modeling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kkrusere/Developing-a-Score-to-Measure-Riskiness-of-Residential-Properties-Insurance/blob/main/modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiSNbaLFD5mU",
        "outputId": "204b6f53-fcb1-4164-eb43-6991aeaa49a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U kaleido"
      ],
      "metadata": {
        "id": "U_nR8R9v4g8X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00622917-c189-4cce-8151-42f6db5fd486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kaleido\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 79.9 MB 1.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: kaleido\n",
            "Successfully installed kaleido-0.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <center> **Developing a Score to Measure Riskiness of Residential Properties Insurance** \n",
        "\n",
        "<center><em>Developing a Score to Measure Riskiness of Residential buildings, Homes, Apartments and Condos as part of an insurance policy underwriting. Insurance underwriting is how an insurance company evaluates its risk. In this project, we identify and explore multiple data sources to collect variables that could be used to develop a score that measures the riskiness of residential buildings to aid the insurance underwriting process.</em></center>\n",
        "\n",
        "\n",
        "<center><img src=\"https://github.com/kkrusere/Developing-a-Score-to-Measure-Riskiness-of-Residential-Properties-Insurance/blob/main/assets/real-estate-risk.jpg?raw=1\" width=600/></center>\n",
        "\n",
        "***Project Contributors:*** Kuzi Rusere and Umair Shaikh<br>\n",
        "**MVP streamlit App URL:** https://kkrusere-developing-a-score-to-measure-prototype-mvp-app-acxav4.streamlitapp.com\n",
        "\n",
        "### **Modeling, Evaluation and Analysis of Clusters** "
      ],
      "metadata": {
        "id": "0i20voH2dr6U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "Em4qpRFbdqsE"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reading Data\n"
      ],
      "metadata": {
        "id": "SCKif9YV3NSu"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zJHf9c923cPa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import mysql.connector as connection\n",
        "# from sqlalchemy import create_engine\n",
        "# import config #this holds our credentials for the database \n",
        "\n",
        "# host= config.host\n",
        "# user= config.user\n",
        "# db_password = config.password\n",
        "# port = config.port\n",
        "# database = config.database\n",
        "\n",
        "# engine = create_engine(f\"mysql+pymysql://{user}:{db_password}@{host}/{database}\")\n",
        "\n",
        "# try:\n",
        "#     query = f\"SELECT * FROM NYC_crime\"\n",
        "#     data = pd.read_sql(query,engine)\n",
        "\n",
        "# except Exception as e:\n",
        "#     print(str(e))"
      ],
      "metadata": {
        "id": "X-Pk_mV03cMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "47klyM-U3cJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/Fire_Incident_Dispatch_Data100000.csv\")"
      ],
      "metadata": {
        "id": "aGVLC94vU9q1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "uJu6JjZxXwrr",
        "outputId": "6468c9d3-68ca-4126-f929-d9216d371710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     INCIDENT_DATETIME INCIDENT_BOROUGH  ZIPCODE HIGHEST_ALARM_LEVEL  \\\n",
              "0  2014-03-15 06:39:53        MANHATTAN  10010.0         First Alarm   \n",
              "1  2021-03-18 08:53:00         BROOKLYN  11232.0         First Alarm   \n",
              "2  2020-07-15 19:26:00        MANHATTAN  10023.0         First Alarm   \n",
              "3  2012-02-10 12:22:46        MANHATTAN  10023.0         First Alarm   \n",
              "4  2014-06-18 18:05:02         BROOKLYN  11212.0         First Alarm   \n",
              "\n",
              "                          INCIDENT_CLASSIFICATION  \\\n",
              "0              Multiple Dwelling 'A' - Other fire   \n",
              "1  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "2  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "3              Multiple Dwelling 'A' - Other fire   \n",
              "4                      Multiple Dwelling 'B' Fire   \n",
              "\n",
              "  INCIDENT_CLASSIFICATION_GROUP  \n",
              "0              Structural Fires  \n",
              "1              Structural Fires  \n",
              "2              Structural Fires  \n",
              "3              Structural Fires  \n",
              "4              Structural Fires  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-929d5c6b-6ecc-44db-beca-b54de57b9a05\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>INCIDENT_DATETIME</th>\n",
              "      <th>INCIDENT_BOROUGH</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>HIGHEST_ALARM_LEVEL</th>\n",
              "      <th>INCIDENT_CLASSIFICATION</th>\n",
              "      <th>INCIDENT_CLASSIFICATION_GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-03-15 06:39:53</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10010.0</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-18 08:53:00</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11232.0</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-07-15 19:26:00</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023.0</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-02-10 12:22:46</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023.0</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-06-18 18:05:02</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11212.0</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'B' Fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-929d5c6b-6ecc-44db-beca-b54de57b9a05')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-929d5c6b-6ecc-44db-beca-b54de57b9a05 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-929d5c6b-6ecc-44db-beca-b54de57b9a05');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we need to covert the zipcode from datatype float to string since it is a categorical data\n",
        "fdnyc_data['ZIPCODE'] = fdnyc_data['ZIPCODE'].astype(int)\n",
        "fdnyc_data['ZIPCODE'] = fdnyc_data['ZIPCODE'].astype(str)\n",
        "fdnyc_data.head()"
      ],
      "metadata": {
        "id": "x7ZHTDptraxZ",
        "outputId": "29a46e59-2643-4005-e454-73ff57b13611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     INCIDENT_DATETIME INCIDENT_BOROUGH ZIPCODE HIGHEST_ALARM_LEVEL  \\\n",
              "0  2014-03-15 06:39:53        MANHATTAN   10010         First Alarm   \n",
              "1  2021-03-18 08:53:00         BROOKLYN   11232         First Alarm   \n",
              "2  2020-07-15 19:26:00        MANHATTAN   10023         First Alarm   \n",
              "3  2012-02-10 12:22:46        MANHATTAN   10023         First Alarm   \n",
              "4  2014-06-18 18:05:02         BROOKLYN   11212         First Alarm   \n",
              "\n",
              "                          INCIDENT_CLASSIFICATION  \\\n",
              "0              Multiple Dwelling 'A' - Other fire   \n",
              "1  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "2  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "3              Multiple Dwelling 'A' - Other fire   \n",
              "4                      Multiple Dwelling 'B' Fire   \n",
              "\n",
              "  INCIDENT_CLASSIFICATION_GROUP  \n",
              "0              Structural Fires  \n",
              "1              Structural Fires  \n",
              "2              Structural Fires  \n",
              "3              Structural Fires  \n",
              "4              Structural Fires  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64744188-eb5b-41b3-b5b7-1016e04fdd7e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>INCIDENT_DATETIME</th>\n",
              "      <th>INCIDENT_BOROUGH</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>HIGHEST_ALARM_LEVEL</th>\n",
              "      <th>INCIDENT_CLASSIFICATION</th>\n",
              "      <th>INCIDENT_CLASSIFICATION_GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-03-15 06:39:53</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10010</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-18 08:53:00</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11232</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-07-15 19:26:00</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-02-10 12:22:46</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-06-18 18:05:02</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11212</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'B' Fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64744188-eb5b-41b3-b5b7-1016e04fdd7e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64744188-eb5b-41b3-b5b7-1016e04fdd7e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-64744188-eb5b-41b3-b5b7-1016e04fdd7e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = fdnyc_data.drop(columns=['INCIDENT_DATETIME','INCIDENT_CLASSIFICATION_GROUP'])"
      ],
      "metadata": {
        "id": "8Mro5CHkratR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "J4MQPjMX5hSQ",
        "outputId": "4ee6adb7-bff5-456a-bd32-bc91dffa6738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  INCIDENT_BOROUGH ZIPCODE HIGHEST_ALARM_LEVEL  \\\n",
              "0        MANHATTAN   10010         First Alarm   \n",
              "1         BROOKLYN   11232         First Alarm   \n",
              "2        MANHATTAN   10023         First Alarm   \n",
              "3        MANHATTAN   10023         First Alarm   \n",
              "4         BROOKLYN   11212         First Alarm   \n",
              "\n",
              "                          INCIDENT_CLASSIFICATION  \n",
              "0              Multiple Dwelling 'A' - Other fire  \n",
              "1  Multiple Dwelling 'A' - Food on the stove fire  \n",
              "2  Multiple Dwelling 'A' - Food on the stove fire  \n",
              "3              Multiple Dwelling 'A' - Other fire  \n",
              "4                      Multiple Dwelling 'B' Fire  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1952d1e6-de95-4ad8-8dd9-4c76baf76781\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>INCIDENT_BOROUGH</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>HIGHEST_ALARM_LEVEL</th>\n",
              "      <th>INCIDENT_CLASSIFICATION</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10010</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11232</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11212</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'B' Fire</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1952d1e6-de95-4ad8-8dd9-4c76baf76781')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1952d1e6-de95-4ad8-8dd9-4c76baf76781 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1952d1e6-de95-4ad8-8dd9-4c76baf76781');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kmodes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLoJBuF1u5w6",
        "outputId": "7d6ed457-e7d8-4475-856a-390ff4bd7ad5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kmodes\n",
            "  Downloading kmodes-0.12.2-py2.py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.7/dist-packages (from kmodes) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.0->kmodes) (3.1.0)\n",
            "Installing collected packages: kmodes\n",
            "Successfully installed kmodes-0.12.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modeling and Evaluation"
      ],
      "metadata": {
        "id": "nP_I2YQb5itq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kmodes.kmodes import KModes"
      ],
      "metadata": {
        "id": "gt7Xpu53l5Dx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RpDcmx5y0Dxu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our modeling is divided into phases, this is the first part where we are going to be clustering our FDNYC fire incidence Data and NYPD Crime Data. The idea here is to cluster the data and analyze the cluster after. The goal of the analysis is to have a perspective of the attributes and features that caused or resulted in the data entries being clustered together.\n",
        "\n",
        "For this, we are going to be using Kmode clustering. The most common unsupervised clustering algorithm is the Kmean clustering, it is a go-to machine learning algorithm when it comes to unlabeled data that needs to be grouped in some way that reviews patterns and insight within the data, provided that the data is numerical. Since our data is far from being numerical except for zip codes, latitudes, and longitudes, we cannot use Kmeans clustering. Hence, comes in Kmodes which is an extension of the Kmeans clustering algorithm that uses simple matching dissimilarity measures for categorical objects, where the means are replaced by modes of clusters.\n",
        "\n",
        "\n",
        "<center><img src=\"https://github.com/kkrusere/Developing-a-Score-to-Measure-Riskiness-of-Residential-Properties-Insurance/blob/main/assets/kmodes.png?raw=1\" width=600/></center>\n",
        "\n",
        "\n",
        "\n",
        "Choosing the Appropriate Number of Clusters\n",
        "\n",
        "We are going to look at the most commonly used method to evaluate the appropriate number of clusters:\n",
        "\n",
        "* The elbow method\n"
      ],
      "metadata": {
        "id": "q7LH7_3e0EcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ieE0aopmgCEc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### For FDNYC"
      ],
      "metadata": {
        "id": "1pnjyCMMgDbU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmodes_param = {\n",
        "    \"init\": \"random\",\n",
        "    \"n_init\": 5,\n",
        "    \"max_iter\": 10,\n",
        "    \"random_state\": 42,\n",
        "}\n",
        "# A list holds the Cost values for each k\n",
        "fdnyc_cost = []\n",
        "for k in range(1, 11):\n",
        "    fdnyc_kmode = KModes(n_clusters=k, **kmodes_param, verbose=1)\n",
        "    fdnyc_kmode.fit_predict(X)\n",
        "    fdnyc_cost.append(fdnyc_kmode.cost_)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jbLnBuNwl-sy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cEZVrcAFk63Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we are going to use the elbow method to \"Evaluate\" the model. A heuristic approach is used in determining the number of clusters in a data set by basically plotting the explained variation as a function of the number of clusters. The ideal number of clusters to use for the modeling correspond to the elbow of the curve.In other words, the method is used to determine the optimal number of clusters in k-means clustering (and in our case, kmodes clustering).\n",
        "\n",
        "<center><img src=\"https://github.com/kkrusere/Developing-a-Score-to-Measure-Riskiness-of-Residential-Properties-Insurance/blob/main/assets/elbow_method.png?raw=1\" width=600/></center>\n"
      ],
      "metadata": {
        "id": "3L5stqgylNwA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#lest plot the elbow\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(range(1, 11), fdnyc_cost)\n",
        "plt.xticks(range(1, 11))\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"cost\")\n",
        "plt.title(\"Cluster Elbow\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "s5b6inI21yik",
        "outputId": "69552b8c-52ed-4ffd-c6b6-2ff9f32c6723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k0O0B4lE2IOt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above, it looks like 7,8 and 9 can be used as clusters. We are going to be choosing 9 as the number of clusters. So, for the training of our model, `n_clusters` = 9."
