+        "end_date": "2023-03-26",
+        "declared_date": "2023-03-23",
+        "lat": 41.1287,
+        "long": -81.54,
+        "radius_miles": 228
+    },
+    {
+        "id": 77,
+        "type": "Landslide",
+        "state": "Mississippi",
+        "name": "Mississippi Landslide",
+        "description": "Phasellus sit amet erat. Nulla tempus. Vivamus in felis eu sapien cursus vestibulum.",
+        "start_date": "2023-01-31",
+        "end_date": "2023-02-10",
+        "declared_date": "2023-02-02",
+        "lat": 30.4158,
+        "long": -89.0684,
+        "radius_miles": 232
+    },
+    {
+        "id": 78,
+        "type": "Landslide",
+        "state": "New York",
+        "name": "New York Landslide",
+        "description": "Cras non velit nec nisi vulputate nonummy. Maecenas tincidunt lacus at velit. Vivamus vel nulla eget eros elementum pellentesque.\n\nQuisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.",
+        "start_date": "2023-02-03",
+        "end_date": "2023-02-16",
+        "declared_date": "2023-02-07",
+        "lat": 40.6964,
+        "long": -74.0253,
+        "radius_miles": 186
+    },
+    {
+        "id": 79,
+        "type": "Hurricane",
+        "state": "Missouri",
+        "name": "Missouri Hurricane Mahmud",
+        "description": "Quisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.\n\nPhasellus in felis. Donec semper sapien a libero. Nam dui.\n\nProin leo odio, porttitor id, consequat in, consequat ut, nulla. Sed accumsan felis. Ut at dolor quis odio consequat varius.",
+        "start_date": "2023-04-20",
+        "end_date": "2023-04-30",
+        "declared_date": "2023-04-27",
+        "lat": 38.8518,
+        "long": -94.3944,
+        "radius_miles": 300
+    },
+    {
+        "id": 80,
+        "type": "Earthquake",
+        "state": "Washington",
+        "name": "Washington Earthquake",
+        "description": "Etiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.\n\nPraesent id massa id nisl venenatis lacinia. Aenean sit amet justo. Morbi ut odio.\n\nCras mi pede, malesuada in, imperdiet et, commodo vulputate, justo. In blandit ultrices enim. Lorem ipsum dolor sit amet, consectetuer adipiscing elit.",
+        "start_date": "2023-02-08",
+        "end_date": "2023-02-20",
+        "declared_date": "2023-02-13",
+        "lat": 47.1591,
+        "long": -122.5485,
+        "radius_miles": 219
+    },
+    {
+        "id": 81,
+        "type": "Winter Storm",
+        "state": "Virginia",
+        "name": "Virginia Winter Storm",
+        "description": "Aenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.\n\nQuisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.\n\nVestibulum ac est lacinia nisi venenatis tristique. Fusce congue, diam id ornare imperdiet, sapien urna pretium nisl, ut volutpat sapien arcu sed augue. Aliquam erat volutpat.",
+        "start_date": "2023-04-28",
+        "end_date": "2023-05-01",
+        "declared_date": "2023-05-02",
+        "lat": 37.3589,
+        "long": -79.9448,
+        "radius_miles": 68
+    },
+    {
+        "id": 82,
+        "type": "Wildfire",
+        "state": "Texas",
+        "name": "Texas Wildfire",
+        "description": "Duis bibendum. Morbi non quam nec dui luctus rutrum. Nulla tellus.",
+        "start_date": "2022-11-20",
+        "end_date": "2022-11-29",
+        "declared_date": "2022-11-27",
+        "lat": 32.7673,
+        "long": -96.7776,
+        "radius_miles": 164
+    },
+    {
+        "id": 83,
+        "type": "Flood",
+        "state": "California",
+        "name": "California Flood",
+        "description": "Integer ac leo. Pellentesque ultrices mattis odio. Donec vitae nisi.",
+        "start_date": "2023-02-04",
+        "end_date": "2023-02-10",
+        "declared_date": "2023-02-09",
+        "lat": 37.7772,
+        "long": -121.9554,
+        "radius_miles": 118
+    },
+    {
+        "id": 84,
+        "type": "Landslide",
+        "state": "Florida",
+        "name": "Florida Landslide",
+        "description": "Mauris enim leo, rhoncus sed, vestibulum sit amet, cursus id, turpis. Integer aliquet, massa id lobortis convallis, tortor risus dapibus augue, vel accumsan tellus nisi eu orci. Mauris lacinia sapien quis libero.\n\nNullam sit amet turpis elementum ligula vehicula consequat. Morbi a ipsum. Integer a nibh.",
+        "start_date": "2023-03-28",
+        "end_date": "2023-04-18",
+        "declared_date": "2023-04-02",
+        "lat": 26.1457,
+        "long": -80.4483,
+        "radius_miles": 91
+    },
+    {
+        "id": 85,
+        "type": "Wildfire",
+        "state": "Georgia",
+        "name": "Georgia Wildfire",
+        "description": "Proin eu mi. Nulla ac enim. In tempor, turpis nec euismod scelerisque, quam turpis adipiscing lorem, vitae mattis nibh ligula nec sem.\n\nDuis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.",
+        "start_date": "2023-06-11",
+        "end_date": "2023-06-29",
+        "declared_date": "2023-06-13",
+        "lat": 33.8123,
+        "long": -84.2819,
+        "radius_miles": 170
+    },
+    {
+        "id": 86,
+        "type": "Wildfire",
+        "state": "California",
+        "name": "California Wildfire",
+        "description": "In quis justo. Maecenas rhoncus aliquam lacus. Morbi quis tortor id nulla ultrices aliquet.\n\nMaecenas leo odio, condimentum id, luctus nec, molestie sed, justo. Pellentesque viverra pede ac diam. Cras pellentesque volutpat dui.\n\nMaecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.",
+        "start_date": "2022-12-08",
+        "end_date": "2022-12-17",
+        "declared_date": "2022-12-09",
+        "lat": 38.3774,
+        "long": -121.4444,
+        "radius_miles": 41
+    },
+    {
+        "id": 87,
+        "type": "Winter Storm",
+        "state": "Washington",
+        "name": "Washington Winter Storm",
+        "description": "Morbi non lectus. Aliquam sit amet diam in magna bibendum imperdiet. Nullam orci pede, venenatis non, sodales sed, tincidunt eu, felis.",
+        "start_date": "2023-06-05",
+        "end_date": "2023-06-22",
+        "declared_date": "2023-06-08",
+        "lat": 45.6644,
+        "long": -122.5212,
+        "radius_miles": 103
+    },
+    {
+        "id": 88,
+        "type": "Wildfire",
+        "state": "Idaho",
+        "name": "Idaho Wildfire",
+        "description": "Praesent blandit. Nam nulla. Integer pede justo, lacinia eget, tincidunt eget, tempus vel, pede.",
+        "start_date": "2023-02-12",
+        "end_date": "2023-02-28",
+        "declared_date": "2023-02-18",
+        "lat": 43.4599,
+        "long": -116.244,
+        "radius_miles": 144
+    },
+    {
+        "id": 89,
+        "type": "Winter Storm",
+        "state": "Pennsylvania",
+        "name": "Pennsylvania Winter Storm",
+        "description": "Maecenas tristique, est et tempus semper, est quam pharetra magna, ac consequat metus sapien ut nunc. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Mauris viverra diam vitae quam. Suspendisse potenti.\n\nNullam porttitor lacus at turpis. Donec posuere metus vitae ipsum. Aliquam non mauris.\n\nMorbi non lectus. Aliquam sit amet diam in magna bibendum imperdiet. Nullam orci pede, venenatis non, sodales sed, tincidunt eu, felis.",
+        "start_date": "2023-01-08",
+        "end_date": "2023-01-12",
+        "declared_date": "2023-01-12",
+        "lat": 40.0018,
+        "long": -75.1179,
+        "radius_miles": 235
+    },
+    {
+        "id": 90,
+        "type": "Hurricane",
+        "state": "North Carolina",
+        "name": "North Carolina Hurricane Rubin",
+        "description": "Sed sagittis. Nam congue, risus semper porta volutpat, quam pede lobortis ligula, sit amet eleifend pede libero quis orci. Nullam molestie nibh in lectus.",
+        "start_date": "2023-01-15",
+        "end_date": "2023-01-23",
+        "declared_date": "2023-01-19",
+        "lat": 35.7977,
+        "long": -78.6253,
+        "radius_miles": 143
+    },
+    {
+        "id": 91,
+        "type": "Hurricane",
+        "state": "Florida",
+        "name": "Florida Hurricane Celle",
+        "description": "Fusce posuere felis sed lacus. Morbi sem mauris, laoreet ut, rhoncus aliquet, pulvinar sed, nisl. Nunc rhoncus dui vel sem.\n\nSed sagittis. Nam congue, risus semper porta volutpat, quam pede lobortis ligula, sit amet eleifend pede libero quis orci. Nullam molestie nibh in lectus.",
+        "start_date": "2023-05-11",
+        "end_date": "2023-05-22",
+        "declared_date": "2023-05-13",
+        "lat": 30.6143,
+        "long": -87.2758,
+        "radius_miles": 144
+    },
+    {
+        "id": 92,
+        "type": "Landslide",
+        "state": "Florida",
+        "name": "Florida Landslide",
+        "description": "Quisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.\n\nVestibulum ac est lacinia nisi venenatis tristique. Fusce congue, diam id ornare imperdiet, sapien urna pretium nisl, ut volutpat sapien arcu sed augue. Aliquam erat volutpat.",
+        "start_date": "2023-05-01",
+        "end_date": "2023-05-08",
+        "declared_date": "2023-05-06",
+        "lat": 26.6644,
+        "long": -80.1741,
+        "radius_miles": 66
+    },
+    {
+        "id": 93,
+        "type": "Wildfire",
+        "state": "Missouri",
+        "name": "Missouri Wildfire",
+        "description": "Aenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.\n\nQuisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.",
+        "start_date": "2022-12-26",
+        "end_date": "2023-01-01",
+        "declared_date": "2022-12-31",
+        "lat": 38.8518,
+        "long": -94.3944,
+        "radius_miles": 278
+    },
+    {
+        "id": 94,
+        "type": "Winter Storm",
+        "state": "Indiana",
+        "name": "Indiana Winter Storm",
+        "description": "Curabitur in libero ut massa volutpat convallis. Morbi odio odio, elementum eu, interdum eu, tincidunt in, leo. Maecenas pulvinar lobortis est.\n\nPhasellus sit amet erat. Nulla tempus. Vivamus in felis eu sapien cursus vestibulum.\n\nProin eu mi. Nulla ac enim. In tempor, turpis nec euismod scelerisque, quam turpis adipiscing lorem, vitae mattis nibh ligula nec sem.",
