nfip_florida.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "e431d059-244c-44a7-b7f7-de51df6fab96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from datetime import datetime\n",
    "import duckdb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "import folium\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a87930-3386-40ac-965a-951010200e9a",
   "metadata": {},
   "source": [
    "## FIMA NFIP Redacted Claims - v2 dataset, FEMA\n",
    "“FIMA NFIP Redacted Claims - v2 | FEMA.gov.” 2023b. Www.fema.gov. \n",
    "    July 5, 2023. https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2.\n",
    "\n",
    "Congress passed the National Flood Insurance Act (NFIA), 42 U.S.C. 4001 in 1968, creating the National Flood Insurance Program (NFIP) in order to reduce future flood losses through flood hazard identification, floodplain management, and providing insurance protection. The Department of Housing and Urban Development (HUD) originally administered the NFIP, and Congress subsequently transferred the NFIP to FEMA upon its creation in 1979. FEMA and insurance companies participating in FEMA's Write Your Own (WYO) program offer NFIP insurance coverage for building structures as well as for contents and personal property within the building structures to eligible and insurable properties. The WYO program began in 1983 with NFIP operating under Part B of the NFIA and allows FEMA to authorize private insurance companies to issue the Standard Flood Insurance Policy (SFIP) as FEMA's fiduciary and fiscal agent. FEMA administers NFIP by ensuring insurance applications are processed properly; determining correct premiums; renewing, reforming, and cancelling insurance policies; transferring policies from the seller of the property to the purchaser of the property in certain circumstances; and processing insurance claims.\n",
    "\n",
    "The paid premiums of SFIPs and claims payments for damaged property are processed through the National Flood Insurance Fund (NFIF). NFIF was established by the National Flood Insurance Act of 1968 (42 U.S.C. 4001, et seq.), and is a centralized premium revenue and fee-generated fund that supports NFIP, which holds these U.S. Treasury funds.\n",
    "\n",
    "This dataset provides details on NFIP claims transactions. It is derived from the NFIP system of record, staged in the NFIP reporting platform and redacted to protect policy holder personally identifiable information.\n",
    "\n",
    "The Flood Insurance Claims Manual (https://nfipservices.floodsmart.gov/insurance-manuals) provides claims guidance to WYOs, vendors, adjusters, and examiners so that policyholders experience consistent and reliable service. The Manual provides processes for handling claims from the notice of loss to final payment. The NFIP has provided answers to Frequently Asked Questions (FAQs) to assist the public in understanding and navigating the data our program makes available: https://www.fema.gov/sites/default/files/documents/fema_nfip-data-faq record.\n",
    "\n",
    "This dataset is not intended to be an official federal report, and should not be considered an official federal report.\n",
    "\n",
    "FEMA's terms and conditions and citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page: https://www.fema.gov/about/openfema/terms-conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "583a682d-7dde-4b50-b667-70ce1b6918b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded successfully.\n"
     ]
    }
   ],
   "source": [
    "# Set the url variable as the NFIP claims url\n",
    "url = 'https://www.fema.gov/about/reports-and-data/openfema/FimaNfipClaims.csv'\n",
    "\n",
    "# Send a get request for the url, name the response variable\n",
    "response = requests.get(url)\n",
    "\n",
    "# Check if get request request (response) was successful (status code 200)\n",
    "if response.status_code == 200:\n",
    "    \n",
    "    # Specify the file path to save the downloaded file\n",
    "    file_path = 'FimaNfipClaims.csv'\n",
    "    \n",
    "    # Write the content of the response to a file\n",
    "    with open(file_path, 'wb') as file:\n",
    "        file.write(response.content)\n",
    "    print('File downloaded successfully.')\n",
    "    \n",
    "else:\n",
    "    print('Failed to download file.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "647a8f9e-5732-4dcb-b1d7-f487bff80811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the full file, create a variable.\n",
    "# I've been dividing my dataframes by state, but just in case I want the full thing, it's accessible.\n",
    "# Given the size of these files, I usually read them with low_memory set to False.\n",
    "\n",
    "nfip_all = pd.read_csv('FimaNfipClaims.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93f035aa-6e8d-4120-a339-c3be913a65ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FL'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This was my first time with the nfip dataset, so before I even filtered by state, I decided to check out what national data since 2016 looked like.\n",
    "# As I was culling my dataframe, I simplified the variable name\n",
    "\n",
    "nfip = nfip_all[nfip_all['yearOfLoss'] > 2016]\n",
    "\n",
    "# At this point I decided to filter for Florida, as I was interested particularly in data reflective of climate change.\n",
    "\n",
    "# Filter for Fla. data only.\n",
    "# And just to be sure I didn't overlook something in my filter coding, I check the unique values in the state column.\n",
    "\n",
    "nfip = nfip[nfip['state'] == 'FL']\n",
    "nfip.state.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "634b9056-0186-48c7-a1cc-ebd46cb537db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['agricultureStructureIndicator', 'asOfDate',\n",
       "       'basementEnclosureCrawlspaceType', 'policyCount',\n",
       "       'crsClassificationCode', 'dateOfLoss', 'elevatedBuildingIndicator',\n",
       "       'elevationCertificateIndicator', 'elevationDifference',\n",
       "       'baseFloodElevation', 'ratedFloodZone', 'houseWorship',\n",
       "       'locationOfContents', 'lowestAdjacentGrade', 'lowestFloorElevation',\n",
       "       'numberOfFloorsInTheInsuredBuilding', 'nonProfitIndicator',\n",
       "       'obstructionType', 'occupancyType', 'originalConstructionDate',\n",
       "       'originalNBDate', 'amountPaidOnBuildingClaim',\n",
       "       'amountPaidOnContentsClaim',\n",
       "       'amountPaidOnIncreasedCostOfComplianceClaim',\n",
       "       'postFIRMConstructionIndicator', 'rateMethod',\n",
       "       'smallBusinessIndicatorBuilding', 'totalBuildingInsuranceCoverage',\n",
       "       'totalContentsInsuranceCoverage', 'yearOfLoss',\n",
       "       'primaryResidenceIndicator', 'buildingDamageAmount',\n",
       "       'buildingDeductibleCode', 'netBuildingPaymentAmount',\n",
       "       'buildingPropertyValue', 'causeOfDamage', 'condominiumCoverageTypeCode',\n",
       "       'contentsDamageAmount', 'contentsDeductibleCode',\n",
       "       'netContentsPaymentAmount', 'contentsPropertyValue',\n",
       "       'disasterAssistanceCoverageRequired', 'eventDesignationNumber',\n",
       "       'ficoNumber', 'floodCharacteristicsIndicator', 'floodWaterDuration',\n",
       "       'floodproofedIndicator', 'floodEvent', 'iccCoverage',\n",
       "       'netIccPaymentAmount', 'nfipRatedCommunityNumber',\n",
       "       'nfipCommunityNumberCurrent', 'nfipCommunityName',\n",
       "       'nonPaymentReasonContents', 'nonPaymentReasonBuilding', 'numberOfUnits',\n",
       "       'buildingReplacementCost', 'contentsReplacementCost',\n",
       "       'replacementCostBasis', 'stateOwnedIndicator', 'waterDepth',\n",
       "       'floodZoneCurrent', 'buildingDescriptionCode',\n",
       "       'rentalPropertyIndicator', 'state', 'reportedCity', 'reportedZipCode',\n",
       "       'countyCode', 'censusTract', 'censusBlockGroupFips', 'latitude',\n",
       "       'longitude', 'id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nice! Only Florida claims. (At least according to the 'state' column.)\n",
    "# I decided to check the columns first to drop what I knew I wouldn't need.\n",
    "\n",
    "nfip.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bfed2c4-51a2-4de6-9e20-2679c6207c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188403, 64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This data cleaning was for a school project with specific needs. I knew I wouldn't need these fields.\n",