      ],
      "metadata": {
        "id": "LIy9z9ajnExn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_kmode = KModes(n_clusters=9, init = \"random\", n_init = 5, verbose=1)\n",
        "fdnyc_clusters = fdnyc_kmode.fit_predict(X)\n",
        "fdnyc_data.insert(0, \"Cluster\", fdnyc_clusters, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ijFnNOXe2ILM",
        "outputId": "fcb96d36-c7bb-4b26-d9c9-a8a5406be0bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 1, iteration: 1/100, moves: 28401, cost: 137653.0\n",
            "Run 1, iteration: 2/100, moves: 7722, cost: 136999.0\n",
            "Run 1, iteration: 3/100, moves: 71, cost: 136999.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 2, iteration: 1/100, moves: 18181, cost: 151814.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 3, iteration: 1/100, moves: 42203, cost: 150687.0\n",
            "Run 3, iteration: 2/100, moves: 4283, cost: 150687.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 4, iteration: 1/100, moves: 24717, cost: 151233.0\n",
            "Run 4, iteration: 2/100, moves: 872, cost: 151233.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 5, iteration: 1/100, moves: 17705, cost: 134804.0\n",
            "Run 5, iteration: 2/100, moves: 89, cost: 134804.0\n",
            "Best run was number 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MNce-xVXQEZn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below is the resulting dataframe after adding adding the cluster column. We are going to save the dataframe as a csv file for use later in the analysis of the clusters section. The idea is to look at the clusters and analyze answering the question `Why did the model cluster these row entries into one cluster?` The answer to thjis question will give us a perspective on how to rank the one cluster with respect to the other clusters. within our analysis, we will keep trac of some `Key performance metrics/indicators` such as for example, given a particular `crime`if we are looking at the NYPD dataset or given a particular `complaint` what are the chances of that occuring in a particular `zipcode` or `borough`. "
      ],
      "metadata": {
        "id": "ihLNMRj7oSJ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data.head()"
      ],
      "metadata": {
        "id": "JQzsmz0m5rHb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "88513b53-4ba0-4c27-cd61-5d2b9492f585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster    INCIDENT_DATETIME INCIDENT_BOROUGH ZIPCODE HIGHEST_ALARM_LEVEL  \\\n",
              "0        0  2014-03-15 06:39:53        MANHATTAN   10010         First Alarm   \n",
              "1        2  2021-03-18 08:53:00         BROOKLYN   11232         First Alarm   \n",
              "2        7  2020-07-15 19:26:00        MANHATTAN   10023         First Alarm   \n",
              "3        0  2012-02-10 12:22:46        MANHATTAN   10023         First Alarm   \n",
              "4        2  2014-06-18 18:05:02         BROOKLYN   11212         First Alarm   \n",
              "\n",
              "                          INCIDENT_CLASSIFICATION  \\\n",
              "0              Multiple Dwelling 'A' - Other fire   \n",
              "1  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "2  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "3              Multiple Dwelling 'A' - Other fire   \n",
              "4                      Multiple Dwelling 'B' Fire   \n",
              "\n",
              "  INCIDENT_CLASSIFICATION_GROUP  \n",
              "0              Structural Fires  \n",
              "1              Structural Fires  \n",
              "2              Structural Fires  \n",
              "3              Structural Fires  \n",
              "4              Structural Fires  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b030748e-a197-4ef1-9f90-b3ce5d6fa167\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>INCIDENT_DATETIME</th>\n",
              "      <th>INCIDENT_BOROUGH</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>HIGHEST_ALARM_LEVEL</th>\n",
              "      <th>INCIDENT_CLASSIFICATION</th>\n",
              "      <th>INCIDENT_CLASSIFICATION_GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2014-03-15 06:39:53</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10010</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2021-03-18 08:53:00</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11232</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>2020-07-15 19:26:00</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2012-02-10 12:22:46</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2014-06-18 18:05:02</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11212</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'B' Fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b030748e-a197-4ef1-9f90-b3ce5d6fa167')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b030748e-a197-4ef1-9f90-b3ce5d6fa167 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b030748e-a197-4ef1-9f90-b3ce5d6fa167');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L_5f9vvuQOSb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#lets save the data to a csv for later use in the analysis of the clusters.\n",
        "fdnyc_data.to_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_Fire_Incident_Dispatch_Data.csv\", index=False)"
      ],
      "metadata": {
        "id": "l3NTRIeej_zy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OMkFS0ufj_wK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### For NYPD"
      ],
      "metadata": {
        "id": "6_Q4_IGggP-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#readin the NYPD Data\n",
        "\n",
        "nypd_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/NYPD_Complaint_Data_Historic10000.csv\")\n",
        "nypd_data.columns = ['RPT_DT', 'OFNS_DESC', 'CRM_ATPT_CPTD_CD', 'LAW_CAT_CD', 'BOROUGH','LOC_OF_OCCUR_DESC', 'PREM_TYP_DESC', 'ZIPCODE']\n",
        "nypd_data['ZIPCODE'] = nypd_data['ZIPCODE'].astype(int)\n",
        "nypd_data['ZIPCODE'] = nypd_data['ZIPCODE'].astype(str)\n",
        "\n",
        "nypd_data.dropna(inplace=True)\n",
        "nypd_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "FwibsveRgjgO",
        "outputId": "7f9b8bf6-3e8a-424a-aad6-3d59bbff9893"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RPT_DT      OFNS_DESC CRM_ATPT_CPTD_CD   LAW_CAT_CD    BOROUGH  \\\n",
              "0  06/12/2012       BURGLARY        COMPLETED       FELONY      BRONX   \n",
              "1  06/11/2021  PETIT LARCENY        COMPLETED  MISDEMEANOR   BROOKLYN   \n",
              "2  10/24/2006  GRAND LARCENY        COMPLETED       FELONY  MANHATTAN   \n",
              "3  12/03/2008  GRAND LARCENY        COMPLETED       FELONY      BRONX   \n",
              "4  05/15/2017  GRAND LARCENY        COMPLETED       FELONY  MANHATTAN   \n",
              "\n",
              "  LOC_OF_OCCUR_DESC           PREM_TYP_DESC ZIPCODE  \n",
              "0            INSIDE  RESIDENCE - APT. HOUSE   10453  \n",
              "1          FRONT OF         RESIDENCE-HOUSE   11208  \n",
              "2            INSIDE  RESIDENCE - APT. HOUSE   10027  \n",
              "3          FRONT OF  RESIDENCE - APT. HOUSE   10452  \n",
              "4            INSIDE  RESIDENCE - APT. HOUSE   10025  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d727fb7e-4285-4c59-ae2f-507554d53a81\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>RPT_DT</th>\n",
              "      <th>OFNS_DESC</th>\n",
              "      <th>CRM_ATPT_CPTD_CD</th>\n",
              "      <th>LAW_CAT_CD</th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>LOC_OF_OCCUR_DESC</th>\n",
              "      <th>PREM_TYP_DESC</th>\n",
              "      <th>ZIPCODE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>06/12/2012</td>\n",
              "      <td>BURGLARY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06/11/2021</td>\n",
              "      <td>PETIT LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>MISDEMEANOR</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE-HOUSE</td>\n",
              "      <td>11208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/24/2006</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12/03/2008</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>05/15/2017</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d727fb7e-4285-4c59-ae2f-507554d53a81')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d727fb7e-4285-4c59-ae2f-507554d53a81 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d727fb7e-4285-4c59-ae2f-507554d53a81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = nypd_data.drop([\"RPT_DT\"],  axis=1)\n"
      ],
      "metadata": {
        "id": "kTtAHVTTirGD"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we model\n",
        "kmodes_param = {\n",
        "    \"init\": \"random\",\n",
        "    \"n_init\": 5,\n",
        "    \"max_iter\": 10,\n",
        "    \"random_state\": 42,\n",
        "}\n",
        "# A list holds the cost values for each k\n",
        "nypd_cost = []\n",
        "for k in range(1, 11):\n",
        "    nypd_kmode = KModes(n_clusters=k, **kmodes_param, verbose=1)\n",
        "    nypd_kmode.fit_predict(X)\n",
        "    nypd_cost.append(nypd_kmode.cost_)\n"
      ],
      "metadata": {
        "id": "iJeSlybOg-DG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#lets plot the elbow\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(range(1, 11), nypd_cost)\n",
        "plt.xticks(range(1, 11))\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"cost\")\n",
        "plt.title(\"Cluster Elbow\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "sp19a0sSiPGs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "54e19eb4-b491-4ef3-bc3c-00977728670a"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nypd_kmode = KModes(n_clusters=6, init = \"random\", n_init = 5, verbose=1)\n",
        "clusters = nypd_kmode.fit_predict(X)\n",
        "nypd_data.insert(0, \"Cluster\", clusters, True)"
      ],
      "metadata": {
        "id": "CxVremNKiaWN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "327ee868-4c1e-4464-aaf0-6877cf6d846c"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 1, iteration: 1/100, moves: 1953, cost: 26341.0\n",
            "Run 1, iteration: 2/100, moves: 498, cost: 26341.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 2, iteration: 1/100, moves: 5023, cost: 27627.0\n",
            "Run 2, iteration: 2/100, moves: 30, cost: 27627.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 3, iteration: 1/100, moves: 1779, cost: 27079.0\n",
            "Run 3, iteration: 2/100, moves: 1940, cost: 25982.0\n",
            "Run 3, iteration: 3/100, moves: 676, cost: 25982.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 4, iteration: 1/100, moves: 1351, cost: 25201.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 5, iteration: 1/100, moves: 918, cost: 26683.0\n",
            "Run 5, iteration: 2/100, moves: 179, cost: 26683.0\n",
            "Best run was number 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nypd_data.head()"
      ],
      "metadata": {
        "id": "Ti2uRBWaik5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f46838ae-b40d-4704-b707-8320a9381acc"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster      RPT_DT      OFNS_DESC CRM_ATPT_CPTD_CD   LAW_CAT_CD  \\\n",
              "0        1  06/12/2012       BURGLARY        COMPLETED       FELONY   \n",
              "1        3  06/11/2021  PETIT LARCENY        COMPLETED  MISDEMEANOR   \n",
              "2        1  10/24/2006  GRAND LARCENY        COMPLETED       FELONY   \n",
              "3        1  12/03/2008  GRAND LARCENY        COMPLETED       FELONY   \n",
              "4        1  05/15/2017  GRAND LARCENY        COMPLETED       FELONY   \n",
              "\n",
              "     BOROUGH LOC_OF_OCCUR_DESC           PREM_TYP_DESC ZIPCODE  \n",