+        "start_date": "2022-10-15",
+        "end_date": "2022-10-24",
+        "declared_date": "2022-10-21",
+        "lat": 41.0938,
+        "long": -85.0707,
+        "radius_miles": 41
+    },
+    {
+        "id": 95,
+        "type": "Flood",
+        "state": "California",
+        "name": "California Flood",
+        "description": "In congue. Etiam justo. Etiam pretium iaculis justo.\n\nIn hac habitasse platea dictumst. Etiam faucibus cursus urna. Ut tellus.",
+        "start_date": "2023-01-07",
+        "end_date": "2023-01-11",
+        "declared_date": "2023-01-14",
+        "lat": 32.8538,
+        "long": -117.1197,
+        "radius_miles": 162
+    },
+    {
+        "id": 96,
+        "type": "Thunderstorm",
+        "state": "California",
+        "name": "California Thunderstorm",
+        "description": "Aenean fermentum. Donec ut mauris eget massa tempor convallis. Nulla neque libero, convallis eget, eleifend luctus, ultricies eu, nibh.\n\nQuisque id justo sit amet sapien dignissim vestibulum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Nulla dapibus dolor vel est. Donec odio justo, sollicitudin ut, suscipit a, feugiat et, eros.\n\nVestibulum ac est lacinia nisi venenatis tristique. Fusce congue, diam id ornare imperdiet, sapien urna pretium nisl, ut volutpat sapien arcu sed augue. Aliquam erat volutpat.",
+        "start_date": "2023-04-26",
+        "end_date": "2023-05-05",
+        "declared_date": "2023-04-28",
+        "lat": 37.7772,
+        "long": -121.9554,
+        "radius_miles": 282
+    },
+    {
+        "id": 97,
+        "type": "Hurricane",
+        "state": "Pennsylvania",
+        "name": "Pennsylvania Hurricane Celene",
+        "description": "Proin eu mi. Nulla ac enim. In tempor, turpis nec euismod scelerisque, quam turpis adipiscing lorem, vitae mattis nibh ligula nec sem.\n\nDuis aliquam convallis nunc. Proin at turpis a pede posuere nonummy. Integer non velit.\n\nDonec diam neque, vestibulum eget, vulputate ut, ultrices vel, augue. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Donec pharetra, magna vestibulum aliquet ultrices, erat tortor sollicitudin mi, sit amet lobortis sapien sapien non mi. Integer ac neque.",
+        "start_date": "2023-03-16",
+        "end_date": "2023-03-20",
+        "declared_date": "2023-03-20",
+        "lat": 39.9832,
+        "long": -75.7481,
+        "radius_miles": 273
+    },
+    {
+        "id": 98,
+        "type": "Wildfire",
+        "state": "Michigan",
+        "name": "Michigan Wildfire",
+        "description": "Quisque porta volutpat erat. Quisque erat eros, viverra eget, congue eget, semper rutrum, nulla. Nunc purus.\n\nPhasellus in felis. Donec semper sapien a libero. Nam dui.",
+        "start_date": "2023-06-17",
+        "end_date": "2023-06-30",
+        "declared_date": "2023-06-20",
+        "lat": 42.2399,
+        "long": -83.1508,
+        "radius_miles": 189
+    },
+    {
+        "id": 99,
+        "type": "Winter Storm",
+        "state": "Tennessee",
+        "name": "Tennessee Winter Storm",
+        "description": "Duis bibendum. Morbi non quam nec dui luctus rutrum. Nulla tellus.",
+        "start_date": "2023-05-16",
+        "end_date": "2023-06-02",
+        "declared_date": "2023-05-22",
+        "lat": 35.9901,
+        "long": -83.9622,
+        "radius_miles": 81
+    },
+    {
+        "id": 100,
+        "type": "Earthquake",
+        "state": "District of Columbia",
+        "name": "District of Columbia Earthquake",
+        "description": "Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus vestibulum sagittis sapien. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.\n\nEtiam vel augue. Vestibulum rutrum rutrum neque. Aenean auctor gravida sem.",
+        "start_date": "2022-10-25",
+        "end_date": "2022-11-01",
+        "declared_date": "2022-10-30",
+        "lat": 38.8933,
+        "long": -77.0146,
+        "radius_miles": 178
+    }
+]
diff --git a/python/data_analysis.ipynb b/python/data_analysis.ipynb
new file mode 100644
index 0000000..0a4cc22
--- /dev/null
+++ b/python/data_analysis.ipynb
@@ -0,0 +1,1810 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GCU_CS\n",
+    "\n",
+    "## Members:\n",
+    "* Gabriel Aracena\n",
+    "    * Senior @ Grand Canyon University \n",
+    "    * Expected Graduation April 2024\n",
+    "    * Pursuing a Bachelor's of Science in Computer Science with an Emphasis on Big Data \n",
+    "    * Originally from Fortaleza, Brazil\n",
+    "    * Hobbies: Playing Intramural Sports, Playing Video Games, Working Out\n",
+    "    * LinkedIn: Gabriel Aracena (https://www.linkedin.com/in/gabriel-aracena-a92363195/)\n",
+    "    * GitHub: G4BE-334 (https://github.com/G4BE-334)\n",
+    "    * Personality traits: Dedicated, Outgoing, Friendly, Analytical, Driven\n",
+    "\n",
+    "![Gabriel Profile Picture](src/pictures/gabriel.jpg)\n",
+    "\n",
+    "* Kyungchan Im (aka Chris)\n",
+    "    * Senior @ Grand Canyon University \n",
+    "    * Expected Graduation April 2024\n",
+    "    * Pursuing a Bachelor's of Science in Computer Science with an Emphasis on Big Data \n",
+    "    * Originally from Seoul, South Korea\n",
+    "    * Hobbies: Playing Basketball, Coding, Learning about AI\n",
+    "    * LinkedIn: Kyungchan (Chris) Im (https://www.linkedin.com/in/kyungchan-im/)\n",
+    "    * GitHub: ChristheIm (https://github.com/ChristheIm)\n",
+    "    * Personality traits: Outgoing, Diligent, Hard-working, Passionate, Humorous\n",
+    "\n",
+    "![Chris Profile Picture](src/pictures/chris.jfif)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Anaysis of Disaster and Claims for State Farm Coding Competition 2023 Finals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'geopandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\VRLab\\Documents\\gabe\\2023-StateFarm-CodingCompetition-Finals\\python\\data_analysis.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/VRLab/Documents/gabe/2023-StateFarm-CodingCompetition-Finals/python/data_analysis.ipynb#Y121sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/VRLab/Documents/gabe/2023-StateFarm-CodingCompetition-Finals/python/data_analysis.ipynb#Y121sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/VRLab/Documents/gabe/2023-StateFarm-CodingCompetition-Finals/python/data_analysis.ipynb#Y121sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mgeopandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mgpd\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/VRLab/Documents/gabe/2023-StateFarm-CodingCompetition-Finals/python/data_analysis.ipynb#Y121sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mshapely\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mgeometry\u001b[39;00m \u001b[39mimport\u001b[39;00m Point\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
+     ]
+    }
+   ],
+   "source": [
+    "## Import libraries\n",
+    "from simple_data_tool import SimpleDataTool\n",
+    "\n",
+    "# For Data Analysis\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# For Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Load\n",
+    "Import JSON dataset into DataFrame in order to work with Python Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Agent JSON data\n",
+    "agents = pd.read_json(\"data/sfcc_2023_agents.json\")\n",
+    "agents.set_index('id', inplace=True) # Set the 'id' column as the index\n",
+    "df_agents = pd.json_normalize(agents.to_dict(orient='records')) # Normalize the columns\n",
+    "\n",
+    "# Load Claim JSON data\n",
+    "claims = pd.read_json(\"data/sfcc_2023_claims.json\")\n",
+    "claims.set_index('id', inplace=True) # Set the 'id' column as the index\n",
+    "df_claims = pd.json_normalize(claims.to_dict(orient='records')) # Normalize the columns\n",
+    "\n",
+    "# Load Claim Handler JSON data\n",
+    "claim_handlers = pd.read_json(\"data/sfcc_2023_claim_handlers.json\")\n",
+    "claim_handlers.set_index('id', inplace=True) # Set the 'id' column as the index\n",
+    "df_claim_handlers = pd.json_normalize(claim_handlers.to_dict(orient='records')) # Normalize the columns\n",
+    "\n",
+    "# Load Disaster JSON data\n",
+    "disasters = pd.read_json(\"data/sfcc_2023_disasters.json\")\n",
+    "disasters.set_index('id', inplace=True) # Set the 'id' column as the index\n",
+    "df_disasters = pd.json_normalize(disasters.to_dict(orient='records')) # Normalize the columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check the info for each individual dataset\n",
+    "\n",
+    "1) Get Data information and description about the data <br>\n",
+    "2) Normalization and Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "first_name            100\n",
+       "last_name             100\n",
+       "state                 100\n",
+       "region                100\n",
+       "primary_language      100\n",
+       "secondary_language     59\n",
+       "years_active          100\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agents.count() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>state</th>\n",
+       "      <th>region</th>\n",
+       "      <th>primary_language</th>\n",
+       "      <th>secondary_language</th>\n",
+       "      <th>years_active</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Catha</td>\n",
+       "      <td>Abrahmer</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>Midwest</td>\n",
+       "      <td>English</td>\n",
+       "      <td>German</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yetta</td>\n",
+       "      <td>Eason</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>West</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Korean</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Janeta</td>\n",
+       "      <td>D'Avaux</td>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>Northeast</td>\n",
+       "      <td>English</td>\n",