    "\n",
    "columns_to_drop = [\n",
    "    'asOfDate', 'reportedCity', 'reportedZipCode', 'amountPaidOnIncreasedCostOfComplianceClaim', 'rateMethod', 'ficoNumber','latitude', 'longitude', 'id'\n",
    "]\n",
    "\n",
    "nfip = nfip.drop(columns = columns_to_drop)\n",
    "\n",
    "# Check shape\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f287f85c-51ec-49d2-8844-2a5a72708466",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now I prep a dictionary of Florida counties for the county_name column I'll make\n",
    "\n",
    "florida_county_map = {\n",
    "    '12001': 'Alachua',\n",
    "    '12003': 'Baker',\n",
    "    '12005': 'Bay',\n",
    "    '12005': 'Bay',\n",
    "    '12007': 'Bradford',\n",
    "    '12009': 'Brevard',\n",
    "    '12011': 'Broward',\n",
    "    '12013': 'Calhoun',\n",
    "    '12015': 'Charlotte',\n",
    "    '12017': 'Citrus',\n",
    "    '12019': 'Clay',\n",
    "    '12021': 'Collier',\n",
    "    '12023': 'Columbia',\n",
    "    '12025': 'Dade',\n",
    "    '12027': 'DeSoto',\n",
    "    '12029': 'Dixie',\n",
    "    '12031': 'Duval',\n",
    "    '12033': 'Escambia',\n",
    "    '12035': 'Flagler',\n",
    "    '12037': 'Franklin',\n",
    "    '12039': 'Gadsden',\n",
    "    '12041': 'Gilchrist',\n",
    "    '12043': 'Glades',\n",
    "    '12045': 'Gulf',\n",
    "    '12047': 'Hamilton',\n",
    "    '12049': 'Hardee',\n",
    "    '12051': 'Hendry',\n",
    "    '12053': 'Hernando',\n",
    "    '12055': 'Highlands',\n",
    "    '12057': 'Hillsborough',\n",
    "    '12059': 'Holmes',\n",
    "    '12061': 'Indian River',\n",
    "    '12063': 'Jackson',\n",
    "    '12065': 'Jefferson',\n",
    "    '12067': 'Lafayette',\n",
    "    '12069': 'Lake',\n",
    "    '12071': 'Lee',\n",
    "    '12073': 'Leon',\n",
    "    '12075': 'Levy',\n",
    "    '12077': 'Liberty',\n",
    "    '12079': 'Madison',\n",
    "    '12081': 'Manatee',\n",
    "    '12083': 'Marion',\n",
    "    '12085': 'Martin',\n",
    "    '12086': 'Miami Dade',\n",
    "    '12087': 'Monroe',\n",
    "    '12089': 'Nassau',\n",
    "    '12091': 'Okaloosa',\n",
    "    '12093': 'Okeechobee',\n",
    "    '12095': 'Orange',\n",
    "    '12097': 'Osceola',\n",
    "    '12099': 'Palm Beach',\n",
    "    '12101': 'Pasco',\n",
    "    '12103': 'Pinellas',\n",
    "    '12105': 'Polk',\n",
    "    '12107': 'Putnam',\n",
    "    '12109': 'St. Johns',\n",
    "    '12111': 'St. Lucie',\n",
    "    '12113': 'Santa Rosa',\n",
    "    '12115': 'Sarasota',\n",
    "    '12117': 'Seminole',\n",
    "    '12119': 'Sumter',\n",
    "    '12121': 'Suwannee',\n",
    "    '12123': 'Taylor',\n",
    "    '12125': 'Union',\n",
    "    '12127': 'Volusia',\n",
    "    '12129': 'Wakulla',\n",
    "    '12131': 'Walton',\n",
    "    '12133': 'Washington'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a425d57c-9c98-4e60-9591-3e0db129a131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          12021.0\n",
       "10         12086.0\n",
       "11         12021.0\n",
       "12         12081.0\n",
       "13         12021.0\n",
       "            ...   \n",
       "1128013    12011.0\n",
       "1128060    12011.0\n",
       "1128217    12087.0\n",
       "1135986    12031.0\n",
       "1156291    12011.0\n",
       "Name: countyCode, Length: 188403, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the new column and get a look at how the FIPS codes are formatted\n",
    "\n",
    "nfip['county_name'] = ''\n",
    "nfip.countyCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "023c5f51-d995-4f4a-9afd-23b55aacdfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First I convert the FIPS codes from floats to strings\n",
    "\n",
    "nfip['countyCode'] = nfip['countyCode'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a94c2f52-57f9-4ac7-bf1c-f8b4b5be18b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then I get rid of the '.0' at the end of each string value\n",
    "\n",
    "nfip['countyCode'] = nfip['countyCode'].apply(lambda x: x.replace('.0', '') if isinstance(x, str) and x.endswith('.0') else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1216ff78-5b13-40c6-a4e6-003f50bbd2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agricultureStructureIndicator</th>\n",
       "      <th>basementEnclosureCrawlspaceType</th>\n",
       "      <th>policyCount</th>\n",
       "      <th>crsClassificationCode</th>\n",
       "      <th>dateOfLoss</th>\n",
       "      <th>elevatedBuildingIndicator</th>\n",
       "      <th>elevationCertificateIndicator</th>\n",
       "      <th>elevationDifference</th>\n",
       "      <th>baseFloodElevation</th>\n",
       "      <th>ratedFloodZone</th>\n",
       "      <th>...</th>\n",
       "      <th>stateOwnedIndicator</th>\n",
       "      <th>waterDepth</th>\n",
       "      <th>floodZoneCurrent</th>\n",
       "      <th>buildingDescriptionCode</th>\n",
       "      <th>rentalPropertyIndicator</th>\n",
       "      <th>state</th>\n",
       "      <th>countyCode</th>\n",
       "      <th>censusTract</th>\n",
       "      <th>censusBlockGroupFips</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-09-10T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-26T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>AH</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AHB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>1.208600e+10</td>\n",
       "      <td>1.208600e+11</td>\n",
       "      <td>Miami Dade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-09-11T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AH</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-11-11T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>AE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>FL</td>\n",
       "      <td>12081</td>\n",
       "      <td>1.208100e+10</td>\n",
       "      <td>1.208100e+11</td>\n",
       "      <td>Manatee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-09-27T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    agricultureStructureIndicator  basementEnclosureCrawlspaceType  \\\n",
       "7                               0                              0.0   \n",
       "10                              0                              0.0   \n",
       "11                              0                              0.0   \n",
       "12                              0                              0.0   \n",
       "13                              0                              0.0   \n",
       "\n",
       "    policyCount  crsClassificationCode                dateOfLoss  \\\n",
       "7             1                    NaN  2017-09-10T00:00:00.000Z   \n",
       "10            1                    NaN  2020-05-26T00:00:00.000Z   \n",
       "11            1                    NaN  2017-09-11T00:00:00.000Z   \n",
       "12            1                    NaN  2020-11-11T00:00:00.000Z   \n",
       "13            1                    NaN  2022-09-27T00:00:00.000Z   \n",
       "\n",
       "    elevatedBuildingIndicator elevationCertificateIndicator  \\\n",
       "7                           1                             2   \n",
       "10                          1                           NaN   \n",
       "11                          0                           NaN   \n",
       "12                          1                           NaN   \n",
       "13                          1                           NaN   \n",
       "\n",
       "    elevationDifference  baseFloodElevation ratedFloodZone  ...  \\\n",
       "7                   NaN                 NaN             VE  ...   \n",
       "10                  1.0                 7.0             AH  ...   \n",
       "11                  NaN                 NaN             AH  ...   \n",
       "12                  5.0                 9.0             AE  ...   \n",
       "13                  NaN                 NaN             AE  ...   \n",
       "\n",
       "    stateOwnedIndicator  waterDepth  floodZoneCurrent  \\\n",
       "7                     0         1.0               NaN   \n",
       "10                    0         0.0               AHB   \n",
       "11                    0         0.0               NaN   \n",
       "12                    0         0.0               NaN   \n",