              "0      BRONX            INSIDE  RESIDENCE - APT. HOUSE   10453  \n",
              "1   BROOKLYN          FRONT OF         RESIDENCE-HOUSE   11208  \n",
              "2  MANHATTAN            INSIDE  RESIDENCE - APT. HOUSE   10027  \n",
              "3      BRONX          FRONT OF  RESIDENCE - APT. HOUSE   10452  \n",
              "4  MANHATTAN            INSIDE  RESIDENCE - APT. HOUSE   10025  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-119537e9-26f0-4aa2-848b-c00ec691cd7c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>RPT_DT</th>\n",
              "      <th>OFNS_DESC</th>\n",
              "      <th>CRM_ATPT_CPTD_CD</th>\n",
              "      <th>LAW_CAT_CD</th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>LOC_OF_OCCUR_DESC</th>\n",
              "      <th>PREM_TYP_DESC</th>\n",
              "      <th>ZIPCODE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>06/12/2012</td>\n",
              "      <td>BURGLARY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>06/11/2021</td>\n",
              "      <td>PETIT LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>MISDEMEANOR</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE-HOUSE</td>\n",
              "      <td>11208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10/24/2006</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>12/03/2008</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>05/15/2017</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-119537e9-26f0-4aa2-848b-c00ec691cd7c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-119537e9-26f0-4aa2-848b-c00ec691cd7c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-119537e9-26f0-4aa2-848b-c00ec691cd7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qmVjwSPk_qev"
      },
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nypd_data.to_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_NYPD_Complaint_Data_Historic.csv\", index=False)"
      ],
      "metadata": {
        "id": "kseDq3OCkKhg"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ByZkZYzVkKYI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### For the NYC Complaints and Service Requests \n",
        "\n",
        "Here, we will do the same thing that we have done with the NYPD and FDNY datasets. we are going to create a clustering "
      ],
      "metadata": {
        "id": "ri1kizUw_rQd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#readin the NYC Complaints and servise request Data = nyc_csr311_data\n",
        "nyc_csr311_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/NYC311Open_Data100000.csv\")\n",
        "nyc_csr311_data.dropna(inplace=True)\n",
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "id": "rXpJUyDn_6EF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "c86f05a8-e6a1-409a-8f62-f27a6cf273d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unique Key        Date Agency  \\\n",
              "0    29491303  2014-12-14   NYPD   \n",
              "1    29234415  2014-11-06    HPD   \n",
              "2    23185964  2012-05-07    HPD   \n",
              "3    40093189  2018-08-24    HPD   \n",
              "4    29693040  2015-01-11    HPD   \n",
              "\n",
              "                                         Agency Name       Complaint Type  \\\n",
              "0                    New York City Police Department  Noise - Residential   \n",
              "1  Department of Housing Preservation and Develop...       HEAT/HOT WATER   \n",
              "2  Department of Housing Preservation and Develop...             NONCONST   \n",
              "3  Department of Housing Preservation and Develop...      FLOORING/STAIRS   \n",
              "4  Department of Housing Preservation and Develop...       HEAT/HOT WATER   \n",
              "\n",
              "         Descriptor               Location Type  Incident Zip  \\\n",
              "0  Loud Music/Party  Residential Building/House       10075.0   \n",
              "1    APARTMENT ONLY        RESIDENTIAL BUILDING       10029.0   \n",
              "2   CARBON MONOXIDE        RESIDENTIAL BUILDING       11434.0   \n",
              "3             FLOOR        RESIDENTIAL BUILDING       10031.0   \n",
              "4   ENTIRE BUILDING        RESIDENTIAL BUILDING       11204.0   \n",
              "\n",
              "      Incident Address      Street Name Address Type      City  \\\n",
              "0   508 EAST 78 STREET   EAST 78 STREET      ADDRESS  NEW YORK   \n",
              "1  127 EAST 107 STREET  EAST 107 STREET      ADDRESS  NEW YORK   \n",
              "2      178-09 114 ROAD         114 ROAD      ADDRESS   JAMAICA   \n",
              "3  583 RIVERSIDE DRIVE  RIVERSIDE DRIVE      ADDRESS  NEW YORK   \n",
              "4   1402 WEST 4 STREET    WEST 4 STREET      ADDRESS  BROOKLYN   \n",
              "\n",
              "                              Resolution Description    Borough   Latitude  \\\n",
              "0  The Police Department responded and upon arriv...  MANHATTAN  40.770452   \n",
              "1  The Department of Housing Preservation and Dev...  MANHATTAN  40.793157   \n",
              "2  The Department of Housing Preservation and Dev...     QUEENS  40.693417   \n",
              "3  The Department of Housing Preservation and Dev...  MANHATTAN  40.821355   \n",
              "4  The complaint you filed is a duplicate of a co...   BROOKLYN  40.611846   \n",
              "\n",
              "   Longitude  \n",
              "0 -73.950341  \n",
              "1 -73.946330  \n",
              "2 -73.770789  \n",
              "3 -73.957193  \n",
              "4 -73.978444  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bec01786-02a1-4ae0-b89b-b381326a03c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unique Key</th>\n",
              "      <th>Date</th>\n",
              "      <th>Agency</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>Incident Zip</th>\n",
              "      <th>Incident Address</th>\n",
              "      <th>Street Name</th>\n",
              "      <th>Address Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Resolution Description</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29491303</td>\n",
              "      <td>2014-12-14</td>\n",
              "      <td>NYPD</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075.0</td>\n",
              "      <td>508 EAST 78 STREET</td>\n",
              "      <td>EAST 78 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Police Department responded and upon arriv...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.770452</td>\n",
              "      <td>-73.950341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29234415</td>\n",
              "      <td>2014-11-06</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029.0</td>\n",
              "      <td>127 EAST 107 STREET</td>\n",
              "      <td>EAST 107 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.793157</td>\n",
              "      <td>-73.946330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23185964</td>\n",
              "      <td>2012-05-07</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434.0</td>\n",
              "      <td>178-09 114 ROAD</td>\n",
              "      <td>114 ROAD</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>QUEENS</td>\n",
              "      <td>40.693417</td>\n",
              "      <td>-73.770789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40093189</td>\n",
              "      <td>2018-08-24</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031.0</td>\n",
              "      <td>583 RIVERSIDE DRIVE</td>\n",
              "      <td>RIVERSIDE DRIVE</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.821355</td>\n",
              "      <td>-73.957193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29693040</td>\n",
              "      <td>2015-01-11</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204.0</td>\n",
              "      <td>1402 WEST 4 STREET</td>\n",
              "      <td>WEST 4 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>The complaint you filed is a duplicate of a co...</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>40.611846</td>\n",
              "      <td>-73.978444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bec01786-02a1-4ae0-b89b-b381326a03c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bec01786-02a1-4ae0-b89b-b381326a03c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bec01786-02a1-4ae0-b89b-b381326a03c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we need to covert the zipcode from datatype float to string since it is a categorical data\n",
        "nyc_csr311_data['Incident Zip'] = nyc_csr311_data['Incident Zip'].astype(int)\n",
        "nyc_csr311_data['Incident Zip'] = nyc_csr311_data['Incident Zip'].astype(str)\n",
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "id": "v1GPTQVA_6Ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "3518a385-23d6-478c-b6e2-7c051178a942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unique Key        Date Agency  \\\n",
              "0    29491303  2014-12-14   NYPD   \n",
              "1    29234415  2014-11-06    HPD   \n",
              "2    23185964  2012-05-07    HPD   \n",
              "3    40093189  2018-08-24    HPD   \n",
              "4    29693040  2015-01-11    HPD   \n",
              "\n",
              "                                         Agency Name       Complaint Type  \\\n",
              "0                    New York City Police Department  Noise - Residential   \n",
              "1  Department of Housing Preservation and Develop...       HEAT/HOT WATER   \n",
              "2  Department of Housing Preservation and Develop...             NONCONST   \n",
              "3  Department of Housing Preservation and Develop...      FLOORING/STAIRS   \n",
              "4  Department of Housing Preservation and Develop...       HEAT/HOT WATER   \n",
              "\n",
              "         Descriptor               Location Type Incident Zip  \\\n",
              "0  Loud Music/Party  Residential Building/House        10075   \n",
              "1    APARTMENT ONLY        RESIDENTIAL BUILDING        10029   \n",
              "2   CARBON MONOXIDE        RESIDENTIAL BUILDING        11434   \n",
              "3             FLOOR        RESIDENTIAL BUILDING        10031   \n",
              "4   ENTIRE BUILDING        RESIDENTIAL BUILDING        11204   \n",
              "\n",
              "      Incident Address      Street Name Address Type      City  \\\n",
              "0   508 EAST 78 STREET   EAST 78 STREET      ADDRESS  NEW YORK   \n",
              "1  127 EAST 107 STREET  EAST 107 STREET      ADDRESS  NEW YORK   \n",
              "2      178-09 114 ROAD         114 ROAD      ADDRESS   JAMAICA   \n",
              "3  583 RIVERSIDE DRIVE  RIVERSIDE DRIVE      ADDRESS  NEW YORK   \n",
              "4   1402 WEST 4 STREET    WEST 4 STREET      ADDRESS  BROOKLYN   \n",
              "\n",
              "                              Resolution Description    Borough   Latitude  \\\n",
              "0  The Police Department responded and upon arriv...  MANHATTAN  40.770452   \n",
              "1  The Department of Housing Preservation and Dev...  MANHATTAN  40.793157   \n",
              "2  The Department of Housing Preservation and Dev...     QUEENS  40.693417   \n",
              "3  The Department of Housing Preservation and Dev...  MANHATTAN  40.821355   \n",
              "4  The complaint you filed is a duplicate of a co...   BROOKLYN  40.611846   \n",
              "\n",
              "   Longitude  \n",
              "0 -73.950341  \n",
              "1 -73.946330  \n",
              "2 -73.770789  \n",
              "3 -73.957193  \n",
              "4 -73.978444  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc92ec15-e485-4104-bb50-b60fbb6fa813\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unique Key</th>\n",
