Prediction_Model_of_Car_Price.ipynb

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        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/fahmizainal17/Car_Price_Prediction_Model/blob/main/Prediction_Model_of_Car_Price.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGTv5Ubao_5w"
      },
      "source": [
        "#1. Import Libraries and Packages\n",
        "\n",
        "Import all basic libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "iA_PqxFWmzIX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "pd.set_option('display.max_columns',None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nX4ywMY1722j",
        "outputId": "3822753a-b189-4288-dd73-807538a5776e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "KfcEAa059BC4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel('/content/drive/MyDrive/Carsome_Project/DS Case Study_ Car Sales Data.xlsx')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omv-6Klfpca1"
      },
      "source": [
        "#2. Data Preparation\n",
        "\n",
        "Import the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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          "height": 295
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        "id": "qjK9gKQTo9gU",
        "outputId": "083f1f5f-3a2a-402e-a4a0-0289efc84a37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                           car_brand  \\\n",
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              "                                           car_model  \\\n",
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              "4  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
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              "                                         car_variant  car_year  car_engine  \\\n",
              "0  b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...      2010         2.0   \n",
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              "0             Auto  282508         1      0   blue             22/08/20   \n",
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              "2             Auto  105276         0      0  white             17/05/20   \n",
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              "4             Auto  157239         1      0  brown             20/10/20   \n",
              "\n",
              "            sales_date  price  \n",
              "0  2020-02-10 00:00:00  30100  \n",
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              "2             25/08/20  34100  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 95426,\n  \"fields\": [\n    {\n      \"column\": \"car_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"d222a18d6474d0819d8ee5da0c2c524f31ff0903a4e6abc3f7957f84f1ee3f0f\",\n          \"bcd40d58afd29be4e0af2dfb8c7582db545efe0f717b9fed903ebd2363e6b898\",\n          \"469dfc0b18c73eb263c4a1476545e7b064a7f862ef53827c22026c97268b2ebf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee\",\n          \"7781113a99f177280ad3e89bcf631f03acb8fa8e626082dd9158eeee0bdd5674\",\n          \"47a0da3ea9dab0799ae8d9a49f7b234fff9de364e8b0851b556193fa279a2fc0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_variant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          \"ef8a03eb87802e242ecf9553eef9155d2c8364dd45492fc544a256f3f7429600\",\n          \"f52d63231eb0dbaac689ec9147e4b131fc95ef92d5bc78135b22934b36d76bf4\",\n          \"284fe8d8db088b0b28a9950444d4966576ee270688f08d9ce5c5c79c56705f14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1981,\n        \"max\": 2020,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1994,\n          2004,\n          1998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_engine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4289680593337917,\n        \"min\": 0.6,\n        \"max\": 8.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          3.6,\n          3.2,\n          4.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Manual\",\n          \"Auto\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85450,\n        \"min\": 1,\n        \"max\": 992910,\n        \"num_unique_values\": 80005,\n        \"samples\": [\n          170422,\n          85157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"blue\",\n          \"black\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purchase_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 704,\n        \"samples\": [\n          \"2020-04-01 00:00:00\",\n          \"30/07/20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-07 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 495,\n        \"samples\": [\n          \"30/09/19\",\n          \"2020-04-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29758,\n        \"min\": 400,\n        \"max\": 1000800,\n        \"num_unique_values\": 1991,\n        \"samples\": [\n          69800,\n          165300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7K1h7HusBgv"
      },
      "source": [
        "### Brief Explanation of Columns\n",
        "\n",
        "| Header           | Description                                                                                            |\n",
        "|------------------|--------------------------------------------------------------------------------------------------------|\n",
        "| car_brand        | Hashed value for a car brand (e.g., Honda, BMW, Proton, etc.)                                          |\n",
        "| car_model        | Hashed value for a car model (e.g., Myvi, Saga, i3, etc.)                                              |\n",
        "| car_variant      | Hashed value for a car variant. Car variants can be repeated with different car brands and models. For example, Perodua Myvi & Honda City can both have variant G |\n",
        "| car_year         | Car production year as a 4-digit integer (e.g., 2010, 2013)                                            |\n",
        "| car_engine       | Car engine capacity (e.g., 1.8, 2.0)                                                                   |\n",
        "| car_transmission | Car transmission type (e.g., Auto, Manual)                                                             |\n",
        "| milage           | Car mileage when it was sold (e.g., 282508, 169475)                                                    |\n",
        "| accident         | Boolean flag to indicate if a car has been through an accident (0 = No accident, 1 = Accident)         |\n",
        "| flood            | Boolean flag to indicate if a car has been through a flood (0 = No flood damage, 1 = Flood damage)     |\n",
        "| color            | Color of the car being sold (e.g., blue, black, white)                                                 |\n",
        "| purchase_date    | Date when the car was purchased (in DD/MM/YY format)                                                   |\n",
        "| sales_date       | Date when the car was sold (in DD/MM/YY format)                                                        |\n",
        "| price            | Price at which the car was sold (e.g., 30100, 32100)                                                   |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXbd5Ofps8UD"
      },
      "source": [
        "#3. Exploratory Data Analysis\n",
        "\n",
        "Check descriptive statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "xr2PZ5Lxt0g4",
        "outputId": "9e2d0bcb-4fb0-4548-f56e-2de61e5bfd95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    count unique  \\\n",
              "car_brand           95426     31   \n",
              "car_model           95424    219   \n",
              "car_variant         95426   2279   \n",
              "car_year          95426.0    NaN   \n",
              "car_engine        95426.0    NaN   \n",
              "car_transmission    95426      2   \n",
              "milage            95426.0    NaN   \n",
              "accident          95426.0    NaN   \n",
              "flood             95426.0    NaN   \n",
              "color               95426     16   \n",
              "purchase_date       95426    704   \n",
              "sales_date          95426    495   \n",
              "price             95426.0    NaN   \n",
              "\n",
              "                                                                top   freq  \\\n",
              "car_brand         d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d...  21606   \n",
              "car_model         528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8...  10187   \n",
              "car_variant       3f79bb7b435b05321651daefd374cdc681dc06faa65e37...   5285   \n",
              "car_year                                                        NaN    NaN   \n",
              "car_engine                                                      NaN    NaN   \n",
              "car_transmission                                               Auto  84885   \n",
              "milage                                                          NaN    NaN   \n",
              "accident                                                        NaN    NaN   \n",
              "flood                                                           NaN    NaN   \n",
              "color                                                         white  23293   \n",
              "purchase_date                                   2020-06-09 00:00:00    317   \n",
              "sales_date                                                 25/11/20    376   \n",
              "price                                                           NaN    NaN   \n",
              "\n",
              "                           mean           std     min      25%       50%  \\\n",
              "car_brand                   NaN           NaN     NaN      NaN       NaN   \n",
              "car_model                   NaN           NaN     NaN      NaN       NaN   \n",
              "car_variant                 NaN           NaN     NaN      NaN       NaN   \n",
              "car_year            2011.374154      4.256181  1981.0   2009.0    2012.0   \n",
              "car_engine              1.66021      0.428968     0.6      1.4       1.6   \n",
              "car_transmission            NaN           NaN     NaN      NaN       NaN   \n",
              "milage            147804.267128  85450.084755     1.0  88359.5  133479.0   \n",
              "accident               0.396475      0.489168     0.0      0.0       0.0   \n",
              "flood                  0.001027       0.03203     0.0      0.0       0.0   \n",
              "color                       NaN           NaN     NaN      NaN       NaN   \n",
              "purchase_date               NaN           NaN     NaN      NaN       NaN   \n",
              "sales_date                  NaN           NaN     NaN      NaN       NaN   \n",
              "price              32200.764991  29758.710619   400.0  12600.0   23700.0   \n",
              "\n",
              "                        75%        max  \n",
              "car_brand               NaN        NaN  \n",
              "car_model               NaN        NaN  \n",
              "car_variant             NaN        NaN  \n",
              "car_year             2014.0     2020.0  \n",
              "car_engine              2.0        8.0  \n",
              "car_transmission        NaN        NaN  \n",
              "milage            191572.75   992910.0  \n",
              "accident                1.0        1.0  \n",
              "flood                   0.0        1.0  \n",
              "color                   NaN        NaN  \n",
              "purchase_date           NaN        NaN  \n",
              "sales_date              NaN        NaN  \n",
              "price               41500.0  1000800.0  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>car_year</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.374154</td>\n",
              "      <td>4.256181</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>2020.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_engine</th>\n",
              "      <td>95426.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1.66021</td>\n",
              "      <td>0.428968</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_transmission</th>\n",
              "      <td>95426</td>\n",
              "      <td>2</td>\n",
              "      <td>Auto</td>\n",
              "      <td>84885</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",
              "      <td>NaN</td>\n",
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              "      <th>milage</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>147804.267128</td>\n",
              "      <td>85450.084755</td>\n",
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              "      <td>88359.5</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.396475</td>\n",
              "      <td>0.489168</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>flood</th>\n",
              "      <td>95426.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.001027</td>\n",
              "      <td>0.03203</td>\n",
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              "        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-49af1348-ef7a-4232-bb41-d81fe458e286\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49af1348-ef7a-4232-bb41-d81fe458e286')\"\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-49af1348-ef7a-4232-bb41-d81fe458e286 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"95424\",\n        \"max\": \"95426\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"95424\",\n          \"95426\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 2,\n        \"max\": 2279,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          31,\n          219\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d779ecc01109a234339\",\n          \"528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8d8e80068e5c5598bba\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"317\",\n        \"max\": \"84885\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"21606\",\n          \"10187\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0010269737807306184,\n        \"max\": 147804.26712845557,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2011.3741537945634,\n          1.6602100056588351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.032030139816054196,\n        \"max\": 85450.08475523326,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.256181022611437,\n          0.4289680593337917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 1981.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 88359.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.4,\n          12600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 133479.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.6,\n          23700.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 191572.75,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2014.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0,\n        \"max\": 1000800.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.0,\n          1000800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.describe(include = 'all').T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZaj9ePdwgAA",
        "outputId": "bb96d1c2-7377-4387-f563-dce78a1f8c69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 95426 entries, 0 to 95425\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   car_brand         95426 non-null  object \n",
            " 1   car_model         95424 non-null  object \n",
            " 2   car_variant       95426 non-null  object \n",
            " 3   car_year          95426 non-null  int64  \n",
            " 4   car_engine        95426 non-null  float64\n",
            " 5   car_transmission  95426 non-null  object \n",
            " 6   milage            95426 non-null  int64  \n",
            " 7   accident          95426 non-null  int64  \n",
            " 8   flood             95426 non-null  int64  \n",
            " 9   color             95426 non-null  object \n",
            " 10  purchase_date     95426 non-null  object \n",
            " 11  sales_date        95426 non-null  object \n",
            " 12  price             95426 non-null  int64  \n",
            "dtypes: float64(1), int64(5), object(7)\n",
            "memory usage: 9.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-bfnprDKhvI",
        "outputId": "bbde754e-e337-43bc-ec1a-633eb840399d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "car_brand                   object\n",
            "car_model                   object\n",
            "car_variant                 object\n",
            "car_year                     int64\n",
            "car_engine                 float64\n",
            "car_transmission            object\n",
            "milage                       int64\n",
            "accident                     int64\n",
            "flood                        int64\n",
            "color                       object\n",
            "purchase_date       datetime64[ns]\n",
            "sales_date          datetime64[ns]\n",
            "price                        int64\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Convert columns to appropriate data types\n",
        "df['car_brand'] = df['car_brand'].astype('object')\n",
        "df['car_model'] = df['car_model'].astype('object')\n",
        "df['car_variant'] = df['car_variant'].astype('object')\n",
        "df['car_year'] = df['car_year'].astype('int64')\n",
        "df['car_engine'] = df['car_engine'].astype('float64')\n",