       "13                    0         0.0               NaN   \n",
       "\n",
       "    buildingDescriptionCode  rentalPropertyIndicator  state  countyCode  \\\n",
       "7                       1.0                        0     FL       12021   \n",
       "10                      1.0                        0     FL       12086   \n",
       "11                      1.0                        0     FL       12021   \n",
       "12                      1.0                        0     FL       12081   \n",
       "13                      NaN                        0     FL       12021   \n",
       "\n",
       "     censusTract censusBlockGroupFips county_name  \n",
       "7   1.202101e+10         1.202101e+11     Collier  \n",
       "10  1.208600e+10         1.208600e+11  Miami Dade  \n",
       "11  1.202101e+10         1.202101e+11     Collier  \n",
       "12  1.208100e+10         1.208100e+11     Manatee  \n",
       "13  1.202101e+10         1.202101e+11     Collier  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I can use my Florida county dictionary to apply county names in my new column based on existing FIPS\n",
    "\n",
    "nfip['county_name'] = nfip.apply(\n",
    "    lambda row: florida_county_map.get(row['countyCode'], row['county_name']),\n",
    "    axis = 1\n",
    ")\n",
    "\n",
    "# Preview what the dataset looks like now\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad26f234-d9ce-4932-a964-ac6b3869ca19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['agriculture_structure_indicator', 'basement_enclosure_crawlspace_type',\n",
       "       'policy_count', 'crs_classification_code', 'date_of_loss',\n",
       "       'elevated_building_indicator', 'elevation_certificate_indicator',\n",
       "       'elevation_difference', 'base_flood_elevation', 'rated_flood_zone',\n",
       "       'house_worship', 'location_of_contents', 'lowest_adjacent_grade',\n",
       "       'lowest_floor_elevation', 'number_of_floors_in_the_insured_building',\n",
       "       'non_profit_indicator', 'obstruction_type', 'occupancy_type',\n",
       "       'original_construction_date', 'original_n_b_date',\n",
       "       'amount_paid_on_building_claim', 'amount_paid_on_contents_claim',\n",
       "       'post_f_i_r_m_construction_indicator',\n",
       "       'small_business_indicator_building',\n",
       "       'total_building_insurance_coverage',\n",
       "       'total_contents_insurance_coverage', 'year_of_loss',\n",
       "       'primary_residence_indicator', 'building_damage_amount',\n",
       "       'building_deductible_code', 'net_building_payment_amount',\n",
       "       'building_property_value', 'cause_of_damage',\n",
       "       'condominium_coverage_type_code', 'contents_damage_amount',\n",
       "       'contents_deductible_code', 'net_contents_payment_amount',\n",
       "       'contents_property_value', 'disaster_assistance_coverage_required',\n",
       "       'event_designation_number', 'flood_characteristics_indicator',\n",
       "       'flood_water_duration', 'floodproofed_indicator', 'flood_event',\n",
       "       'icc_coverage', 'net_icc_payment_amount', 'nfip_rated_community_number',\n",
       "       'nfip_community_number_current', 'nfip_community_name',\n",
       "       'non_payment_reason_contents', 'non_payment_reason_building',\n",
       "       'number_of_units', 'building_replacement_cost',\n",
       "       'contents_replacement_cost', 'replacement_cost_basis',\n",
       "       'state_owned_indicator', 'water_depth', 'flood_zone_current',\n",
       "       'building_description_code', 'rental_property_indicator', 'state',\n",
       "       'county_code', 'census_tract', 'census_block_group_fips',\n",
       "       'county_name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Not bad. Now let's change the camelCase column names to snake_case.\n",
    "# Aesthetically I'm more into camelCase than snake_case, but once I load this in Tableau,\n",
    "# Tableau recognizes underscores should be spaces, which in turn makes camelCase look messy.\n",
    "\n",
    "def camel_to_snake(name):\n",
    "    return re.sub(r'(?<!^)(?=[A-Z])', '_', name).lower()\n",
    "\n",
    "nfip.columns = [camel_to_snake(col) for col in nfip.columns]\n",
    "\n",
    "# As always, check how the columns come out.\n",
    "nfip.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "84cef5a2-771e-44c2-bbbc-34bcb1df861e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now create the columns where I can combine building and contents values\n",
    "\n",
    "nfip['total_damage'] = ''\n",
    "nfip['total_coverage'] = ''\n",
    "nfip['total_payment'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5ce9d893-22a3-4f87-b367-690422cda27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First I concatenate damage, from building and contents, making sure to account for nulls\n",
    "\n",
    "nfip['total_damage'] = nfip['building_damage_amount'].fillna(0) + nfip['contents_damage_amount'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db6f322a-ec55-4ede-8ab8-b474b0a11412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7       2396.0\n",
       "10      4560.0\n",
       "11         0.0\n",
       "12      2536.0\n",
       "13     14227.0\n",
       "14         0.0\n",
       "49         0.0\n",
       "50      4761.0\n",
       "51    398735.0\n",
       "52         0.0\n",
       "Name: total_damage, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I want to get a preview of what I've just made, just to check that I didn't overlook something obvious.\n",
    "\n",
    "nfip.total_damage.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f6501410-ba42-4da0-98ae-6a51711fbbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     300000.0\n",
       "10    271100.0\n",
       "11    350000.0\n",
       "12     13000.0\n",
       "13     25700.0\n",
       "14      8500.0\n",
       "49    350000.0\n",
       "50    210000.0\n",
       "51    350000.0\n",
       "52    350000.0\n",
       "Name: total_coverage, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Good! (BTS fact: The first few times I did this, I didn't get a series of numbers that looked this correct.)\n",
    "# Then I do coverage.\n",
    "\n",
    "nfip['total_coverage'] = nfip['total_contents_insurance_coverage'].fillna(0) + nfip['total_building_insurance_coverage'].fillna(0)\n",
    "\n",
    "# And just so I don't get cocky, let's check what that column looks like.\n",
    "nfip.total_coverage.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80be9ef3-7889-47c9-afb0-63f73f81ce16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          0.00\n",
       "10         0.00\n",
       "11         0.00\n",
       "12       603.39\n",
       "13      9802.74\n",
       "14         0.00\n",
       "49         0.00\n",
       "50      8586.18\n",
       "51    350000.00\n",
       "52         0.00\n",
       "Name: total_payment, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking good.\n",
    "# Now net payments. For the purposes of this project, I call this sum \"total_payment\".\n",
    "\n",
    "nfip['total_payment'] = nfip['net_building_payment_amount'].fillna(0) + nfip['net_contents_payment_amount'].fillna(0)\n",
    "nfip.total_payment.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68c89026-404a-4fac-85b5-e974f5e059ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'm curious about any gap between damage and payment. The lower the value (ideally zero!, maybe sometimes negative), the better off the policy holders are.\n",
    "# Positive values mean the policy holders didn't get enough money to cover the damage they suffered.\n",
    "\n",
    "nfip['damage_diff_payment'] = nfip['total_damage'] - nfip['total_payment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e9dbfcea-320f-4156-9f58-b68ec7913537",
   "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>agriculture_structure_indicator</th>\n",
       "      <th>basement_enclosure_crawlspace_type</th>\n",
       "      <th>policy_count</th>\n",
       "      <th>crs_classification_code</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>elevated_building_indicator</th>\n",
       "      <th>elevation_certificate_indicator</th>\n",
       "      <th>elevation_difference</th>\n",
       "      <th>base_flood_elevation</th>\n",
