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{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "rAD4175xntuK"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import xgboost as xgb\n",
        "from tabulate import tabulate\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', color_codes=True)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "import random\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "import scipy.stats as stats\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Real_Estate_Sales_2001-2022_GL (1).csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "2wjyexOuor31",
        "outputId": "c7187c85-7afb-40b0-c196-6286136f5f5f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-91f2065255b4>:1: DtypeWarning: Columns (8,9,10,11,12) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/Real_Estate_Sales_2001-2022_GL (1).csv')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Serial Number  List Year Date Recorded     Town          Address  \\\n",
              "0        2020177       2020    04/14/2021  Ansonia    323 BEAVER ST   \n",
              "1        2020225       2020    05/26/2021  Ansonia   152 JACKSON ST   \n",
              "2        2020348       2020    09/13/2021  Ansonia  230 WAKELEE AVE   \n",
              "3        2020090       2020    12/14/2020  Ansonia      57 PLATT ST   \n",
              "4         210288       2021    06/20/2022     Avon   12 BYRON DRIVE   \n",
              "\n",
              "   Assessed Value  Sale Amount  Sales Ratio Property Type Residential Type  \\\n",
              "0        133000.0     248400.0       0.5354   Residential    Single Family   \n",
              "1        110500.0     239900.0       0.4606   Residential     Three Family   \n",
              "2        150500.0     325000.0       0.4630    Commercial              NaN   \n",
              "3        127400.0     202500.0       0.6291   Residential       Two Family   \n",
              "4        179990.0     362500.0       0.4965   Residential            Condo   \n",
              "\n",
              "  Non Use Code Assessor Remarks OPM remarks  \\\n",
              "0          NaN              NaN         NaN   \n",
              "1          NaN              NaN         NaN   \n",
              "2          NaN              NaN         NaN   \n",
              "3          NaN              NaN         NaN   \n",
              "4          NaN              NaN         NaN   \n",
              "\n",
              "                             Location  \n",
              "0          POINT (-73.06822 41.35014)  \n",
              "1                                 NaN  \n",
              "2                                 NaN  \n",
              "3                                 NaN  \n",
              "4  POINT (-72.879115982 41.773452988)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14a7441d-a3b0-41a1-85cd-8f9947a6e856\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>Serial Number</th>\n",
              "      <th>List Year</th>\n",
              "      <th>Date Recorded</th>\n",
              "      <th>Town</th>\n",
              "      <th>Address</th>\n",
              "      <th>Assessed Value</th>\n",
              "      <th>Sale Amount</th>\n",
              "      <th>Sales Ratio</th>\n",
              "      <th>Property Type</th>\n",
              "      <th>Residential Type</th>\n",
              "      <th>Non Use Code</th>\n",
              "      <th>Assessor Remarks</th>\n",
              "      <th>OPM remarks</th>\n",
              "      <th>Location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020177</td>\n",
              "      <td>2020</td>\n",
              "      <td>04/14/2021</td>\n",
              "      <td>Ansonia</td>\n",
              "      <td>323 BEAVER ST</td>\n",
              "      <td>133000.0</td>\n",
              "      <td>248400.0</td>\n",
              "      <td>0.5354</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Single Family</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (-73.06822 41.35014)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020225</td>\n",
              "      <td>2020</td>\n",
              "      <td>05/26/2021</td>\n",
              "      <td>Ansonia</td>\n",
              "      <td>152 JACKSON ST</td>\n",
              "      <td>110500.0</td>\n",
              "      <td>239900.0</td>\n",
              "      <td>0.4606</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Three Family</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020348</td>\n",
              "      <td>2020</td>\n",
              "      <td>09/13/2021</td>\n",
              "      <td>Ansonia</td>\n",
              "      <td>230 WAKELEE AVE</td>\n",
              "      <td>150500.0</td>\n",
              "      <td>325000.0</td>\n",
              "      <td>0.4630</td>\n",
              "      <td>Commercial</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020090</td>\n",