              "      <th>Date</th>\n",
              "      <th>Agency</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>Incident Zip</th>\n",
              "      <th>Incident Address</th>\n",
              "      <th>Street Name</th>\n",
              "      <th>Address Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Resolution Description</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29491303</td>\n",
              "      <td>2014-12-14</td>\n",
              "      <td>NYPD</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>508 EAST 78 STREET</td>\n",
              "      <td>EAST 78 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Police Department responded and upon arriv...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.770452</td>\n",
              "      <td>-73.950341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29234415</td>\n",
              "      <td>2014-11-06</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029</td>\n",
              "      <td>127 EAST 107 STREET</td>\n",
              "      <td>EAST 107 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.793157</td>\n",
              "      <td>-73.946330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23185964</td>\n",
              "      <td>2012-05-07</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434</td>\n",
              "      <td>178-09 114 ROAD</td>\n",
              "      <td>114 ROAD</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>QUEENS</td>\n",
              "      <td>40.693417</td>\n",
              "      <td>-73.770789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40093189</td>\n",
              "      <td>2018-08-24</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>583 RIVERSIDE DRIVE</td>\n",
              "      <td>RIVERSIDE DRIVE</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>The Department of Housing Preservation and Dev...</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>40.821355</td>\n",
              "      <td>-73.957193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29693040</td>\n",
              "      <td>2015-01-11</td>\n",
              "      <td>HPD</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204</td>\n",
              "      <td>1402 WEST 4 STREET</td>\n",
              "      <td>WEST 4 STREET</td>\n",
              "      <td>ADDRESS</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>The complaint you filed is a duplicate of a co...</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>40.611846</td>\n",
              "      <td>-73.978444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc92ec15-e485-4104-bb50-b60fbb6fa813')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc92ec15-e485-4104-bb50-b60fbb6fa813 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc92ec15-e485-4104-bb50-b60fbb6fa813');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_data = nyc_csr311_data[['Unique Key', 'Agency Name', 'Complaint Type','Descriptor', 'Location Type', 'Incident Zip','City','Borough']]\n",
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "djvWvJyI17_d",
        "outputId": "7d23d8c6-6c22-4e20-d51c-90f51cf5bd40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unique Key                                        Agency Name  \\\n",
              "0    29491303                    New York City Police Department   \n",
              "1    29234415  Department of Housing Preservation and Develop...   \n",
              "2    23185964  Department of Housing Preservation and Develop...   \n",
              "3    40093189  Department of Housing Preservation and Develop...   \n",
              "4    29693040  Department of Housing Preservation and Develop...   \n",
              "\n",
              "        Complaint Type        Descriptor               Location Type  \\\n",
              "0  Noise - Residential  Loud Music/Party  Residential Building/House   \n",
              "1       HEAT/HOT WATER    APARTMENT ONLY        RESIDENTIAL BUILDING   \n",
              "2             NONCONST   CARBON MONOXIDE        RESIDENTIAL BUILDING   \n",
              "3      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING   \n",
              "4       HEAT/HOT WATER   ENTIRE BUILDING        RESIDENTIAL BUILDING   \n",
              "\n",
              "  Incident Zip      City    Borough  \n",
              "0        10075  NEW YORK  MANHATTAN  \n",
              "1        10029  NEW YORK  MANHATTAN  \n",
              "2        11434   JAMAICA     QUEENS  \n",
              "3        10031  NEW YORK  MANHATTAN  \n",
              "4        11204  BROOKLYN   BROOKLYN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dc23b09-4f20-4e92-99d2-7a2c8833e0ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unique Key</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>Incident Zip</th>\n",
              "      <th>City</th>\n",
              "      <th>Borough</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29491303</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>29234415</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23185964</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>QUEENS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40093189</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29693040</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>BROOKLYN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dc23b09-4f20-4e92-99d2-7a2c8833e0ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7dc23b09-4f20-4e92-99d2-7a2c8833e0ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7dc23b09-4f20-4e92-99d2-7a2c8833e0ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K0pRDiLM3iZe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = nyc_csr311_data[['Agency Name', 'Complaint Type','Descriptor', 'Location Type', 'Incident Zip','City','Borough']]"
      ],
      "metadata": {
        "id": "XCLYb8uK3rnG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we model\n",
        "kmodes_param = {\n",
        "    \"init\": \"random\",\n",
        "    \"n_init\": 5,\n",
        "    \"max_iter\": 10,\n",
        "    \"random_state\": 42,\n",
        "}\n",
        "# A list holds the cost values for each k\n",
        "nyc_csr311_cost = []\n",
        "for k in range(1, 11):\n",
        "    nyc_csr311_kmode = KModes(n_clusters=k, **kmodes_param, verbose=1)\n",
        "    nyc_csr311_kmode.fit_predict(X)\n",
        "    nyc_csr311_cost.append(nyc_csr311_kmode.cost_)\n"
      ],
      "metadata": {
        "id": "ovPrV23f_58i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#lets plot the elbow\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(range(1, 11), nyc_csr311_cost)\n",
        "plt.xticks(range(1, 11))\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"cost\")\n",
        "plt.title(\"Cluster Elbow\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "aBU1K7uWD2eA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "a08e137e-dc61-46b1-a99f-48d90dbc3d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ls-5MBK5D2an"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_kmode = KModes(n_clusters=4, init = \"random\", n_init = 5, verbose=1)\n",
        "nyc_csr311_clusters = nyc_csr311_kmode.fit_predict(X)\n",
        "nyc_csr311_data.insert(0, \"Cluster\", nyc_csr311_clusters, True)"
      ],
      "metadata": {
        "id": "SJzNMblZEYPO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd7cb2a-1182-4778-a706-84adbaf39d61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 1, iteration: 1/100, moves: 3590, cost: 306318.0\n",
            "Run 1, iteration: 2/100, moves: 109, cost: 306318.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 2, iteration: 1/100, moves: 10111, cost: 342587.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 3, iteration: 1/100, moves: 12224, cost: 303001.0\n",
            "Run 3, iteration: 2/100, moves: 824, cost: 303001.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 4, iteration: 1/100, moves: 21775, cost: 302234.0\n",
            "Run 4, iteration: 2/100, moves: 17716, cost: 301137.0\n",
            "Run 4, iteration: 3/100, moves: 4118, cost: 300858.0\n",
            "Run 4, iteration: 4/100, moves: 344, cost: 300858.0\n",
            "Init: initializing centroids\n",
            "Init: initializing clusters\n",
            "Starting iterations...\n",
            "Run 5, iteration: 1/100, moves: 4500, cost: 307965.0\n",
            "Best run was number 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "id": "5FItsPvSEYLm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "3bb5d682-23fd-411a-db7f-1c9cb02ae242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster  Unique Key                                        Agency Name  \\\n",
              "0        0    29491303                    New York City Police Department   \n",
              "1        1    29234415  Department of Housing Preservation and Develop...   \n",
              "2        1    23185964  Department of Housing Preservation and Develop...   \n",
              "3        0    40093189  Department of Housing Preservation and Develop...   \n",
              "4        1    29693040  Department of Housing Preservation and Develop...   \n",
              "\n",
              "        Complaint Type        Descriptor               Location Type  \\\n",
              "0  Noise - Residential  Loud Music/Party  Residential Building/House   \n",
              "1       HEAT/HOT WATER    APARTMENT ONLY        RESIDENTIAL BUILDING   \n",
              "2             NONCONST   CARBON MONOXIDE        RESIDENTIAL BUILDING   \n",
              "3      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING   \n",
              "4       HEAT/HOT WATER   ENTIRE BUILDING        RESIDENTIAL BUILDING   \n",
              "\n",
              "  Incident Zip      City    Borough  \n",
              "0        10075  NEW YORK  MANHATTAN  \n",
              "1        10029  NEW YORK  MANHATTAN  \n",
              "2        11434   JAMAICA     QUEENS  \n",
              "3        10031  NEW YORK  MANHATTAN  \n",
              "4        11204  BROOKLYN   BROOKLYN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ed3347d-a770-488c-bf58-d6b88b9e6355\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Unique Key</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>Incident Zip</th>\n",
              "      <th>City</th>\n",
              "      <th>Borough</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>29491303</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>29234415</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>23185964</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>QUEENS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>40093189</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>29693040</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>BROOKLYN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ed3347d-a770-488c-bf58-d6b88b9e6355')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ed3347d-a770-488c-bf58-d6b88b9e6355 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ed3347d-a770-488c-bf58-d6b88b9e6355');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_data.to_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_NYC311Open_Data.csv\", index=False)"
      ],
      "metadata": {
        "id": "jfeAMB8hD2YB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ixqdoSEEk1AN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "htqcRhtdk081"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analysis of Clusters "
      ],
      "metadata": {
        "id": "rMw3gSLu5rpC"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lmsQjny-kDdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to create a pandas groupedby object using the cluster column. This will be usefull and will enable us to create reusable fuctions for fetching the same kind of data for different clusters."