        "df['car_transmission'] = df['car_transmission'].astype('object')\n",
        "df['milage'] = df['milage'].astype('int64')\n",
        "df['accident'] = df['accident'].astype('int64')\n",
        "df['flood'] = df['flood'].astype('int64')\n",
        "df['color'] = df['color'].astype('object')\n",
        "df['purchase_date'] = pd.to_datetime(df['purchase_date'], dayfirst=True)\n",
        "df['sales_date'] = pd.to_datetime(df['sales_date'])\n",
        "df['price'] = df['price'].astype('int64')\n",
        "\n",
        "# Verify the data types\n",
        "print(df.dtypes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MHeER7qyC_z"
      },
      "source": [
        "## 3.1 Data Cleaning\n",
        "\n",
        "check missing values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tn0d62Hbsta-",
        "outputId": "1dcc4ad6-f990-45ea-f2b0-71b141f9b9aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Missing Values Percentage Missing\n",
            "car_model                      2              0.00%\n",
            "car_brand                      0              0.00%\n",
            "car_variant                    0              0.00%\n",
            "car_year                       0              0.00%\n",
            "car_engine                     0              0.00%\n",
            "car_transmission               0              0.00%\n",
            "milage                         0              0.00%\n",
            "accident                       0              0.00%\n",
            "flood                          0              0.00%\n",
            "color                          0              0.00%\n",
            "purchase_date                  0              0.00%\n",
            "sales_date                     0              0.00%\n",
            "price                          0              0.00%\n"
          ]
        }
      ],
      "source": [
        "missing_values = df.isnull().sum().sort_values(ascending=False)\n",
        "percentage_missing = (missing_values / len(df)) * 100\n",
        "missing_info = pd.concat([missing_values, percentage_missing], axis=1)\n",
        "missing_info.columns = ['Missing Values', 'Percentage Missing']\n",
        "\n",
        "# Format percentage_missing column with two decimal places and percentage symbol\n",
        "missing_info['Percentage Missing'] = missing_info['Percentage Missing'].apply(lambda x: \"{:.2f}%\".format(x))\n",
        "\n",
        "print(missing_info)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hajc53Q3ODo"
      },
      "source": [
        "As we can see, most of our columns contain misssing values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYTnsOfX4jvF"
      },
      "source": [
        "**Approach to Missing Values**\n",
        "\n",
        "FootNote\n",
        "- mean\n",
        "- median\n",
        "- mode\n",
        "- Previous row\n",
        "- Fully Drop\n",
        "---\n",
        "Ways\n",
        "- mean\n",
        "- median\n",
        "- mode\n",
        "- Fully Drop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ykXhiL6qJeHx"
      },
      "outputs": [],
      "source": [
        "# Handling missing values for 'car_model'\n",
        "df['car_model'].fillna(df['car_model'].mode()[0], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wbpdJk-4Jhfp",
        "outputId": "0d3bf391-77d3-4507-a9ed-c14ca5b586a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Missing Values Percentage Missing\n",
            "car_brand                      0              0.00%\n",
            "car_model                      0              0.00%\n",
            "car_variant                    0              0.00%\n",
            "car_year                       0              0.00%\n",
            "car_engine                     0              0.00%\n",
            "car_transmission               0              0.00%\n",
            "milage                         0              0.00%\n",
            "accident                       0              0.00%\n",
            "flood                          0              0.00%\n",
            "color                          0              0.00%\n",
            "purchase_date                  0              0.00%\n",
            "sales_date                     0              0.00%\n",
            "price                          0              0.00%\n"
          ]
        }
      ],
      "source": [
        "missing_values = df.isnull().sum().sort_values(ascending=False)\n",
        "percentage_missing = (missing_values / len(df)) * 100\n",
        "missing_info = pd.concat([missing_values, percentage_missing], axis=1)\n",
        "missing_info.columns = ['Missing Values', 'Percentage Missing']\n",
        "\n",
        "# Format percentage_missing column with two decimal places and percentage symbol\n",
        "missing_info['Percentage Missing'] = missing_info['Percentage Missing'].apply(lambda x: \"{:.2f}%\".format(x))\n",
        "\n",
        "print(missing_info)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQuFKlP-7ogC"
      },
      "source": [
        "Footnote\n",
        "\n",
        "KDEplot, Histogram\n",
        "- All Features\n",
        "- Do not include target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZFHshfn6AVV"
      },
      "source": [
        "**Check distribution of numerical features**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4ex0eRL79Ic",
        "outputId": "7fcdf093-c617-4a3c-b36a-b886d60b462f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['car_brand', 'car_model', 'car_variant', 'car_year', 'car_engine',\n",
              "       'car_transmission', 'milage', 'accident', 'flood', 'color',\n",
              "       'purchase_date', 'sales_date', 'price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "kZNtzxKr8Drj"
      },
      "outputs": [],
      "source": [
        "num = df.select_dtypes(include=['int64', 'float64']).columns  # numerical columns\n",
        "cat = df.select_dtypes(include=['object']).columns  # non-numerical (categorical) columns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LZsJ8m49qdb",
        "outputId": "6117c39b-abd5-4073-822f-28fe44e2a763"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['car_year', 'car_engine', 'milage', 'accident', 'flood', 'price'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "num"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a0WJdxrK90B0",
        "outputId": "cc7f18f4-12d0-4176-e854-d2d267b197d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x1000 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define the number of columns for subplots\n",
        "num_columns = 2\n",
        "num_rows = int(np.ceil(len(num) / num_columns))\n",
        "\n",
        "# Create a figure and axis for subplots\n",
        "fig, axes = plt.subplots(num_rows, num_columns, figsize=(6, 10))\n",
        "\n",
        "# Flatten the 2D axes array to simplify indexing\n",
        "axes = axes.ravel()\n",
        "\n",
        "# Loop through the features and create histograms\n",
        "for i, feature in enumerate(num):\n",
        "    ax = axes[i]\n",
        "    sns.histplot(data=df, x=feature, ax=ax, kde=True)\n",
        "    ax.set_title(f'{feature}')\n",
        "    ax.set_xlabel('')\n",
        "\n",
        "# Remove any empty subplots\n",
        "for i in range(len(num), num_rows * num_columns):\n",
        "    fig.delaxes(axes[i])\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YpftvV3QVP-"
      },
      "source": [
        "**Check distribution of Categorical features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bd5dKTU1QzKU",
        "outputId": "4f7ef090-d9c5-4e20-da9d-c0832e3ef5e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['car_brand', 'car_model', 'car_variant', 'car_transmission', 'color'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "cat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "QaF6U3ORQg7K",
        "outputId": "4f960c28-e8c3-48be-9658-a2f546c4df4d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 5 Axes>"
            ],
            "image/png": 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emSn/Vdk+Pj6XfJV33uU9312XJJlPPvnEtG3b1mNYhw4djCTTrVu3IqcLCwszkydPLnKbybtvXlRUr169wJ2eLna53I9EGTZs2CWvo/y3zXc4HBfc9n18fIyvr2+h+/7590/ddfn5+Vl9kVdeecVIMu3atStyHgEBAUaS+frrrwu8fxe6/WD9+vWtq8wjIyNN69atzcCBA82OHTuMJPPNN9+Yhx56qNAr7O+55x4zYMAAc8stt5gTJ04Yh8NhVqxYYebMmWNq1qxpcnNzTevWrU2HDh1My5Ytzfz5803NmjWNv7+/6devn8nMzLT6k7feeqvp2bOnRx8zMTHR3HPPPdbrkydPmgkTJpiaNWsap9Np6tata15++eWL6vfmN3HiRJOUlGTq169v7rzzzgKP6nKvR/et6oOCgozNZjNBQUHm6aeftt6ratWqmU6dOpn27dsXuY4rUt+KIAiCIAiCIAiCqFyR/1hJcY5XFXY37Pzhvs26e15Op9PUqlXLNGjQ4JL626WeIA8KCjIPPvig+fbbb80///lPExAQYBYsWGCMOZcgDw0NNeHh4aZBgwZm5MiR5r///W+hdRWVIH/00Uc9Lp//7rvvrJX15ZdfFig/aNAgI527NXlqaqpH8rt169YmJSXFI3F89OhRM2zYMHPVVVdZw5o0aWJ8fX2t539KMu+8846Jj483t9xyi5H+dyDQvUH4+vqa4OBgj3qkc89Jcx+McpdzH9Rp166dlch3b0gvvPCCee+998zSpUutOtztyPschrzP03QP9/X1NfHx8R7D3fW6D5rlfX5rYRtvp06drAOL0dHRF3x+g3Tu2Wzu/y/0TI2//OUvhSZl87bFfYJBWT2fo7gf5LKIu+6664JlinrGR0VdJoIgCIKoTFERnrd5viisU1HRkl/nOymwsAR01apVC+y/uBOu7sg7vrB9Hfewwh6t5A7345uK2leKiooqcKJiu3btPE4EKGzaqlWrFthuXC6Xx7L6+vp69EUCAwNNkyZNPN7DWrVqWY/mCQsLsxKkeffz8yZGw8LCPB6rVNi2EBUVZWw2m+ndu7fHPqQ7Ye50Oo3dbrfeM/d6z/usuBtvvNHExMSYsLAwU6NGDZOUlGQCAwOtfXen02n69u1rAgICTLdu3Uz16tVNdHS0lbB1nzThXo6mTZua4OBgc+eddxofHx8TExNjtm7dap2YGxwcbGJjY01AQIAJDAw0ffr0Me3btzdLly41o0ePNk6n0zgcDjNmzBjjcDhM586dzR133GGys7ONJPPRRx9Zz2beu3evueaaa0xcXJwJDAw0Xbp0Mdu2bTPr1q0z9erVM7fffrvVn4yNjS3wzPrU1FTTokUL6/Wtt95qYmNjzf/7f//P/PDDD+ajjz4yb775ZjF60v/TuHFj07hxY1O/fn1rvTudThMfH2/69+9vvZf5+2F9+vTx+HxER0d7vLeX2xegL0EQBEEQBEEQBEEUFXmPO5wvl5f3mEhRx3wCAwPNiy++aKT/5Qdr1aplduzYYa666irj4+NjPRa7OEo9Qd64cWPrinFjzp0B37hxY2OMMW+88YZ55513zI4dO8yyZctM48aNTdu2bc2ZM2cK1FVUgvzuu+823bp1M8acuzq8Z8+e1gGr9957z6Nsdna2cTgcJjg42FSpUsVkZGQYY4ypUaOGkc4daJg4caLp06eP9QY8/vjjxuFwmI8++sha+cYY07VrV9O9e3erXI0aNcy8efOsN9B9gMdut5vmzZub0NBQ85e//MXjzQ0NDTVdu3Y1NpvN/POf/7TaULt2batMvXr1rDobNGhgTp06ZQ4fPuyRnM8b9erVM9K5AyAX2kADAwMLPfiY/+oNm81mHXBxOBwmPDy8wPMh3ZE/Ye5wOEyDBg2sZSjqQIr7YNvGjRtNrVq1ztvuO+64w8TGxhZZV972ni+uvfZaq40X84HOv2yXEiV1BZr7+fUEQRAEQVTMKM9nBVf05L07SjPB9tBDD13WfC90F6OQkBCTkJBglc2fqJcKJizzzzcpKanA/vSoUaOsvklR72uHDh3M//3f/xmHw2FcLpfVL8jbPxg6dGiB+TocDjN9+vQCbYuIiDAxMTHG39/fvPTSS9ZyuaNTp04mKirKSkz369fP/P3vfzdTpkwxV199dYFlfPLJJ83Ro0etq/Tdy3jvvfeau+++2yQmJhrpXAf5xhtvtK5AHzVqlJFk/vGPf3jU6efnZ95//30zceJEY7PZPNaZ3W43cXFxJjk52UjnriJ3Cw0NNU2aNDGTJk0yPXv2NNdff70JCAgwH330kWnXrp1p27at1XdKTU01drvdOonhl19+sep5//33jd1uN7/99psxxhhfX1+zePFij37m3LlzTUREhDHGmD179hhJJi0t7eI6zRfgPklh/Pjx5vnnnzdRUVHGbrebhx56yOOk6KL6Z4VF3u26WrVqpf5ZJwiCIAiCIAiCILw33Cf5XyiKOgazePFic/z4cSPJPPjgg0Y6dzGxMcbqz7/77rvF7m+XeoJ86NChHsOWL19ufHx8Ck2C//DDD0Y6dzZ/fheTIB85cqSJi4sz//nPf4xUMEG+bt06ayXnPWBx/fXXWyva4XCY+Ph46wCU+6BJYQnX/AcffX19rTc6720ji3pTAwICTI0aNUxAQIDJzMw00rmDSu6zKSZNmmSdDWG32014eLh10KOwgx/usuc76HfddddZ/9vtdutgnruN7qvWL2djP9/8897y0L2u8iaeg4KCrPkXdjtH93oviVuZFzcu9tbnJRVFnQRBEARBEETFj/y3FXdHSSXOL2V/7VKmcT8mqKjxjRo1KpHluZRkeWFJ6fxR1COFzhctW7Y0EyZMOO/6euCBB6x9wypVqphvv/3WNGvWrNjLfKEr/QvbXux2u7VcefeL8+4f33rrrVbiMzo62jidThMQEFDs274X9v7s3LnT6kd17tzZ2Gw2Ex0dbfz8/ExoaKgJCwszMTExHifY5l0e9/JdffXVplevXtbJse7PTN7yfn5+plevXsbf399ERkaa1157zerz+Pn5mcmTJ5u+ffua2NhYax0EBgZ6fP4cDofx8fExt9xyi5kwYYLx8/OzhrnLNG7c2AwcONBUq1bNxMfHe/Qh3f20tWvXGmMunCB/6623jMPhMDk5OQX6rsW1b98+a5t069Spk2nZsqVp166dx92/8p68LZ07AaGi3TWCIAiCIAiCIAiC8O7In2e7mONBHTp0sBLk7otHo6OjTUhIiFXf3Llzi93ntqsCqVOnjqpXr67vv//+oqeJiopSRkaGRo8erRUrVujjjz+Wj4+PNS6vF198UZI0c+ZMJScnW8MDAwMVGBioQYMGaf/+/UpNTbXGHTp0SA6HwxrWpk0bffTRR/roo4/0ySefaN68eZLOPYz+9OnTOn78uCTp2LFjVh1nzpyRzWZTYmKiR3tOnDihX3/9VSdOnFDVqlUlScYYHTlyRJL0t7/9Tb/88oskKTc3V35+fgoLC1NMTIz1cPq83GV9fX0VHh5e6Ppat26d9X9ubq5OnDhhtVGSNm/e7FG3u11uAQEBkiSbzaaYmBgFBQUpLi7Oo4wxptB5S+fWp5t7XeUtP378eIWEhEiSgoKCVLdu3QJ1JCcn66677pJ0br1LUp8+fRQYGFigrHt8STh9+nShw93b2+Vo0aJFgWEZGRnFqqNmzZqX1QaXy3VZ0wMAKjebzVbeTahw7Paid5UvtL5ycnIKHe7e5yrKxf6e5+bmym63F+t9K2z/8UJsNtt596cOHjxY7Drd9eZ13XXXXXCa+++/3+M9ce/H5uXr6+vx+uTJk8VuW7NmzRQeHq7c3FzZbDarrQ0aNLDKvPDCC9a+4dGjR9WxY0ft3LnTmn9ISIgef/xxj3p9fHxUo0YN63VR+8x598kK215yc3O1YsUKtW3bVsYYnTp1SpLUuHFjq8xLL71k7WufPXtWDodDJ06c0O+//15gHtWrV5fD4dC1116rgQMHWsOdTqdee+01dejQQTVr1rT6Be5pR48erU8++UQ9evSQMUY33HCDjh07Jh8fHx04cECzZs1SUFCQnE6nJCkiIkINGjRQ7dq1reWQZLV/xowZVnsl6eOPP5bT6VTv3r2Vm5urevXqefS37rjjDi1dulSLFy+29puHDBmikSNHKiAgQC6XSw888IA++eQTpaSk6PTp03rqqad07Ngx/fzzzzp+/Lj+3//7f5LO9ZFOnz6tKlWqFPqe5OXuf+aVkZFh9T39/f0vWMfF2rp1qyRpx44d8vHxkY+Pj9atW6evvvpKmzdv1oEDB1SrVi3ZbDZVr15d0v8+W9WqVbukzzwAAAAAAEUpLI908803X/T0efNsNptNrVq1sl67+9PBwcGqW7eu4uPjJUmffPKJvv76a0nSzz//LElKT0/X8ePHreMm999/v9asWVOsZSn1BPmWLVs8Xm/evFn169cv9EDbL7/8osOHDys6Ovqi62/Xrp2++uor/etf/9KaNWsUHx+vtLQ0hYSEqEmTJla5N954Q2+99ZbsdruCgoI86mjcuLF8fHz0448/Kjo6WsePH7cOzNSqVUtnz561Es6//PKLunbtqq5duyopKcl6M9avX6933nlHERERcjqdHsnamjVrymazqVmzZrLZbLrpppsknTsY5T7IN336dEnnDurNnz9f0rmDc+6NbciQIZo1a5aOHDmif/3rX+rXr58keSxLs2bN5HQ6debMGevAU1533nmnRo4cab12Op3W+zBkyBBJ5w74uNe/y+XSxo0b5efnJ0mKjIzUH3/8oYCAAJlzdx9Q//79rXUgFTyQ3KxZM82fP9/asPMmk91tdx8Uk6RPP/1UmZmZks4dWD5w4ECB5Thx4oQWLFjgMb/3339fx48ft9rq5n4fC5O/bF75D66eT/7tSTp3QKqwA9aFJdOdTqeysrKKrN/hcBR6gD7/Z6hOnTrnbWdRiXx3PXnfh+I4X/IAV5aSPOEEQMVzuSd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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define the number of columns for subplots\n",
        "num_columns = 2\n",
        "num_rows = int(np.ceil(len(cat) / num_columns))\n",
        "\n",
        "# Create a figure and axis for subplots\n",
        "fig, axes = plt.subplots(num_rows, num_columns, figsize=(20, 10))\n",
        "\n",
        "# Flatten the 2D axes array to simplify indexing\n",
        "axes = axes.ravel()\n",
        "\n",
        "# Loop through the features and create histograms\n",
        "for i, feature in enumerate(cat):\n",
        "    ax = axes[i]\n",
        "    sns.countplot(data=df, x=feature, ax=ax)\n",
        "    ax.set_title(f'{feature}')\n",
        "    ax.set_xlabel('')\n",
        "\n",
        "# Remove any empty subplots\n",
        "for i in range(len(cat), num_rows * num_columns):\n",
        "    fig.delaxes(axes[i])\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhkXZfoMSmNT"
      },
      "source": [
        "Approach for missing values imputation\n",
        "\n",
        "Everything is fine because we got 0.00% for our missing value\n",
        "Create Backup Data for impute note in case we want to go back later for correction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Fs5hnM8gVwPO"
      },
      "outputs": [],
      "source": [
        "#create a backup first before running the code to retrieve back later the original one\n",
        "df_backup = df.copy()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "xvtCfU5VYBiI"
      },
      "outputs": [],
      "source": [
        "df = df_backup.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK5w873CZPp0",
        "outputId": "b29b237c-2aca-438b-a8ed-b037213f47ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 95426 entries, 0 to 95425\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype         \n",
            "---  ------            --------------  -----         \n",
            " 0   car_brand         95426 non-null  object        \n",
            " 1   car_model         95426 non-null  object        \n",
            " 2   car_variant       95426 non-null  object        \n",
            " 3   car_year          95426 non-null  int64         \n",
            " 4   car_engine        95426 non-null  float64       \n",
            " 5   car_transmission  95426 non-null  object        \n",
            " 6   milage            95426 non-null  int64         \n",
            " 7   accident          95426 non-null  int64         \n",
            " 8   flood             95426 non-null  int64         \n",
            " 9   color             95426 non-null  object        \n",
            " 10  purchase_date     95426 non-null  datetime64[ns]\n",
            " 11  sales_date        95426 non-null  datetime64[ns]\n",
            " 12  price             95426 non-null  int64         \n",
            "dtypes: datetime64[ns](2), float64(1), int64(5), object(5)\n",
            "memory usage: 9.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxWtExfacMP1"
      },
      "source": [
        "###Check Duplicates\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "d-4KtfZVceXR",
        "outputId": "b611e296-ba1d-46d8-9cd2-2ba140c9092f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "car_brand           0\n",
              "car_model           0\n",
              "car_variant         0\n",
              "car_year            0\n",
              "car_engine          0\n",
              "car_transmission    0\n",
              "milage              0\n",
              "accident            0\n",
              "flood               0\n",
              "color               0\n",
              "purchase_date       0\n",
              "sales_date          0\n",
              "price               0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>car_brand</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_variant</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_engine</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_transmission</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>milage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accident</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flood</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purchase_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sales_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df[df.duplicated()].count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMAgxwr_dluq"
      },
      "source": [
        "###Check Inconsistencies\n",
        "\n",