              "      <td>2020</td>\n",
              "      <td>12/14/2020</td>\n",
              "      <td>Ansonia</td>\n",
              "      <td>57 PLATT ST</td>\n",
              "      <td>127400.0</td>\n",
              "      <td>202500.0</td>\n",
              "      <td>0.6291</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Two Family</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>210288</td>\n",
              "      <td>2021</td>\n",
              "      <td>06/20/2022</td>\n",
              "      <td>Avon</td>\n",
              "      <td>12 BYRON DRIVE</td>\n",
              "      <td>179990.0</td>\n",
              "      <td>362500.0</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>Residential</td>\n",
              "      <td>Condo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (-72.879115982 41.773452988)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14a7441d-a3b0-41a1-85cd-8f9947a6e856')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-14a7441d-a3b0-41a1-85cd-8f9947a6e856 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-14a7441d-a3b0-41a1-85cd-8f9947a6e856');\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",
              "\n",
              "\n",
              "    <div id=\"df-e49d6d24-d1ef-40db-a934-37231d257b6a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e49d6d24-d1ef-40db-a934-37231d257b6a')\"\n",
              "                title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e49d6d24-d1ef-40db-a934-37231d257b6a button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "Rz2Fr2ZTo9Fd",
        "outputId": "8fa70fb0-c567-45e9-f012-689e4e050e93"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Serial Number  List Year Date Recorded           Town  \\\n",
              "254789          30025       2003    11/04/2003         Orange   \n",
              "254790          30725       2003    06/24/2004      Fairfield   \n",
              "254791          31526       2003    09/20/2004        Bristol   \n",
              "254792          30371       2003    03/01/2004        Enfield   \n",
              "254793          30641       2003    05/20/2004  East Hartford   \n",
              "\n",
              "                  Address  Assessed Value  Sale Amount  Sales Ratio  \\\n",
              "254789  826 RAIL FENCE RD        260300.0     545000.0     0.477615   \n",
              "254790     150 LONDON TER        198590.0     535200.0     0.371058   \n",
              "254791       65 SANDRA ST         94580.0     169000.0     0.559645   \n",
              "254792     940 ENFIELD ST         76510.0     200000.0     0.382550   \n",
              "254793     85 BEDFORD AVE             NaN          NaN          NaN   \n",
              "\n",
              "       Property Type Residential Type Non Use Code Assessor Remarks  \\\n",
              "254789           NaN              NaN          7.0              NaN   \n",
              "254790           NaN              NaN          NaN              NaN   \n",
              "254791           NaN              NaN          NaN              NaN   \n",
              "254792           NaN              NaN          7.0              NaN   \n",
              "254793           NaN              NaN          NaN              NaN   \n",
              "\n",
              "       OPM remarks                    Location  \n",
              "254789         NaN                         NaN  \n",
              "254790         NaN                         NaN  \n",
              "254791         NaN                         NaN  \n",
              "254792         NaN  POINT (-72.59445 41.99622)  \n",
              "254793         NaN                         NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae56aa1c-ff5a-46fb-8675-75065777b73d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Serial Number</th>\n",
              "      <th>List Year</th>\n",
              "      <th>Date Recorded</th>\n",
              "      <th>Town</th>\n",
              "      <th>Address</th>\n",
              "      <th>Assessed Value</th>\n",
              "      <th>Sale Amount</th>\n",
              "      <th>Sales Ratio</th>\n",
              "      <th>Property Type</th>\n",
              "      <th>Residential Type</th>\n",
              "      <th>Non Use Code</th>\n",
              "      <th>Assessor Remarks</th>\n",
              "      <th>OPM remarks</th>\n",
              "      <th>Location</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>254789</th>\n",
              "      <td>30025</td>\n",
              "      <td>2003</td>\n",
              "      <td>11/04/2003</td>\n",
              "      <td>Orange</td>\n",
              "      <td>826 RAIL FENCE RD</td>\n",
              "      <td>260300.0</td>\n",
              "      <td>545000.0</td>\n",
              "      <td>0.477615</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254790</th>\n",
              "      <td>30725</td>\n",