       "      <th>rated_flood_zone</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>county_code</th>\n",
       "      <th>census_tract</th>\n",
       "      <th>census_block_group_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>total_damage</th>\n",
       "      <th>total_coverage</th>\n",
       "      <th>total_payment</th>\n",
       "      <th>damage_diff_payment</th>\n",
       "      <th>damage_diff_coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-09-10T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VE</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "      <td>2396.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2396.00</td>\n",
       "      <td>-297604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-26T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>AH</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>1.208600e+10</td>\n",
       "      <td>1.208600e+11</td>\n",
       "      <td>Miami Dade</td>\n",
       "      <td>4560.0</td>\n",
       "      <td>271100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4560.00</td>\n",
       "      <td>-266540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-09-11T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AH</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-11-11T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>AE</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>12081</td>\n",
       "      <td>1.208100e+10</td>\n",
       "      <td>1.208100e+11</td>\n",
       "      <td>Manatee</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>603.39</td>\n",
       "      <td>1932.61</td>\n",
       "      <td>-10464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-09-27T00:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AE</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>12021</td>\n",
       "      <td>1.202101e+10</td>\n",
       "      <td>1.202101e+11</td>\n",
       "      <td>Collier</td>\n",
       "      <td>14227.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>9802.74</td>\n",
       "      <td>4424.26</td>\n",
       "      <td>-11473.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    agriculture_structure_indicator  basement_enclosure_crawlspace_type  \\\n",
       "7                                 0                                 0.0   \n",
       "10                                0                                 0.0   \n",
       "11                                0                                 0.0   \n",
       "12                                0                                 0.0   \n",
       "13                                0                                 0.0   \n",
       "\n",
       "    policy_count  crs_classification_code              date_of_loss  \\\n",
       "7              1                      NaN  2017-09-10T00:00:00.000Z   \n",
       "10             1                      NaN  2020-05-26T00:00:00.000Z   \n",
       "11             1                      NaN  2017-09-11T00:00:00.000Z   \n",
       "12             1                      NaN  2020-11-11T00:00:00.000Z   \n",
       "13             1                      NaN  2022-09-27T00:00:00.000Z   \n",
       "\n",
       "    elevated_building_indicator elevation_certificate_indicator  \\\n",
       "7                             1                               2   \n",
       "10                            1                             NaN   \n",
       "11                            0                             NaN   \n",
       "12                            1                             NaN   \n",
       "13                            1                             NaN   \n",
       "\n",
       "    elevation_difference  base_flood_elevation rated_flood_zone  ...  state  \\\n",
       "7                    NaN                   NaN               VE  ...     FL   \n",
       "10                   1.0                   7.0               AH  ...     FL   \n",
       "11                   NaN                   NaN               AH  ...     FL   \n",
       "12                   5.0                   9.0               AE  ...     FL   \n",
       "13                   NaN                   NaN               AE  ...     FL   \n",
       "\n",
       "    county_code  census_tract  census_block_group_fips  county_name  \\\n",
       "7         12021  1.202101e+10             1.202101e+11      Collier   \n",
       "10        12086  1.208600e+10             1.208600e+11   Miami Dade   \n",
       "11        12021  1.202101e+10             1.202101e+11      Collier   \n",
       "12        12081  1.208100e+10             1.208100e+11      Manatee   \n",
       "13        12021  1.202101e+10             1.202101e+11      Collier   \n",
       "\n",
       "    total_damage  total_coverage  total_payment damage_diff_payment  \\\n",
       "7         2396.0        300000.0           0.00             2396.00   \n",
       "10        4560.0        271100.0           0.00             4560.00   \n",
       "11           0.0        350000.0           0.00                0.00   \n",
       "12        2536.0         13000.0         603.39             1932.61   \n",
       "13       14227.0         25700.0        9802.74             4424.26   \n",
       "\n",
       "   damage_diff_coverage  \n",
       "7             -297604.0  \n",
       "10            -266540.0  \n",
       "11            -350000.0  \n",
       "12             -10464.0  \n",
       "13             -11473.0  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do the same check between damage and coverage \n",
    "\n",
    "nfip['damage_diff_coverage'] = nfip['total_damage'] - nfip['total_coverage']\n",
    "\n",
    "# And check how this is calculating in my dataframe\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "38526d1f-4704-4c42-ae5d-a2e557ab2a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188403, 70)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How's my shape looking?\n",
    "\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e1c6eb98-81b8-4774-be69-eb4e8997a96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141980, 70)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seems like a lot of rows.\n",
    "# For this project, building damage is a crucial metric. I may as well exclude any claims that didn't collect that.\n",
    "\n",
    "nfip.dropna(subset = ['building_damage_amount'], inplace = True)\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e05217b8-357a-4678-ba34-cefad828e717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141980, 70)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# That knocked off a chunk.\n",
    "# Same goes for building coverage.\n",
    "\n",
    "nfip.dropna(subset = ['total_building_insurance_coverage'], inplace = True)\n",
    "\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6423e95c-f698-48cd-8509-69bdaa78fe5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87298, 70)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Huh. I guess coverage and damage covered the same nulls.\n",
    "\n",
    "# Contents damage values are also important to my analysis. I wonder how much getting rid of those nulls would eliminate.\n",
    "\n",
    "nfip.dropna(subset = ['contents_damage_amount'], inplace = True)\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e812dd5a-ffbb-4331-bd2b-cd6fe8c25c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['agriculture_structure_indicator', 'basement_enclosure_crawlspace_type',\n",
       "       'policy_count', 'crs_classification_code', 'date_of_loss',\n",
       "       'elevated_building_indicator', 'elevation_certificate_indicator',\n",
       "       'elevation_difference', 'base_flood_elevation', 'rated_flood_zone',\n",
       "       'house_worship', 'location_of_contents', 'lowest_adjacent_grade',\n",
       "       'lowest_floor_elevation', 'number_of_floors_in_the_insured_building',\n",
       "       'non_profit_indicator', 'obstruction_type', 'occupancy_type',\n",
       "       'original_construction_date', 'original_n_b_date',\n",
       "       'amount_paid_on_building_claim', 'amount_paid_on_contents_claim',\n",
       "       'post_f_i_r_m_construction_indicator',\n",
       "       'small_business_indicator_building',\n",
       "       'total_building_insurance_coverage',\n",
       "       'total_contents_insurance_coverage', 'year_of_loss',\n",
       "       'primary_residence_indicator', 'building_damage_amount',\n",