+       "      <td>None</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kalie</td>\n",
+       "      <td>Tomkins</td>\n",
+       "      <td>Virginia</td>\n",
+       "      <td>South</td>\n",
+       "      <td>English</td>\n",
+       "      <td>None</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Teddy</td>\n",
+       "      <td>Dennitts</td>\n",
+       "      <td>Illinois</td>\n",
+       "      <td>Midwest</td>\n",
+       "      <td>English</td>\n",
+       "      <td>Chinese</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   first_name last_name        state     region primary_language  \\\n",
+       "id                                                                 \n",
+       "1       Catha  Abrahmer    Minnesota    Midwest          English   \n",
+       "2       Yetta     Eason       Oregon       West          English   \n",
+       "3      Janeta   D'Avaux  Connecticut  Northeast          English   \n",
+       "4       Kalie   Tomkins     Virginia      South          English   \n",
+       "5       Teddy  Dennitts     Illinois    Midwest          English   \n",
+       "\n",
+       "   secondary_language  years_active  \n",
+       "id                                   \n",
+       "1              German            10  \n",
+       "2              Korean            12  \n",
+       "3                None            47  \n",
+       "4                None            33  \n",
+       "5             Chinese            48  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agents.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "disaster_id                  1000\n",
+       "status                       1000\n",
+       "total_loss                   1000\n",
+       "loss_of_life                 1000\n",
+       "type                         1000\n",
+       "severity_rating              1000\n",
+       "estimate_cost                1000\n",
+       "agent_assigned_id            1000\n",
+       "claim_handler_assigned_id    1000\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "claims.count() # count the number of each columns and missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>disaster_id</th>\n",
+       "      <th>status</th>\n",
+       "      <th>total_loss</th>\n",
+       "      <th>loss_of_life</th>\n",
+       "      <th>type</th>\n",
+       "      <th>severity_rating</th>\n",
+       "      <th>estimate_cost</th>\n",
+       "      <th>agent_assigned_id</th>\n",
+       "      <th>claim_handler_assigned_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>Received</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Auto</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3580.02</td>\n",
+       "      <td>97</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24</td>\n",
+       "      <td>In Review</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Auto</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1741.32</td>\n",
+       "      <td>36</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Received</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Auto</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15224.30</td>\n",
+       "      <td>94</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Received</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Auto</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6542.46</td>\n",
+       "      <td>92</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Closed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Auto</td>\n",
+       "      <td>1</td>\n",
+       "      <td>979.81</td>\n",
+       "      <td>86</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    disaster_id     status  total_loss  loss_of_life  type  severity_rating  \\\n",
+       "id                                                                            \n",
+       "1            15   Received       False         False  Auto                4   \n",
+       "2            24  In Review       False         False  Auto                6   \n",
+       "3            31   Received        True         False  Auto                4   \n",
+       "4            14   Received       False          True  Auto                8   \n",
+       "5            30     Closed       False         False  Auto                1   \n",
+       "\n",
+       "    estimate_cost  agent_assigned_id  claim_handler_assigned_id  \n",
+       "id                                                               \n",
+       "1         3580.02                 97                         50  \n",
+       "2         1741.32                 36                         92  \n",
+       "3        15224.30                 94                        133  \n",
+       "4         6542.46                 92                         39  \n",
+       "5          979.81                 86                        103  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "claims.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "first_name    156\n",
+       "last_name     156\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "claim_handlers.count() # count the number of each columns and missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Barnabe</td>\n",
+       "      <td>Clynman</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Valdemar</td>\n",
+       "      <td>Size</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Edithe</td>\n",
+       "      <td>Bleakley</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sonja</td>\n",
+       "      <td>Diamant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Elsey</td>\n",
+       "      <td>Sreenan</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   first_name last_name\n",
+       "id                     \n",
+       "1     Barnabe   Clynman\n",
+       "2    Valdemar      Size\n",
+       "3      Edithe  Bleakley\n",
+       "4       Sonja   Diamant\n",
+       "5       Elsey   Sreenan"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "claim_handlers.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type             100\n",
+       "state            100\n",
+       "name             100\n",
+       "description      100\n",
+       "start_date       100\n",
+       "end_date         100\n",
+       "declared_date    100\n",
+       "lat              100\n",
+       "long             100\n",
+       "radius_miles     100\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "disasters.count() # count the number of each columns and missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>state</th>\n",
+       "      <th>name</th>\n",
+       "      <th>description</th>\n",
+       "      <th>start_date</th>\n",
+       "      <th>end_date</th>\n",
+       "      <th>declared_date</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>radius_miles</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Flood</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska Flood</td>\n",
+       "      <td>Fusce consequat. Nulla nisl. Nunc nisl.\\n\\nDui...</td>\n",
+       "      <td>2023-06-13</td>\n",
+       "      <td>2023-06-20</td>\n",
+       "      <td>2023-06-17</td>\n",
+       "      <td>58.3271</td>\n",
+       "      <td>-134.4742</td>\n",
+       "      <td>168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Wildfire</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>Texas Wildfire</td>\n",
+       "      <td>Duis consequat dui nec nisi volutpat eleifend....</td>\n",
+       "      <td>2023-04-11</td>\n",
+       "      <td>2023-04-19</td>\n",
+       "      <td>2023-04-12</td>\n",
+       "      <td>33.5693</td>\n",
+       "      <td>-101.8904</td>\n",
+       "      <td>235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Tornado</td>\n",
+       "      <td>Virginia</td>\n",
+       "      <td>Virginia Tornado</td>\n",
+       "      <td>Nullam sit amet turpis elementum ligula vehicu...</td>\n",
+       "      <td>2023-04-29</td>\n",
+       "      <td>2023-05-07</td>\n",
+       "      <td>2023-05-06</td>\n",
+       "      <td>38.9776</td>\n",
+       "      <td>-77.3860</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Earthquake</td>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>Connecticut Earthquake</td>\n",
+       "      <td>Fusce consequat. Nulla nisl. Nunc nisl.</td>\n",
+       "      <td>2023-04-24</td>\n",
+       "      <td>2023-05-09</td>\n",
+       "      <td>2023-04-28</td>\n",
+       "      <td>41.3657</td>\n",
+       "      <td>-72.9275</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Winter Storm</td>\n",
+       "      <td>Missouri</td>\n",
+       "      <td>Missouri Winter Storm</td>\n",
+       "      <td>Suspendisse potenti. In eleifend quam a odio. ...</td>\n",
+       "      <td>2023-03-11</td>\n",
+       "      <td>2023-03-21</td>\n",
+       "      <td>2023-03-14</td>\n",
+       "      <td>39.0663</td>\n",
+       "      <td>-94.5674</td>\n",
+       "      <td>155</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            type        state                    name  \\\n",
+       "id                                                      \n",
+       "1          Flood       Alaska            Alaska Flood   \n",
+       "2       Wildfire        Texas          Texas Wildfire   \n",
+       "3        Tornado     Virginia        Virginia Tornado   \n",
+       "4     Earthquake  Connecticut  Connecticut Earthquake   \n",
+       "5   Winter Storm     Missouri   Missouri Winter Storm   \n",
+       "\n",
+       "                                          description  start_date    end_date  \\\n",
+       "id                                                                              \n",
+       "1   Fusce consequat. Nulla nisl. Nunc nisl.\\n\\nDui...  2023-06-13  2023-06-20   \n",
+       "2   Duis consequat dui nec nisi volutpat eleifend....  2023-04-11  2023-04-19   \n",
+       "3   Nullam sit amet turpis elementum ligula vehicu...  2023-04-29  2023-05-07   \n",
+       "4             Fusce consequat. Nulla nisl. Nunc nisl.  2023-04-24  2023-05-09   \n",