        "check logic or not whether per say age 1000 years old ,not happen here (check min and max)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dcotzBPpdrJl",
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                 count                           mean                  min  \\\n",
              "car_year       95426.0                    2011.374154               1981.0   \n",
              "car_engine     95426.0                        1.66021                  0.6   \n",
              "milage         95426.0                  147804.267128                  1.0   \n",
              "accident       95426.0                       0.396475                  0.0   \n",
              "flood          95426.0                       0.001027                  0.0   \n",
              "purchase_date    95426  2020-04-07 00:00:10.864963072  2019-01-04 00:00:00   \n",
              "sales_date       95426  2020-06-16 15:42:30.323811072  2019-01-07 00:00:00   \n",
              "price          95426.0                   32200.764991                400.0   \n",
              "\n",
              "                               25%                  50%                  75%  \\\n",
              "car_year                    2009.0               2012.0               2014.0   \n",
              "car_engine                     1.4                  1.6                  2.0   \n",
              "milage                     88359.5             133479.0            191572.75   \n",
              "accident                       0.0                  0.0                  1.0   \n",
              "flood                          0.0                  0.0                  0.0   \n",
              "purchase_date  2019-11-20 00:00:00  2020-05-18 00:00:00  2020-09-04 00:00:00   \n",
              "sales_date     2020-01-28 00:00:00  2020-07-23 00:00:00  2020-11-09 00:00:00   \n",
              "price                      12600.0              23700.0              41500.0   \n",
              "\n",
              "                               max           std  \n",
              "car_year                    2020.0      4.256181  \n",
              "car_engine                     8.0      0.428968  \n",
              "milage                    992910.0  85450.084755  \n",
              "accident                       1.0      0.489168  \n",
              "flood                          1.0       0.03203  \n",
              "purchase_date  2021-12-01 00:00:00           NaN  \n",
              "sales_date     2021-12-01 00:00:00           NaN  \n",
              "price                    1000800.0  29758.710619  "
            ],
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              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>min</th>\n",
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              "      <th>car_year</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>2011.374154</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>4.256181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car_engine</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>1.66021</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.428968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>milage</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>147804.267128</td>\n",
              "      <td>1.0</td>\n",
              "      <td>88359.5</td>\n",
              "      <td>133479.0</td>\n",
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              "      <td>992910.0</td>\n",
              "      <td>85450.084755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accident</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>0.396475</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.489168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flood</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>0.001027</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.03203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purchase_date</th>\n",
              "      <td>95426</td>\n",
              "      <td>2020-04-07 00:00:10.864963072</td>\n",
              "      <td>2019-01-04 00:00:00</td>\n",
              "      <td>2019-11-20 00:00:00</td>\n",
              "      <td>2020-05-18 00:00:00</td>\n",
              "      <td>2020-09-04 00:00:00</td>\n",
              "      <td>2021-12-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sales_date</th>\n",
              "      <td>95426</td>\n",
              "      <td>2020-06-16 15:42:30.323811072</td>\n",
              "      <td>2019-01-07 00:00:00</td>\n",
              "      <td>2020-01-28 00:00:00</td>\n",
              "      <td>2020-07-23 00:00:00</td>\n",
              "      <td>2020-11-09 00:00:00</td>\n",
              "      <td>2021-12-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>95426.0</td>\n",
              "      <td>32200.764991</td>\n",
              "      <td>400.0</td>\n",
              "      <td>12600.0</td>\n",
              "      <td>23700.0</td>\n",
              "      <td>41500.0</td>\n",
              "      <td>1000800.0</td>\n",
              "      <td>29758.710619</td>\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 95426.0,\n        \"max\": 95426.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          95426.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2020-06-16 15:42:30.323811072\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.6602100056588351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2019-01-07 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1981.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2020-01-28 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2009.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2020-07-23 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2012.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2020-11-09 00:00:00\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000001\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2020.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.032030139816054196,\n        \"max\": 85450.08475523326,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.256181022611437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmurhPAveO-w"
      },
      "source": [
        "It seems that our data all has consistent values\n",
        "\n",
        "Now ,our dataset already cleaned"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Okf5Ecz2eePo"
      },
      "source": [
        "##3.2 Data Formatting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Kf0X4wzIecZQ",
        "outputId": "0e90bda4-591a-4736-ce03-7d1872f6b84a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           car_brand  \\\n",
              "0  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "1  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "2  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "3  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "4  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "\n",
              "                                           car_model  \\\n",
              "0  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "1  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "2  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "3  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "4  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "\n",
              "                                         car_variant  car_year  car_engine  \\\n",
              "0  b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...      2010         2.0   \n",
              "1  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010         1.8   \n",
              "2  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010         1.8   \n",
              "3  bc8c75c6d926e32a4dd2119e279211bae53ad4024822c4...      2010         2.0   \n",
              "4  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2013         1.8   \n",
              "\n",
              "  car_transmission  milage  accident  flood  color purchase_date sales_date  \\\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 95426,\n  \"fields\": [\n    {\n      \"column\": \"car_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"d222a18d6474d0819d8ee5da0c2c524f31ff0903a4e6abc3f7957f84f1ee3f0f\",\n          \"bcd40d58afd29be4e0af2dfb8c7582db545efe0f717b9fed903ebd2363e6b898\",\n          \"469dfc0b18c73eb263c4a1476545e7b064a7f862ef53827c22026c97268b2ebf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee\",\n          \"16188709fb9e80689a08d1dad58c0d52073628fc52ff213a6c31a77dd59d071f\",\n          \"47a0da3ea9dab0799ae8d9a49f7b234fff9de364e8b0851b556193fa279a2fc0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_variant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          \"ef8a03eb87802e242ecf9553eef9155d2c8364dd45492fc544a256f3f7429600\",\n          \"f52d63231eb0dbaac689ec9147e4b131fc95ef92d5bc78135b22934b36d76bf4\",\n          \"284fe8d8db088b0b28a9950444d4966576ee270688f08d9ce5c5c79c56705f14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1981,\n        \"max\": 2020,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1994,\n          2004,\n          1998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_engine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4289680593337917,\n        \"min\": 0.6,\n        \"max\": 8.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          3.6,\n          3.2,\n          4.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Manual\",\n          \"Auto\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85450,\n        \"min\": 1,\n        \"max\": 992910,\n        \"num_unique_values\": 80005,\n        \"samples\": [\n          170422,\n          85157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"blue\",\n          \"black\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purchase_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-04 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 704,\n        \"samples\": [\n          \"2020-04-01 00:00:00\",\n          \"2020-07-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-07 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 495,\n        \"samples\": [\n          \"2019-09-30 00:00:00\",\n          \"2020-04-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29758,\n        \"min\": 400,\n        \"max\": 1000800,\n        \"num_unique_values\": 1991,\n        \"samples\": [\n          69800,\n          165300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RA_KZN5RNERI",
        "outputId": "c903f34f-201d-4496-af3e-a2aa0ea99f6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in column 'car_brand': ['b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b37ce3d00010b09af252'\n",
            " '27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421ca40f1de6cfd4cbb06'\n",
            " 'c1ddb780f9790c61ff6e942d11bf621fce0863216f84349a02a2f46f46485e74'\n",
            " '9e48bad6e31c128258257bd997eacca666f0dae2049027b545ea48a0cf43ed71'\n",
            " '1c319d594acddfd6e07e776239d8af8082c7ec3dd11056e4ffce37991b2f9407'\n",
            " 'b2808c947ea04c193e096f4923d13599ab96317dee1e33c8705bfc863f354903'\n",
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            " 'bccae4ce7be65100a8472740c829b76610a653a6816a7b88c7f7def8ae8e9b0c']\n",
            "Unique values in column 'car_model': ['4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb81c8ba27361ad476d6b'\n",
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            " '472c45416dfbf344d12fc1e4d36b8e4cc4c5d742d8908cff726e13451dc3694a'\n",
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            " '5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee'\n",
            " 'f67b179d9ed401be108683683fe20139bcc3b3cc659ad3b069af3608f77cd752'\n",
            " 'e5e53c784d5d49de1cabb6e904bf3380026aadcb9769775a268dd304dd9aa2df'\n",
            " '968076be2e38cf897d4d6cea3faca9c037e1a4e3b4b7744fb2533e07751bd30a'\n",
            " 'ae1352739c5a08d4fdc55ed5307492f16b4f2b4f20d518ce97d2ebfcdcd4888f'\n",
            " '936fccfc8fe9a540965b08e35f46a4a5cc81dec92ac1b1dedfcb4810d017ffaf'\n",
            " 'eb7a4aab93b1d94b090d3a890116064e0a617f504d8acc7d2d58dcc82bbc550d'\n",
            " '3adda1a6412201ac40ff6a13b2af60f855938088b9a129dd694116f5cf5b86ce'\n",
            " '770d5230659f441fdf367769b7470a466237768cb850c2767c3507566cea7d10'\n",
            " '2b6720f0a0fafec88b7da78d2409e5595e7cc5b00b4db8b4058ab1566925aa78'\n",
            " '44554623ac5712766fb912b1a761a9dc75dbac4726f41e8a0d13a5f7f4dfabcc'\n",
            " '55dac4b0b4be82f0219616b369b848675116ea7edd6c53390d9d1e341f99ff7e'\n",
            " '93a9ff2444929393be0ca9974be75f69c6421af604ccb1124ec49557316b88ac'\n",
            " '535724adfbc63b05cfcdfadafff4448ad5e26bdab278a9413011a039f15e5049'\n",
            " 'e5dd4aa5ea21461846a3b45909525480a3dc4e7f399754b86ba9a9f8ac876a38'\n",
            " '994b13a056ee0664499646c0f520aa0c76b25a8bfcb34dca8e78d3c0b8b09155'\n",
            " '490ddef24b9665eac31a08954fc1771baf772b2235b2fc3ca0fa646fc84b113d'\n",
            " '175d374a1e9f8545f79f430abdd122afec28d8e81c008cd2a86d201e38382ff6'\n",
            " 'ef7794d43813f925613cc5ad4bebe37127b84d1d2708e1d377068b8f5481bca7'\n",
            " 'a33603bf79f74b056172d43b78358a9d6a51ae59d2720741fbb33ce78e3ad607'\n",
            " '24aa915e58ac08ad1a13d57414374439efcf900f4c40c47effe0e1add7011644'\n",
            " '6de0dded876c2aaac9cd59cfaf8444b36ec8cf832cdaf6008fbeea809f7c9a7c'\n",
            " '81859055b06562b47a36ca6418c2ecd3e9c0e3ea22b2f2a80bde1b7d7a4665b0'\n",
            " '8c58c9e154bb46b9116767ca4cb476fc6188e16e990673053c29867a0c60d729'\n",
            " '35bab8adf7600be12b5f9667d5bbcff3497dd92b79f7fb443a32fd9d4f324689'\n",
            " '3ae83660f67600220d1793cab11212571c61529952f4fccc4e1028272ece4d98'\n",
            " 'a323a2ca0a8ef6ca40667093afb3d26d45d00fe2a2750c5ddc7b0f4dd413b021'\n",
            " '0caf4b03f45594db97b39928aed56f0197a8121ad52971238b63c66575f1e7ae'\n",
            " '67814213b688c144f85ac79d62e499f1fcb29de51db696dc9f6292beca48462d'\n",
            " '5d38241db05d96dc56774e678c304ab3fd39641e68684b5c323b3673e19df8f5'\n",
            " 'c53f49425e9e6743211e3168607801b7aa23a2d15121ea9e4f6da984a097b9cf'\n",
            " '8d3b9db31d6ccf7c1b21fefe8decc3cfa57b25eb3019048d97d2279dd89bba59'\n",
            " '90b3783af039308f9b77056beedb076087b77704ff7ca030ca4aa424338998d3'\n",
            " 'f8dea8da9e08876c6078a772d5e93ec308d2a0dc6a6e81eaf4a339d99476d6b0'\n",
            " 'd0fc226cbf7d132300a8b822ab7350f4d6b205d3c294e3f71217c06e5db437fa'\n",
            " '74bf165dd4258809ecdafda007b91521d5d0082bf3b369f470f783b3de03e390'\n",
            " '5bc52e4c7ea7082c1e22869880d3bf4eda10582da54ea42261df5ef45e9ddf31'\n",
            " '83d02340f51606385f49880ae87d293c193b110ab262e0a5d726531fc904d6f6'\n",
            " '2d1fd0086934aca9129323fd90ba6b15f8bf49189888307a3a0a743e9211d084'\n",
            " 'bed6b6065b4d7611e032039d5f74892403413900fc7b3e3af6dbdb7fcc4ab7f9'\n",
            " '6910d47b0baf576f8ca30ed167fb06c6f5581aed3b7865e08aea270c5a02a729'\n",
            " 'c435de4d8c1745e600a45c17254162c4d14d5add64a7f00a560d620d7c7cf5da'\n",
            " 'a344e996d98aa7ad4aa36338a6cfc1d6e38d0e2467b3ba5becca99e39620e6f7'\n",
            " '091e606752615c91da70540d9951464cd898f7c2a420fc7181b4f6793772e723'\n",
            " 'fd109ddf273f07f7fee33ef0733625c056e6a557a25a2adb9d70567f9a5e4004'\n",
            " '7b3e7cdfdbca8bb5463ebc638eed83c93111f98f7464cbddd66a012630a88f29'\n",
            " '625fe74cad4600b5e8b76a9283333eb79052ae50d6af7f660feb4831d87af5d2'\n",
            " '367728b04ad7c2ff9151c2ea9eecd94d2aa37486eaaa62f5167e0e2cb120439f'\n",
            " 'ad761731c8810ecd3742bce4df193beb40c145f939fc77a61bc707e23b272158'\n",
            " '42ce1e72d529cfe94e9ee314ff1512cd57a6fc0a8fa80d8c57b68a3ed2aa047f'\n",
            " '1317dfa6a0c51245a1fbd37c6de9819ac469d2e5f71f70a42eec6c6181a30fa7'\n",
            " '1a2237466d3cfb499304bb3a9175ea92631be4c18c8459ad4018210ce273aa32'\n",
            " '0b9c8acadd9f05997432738a1e38fa9561a0e781c39aba14fac7adee104559b1'\n",
            " 'd091f2afe988afd4cea4181b84eaaf780749aed70cbca513c7f900a4fb986b99'\n",
            " 'b165d7267598f60c158d37bfd6ab06efc45f6f56a5f26625735ff3d9fc6a2812'\n",
            " 'd5209575bb9d933277bb7a3fa1ce14e91488ff2dc9ef9c26e0aa86c7e3409d90'\n",
            " '7f99454e9601b52cd503486b508cb1727c9b1284cee9b98601a7ae30453cdd1e'\n",
            " '2e9363371b90161308fac281c186f6c8e2fb7519128524ebb9dcc918ffde374b'\n",
            " 'b2b2f3cb7412d108972add0deb7ae152876c10195833123ca11ee81f4a66b28c'\n",
            " 'ea885fec36d91612f54bb17555072967002381045481ef729f9e31741b4f3720'\n",
            " '355b1bbfc96725cdce8f4a2708fda310a80e6d13315aec4e5eed2a75fe8032ce'\n",
            " '861827ebacf9b14665c81ba76aff3cd02da17a52c55118ca112821770b56279d'\n",
            " '47a0da3ea9dab0799ae8d9a49f7b234fff9de364e8b0851b556193fa279a2fc0'\n",
            " '761a9b787871f922d2f9deb8330a109b3b9d3dc75fc83b2ae42acd10d925678a'\n",
            " '0d7851166d237c9c086fe95bd43d67d92f91a2d4151fa6130cb7f2e7bb8196f4']\n",
            "Unique values in column 'car_variant': ['b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f81e900008da4bc54130'\n",
            " '1dff83b33d877727a06f16e2d53e96834dccb80af01f8e876312f29e4713cfde'\n",
            " 'bc8c75c6d926e32a4dd2119e279211bae53ad4024822c40817376595c43d6190' ...\n",
            " 'a8f2b27c11c246651829c36769de44946b0fb8f73759fdeda5405278d627dc09'\n",
            " 'e8f23d0baf35c38bf34e4f0b08c7832d92109cc2bc7ae4dae843f5aef56a2c97'\n",
            " 'e27f4db69302584a96a26911f4af64122d9bd6cbec70421f23fd606275162a73']\n",
            "Unique values in column 'car_year': [2010 2013 2014 2015 2012 2016 2003 2007 2005 2008 2002 2006 2000 2004\n",