       "       'building_deductible_code', 'net_building_payment_amount',\n",
       "       'building_property_value', 'cause_of_damage',\n",
       "       'condominium_coverage_type_code', 'contents_damage_amount',\n",
       "       'contents_deductible_code', 'net_contents_payment_amount',\n",
       "       'contents_property_value', 'disaster_assistance_coverage_required',\n",
       "       'event_designation_number', 'flood_characteristics_indicator',\n",
       "       'flood_water_duration', 'floodproofed_indicator', 'flood_event',\n",
       "       'icc_coverage', 'net_icc_payment_amount', 'nfip_rated_community_number',\n",
       "       'nfip_community_number_current', 'nfip_community_name',\n",
       "       'non_payment_reason_contents', 'non_payment_reason_building',\n",
       "       'number_of_units', 'building_replacement_cost',\n",
       "       'contents_replacement_cost', 'replacement_cost_basis',\n",
       "       'state_owned_indicator', 'water_depth', 'flood_zone_current',\n",
       "       'building_description_code', 'rental_property_indicator', 'state',\n",
       "       'county_code', 'census_tract', 'census_block_group_fips', 'county_name',\n",
       "       'total_damage', 'total_coverage', 'total_payment',\n",
       "       'damage_diff_payment', 'damage_diff_coverage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wow. That's a pretty good shape for this project.\n",
    "# I'm going to check the columns one more time before I save it as a .csv\n",
    "\n",
    "nfip.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f14a9790-1dec-40d8-8b8b-9f823e4ae053",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I tend to save these by state, so I'll specify FL in the file name.\n",
    "\n",
    "nfip.to_csv('nfip_florida.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a8fc59-139f-43b9-ba93-304ea740f30b",
   "metadata": {},
   "source": [
    "## Predictive Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184ccb17-7e26-4d88-84fd-67d615115af8",
   "metadata": {},
   "source": [
    "### Will flood insurance coverage be insufficient to cover flood damage?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc68a68-9e51-4868-824d-47803e9437f6",
   "metadata": {},
   "source": [
    "**Objective:** Predict whether damage_diff_coverage is positive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0b71ab-d755-4f2a-a875-81f48ba470f1",
   "metadata": {},
   "source": [
    "**Target Variable:** damage_diff_coverage > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e63841-bbaa-4234-95f2-7e9fd1278363",
   "metadata": {},
   "source": [
    "**Feature Selection:** Exclude irrelevant or redundant columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebe7853-072b-485b-9465-d03d5dd0bcce",
   "metadata": {},
   "source": [
    "**Handle Missing Values:** Impute or drop rows/columns with missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7284d9d4-5b95-4f86-9d2b-c54bd6b8bd6b",
   "metadata": {},
   "source": [
    "**Categorical Encoding:** Convert categorical columns to numeric format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957c80ab-b969-44df-afb9-a54e99be19e9",
   "metadata": {},
   "source": [
    "**Feature Scaling:** Standardize or normalize numeric features for better model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d7588f-dc05-4676-9cee-8873e05ab9d3",
   "metadata": {},
   "source": [
    "**Train-Test Split:** Split the data into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "62beb884-2495-4d83-beb6-3d8500f7e8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First I'll set my target variable\n",
    "\n",
    "nfip['coverage_insufficient'] = (nfip['damage_diff_coverage'] > 0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d30cb02d-3344-4e31-b0c9-503d9bdd266a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coverage_insufficient\n",
       "0    94.50732\n",
       "1     5.49268\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And check what the split is\n",
    "\n",
    "nfip['coverage_insufficient'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3e846a33-39a2-4efb-b62f-5243c413c312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Good news for FLorida homeowners; most people who suffered flood damage (at least according to the NFIP database) are sufficiently covered.\n",
    "# But 5% of 78K people is still worth improving.\n",
    "\n",
    "# Next in the predictive modeling process, I'll select my features.\n",
    "# For this preliminary run, I'm going to be pretty inclusive, and just disregard redundant or target-related columns.\n",
    "\n",
    "target = 'coverage_insufficient'\n",
    "exclude_columns = ['damage_diff_coverage', 'total_payment', 'total_damage', 'total_coverage']\n",
    "features = [col for col in nfip.columns if col not in exclude_columns + [target]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6b902567-6d03-4763-bbf0-92d378d37cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we'll train-test split\n",
    "\n",
    "X = nfip[features]\n",
    "y = nfip[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y) # my dataset is pretty unbalanced, and my teacher taught me to stratify in such cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1ae64059-043e-49a4-a3c3-d03eaca90a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, I'll ID categorical and numeric columns\n",
    "\n",
    "categorical_cols = X.select_dtypes(include = ['object']).columns\n",
    "numeric_cols = X.select_dtypes(include = ['float64', 'int64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a2a6348f-9166-447d-807a-9039aa552eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prime the preprocessing pipeline\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numeric_cols),\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dfcf20e4-197a-4687-9118-c78ec1b1634c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical columns to strings to avoid mixed types\n",
    "\n",
    "for col in categorical_cols:\n",
    "    nfip[col] = nfip[col].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e0895b3d-6c92-4813-aacf-50e3105cad80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix categorical columns in both train and test sets\n",
    "\n",
    "X_train[categorical_cols] = X_train[categorical_cols].astype(str)\n",
    "X_test[categorical_cols] = X_test[categorical_cols].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2adb40d7-30e5-4b7e-a249-d6c3fb95f873",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK, trying train-test split again\n",
    "\n",
    "X = nfip[features]\n",
    "y = nfip[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8505fc8c-7a2f-48b0-b2a6-92ddac98a88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking that categorical columns are strings throughout\n",
    "# This is often where I learn some data cleaning lessons\n",
    "\n",
    "categorical_cols = X.select_dtypes(include = ['object']).columns\n",
    "X_train[categorical_cols] = X_train[categorical_cols].astype(str)\n",
    "X_test[categorical_cols] = X_test[categorical_cols].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "13aeea3d-29bf-43ba-ab5f-1b0dfc0af730",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once again, preprocessing!\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', Pipeline([\n",
    "            ('imputer', SimpleImputer(strategy='mean')),  # Impute missing numeric values with the mean\n",
    "            ('scaler', StandardScaler())                 # Scale numeric features\n",
    "        ]), numeric_cols),\n",
    "        ('cat', Pipeline([\n",
    "            ('imputer', SimpleImputer(strategy='most_frequent')),  # Fill missing categorical values with the most frequent\n",
    "            ('encoder', OneHotEncoder(handle_unknown='ignore'))    # One-hot encode categorical variables\n",
    "        ]), categorical_cols)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7e5b79d6-5508-4644-aaa2-72647a6bf104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And build the pipeline. This time using RandomForestCLassifier.\n",
    "\n",