              "      <td>2003</td>\n",
              "      <td>06/24/2004</td>\n",
              "      <td>Fairfield</td>\n",
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              "      <td>198590.0</td>\n",
              "      <td>535200.0</td>\n",
              "      <td>0.371058</td>\n",
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              "      <th>254791</th>\n",
              "      <td>31526</td>\n",
              "      <td>2003</td>\n",
              "      <td>09/20/2004</td>\n",
              "      <td>Bristol</td>\n",
              "      <td>65 SANDRA ST</td>\n",
              "      <td>94580.0</td>\n",
              "      <td>169000.0</td>\n",
              "      <td>0.559645</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254792</th>\n",
              "      <td>30371</td>\n",
              "      <td>2003</td>\n",
              "      <td>03/01/2004</td>\n",
              "      <td>Enfield</td>\n",
              "      <td>940 ENFIELD ST</td>\n",
              "      <td>76510.0</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>0.382550</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (-72.59445 41.99622)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254793</th>\n",
              "      <td>30641</td>\n",
              "      <td>2003</td>\n",
              "      <td>05/20/2004</td>\n",
              "      <td>East Hartford</td>\n",
              "      <td>85 BEDFORD AVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae56aa1c-ff5a-46fb-8675-75065777b73d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ae56aa1c-ff5a-46fb-8675-75065777b73d 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-ae56aa1c-ff5a-46fb-8675-75065777b73d');\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",
              "\n",
              "\n",
              "    <div id=\"df-d89d3d6c-4c9d-460c-b036-e148aa120343\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d89d3d6c-4c9d-460c-b036-e148aa120343')\"\n",
              "                title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d89d3d6c-4c9d-460c-b036-e148aa120343 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Or72Yaqo_AD",
        "outputId": "86363554-4fb5-40a0-9467-b3367723da5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(254794, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T.plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "lqHPc5ampBSa",
        "outputId": "df68be08-e822-402e-8eeb-e9f1c17fba7e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns.to_list()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhcY5vFMpFyZ",
        "outputId": "82c1173c-bb53-441c-8769-ae9be891cc95"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Serial Number',\n",
              " 'List Year',\n",
              " 'Date Recorded',\n",
              " 'Town',\n",
              " 'Address',\n",
              " 'Assessed Value',\n",
              " 'Sale Amount',\n",
              " 'Sales Ratio',\n",
              " 'Property Type',\n",
              " 'Residential Type',\n",
              " 'Non Use Code',\n",
              " 'Assessor Remarks',\n",
              " 'OPM remarks',\n",
              " 'Location']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UD9ztgMpJu2",
        "outputId": "32492d8b-c61f-4050-ca7b-94d6d8fa7f6e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.int64(0)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uL2uY9MpMaB",
        "outputId": "933c181e-82e2-4ed9-e038-8b8bbc66190a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 254794 entries, 0 to 254793\n",
            "Data columns (total 14 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   Serial Number     254794 non-null  int64  \n",
            " 1   List Year         254794 non-null  int64  \n",
            " 2   Date Recorded     254792 non-null  object \n",
            " 3   Town              254794 non-null  object \n",
            " 4   Address           254787 non-null  object \n",
            " 5   Assessed Value    254793 non-null  float64\n",
            " 6   Sale Amount       254793 non-null  float64\n",
            " 7   Sales Ratio       254793 non-null  float64\n",
            " 8   Property Type     123889 non-null  object \n",
            " 9   Residential Type  112451 non-null  object \n",
            " 10  Non Use Code      57857 non-null   object \n",
            " 11  Assessor Remarks  25992 non-null   object \n",
            " 12  OPM remarks       3619 non-null    object \n",
            " 13  Location          113115 non-null  object \n",
            "dtypes: float64(3), int64(2), object(9)\n",
            "memory usage: 27.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "sns.heatmap(df.isnull(), cbar=False, cmap='viridis')\n",
        "plt.title('Missing Values Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "sZxwO9MXpOYs",
        "outputId": "5cf8a9ec-661e-43a3-956f-686933f5a3c3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}