      ],
      "metadata": {
        "id": "t3aTPJKAWIT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#lets create some helper fuctions\n",
        "\n",
        "def get_cluster_df(cluster_name, cluster_grouped_dataset):\n",
        "  \"\"\"\n",
        "  This function basically takes in the name of the cluster and uses that to filter the `cluster_grouped_dataset`\n",
        "  The function returns a dataframe filtered for the specific cluster (the cluster is also reindexed)\n",
        "  \"\"\"\n",
        "  cluster_filtered_df = cluster_grouped_dataset.get_group(cluster_name)\n",
        "  cluster_filtered_df = cluster_filtered_df.reset_index(drop=True)\n",
        "\n",
        "  return cluster_filtered_df\n",
        "\n",
        "def get_borough_df(cluster_df, borough_name):\n",
        "  borough_grouped_ob = cluster_df.groupby('INCIDENT_BOROUGH')\n",
        "  \"\"\"\n",
        "  This fuction basically takes in the specific cluster filtered dataframe and return the borough filtered dataframe\n",
        "  We will have a try~catch here to handle cases where the requested borough is not in the dataset/dataframe\n",
        "  \"\"\"\n",
        "  try:\n",
        "    borough_df = borough_grouped_ob.get_group(borough_name)\n",
        "    borough_df = borough_df.reset_index(drop= True)\n",
        "  except:\n",
        "    return f\"We could not process your request for the {borough_name} filtered dataframe. The borough might not be present in the cluster specified\"\n",
        "  return borough_df\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "wLSpnhrsW-X5"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### For FDNYC"
      ],
      "metadata": {
        "id": "S_7ATAw0kD7S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will read the data."
      ],
      "metadata": {
        "id": "G0r1wq04F_ql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_Fire_Incident_Dispatch_Data.csv\")"
      ],
      "metadata": {
        "id": "aKxcH-5KF-V6"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data.columns = ['Cluster', 'INCIDENT_DATETIME', 'BOROUGH', 'ZIPCODE','HIGHEST_ALARM_LEVEL', 'INCIDENT_CLASSIFICATION','INCIDENT_CLASSIFICATION_GROUP']"
      ],
      "metadata": {
        "id": "VzKK0Z-B8iof"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fdnyc_data['ZIPCODE'] = fdnyc_data['ZIPCODE'].astype(str)\n",
        "fdnyc_data['Cluster'] = fdnyc_data['Cluster'].astype(str)\n",
        "fdnyc_data = fdnyc_data.replace(to_replace=\"RICHMOND / STATEN ISLAND\", value=\"STATEN ISLAND\")\n",
        "fdnyc_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sKTZDiZFKM0F",
        "outputId": "9c096d34-a25b-441c-a769-ff5f6f776722"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster    INCIDENT_DATETIME    BOROUGH ZIPCODE HIGHEST_ALARM_LEVEL  \\\n",
              "0       0  2014-03-15 06:39:53  MANHATTAN   10010         First Alarm   \n",
              "1       2  2021-03-18 08:53:00   BROOKLYN   11232         First Alarm   \n",
              "2       7  2020-07-15 19:26:00  MANHATTAN   10023         First Alarm   \n",
              "3       0  2012-02-10 12:22:46  MANHATTAN   10023         First Alarm   \n",
              "4       2  2014-06-18 18:05:02   BROOKLYN   11212         First Alarm   \n",
              "\n",
              "                          INCIDENT_CLASSIFICATION  \\\n",
              "0              Multiple Dwelling 'A' - Other fire   \n",
              "1  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "2  Multiple Dwelling 'A' - Food on the stove fire   \n",
              "3              Multiple Dwelling 'A' - Other fire   \n",
              "4                      Multiple Dwelling 'B' Fire   \n",
              "\n",
              "  INCIDENT_CLASSIFICATION_GROUP  \n",
              "0              Structural Fires  \n",
              "1              Structural Fires  \n",
              "2              Structural Fires  \n",
              "3              Structural Fires  \n",
              "4              Structural Fires  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-42c604a0-4a77-4f77-973e-5fa5605c868d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>INCIDENT_DATETIME</th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>HIGHEST_ALARM_LEVEL</th>\n",
              "      <th>INCIDENT_CLASSIFICATION</th>\n",
              "      <th>INCIDENT_CLASSIFICATION_GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2014-03-15 06:39:53</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10010</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2021-03-18 08:53:00</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11232</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>2020-07-15 19:26:00</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Food on the stove fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2012-02-10 12:22:46</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>10023</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'A' - Other fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2014-06-18 18:05:02</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>11212</td>\n",
              "      <td>First Alarm</td>\n",
              "      <td>Multiple Dwelling 'B' Fire</td>\n",
              "      <td>Structural Fires</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42c604a0-4a77-4f77-973e-5fa5605c868d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42c604a0-4a77-4f77-973e-5fa5605c868d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42c604a0-4a77-4f77-973e-5fa5605c868d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_opuQTk0KVE9"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets start by looking at the number of cluster in our data modeled data \n",
        "temp_df = pd.DataFrame(fdnyc_data['Cluster'].value_counts())\n",
        "temp_df = temp_df.reset_index()\n",
        "temp_df.columns = [temp_df.columns[1], 'Count']\n",
        "temp_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "ZKCcLT8IKVe4",
        "outputId": "a38f8d15-59e0-4e9b-9bf1-5a70032ed79d"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster  Count\n",
              "0       2  25793\n",
              "1       0  21148\n",
              "2       5  11868\n",
              "3       6  11535\n",
              "4       7  10864\n",
              "5       8   7295\n",
              "6       4   4242\n",
              "7       3   3669\n",
              "8       1   3586"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f6ef8cc-b472-4808-8e20-2dadb5e65b59\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>25793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>21148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>11868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>11535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>10864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>7295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4</td>\n",
              "      <td>4242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>3669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f6ef8cc-b472-4808-8e20-2dadb5e65b59')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f6ef8cc-b472-4808-8e20-2dadb5e65b59 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f6ef8cc-b472-4808-8e20-2dadb5e65b59');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters \n",
        "fig = px.histogram(fdnyc_data, x = 'Cluster', title=\"The Frequency Distribution of the Clusters in the FDNY Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "fw7nTg52ZywQ",
        "outputId": "aa776b71-1d8e-4184-c841-f6bfebe0cd49"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J88O5lLQbmfL"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters stratified by Borough distribution\n",
        "fig = px.histogram(fdnyc_data, x = 'Cluster',color=\"BOROUGH\", title=\"The Frequency Distribution of the Clusters in the FDNY Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "Htj42Yi2bkKc",
        "outputId": "14b00a1b-20f1-4456-a6d2-bce52d2b528d"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RFtT1GPcbkHG"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a picture of the above dataframe\n",
        "fig = px.pie(temp_df, names='Cluster', values='Count', title=\"The Frequency Distribution of the Clusters in the FDNY Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "TqCgJj0HMTH2",
        "outputId": "b1819eff-30ee-4bce-b386-29f80659f864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Above picture shows the frequency distribution of the clusters in our FDNY kmodes modeled dataset. The picture shows that cluster `2` is the largest cluster at 25% followed by cluster `0` at 23% the data.Clusters `1`, `3` and `4` are the smaller clusters with each being less than 5% of the data. So now, the task becomes, lets go through each of the clusters doing an exploratory anaysis as part of building our perspective on ranking the clusters."