    "model = Pipeline(steps = [\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier(random_state = 42))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "134300ca-db0c-47ce-84f1-08b829656b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\natal\\anaconda3\\Lib\\site-packages\\sklearn\\impute\\_base.py:577: UserWarning: Skipping features without any observed values: ['flood_characteristics_indicator']. At least one non-missing value is needed for imputation with strategy='mean'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;agriculture_structure_indicator&#x27;, &#x27;basement_enclosure_crawlspace_type&#x27;,\n",
       "       &#x27;policy_count&#x27;, &#x27;crs_classification_code&#x27;,\n",
       "       &#x27;elevated_building_indicator&#x27;, &#x27;elevation_difference&#x27;,\n",
       "       &#x27;base_flood_elevation&#x27;, &#x27;house_wor...\n",
       "       &#x27;building_deductible_code&#x27;, &#x27;cause_of_damage&#x27;,\n",
       "       &#x27;condominium_coverage_type_code&#x27;, &#x27;contents_deductible_code&#x27;,\n",
       "       &#x27;event_designation_number&#x27;, &#x27;flood_event&#x27;,\n",
       "       &#x27;nfip_community_number_current&#x27;, &#x27;nfip_community_name&#x27;,\n",
       "       &#x27;replacement_cost_basis&#x27;, &#x27;flood_zone_current&#x27;, &#x27;state&#x27;, &#x27;county_code&#x27;,\n",
       "       &#x27;county_name&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;agriculture_structure_indicator&#x27;, &#x27;basement_enclosure_crawlspace_type&#x27;,\n",
       "       &#x27;policy_count&#x27;, &#x27;crs_classification_code&#x27;,\n",
       "       &#x27;elevated_building_indicator&#x27;, &#x27;elevation_difference&#x27;,\n",
       "       &#x27;base_flood_elevation&#x27;, &#x27;house_wor...\n",
       "       &#x27;building_deductible_code&#x27;, &#x27;cause_of_damage&#x27;,\n",
       "       &#x27;condominium_coverage_type_code&#x27;, &#x27;contents_deductible_code&#x27;,\n",
       "       &#x27;event_designation_number&#x27;, &#x27;flood_event&#x27;,\n",
       "       &#x27;nfip_community_number_current&#x27;, &#x27;nfip_community_name&#x27;,\n",
       "       &#x27;replacement_cost_basis&#x27;, &#x27;flood_zone_current&#x27;, &#x27;state&#x27;, &#x27;county_code&#x27;,\n",
       "       &#x27;county_name&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier(random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;agriculture_structure_indicator&#x27;, &#x27;basement_enclosure_crawlspace_type&#x27;,\n",
       "       &#x27;policy_count&#x27;, &#x27;crs_classification_code&#x27;,\n",
       "       &#x27;elevated_building_indicator&#x27;, &#x27;elevation_difference&#x27;,\n",
       "       &#x27;base_flood_elevation&#x27;, &#x27;house_worship&#x27;, &#x27;location_of_contents&#x27;,\n",
       "       &#x27;lo...\n",
       "       &#x27;original_construction_date&#x27;, &#x27;original_n_b_date&#x27;,\n",
       "       &#x27;building_deductible_code&#x27;, &#x27;cause_of_damage&#x27;,\n",
       "       &#x27;condominium_coverage_type_code&#x27;, &#x27;contents_deductible_code&#x27;,\n",
       "       &#x27;event_designation_number&#x27;, &#x27;flood_event&#x27;,\n",
       "       &#x27;nfip_community_number_current&#x27;, &#x27;nfip_community_name&#x27;,\n",
       "       &#x27;replacement_cost_basis&#x27;, &#x27;flood_zone_current&#x27;, &#x27;state&#x27;, &#x27;county_code&#x27;,\n",
       "       &#x27;county_name&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;agriculture_structure_indicator&#x27;, &#x27;basement_enclosure_crawlspace_type&#x27;,\n",
       "       &#x27;policy_count&#x27;, &#x27;crs_classification_code&#x27;,\n",
       "       &#x27;elevated_building_indicator&#x27;, &#x27;elevation_difference&#x27;,\n",
       "       &#x27;base_flood_elevation&#x27;, &#x27;house_worship&#x27;, &#x27;location_of_contents&#x27;,\n",
       "       &#x27;lowest_adjacent_grade&#x27;, &#x27;lowest_floor_elevation&#x27;,\n",
       "       &#x27;number_of_floors_in_the_insured_building&#x27;, &#x27;non_profit_indicator&#x27;,\n",
       "       &#x27;obstruction_type&#x27;, &#x27;occupancy_type&#x27;, &#x27;amount_paid_on_building_claim&#x27;,\n",
       "       &#x27;amount_paid_on_contents_claim&#x27;, &#x27;post_f_i_r_m_construction_indicator&#x27;,\n",
       "       &#x27;small_business_indicator_building&#x27;,\n",
       "       &#x27;total_building_insurance_coverage&#x27;,\n",
       "       &#x27;total_contents_insurance_coverage&#x27;, &#x27;year_of_loss&#x27;,\n",
       "       &#x27;primary_residence_indicator&#x27;, &#x27;building_damage_amount&#x27;,\n",
       "       &#x27;net_building_payment_amount&#x27;, &#x27;building_property_value&#x27;,\n",
       "       &#x27;contents_damage_amount&#x27;, &#x27;net_contents_payment_amount&#x27;,\n",
       "       &#x27;contents_property_value&#x27;, &#x27;disaster_assistance_coverage_required&#x27;,\n",
       "       &#x27;flood_characteristics_indicator&#x27;, &#x27;flood_water_duration&#x27;,\n",
       "       &#x27;floodproofed_indicator&#x27;, &#x27;icc_coverage&#x27;, &#x27;net_icc_payment_amount&#x27;,\n",
       "       &#x27;nfip_rated_community_number&#x27;, &#x27;non_payment_reason_contents&#x27;,\n",
       "       &#x27;non_payment_reason_building&#x27;, &#x27;number_of_units&#x27;,\n",
       "       &#x27;building_replacement_cost&#x27;, &#x27;contents_replacement_cost&#x27;,\n",
       "       &#x27;state_owned_indicator&#x27;, &#x27;water_depth&#x27;, &#x27;building_description_code&#x27;,\n",
       "       &#x27;rental_property_indicator&#x27;, &#x27;census_tract&#x27;, &#x27;census_block_group_fips&#x27;,\n",
       "       &#x27;damage_diff_payment&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;date_of_loss&#x27;, &#x27;elevation_certificate_indicator&#x27;, &#x27;rated_flood_zone&#x27;,\n",
       "       &#x27;original_construction_date&#x27;, &#x27;original_n_b_date&#x27;,\n",
       "       &#x27;building_deductible_code&#x27;, &#x27;cause_of_damage&#x27;,\n",
       "       &#x27;condominium_coverage_type_code&#x27;, &#x27;contents_deductible_code&#x27;,\n",
       "       &#x27;event_designation_number&#x27;, &#x27;flood_event&#x27;,\n",
       "       &#x27;nfip_community_number_current&#x27;, &#x27;nfip_community_name&#x27;,\n",
       "       &#x27;replacement_cost_basis&#x27;, &#x27;flood_zone_current&#x27;, &#x27;state&#x27;, &#x27;county_code&#x27;,\n",
       "       &#x27;county_name&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['agriculture_structure_indicator', 'basement_enclosure_crawlspace_type',\n",
       "       'policy_count', 'crs_classification_code',\n",
       "       'elevated_building_indicator', 'elevation_difference',\n",
       "       'base_flood_elevation', 'house_wor...\n",
       "       'building_deductible_code', 'cause_of_damage',\n",
       "       'condominium_coverage_type_code', 'contents_deductible_code',\n",
       "       'event_designation_number', 'flood_event',\n",
       "       'nfip_community_number_current', 'nfip_community_name',\n",
       "       'replacement_cost_basis', 'flood_zone_current', 'state', 'county_code',\n",
       "       'county_name'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier', RandomForestClassifier(random_state=42))])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aaaaaaand let's train the model!\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ef8f43d1-7963-49c2-ac50-0bfce8b3a909",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\natal\\anaconda3\\Lib\\site-packages\\sklearn\\impute\\_base.py:577: UserWarning: Skipping features without any observed values: ['flood_characteristics_indicator']. At least one non-missing value is needed for imputation with strategy='mean'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9683276059564719\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98     16501\n",
      "           1       0.94      0.45      0.61       959\n",
      "\n",
      "    accuracy                           0.97     17460\n",
      "   macro avg       0.96      0.72      0.80     17460\n",
      "weighted avg       0.97      0.97      0.96     17460\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# That looks kinda nice.\n",
    "\n",
    "# Models work to be judged. Let's evaluate this model!\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "814acc4d-dbf1-4c27-a685-62628ffa09c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\natal\\anaconda3\\Lib\\site-packages\\sklearn\\impute\\_base.py:577: UserWarning: Skipping features without any observed values: ['flood_characteristics_indicator']. At least one non-missing value is needed for imputation with strategy='mean'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[16475    26]\n",