            " 2009 2011 2017 1996 2020 2001 2019 2018 1997 1999 1998 1992 1994 1993\n",
            " 1995 1991 1990 1988 1989 1981 1986]\n",
            "Unique values in column 'car_engine': [2.  1.8 1.4 1.6 1.5 2.5 3.  2.2 2.8 2.4 1.9 2.3 1.  3.2 1.7 1.3 1.1 1.2\n",
            " 3.5 3.7 2.6 2.1 2.7 0.6 0.8 0.7 4.2 4.8 3.4 4.7 3.8 4.  8.  2.9 4.6 3.6]\n",
            "Unique values in column 'car_transmission': ['Auto' 'Manual']\n",
            "Unique values in column 'milage': [282508 169475 105276 ...  61907  45704  37815]\n",
            "Unique values in column 'accident': [1 0]\n",
            "Unique values in column 'flood': [0 1]\n",
            "Unique values in column 'color': ['blue' 'black' 'white' 'brown' 'grey' 'red' 'silver' 'others' 'purple'\n",
            " 'gold' 'green' 'orange' 'beige' 'yellow' 'maroon' 'pink']\n",
            "Unique values in column 'purchase_date': <DatetimeArray>\n",
            "['2020-08-22 00:00:00', '2020-04-09 00:00:00', '2020-05-17 00:00:00',\n",
            " '2020-01-16 00:00:00', '2020-10-20 00:00:00', '2020-06-15 00:00:00',\n",
            " '2019-09-27 00:00:00', '2019-01-12 00:00:00', '2020-04-05 00:00:00',\n",
            " '2020-09-25 00:00:00',\n",
            " ...\n",
            " '2019-05-03 00:00:00', '2019-03-22 00:00:00', '2019-03-13 00:00:00',\n",
            " '2019-10-03 00:00:00', '2019-06-03 00:00:00', '2019-09-03 00:00:00',\n",
            " '2021-03-02 00:00:00', '2019-12-03 00:00:00', '2021-07-02 00:00:00',\n",
            " '2021-02-02 00:00:00']\n",
            "Length: 704, dtype: datetime64[ns]\n",
            "Unique values in column 'sales_date': <DatetimeArray>\n",
            "['2020-02-10 00:00:00', '2020-09-12 00:00:00', '2020-08-25 00:00:00',\n",
            " '2020-08-05 00:00:00', '2020-07-12 00:00:00', '2020-09-14 00:00:00',\n",
            " '2019-10-24 00:00:00', '2020-05-03 00:00:00', '2020-08-07 00:00:00',\n",
            " '2020-04-11 00:00:00',\n",
            " ...\n",
            " '2021-07-02 00:00:00', '2019-04-07 00:00:00', '2020-05-17 00:00:00',\n",
            " '2019-11-18 00:00:00', '2020-05-18 00:00:00', '2019-07-22 00:00:00',\n",
            " '2020-01-20 00:00:00', '2019-07-26 00:00:00', '2019-11-23 00:00:00',\n",
            " '2019-06-21 00:00:00']\n",
            "Length: 495, dtype: datetime64[ns]\n",
            "Unique values in column 'price': [ 30100  32100  34100 ... 188800 242100 267000]\n"
          ]
        }
      ],
      "source": [
        "for column in df.columns:\n",
        "    unique_values = df[column].unique()\n",
        "    print(f\"Unique values in column '{column}': {unique_values}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqOy8Du-hCnx"
      },
      "source": [
        "### Features to be Formatted\n",
        "\n",
        "To improve the model's performance and make the data easier for the machine to process, we need to format certain features. The following categorical features will be transformed:\n",
        "\n",
        "- `car_brand`\n",
        "- `car_model`\n",
        "- `car_variant`\n",
        "- `car_transmission`\n",
        "- `color`\n",
        "\n",
        "### Categorical Feature Transformation\n",
        "\n",
        "#### Encoding Approaches:\n",
        "1. **One-Hot Encoding**:\n",
        "   - **Use Case**: Apply when there is no inherent ranking between categories. This will create separate binary columns for each category.\n",
        "   - **Examples**: `car_brand`, `car_model`, `car_variant`, `color`\n",
        "\n",
        "2. **Label Encoding**:\n",
        "   - **Use Case**: Apply when there is a ranking or order between the categories.\n",
        "   - **Examples**: This approach is not typically used for the features in our dataset, as all categorical features do not have a natural ranking.\n",
        "\n",
        "### Footnote\n",
        "- **Dictionary**: A data structure to map categorical values to numerical representations.\n",
        "- **List**: A sequence of categorical values that need to be encoded.\n",
        "\n",
        "### Example Encoding:\n",
        "For one-hot encoding, each unique value in the categorical features will be transformed into a new binary column. For instance:\n",
        "\n",
        "- `car_transmission`: This will create two binary columns: `car_transmission_Auto` and `car_transmission_Manual`.\n",
        "- `color`: Will result in binary columns for each color, such as `color_Blue`, `color_Black`, `color_White`, etc.\n",
        "\n",
        "The encoding process helps convert categorical data into a format suitable for machine learning models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "tIu_acCGN9uI"
      },
      "outputs": [],
      "source": [
        "# # List of categorical features to encode using One-Hot Encoding\n",
        "# categorical_features = ['car_model', 'car_variant', 'car_transmission', 'color']\n",
        "\n",
        "# # Apply One-Hot Encoding with numerical 1/0 instead of True/False\n",
        "# df_encoded = pd.get_dummies(df, columns=categorical_features, drop_first=True, dtype=int)\n",
        "\n",
        "# # Get the list of one-hot encoded columns\n",
        "# one_hot_encoded_cols = [col for col in df_encoded.columns if col.startswith('car_transmission_') or col.startswith('color_')]\n",
        "\n",
        "# # Since 'car_brand' was removed, we won't place it after one-hot encoded columns\n",
        "# columns_order = (\n",
        "#     [col for col in df_encoded.columns if col not in one_hot_encoded_cols] +\n",
        "#     one_hot_encoded_cols\n",
        "# )\n",
        "\n",
        "# # Reorder the DataFrame\n",
        "# df_encoded = df_encoded[columns_order]\n",
        "\n",
        "# # Verify the transformation\n",
        "# print(df_encoded.head())\n",
        "\n",
        "# # Check for any missing values before proceeding with model training\n",
        "# missing_values = df_encoded.isnull().sum()\n",
        "# print(f'Missing values:\\n{missing_values[missing_values > 0]}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-t4bJ1jFOK7t"
      },
      "outputs": [],
      "source": [
        "# # Count the number of rows before dropping missing values\n",
        "# count_before_dropna = df_encoded.shape[0]\n",
        "\n",
        "# # Drop rows with missing values\n",
        "# df_cleaned = df_encoded.dropna()\n",
        "\n",
        "# # Count the number of rows after dropping missing values\n",
        "# count_after_dropna = df_cleaned.shape[0]\n",
        "\n",
        "# # Print the counts\n",
        "# print(f\"Count before dropna: {count_before_dropna}\")\n",
        "# print(f\"Count after dropna: {count_after_dropna}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "Hq17PZQ4OMdo",
        "outputId": "b6beabca-34b0-463d-86f9-0ac3997b8b99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               car_brand  \\\n",
              "658    27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421...   \n",
              "34583  469dfc0b18c73eb263c4a1476545e7b064a7f862ef5382...   \n",
              "65321  d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d...   \n",
              "90645  337b8d2c1e132acd75171f1acf0e73b20bc9541720d500...   \n",
              "16597  8417659f46b5b43b2bb9396f794ab19728546f699babdb...   \n",
              "44841  8417659f46b5b43b2bb9396f794ab19728546f699babdb...   \n",
              "482    27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421...   \n",
              "47183  8417659f46b5b43b2bb9396f794ab19728546f699babdb...   \n",
              "53016  eaac0f48d2ccec4d9cfca2115cb16800ecd23f60265f0c...   \n",
              "60999  d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d...   \n",
              "\n",
              "                                               car_model  \\\n",
              "658    4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...   \n",
              "34583  5e815286bca594454b291f3b0350ec22aab6de20b6d9ef...   \n",
              "65321  528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8...   \n",
              "90645  c435de4d8c1745e600a45c17254162c4d14d5add64a7f0...   \n",
              "16597  f964b9b45e177f9a3bb1b8d815e214416c309c1b597cdf...   \n",
              "44841  e43ba08557cbb4f19ed7684fe3cdbf5426fa2c145b8bac...   \n",
              "482    4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...   \n",
              "47183  f964b9b45e177f9a3bb1b8d815e214416c309c1b597cdf...   \n",
              "53016  9df92a09c9386e8748c964d0dbf6cc9bea395d04559d50...   \n",
              "60999  528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8...   \n",
              "\n",
              "                                             car_variant  car_year  \\\n",
              "658    97b16f57c20c4d60f8872fc09e27bed3e375098ba102e6...      2017   \n",
              "34583  fc8fcfa104ef1f5484aadb46662a0b3eb9ad26f0ada96e...      2010   \n",
              "65321  cd0aa9856147b6c5b4ff2b7dfee5da20aa38253099ef1b...      2016   \n",
              "90645  043a718774c572bd8a25adbeb1bfcd5c0256ae11cecf9f...      2015   \n",
              "16597  b9bb63d4eabf20d5eb50432f32fa12a844e7826e71f8ff...      2008   \n",
              "44841  26a85a68ec4b590c5924e834819eaabf6eab56157e45a0...      2012   \n",
              "482    58f93c78bc73bb8eb5f8ae69aa01148d917104f1688562...      2014   \n",
              "47183  b9bb63d4eabf20d5eb50432f32fa12a844e7826e71f8ff...      2010   \n",
              "53016  06ea4237dd61baa02f047d6703c6334930d26e9d2f547a...      2014   \n",
              "60999  aaae255bfc0f64c3d7a239694bb452ce6e001adfdc4f83...      2014   \n",
              "\n",
              "       car_engine car_transmission  milage  accident  flood   color  \\\n",
              "658           1.5             Auto   74497         0      0    grey   \n",
              "34583         1.6             Auto  158031         0      0   brown   \n",
              "65321         1.3             Auto   72683         1      0   white   \n",
              "90645         1.5             Auto   52279         0      0   white   \n",
              "16597         2.0             Auto  187141         0      0    grey   \n",
              "44841         1.3             Auto   56527         0      0   white   \n",
              "482           2.0             Auto  128758         0      0   white   \n",
              "47183         2.0             Auto  154196         0      0   white   \n",
              "53016         2.0             Auto   47071         0      0  silver   \n",
              "60999         1.3             Auto  144433         1      0  silver   \n",
              "\n",
              "      purchase_date sales_date   price  \n",
              "658      2019-10-10 2019-11-29   97300  \n",
              "34583    2020-08-06 2020-01-09    9900  \n",
              "65321    2019-09-13 2019-11-25   22100  \n",
              "90645    2020-12-09 2020-12-28   48500  \n",
              "16597    2019-04-11 2020-07-01   24000  \n",
              "44841    2019-08-24 2019-11-11   23700  \n",
              "482      2019-10-25 2019-10-12   83500  \n",
              "47183    2020-02-18 2020-05-16   32900  \n",
              "53016    2019-11-15 2020-01-22  154200  \n",
              "60999    2020-10-06 2020-08-08   22000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1020342-b57a-40c0-85a1-8b485df83cfd\" 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>car_brand</th>\n",
              "      <th>car_model</th>\n",
              "      <th>car_variant</th>\n",
              "      <th>car_year</th>\n",
              "      <th>car_engine</th>\n",
              "      <th>car_transmission</th>\n",
              "      <th>milage</th>\n",
              "      <th>accident</th>\n",
              "      <th>flood</th>\n",
              "      <th>color</th>\n",
              "      <th>purchase_date</th>\n",
              "      <th>sales_date</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>658</th>\n",
              "      <td>27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421...</td>\n",
              "      <td>4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...</td>\n",
              "      <td>97b16f57c20c4d60f8872fc09e27bed3e375098ba102e6...</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Auto</td>\n",
              "      <td>74497</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>grey</td>\n",
              "      <td>2019-10-10</td>\n",
              "      <td>2019-11-29</td>\n",
              "      <td>97300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34583</th>\n",
              "      <td>469dfc0b18c73eb263c4a1476545e7b064a7f862ef5382...</td>\n",
              "      <td>5e815286bca594454b291f3b0350ec22aab6de20b6d9ef...</td>\n",
              "      <td>fc8fcfa104ef1f5484aadb46662a0b3eb9ad26f0ada96e...</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Auto</td>\n",
              "      <td>158031</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>brown</td>\n",
              "      <td>2020-08-06</td>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>9900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65321</th>\n",
              "      <td>d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d...</td>\n",
              "      <td>528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8...</td>\n",
              "      <td>cd0aa9856147b6c5b4ff2b7dfee5da20aa38253099ef1b...</td>\n",
              "      <td>2016</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Auto</td>\n",
              "      <td>72683</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>2019-11-25</td>\n",
              "      <td>22100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90645</th>\n",
              "      <td>337b8d2c1e132acd75171f1acf0e73b20bc9541720d500...</td>\n",
              "      <td>c435de4d8c1745e600a45c17254162c4d14d5add64a7f0...</td>\n",
              "      <td>043a718774c572bd8a25adbeb1bfcd5c0256ae11cecf9f...</td>\n",
              "      <td>2015</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Auto</td>\n",
              "      <td>52279</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2020-12-09</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>48500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16597</th>\n",
              "      <td>8417659f46b5b43b2bb9396f794ab19728546f699babdb...</td>\n",
              "      <td>f964b9b45e177f9a3bb1b8d815e214416c309c1b597cdf...</td>\n",
              "      <td>b9bb63d4eabf20d5eb50432f32fa12a844e7826e71f8ff...</td>\n",
              "      <td>2008</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Auto</td>\n",
              "      <td>187141</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>grey</td>\n",
              "      <td>2019-04-11</td>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>24000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44841</th>\n",
              "      <td>8417659f46b5b43b2bb9396f794ab19728546f699babdb...</td>\n",
              "      <td>e43ba08557cbb4f19ed7684fe3cdbf5426fa2c145b8bac...</td>\n",
              "      <td>26a85a68ec4b590c5924e834819eaabf6eab56157e45a0...</td>\n",
              "      <td>2012</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Auto</td>\n",
              "      <td>56527</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2019-08-24</td>\n",
              "      <td>2019-11-11</td>\n",
              "      <td>23700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>482</th>\n",
              "      <td>27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421...</td>\n",
              "      <td>4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...</td>\n",
              "      <td>58f93c78bc73bb8eb5f8ae69aa01148d917104f1688562...</td>\n",
              "      <td>2014</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Auto</td>\n",
              "      <td>128758</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>2019-10-12</td>\n",
              "      <td>83500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47183</th>\n",
              "      <td>8417659f46b5b43b2bb9396f794ab19728546f699babdb...</td>\n",
              "      <td>f964b9b45e177f9a3bb1b8d815e214416c309c1b597cdf...</td>\n",
              "      <td>b9bb63d4eabf20d5eb50432f32fa12a844e7826e71f8ff...</td>\n",
              "      <td>2010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Auto</td>\n",
              "      <td>154196</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2020-02-18</td>\n",
              "      <td>2020-05-16</td>\n",
              "      <td>32900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53016</th>\n",
              "      <td>eaac0f48d2ccec4d9cfca2115cb16800ecd23f60265f0c...</td>\n",
              "      <td>9df92a09c9386e8748c964d0dbf6cc9bea395d04559d50...</td>\n",
              "      <td>06ea4237dd61baa02f047d6703c6334930d26e9d2f547a...</td>\n",
              "      <td>2014</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Auto</td>\n",
              "      <td>47071</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>silver</td>\n",
              "      <td>2019-11-15</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>154200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60999</th>\n",
              "      <td>d37b8da02160dc847057988f20d49b8140f0b08a0e7a9d...</td>\n",
              "      <td>528d74b0fd396b7d0f9869d2d5dc642662abc6aea4b8a8...</td>\n",
              "      <td>aaae255bfc0f64c3d7a239694bb452ce6e001adfdc4f83...</td>\n",
              "      <td>2014</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Auto</td>\n",