+       "5   Suspendisse potenti. In eleifend quam a odio. ...  2023-03-11  2023-03-21   \n",
+       "\n",
+       "   declared_date      lat      long  radius_miles  \n",
+       "id                                                 \n",
+       "1     2023-06-17  58.3271 -134.4742           168  \n",
+       "2     2023-04-12  33.5693 -101.8904           235  \n",
+       "3     2023-05-06  38.9776  -77.3860           273  \n",
+       "4     2023-04-28  41.3657  -72.9275           120  \n",
+       "5     2023-03-14  39.0663  -94.5674           155  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "disasters.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find some insight from the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a SimpleDataTool object for use calculation functions inside the class\n",
+    "data = SimpleDataTool()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Find Regional Disaster Counts #####"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the total number of disaster occurrend in the simulation\n",
+    "regional_disaster_map = data.get_regional_disaster_map()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                               Disaster Type  Count\n",
+      "Region                                                             \n",
+      "south      WildfireTornadoEarthquakeLandslideHurricaneFlo...     36\n",
+      "midwest    Winter StormWildfireEarthquakeThunderstormFloo...     25\n",
+      "northeast  EarthquakeThunderstormFloodLandslideHurricaneW...     20\n",
+      "west       FloodTornadoThunderstormWildfireWinter StormHu...     19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize lists to store the data\n",
+    "disaster_regions = []\n",
+    "disaster_types = []\n",
+    "disaster_counts = []\n",
+    "\n",
+    "# Populate the lists with data\n",
+    "for region, disaster in regional_disaster_map.items():\n",
+    "    for disaster_type, count in disaster.items():\n",
+    "        disaster_regions.append(region)\n",
+    "        disaster_types.append(disaster_type)\n",
+    "        disaster_counts.append(count)\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "disaster_map = pd.DataFrame({\n",
+    "    'Region': disaster_regions,\n",
+    "    'Disaster Type': disaster_types,\n",
+    "    'Count': disaster_counts\n",
+    "})\n",
+    "\n",
+    "# Print the sum of the disaster regionally.\n",
+    "print(disaster_map.groupby(['Region']).sum().sort_values(by='Count', ascending=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x='Region', y='Count', hue='Disaster Type', data=disaster_map, errorbar=None, palette='muted')\n",
+    "plt.title('Count of Each Disaster by Region')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Region')\n",
+    "plt.legend(title='Disaster Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Find the number of claims by region #####"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regional_claim_map = data.get_total_claims_per_regional_disaster()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                               Disaster Type  Count\n",
+      "Region                                                             \n",
+      "south      North CarolinaFloridaMississippiTexasGeorgiaVi...    328\n",
+      "midwest    MichiganMinnesotaNebraskaIowaKansasIndianaOhio...    240\n",
+      "west       CaliforniaArizonaWashingtonUtahIdahoColoradoAl...    224\n",
+      "northeast  New YorkConnecticutPennsylvaniaDistrict of Col...    208\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize lists to store the data\n",
+    "claim_map_regions = []\n",
+    "claim_map_state = []\n",
+    "claim_map_counts = []\n",
+    "\n",
+    "# Populate the lists with data\n",
+    "for region, state in regional_claim_map.items():\n",
+    "    for state_name, count in state.items():\n",
+    "        claim_map_regions.append(region)\n",
+    "        claim_map_state.append(state_name)\n",
+    "        claim_map_counts.append(count)\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "claim_map = pd.DataFrame({\n",
+    "    'Region': claim_map_regions,\n",
+    "    'Disaster Type': claim_map_state,\n",
+    "    'Count': claim_map_counts\n",
+    "})\n",
+    "\n",
+    "\n",
+    "# Print the sum of the disaster regionally.\n",
+    "total_claim_map = claim_map.groupby(['Region']).sum().sort_values(by='Count', ascending=False)\n",
+    "print(total_claim_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x='Region', y='Count', data=total_claim_map, legend=False, palette='muted', hue='Region')\n",
+    "\n",
+    "plt.title('Total Count of Claims by Region')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('Region')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Agent and Claims per region analysis\n",
+    "\n",
+    "We want to analyse the number of agents and claims per region to see if there is a relationship between them. Ideally, it is expected that a region with a higher number of claims would have a higher number of agents to deal with them and the contrary as well. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Finding the number of Agents per region\n",
+    "\n",
+    "regional_agents = data.get_total_agents_per_region()\n",
+    "\n",
+    "regions = list(regional_agents.keys())\n",
+    "agent_counts = [len(agents) for agents in regional_agents.values()]\n",
+    "\n",
+    "\n",
+    "colors = ['green', 'orange', 'blue', 'red']\n",
+    "\n",
+    "# Create a bar chart with different colors\n",
+    "plt.bar(regions, agent_counts, color=colors)\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Number of Agents')\n",
+    "plt.title('Count of Agents per Region')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can already start to see, there seems to be a discrepancy between the number of agents per claim on each region. The south region it is expected to have the most amount of agents but the west region is the one that actually has it.\n",
+    "\n",
+    "Let's dive deeper into that and see a normalized comparison between them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Convert agents data to a DataFrame\n",
+    "agents_data = pd.DataFrame({\n",
+    "    'Region': [region.lower() for region in regional_agents.keys()],\n",
+    "    'Agents': [len(agents) for agents in regional_agents.values()],\n",
+    "    'Type': 'Agents'\n",
+    "})\n",
+    "\n",
+    "# Convert claim data to a DataFrame and add a 'Type' column\n",
+    "claims_data = total_claim_map.reset_index()\n",
+    "claims_data['Type'] = 'Claims'\n",
+    "\n",
+    "# Rename columns for consistency\n",
+    "claims_data.rename(columns={'Count': 'Claims'}, inplace=True)\n",
+    "\n",
+    "# Combine the data\n",
+    "combined_data = pd.concat([agents_data, claims_data], ignore_index=True)\n",
+    "\n",
+    "# Calculate total number of agents and claims\n",
+    "total_agents = agents_data['Agents'].sum()\n",
+    "total_claims = claims_data['Claims'].sum()\n",
+    "\n",
+    "# Normalize the data by converting counts to proportions\n",
+    "agents_data['Agents'] = agents_data['Agents'] / total_agents\n",
+    "claims_data['Claims'] = claims_data['Claims'] / total_claims\n",
+    "\n",
+    "# Combine the normalized data\n",
+    "combined_normalized_data = pd.concat([agents_data, claims_data], ignore_index=True)\n",
+    "\n",
+    "# Set the size of the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Create the bar plot using the normalized data\n",
+    "sns.barplot(x='Region', y='value', hue='Type', data=pd.melt(combined_normalized_data, ['Region', 'Type']))\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Normalized Comparison of Claims and Agents by Region')\n",
+    "plt.ylabel('Proportion')\n",
+    "plt.xlabel('Region')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see on the normalized graph above, there is a big issue that needs to be mitigated on our State Farm agency. Some balancing needs to be done to ensure effectiveness by our agents and each individual branch as well as fairness. We don't want a branch to be left out with way less agents than needed. \n",
+    "\n",
+    "Lastly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Region  Agents per Claim\n",
+      "0    midwest          0.916667\n",
+      "1  northeast          1.105769\n",
+      "2      south          0.670732\n",
+      "3       west          1.473214\n"
+     ]
+    }
+   ],
+   "source": [
+    "claims_data['Region'] = claims_data['Region'].str.lower()\n",
+    "\n",
+    "# Set 'Region' as index for easy division\n",
+    "agents_data.set_index('Region', inplace=True)\n",
+    "claims_data.set_index('Region', inplace=True)\n",
+    "\n",
+    "# Calculate the ratio of agents to claims\n",
+    "agents_per_claims_ratio = agents_data['Agents'] / claims_data['Claims']\n",
+    "\n",
+    "# Reset index to bring 'Region' back as a column\n",
+    "agents_per_claims_ratio = agents_per_claims_ratio.reset_index()\n",
+    "\n",
+    "# Rename the columns for clarity\n",
+    "agents_per_claims_ratio.columns = ['Region', 'Agents per Claim']\n",
+    "\n",
+    "# Print the result\n",
+    "print(agents_per_claims_ratio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geographical Disaster Map\n",
+    "\n",
+    "- By using Geographical shapefile, we can plot specific points within the map.\n",
+    "- shapefile resources are from ScienceBase.gov portal (https://www.sciencebase.gov/catalog/item/4f4e4783e4b07f02db4837ce)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group by 'lat' and 'long' and count the occurrences\n",
+    "location_counts = disasters.groupby(['lat', 'long']).size().reset_index(name='counts')\n",
+    "\n",
+    "# Merge the counts back into the original DataFrame\n",
+    "disasters = disasters.merge(location_counts, on=['lat', 'long'])\n",
+    "\n",
+    "# Normalize the counts for plotting purposes\n",
+    "# This will scale the counts to a range suitable for the markersize or alpha\n",
+    "max_count = disasters['counts'].max()\n",