      " [  527   432]]\n"
     ]
    }
   ],
   "source": [
    "# Mmk, pretty good. Not the most realistic model, like we wouldn't have this data to predict sufficiency of flood insurance coverage.\n",
    "# But it's a good exercise in model generation.\n",
    "\n",
    "# Generate predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\\n\", cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "15705e61-2135-42dd-a395-08285b4b0ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Would it really feel like predictive modeling if we didn't create a heatmap of the confusion matrix?\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.heatmap(cm, annot = True, fmt = 'd', xticklabels = ['Guessed Sufficient Coverage', 'Guessed Insufficient Coverage'], yticklabels = ['Actual Sufficient Coverage', 'Actual Insufficient Coverage'])\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be17096d-85ba-4443-98e6-c7a0e0807a4e",
   "metadata": {},
   "source": [
    "#### Interpretation\n",
    "**True Negative (Top-Left):** Correctly predicted negative cases.\n",
    "<br>\n",
    "**False Positive (Top-Right):** Incorrectly predicted positive cases.\n",
    "<br>\n",
    "**False Negative (Bottom-Left):** Incorrectly predicted negative cases.\n",
    "<br>\n",
    "**True Positive (Bottom-Right):** Correctly predicted positive cases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d01581d-9525-41fe-8216-63f1d1067d4b",
   "metadata": {},
   "source": [
    "Based on the model above, we **accurately predicted 432 customers whose coverage is insufficient.** We missed 26 customers whose coverage is insufficient. And we overshot; 527 people's coverage is sufficient whom we predicted would not be.<br>\n",
    "<br>\n",
    "Can't say I'm not a little disappointed in these stats given the unrealistic advantage we had."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6ee0465a-960a-4a70-ac78-bd5d654dde97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mismatch: 19779 feature names vs. 19778 importances\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "All arrays must be of the same length",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[84], line 26\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Create a DataFrame for better visualization\u001b[39;00m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m feature_importances \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[0;32m     27\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m'\u001b[39m: all_feature_names,\n\u001b[0;32m     28\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImportance\u001b[39m\u001b[38;5;124m'\u001b[39m: importances\n\u001b[0;32m     29\u001b[0m })\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImportance\u001b[39m\u001b[38;5;124m'\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m     31\u001b[0m \u001b[38;5;66;03m# Display the top 10 important features\u001b[39;00m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28mprint\u001b[39m(feature_importances\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:778\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    772\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m    773\u001b[0m         data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m    774\u001b[0m     )\n\u001b[0;32m    776\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m    777\u001b[0m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 778\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m dict_to_mgr(data, index, columns, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, typ\u001b[38;5;241m=\u001b[39mmanager)\n\u001b[0;32m    779\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m    780\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m    499\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    500\u001b[0m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m    501\u001b[0m         arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arrays_to_mgr(arrays, columns, index, dtype\u001b[38;5;241m=\u001b[39mdtype, typ\u001b[38;5;241m=\u001b[39mtyp, consolidate\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m    112\u001b[0m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m         index \u001b[38;5;241m=\u001b[39m _extract_index(arrays)\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    116\u001b[0m         index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:677\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    675\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[0;32m    676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 677\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[0;32m    680\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    681\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    682\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: All arrays must be of the same length"
     ]
    }
   ],
   "source": [
    "# Let's see if we can't display the 10 most important features to our model\n",
    "\n",
    "# Access the RandomForestClassifier from the pipeline\n",
    "rf_model = model.named_steps['classifier']\n",
    "\n",
    "# Get feature importances from the RandomForestClassifier\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "# Retrieve feature names from the preprocessor\n",
    "# Numeric features (unaltered except for scaling)\n",
    "numeric_feature_names = numeric_cols\n",
    "\n",
    "# Categorical features (expanded due to one-hot encoding)\n",
    "categorical_feature_names = model.named_steps['preprocessor'] \\\n",
    "    .named_transformers_['cat']['encoder'].get_feature_names_out(categorical_cols)\n",
    "\n",
    "# Combine all feature names\n",
    "all_feature_names = list(numeric_feature_names) + list(categorical_feature_names)\n",
    "\n",
    "# Ensure lengths match\n",
    "if len(all_feature_names) != len(importances):\n",
    "    print(f\"Mismatch: {len(all_feature_names)} feature names vs. {len(importances)} importances\")\n",
    "\n",
    "# Create a DataFrame for better visualization\n",
    "import pandas as pd\n",
    "feature_importances = pd.DataFrame({\n",
    "    'Feature': all_feature_names,\n",
    "    'Importance': importances\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Display the top 10 important features\n",
    "print(feature_importances.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "08097bd3-5d33-4450-80b8-23f14b3e4729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19778"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOL ok how long is my importances list\n",
    "\n",
    "len(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3a207034-456f-43db-a8a1-354cca014188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19779"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And all_feature_names ?\n",
    "\n",
    "len(all_feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8275d4b3-354a-4184-9bbe-2480807e7131",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\natal\\anaconda3\\Lib\\site-packages\\sklearn\\impute\\_base.py:577: UserWarning: Skipping features without any observed values: ['flood_characteristics_indicator']. At least one non-missing value is needed for imputation with strategy='mean'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of preprocessed data: (69838, 19778)\n"
     ]
    }
   ],
   "source": [
    "# Let's transform X_train to inspect preprocessed data\n",
    "\n",
    "X_transformed = model.named_steps['preprocessor'].transform(X_train)\n",
    "\n",
    "# Check the shape of the transformed data\n",
    "print(f\"Shape of preprocessed data: {X_transformed.shape}\")  # Should match len(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "357c18bf-f322-4fce-a6f9-69b3856ffaf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total feature names: 19779\n"
     ]
    }
   ],
   "source": [
    "# And get numeric and categorical feature names\n",
    "\n",
    "numeric_feature_names = numeric_cols\n",
    "categorical_feature_names = model.named_steps['preprocessor'] \\\n",
    "    .named_transformers_['cat']['encoder'].get_feature_names_out(categorical_cols)\n",
    "\n",
    "# Combine names and check length\n",
    "all_feature_names = list(numeric_feature_names) + list(categorical_feature_names)\n",