              "      <td>144433</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>silver</td>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-08-08</td>\n",
              "      <td>22000</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-a1020342-b57a-40c0-85a1-8b485df83cfd')\"\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",
              "      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-a1020342-b57a-40c0-85a1-8b485df83cfd 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-a1020342-b57a-40c0-85a1-8b485df83cfd');\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-fe23f8fd-cdf7-40b1-a633-36e1a053d75e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe23f8fd-cdf7-40b1-a633-36e1a053d75e')\"\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-fe23f8fd-cdf7-40b1-a633-36e1a053d75e 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"car_brand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"27df9ed9a477af0fcfe369c8ef3474a75cebf357d8b421ca40f1de6cfd4cbb06\",\n          \"469dfc0b18c73eb263c4a1476545e7b064a7f862ef53827c22026c97268b2ebf\",\n          \"eaac0f48d2ccec4d9cfca2115cb16800ecd23f60265f0ca0995ef41b8c55ac60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"4e07408562bedb8b60ce05c1decfe3ad16b72230967de01f640b7e4729b49fce\",\n          \"5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee\",\n          \"e43ba08557cbb4f19ed7684fe3cdbf5426fa2c145b8baced5e4fcec410c4e46c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_variant\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"06ea4237dd61baa02f047d6703c6334930d26e9d2f547a268852089befa4ec13\",\n          \"fc8fcfa104ef1f5484aadb46662a0b3eb9ad26f0ada96ee07a4a6379a8fc88ed\",\n          \"26a85a68ec4b590c5924e834819eaabf6eab56157e45a01b9163e49d94cbdf97\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2008,\n        \"max\": 2017,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2010,\n          2012\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_engine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3171049598406741,\n        \"min\": 1.3,\n        \"max\": 2.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.6,\n          2.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Auto\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52167,\n        \"min\": 47071,\n        \"max\": 187141,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          47071\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"brown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purchase_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-04-11 00:00:00\",\n        \"max\": \"2020-12-09 00:00:00\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2019-11-15 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-10-12 00:00:00\",\n        \"max\": \"2020-12-28 00:00:00\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2020-01-22 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45967,\n        \"min\": 9900,\n        \"max\": 154200,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          154200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df.sample(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzgpeSq_jRAK"
      },
      "source": [
        "### Features Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "z94hc3NMV6WU",
        "outputId": "4a0bb093-6743-4a9e-eb41-4baf8956d545"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               car_brand  \\\n",
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              "...                                                  ...   \n",
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              "95425  bccae4ce7be65100a8472740c829b76610a653a6816a7b...   \n",
              "\n",
              "                                               car_model  \\\n",
              "0      4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
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              "...                                                  ...   \n",
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              "95425  2e9363371b90161308fac281c186f6c8e2fb7519128524...   \n",
              "\n",
              "                                             car_variant  car_year  \\\n",
              "0      b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...      2010   \n",
              "1      1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010   \n",
              "2      1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010   \n",
              "3      bc8c75c6d926e32a4dd2119e279211bae53ad4024822c4...      2010   \n",
              "4      1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2013   \n",
              "...                                                  ...       ...   \n",
              "95421  d5fa38a1f8a14002509297c163336a28806979e6195592...      2016   \n",
              "95422  e27f4db69302584a96a26911f4af64122d9bd6cbec7042...      2016   \n",
              "95423  d5fa38a1f8a14002509297c163336a28806979e6195592...      2016   \n",
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              "95425  d5fa38a1f8a14002509297c163336a28806979e6195592...      2018   \n",
              "\n",
              "       car_engine  car_transmission  milage  accident  flood  color  \\\n",
              "0             2.0                 1  282508         1      0   blue   \n",
              "1             1.8                 1  169475         1      0  black   \n",
              "2             1.8                 1  105276         0      0  white   \n",
              "3             2.0                 1   81123         0      0  black   \n",
              "4             1.8                 1  157239         1      0  brown   \n",
              "...           ...               ...     ...       ...    ...    ...   \n",
              "95421         2.0                 1  100163         0      0  black   \n",
              "95422         2.0                 1   28115         0      0  white   \n",
              "95423         2.0                 1   61907         0      0  black   \n",
              "95424         2.0                 1   45704         0      0   grey   \n",
              "95425         2.0                 1   37815         0      0  white   \n",
              "\n",
              "      purchase_date sales_date   price  days_on_market  car_age_at_sale  \n",
              "0        2020-08-22 2020-02-10   30100            -194               10  \n",
              "1        2020-04-09 2020-09-12   32100             156               10  \n",
              "2        2020-05-17 2020-08-25   34100             100               10  \n",
              "3        2020-01-16 2020-08-05   40100             202               10  \n",
              "4        2020-10-20 2020-07-12   42000            -100                7  \n",
              "...             ...        ...     ...             ...              ...  \n",
              "95421    2020-09-19 2020-11-13  170100              55                4  \n",
              "95422    2020-12-17 2021-02-24  188800              69                5  \n",
              "95423    2020-06-19 2020-09-18  203400              91                4  \n",
              "95424    2019-07-08 2019-09-23  242100              77                3  \n",
              "95425    2020-08-13 2020-10-23  267000              71                2  \n",
              "\n",
              "[95426 rows x 15 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car_brand</th>\n",
              "      <th>car_model</th>\n",
              "      <th>car_variant</th>\n",
              "      <th>car_year</th>\n",
              "      <th>car_engine</th>\n",
              "      <th>car_transmission</th>\n",
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              "      <th>color</th>\n",
              "      <th>purchase_date</th>\n",
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              "      <th>days_on_market</th>\n",
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              "      <td>2019-09-23</td>\n",
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              "      <td>2018</td>\n",
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              "      <td>2020-10-23</td>\n",
              "      <td>267000</td>\n",
              "      <td>71</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 95426,\n  \"fields\": [\n    {\n      \"column\": \"car_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"d222a18d6474d0819d8ee5da0c2c524f31ff0903a4e6abc3f7957f84f1ee3f0f\",\n          \"bcd40d58afd29be4e0af2dfb8c7582db545efe0f717b9fed903ebd2363e6b898\",\n          \"469dfc0b18c73eb263c4a1476545e7b064a7f862ef53827c22026c97268b2ebf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee\",\n          \"16188709fb9e80689a08d1dad58c0d52073628fc52ff213a6c31a77dd59d071f\",\n          \"47a0da3ea9dab0799ae8d9a49f7b234fff9de364e8b0851b556193fa279a2fc0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_variant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          \"ef8a03eb87802e242ecf9553eef9155d2c8364dd45492fc544a256f3f7429600\",\n          \"f52d63231eb0dbaac689ec9147e4b131fc95ef92d5bc78135b22934b36d76bf4\",\n          \"284fe8d8db088b0b28a9950444d4966576ee270688f08d9ce5c5c79c56705f14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1981,\n        \"max\": 2020,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1994,\n          2004,\n          1998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_engine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4289680593337917,\n        \"min\": 0.6,\n        \"max\": 8.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          3.6,\n          3.2,\n          4.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_transmission\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85450,\n        \"min\": 1,\n        \"max\": 992910,\n        \"num_unique_values\": 80005,\n        \"samples\": [\n          170422,\n          85157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"blue\",\n          \"black\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purchase_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-04 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 704,\n        \"samples\": [\n          \"2020-04-01 00:00:00\",\n          \"2020-07-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-07 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 495,\n        \"samples\": [\n          \"2019-09-30 00:00:00\",\n          \"2020-04-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29758,\n        \"min\": 400,\n        \"max\": 1000800,\n        \"num_unique_values\": 1991,\n        \"samples\": [\n          69800,\n          165300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_on_market\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 126,\n        \"min\": -334,\n        \"max\": 691,\n        \"num_unique_values\": 723,\n        \"samples\": [\n          -186,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_age_at_sale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 39,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          0,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Encode 'Auto' as 1 and 'Manual' as 0 for car_transmission\n",
        "df['car_transmission'] = df['car_transmission'].apply(lambda x: 1 if x == 'Auto' else 0)\n",
        "\n",
        "# Calculate days on market (difference between sales_date and purchase_date)\n",
        "df['days_on_market'] = (pd.to_datetime(df['sales_date']) - pd.to_datetime(df['purchase_date'])).dt.days\n",
        "\n",
        "# Calculate car age at sale (difference between sales date year and car year)\n",
        "df['car_age_at_sale'] = pd.to_datetime(df['sales_date']).dt.year - df['car_year']\n",
        "\n",
        "# Move the new features after the last column (car_brand)\n",
        "columns_order = df.columns.tolist()  # Get the current column order\n",
        "columns_order.remove('days_on_market')\n",
        "columns_order.remove('car_age_at_sale')\n",
        "columns_order.extend(['days_on_market', 'car_age_at_sale'])\n",
        "\n",
        "# Reorder the DataFrame\n",
        "df = df[columns_order]\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Npe_bEU9YtdR"
      },
      "source": [
        "one-hot encode\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zX0MylstZY8O"
      },
      "source": [
        "### 1. Label Encoding:\n",
        "- **car_transmission**: Encoding 'Auto' as 1 and 'Manual' as 0.\n",
        "- **color**: Encoding car colors by assigning an integer value to each unique color.\n",
        "- **car_year**: Encoding car production years with a unique integer value for each year.\n",
        "\n",
        "### 2. One-Hot Encoding:\n",
        "- **car_brand**: Encoding car brands with one-hot encoding to create binary columns for each brand.\n",
        "- **car_model**: Encoding car models with one-hot encoding to create binary columns for each model.\n",
        "- **car_variant**: Encoding car variants with one-hot encoding to create binary columns for each variant.\n",
        "\n",
        "This approach ensures that features with natural rankings are encoded appropriately with label encoding, while categorical variables without inherent rankings are encoded using one-hot encoding to facilitate machine learning model training.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vr92FjfZahZ9"
      },
      "source": [
        "Before we do encoding ,create backup first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Md6mMQixafTR"
      },
      "outputs": [],
      "source": [
        "df_backup = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ZlfQajDPbKgQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# # Label Encoding for 'color'\n",
        "# label_encoder = LabelEncoder()\n",
        "# df['color'] = label_encoder.fit_transform(df['color'])\n",
        "\n",
        "# # One-Hot Encoding for 'car_brand', 'car_model', and 'car_variant'\n",
        "# df = pd.get_dummies(df, columns=['car_brand', 'car_model', 'car_variant'], drop_first=True)\n",
        "\n",
        "# Moving 'days_on_market' and 'car_age_at_sale' to be before 'price'\n",
        "columns_order = (\n",
        "    [col for col in df.columns if col not in ['price', 'days_on_market', 'car_age_at_sale']] +\n",
        "    ['days_on_market', 'car_age_at_sale', 'price']\n",
        ")\n",
        "df = df[columns_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "NqMwno81NUSV"
      },
      "outputs": [],
      "source": [
        "# # Apply One-Hot Encoding to the 'color' column\n",
        "# df_encoded = pd.get_dummies(df, columns=['color'], drop_first=True, dtype=int)\n",
        "\n",
        "# # Reorder the columns so that the encoded 'color' columns appear just before 'price'\n",
        "# columns_order = (\n",
        "#     [col for col in df_encoded.columns if col not in ['price'] and not col.startswith('color_')] +\n",
        "#     [col for col in df_encoded.columns if col.startswith('color_')] +\n",
        "#     ['price']\n",
        "# )\n",
        "\n",
        "# # Reorder the DataFrame\n",
        "# df_encoded = df_encoded[columns_order]\n",
        "# df = df_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "tNnTprA-hLSH",
        "outputId": "affd3d0c-b39e-4ac6-ea4a-b0ae7296ad94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           car_brand  \\\n",
              "0  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "1  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
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              "3  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "4  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
              "\n",
              "                                           car_model  \\\n",
              "0  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "1  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "2  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "3  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "4  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
              "\n",
              "                                         car_variant  car_year  car_engine  \\\n",
              "0  b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...      2010         2.0   \n",
              "1  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010         1.8   \n",
              "2  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2010         1.8   \n",
              "3  bc8c75c6d926e32a4dd2119e279211bae53ad4024822c4...      2010         2.0   \n",
              "4  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...      2013         1.8   \n",
              "\n",
              "   car_transmission  milage  accident  flood  color purchase_date sales_date  \\\n",
              "0                 1  282508         1      0   blue    2020-08-22 2020-02-10   \n",
              "1                 1  169475         1      0  black    2020-04-09 2020-09-12   \n",
              "2                 1  105276         0      0  white    2020-05-17 2020-08-25   \n",
              "3                 1   81123         0      0  black    2020-01-16 2020-08-05   \n",
              "4                 1  157239         1      0  brown    2020-10-20 2020-07-12   \n",
              "\n",
              "   days_on_market  car_age_at_sale  price  \n",
              "0            -194               10  30100  \n",
              "1             156               10  32100  \n",
              "2             100               10  34100  \n",
              "3             202               10  40100  \n",