      ],
      "metadata": {
        "id": "z9CS147lN8-c"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tNlmN9x4QaWX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Let's start with cluster `0`"
      ],
      "metadata": {
        "id": "LETUch1TQbev"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_0_df = get_cluster_df('0', fdnyc_data.groupby(\"Cluster\"))"
      ],
      "metadata": {
        "id": "LV9oNvRUQa7V"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we can look at the distribution the boroughs in cluster\n",
        "cluster_0_df[\"BOROUGH\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zJhiwzbOqeD",
        "outputId": "dbe5016d-d6e7-436f-90e0-1b5eb56f5065"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BRONX            9975\n",
              "MANHATTAN        4607\n",
              "BROOKLYN         4206\n",
              "QUEENS           1981\n",
              "STATEN ISLAND     379\n",
              "Name: INCIDENT_BOROUGH, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qM_R6yFuue8_"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_0_df['BOROUGH'].value_counts().plot(kind='pie', autopct='%1.1f%%', figsize=(10,8))\n",
        "\n",
        "plt.axis('equal')\n",
        "plt.title('Distribution of The Boroughs in cluster 0')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "CrUYlca1O94s",
        "outputId": "a74e709f-6b5c-4904-e0b3-013442886d79"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#still at cluster 0 lets look at the INCIDENT_CLASSIFICATION which is the types of fires that the FDNYC responded to\n",
        "cluster_0_df[\"INCIDENT_CLASSIFICATION\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BginoVUrSFSj",
        "outputId": "9d419c19-4630-41d6-a5ed-90fdf9ba88cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Multiple Dwelling 'A' - Other fire                16547\n",
              "Private Dwelling Fire                              2082\n",
              "Multiple Dwelling 'A' - Food on the stove fire     1055\n",
              "Multiple Dwelling 'B' Fire                          822\n",
              "Multiple Dwelling 'A' - Compactor fire              642\n",
              "Name: INCIDENT_CLASSIFICATION, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_0_df['INCIDENT_CLASSIFICATION'].value_counts().plot(kind='pie', autopct='%1.1f%%', figsize=(10,8))\n",
        "\n",
        "plt.axis('equal')\n",
        "plt.title('Distribution of The INCIDENT_CLASSIFICATION in cluster 0')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "M9zEAddzSpMi",
        "outputId": "e49a8b6d-66fe-4347-9870-bd5babca19a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multiple Dwelling ‘A’ - Compactor Fire: this is a commercial compactor fire that is confined to the contents of a compactor. This excludes home trash compactors. This is in a multiple dwelling that is occupied by three or more families living independently of each other.\n",
        "\n",
        "Multiple Dwelling ‘A’ - Food on the stove fire: this is a fire involving the contents of a cooking vessel that does not extend beyond the cooking vessel. This is in a multiple dwelling that is occupied by three or more families living independently of each other.\n",
        "\n",
        "Private Dwelling Fire: a fire in a residence for not more than two families living independently of each other with separate cooking facilities.\n",
        "\n"
      ],
      "metadata": {
        "id": "xSIVAhR8fi9I"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rJuNpzyTXWrv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7EWdaGQYkObZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### For NYPD"
      ],
      "metadata": {
        "id": "m6PbMG_4kPiw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nypd_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_NYPD_Complaint_Data_Historic.csv\")\n",
        "nypd_data['ZIPCODE'] = nypd_data['ZIPCODE'].astype(str)\n",
        "nypd_data['Cluster'] = nypd_data['Cluster'].astype(str)"
      ],
      "metadata": {
        "id": "nDuFxgh1kUKI"
      },
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nypd_data.head()"
      ],
      "metadata": {
        "id": "JoERhwEBEJkZ",
        "outputId": "9a0ba701-b486-4b58-8b1d-134dc642c4b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster      RPT_DT      OFNS_DESC CRM_ATPT_CPTD_CD   LAW_CAT_CD    BOROUGH  \\\n",
              "0       1  06/12/2012       BURGLARY        COMPLETED       FELONY      BRONX   \n",
              "1       3  06/11/2021  PETIT LARCENY        COMPLETED  MISDEMEANOR   BROOKLYN   \n",
              "2       1  10/24/2006  GRAND LARCENY        COMPLETED       FELONY  MANHATTAN   \n",
              "3       1  12/03/2008  GRAND LARCENY        COMPLETED       FELONY      BRONX   \n",
              "4       1  05/15/2017  GRAND LARCENY        COMPLETED       FELONY  MANHATTAN   \n",
              "\n",
              "  LOC_OF_OCCUR_DESC           PREM_TYP_DESC ZIPCODE  \n",
              "0            INSIDE  RESIDENCE - APT. HOUSE   10453  \n",
              "1          FRONT OF         RESIDENCE-HOUSE   11208  \n",
              "2            INSIDE  RESIDENCE - APT. HOUSE   10027  \n",
              "3          FRONT OF  RESIDENCE - APT. HOUSE   10452  \n",
              "4            INSIDE  RESIDENCE - APT. HOUSE   10025  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90985005-8aae-47d4-92d0-bec6cf550a98\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>RPT_DT</th>\n",
              "      <th>OFNS_DESC</th>\n",
              "      <th>CRM_ATPT_CPTD_CD</th>\n",
              "      <th>LAW_CAT_CD</th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>LOC_OF_OCCUR_DESC</th>\n",
              "      <th>PREM_TYP_DESC</th>\n",
              "      <th>ZIPCODE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>06/12/2012</td>\n",
              "      <td>BURGLARY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>06/11/2021</td>\n",
              "      <td>PETIT LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>MISDEMEANOR</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE-HOUSE</td>\n",
              "      <td>11208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10/24/2006</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>12/03/2008</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>BRONX</td>\n",
              "      <td>FRONT OF</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>05/15/2017</td>\n",
              "      <td>GRAND LARCENY</td>\n",
              "      <td>COMPLETED</td>\n",
              "      <td>FELONY</td>\n",
              "      <td>MANHATTAN</td>\n",
              "      <td>INSIDE</td>\n",
              "      <td>RESIDENCE - APT. HOUSE</td>\n",
              "      <td>10025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90985005-8aae-47d4-92d0-bec6cf550a98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-90985005-8aae-47d4-92d0-bec6cf550a98 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-90985005-8aae-47d4-92d0-bec6cf550a98');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets start by looking at the number of cluster in our data modeled data \n",
        "temp_df = pd.DataFrame(nypd_data['Cluster'].value_counts())\n",
        "temp_df = temp_df.reset_index()\n",
        "temp_df.columns = [temp_df.columns[1], 'Count']\n",
        "temp_df"
      ],
      "metadata": {
        "id": "sakF4l4oEOSQ",
        "outputId": "7e4e46f4-4178-42a5-af6c-ff80341321cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster  Count\n",
              "0       1   2613\n",
              "1       0   2228\n",
              "2       2   1881\n",
              "3       4   1749\n",
              "4       5    844\n",
              "5       3    685"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32043a69-8327-4224-baeb-c0f86486a0b9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>685</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32043a69-8327-4224-baeb-c0f86486a0b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-32043a69-8327-4224-baeb-c0f86486a0b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32043a69-8327-4224-baeb-c0f86486a0b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters \n",
        "fig = px.histogram(nypd_data, x = 'Cluster', title=\"The Frequency Distribution of the Clusters in the NYPD Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "id": "u-T1ZjSWEONY",
        "outputId": "0118a4ef-124a-4ef6-eaa3-67577bb7825c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zNoMcegdEOJ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters stratified by Borough distribution\n",
        "fig = px.histogram(nypd_data, x = 'Cluster',color=\"BOROUGH\", title=\"The Frequency Distribution of the Clusters in the NYPD Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "id": "dl6e9gpkEOHV",
        "outputId": "4b726df7-3523-4004-ed51-74d2a401ba7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kF5ufG9QFIIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters stratified by Borough distribution\n",
        "fig = px.histogram(nypd_data, x = 'Cluster',color=\"OFNS_DESC\", title=\"The Frequency Distribution of the Clusters in the NYPD Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=1000)"
      ],
      "metadata": {
        "id": "rlC5joTIFIFp",
        "outputId": "04f60f03-822a-47e0-c368-723293a8bfd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NnF23itrFICR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rr00ASlGFH_Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D1-oEI6mEOD5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### For NYC 311 Complaints and Service Requests"
      ],
      "metadata": {
        "id": "TxT4gLs6Lzv3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKzbepii9EY_",
        "outputId": "08516eb4-96d1-4ec6-ddde-344a222c0673"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Cluster', 'Unique Key', 'Agency Name', 'Complaint Type', 'Descriptor',\n",
              "       'Location Type', 'Incident Zip', 'City', 'Borough'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We will do the same thing for the 311 complaints and service requests data \n",
        "nyc_csr311_data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/MODEL_NYC311Open_Data.csv\")\n",
        "nyc_csr311_data.columns = ['Cluster', 'Unique Key', 'Agency Name', 'Complaint Type', 'Descriptor','Location Type', 'ZIPCODE', 'City', 'BOROUGH']\n",
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "id": "Pd5o5XqaMDii",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "eeacb31e-2f7a-495b-b286-b93866a31a31"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cluster  Unique Key                                        Agency Name  \\\n",
              "0        0    29491303                    New York City Police Department   \n",
              "1        1    29234415  Department of Housing Preservation and Develop...   \n",
              "2        1    23185964  Department of Housing Preservation and Develop...   \n",
              "3        0    40093189  Department of Housing Preservation and Develop...   \n",
              "4        1    29693040  Department of Housing Preservation and Develop...   \n",
              "\n",
              "        Complaint Type        Descriptor               Location Type  ZIPCODE  \\\n",
              "0  Noise - Residential  Loud Music/Party  Residential Building/House    10075   \n",
              "1       HEAT/HOT WATER    APARTMENT ONLY        RESIDENTIAL BUILDING    10029   \n",
              "2             NONCONST   CARBON MONOXIDE        RESIDENTIAL BUILDING    11434   \n",
              "3      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING    10031   \n",
              "4       HEAT/HOT WATER   ENTIRE BUILDING        RESIDENTIAL BUILDING    11204   \n",
              "\n",
              "       City    BOROUGH  \n",
              "0  NEW YORK  MANHATTAN  \n",
              "1  NEW YORK  MANHATTAN  \n",
              "2   JAMAICA     QUEENS  \n",