+    "disasters['normalized_counts'] = disasters['counts'] / max_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the shapefile\n",
+    "us_states = gpd.read_file('data/map/US_States.shp')\n",
+    "\n",
+    "# Convert the disaster data into GeoDataFrame\n",
+    "gdf = gpd.GeoDataFrame(disasters, geometry=gpd.points_from_xy(disasters['long'], disasters['lat']))\n",
+    "\n",
+    "# Plot the states\n",
+    "fig, ax = plt.subplots(figsize=(15, 15))\n",
+    "us_states.plot(ax=ax, color='lightgrey')\n",
+    "\n",
+    "# Plot each disaster point with a different color based on the type of disaster\n",
+    "disaster_types = gdf['type'].unique()\n",
+    "colors = ['blue', 'red', 'orange', 'pink', 'purple', 'green', 'grey', 'brown']  # Define more colors if you have more types\n",
+    "color_dict = dict(zip(disaster_types, colors))\n",
+    "\n",
+    "# Iterate through the disaster types and plot them\n",
+    "for idx, row in gdf.iterrows():\n",
+    "    ax.scatter(row['geometry'].x, row['geometry'].y, s = 100 * row['normalized_counts'],  # scale size by normalized count\n",
+    "               color=color_dict[row['type']], alpha=0.5)  # scale alpha by normalized count\n",
+    "\n",
+    "\n",
+    "# Set the x and y axis limits to fit the contiguous US\n",
+    "ax.set_xlim(-180, -66)\n",
+    "ax.set_ylim(24, 80)\n",
+    "ax.set_title('Disaster Map of the USA')\n",
+    "\n",
+    "# Create a legend for the disaster types\n",
+    "legend_elements = [plt.Line2D([0], [0], marker='o', color='w', label=disaster_type,\n",
+    "                              markerfacecolor=color, markersize=10)\n",
+    "                   for disaster_type, color in color_dict.items()]\n",
+    "ax.legend(handles=legend_elements, title='Disaster Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "\n",
+    "\n",
+    "# Remove the x and y axis since they often aren't useful in a geographical plot\n",
+    "ax.set_axis_off()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now we want to see the heatmap of total claims distribution per state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the total number of disaster occurrend in individual region/states\n",
+    "total_claim_per_state = data.get_total_claims_per_regional_disaster()\n",
+    "\n",
+    "# Separte the data into two lists for plotting\n",
+    "claims_list = [(state, claims) for region, states in total_claim_per_state.items() for state, claims in states.items()]\n",
+    "claims_per_state_table = pd.DataFrame(claims_list, columns=['state', 'claims'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>claims</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Texas</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Florida</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Pennsylvania</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Tennessee</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Georgia</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Indiana</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Virginia</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>District of Columbia</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Missouri</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Massachusetts</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Iowa</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Idaho</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Nebraska</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Louisiana</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Illinois</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Utah</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Mississippi</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Maine</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Oklahoma</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Maryland</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>New Mexico</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   state  claims\n",
+       "0             California     100\n",
+       "20                 Texas      89\n",
+       "27              New York      86\n",
+       "18               Florida      67\n",
+       "14                  Ohio      52\n",
+       "29          Pennsylvania      44\n",
+       "8               Michigan      40\n",
+       "25             Tennessee      39\n",
+       "21               Georgia      32\n",
+       "13               Indiana      32\n",
+       "22              Virginia      28\n",
+       "9              Minnesota      28\n",
+       "2             Washington      26\n",
+       "30  District of Columbia      24\n",
+       "1                Arizona      24\n",
+       "16              Missouri      23\n",
+       "5               Colorado      22\n",
+       "24               Alabama      22\n",
+       "32         Massachusetts      20\n",
+       "11                  Iowa      20\n",
+       "4                  Idaho      18\n",
+       "17        North Carolina      17\n",
+       "12                Kansas      16\n",
+       "10              Nebraska      16\n",
+       "6                 Alaska      15\n",
+       "28           Connecticut      15\n",
+       "23             Louisiana      13\n",
+       "15              Illinois      13\n",
+       "3                   Utah      13\n",
+       "19           Mississippi      11\n",
+       "31                 Maine      11\n",
+       "26              Oklahoma      10\n",
+       "33              Maryland       8\n",
+       "7             New Mexico       6"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "claims_per_state_table.sort_values(by='claims', ascending=False, inplace=True)\n",
+    "claims_per_state_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>STATE</th>\n",
+       "      <th>STPOSTAL</th>\n",
+       "      <th>VERSION</th>\n",
+       "      <th>DotRegion</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>05</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1033.192917</td>\n",
+       "      <td>278.612681</td>\n",
+       "      <td>MULTIPOLYGON (((-148.62042 60.90747, -148.6182...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Montana</td>\n",
+       "      <td>MT</td>\n",
+       "      <td>05</td>\n",
+       "      <td>8</td>\n",
+       "      <td>36.656062</td>\n",
+       "      <td>45.078808</td>\n",
+       "      <td>POLYGON ((-104.05770 44.99743, -104.05954 44.9...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</td>\n",
+       "      <td>Idaho</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>05</td>\n",
+       "      <td>10</td>\n",
+       "      <td>31.141332</td>\n",
+       "      <td>24.456703</td>\n",
+       "      <td>POLYGON ((-117.12337 44.60405, -117.12187 44.6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>North Dakota</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>05</td>\n",
+       "      <td>8</td>\n",
+       "      <td>25.154998</td>\n",
+       "      <td>21.839700</td>\n",
+       "      <td>POLYGON ((-96.78089 46.61976, -96.78022 46.619...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>MN</td>\n",
+       "      <td>05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>36.513438</td>\n",
+       "      <td>25.535173</td>\n",
+       "      <td>MULTIPOLYGON (((-92.20109 46.70614, -92.20159 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  STFIPS         STATE STPOSTAL VERSION  DotRegion   Shape_Leng  Shape_Area  \\\n",
+       "0     02        Alaska       AK      05         10  1033.192917  278.612681   \n",
+       "1     30       Montana       MT      05          8    36.656062   45.078808   \n",
+       "2     16         Idaho       ID      05         10    31.141332   24.456703   \n",
+       "3     38  North Dakota       ND      05          8    25.154998   21.839700   \n",
+       "4     27     Minnesota       MN      05          5    36.513438   25.535173   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  MULTIPOLYGON (((-148.62042 60.90747, -148.6182...  \n",
+       "1  POLYGON ((-104.05770 44.99743, -104.05954 44.9...  \n",
+       "2  POLYGON ((-117.12337 44.60405, -117.12187 44.6...  \n",
+       "3  POLYGON ((-96.78089 46.61976, -96.78022 46.619...  \n",
+       "4  MULTIPOLYGON (((-92.20109 46.70614, -92.20159 ...  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "us_states.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the claims data with the shapefile to get the geometry\n",
+    "heatmap_gdf = us_states.merge(claims_per_state_table, left_on='STATE', right_on='state')\n",
+    "heatmap_gdf['claims'].fillna(0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Heatmap Definition\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(15, 15))\n",
+    "heatmap_gdf.plot(column='claims', ax=ax, cmap='OrRd', legend=True,\n",
+    "                legend_kwds={'label': \"Number of Claims by State\",\n",
+    "                             'orientation': \"horizontal\"})\n",
+    "\n",
+    "# Plot state borders on top of the heatmap\n",
+    "heatmap_gdf.boundary.plot(ax=ax, edgecolor='black', linewidth=0.5)  # Adjust linewidth as needed\n",
+    "\n",
+    "\n",
+    "# Remove the axis for a cleaner look\n",
+    "ax.set_axis_off()\n",
+    "\n",
+    "# Set a title for the map\n",
+    "ax.set_title('Number of Claims by State')\n",
+    "\n",
+    "# Set figure size\n",
+    "ax.set_xlim(-180, -66)\n",
+    "ax.set_ylim(24, 80)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Severity and Estimated Cost Analaisys\n",
+    "\n",
+    "We also want to analyse how severity and other variables such as total loss, life loss and claim type affect the estimated cost. In the end, state farm is a business and we want to be able to analyse our margins and cost to maintain profitability, also enabling us to find possible outliers that needs further investigation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "claims_df = pd.DataFrame(data.get_claim_data())\n",
+    "\n",