              "4            -100                7  42000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car_brand</th>\n",
              "      <th>car_model</th>\n",
              "      <th>car_variant</th>\n",
              "      <th>car_year</th>\n",
              "      <th>car_engine</th>\n",
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              "      <th>flood</th>\n",
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              "      <th>purchase_date</th>\n",
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              "      <th>days_on_market</th>\n",
              "      <th>car_age_at_sale</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...</td>\n",
              "      <td>4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...</td>\n",
              "      <td>b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...</td>\n",
              "      <td>2010</td>\n",
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              "      <td>b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...</td>\n",
              "      <td>4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...</td>\n",
              "      <td>1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...</td>\n",
              "      <td>2010</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>black</td>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>2020-09-12</td>\n",
              "      <td>156</td>\n",
              "      <td>10</td>\n",
              "      <td>32100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...</td>\n",
              "      <td>4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...</td>\n",
              "      <td>1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>105276</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>white</td>\n",
              "      <td>2020-05-17</td>\n",
              "      <td>2020-08-25</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>34100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...</td>\n",
              "      <td>4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...</td>\n",
              "      <td>bc8c75c6d926e32a4dd2119e279211bae53ad4024822c4...</td>\n",
              "      <td>2010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>81123</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>black</td>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>2020-08-05</td>\n",
              "      <td>202</td>\n",
              "      <td>10</td>\n",
              "      <td>40100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...</td>\n",
              "      <td>4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...</td>\n",
              "      <td>1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>157239</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>brown</td>\n",
              "      <td>2020-10-20</td>\n",
              "      <td>2020-07-12</td>\n",
              "      <td>-100</td>\n",
              "      <td>7</td>\n",
              "      <td>42000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14c86f0a-b1fb-41c0-8fa7-839a13bac3e6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-14c86f0a-b1fb-41c0-8fa7-839a13bac3e6 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-14c86f0a-b1fb-41c0-8fa7-839a13bac3e6');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    20% {\n",
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              "      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-7a340443-28ee-4b08-a5f1-8d9cc9da6d74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 95426,\n  \"fields\": [\n    {\n      \"column\": \"car_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"d222a18d6474d0819d8ee5da0c2c524f31ff0903a4e6abc3f7957f84f1ee3f0f\",\n          \"bcd40d58afd29be4e0af2dfb8c7582db545efe0f717b9fed903ebd2363e6b898\",\n          \"469dfc0b18c73eb263c4a1476545e7b064a7f862ef53827c22026c97268b2ebf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 219,\n        \"samples\": [\n          \"5e815286bca594454b291f3b0350ec22aab6de20b6d9efeec67d604f6bce65ee\",\n          \"16188709fb9e80689a08d1dad58c0d52073628fc52ff213a6c31a77dd59d071f\",\n          \"47a0da3ea9dab0799ae8d9a49f7b234fff9de364e8b0851b556193fa279a2fc0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_variant\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          \"ef8a03eb87802e242ecf9553eef9155d2c8364dd45492fc544a256f3f7429600\",\n          \"f52d63231eb0dbaac689ec9147e4b131fc95ef92d5bc78135b22934b36d76bf4\",\n          \"284fe8d8db088b0b28a9950444d4966576ee270688f08d9ce5c5c79c56705f14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1981,\n        \"max\": 2020,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          1994,\n          2004,\n          1998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_engine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4289680593337917,\n        \"min\": 0.6,\n        \"max\": 8.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          3.6,\n          3.2,\n          4.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_transmission\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85450,\n        \"min\": 1,\n        \"max\": 992910,\n        \"num_unique_values\": 80005,\n        \"samples\": [\n          170422,\n          85157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"blue\",\n          \"black\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purchase_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-04 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 704,\n        \"samples\": [\n          \"2020-04-01 00:00:00\",\n          \"2020-07-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-01-07 00:00:00\",\n        \"max\": \"2021-12-01 00:00:00\",\n        \"num_unique_values\": 495,\n        \"samples\": [\n          \"2019-09-30 00:00:00\",\n          \"2020-04-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_on_market\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 126,\n        \"min\": -334,\n        \"max\": 691,\n        \"num_unique_values\": 723,\n        \"samples\": [\n          -186,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"car_age_at_sale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 39,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          0,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29758,\n        \"min\": 400,\n        \"max\": 1000800,\n        \"num_unique_values\": 1991,\n        \"samples\": [\n          69800,\n          165300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMOKF8SaxL1l"
      },
      "source": [
        "## 3.3 Data Distribution (Analytics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCsuvySVlSsD"
      },
      "source": [
        "## Mileage VS Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-Z7NjJfriTjB",
        "outputId": "2d156572-65f6-4261-84a5-c318d657deb3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Scatter plot: Mileage vs Price colored by Car Transmission\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x='milage', y='price', hue='car_transmission', data=df)\n",
        "plt.title('Mileage vs Price by Car Transmission')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "By4CK0wTSpqf"
      },
      "source": [
        "## Accident History vs Flood Damage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nW729bi1Sxji",
        "outputId": "831d766e-9b44-439a-98c4-f4baff549ded"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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KLvgBfn9/f3l5eZV6+97u3btLbN+yLNWtW1cNGza8oO2fj6n/gPfz89OAAQM0YMAAHT9+XHfeeacmTpxoB7Jz9VWnTh19/vnnOnbsmNOVlV27dtnrL3T/sbGxSkxMVJ8+fbRhwwbNmDHjgj5bt25dtW7dWosXL9awYcP00UcfqUuXLiUm3HB3d1fnzp3VuXNnFRUV6fHHH9cbb7yh5557rszfc1enTh0VFRVpz5499tVCScrMzFR2drZ9Xor/unv3bqcrXfn5+UpPT7evyhXX7dmzR3fddZddV1BQoPT0dDVv3vyy+j1+/LiWLFmikJAQu98PP/xQnp6eWrVqldO5XLBgwWXt62zbtm3Tf//7Xy1cuNDpXW9n315afA5++OGHEts4e6xevXr6/PPPddttt12X74sDcOXwDBkAnMOJEyf00Ucf6b777lP37t1LLMOGDdOxY8e0bNkySX88l/Ldd985zVZXrPj/vHfr1k2HDx/WrFmzzllzNldXV8XExGjp0qXat2+fPb5z506tWrXKqbZr165ydXXVpEmTSmzPsiz99ttvF3cSJFWqVEmSSp3y/Eo5u8/KlSurfv36Treonauve++9V4WFhSXO8fTp0+VwONSpU6cL7uPhhx/Wjh07NHr0aLm6utozJF6Inj176uuvv9Y777yjw4cPO92uKJU8RhcXF/uWzNKmob9c9957rySVCJXFL14unqEwOjpa7u7ueu2115x+h95++23l5OTYdS1btpS/v7/mzZun/Px8uy4hIeGyf1dOnDihhx9+WEeOHNEzzzxjh29XV1c5HA6nZzd/+uknLV269LL2d7biK2hnHr9lWSVeSRAcHKymTZvq3XfftW+vlKR169bZ0+sX69GjhwoLC/WPf/yjxP5Onz59Vf/5AnBt4QoZAJzDsmXLdOzYMd1///2lrm/Tpo38/f2VmJionj17avTo0frggw/00EMP6ZFHHlFERISOHDmiZcuWad68eWrevLn69u2rd999V/Hx8dq0aZPuuOMO5eXl6fPPP9fjjz9e6ruvJGnSpElKTk7WHXfcoccff1ynT5/W66+/riZNmjg971WvXj09//zzGjdunH766Sd16dJFVapUUXp6upYsWaJHH31UTz311EWdh3r16snX11fz5s1TlSpVVKlSJUVGRp7z2auyEBYWpnbt2ikiIkJ+fn7asmWLPvjgAw0bNsyuiYiIkCQ98cQTiomJsQNT586d1b59ez3zzDP66aef1Lx5c3322Wf6+OOPNWLEiHO+WqA0sbGxqlatmpKSktSpU6dzPv9Tmh49euipp57SU089JT8/vxLPew0aNEhHjhzRXXfdpVq1aunnn3/W66+/rvDwcKcrWGWlefPm6tevn+bPn6/s7Gy1bdtWmzZt0sKFC9WlSxd70hp/f3+NGzdOkyZNUseOHXX//fdr9+7dmjNnjlq1amVPAuPm5qbnn39eQ4YM0V133aWePXsqPT1dCxYsuKhnyH799Vf961//kvTHVbEdO3YoKSlJGRkZGjVqlNM732JjY/Xqq6+qY8eO+utf/6qsrCzNnj1b9evXL5PnHos1atRI9erV01NPPaVff/1V3t7e+vDDD0t9X9iLL76oBx54QLfddpsGDBigo0ePatasWWratKlTSGvbtq2GDBmiKVOmaOvWrerQoYPc3Ny0Z88eJSUlaebMmU7vqANQjhiZ2xEArgOdO3e2PD09rby8vHPW9O/f33Jzc7MOHz5sWZZl/fbbb9awYcOsmjVrWu7u7latWrWsfv362est64/ptJ955hmrbt26lpubmxUUFGR1797d2rt3r12js6a9tyzLWrdunRUREWG5u7tbN910kzVv3rwSU5sX+/DDD63bb7/dqlSpklWpUiWrUaNGVlxcnLV79267pm3btiWmn7csy+rXr5/TdN2WZVkff/yxFRYWZlWoUOG8U+AXT3u/efPmUteXtt+zp71//vnnrdatW1u+vr6Wl5eX1ahRI+uFF16w8vPz7ZrTp09bw4cPt/z9/S2Hw+F0Ho4dO2aNHDnSCg4Ottzc3KwGDRpY06ZNKzFdvSQrLi7unMdiWX9MVy/Jeu+99/60rjS33XZbqVPNW5ZlffDBB1aHDh2sgIAAy93d3apdu7Y1ZMgQ6+DBg+fd7oX0XdrvRkFBgTVp0iT7dy8kJMQaN26cdfLkyRKfnzVrltWoUSPLzc3NCgwMtIYOHWodPXq0RN2cOXOsunXrWh4eHlbLli2tL7/80mrbtu0FT3uv/5uy3+FwWN7e3laTJk2swYMHWxs3biz1M2+//bbVoEEDy8PDw2rUqJG1YMGCUo+1tHOUnp5uSbKmTZvmNP7FF19YkqykpCR7bMeOHVZ0dLRVuXJlq3r16tbgwYOt7777rtTf/0WLFlmNGjWyPDw8rKZNm1rLli2zunXrZjVq1KhE//Pnz7ciIiIsLy8vq0qVKlazZs2sMWPGWAcOHDjv+QJwY3JYluEntwEAuIaNHDlSb7/9tjIyMlSxYkXT7eA6ER4eLn9//0t+rQGA8oNnyAAAOIeTJ0/qX//6l7p160YYQ6kKCgp0+vRpp7G1a9fqu+++U7t27cw0BeC6wjNkAACcJSsrS59//rk++OAD/fbbb3ryySdNt4Rr1K+//qro6Gj97W9/U3BwsHbt2qV58+YpKChIjz32mOn2AFwHCGQAAJxlx44d6tOnjwICAvTaa68pPDzcdEu4RlWtWlURERF66623dOjQIVWqVEmxsbF66aWXVK1aNdPtAbgO8AwZAAAAABjCM2QAAAAAYAiBDAAAAAAMMf4M2a+//qqxY8dq5cqV+v3331W/fn0tWLBALVu2lCRZlqUJEybozTffVHZ2tm677TbNnTtXDRo0sLdx5MgRDR8+XJ988olcXFzUrVs3zZw5U5UrV7Zr/vOf/yguLk6bN2+Wv7+/hg8frjFjxjj1kpSUpOeee04//fSTGjRooJdffln33nvvBR1HUVGRDhw4oCpVqsjhcJTBmQEAAABwPbIsS8eOHVNwcLBcXM5zDczkS9COHDli1alTx+rfv7+1ceNG68cff7RWrVpl/fDDD3bNSy+9ZPn4+FhLly61vvvuO+v++++36tata504ccKu6dixo9W8eXPr66+/ttavX2/Vr1/f6t27t70+JyfHCgwMtPr06WNt377d+t///V/Ly8vLeuONN+yaDRs2WK6urtbUqVOtHTt2WM8++6zl5uZmbdu27YKOZf/+/fbLLVlYWFhYWFhYWFhYWPbv33/eHGF0Uo+nn35aGzZs0Pr160tdb1mWgoODNWrUKD311FOSpJycHAUGBiohIUG9evXSzp07FRYWps2bN9tX1ZKTk3Xvvffql19+UXBwsObOnatnnnlGGRkZcnd3t/e9dOlS7dq1S5LUs2dP5eXlafny5fb+27Rpo/DwcM2bN++8x5KTkyNfX1/t379f3t7el3VeAAAAAFy/cnNzFRISouzsbPn4+PxprdFbFpctW6aYmBg99NBDWrdunWrWrKnHH39cgwcPliSlp6crIyND0dHR9md8fHwUGRmp1NRU9erVS6mpqfL19bXDmCRFR0fLxcVFGzdu1IMPPqjU1FTdeeeddhiTpJiYGL388ss6evSoqlatqtTUVMXHxzv1FxMTo6VLl5ba+6lTp3Tq1Cn752PHjkmSvL29CWQAAAAALuhRJqOTevz444/282CrVq3S0KFD9cQTT2jhwoWSpIyMDElSYGCg0+cCAwPtdRkZGQoICHBaX6FCBfn5+TnVlLaNM/dxrpri9WebMmWKfHx87CUkJOSijx8AAABA+WY0kBUVFalFixZ68cUX9Ze//EWPPvqoBg8efEG3CJo2btw45eTk2Mv+/ftNtwQAAADgOmM0kNWoUUNhYWFOY40bN9a+ffskSUFBQZKkzMxMp5rMzEx7XVBQkLKyspzWnz59WkeOHHGqKW0bZ+7jXDXF68/m4eFh357IbYoAAAAALoXRZ8huu+027d6922nsv//9r+rUqSNJqlu3roKCgpSSkqLw8HBJfzwgt3HjRg0dOlSSFBUVpezsbKWlpSkiIkKStGbNGhUVFSkyMtKueeaZZ1RQUCA3NzdJ0urVq3XzzTeratWqdk1KSopGjBhh97J69WpFRUVdseMHAAAAbkSWZen06dMqLCw03coV4erqqgoVKpTJ666MBrKRI0fq1ltv1YsvvqgePXpo06ZNmj9/vubPny/pj4fgRowYoeeff14NGjRQ3bp19dxzzyk4OFhdunSR9McVtY4dO9q3OhYUFGjYsGHq1auXgoODJUl//etfNWnSJA0cOFBjx47V9u3bNXPmTE2fPt3u5cknn1Tbtm31yiuvKDY2VosWLdKWLVvsXgAAAACcX35+vg4ePKjff//ddCtXVMWKFVWjRg2niQMvhdFp7yVp+fLlGjdunPbs2aO6desqPj7enmVRkv1i6Pnz5ys7O1u333675syZo4YNG9o1R44c0bBhw5xeDP3aa6+d88XQ1atX1/DhwzV27FinXpKSkvTss8/aL4aeOnXqBb8YOjc3Vz4+PsrJyeH2RQAAAJRLRUVF2rNnj1xdXeXv7y93d/cyuYp0LbEsS/n5+Tp06JAKCwvVoEGDEi9/vphsYDyQ3SgIZAAAACjvTp48qfT0dNWpU0cVK1Y03c4V9fvvv+vnn39W3bp15enp6bTuYrKB0Uk9AAAAANx4zr5idCMqq2O88c8UAAAAAFyjCGQAAAAAYAiBDAAAAIBxlmXp0UcflZ+fnxwOh3x9fZ1eSXWlhIaGasaMGVd8P+dCIAMAAABgXHJyshISErR8+XIdPHhQTZs2Nd3SVWH0PWQAAAAAIEl79+5VjRo1dOutt0qSKlQoH1GFK2QAAAAAjOrfv7+GDx+uffv2yeFwKDQ0tETN0aNH1bdvX1WtWlUVK1ZUp06dtGfPHqeaDz/8UE2aNJGHh4dCQ0P1yiuvOK3PyspS586d5eXlpbp16yoxMfFKHtYFIZABAAAAMGrmzJmaPHmyatWqpYMHD2rz5s0lavr3768tW7Zo2bJlSk1NlWVZuvfee1VQUCBJSktLU48ePdSrVy9t27ZNEydO1HPPPaeEhASnbezfv19ffPGFPvjgA82ZM0dZWVlX6zBLVT6uAwIAAAC4Zvn4+KhKlSpydXVVUFBQifV79uzRsmXLtGHDBvuWxsTERIWEhGjp0qV66KGH9Oqrr+ruu+/Wc889J0lq2LChduzYoWnTpql///7673//q5UrV2rTpk1q1aqVJOntt99W48aNr96BloIrZAAAAACuaTt37lSFChUUGRlpj1WrVk0333yzdu7cadfcdtttTp+77bbbtGfPHhUWFtrbiIiIsNc3atRIvr6+V+UYzoVABgAAAACGEMgAAAAAXNMaN26s06dPa+PGjfbYb7/9pt27dyssLMyu2bBhg9PnNmzYoIYNG8rV1VWNGjXS6dOnlZaWZq/fvXu3srOzr8oxnAvPkJUTEaPfNd0Cyom0aX1NtwAAAG4wDRo00AMPPKDBgwfrjTfeUJUqVfT000+rZs2aeuCBByRJo0aNUqtWrfSPf/xDPXv2VGpqqmbNmqU5c+ZIkm6++WZ17NhRQ4YM0dy5c1WhQgWNGDFCXl5eJg+NK2QAAAAArn0LFixQRESE7rvvPkVFRcmyLH366adyc3OTJLVo0ULvv/++Fi1apKZNm2r8+PGaPHmy+vfv77SN4OBgtW3bVl27dtWjjz6qgIAAQ0f0B4dlWZbRDm4Qubm58vHxUU5Ojry9vU23UwJXyHC1cIUMAIDy6+TJk0pPT1fdunXl6elpup0r6s+O9WKyAVfIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAkAqmGwAAAABw44sY/e5V3V/atL5XdX+XiitkAAAAAPB/Zs+erdDQUHl6eioyMlKbNm26ovsjkAEAAACApMWLFys+Pl4TJkzQN998o+bNmysmJkZZWVlXbJ8EMgAAAACQ9Oqrr2rw4MEaMGCAwsLCNG/ePFWsWFHvvPPOFdsngQwAAABAuZefn6+0tDRFR0fbYy4uLoqOjlZqauoV2y+BDAAAAEC5d/jwYRUWFiowMNBpPDAwUBkZGVdsvwQyAAAAADCEQAYAAACg3KtevbpcXV2VmZnpNJ6ZmamgoKArtl8CGQAAAIByz93dXREREUpJSbHHioqKlJKSoqioqCu2X14MDQAAAACS4uPj1a9fP7Vs2VKtW7fWjBkzlJeXpwEDBlyxfRLIAAAAAFxxadP6mm7hvHr27KlDhw5p/PjxysjIUHh4uJKTk0tM9FGWCGQAAAAA8H+GDRumYcOGXbX98QwZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYUsF0AwAAAABufPsmN7uq+6s9fttV3d+l4goZAAAAgHLvyy+/VOfOnRUcHCyHw6GlS5delf0SyAAAAACUe3l5eWrevLlmz559VffLLYsAAAAAyr1OnTqpU6dOV32/XCEDAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEWRYBAAAAlHvHjx/XDz/8YP+cnp6urVu3ys/PT7Vr175i+yWQAQAAALjiao/fZrqFP7Vlyxa1b9/e/jk+Pl6S1K9fPyUkJFyx/RLIAAAAAJR77dq1k2VZV32/PEMGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAMqUickxrrayOkYCGQAAAIAy4ebmJkn6/fffDXdy5RUfY/ExXyqmvQcAAABQJlxdXeXr66usrCxJUsWKFeVwOAx3VbYsy9Lvv/+urKws+fr6ytXV9bK2RyADAAAAUGaCgoIkyQ5lNypfX1/7WC8HgQwAAABAmXE4HKpRo4YCAgJUUFBgup0rws3N7bKvjBUz+gzZxIkT5XA4nJZGjRrZ60+ePKm4uDhVq1ZNlStXVrdu3ZSZmem0jX379ik2NlYVK1ZUQECARo8erdOnTzvVrF27Vi1atJCHh4fq16+vhISEEr3Mnj1boaGh8vT0VGRkpDZt2nRFjhkAAAAoD1xdXeXp6XlDLmUVxqRrYFKPJk2a6ODBg/by73//2143cuRIffLJJ0pKStK6det04MABde3a1V5fWFio2NhY5efn66uvvtLChQuVkJCg8ePH2zXp6emKjY1V+/bttXXrVo0YMUKDBg3SqlWr7JrFixcrPj5eEyZM0DfffKPmzZsrJibmhr/MCgAAAMAsh2VwTsqJEydq6dKl2rp1a4l1OTk58vf313vvvafu3btLknbt2qXGjRsrNTVVbdq00cqVK3XffffpwIEDCgwMlCTNmzdPY8eO1aFDh+Tu7q6xY8dqxYoV2r59u73tXr16KTs7W8nJyZKkyMhItWrVSrNmzZIkFRUVKSQkRMOHD9fTTz9dau+nTp3SqVOn7J9zc3MVEhKinJwceXt7l8n5KUsRo9813QLKibRpfU23AAAAYFRubq58fHwuKBsYv0K2Z88eBQcH66abblKfPn20b98+SVJaWpoKCgoUHR1t1zZq1Ei1a9dWamqqJCk1NVXNmjWzw5gkxcTEKDc3V99//71dc+Y2imuKt5Gfn6+0tDSnGhcXF0VHR9s1pZkyZYp8fHzsJSQk5DLPBAAAAIDyxmggi4yMVEJCgpKTkzV37lylp6frjjvu0LFjx5SRkSF3d3f5+vo6fSYwMFAZGRmSpIyMDKcwVry+eN2f1eTm5urEiRM6fPiwCgsLS60p3kZpxo0bp5ycHHvZv3//JZ0DAAAAAOWX0VkWO3XqZP/9LbfcosjISNWpU0fvv/++vLy8DHZ2fh4eHvLw8DDdBgAAAIDrmPFbFs/k6+urhg0b6ocfflBQUJDy8/OVnZ3tVJOZmWnP9x8UFFRi1sXin89X4+3tLS8vL1WvXl2urq6l1pTFewUAAAAA4FyuqUB2/Phx7d27VzVq1FBERITc3NyUkpJir9+9e7f27dunqKgoSVJUVJS2bdvmNBvi6tWr5e3trbCwMLvmzG0U1xRvw93dXREREU41RUVFSklJsWsAAAAA4EowGsieeuoprVu3Tj/99JO++uorPfjgg3J1dVXv3r3l4+OjgQMHKj4+Xl988YXS0tI0YMAARUVFqU2bNpKkDh06KCwsTA8//LC+++47rVq1Ss8++6zi4uLs2wkfe+wx/fjjjxozZox27dqlOXPm6P3339fIkSPtPuLj4/Xmm29q4cKF2rlzp4YOHaq8vDwNGDDAyHkBAAAAUD4YfYbsl19+Ue/evfXbb7/J399ft99+u77++mv5+/tLkqZPny4XFxd169ZNp06dUkxMjObMmWN/3tXVVcuXL9fQoUMVFRWlSpUqqV+/fpo8ebJdU7duXa1YsUIjR47UzJkzVatWLb311luKiYmxa3r27KlDhw5p/PjxysjIUHh4uJKTk0tM9AEAAAAAZcnoe8huJBfzrgETeA8ZrhbeQwYAAMq76+o9ZAAAAABQXhHIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGXDOB7KWXXpLD4dCIESPssZMnTyouLk7VqlVT5cqV1a1bN2VmZjp9bt++fYqNjVXFihUVEBCg0aNH6/Tp0041a9euVYsWLeTh4aH69esrISGhxP5nz56t0NBQeXp6KjIyUps2bboShwkAAAAAtmsikG3evFlvvPGGbrnlFqfxkSNH6pNPPlFSUpLWrVunAwcOqGvXrvb6wsJCxcbGKj8/X1999ZUWLlyohIQEjR8/3q5JT09XbGys2rdvr61bt2rEiBEaNGiQVq1aZdcsXrxY8fHxmjBhgr755hs1b95cMTExysrKuvIHDwAAAKDccliWZZls4Pjx42rRooXmzJmj559/XuHh4ZoxY4ZycnLk7++v9957T927d5ck7dq1S40bN1ZqaqratGmjlStX6r777tOBAwcUGBgoSZo3b57Gjh2rQ4cOyd3dXWPHjtWKFSu0fft2e5+9evVSdna2kpOTJUmRkZFq1aqVZs2aJUkqKipSSEiIhg8frqeffvqCjiM3N1c+Pj7KycmRt7d3WZ6iMhEx+l3TLaCcSJvW13QLAAAARl1MNjB+hSwuLk6xsbGKjo52Gk9LS1NBQYHTeKNGjVS7dm2lpqZKklJTU9WsWTM7jElSTEyMcnNz9f3339s1Z287JibG3kZ+fr7S0tKcalxcXBQdHW3XlObUqVPKzc11WgAAAADgYlQwufNFixbpm2++0ebNm0usy8jIkLu7u3x9fZ3GAwMDlZGRYdecGcaK1xev+7Oa3NxcnThxQkePHlVhYWGpNbt27Tpn71OmTNGkSZMu7EABAAAAoBTGrpDt379fTz75pBITE+Xp6WmqjUs2btw45eTk2Mv+/ftNtwQAAADgOmMskKWlpSkrK0stWrRQhQoVVKFCBa1bt06vvfaaKlSooMDAQOXn5ys7O9vpc5mZmQoKCpIkBQUFlZh1sfjn89V4e3vLy8tL1atXl6ura6k1xdsojYeHh7y9vZ0WAAAAALgYxgLZ3XffrW3btmnr1q320rJlS/Xp08f+ezc3N6WkpNif2b17t/bt26eoqChJUlRUlLZt2+Y0G+Lq1avl7e2tsLAwu+bMbRTXFG/D3d1dERERTjVFRUVKSUmxawAAAADgSjD2DFmVKlXUtGlTp7FKlSqpWrVq9vjAgQMVHx8vPz8/eXt7a/jw4YqKilKbNm0kSR06dFBYWJgefvhhTZ06VRkZGXr22WcVFxcnDw8PSdJjjz2mWbNmacyYMXrkkUe0Zs0avf/++1qxYoW93/j4ePXr108tW7ZU69atNWPGDOXl5WnAgAFX6WwAAAAAKI+MTupxPtOnT5eLi4u6deumU6dOKSYmRnPmzLHXu7q6avny5Ro6dKiioqJUqVIl9evXT5MnT7Zr6tatqxUrVmjkyJGaOXOmatWqpbfeeksxMTF2Tc+ePXXo0CGNHz9eGRkZCg8PV3JycomJPgAAAACgLBl/D9mNgveQAX/gPWQAAKC8u67eQwYAAAAA5RWBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADKlgugEAAHB1RIx+13QLKCfSpvU13QJw3eAKGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAkEsKZHfddZeys7NLjOfm