              "3  NEW YORK  MANHATTAN  \n",
              "4  BROOKLYN   BROOKLYN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b035cdaf-d063-4d45-b37a-f813d38e8993\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Unique Key</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>City</th>\n",
              "      <th>BOROUGH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>29491303</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>29234415</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>23185964</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>QUEENS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>40093189</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>29693040</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>BROOKLYN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b035cdaf-d063-4d45-b37a-f813d38e8993')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b035cdaf-d063-4d45-b37a-f813d38e8993 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b035cdaf-d063-4d45-b37a-f813d38e8993');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_data['ZIPCODE'] = nyc_csr311_data['ZIPCODE'].astype(str)\n",
        "nyc_csr311_data['Cluster'] = nyc_csr311_data['Cluster'].astype(str)\n",
        "nyc_csr311_data.head()"
      ],
      "metadata": {
        "id": "1OZReLhUMDc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5315cd12-d725-4a65-f208-9ade39e8740f"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster  Unique Key                                        Agency Name  \\\n",
              "0       0    29491303                    New York City Police Department   \n",
              "1       1    29234415  Department of Housing Preservation and Develop...   \n",
              "2       1    23185964  Department of Housing Preservation and Develop...   \n",
              "3       0    40093189  Department of Housing Preservation and Develop...   \n",
              "4       1    29693040  Department of Housing Preservation and Develop...   \n",
              "\n",
              "        Complaint Type        Descriptor               Location Type ZIPCODE  \\\n",
              "0  Noise - Residential  Loud Music/Party  Residential Building/House   10075   \n",
              "1       HEAT/HOT WATER    APARTMENT ONLY        RESIDENTIAL BUILDING   10029   \n",
              "2             NONCONST   CARBON MONOXIDE        RESIDENTIAL BUILDING   11434   \n",
              "3      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING   10031   \n",
              "4       HEAT/HOT WATER   ENTIRE BUILDING        RESIDENTIAL BUILDING   11204   \n",
              "\n",
              "       City    BOROUGH  \n",
              "0  NEW YORK  MANHATTAN  \n",
              "1  NEW YORK  MANHATTAN  \n",
              "2   JAMAICA     QUEENS  \n",
              "3  NEW YORK  MANHATTAN  \n",
              "4  BROOKLYN   BROOKLYN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e57e01da-2a81-43ff-a854-bb5a30e2a401\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Unique Key</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>ZIPCODE</th>\n",
              "      <th>City</th>\n",
              "      <th>BOROUGH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>29491303</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>29234415</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>APARTMENT ONLY</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10029</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>23185964</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>NONCONST</td>\n",
              "      <td>CARBON MONOXIDE</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11434</td>\n",
              "      <td>JAMAICA</td>\n",
              "      <td>QUEENS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>40093189</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>29693040</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>HEAT/HOT WATER</td>\n",
              "      <td>ENTIRE BUILDING</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>11204</td>\n",
              "      <td>BROOKLYN</td>\n",
              "      <td>BROOKLYN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e57e01da-2a81-43ff-a854-bb5a30e2a401')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e57e01da-2a81-43ff-a854-bb5a30e2a401 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e57e01da-2a81-43ff-a854-bb5a30e2a401');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets start by looking at the number of cluster in our data modeled data \n",
        "temp_df = pd.DataFrame(nyc_csr311_data['Cluster'].value_counts())\n",
        "temp_df = temp_df.reset_index()\n",
        "temp_df.columns = [temp_df.columns[1], 'Count']\n",
        "temp_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "jjbNEedbWv1p",
        "outputId": "031b80b6-ec00-4ef5-853d-7b1a68e40f8e"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster  Count\n",
              "0       0  33467\n",
              "1       2  24635\n",
              "2       3  21737\n",
              "3       1  20161"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d16d724a-5860-4f6b-9bca-2c8640f379d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>33467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>24635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>21737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>20161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d16d724a-5860-4f6b-9bca-2c8640f379d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d16d724a-5860-4f6b-9bca-2c8640f379d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d16d724a-5860-4f6b-9bca-2c8640f379d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters \n",
        "fig = px.histogram(nyc_csr311_data, x = 'Cluster', title=\"The Frequency Distribution of the Clusters in the 311 Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "id": "QQ9EqDRVMCsp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "edff4e93-ca46-47f0-a59d-ea7919abd694"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZF13OTUhasTw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X_IVE-zffICX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a histogram of the clusters stratified by Borough distribution\n",
        "fig = px.histogram(nyc_csr311_data, x = 'Cluster', color='BOROUGH',title=\"The Frequency Distribution of the Clusters in the 311 Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "BRKUVDa0fH_m",
        "outputId": "8f77c9d0-7ac5-4fae-930b-6f2312e1758b"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XDt1Vq1Xajf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let look at a picture of the above dataframe\n",
        "fig = px.pie(temp_df, names='Cluster', values='Count', title=\"The Frequency Distribution of the Clusters in the 311 Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "eQb3ewQPW9YB",
        "outputId": "341cda51-d809-4163-fda7-51ccff5f0748"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 3 looks to be having the largest cluster of the 4 with 33.5% followed by cluster 2. Let do the analysis of the cluster and uncover the different patterns and attributes of the clusters. Bassically, again, answering the question `Why were these data point clustered together?`."
      ],
      "metadata": {
        "id": "wNLrF2DkXHfv"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2s4H_79Aan__"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_0_df = get_cluster_df('0',cluster_grouped_dataset=nyc_csr311_data.groupby('Cluster'))\n",
        "cluster_0_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "VQd0NM8yXFeT",
        "outputId": "15f65056-1723-40bd-b4df-02dd4d3ba305"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cluster  Unique Key                                        Agency Name  \\\n",
              "0       0    29491303                    New York City Police Department   \n",
              "1       0    40093189  Department of Housing Preservation and Develop...   \n",
              "2       0    27406518                    New York City Police Department   \n",
              "3       0    36313197                    New York City Police Department   \n",
              "4       0    41395550  Department of Housing Preservation and Develop...   \n",
              "\n",
              "        Complaint Type        Descriptor               Location Type  \\\n",
              "0  Noise - Residential  Loud Music/Party  Residential Building/House   \n",
              "1      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING   \n",
              "2  Noise - Residential   Loud Television  Residential Building/House   \n",
              "3  Noise - Residential  Banging/Pounding  Residential Building/House   \n",
              "4      FLOORING/STAIRS             FLOOR        RESIDENTIAL BUILDING   \n",
              "\n",
              "  Incident Zip      City    Borough  \n",
              "0        10075  NEW YORK  MANHATTAN  \n",
              "1        10031  NEW YORK  MANHATTAN  \n",
              "2        10075  NEW YORK  MANHATTAN  \n",
              "3        10024  NEW YORK  MANHATTAN  \n",
              "4        10019  NEW YORK  MANHATTAN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5ffa9b5-db6d-451c-ac1b-c664e5bf1c6d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Cluster</th>\n",
              "      <th>Unique Key</th>\n",
              "      <th>Agency Name</th>\n",
              "      <th>Complaint Type</th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Location Type</th>\n",
              "      <th>Incident Zip</th>\n",
              "      <th>City</th>\n",
              "      <th>Borough</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>29491303</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Music/Party</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>40093189</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10031</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>27406518</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Loud Television</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10075</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>36313197</td>\n",
              "      <td>New York City Police Department</td>\n",
              "      <td>Noise - Residential</td>\n",
              "      <td>Banging/Pounding</td>\n",
              "      <td>Residential Building/House</td>\n",
              "      <td>10024</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>41395550</td>\n",
              "      <td>Department of Housing Preservation and Develop...</td>\n",
              "      <td>FLOORING/STAIRS</td>\n",
              "      <td>FLOOR</td>\n",
              "      <td>RESIDENTIAL BUILDING</td>\n",
              "      <td>10019</td>\n",
              "      <td>NEW YORK</td>\n",
              "      <td>MANHATTAN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5ffa9b5-db6d-451c-ac1b-c664e5bf1c6d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5ffa9b5-db6d-451c-ac1b-c664e5bf1c6d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d5ffa9b5-db6d-451c-ac1b-c664e5bf1c6d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#first we take a look at the distribution of the boroughs in the cluster\n",
        "fig = px.histogram(cluster_0_df, x = 'BOROUGH', title=\"The Frequency Distribution of the Borough in Cluster 0 of the 311 Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "0pya85sMW9VJ",
        "outputId": "3f4fc7d1-c377-48dc-9b41-505d0d1dbbe5"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#first we take a look at the distribution of the boroughs in the cluster\n",
        "fig = px.histogram(cluster_0_df, x = 'BOROUGH', color='Complaint Type', title=\"The Frequency Distribution of the Borough in Cluster 0 of the 311 Modeled Dataset\")\n",
        "fig.show(renderer='png', height=500, width=800)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "OUSyTWIWkV1a",