+    "plt.figure(figsize=(10, 12))\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "# Create a legend dictionary to handle legend entries\n",
+    "legend_labels = {}\n",
+    "\n",
+    "# Iterate through the DataFrame and plot each point with appropriate color and marker\n",
+    "for index, row in claims_df.iterrows():\n",
+    "    color = 'red' if row['total_loss'] else 'blue'\n",
+    "    marker = 'x' if row['loss_of_life'] else 'o'\n",
+    "    label = ('Total Loss' if row['total_loss'] else 'Partial Loss') + (' with Loss of Life' if row['loss_of_life'] else '')\n",
+    "    \n",
+    "    # Add to legend_labels if not already present\n",
+    "    if label not in legend_labels:\n",
+    "        legend_labels[label] = plt.scatter([], [], color=color, marker=marker)\n",
+    "\n",
+    "    # Plot each point\n",
+    "    plt.scatter(row['severity_rating'], row['estimate_cost'], color=color, marker=marker)\n",
+    "\n",
+    "# Add the legend to the plot with the unique handles and labels\n",
+    "plt.legend(legend_labels.values(), legend_labels.keys())\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.xlabel('Severity Rating')\n",
+    "plt.ylabel('Estimated Cost')\n",
+    "plt.title('Claims Data: Severity vs Estimated Cost')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from numpy.polynomial.polynomial import Polynomial\n",
+    "\n",
+    "# Defining type of claims\n",
+    "claim_types = ['Auto', 'Home', 'Life']\n",
+    "\n",
+    "# Set up the figure and axes for the subplots\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(20, 12))  # 1 row, 3 columns\n",
+    "\n",
+    "# Loop through the claim types and create a subplot for each\n",
+    "for i, claim_type in enumerate(claim_types):\n",
+    "    # Filter the DataFrame for the current claim type\n",
+    "    df_filtered = claims_df[claims_df['type'] == claim_type]\n",
+    "\n",
+    "    # Prepare data for polynomial fit\n",
+    "    xdata = df_filtered['severity_rating']\n",
+    "    ydata = df_filtered['estimate_cost']\n",
+    "    \n",
+    "    # Fit a Polynomial (degree can be adjusted)\n",
+    "    degree = 2  # Degree of the polynomial\n",
+    "    coefs = Polynomial.fit(xdata, ydata, degree).convert().coef\n",
+    "\n",
+    "    # Plotting the polynomial fit line\n",
+    "    x_fit = np.linspace(min(xdata), max(xdata), 100)\n",
+    "    y_fit = sum(coefs[j] * x_fit**j for j in range(len(coefs)))\n",
+    "    axes[i].plot(x_fit, y_fit, \"r--\", label='Polynomial Fit')\n",
+    "\n",
+    "    # Iterate through the filtered DataFrame and plot each point with the appropriate color and marker\n",
+    "    for index, row in df_filtered.iterrows():\n",
+    "        color = 'red' if row['total_loss'] else 'blue'\n",
+    "        marker = 'x' if row['loss_of_life'] else 'o'\n",
+    "        axes[i].scatter(row['severity_rating'], row['estimate_cost'], color=color, marker=marker)\n",
+    "\n",
+    "    # Add titles and labels to each subplot\n",
+    "    axes[i].set_title(f'{claim_type} Claims')\n",
+    "    axes[i].set_xlabel('Severity Rating')\n",
+    "    axes[i].set_ylabel('Estimated Cost')\n",
+    "\n",
+    "    # Only add legend to the first subplot for clarity\n",
+    "    if i == 0:\n",
+    "        handles, labels = axes[i].get_legend_handles_labels()\n",
+    "        by_label = dict(zip(labels, handles))\n",
+    "        axes[i].legend(by_label.values(), by_label.keys())\n",
+    "\n",
+    "# Adjust the layout so labels and titles don't overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/python/simple_data_tool.py b/python/simple_data_tool.py
new file mode 100644
index 0000000..0181b80
--- /dev/null
+++ b/python/simple_data_tool.py
@@ -0,0 +1,533 @@
+# ------------------------------------ Package Installation ------------------------------------ #
+import json
+import math
+
+# from statistics import mean # Deprecated since we are not using it
+from datetime import datetime # For formatting datetime strings
+
+
+# ------------------------------------ Class Declaration ------------------------------------ #
+class SimpleDataTool:
+
+    AGENTS_FILEPATH = 'data/sfcc_2023_agents.json'
+    CLAIM_HANDLERS_FILEPATH = 'data/sfcc_2023_claim_handlers.json'
+    CLAIMS_FILEPATH = 'data/sfcc_2023_claims.json'
+    DISASTERS_FILEPATH = 'data/sfcc_2023_disasters.json'
+
+    REGION_MAP = {
+        'west': 'Alaska,Hawaii,Washington,Oregon,California,Montana,Idaho,Wyoming,Nevada,Utah,Colorado,Arizona,New Mexico',
+        'midwest': 'North Dakota,South Dakota,Minnesota,Wisconsin,Michigan,Nebraska,Iowa,Illinois,Indiana,Ohio,Missouri,Kansas',
+        'south': 'Oklahoma,Texas,Arkansas,Louisiana,Kentucky,Tennessee,Mississippi,Alabama,West Virginia,Virginia,North Carolina,South Carolina,Georgia,Florida',
+        'northeast': 'Maryland,Delaware,District of Columbia,Pennsylvania,New York,New Jersey,Connecticut,Massachusetts,Vermont,New Hampshire,Rhode Island,Maine'
+    }
+
+    def __init__(self):
+        self.__agent_data = self.load_json_from_file(self.AGENTS_FILEPATH)
+        self.__claim_handler_data = self.load_json_from_file(
+            self.CLAIM_HANDLERS_FILEPATH)
+        self.__claim_data = self.load_json_from_file(self.CLAIMS_FILEPATH)
+        self.__disaster_data = self.load_json_from_file(
+            self.DISASTERS_FILEPATH)
+
+    # Helper Methods
+
+    def load_json_from_file(self, filename):
+        data = None
+
+        with open(filename, 'r', encoding='utf-8') as file:
+            data = json.load(file)
+
+        return data
+
+    def get_agent_data(self):
+        return self.__agent_data
+
+    def get_claim_handler_data(self):
+        return self.__claim_handler_data
+
+    def get_disaster_data(self):
+        return self.__disaster_data
+
+    def get_claim_data(self):
+        return self.__claim_data
+
+    # Unit Test Methods
+
+# ------------------------------------ Unit Test One ------------------------------------ #
+    # region Test Set One
+
+    def get_num_closed_claims(self):
+        """Calculates the number of claims where that status is "Closed"
+
+        Returns:
+            int: number of closed claims
+        """
+        claims = self.get_claim_data()
+        closed_claims_count = sum(1 for claim in claims if claim['status'] == 'Closed')
+        return closed_claims_count
+
+
+    def get_num_claims_for_claim_handler_id(self, claim_handler_id):
+        """Calculates the number of claims assigned to a specific claim handler
+
+        Args:
+            claim_handler_id (int): id of claim handler
+
+        Returns:
+            int: number of claims assigned to claim handler
+        """
+        claims = self.get_claim_data()
+        claim_handler_claims = sum(1 for claim in claims if claim['claim_handler_assigned_id'] == claim_handler_id)
+        return claim_handler_claims
+
+
+
+    def get_num_disasters_for_state(self, state):
+        """Calculates the number of disasters for a specific state
+
+        Args:
+            state (string): name of a state in the United States of America,
+                            including the District of Columbia
+
+        Returns:
+            int: number of disasters for state
+        """
+        disasters = self.get_disaster_data()
+        state_disasters = sum(1 for disaster in disasters if disaster['state'] == state)
+        return state_disasters
+
+
+
+    # endregion
+
+# ------------------------------------ Unit Test Two ------------------------------------ #
+    # region Test Set Two
+
+    def get_total_claim_cost_for_disaster(self, disaster_id):
+        """Sums the estimated cost of a specific disaster by its claims
+
+        Args:
+            disaster_id (int): id of disaster
+
+        Returns:
+            float | None: estimate cost of disaster, rounded to the nearest hundredths place
+                          returns None if no claims are found
+        """
+        # Assign dataset to variable
+        claims = self.get_claim_data()
+        
+        # Total_cost to calculate all sums of estimate_cost
+        total_cost = 0  
+
+        # Looping through claims to find those related to the disaster_id
+        for claim in claims:
+            if claim['disaster_id'] == disaster_id:
+                # add to total cost if disaster_id matches
+                total_cost += claim['estimate_cost']
+
+        # If there is no cost, return None
+        return round(total_cost, 2) if total_cost > 0 else None
+
+
+    def get_average_claim_cost_for_claim_handler(self, claim_handler_id):
+        """Gets the average estimated cost of all claims assigned to a claim handler
+
+        Args:
+            claim_handler_id (int): id of claim handler
+
+        Returns:
+            float | None : average cost of claims, rounded to the nearest hundredths place
+                           or None if no claims are found
+        """
+        # Initialize variables for total_cost and count
+        total_cost = 0
+        count = 0
+
+        # Assign dataset to variable
+        claims = self.get_claim_data()
+        
+        # Iterate through claims, add estimate_cost if claim_handler_assigned_id matches
+        for claim in claims:
+            if claim['claim_handler_assigned_id'] == claim_handler_id:
+                # If the dataset matches, add to total_cost and increment count
+                total_cost += claim['estimate_cost']
+                count += 1
+        
+        # If count is 0, return None, else return average cost rounded to nearest hundredths place
+        return None if count == 0 else round(total_cost / count, 2)
+
+    def get_state_with_most_disasters(self):
+        """Returns the name of the state with the most disasters based on disaster data
+
+        If two states have the same number of disasters, then sort by alphabetical (a-z)
+        and take the first.
+
+        Example: Say New Jersey and Delaware both have the highest number of disasters at
+                 12 disasters each. Then, this method would return "Delaware" since "D"
+                 comes before "N" in the alphabet. 