5uquu+663J4AAAAAoFy4pEC2du1a5efnlxg/efKk1q9ff9lNAQAAAEB5UOFiiv/zn//Yf79jxw5lZGTYPxcWFio5OVk1a9Ysu+4AAAAA4AZ2UYEsPDxcDodDDoej1FsTvby89Prrr5dZcwAAAABwI7uoQJaeni7LsnTTTTdp06ZN8vf3t9e5u7srICBArq6uZd4kAAAAANyILiqQ1alTR5JUVFR0RZoBAAAAgPLkogLZmfbs2aMvvvhCWVlZJQLa+PHjL7sxAAAAALjRXVIge/PNNzV06FBVr15dQUFBcjgc9jqHw0EgAwAAAIALcEmB7Pnnn9cLL7ygsWPHlnU/AAAAAFBuXNJ7yI4ePaqHHnqorHsBAAAAgHLlkgLZQw89pM8++6ysewEAAACAcuWSblmsX7++nnvuOX399ddq1qyZ3NzcnNY/8cQTZdIcAAAAANzILimQzZ8/X5UrV9a6deu0bt06p3UOh4NABgAAAAAX4JICWXp6eln3AQAAAADlziU9Q1ZW5s6dq1tuuUXe3t7y9vZWVFSUVq5caa8/efKk4uLiVK1aNVWuXFndunVTZmam0zb27dun2NhYVaxYUQEBARo9erROnz7tVLN27Vq1aNFCHh4eql+/vhISEkr0Mnv2bIWGhsrT01ORkZHatGnTFTlmAAAAACh2SVfIHnnkkT9d/84771zQdmrVqqWXXnpJDRo0kGVZWrhwoR544AF9++23atKkiUaOHKkVK1YoKSlJPj4+GjZsmLp27aoNGzZIkgoLCxUbG6ugoCB99dVXOnjwoPr27Ss3Nze9+OKLkv64mhcbG6vHHntMiYmJSklJ0aBBg1SjRg3FxMRIkhYvXqz4+HjNmzdPkZGRmjFjhmJiYrR7924FBARcyikCAAAAgPNyWJZlXeyHHnzwQaefCwoKtH37dmVnZ+uuu+7SRx99dMkN+fn5adq0aerevbv8/f313nvvqXv37pKkXbt2qXHjxkpNTVWbNm20cuVK3XfffTpw4IACAwMlSfPmzdPYsWN16NAhubu7a+zYsVqxYoW2b99u76NXr17Kzs5WcnKyJCkyMlKtWrXSrFmzJElFRUUKCQnR8OHD9fTTT19Q37m5ufLx8VFOTo68vb0v+fivlIjR75puAeVE2rS+plsAcA58F+Bq4bsA5d3FZINLukK2ZMmSEmNFRUUaOnSo6tWrdymbVGFhoZKSkpSXl6eoqCilpaWpoKBA0dHRdk2jRo1Uu3ZtO5ClpqaqWbNmdhiTpJiYGA0dOlTff/+9/vKXvyg1NdVpG8U1I0aMkCTl5+crLS1N48aNs9e7uLgoOjpaqamp5+z31KlTOnXqlP1zbm7uJR03AAAAgPKrzJ4hc3FxUXx8vKZPn35Rn9u2bZsqV64sDw8PPfbYY1qyZInCwsKUkZEhd3d3+fr6OtUHBgYqIyNDkpSRkeEUxorXF6/7s5rc3FydOHFChw8fVmFhYak1xdsozZQpU+Tj42MvISEhF3XcAAAAAFCmk3rs3bu3xIQa53PzzTdr69at2rhxo4YOHap+/fppx44dZdnWFTFu3Djl5OTYy/79+023BAAAAOA6c0m3LMbHxzv9bFmWDh48qBUrVqhfv34XtS13d3fVr19fkhQREaHNmzdr5syZ6tmzp/Lz85Wdne10lSwzM1NBQUGSpKCgoBKzIRbPwnhmzdkzM2ZmZsrb21teXl5ydXWVq6trqTXF2yiNh4eHPDw8LupYAQAAAOBMl3SF7Ntvv3Va/vOf/0iSXnnlFc2YMeOyGioqKtKpU6cUEREhNzc3paSk2Ot2796tffv2KSoqSpIUFRWlbdu2KSsry65ZvXq1vL29FRYWZtecuY3imuJtuLu7KyIiwqmmqKhIKSkpdg0AAAAAXAmXdIXsiy++KJOdjxs3Tp06dVLt2rV17Ngxvffee1q7dq1WrVolHx8fDRw4UPHx8fLz85O3t7eGDx+uqKgotWnTRpLUoUMHhYWF6eGHH9bUqVOVkZGhZ599VnFxcfbVq8cee0yzZs3SmDFj9Mgjj2jNmjV6//33tWLFCruP+Ph49evXTy1btlTr1q01Y8YM5eXlacCAAWVynAAAAABQmksKZMUOHTqk3bt3S/rjWTB/f/+L+nxWVpb69u2rgwcPysfHR7fccotWrVqle+65R5I0ffp0ubi4qFu3bjp16pRiYmI0Z84c+/Ourq5avny5hg4dqqioKFWqVEn9+vXT5MmT7Zq6detqxYoVGjlypGbOnKlatWrprbfest9BJkk9e/bUoUOHNH78eGVkZCg8PFzJycklJvoAAAAAgLJ0Se8hy8vL0/Dhw/Xuu++qqKhI0h/hqG/fvnr99ddVsWLFMm/0Wsd7yIA/8O4Z4NrFdwGuFr4LUN5dTDa4pGfI4uPjtW7dOn3yySfKzs5Wdna2Pv74Y61bt06jRo26pKYBAAAAoLy5pFsWP/zwQ33wwQdq166dPXbvvffKy8tLPXr00Ny5c8uqPwAAAAC4YV3SFbLff/+91OerAgIC9Pvvv192UwAAAABQHlxSIIuKitKECRN08uRJe+zEiROaNGkSU8UDAAAAwAW6pFsWZ8yYoY4dO6pWrVpq3ry5JOm7776Th4eHPvvsszJtEAAAAABuVJcUyJo1a6Y9e/YoMTFRu3btkiT17t1bffr0kZeXV5k2CAAAAAA3qksKZFOmTFFgYKAGDx7sNP7OO+/o0KFDGjt2bJk0BwAAAAA3skt6huyNN95Qo0aNSow3adJE8+bNu+ymAAAAAKA8uKRAlpGRoRo1apQY9/f318GDBy+7KQAAAAAoDy4pkIWEhGjDhg0lxjds2KDg4ODLbgoAAAAAyoNLeoZs8ODBGjFihAoKCnTXXXdJklJSUjRmzBiNGjWqTBsEAAAAgBvVJQWy0aNH67ffftPjjz+u/Px8SZKnp6fGjh2rcePGlWmDAAAAAHCjuqRA5nA49PLLL+u5557Tzp075eXlpQYNGsjDw6Os+wMAAACAG9YlBbJilStXVqtWrcqqFwAAAAAoVy5pUg8AAAAAwOUjkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhgNZFOmTFGrVq1UpUoVBQQEqEuXLtq9e7dTzcmTJxUXF6dq1aqpcuXK6tatmzIzM51q9u3bp9jYWFWsWFEBAQEaPXq0Tp8+7VSzdu1atWjRQh4eHqpfv74SEhJK9DN79myFhobK09NTkZGR2rRpU5kfMwAAAAAUMxrI1q1bp7i4OH399ddavXq1CgoK1KFDB+Xl5dk1I0eO1CeffKKkpCStW7dOBw4cUNeuXe31hYWFio2NVX5+vr766istXLhQCQkJGj9+vF2Tnp6u2NhYtW/fXlu3btWIESM0aNAgrVq1yq5ZvHix4uPjNWHCBH3zzTdq3ry5YmJilJWVdXVOBgAAAIByx2FZlmW6iWKHDh1SQECA1q1bpzvvvFM5OTny9/fXe++9p+7du0uSdu3apcaNGys1NVVt2rTRypUrdd999+nAgQMKDAyUJM2bN09jx47VoUOH5O7urrFjx2rFihXavn27va9evXopOztbycnJkqTIyEi1atVKs2bNkiQVFRUpJCREw4cP19NPP33e3nNzc+Xj46OcnBx5e3uX9am5bBGj3zXdAsqJtGl9TbcA4Bz4LsDVwncByruLyQbX1DNkOTk5kiQ/Pz9JUlpamgoKChQdHW3XNGrUSLVr11ZqaqokKTU1Vc2aNbPDmCTFxMQoNzdX33//vV1z5jaKa4q3kZ+fr7S0NKcaFxcXRUdH2zVnO3XqlHJzc50WAAAAALgY10wgKyoq0ogRI3TbbbepadOmkqSMjAy5u7vL19fXqTYwMFAZGRl2zZlhrHh98bo/q8nNzdWJEyd0+PBhFRYWllpTvI2zTZkyRT4+PvYSEhJyaQcOAAAAoNy6ZgJZXFyctm/frkWLFplu5YKMGzdOOTk59rJ//37TLQEAAAC4zlQw3YAkDRs2TMuXL9eXX36pWrVq2eNBQUHKz89Xdna201WyzMxMBQUF2TVnz4ZYPAvjmTVnz8yYmZkpb29veXl5ydXVVa6urqXWFG/jbB4eHvLw8Li0AwYAAAAAGb5CZlmWhg0bpiVLlmjNmjWqW7eu0/qIiAi5ubkpJSXFHtu9e7f27dunqKgoSVJUVJS2bdvmNBvi6tWr5e3trbCwMLvmzG0U1xRvw93dXREREU41RUVFSklJsWsAAAAAoKwZvUIWFxen9957Tx9//LGqVKliP6/l4+MjLy8v+fj4aODAgYqPj5efn5+8vb01fPhwRUVFqU2bNpKkDh06KCwsTA8//LCmTp2qjIwMPfvss4qLi7OvYD322GOaNWuWxowZo0ceeURr1qzR+++/rxUrVti9xMfHq1+/fmrZsqVat26tGTNmKC8vTwMGDLj6JwYAAABAuWA0kM2dO1eS1K5dO6fxBQsWqH///pKk6dOny8XFRd26ddOpU6cUExOjOXPm2LWurq5avny5hg4dqqioKFWqVEn9+vXT5MmT7Zq6detqxYoVGjlypGbOnKlatWrprbfeUkxMjF3Ts2dPHTp0SOPHj1dGRobCw8OVnJxcYqIPAAAAACgr19R7yK5nvIcM+APvngGuXXwX4GrhuwDl3XX7HjIAAAAAKE8IZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQ4wGsi+//FKdO3dWcHCwHA6Hli5d6rTesiyNHz9eNWrUkJeXl6Kjo7Vnzx6nmiNHjqhPnz7y9vaWr6+vBg4cqOPHjzvV/Oc//9Edd9whT09PhYSEaOrUqSV6SUpKUqNGjeTp6almzZrp008/LfPjBQAAAIAzGQ1keXl5at68uWbPnl3q+qlTp+q1117TvHnztHHjRlWqVEkxMTE6efKkXdOnTx99//33Wr16tZYvX64vv/xSjz76qL0+NzdXHTp0UJ06dZSWlqZp06Zp4sSJmj9/vl3z1VdfqXfv3ho4cKC+/fZbdenSRV26dNH27duv3MEDAAAAKPcclmVZppuQJIfDoSVLlqhLly6S/rg6FhwcrFGjRumpp56SJOXk5CgwMFAJCQnq1auXdu7cqbCwMG3evFktW7aUJCUnJ+vee+/VL7/8ouDgYM2dO1fPPPOMMjIy5O7uLkl6+umntXTpUu3atUuS1LNnT+Xl5Wn58uV2P23atFF4eLjmzZt3Qf3n5ubKx8dHOTk58vb2LqvTUmYiRr9rugWUE2nT+ppuAcA58F2Aq4XvApR3F5MNrtlnyNLT05WRkaHo6Gh7zMfHR5GRkUpNTZUkpaamytfX1w5jkhQdHS0XFxdt3LjRrrnzzjvtMCZJMTEx2r17t44ePWrXnLmf4pri/ZTm1KlTys3NdVoAAAAA4GJcs4EsIyNDkhQYGOg0HhgYaK/LyMhQQECA0/oKFSrIz8/Pqaa0bZy5j3PVFK8vzZQpU+Tj42MvISEhF3uIAAAAAMq5azaQXevGjRunnJwce9m/f7/plgAAAABcZ67ZQBYUFCRJyszMdBrPzMy01wUFBSkrK8tp/enTp3XkyBGnmtK2ceY+zlVTvL40Hh4e8vb2dloAAAAA4GJcs4Gsbt26CgoKUkpKij2Wm5urjRs3KioqSpIUFRWl7OxspaWl2TVr1qxRUVGRIiMj7Zovv/xSBQUFds3q1at18803q2rVqnbNmfsprineDwAAAABcCUYD2fHjx7V161Zt3bpV0h8TeWzdulX79u2Tw+HQiBEj9Pzzz2vZsmXatm2b+vbtq+DgYHsmxsaNG6tjx44aPHiwNm3apA0bNmjYsGHq1auXgoODJUl//etf5e7uroEDB+r777/X4sWLNXPmTMXHx9t9PPnkk0pOTtYrr7yiXbt2aeLEidqyZYuGDRt2tU8JAAAAgHKkgsmdb9myRe3bt7d/Lg5J/fr1U0JCgsaMGaO8vDw9+uijys7O1u23367k5GR5enran0lMTNSwYcN09913y8XFRd26ddNrr71mr/fx8dFnn32muLg4RUREqHr16ho/frzTu8puvfVWvffee3r22Wf197//XQ0aNNDSpUvVtGnTq3AWAAAAAJRX18x7yK53vIcM+APvngGuXXwX4GrhuwDl3Q3xHjIAAAAAuNERyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAtlZZs+erdDQUHl6eioyMlKbNm0y3RIAAACAGxSB7AyLFy9WfHy8JkyYoG+++UbNmzdXTEyMsrKyTLcGAAAA4AZEIDvDq6++qsGDB2vAgAEKCwvTvHnzVLFiRb3zzjumWwMAAABwA6pguoFrRX5+vtLS0jRu3Dh7zMXFRdHR0UpNTS1Rf+rUKZ06dcr+OScnR5KUm5t75Zu9BIWnTphuAeXE938PM90CyomQp7823cJ1h+8CXC18F+BquVa/C4ozgWVZ560lkP2fw4cPq7CwUIGBgU7jgYGB2rVrV4n6KVOmaNKkSSXGQ0JCrliPwPWgqekGUH5M8THdAYBz4LsAV801/l1w7Ngx+fj8eY8Esks0btw4xcfH2z8XFRXpyJEjqlatmhwOh8HOAHNyc3MVEhKi/fv3y9vb23Q7AAAD+C4A/rgyduzYMQUHB5+3lkD2f6pXry5XV1dlZmY6jWdmZiooKKhEvYeHhzw8PJzGfH19r2SLwHXD29ubL2EAKOf4LkB5d74rY8WY1OP/uLu7KyIiQikpKfZYUVGRUlJSFBUVZbAzAAAAADcqrpCdIT4+Xv369VPLli3VunVrzZgxQ3l5eRowYIDp1gAAAADcgAhkZ+jZs6cOHTqk8ePHKyMjQ+Hh4UpOTi4x0QeA0nl4eGjChAklbucFAJQffBcAF8dhXchcjAAAAACAMsczZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAagTMyePVuhoaHy9PRUZGSkNm3aZLolAMBV9uWXX6pz584KDg6Ww+HQ0qVLTbcEXPMIZAAu2+LFixUfH68JEybom2++UfPmzRUTE6OsrCzTrQEArqK8vDw1b95cs2fPNt0KcN1g2nsAly0yMlKtWrXSrFmzJElFRUUKCQnR8OHD9fTTTxvuDgBggsPh0JIlS9SlSxfTrQDXNK6QAbgs+fn5SktLU3R0tD3m4uKi6OhopaamGuwMAADg2kcgA3BZDh8+rMLCQgUGBjqNBwYGKiMjw1BXAAAA1wcCGQAAAAAYQiADcFmqV68uV1dXZWZmOo1nZmYqKCjIUFcAAADXBwIZgMvi7u6uiIgIpaSk2GNFRUVKSUlRVFSUwc4AAACufRVMNwDg+hcfH69+/fqpZcuWat26tWbMmKG8vDwNGDDAdGsAgKvo+PHj+uGHH+yf09PTtXXrVvn5+al27doGOwOuXUx7D6BMzJo1S9OmTVNGRobCw8P12muvKTIy0nRbAICraO3atWrfvn2J8X79+ikhIeHqNwRcBwhkAAAAAGAIz5ABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQBwGUJDQzVjxow/rXE4HFq6dOlV6QcAcH0hkAEAcBk2b96sRx999Krv90KCIADg2lfBdAMAAFzP/P39TbcAALiOcYUMAHDDS05O1u233y5fX19Vq1ZN9913n/bu3Wuv/+WXX9S7d2/5+fmpUqVKatmypTZu3Giv/+STT9SqVSt5enqqevXqevDBB+11Z1+p2rNnj+688055enoqLCxMq1evLtHP/v371aNHD/n6+srPz08PPPCAfvrpJ3t9//791aVLF/3zn/9UjRo1VK1aNcXFxamgoECS1K5dO/38888aOXKkHA6HHA5HGZ4tAMDVRCADANzw8vLyFB8fry1btiglJUUuLi568MEHVVRUpOPHj6tt27b69ddftWzZMn333XcaM2aMioqKJEkrVqzQgw8+qHvvvVfffvutUlJS1Lp161L3U1RUpK5du8rd3V0bN27UvHnzNHbsWKeagoICxcTEqEqVKlq/fr02bNigypUrq2PHjsrPz7frvvjiC+3du1dffPGFFi5cqISEBCUkJEiSPvroI9WqVUuTJ0/WwYMHdfDgwStz4gAAVxy3LAIAbnjdunVz+vmdd96Rv7+/duzYoa+++kqHDh3S5s2b5efnJ0mqX7++XfvCCy+oV69emjRpkj3WvHnzUvfz+eefa9euXVq1apWCg4MlSS+++KI6depk1yxevFhFRUV666237CtbCxYskK+vr9auXasOHTpIkqpWrapZs2bJ1dVVjRo1UmxsrFJSUjR48GD5+fnJ1dVVVapUUVBQUBmcIQCAKVwhAwDc8Pbs2aPevXvrpptukre3t0JDQyVJ+/bt09atW/WXv/zFDmNn27p1q+6+++4L2s/OnTsVEhJihzFJioqKcqr57rvv9MMPP6hKlSqqXLmyKleuLD8/P508edLpNsomTZrI1dXV/rlGjRrKysq60EMGAFwnuEIGALjhde7cWXXq1NGbb76p4OBgFRUVqWnTpsrPz5eXl9effvZ86y/W8ePHFRERocTExBLrzpwgxM3NzWmdw+Gwb6MEANw4uEIGALih/fbbb9q9e7eeffZZ3X333WrcuLGOHj1qr7/lllu0detWHTlypNTP33LLLUpJSbmgfTVu3Fj79+93eqbr66+/dqpp0aKF9uzZo4CAANWvX99p8fHxueDjcnd3V2Fh4QXXAwCuTQQyAMANrWrVqqpWrZrmz5+vH374QWvWrFF8fLy9vnfv3goKClKXLl20YcMG/fjjj/rwww+VmpoqSZowYYL+93//VxMmTNDOnTu1bds2vfzyy6XuKzo6Wg0bNlS/fv303Xffaf369XrmmWecavr06aPq1avrgQce0Pr165Wenq61a9fqiSee0C+//HLBxxUaGqovv/xSv/76qw4fPnwJZwYAcC0gkAEAbmguLi5atGiR0tLS1LRpU40cOVLTpk2z17u7u+uzzz5TQECA7r33XjVr1kwvvfSS/fxWu3btlJSUpGXLlik8PFx33XWXNm3adM59LVmyRCdOnFDr1q01aNAgvfDCC041FStW1JdffqnatWura9euaty4sQYOHKiTJ0/K29v7go9r8uTJ+umnn1SvXj3ehQYA1zGHZVmW6SYAAAAAoDziChkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGDI/wO4vyBJn4zqnAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Count plot: Accident History vs Flood Damage\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='accident', hue='flood', data=df)\n",
        "plt.title('Accident History vs Flood Damage')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "xZANOkI2hpns"
      },
      "outputs": [],
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# sns.pairplot(df, hue='price')\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boWYUQn1nH5N"
      },
      "source": [
        "For now, we can export our cleaned data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "UARSgh3xnNqM"
      },
      "outputs": [],
      "source": [
        "# Save the cleaned data to a CSV file\n",
        "df.to_csv('cleaned_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqpY3QDPxH7e"
      },
      "source": [
        "##3.4 Data Transformation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_SnaxMZYHlj",
        "outputId": "2270efe0-4312-4d96-dd19-95d8331a9748"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['car_brand', 'car_model', 'car_variant', 'car_year', 'car_engine',\n",
              "       'car_transmission', 'milage', 'accident', 'flood', 'color',\n",
              "       'purchase_date', 'sales_date', 'days_on_market', 'car_age_at_sale',\n",
              "       'price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03hDhIzvYLoF",
        "outputId": "cca23189-58b4-4c9d-f5c4-3f9994f22d12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "len(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "R87Vz_9IpzDF"
      },
      "outputs": [],
      "source": [
        "right_skew_numerical_transform = [\n",
        "    'milage',               # Mileage of the car\n",
        "    'car_year',             # Year the car was made\n",
        "    'car_engine',           # Engine size of the car\n",
        "    'days_on_market',       # Days the car was on the market before sale\n",
        "    'car_age_at_sale'       # Age of the car at the time of sale\n",
        "]\n",
        "\n",
        "\n",
        "categorical_features = [\n",
        "    'color',                # Color of the car (One-hot encoded)\n",
        "    'car_brand',            # Car brand (One-hot encoded)\n",
        "    'car_model',            # Car model (One-hot encoded)\n",
        "    'car_variant'           # Car variant (One-hot encoded)\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmJz4bTIY7E3",
        "outputId": "2418aaf4-94e5-43d8-bb76-f7177e8ad215"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "len(right_skew_numerical_transform)+len(categorical_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTvwPDQAqvp9"
      },
      "source": [
        "### We will construct and apply a column transformer pipeline with the following steps:\n",
        "\n",
        "1. **Right-Skewed Numerical Features**:\n",
        "   - **Transformation Steps**:\n",
        "     - **RobustScaler**: To handle outliers effectively in the numerical data.\n",
        "     - **StandardScaler**: To ensure uniform feature scaling across the numerical features.\n",
        "\n",
        "2. **Categorical Features**:\n",
        "   - **Transformation Steps**:\n",
        "     - **Passthrough**: The categorical features are passed through without any additional transformation (assuming they are already one-hot encoded or pre-processed accordingly).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHA1o79rtFjA",
        "outputId": "69bb1199-54a0-4176-8a42-c9217ea6e70b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     milage  car_year car_engine days_on_market car_age_at_sale  color  \\\n",
            "0  1.576411 -0.322862   0.792114      -2.086114        0.345494   blue   \n",
            "1  0.253608 -0.322862   0.325877       0.672729        0.345494  black   \n",
            "2   -0.4977 -0.322862   0.325877       0.231314        0.345494  white   \n",
            "3 -0.780358 -0.322862   0.792114        1.03532        0.345494  black   \n",
            "4  0.110413  0.381998   0.325877      -1.345167       -0.355245  brown   \n",
            "\n",
            "                                           car_brand  \\\n",
            "0  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
            "1  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
            "2  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
            "3  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
            "4  b51026e4444f98ecdbe1d7cb1f310427a47d7a6e7659b3...   \n",
            "\n",
            "                                           car_model  \\\n",
            "0  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
            "1  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
            "2  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
            "3  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
            "4  4539e4b4889079c2a00afeae0bfc1439840ef2379a1fb8...   \n",
            "\n",
            "                                         car_variant car_transmission  \\\n",
            "0  b20b25f3d8e86acc926ec233aac7485058f71a82cbb6f8...                1   \n",
            "1  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...                1   \n",
            "2  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...                1   \n",
            "3  bc8c75c6d926e32a4dd2119e279211bae53ad4024822c4...                1   \n",
            "4  1dff83b33d877727a06f16e2d53e96834dccb80af01f8e...                1   \n",
            "\n",
            "  accident flood purchase_date sales_date  price  \n",
            "0        1     0    2020-08-22 2020-02-10  30100  \n",
            "1        1     0    2020-04-09 2020-09-12  32100  \n",
            "2        0     0    2020-05-17 2020-08-25  34100  \n",
            "3        0     0    2020-01-16 2020-08-05  40100  \n",
            "4        1     0    2020-10-20 2020-07-12  42000  \n"
          ]
        }
      ],
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "import pandas as pd\n",
        "\n",
        "# Define the scaling pipeline\n",
        "preprocessing_pipeline = Pipeline([\n",
        "    ('robust_scaling', RobustScaler()),  # Robust scaling to handle outliers\n",
        "    ('standard_scaling', StandardScaler())  # Standard scaling for uniform feature scaling\n",
        "])\n",
        "\n",
        "# Define the column transformer\n",
        "transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('Scaling', preprocessing_pipeline, right_skew_numerical_transform),  # Apply to numerical features\n",
        "        ('OneHot', 'passthrough', categorical_features)  # Pass categorical features as is\n",
        "    ],\n",
        "    remainder='passthrough'  # Keep the other features as is\n",
        ")\n",
        "\n",
        "# Apply the transformation to the dataset\n",
        "df_transformed = transformer.fit_transform(df)\n",
        "\n",
        "# Convert the transformed data back to a DataFrame\n",
        "# Adjust the column names by including all required features plus 'price'\n",
        "df_transformed = pd.DataFrame(df_transformed, columns=[*right_skew_numerical_transform, *categorical_features, 'car_transmission', 'accident', 'flood', 'purchase_date', 'sales_date', 'price'])\n",
        "\n",
        "# Verify the transformation and check for any anomalies\n",
        "print(df_transformed.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "vzGvOkTytPzE",
        "outputId": "7ab1ddc1-a321-498c-80a2-8e61b09a9f5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(remainder='passthrough',\n",
              "                  transformers=[('Scaling',\n",
              "                                 Pipeline(steps=[('robust_scaling',\n",
              "                                                  RobustScaler()),\n",
              "                                                 ('standard_scaling',\n",
              "                                                  StandardScaler())]),\n",
              "                                 ['milage', 'car_year', 'car_engine',\n",
              "                                  'days_on_market', 'car_age_at_sale']),\n",
              "                                ('OneHot', 'passthrough',\n",
              "                                 ['color', 'car_brand', 'car_model',\n",
              "                                  'car_variant'])])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;Scaling&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;robust_scaling&#x27;,\n",