        "outputId": "d2e00dde-8df2-4912-abcd-2ac24551ccd9"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wq6HmgMOW9SV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kKWkdGV-lMW1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Commentary and Next steps**\n",
        "\n",
        "Continuing with analyzing the clusters generated from the Kmodes clustering models for both the Fire Department and NYPD.\n",
        " \n",
        "> For the FDNYC (Fire department) clusters, we need to create a ranking measure that we can use for ranking which borough is riskier to fires and which types of fires. We also have to have a better understanding of the types of fires using the fire department codes. \n",
        "\n",
        "> The NYPD, we are going to do the same thing, continued analysis to uncover the types of crimes in each of the clusters and the distribution of the borough in relation to the crime. We will use this to develop a ranking measure of the borough and crime. \n",
        "\n",
        "> This is very important as crime is one of the determinants of riskiness as far as homeowners insurance. The other determinants are the zipcode or location of the property and the average cost of residential properties in the location. With this in mind, the end goal is to join the 311 complaints and service request data, borough ranking measures, the FDNYC fire borough ranking measures, NYPD crime borough ranking measures, and the average cost of properties and average residential insurance cost per borough. \n",
        "\n",
        "This is still a work in progress \n",
        "> This joined dataset will be used to create a linear combination of fire, crime, complaints/service requests, cost of residential property, and the average insurance cost per borough as the target. "
      ],
      "metadata": {
        "id": "Ci5nL352mIlE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_csr311_cluster_borough_probs_dict = dict()\n",
        "nyc_csr311_cluster_zipcode_probs_dict = dict()\n",
        "for cluster in list(nyc_csr311_data['Cluster'].unique()):\n",
        "  #create the cluster df\n",
        "  cluster_df = get_cluster_df(cluster, cluster_grouped_dataset=nyc_csr311_data.groupby('Cluster'))\n",
        "  tempdf = pd.DataFrame(cluster_df['BOROUGH'].value_counts())\n",
        "  tempdf = tempdf.reset_index()\n",
        "  tempdf.columns = ['BOROUGH', 'count']\n",
        "  cluster_dict = tempdf.set_index('BOROUGH').to_dict()['count']\n",
        "  nyc_csr311_cluster_borough_probs_dict[cluster] = cluster_dict\n",
        "\n",
        "  tempdfz = pd.DataFrame(cluster_df['ZIPCODE'].value_counts())\n",
        "  tempdfz = tempdfz.reset_index()\n",
        "  tempdfz.columns = ['ZIPCODE', 'count']\n",
        "  cluster_dict = tempdfz.set_index('ZIPCODE').to_dict()['count']\n",
        "  nyc_csr311_cluster_zipcode_probs_dict[cluster] = cluster_dict\n"
      ],
      "metadata": {
        "id": "_XiPK2F-yrvO"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_dict  = dict()\n",
        "cluster_belonging_dict = dict()\n",
        "for cluster in nyc_csr311_cluster_borough_probs_dict:\n",
        "  for borough in nyc_csr311_cluster_borough_probs_dict[cluster]:\n",
        "    if borough not in max_dict:\n",
        "      max_dict[borough] = nyc_csr311_cluster_borough_probs_dict[cluster][borough]\n",
        "      cluster_belonging_dict[borough] = cluster\n",
        "\n",
        "    elif max_dict[borough] < nyc_csr311_cluster_borough_probs_dict[cluster][borough]:\n",
        "      max_dict[borough] = nyc_csr311_cluster_borough_probs_dict[cluster][borough]\n",
        "      cluster_belonging_dict[borough] = cluster\n",
        "\n",
        "max_dict\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5wtKVVM4h0L",
        "outputId": "2380f726-9a92-4adc-ec4b-ab3b28fe1db3"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MANHATTAN': 15075,\n",
              " 'BROOKLYN': 19577,\n",
              " 'BRONX': 21737,\n",
              " 'QUEENS': 8617,\n",
              " 'STATEN ISLAND': 1192}"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_belonging_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWFDLS1h6Tll",
        "outputId": "f6c20f79-8451-47b0-c7ba-0ccee8ff836e"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MANHATTAN': '0',\n",
              " 'BROOKLYN': '2',\n",
              " 'BRONX': '3',\n",
              " 'QUEENS': '1',\n",
              " 'STATEN ISLAND': '1'}"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_dict  = dict()\n",
        "cluster_belonging_dict = dict()\n",
        "for cluster in nyc_csr311_cluster_zipcode_probs_dict:\n",
        "  for zipcode in nyc_csr311_cluster_zipcode_probs_dict[cluster]:\n",
        "    if zipcode not in max_dict:\n",
        "      max_dict[zipcode] = nyc_csr311_cluster_zipcode_probs_dict[cluster][zipcode]\n",
        "      cluster_belonging_dict[zipcode] = cluster\n",
        "\n",
        "    elif max_dict[zipcode] < nyc_csr311_cluster_zipcode_probs_dict[cluster][zipcode]:\n",
        "      max_dict[zipcode] = nyc_csr311_cluster_zipcode_probs_dict[cluster][zipcode]\n",
        "      cluster_belonging_dict[zipcode] = cluster\n",
        "\n",
        "max_dict"
      ],
      "metadata": {
        "id": "EBvTsVy0t5bp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_belonging_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8o6Uq3fs-yU",
        "outputId": "ae86ea59-d565-4a3e-8982-b6ee0c883cc4"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'10031': '0',\n",
              " '10032': '0',\n",
              " '10034': '0',\n",
              " '10033': '0',\n",
              " '10040': '0',\n",
              " '10029': '0',\n",
              " '10027': '0',\n",
              " '10025': '0',\n",
              " '10030': '0',\n",
              " '11226': '2',\n",
              " '10467': '3',\n",
              " '10468': '3',\n",
              " '10002': '0',\n",
              " '10026': '0',\n",
              " '10039': '0',\n",
              " '10009': '0',\n",
              " '10035': '0',\n",
              " '10456': '3',\n",
              " '10452': '3',\n",
              " '10458': '3',\n",
              " '10003': '0',\n",
              " '10453': '3',\n",
              " '10457': '3',\n",
              " '11221': '2',\n",
              " '10463': '3',\n",
              " '11207': '2',\n",
              " '11237': '2',\n",
              " '11206': '2',\n",
              " '11212': '2',\n",
              " '10019': '0',\n",
              " '10011': '0',\n",
              " '11213': '2',\n",
              " '10472': '3',\n",
              " '10024': '0',\n",
              " '11216': '2',\n",
              " '10462': '3',\n",
              " '11385': '1',\n",
              " '11205': '0',\n",
              " '10460': '3',\n",
              " '11225': '2',\n",
              " '11233': '2',\n",
              " '11208': '2',\n",
              " '11368': '1',\n",
              " '10466': '3',\n",
              " '11238': '2',\n",
              " '10037': '0',\n",
              " '10128': '0',\n",
              " '10023': '0',\n",
              " '10016': '0',\n",
              " '11211': '2',\n",
              " '10036': '0',\n",
              " '11203': '2',\n",
              " '11419': '0',\n",
              " '11236': '2',\n",
              " '10459': '3',\n",
              " '10028': '0',\n",
              " '10451': '3',\n",
              " '10469': '3',\n",
              " '11373': '1',\n",
              " '10473': '3',\n",
              " '10014': '0',\n",
              " '11230': '2',\n",
              " '11372': '1',\n",
              " '10012': '0',\n",
              " '10454': '3',\n",
              " '10001': '0',\n",
              " '11435': '1',\n",
              " '11377': '1',\n",
              " '10455': '3',\n",
              " '11210': '2',\n",
              " '11201': '0',\n",
              " '11235': '2',\n",
              " '10022': '0',\n",
              " '10010': '0',\n",
              " '11220': '2',\n",
              " '10461': '3',\n",
              " '11209': '2',\n",
              " '11691': '1',\n",
              " '11218': '2',\n",
              " '11234': '2',\n",
              " '11229': '2',\n",
              " '11420': '1',\n",
              " '11421': '1',\n",
              " '10304': '1',\n",
              " '10013': '0',\n",
              " '10021': '0',\n",
              " '11106': '1',\n",
              " '11101': '1',\n",
              " '11375': '1',\n",
              " '10314': '0',\n",
              " '11223': '2',\n",
              " '10301': '1',\n",
              " '11214': '2',\n",
              " '11434': '1',\n",
              " '10465': '0',\n",
              " '11369': '1',\n",
              " '11433': '1',\n",
              " '11217': '2',\n",
              " '11432': '1',\n",
              " '11224': '2',\n",
              " '10065': '0',\n",
              " '11215': '2',\n",
              " '10075': '0',\n",
              " '11222': '2',\n",
              " '11204': '2',\n",
              " '11249': '2',\n",
              " '11355': '1',\n",
              " '11102': '1',\n",
              " '11354': '1',\n",
              " '11374': '1',\n",
              " '10310': '1',\n",
              " '10038': '0',\n",
              " '11417': '1',\n",
              " '11418': '1',\n",
              " '11231': '2',\n",
              " '11103': '1',\n",
              " '10306': '1',\n",
              " '11232': '2',\n",
              " '11104': '1',\n",
              " '11219': '2',\n",
              " '10474': '3',\n",
              " '11436': '1',\n",
              " '10302': '1',\n",
              " '11423': '1',\n",
              " '11415': '1',\n",
              " '11365': '0',\n",
              " '11370': '0',\n",
              " '11367': '1',\n",
              " '11416': '1',\n",
              " '10017': '0',\n",
              " '11105': '1',\n",
              " '10303': '1',\n",
              " '10312': '0',\n",
              " '11366': '0',\n",
              " '10470': '3',\n",
              " '11379': '0',\n",
              " '10305': '1',\n",
              " '11414': '1',\n",
              " '11356': '1',\n",
              " '11358': '1',\n",
              " '10471': '3',\n",
              " '11693': '1',\n",
              " '11413': '1',\n",
              " '11412': '1',\n",
              " '11428': '1',\n",
              " '10018': '0',\n",
              " '11427': '1',\n",
              " '11692': '1',\n",
              " '11378': '1',\n",
              " '11429': '1',\n",
              " '11422': '1',\n",
              " '11228': '2',\n",
              " '11694': '1',\n",
              " '11357': '0',\n",
              " '11426': '0',\n",
              " '11361': '1',\n",
              " '10308': '0',\n",
              " '10309': '1',\n",
              " '11360': '0',\n",
              " '11411': '1',\n",
              " '10044': '0',\n",
              " '10007': '0',\n",
              " '11364': '1',\n",
              " '10005': '0',\n",
              " '10475': '3',\n",
              " '10282': '0',\n",
              " '10307': '0',\n",
              " '11239': '2',\n",
              " '10006': '0',\n",
              " '10069': '0',\n",
              " '11362': '0',\n",
              " '10464': '3',\n",
              " '11004': '1',\n",
              " '10280': '0',\n",
              " '11363': '1',\n",
              " '10004': '0',\n",
              " '11001': '1',\n",
              " '11109': '0',\n",
              " '11040': '0',\n",
              " '10123': '0',\n",
              " '11697': '1',\n",
              " '10129': '1',\n",
              " '11430': '1'}"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GFBASMMSwYrH"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.5 ('capstone')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.5"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "6c67f8adbebdea49f93c63d2a2dcd5cb4fed3a722249876be11f90d0fd0a91fc"
      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}