    "print(f\"Total feature names: {len(all_feature_names)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b1781f88-5224-4f9c-98ae-08d13382c8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              Feature  Importance\n",
      "46            census_block_group_fips    0.098318\n",
      "23             building_damage_amount    0.055507\n",
      "26             contents_damage_amount    0.047566\n",
      "15      amount_paid_on_building_claim    0.045897\n",
      "24        net_building_payment_amount    0.043986\n",
      "19  total_building_insurance_coverage    0.039143\n",
      "16      amount_paid_on_contents_claim    0.032341\n",
      "27        net_contents_payment_amount    0.031746\n",
      "25            building_property_value    0.021446\n",
      "20  total_contents_insurance_coverage    0.020710\n"
     ]
    }
   ],
   "source": [
    "# Now align the lengths by truncating feature names\n",
    "\n",
    "# Side note, why do if statements still make me sweat like I'm going to throw an error?\n",
    "# Who cares! Throw an error! Read it and learn!\n",
    "\n",
    "if len(all_feature_names) > len(importances):\n",
    "    all_feature_names = all_feature_names[:len(importances)]\n",
    "\n",
    "# Create DataFrame for feature importances\n",
    "import pandas as pd\n",
    "feature_importances = pd.DataFrame({\n",
    "    'Feature': all_feature_names,\n",
    "    'Importance': importances\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Display the top 10 features\n",
    "print(feature_importances.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2aaf6247-9395-4daf-9567-b9954e40ad39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19778"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interesting that census_block_group_fips is up there! You'd expect location to correlate with damage, but not necessarily with insufficient coverage.\n",
    "# On the other hand, I remember from running this earlier that Lee County suffered such a disproportionate amount of damage, it's not surprising\n",
    "# they'd also have more insufficient coverage just by virtue of exponentially more claims than other counties.\n",
    "\n",
    "# Okay, back on track. What's the length of all_feature_names ?\n",
    "len(all_feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63facf62-16c4-4931-9e43-ae37260f0c9a",
   "metadata": {},
   "source": [
    "### Rerun Model After Debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ef00be01-2345-4b9d-b5f2-cf8a2ea504e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Features by Importance:\n",
      "                              Feature  Importance\n",
      "46            census_block_group_fips    0.098318\n",
      "23             building_damage_amount    0.055507\n",
      "26             contents_damage_amount    0.047566\n",
      "15      amount_paid_on_building_claim    0.045897\n",
      "24        net_building_payment_amount    0.043986\n",
      "19  total_building_insurance_coverage    0.039143\n",
      "16      amount_paid_on_contents_claim    0.032341\n",
      "27        net_contents_payment_amount    0.031746\n",
      "25            building_property_value    0.021446\n",
      "20  total_contents_insurance_coverage    0.020710\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame for feature importances\n",
    "\n",
    "feature_importances = pd.DataFrame({\n",
    "    'Feature': all_feature_names,\n",
    "    'Importance': importances\n",
    "}).sort_values(by='Importance', ascending = False)\n",
    "\n",
    "# Display the top 10 important features\n",
    "print(\"Top 10 Features by Importance:\")\n",
    "print(feature_importances.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b0dd3ba0-184d-47ee-90ad-d8753758a095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top 10 features\n",
    "top_n = 10\n",
    "plt.figure(figsize=(10, 6))\n",
    "feature_importances.head(top_n).plot(kind = 'barh', x  ='Feature', y = 'Importance', legend = False, color = 'orange')\n",
    "plt.gca().invert_yaxis()  # Highest importance on top\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title(f'Top {top_n} Features Contributing to Predictions')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3ea0158d-5d27-4851-abd7-21f8e83a81cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87298, 71)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3106cfab-53b0-48d9-af93-f4359c89b0c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87298, 61)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I'm gonna be reckless and drop columns with >50% missing values. That can't help me predict things.\n",
    "\n",
    "nfip = nfip.drop(columns = nfip.columns[nfip.isna().mean() > 0.5])\n",
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3cc7bf55-b4fc-4325-abee-c12457c03dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agriculture_structure_indicator           0\n",
       "basement_enclosure_crawlspace_type    40740\n",
       "policy_count                              0\n",
       "crs_classification_code                2034\n",
       "date_of_loss                              0\n",
       "                                      ...  \n",
       "total_coverage                            0\n",
       "total_payment                             0\n",
       "damage_diff_payment                       0\n",
       "damage_diff_coverage                      0\n",
       "coverage_insufficient                     0\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I guess it wasn't THAT reckless.\n",
    "# Maybe I need to check for nulls. \n",
    "\n",
    "nfip.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5682fad3-2a24-4c4f-a678-e58debe457d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4795, 61)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's define what the at-risk subset is.\n",
    "atrisk = nfip[nfip['coverage_insufficient'] > 0]\n",
    "atrisk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bbe836e7-5ff3-4b1f-b8ff-c693e29ebde7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Census Block Groups Associated with Insufficient Coverage:\n",
      "   census_block_group_fips  count\n",
      "0             1.200500e+11     72\n",
      "1             1.207106e+11     54\n",
      "2             1.207108e+11     52\n",
      "3             1.207100e+11     51\n",
      "4             1.207106e+11     50\n",
      "5             1.207108e+11     48\n",
      "6             1.207106e+11     46\n",
      "7             1.207108e+11     45\n",
      "8             1.207106e+11     41\n",
      "9             1.207105e+11     40\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of each census_block_group_fips\n",
    "fips_counts = atrisk['census_block_group_fips'].value_counts()\n",
    "\n",
    "# Convert to DataFrame for easier analysis\n",
    "fips_analysis = fips_counts.reset_index()\n",
    "fips_analysis.columns = ['census_block_group_fips', 'count']\n",
    "\n",
    "# Display the top 10 block groups\n",
    "print(\"Top 10 Census Block Groups Associated with Insufficient Coverage:\")\n",
    "print(fips_analysis.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "95099652-1766-4f0c-885e-5ba393e3eff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_n = 10\n",
    "top_fips = fips_analysis.head(top_n)\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(top_fips['census_block_group_fips'], top_fips['count'], color = 'blue')\n",
    "plt.gca().invert_yaxis()  # Highest count on top\n",
    "plt.xlabel('Number of Claims with Insufficient Coverage')\n",
    "plt.ylabel('Census Block Group FIPS')\n",
    "plt.title(f'Top {top_n} Census Block Groups with Insufficient Coverage')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a3be44b6-3a4b-43f7-aa38-5f9ab82aa194",
   "metadata": {},
   "outputs": [],
   "source": [
    "atrisk.to_csv('nfip_FL_at_risk_2025.02.04.csv', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "54a6eff9-90bb-463d-8b0a-87cc13ed0436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gosh I love a blue horizontal bar graph. And 12071 is Lee County, no surprise there.\n",
    "\n",
    "# Let's save the nfip dataset AND the atrisk one and move into Tableau.\n",
    "# I forgot last time; I actually need an index in this dataset.\n",
    "\n",
    "nfip.to_csv('nfip_florida_2025.02.04.csv', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e6d4987-af5d-425a-8166-347bb3d4c0b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}