+
+        Returns:
+            string: single name of state
+        """
+        # Create a dictionary to hold counts of disasters per state
+        disaster_counts = {}
+        
+        # Assign dataset to variable
+        disasters = self.get_disaster_data()
+        
+        # Iterate through disasters, add to dictionary for each state
+        for disaster in disasters:
+            state = disaster['state']
+            disaster_counts[state] = disaster_counts.get(state, 0) + 1
+            
+        # Initialize variables to store the state with most disasters
+        most_disasters_state = None
+        max_disasters_count = 0
+        
+        # Sorted list of states alphabetically by using keys() method
+        states = sorted(disaster_counts.keys())
+        
+        # Iterate through states and find the state with the most disasters
+        for state in states:
+            if disaster_counts[state] > max_disasters_count:
+                most_disasters_state = state
+                max_disasters_count = disaster_counts[state]
+        
+        return most_disasters_state
+
+    def get_state_with_least_disasters(self):
+        """Returns the name of the state with the least disasters based on disaster data
+
+        If two states have the same number of disasters, then sort by alphabetical (a-z)
+        and take the first.
+
+        Example: Say New Mexico and West Virginia both have the least number of disasters at
+                 1 disaster each. Then, this method would return "New Mexico" since "N"
+                 comes before "W" in the alphabet. 
+
+        Returns:
+            string: single name of state
+        """
+        # Create a dictionary to hold counts of disasters per state
+        disaster_counts = {}
+        
+        # Assign dataset to variable
+        disasters = self.get_disaster_data()
+        
+        # Iterate through disasters, add to dictionary for each state
+        for disaster in disasters:
+            state = disaster['state']
+            disaster_counts[state] = disaster_counts.get(state, 0) + 1
+        
+        # Initialize variables to store the state with the least disasters
+        least_disasters_state = None
+        min_disasters_count = float('inf')  # Set initial value to positive infinity
+        
+        # Sorted list of states alphabetically
+        states = sorted(disaster_counts.keys())
+        
+        # Iterate through states and find the state with the least disasters
+        for state in states:
+            if disaster_counts[state] < min_disasters_count:
+                least_disasters_state = state
+                min_disasters_count = disaster_counts[state]
+        
+        return least_disasters_state
+    
+    def get_most_spoken_agent_language_by_state(self, state):
+        """Returns the name of the most spoken language by agents (besides English) for a specific state
+
+        Args:
+            state (string): name of state
+
+        Returns:
+            string: name of language
+                    or empty string if state doesn't exist
+        """
+        # Create a dictionary to hold counts of languages per state
+        language_counts = {}  
+        
+        # Assign dataset to variable
+        agents = self.get_agent_data()
+        
+        # Traverse through agents data
+        for agent in agents:
+            # Check if the agent is from the specified state
+            if agent['state'] == state:
+                # Get the languages spoken by the agent excluding English
+                languages = [lang for lang in [agent['primary_language'], agent['secondary_language']] if lang != 'English']
+                # Increment count for each language in the dictionary
+                for lang in languages:
+                    language_counts[lang] = language_counts.get(lang, 0) + 1
+        
+        # Check if no languages were found return empty string
+        if not language_counts:
+            return ''
+    
+        # Find and return the most spoken language / get function returns 0 if key not found
+        most_spoken_language = max(language_counts, key=language_counts.get)
+        return most_spoken_language
+
+    def get_num_of_open_claims_for_agent_and_severity(self, agent_id, min_severity_rating):
+        """Returns the number of open claims for a specific agent and for a minimum severity level and higher
+
+        Note: Severity rating scale for claims is 1 to 10, inclusive.
+        
+        Args:
+            agent_id (int): ID of the agent
+            min_severity_rating (int): minimum claim severity rating
+
+        Returns:
+            int | None: number of claims that are not closed and have minimum severity rating or greater
+                        -1 if severity rating out of bounds
+                        None if agent does not exist, or agent has no claims (open or not)
+        """
+        # Assign dataset to variable
+        agents = self.get_agent_data()
+        claims = self.get_claim_data()
+        
+        # Check if the severity rating is out of bounds
+        if min_severity_rating < 1 or min_severity_rating > 10:
+            return -1
+        
+        # Check if the agent exists / any() returns True if any element of the iterable is true
+        agent_exists = any(agent['id'] == agent_id for agent in agents)
+        if not agent_exists:
+            return None
+        
+        
+        # Initialize a counter for open claims
+        open_claims_count = 0
+        # Check every claim
+        for claim in claims:
+            # Check if the claim is assigned to the agent, is not closed, and has sufficient severity
+            if (
+                claim['agent_assigned_id'] == agent_id and
+                claim['status'] != "Closed" and
+                claim['severity_rating'] >= min_severity_rating
+            ):
+                # If all conditions are met, increment the counter
+                open_claims_count += 1
+        
+        # Return None if there are no qualifying claims
+        return open_claims_count if open_claims_count > 0 else None
+
+    # endregion
+
+# ------------------------------------ Unit Test Three ------------------------------------ #
+    # region TestSetThree
+
+    def get_num_disasters_declared_after_end_date(self):
+        """Gets the number of disasters where it was declared after it ended
+
+        Returns:
+            int: number of disasters where the declared date is after the end date
+        """
+        disasters = self.get_disaster_data()
+        # Initialize a counter
+        disasters_declared = 0
+
+        # Loop through each disaster in the disasters list
+        for disaster in disasters:
+            # Convert string dates to datetime.date objects
+            declared_date = datetime.strptime(disaster['declared_date'], '%Y-%m-%d').date()
+            end_date = datetime.strptime(disaster['end_date'], '%Y-%m-%d').date()
+            
+            # Check if declared_date is greater than end_date and increment the counter if true
+            if declared_date > end_date:
+                disasters_declared += 1
+                
+        return disasters_declared
+
+
+
+    def build_map_of_agents_to_total_claim_cost(self):
+        """Builds a map of agent and their total claim cost
+
+        Hints:
+            An agent with no claims should return 0
+            Invalid agent id should have a value of None
+            You should round your total_claim_cost to the nearest hundredths
+
+        Returns:
+            dict: key is agent id, value is total cost of claims associated to the agent
+        """
+        # Assign dataset to variable
+        claims = self.get_claim_data()
+        
+        # Initialize a dictionary to store the total cost of claims per agent
+        agent_costs = {agent_id: 0 for agent_id in range(1, 101)}
+
+        # Loop through each claim
+        for claim in claims:
+            agent_id = claim["agent_assigned_id"]   # Get the agent id
+            cost = claim["estimate_cost"]           # Get the cost of the claim
+
+            # Add the cost of this claim to the total cost for this agent
+            agent_costs[agent_id] = round(agent_costs[agent_id] + cost, 2)
+            
+        return agent_costs
+
+
+    def calculate_disaster_claim_density(self, disaster_id):
+        """Calculates density of a diaster based on the number of claims and impact radius
+
+        Hints:
+            Assume uniform spacing between claims
+            Assume disaster impact area is a circle
+
+        Args:
+            disaster_id (int): id of diaster
+
+        Returns:
+            float: density of claims to disaster area, rounded to the nearest thousandths place
+            None if disaster does not exist
+        """
+        disaster = next((d for d in self.get_disaster_data() if d['id'] == disaster_id), None)
+    
+        if not disaster:
+            return None
+
+        # Calculate disaster impact area
+        radius = disaster['radius_miles']
+        disaster_area = math.pi * (radius ** 2)
+
+        # Count claims associated with this disaster
+        num_claims = sum(1 for claim in self.get_claim_data() if claim['disaster_id'] == disaster_id)
+
+        # Compute claim density
+        density = num_claims / disaster_area
+
+        return round(density, 5)
+
+
+    # endregion
+
+# ------------------------------------ Unit Test Four ------------------------------------ #
+    # region TestSetFour
+
+    def get_top_three_months_with_highest_num_of_claims_desc(self):
+        """Gets the top three months with the highest total claims
+
+        Hint:
+            Month should be full name like 01 is January and 12 is December
+            Year should be full four-digit year
+            List should be in descending order
+
+        Returns:
+            list: three strings of month and year, descending order of highest claims
+        """
+        
+        # Dictionary to store the total cost of claims per month
+        monthly_costs = {}
+
+        # Assign dataset to variable
+        claims = self.get_claim_data()
+
+        # Convert disasters list to a dictionary for O(1) lookups
+        disasters_dict = {disaster['id']: disaster for disaster in self.get_disaster_data()}
+
+        # Loop through each claim
+        for claim in claims:
+            disaster_id = claim['disaster_id']
+            disaster = disasters_dict.get(disaster_id)
+
+            if disaster:   
+                claim_date = datetime.strptime(disaster['declared_date'], '%Y-%m-%d')
+                month_year = claim_date.strftime('%B %Y')
+
+                # Add the cost of this claim to the monthly total
+                monthly_costs[month_year] = monthly_costs.get(month_year, 0) + 1
+        
+        # Get the top three months by total claim cost
+        top_three_months = sorted(monthly_costs.keys(), key=lambda month: monthly_costs[month], reverse=True)[:3]
+        
+        return top_three_months
+    
+    # endregion
+    
+# ------------------------------------ Nice to Have! ------------------------------------ #
+    # Helper function to get the region of a state
+    def get_region(self, state):
+        for region, states in self.REGION_MAP.items():
+            if state in states:
+                return region
+        return None  # or 'unknown'
+    
+    
+    # This functions maps out each regional disaster by type and counts the number of each type
+    def get_regional_disaster_map(self):\
+        
+        # Assign dataset to variable
+        disasters = self.get_disaster_data()
+        
+        # Initialize a dictionary to store disaster types per region
+        regional_disaster_map = {
+        'west': {},
+        'midwest': {},
+        'south': {},
+        'northeast': {}
+        }
+
+        # Accumulate disaster types per region
+        for disaster in disasters:
+            region = self.get_region(disaster['state'])
+            if region:
+                disaster_type = disaster['type']
+            if disaster_type in regional_disaster_map[region]:
+                regional_disaster_map[region][disaster_type] += 1
+            else:
+                regional_disaster_map[region][disaster_type] = 1
+
+        return regional_disaster_map
+
+
+    # This functions maps out each regional claims by type and counts the total claims of each type
+    def get_total_claims_per_regional_disaster(self):
+        
+        # Assign dataset to variable
+        claims = self.get_claim_data()
+        disasters = self.get_disaster_data()
+        
+        # Map disasters by id for quick lookup
+        disaster_map = {disaster['id']: disaster for disaster in disasters}
+
+        # Initialize the result data structure
+        regional_claims = {region: {} for region in self.REGION_MAP.keys()}
+
+        # Iterate through each claim, map to disaster, and update counts
+        for claim in claims:
+            # Map claim to disaster using disaster_id
+            disaster = disaster_map.get(claim['disaster_id'])
+            
+            # Ensure disaster exists
+            if disaster:
+                state = disaster['state']
+                region = self.get_region(state)
+                
+                # Ensure region is found and update the counts
+                if region is not None:
+                    if state in regional_claims[region]:
+                        regional_claims[region][state] += 1
+                    else:
+                        regional_claims[region][state] = 1
+        
+        # Return the resulting data structure
+        return regional_claims
+    
+    # This functions maps out each regional agents
+    def get_total_agents_per_region(self):
+        # Assign dataset to variable
+        agents = self.get_agent_data()
+        regional_agents = {"West": [], "Midwest": [], "South": [], "Northeast": []}
+        
+        for agent in agents:
+            for region in regional_agents:
+                if agent['region'] == region:
+                    regional_agents[region].append(agent)
+                    break  # Break out of the inner loop once the correct region is found
+        
+        return regional_agents
+
+
+
diff --git a/python/src/pictures/chris.jfif b/python/src/pictures/chris.jfif
new file mode 100644
index 0000000..9e970aa
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diff --git a/python/src/pictures/gabriel.jpg b/python/src/pictures/gabriel.jpg
new file mode 100644
index 0000000..ff571b8
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diff --git a/simple.json b/simple.json
new file mode 100644
index 0000000..ce52fd9
--- /dev/null
+++ b/simple.json
@@ -0,0 +1,7 @@
+[
+    {
+        "name": "John Smith",
+        "integer": 1,
+        "decimal": 1.99
+    }
+]
diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_simple_data_tool.py b/test/test_simple_data_tool.py
new file mode 100644
index 0000000..049250d
--- /dev/null
+++ b/test/test_simple_data_tool.py
@@ -0,0 +1,150 @@
+import pytest
+import random
+from simple_data_tool import SimpleDataTool
+
+
+@pytest.fixture
+def controller():
+    return SimpleDataTool()
+
+
+def test_read_data_files(controller):
+    """Making sure that JSON files load properly. This test does not count towards your score.
+
+    Args:
+        controller (SimpleDataTool): utility script that loads JSON files and does comparisons
+    """
+    assert (len(controller.get_agent_data()) == 100)
+    assert (len(controller.get_claim_handler_data()) == 156)
+    assert (len(controller.get_claim_data()) == 1000)
+    assert (len(controller.get_disaster_data()) == 100)
+
+
+class TestSetOne:
+    def test_get_num_closed_claims(self, controller):
+        """Test 1"""
+        actual_num_closed_claims = controller.get_num_closed_claims()
+        assert actual_num_closed_claims == 362
+
+    def test_get_num_claims_for_claim_handler_id(self, controller):
+        """Test 2"""
+        assert controller.get_num_claims_for_claim_handler_id(1) == 9
+        assert controller.get_num_claims_for_claim_handler_id(93) == 4
+        assert controller.get_num_claims_for_claim_handler_id(127) == 6
+
+    def test_get_num_disasters_for_state(self, controller):
+        """Test 3"""
+        assert controller.get_num_disasters_for_state(
+            'Arizona') == 2
+        assert controller.get_num_disasters_for_state(
+            'Georgia') == 5
+        assert controller.get_num_disasters_for_state(
+            'Illinois') == 2
+        assert controller.get_num_disasters_for_state(
+            'Texas') == 9
+        assert controller.get_num_disasters_for_state(
+            'District of Columbia') == 2
+
+
+class TestSetTwo:
+
+    def test_get_total_claim_cost_for_disaster(self, controller):
+        """Test 4"""
+        assert controller.get_total_claim_cost_for_disaster(5) == 377726.38
+        assert controller.get_total_claim_cost_for_disaster(0) == None
+        assert controller.get_total_claim_cost_for_disaster(
+            56) == 1287476.19
+        assert controller.get_total_claim_cost_for_disaster(101) == None
+        assert controller.get_total_claim_cost_for_disaster(
+            78) == 614822.68
+
+    def test_get_average_claim_cost_for_claim_handler(self, controller):
+        """Test 5"""
+        assert controller.get_average_claim_cost_for_claim_handler(
+            2) == 87330.89
+        assert round(
+            controller.get_average_claim_cost_for_claim_handler(42), 2) == 122195.90
+        assert controller.get_average_claim_cost_for_claim_handler(-5) == None
+        assert controller.get_average_claim_cost_for_claim_handler(225) == None
+        assert round(
+            controller.get_average_claim_cost_for_claim_handler(151), 2) == 242134.96
+
+    def test_get_state_with_most_and_least_disasters(self, controller):
+        """Test 6"""
+        assert controller.get_state_with_most_disasters() == 'California'
+        assert controller.get_state_with_least_disasters() == 'Alaska'
+
+    def test_get_most_spoken_agent_language_by_state(self, controller):
+        """Test 7"""
+        assert controller.get_most_spoken_agent_language_by_state(
+            'New Hampshire') == 'Arabic'
+        assert controller.get_most_spoken_agent_language_by_state(
+            'Wisconsin') == ''
+        assert controller.get_most_spoken_agent_language_by_state(
+            'Florida') == 'Spanish'
+
+    def test_get_num_of_open_claims_for_agent_and_severity(self, controller):
+        """Test 8"""
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            0, 0) == -1
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            25, 11) == -1
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            65, 3) == None
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            24, 1) == 16
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            87, 6) == 3
+        assert controller.get_num_of_open_claims_for_agent_and_severity(
+            85, 6) == 2
+
+
+class TestSetThree:
+
+    def test_get_num_disasters_declared_after_end_date(self, controller):
+        """Test 9"""
+        assert controller.get_num_disasters_declared_after_end_date() == 8
+
+    def test_build_map_of_agents_to_total_claim_cost(self, controller):
+        """Test 10"""
+        agent_cost_map = controller.build_map_of_agents_to_total_claim_cost()
+        assert len(agent_cost_map.keys()) == 100, 'Missing agents in map'
+
+        # Normal cases
+        assert agent_cost_map.get(1) == 27856.13
+        assert agent_cost_map.get(3) == 2253847.27
+        assert agent_cost_map.get(5) == 529685.97
+        assert agent_cost_map.get(8) == 282307.93
+        assert agent_cost_map.get(13) == 2310862.86
+
+        # Spot-check random agent ids that we expect to have no cost
+        expected_agent_ids_without_cost = [2, 6, 9, 12, 16, 22, 25, 32, 33, 37, 38, 40,
+                                           41, 44, 45, 48, 50, 51, 52, 53, 54, 61, 64,
+                                           65, 67, 69, 72, 81, 90, 93, 96]
+        num_agent_ids_without_cost = len(expected_agent_ids_without_cost)
+        for i in range(3):
+            random_agent_id = expected_agent_ids_without_cost[random.randint(
+                0, num_agent_ids_without_cost - 1)]
+            assert agent_cost_map.get(random_agent_id) == 0
+
+        # Testing invalid agent ids
+        assert agent_cost_map.get(-5) == None
+        assert agent_cost_map.get(255) == None
+
+    def test_calculate_disaster_claim_density(self, controller):
+        """Test 11"""
+        assert controller.calculate_disaster_claim_density(15) == 0.00172
+        assert controller.calculate_disaster_claim_density(68) == 0.00029
+        assert controller.calculate_disaster_claim_density(101) == None
+        assert controller.calculate_disaster_claim_density(64) == 0.01624
+
+
+class TestSetFour:
+
+    def test_get_top_three_months_with_highest_num_of_claims_desc(self, controller):
+        """Test 12"""
+        top_three_months = controller.get_top_three_months_with_highest_num_of_claims_desc()
+        assert len(top_three_months) == 3
+        assert top_three_months[0] == 'April 2023'
+        assert top_three_months[1] == 'November 2022'
+        assert top_three_months[2] == 'February 2023'