              "                                                  RobustScaler()),\n",
              "                                                 (&#x27;standard_scaling&#x27;,\n",
              "                                                  StandardScaler())]),\n",
              "                                 [&#x27;milage&#x27;, &#x27;car_year&#x27;, &#x27;car_engine&#x27;,\n",
              "                                  &#x27;days_on_market&#x27;, &#x27;car_age_at_sale&#x27;]),\n",
              "                                (&#x27;OneHot&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;color&#x27;, &#x27;car_brand&#x27;, &#x27;car_model&#x27;,\n",
              "                                  &#x27;car_variant&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;Scaling&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;robust_scaling&#x27;,\n",
              "                                                  RobustScaler()),\n",
              "                                                 (&#x27;standard_scaling&#x27;,\n",
              "                                                  StandardScaler())]),\n",
              "                                 [&#x27;milage&#x27;, &#x27;car_year&#x27;, &#x27;car_engine&#x27;,\n",
              "                                  &#x27;days_on_market&#x27;, &#x27;car_age_at_sale&#x27;]),\n",
              "                                (&#x27;OneHot&#x27;, &#x27;passthrough&#x27;,\n",
              "                                 [&#x27;color&#x27;, &#x27;car_brand&#x27;, &#x27;car_model&#x27;,\n",
              "                                  &#x27;car_variant&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Scaling</label><div class=\"sk-toggleable__content\"><pre>[&#x27;milage&#x27;, &#x27;car_year&#x27;, &#x27;car_engine&#x27;, &#x27;days_on_market&#x27;, &#x27;car_age_at_sale&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RobustScaler</label><div class=\"sk-toggleable__content\"><pre>RobustScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;color&#x27;, &#x27;car_brand&#x27;, &#x27;car_model&#x27;, &#x27;car_variant&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;car_transmission&#x27;, &#x27;accident&#x27;, &#x27;flood&#x27;, &#x27;purchase_date&#x27;, &#x27;sales_date&#x27;, &#x27;price&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlation Analysis for Numerical Features\n",
        "Pearson Correlation Matrix"
      ],
      "metadata": {
        "id": "e83izUN9OniE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.stats import f_oneway\n",
        "\n",
        "# Assuming df is your DataFrame containing the data\n",
        "\n",
        "# Select numerical features for correlation analysis\n",
        "numerical_features = [\n",
        "    'milage',\n",
        "    'car_year',\n",
        "    'car_engine',\n",
        "    'days_on_market',\n",
        "    'car_age_at_sale',\n",
        "    'price'  # Include price as the dependent variable\n",
        "]\n",
        "\n",
        "# Compute the Pearson correlation matrix\n",
        "correlation_matrix = df[numerical_features].corr()\n",
        "\n",
        "# Plot the Pearson correlation matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\n",
        "plt.title('Pearson Correlation Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "tmIhGPPPOssh",
        "outputId": "16414f97-c7f5-4d3e-cc94-4dc4e4ad0f2b"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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8+DGMjIywbdu2D7pO6siRIxg+fDi6du0KV1dX5ObmYt26dZBIJPJ7wdWsWRN9+/bFr7/+ipSUFDRt2hTnzp3D2rVr0bFjR3z22Wcqa3tYWBhatWqFzp07o3379mjRogX09fVx69YtbNq0CU+fPpXfi+5///sf2rRpA19fX/Tv319+2wJjY2NMnTr1P9Vj7Nix2LVrF9q1aye/5UJ6ejquXLmCrVu34v79+wo33H5Tu3btsG7dOhgbG8PT0xNnzpzBoUOH5LekKODl5QWJRII5c+bg+fPn0NbWlt/H7W2DBg3CL7/8goCAAFy4cAH29vbYunUrTp06hUWLFhVrIZl/q0OHDujQocM787Rq1QpaWlpo3749Bg8ejLS0NKxcuRIVK1bE06dPFfLWrl0by5cvx8yZM+Hs7IyKFSvK78NYXJmZmejbty9cXFwwa9YsAMC0adOwe/du9OvXD1euXPlPEVoiovKCAzoioo9kwoQJcHV1xcKFCzFt2jQAgK2tLVq1aoUvvvgCQP6iFbt375Zf86ajo4NOnTph+PDhqFmzZrHOU7NmTfj7+2P37t14/Pgx9PT0ULNmTfz111+oX7++PN+qVavg6OiI0NBQ7NixA1ZWVggKCsKUKVNU2u4KFSrg9OnT+Pnnn7F582ZMnDgR2dnZqFKlCr744guF+635+flh3759mDJlCiZPngxNTU00bdoUc+bM+aCFNZTR09PDsWPH8OOPP2LLli0ICwuDkZERXF1dMW3atHcu8PHTTz9BIpFgw4YNyMzMRMOGDXHo0KFCK2laWVlhxYoVCAkJQf/+/ZGXl4ejR48qHdDp6uoiIiICEyZMwNq1a5Gamgo3NzesWbMGAQEB/6mtquDm5oatW7di0qRJGDNmDKysrDBkyBBUqFCh0EIlkydPxoMHDzB37ly8ePECTZs2/eAB3Q8//IDbt2/j9OnT0NHRAQBoaWlh7dq1qF+/PsaOHYuff/5ZZe0jIvpUiYQPvbqaiIiIiIiIygReQ0dERERERKSmOKAjIiIiIiJSUxzQERERERERqSkO6IiIiIiIqNw5fvw42rdvDxsbG4hEIqW323lbREQEvL29oa2tDWdnZ4SGhhbKs2zZMtjb20NHRwf16tXDuXPnVF/5N3BAR0RERERE5U56ejpq1qyJZcuWFSv/vXv30LZtW3z22WeIjIzE999/jwEDBijc73Tz5s0IDAzElClTcPHiRflK1AkJCR+rGVzlkoiIiIiIyjeRSIQdO3agY8eOReYZP3489uzZg6tXr8rTevTogZSUFOzbtw8AUK9ePdSpUwdLly4FAMhkMtja2mLEiBGYMGHCR6k7I3RERERERPRJyMrKQmpqqsKWlZWlkmOfOXMGfn5+Cmn+/v44c+YMACA7OxsXLlxQyCMWi+Hn5yfP8zHwxuJUyB5Nt9KuAr3D+TVX35+JSsVgHz43ZVWGplFpV4HewWzrwtKuAhVhb8MlpV0FKkKvRqLSrkKRSvO75D8Te2LatGkKaVOmTMHUqVP/87Hj4uJgaWmpkGZpaYnU1FS8fPkSUqkUeXl5SvNER0f/5/MXhQM6IiIiIiL6JAQFBSEwMFAhTVtbu5RqUzI4oCMiIiIiok+Ctrb2RxvAWVlZIT4+XiEtPj4eRkZG0NXVhUQigUQiUZrHysrqo9QJ4DV0RERERERE7+Xr64vDhw8rpB08eBC+vr4AAC0tLdSuXVshj0wmw+HDh+V5PgZG6IiIiIiISGVEmmX3+r43paWl4fbt2/LH9+7dQ2RkJMzMzGBnZ4egoCA8fvwYYWFhAIBvv/0WS5cuxbhx4/DNN9/gyJEj+OOPP7Bnzx75MQIDA9G3b1/4+Pigbt26WLRoEdLT09GvX7+P1g4O6IiIiIiIqNw5f/48PvvsM/njgmvv+vbti9DQUDx9+hSxsbHy/Q4ODtizZw9GjRqFn376CZUrV8aqVavg7+8vz9O9e3c8e/YMkydPRlxcHLy8vLBv375CC6WoEu9DR4Vwlcuyjatcll1c5bLs4iqXZRtXuSy7uMpl2VWWV7ncq+deauf+POPjrSZZVjFCR0REREREKiPWKLuDzU8RF0UhIiIiIiJSU4zQERERERGRyog0GTMqSextIiIiIiIiNcUBHRERERERkZrilEsiIiIiIlIZLopSshihIyIiIiIiUlOM0BERERERkcqINBmhK0mM0BEREREREakpRuiIiIiIiEhleA1dyWKEjoiIiIiISE1xQEdERERERKSmOOWSiIiIiIhUhouilCxG6IiIiIiIiNQUI3RERERERKQyXBSlZDFCR0REREREpKY4oCMiIiIiIlJTnHJJREREREQqI5JwymVJYoSOiIiIiIhITTFCR0REREREKiNmhK5EMUJHRERERESkpjigIyIiIiIiUlOccklERERERCojEnPKZUlihI6IiIiIiEhNMUJHREREREQqI5IwZlSS2NtERERERERqihE6IiIiIiJSGd62oGQxQkdERERERKSmOKAjIiIiIiJSU5xySUREREREKsPbFpQsRuiIiIiIiIjUFCN0RERERESkMlwUpWQxQkdERERERKSmOKAjIiIiIiJSU5xySUREREREKiPilMsSxQgdERERERGRmmKEjoiIiIiIVEYkZsyoJLG3iYiIiIiI1BQHdCUkIiICIpEIKSkpAIDQ0FCYmJiUap2IiIiIiFRNJBaV2lYeccplCWnQoAGePn0KY2Pj0q5KuWDWyAeOo/vD2LsadGwq4vyXQxG/63BpV6tcaFZDDG8XMXQ0gYfPBOw5l4fkF0Xnb1pDjGY1JAppic8FLNudK3+srwO09JbAyVoELU0gKRU4cSUPNx4KH6sZam/Hnv3YFL4bydLncLK3w3eD+sHD1bnI/EdP/Y3VG/5AXMIzVLKxwrd9eqG+Ty35/qYdeigt923fr9Czc3sAQPeBwxGXkKiwf1DvnviqSwcVtEh9CIKAdevXY9++fUhPT4enpyeGDxuGSpUqvbPc7t27sXXbNkilUjg6OGDIkCFwc3OT78/OzsbKlStx7Phx5OTkoLa3N4YNGwZTU1N5noSEBCxdtgyXL1+Gjo4O/Pz80C8gABLJ69dYdk4ONm7ciKNHjiBZKoWZmRl69eoF/1atAAC5ubnY/McfOHToEJKSklC5cmV8068ffHx8VNxT6kGrVhPo1GsBkb4R8hIe4+WhLch7+kB53mr1oNe2t0KakJuD5/NHyR+bjF+qtOzLozuQdY6fUx9CEARE7FyCi8e3IDMjFbbO3mjbewrMLe2LLPPP0d9xPuJ3pCQ+BgBUtHFGky+GwaV6E4V8D29fwpEdi/D47mWIxGJY2Xrg68BV0NTS+ZhNIvpXOKArIVpaWrCysirtapQbEn09pF6OwcPQbfDZuqy0q1NuNPQUo567GOGn8yBNE/BZTQm+bq6BZbtzkScrulxCioCwQ68HcLK3xmmdGkigowX8HpGHjCwB1e3F6NJYgpV/5SJO+pEao8aOnDiNZavXIXDIAHi6OmPL7r0YMzUE639eAFOTwj8qXb0RgxnzFmNg757wreONw8dPYmLIPKxcMBuOVWwBANtDVyiUOXshEnOX/oKmDeoqpH/TqyvatWohf6ynW/6+/GzZuhW7du3C6MBAWFlZIWzdOkwKDsYvK1ZAS0tLaZljx47h15UrMWL4cLi5uyM8PByTgoOx8tdf5bM5fvn1V/zzzz/4ISgI+vr6+Hn5csycORPz588HAOTl5WHKlCkwNTXF/HnzkJycjHnz50NDIkFAQID8XCEhIZBKpfj+++9hY2OD5ORkyGSvX6Brw8Jw9OhRjBw5EraVK+PCxYuY8eo8zk5OH63fyiJNd2/oNu+Elwc2I/fJfWj7fAb9bsPwYuV0CBlpSssIWS+RunL6GwmK+58vDVI8h2NV6LbphZyYSBXX/tN36q9VOHtoHTr2nw1Ti8o4Gv4T1i8YgGEz90BDU1tpGSNTS/h9ORpmllUAQUDk6XBsWjIMg6dsR8VKLgDyB3MbFg1Eo88HoU2vSRBLJIh/GAORiBPbqGziX+a/1KxZM4wYMQLff/89TE1NYWlpiZUrVyI9PR39+vWDoaEhnJ2d8ddffwEoPOXybXfu3EGHDh1gaWkJAwMD1KlTB4cOHVLI8/TpU7Rt2xa6urpwcHDAxo0bYW9vj0WLFsnzpKSkYMCAAahQoQKMjIzQvHlzREVFfaxuKLOe7T+Om1MWIX7nofdnJpWp5yHG8SsyxDwSkJAChJ/Og6Ee4G777ikQMhmQnvl6e5mluN+2ggjnYmR4kiQgJQ04cVWGzBzA2rx8Tq14nz927kG7Vs3xuV8z2NtVxughA6CjrYW9hyKU5t+6+y/U9a6Jnp3bw962Evp/1R2ujg7YsWe/PI+5qYnCdurcedSq7gkbK0uFY+np6irk09UpXwM6QRAQHh6OHj16wNfXFw4ODhgzejSSkpJw+syZIsvt2LEDbVq3RqtWrVDFzg4jhg+HtrY2Dhw4AABIT0/HgQMHMHDgQHh5ecHFxQWBo0bh+o0buBEdDQC4ePEiYh8+xNixY+Hk5IQ6deqgT+/e2P3nn8jJyQEAnD9/HleuXMGM6dNRq1YtWFpawsPDA1WrVpXX5ciRI+jerRvq1qkDa2trtGvbFnV8fLB9+/aP2HNlk3ad5siOOo3sK39DlhSHl/s3ATnZ0KruW3QhQYCQ/uL1lqE4RUFhX/oLaDpXR+6DW5A9T/rIrfm0CIKAs4fC0KTdt3Cv1QKWtm7o2H8OXqQkIPpi0Z/9bl7N4VKjKcwt7WFu5YAWnUdBS1sPj+6+/q60f/Ns1G3RG40+H4SKlVxgYeWIqnXaQENT+Q8yVJhYIiq1rTzigO4/WLt2LSwsLHDu3DmMGDECQ4YMQdeuXdGgQQNcvHgRrVq1Qu/evZGRkfHeY6WlpeHzzz/H4cOHcenSJbRu3Rrt27dHbGysPE+fPn3w5MkTREREYNu2bfj111+RkJCgcJyuXbsiISEBf/31Fy5cuABvb2+0aNECycnJKm8/0ZtMDABDXRHuxr3+pT8rB3iUKMC2wrvfYM2MgMDOGhjZQQOdGkpgpKe4/+EzAVWriKHz6rO0ahURNCTA/ThOuXxbTk4ubt65h9o1q8vTxGIxatesjmsxN5WWuRZzSyE/ANSpVbPI/MkpKThz/hI+9/us0L6N23ai/dcD0P/7Cfh9+27k5uX9h9aon7i4OEilUtTy8pKn6evrw83NDdE3bigtk5OTg1u3b8PrjTJisRheXl7ywdqtW7eQm5urcFxbW1tUrFBBftwb0dGwt7dXmIJZu3ZtZGRk4MGrz5K/z56Fi4sLtmzdiq9798aAAQOwctUqZGW9/hUlJyenUCRRS1sb165d+1d9orbEEkisbJH7IOaNRAG592OgUcmh6HJa2jD6djqMhsyAfudBEFsUPTtHpGcIDadqyL5c9GCflEtJfIS058/g6NlAnqajZ4jKjjXw8E5ksY4hk+Xh6tk9yMnOgK2TFwAgPTUJj+9GQd/IDL/92APzRjVE6JyvEXvrwkdoBZFqcMrlf1CzZk1MmjQJABAUFITZs2fDwsICAwcOBABMnjwZy5cvx+XLl4t1rJo1a8ofz5gxAzt27MCuXbswfPhwREdH49ChQ/jnn3/k1zGsWrUKLi4u8jInT57EuXPnkJCQAG3t/KkG8+bNQ3h4OLZu3YpBgwYVOm9WVpbCBzkA5AgyaHJaAX0gA538QVt6pmJ6eiagr1P0gO5xooCdp/OQmCrAUFeEpjXE6NdKA8v/zEX2q1mYW07koUtjCcZ300SeTEBOLrD5WB6kymc8lWvPU1ORJ5MVmlppamKM2EePlZZJTklRmj9Z+lxp/n1HjkNPVwdNfBWnW3Zu1waujvYwMjTA1Rs38eu6TUiSSjG8f5//0CL1IpXmzwF+c1AFAKYmJvJ9b0tNTYVMJlNa5tHDh/LjamhowMDAQCGPiakpkl8dVyqVFlpsq+CxNDkZcHJCXFwcrl27Bi1NTQRPmoTnqalYtmwZXqSmIjAwEABQ29sb23fsQLVq1WBtbY3IyEicPn0aeeVscC7SM4BILIEsXTHCJstIhYa5pdIyeckJyNi7AbJnjwFtXejUbQHDr0cj9bdZEF6kFMqvVa0ehOxM5NyM/Agt+LSlPX8GANA3MldI1zeyQHpqorIicvGPYvDbjz2Rm5MFLW09dB+2FBVs8q8xlj7Lf80d27kULbuNg5WtB6LO7ETYvAAMmb77ndfn0WvldXGS0sIB3X9Qo0YN+f8lEgnMzc1RvfrrX7ktLfPf8BMSEmBkZPTOY6WlpWHq1KnYs2cPnj59itzcXLx8+VIeoYuJiYGGhga8vb3lZZydnRW+AERFRSEtLQ3m5opvbi9fvsSdO3eUnjckJATTpk1TSOspMsNXEot31peour0I7eq9Xmhh49F/92Xv9pPXUbaEFAGPEvPwfScNVK0iwqU7+fua18yPzoUdykVGpgB3WzG6NpZgzYFcJKT8p2bQv/DXoQj4NW0E7beiON07tJX/38m+CjQ0NTD/51UY1KcntDQ1S7qaJeLI0aNYsmSJ/PHb76dljUwmg0gkwrhx46Cvrw8AyBk4ELN+/BHDhg2DtrY2Bn/7LRb/9BMGDR4MALC2tkZLPz8cOHiwNKuuFvKe3EPek3vyx+mP78JwQDC0vRoi88SeQvm1atRHzvXzQF5uoX2k6PLfu/Fn2BT5417frXhH7nezsHLAt1N2IPPlC1y/sB/hv01AwPh1qGDjDEHIn2VSu2l31Gr0JQDAuoon7t04g0snt8Hvy9H/rSFEHwEHdP+B5ltfUEQikUKaSJT/68SbF5sXZcyYMTh48CDmzZsHZ2dn6OrqokuXLsjOzi52fdLS0mBtbY2IiIhC+4q6RUJQUJD8V9kCR8xqF/ucVH7FPBLwKPH1lxCNV2M7fR0g7eXrfPo6QLy0+FMjs3KApBcCzAxFAASYGgB13SX4eXcOnr0KGMWnyGBXUYQ6rmLsOff+11d5YmxkBIlYDGmKYnRNmvIcZqYmSsuYmZgUkb/wAipR124g9vETTBn73Xvr4unqjLy8PMTFP4NdZZviN0KN1K9XD+5vrERZcK2a9NXqkQWkKSlwcnRUegwjIyOIxeJCETxpSgpMXx3D1NQUubm5SEtLU4jSpUilMHv1w56pqSlu3lScJltw3XbBcczMzGBubi4fzAH5UzcFQUBiYiIqVaoEE2NjTJ48GdnZ2UhNTYW5uTlWr1lT7hb2EjLSIMjyINY3xJs/V4n1jCCkpxbvIDIZ8uIfQmxSodAuSWUnSMytkL5zjWoq/Ilzq/kZKk95/UN6bm7+96P01CQYmlSUp6enJsLS1uOdx5JoaOUvigLAxr4anty7ir8PhaF9n+kwMM4/VkHErkAFayekJj1VSVuIVI3z6sqIU6dOISAgAJ06dUL16tVhZWWF+/fvy/e7ubkhNzcXly5dkqfdvn1b4QuAt7c34uLioKGhAWdnZ4XNwkJ5xE1bWxtGRkYKG6dbUnFk5wLStNfbs+fAi5cCHK1e//1oaQKVLUR4+Kz4AzpNDcDMQIQXL18/BgDhrUPIhNc/mtBrmpoacHVywIXLV+VpMpkMFy9fRVU3V6Vlqrq5KOQHgPORl5Xm33voKNycHOHsUOW9dbl99wHEYhFMTd49Q0Gd6enpwcbGRr7Z2dnB1NQUkW8sRpWekYGYmBi4eyj/kqmpqQkXZ2eFMjKZDJGRkfBwdwcAuLi4QENDA5GRkfI8jx49QsKzZ/Ljeri74/79+wqLb128dAl6enqws7MDAHh6eiI5ORkvX77+1eXx48cQi8WFPie0tLRgYWGBvLw8nDp1Cr716/+7TlJXsjzkxT2ERhW3NxJF0LB3Re7je0UWUyASQVLBBjIlA0DtGr7IfRqbPz2T3ktb1wBmllXkWwUbZxgYV8DdG6+vP8x6mYZHdy/Lr4crLkGQIS8nf4BoYlEJhiYVkRin+Bwnxd2Hsfmn+cPUxyASi0ttK4/KZ6vLIBcXF2zfvh2RkZGIiopCr169FCJ77u7u8PPzw6BBg3Du3DlcunQJgwYNgq6urvxLrZ+fH3x9fdGxY0ccOHAA9+/fx+nTpzFx4kScP3++tJpWKiT6ejCq6Q6jmvlfhvQcKsOopjt0bK1LuWaftrM3ZGhcTQzXyiJUNMm/3cCLDCD6jfvF9W4hQR3X1289Lb3FqFJRBGP9/MFf96YSyATg6v38v//E50BSqoB29SSwMRfB1ADw9RDDyVqE6IeMzinTrUNb7DlwBPuOHMP9h4+xYMVveJmZhTZ+TQEAsxYuw69hv8vzd2nfBucuRmFz+J948Ogx1vy+BTF37qJTW3+F46ZnZCDi1Fm0bVl4MZSr0TexZdde3L73AE/i4nEw4iSWrg5Dy6aNYfjWdV+fMpFIhI4dO2LTpk34+++/ce/ePcyfNw/m5uZo4Pt6ZcQJQUHYtXu3/HGnTp2wb98+HDx0CLGxsVi6bBmysrLQsmVLAPkLq7Rq1QorV65EVFQUbt26hQULF8LDw0M+6PP29oadrS3+N28e7t69iwsXLiAsLAzt27WTT3n9rFkzGBoaYsHChXgQG4srV67gt99+Q6uWLeXXXkdHR+PUqVN4+vQprl69iknBwRAEAV26dCmpbiwzsv45Aq2aDaBZrR7E5pbQ9e8OaGoj+8rfAAC9tr2h0+QLeX7tBq2hYe8OsbE5JJaVodeuL8RGZsiOOq14YC0daLrVQvblt9Kp2EQiEer59cGJP1cgJvII4h/FYMeq8TA0qQh3bz95vrD/BeDc4fXyx4e2zceDmH+QkvgI8Y9icGjbfNyPOYfq9dvLj9ugdX+cO7wO18/vQ3L8AxzZ8RMS4+6iVuPy9xog9cApl2XEggUL8M0336BBgwawsLDA+PHjkZqq+IteWFgY+vfvjyZNmsDKygohISG4du0adF4tCy4SibB3715MnDgR/fr1w7Nnz2BlZYUmTZrIr+crL4xrV4Pv4XXyx57zfgAAPAzbjsv9g4oqRv/RqesyaGoA7evl3zcuNkHA+iOK96AzMxRBT+f1AM9IT4QvG4mhqw1kZAKxzwT8ti8XGa/W6pEJwMajuWhRS4KezSTQ0gSSX+TfEuHN6+/oteaNGyAlNRWrN25BsjQFzg5V8L8pE2D2aup1QmIixG9csF7Nww3Bo0fgt/WbsXLdJlS2scKsoDHye9AVOHziNARBQIsmDQudU0tTE0dOnEbopq3IzsmBdcWK6PrF5+j2xnV15UXXLl2QmZmJxUuWIC0tDVWrVsWM6dMVVo58+vQpUp+/nubatGlTPE9Nxfp165AslcLJ0REzpk9XuE568KBBEItEmDlrVv6NxWvXxrChQ+X7JRIJpk6diqXLliFw9Ghoa2vDz88PvXu/vtG1rq4ufpw1C8uXL8d3330HQ0NDNGncGH36vF64JjsnB2vDwhAXFwddXV3U8fHB2DFjCi3IUh7kRF/ESz0D6DZqC5G+IfISHiP9j2XyWxGIjcwUpg+IdPSg17oXRPqGEDJfIi8+FmnrF0CWFKdwXC2P2oBIhOzr5evHVlVr2GYAcrJfYvfaycjMSIWdS218PWqlwj3okp/FIiPt9Wym9NRk7PhtPNKeP4O2riEsK7vh61Gr4FT19fta/ZZ9kZuThf2bZuNl+nNY2rqh9+jVMKtoV6LtU2dcFKVkiQTh7YlMpC4ePXoEW1tbHDp0CC1atHh/gWLao+n2/kxUas6vufr+TFQqBvvwuSmrMjQ/3WmfnwKzrQtLuwpUhL0Nl7w/E5WKXo3K7qDpSrvCMzlKSvU/j5bauUsLI3Rq5MiRI0hLS0P16tXx9OlTjBs3Dvb29mjSpElpV42IiIiICADK7Q2+SwsHdGokJycHP/zwA+7evQtDQ0M0aNAAGzZsKLTaJhERERERlQ8c0KkRf39/+Pv7vz8jERERERGVCxzQERERERGRynBRlJLF2xYQERERERGpKUboiIiIiIhIZcrrDb5LC3ubiIiIiIhITXFAR0REREREpKY45ZKIiIiIiFSGi6KULEboiIiIiIiI1BQjdEREREREpDKM0JUsRuiIiIiIiIjUFCN0RERERESkMozQlSxG6IiIiIiIiNQUB3RERERERERqilMuiYiIiIhIZURixoxKEnubiIiIiIjKpWXLlsHe3h46OjqoV68ezp07V2TeZs2aQSQSFdratm0rzxMQEFBof+vWrT9qGxihIyIiIiIilRFL1GNRlM2bNyMwMBArVqxAvXr1sGjRIvj7+yMmJgYVK1YslH/79u3Izs6WP05KSkLNmjXRtWtXhXytW7fGmjVr5I+1tbU/XiPACB0REREREZVDCxYswMCBA9GvXz94enpixYoV0NPTw+rVq5XmNzMzg5WVlXw7ePAg9PT0Cg3otLW1FfKZmpp+1HZwQEdERERERJ+ErKwspKamKmxZWVmF8mVnZ+PChQvw8/OTp4nFYvj5+eHMmTPFOtdvv/2GHj16QF9fXyE9IiICFStWhJubG4YMGYKkpKT/1qj34ICOiIiIiIhURiQWldoWEhICY2NjhS0kJKRQHRMTE5GXlwdLS0uFdEtLS8TFxb23jefOncPVq1cxYMAAhfTWrVsjLCwMhw8fxpw5c3Ds2DG0adMGeXl5/61T34HX0BERERER0SchKCgIgYGBCmkf4xq23377DdWrV0fdunUV0nv06CH/f/Xq1VGjRg04OTkhIiICLVq0UHk9AA7oiIiIiIhIhUrztgXa2trFGsBZWFhAIpEgPj5eIT0+Ph5WVlbvLJueno5NmzZh+vTp7z2Po6MjLCwscPv27Y82oOOUSyIiIiIiKle0tLRQu3ZtHD58WJ4mk8lw+PBh+Pr6vrPsli1bkJWVha+//vq953n06BGSkpJgbW39n+tcFEboiIiIiIhIZURi9bhtQWBgIPr27QsfHx/UrVsXixYtQnp6Ovr16wcA6NOnDypVqlToGrzffvsNHTt2hLm5uUJ6Wloapk2bhi+//BJWVla4c+cOxo0bB2dnZ/j7+3+0dnBAR0RERERE5U737t3x7NkzTJ48GXFxcfDy8sK+ffvkC6XExsZC/Nb00ZiYGJw8eRIHDhwodDyJRILLly9j7dq1SElJgY2NDVq1aoUZM2Z81HvRcUBHRERERETl0vDhwzF8+HCl+yIiIgqlubm5QRAEpfl1dXWxf/9+VVavWDigIyIiIiIilVGXKZefCi6KQkREREREpKYYoSMiIiIiIpUpzdsWlEfsbSIiIiIiIjXFAR0REREREZGa4pRLIiIiIiJSGS6KUrIYoSMiIiIiIlJTjNAREREREZHKcFGUksXeJiIiIiIiUlOM0BERERERkeqIeA1dSWKEjoiIiIiISE1xQEdERERERKSmOOWSiIiIiIhUhrctKFmM0BEREREREakpRuiIiIiIiEhleNuCksXeJiIiIiIiUlMc0BEREREREakpTrkkIiIiIiKV4aIoJYsDOirk/JqrpV0FegefftVKuwpUhNTog6VdBSqCBnJKuwr0Du1Pdy/tKlAR/jIMKu0qUFEazS7tGlAZwQEdERERERGpDBdFKVnsbSIiIiIiIjXFAR0REREREZGa4pRLIiIiIiJSGS6KUrIYoSMiIiIiIlJTjNAREREREZHKMEJXshihIyIiIiIiUlOM0BERERERkerwtgUlir1NRERERESkpjigIyIiIiIiUlOccklERERERCojEnFRlJLECB0REREREZGaYoSOiIiIiIhURsRFUUoUe5uIiIiIiEhNcUBHRERERESkpjjlkoiIiIiIVEYk5qIoJYkROiIiIiIiIjXFCB0REREREakOF0UpUextIiIiIiIiNcUIHRERERERqQyvoStZjNARERERERGpKQ7oiIiIiIiI1BSnXBIRERERkcqIRIwZlST2NhERERERkZpihI6IiIiIiFSHi6KUKEboiIiIiIiI1BQHdERERERERGqKUy6JiIiIiEhlRGLGjEoSe5uIiIiIiEhNMUJHREREREQqI+KiKCWKEToiIiIiIiI1xQgdERERERGpDm8sXqLY20RERERERGqKAzoiIiIiIiI1xSmXRERERESkMlwUpWQxQkdERERERKSmGKEjIiIiIiLV4Y3FSxR7m4iIiIiISE1xQEdERERERKSmOOWSiIiIiIhURiTioigliRE6IiIiIiIiNcUIHRERERERqQ4XRSlR7G0iIiIiIiI1xQgdERERERGpDG8sXrIYoSMiIiIiIlJTjNCRWmpWQwxvFzF0NIGHzwTsOZeH5BdF529aQ4xmNSQKaYnPBSzbnSt/rK8DtPSWwMlaBC1NICkVOHElDzceCh+rGeWSWSMfOI7uD2PvatCxqYjzXw5F/K7DpV2tckEQBGxYvxYH9v2F9PQ0eHhWxdBhI2FTqfI7y+3ZvRPbt22BVJoMBwcnDB4yDK5u7vL9S5csQtSli0hOToKOji48PD3Rt98A2NrafewmqYXdu3dj67ZtkEqlcHRwwJAhQ+Dm5lZk/hMnTiBs3TrEx8ejko0N+n3zDerWqSPfLwgC1q1fj3379iE9PR2enp4YPmwYKlWqBACIj4/Hxt9/R1RUFKRSKczMzNC8eXP06N4dmpqahc735MkTDB8xAmKxGFu3bFF9B3wC+veqgvYtrWCgL8GV6FTMX34bj55mFplfV1eCAb2qoEl9c5gaa+LmvXQsXnkH0bfT5HlMjTUxpK8D6tQygYG+BqKuPceiX++887ik6I+ouwi7cAtJGZlwsTDGuGY1UM3KTGneXdcfYNrBiwppWhIxzgzvIH985PZjbL1yH9EJUjzPzMHGXp/BrYLJx2wCkUowQqdi2dnZpV0FpfLy8iCTyUq7GirR0FOMeu5i7Dmbh1X7cpGdC3zdXAOS9/w1J6QImLc1R76tPpCrsL9TAwksjIDfI/Kw/M9c3IiVoUtjCaxMP2JjyiGJvh5SL8fg6shppV2Vcmfb1s34c1c4hg7/DvMWLoGOjg4mBwe9833rxLEIrFr5C3r2+hqLliyHg6MjJgcHISVFKs/j7OyC70aNwc+//IZpM0MgCAImT5qAvLy8kmhWmXbs2DH8unIlvurVC0uWLIGDoyMmBQcjJSVFaf7r169j9pw58G/VCkuXLIGvry9mzJiB+/fvy/Ns2boVu3btwojhw7Fo4ULo6OhgUnCw/Hl8+PAhBJkMI0aMwIrlyzF40CDs3bsXoWvXFjpfbm4uZs+Zg6pVq36M5n8SenWujC/b2mDe8lsYPDYSLzNlmD+1GrQ0i55SNn64C+p4mWDmwhj0HXkR/1ySYuH06rAw05Ln+fEHT1hb6SBo1nV8M+oS4hKysHB6deho86tZcRy4+QgLTlzBoHru2NDzM7hWMMbw8NNIzsgqsoy+lgb2D2gj3/7s56+w/2VOHrxszDGiYbWPXf1Pn0hcels5VD5b/RaZTIa5c+fC2dkZ2trasLOzw6xZswAA48ePh6urK/T09ODo6Ijg4GDk5OTIy06dOhVeXl5YtWoVHBwcoKOj885zhYWFwdzcHFlZim84HTt2RO/eveWPd+7cCW9vb+jo6MDR0RHTpk1Dbu7rAciCBQtQvXp16Ovrw9bWFkOHDkVa2utf/kJDQ2FiYoJdu3bB09MT2traiI2N/U/9VFbU8xDj+BUZYh4JSEgBwk/nwVAPcLd993xtmQxIz3y9vXzrPd+2ggjnYmR4kiQgJQ04cVWGzBzA2pzzwFXp2f7juDllEeJ3HirtqpQrgiBgV/gOdOvxFer7NoCDgyNGjR6P5KQk/H3mVJHlwndsg3/rNvBr1Rp2dlUwdPh30NbWxsED++V5Wrdpi2rVa8DS0grOzi74uk8/JD57hoSE+JJoWpm2Y8cOtGndGq1atUIVOzuMGD4c2traOHDggNL8O3fuhE/t2ujSpQvs7OzQp08fODk5Yffu3QDyn8fw8HD06NEDvr6+cHBwwJjRo5GUlITTZ84AAHx8fBAYGIja3t6wtrZG/fr18WXnzjh9+nSh860NC4Nt5cpo0rjxx+sENdetfSWEbYnFyXPJuPMgA7MWxcDcTBuN61soza+lJUZTXwssD72HqOupeByXiTWbYvH46Ut0bGMNALC10UU1dyPMX34b0bfT8PDxS8xfcRvaWmL4NalQks1TW+sv3kanqvb4omoVOJob4YfmXtDRkGDntftFlhFBBAt9Hflmrq/4na2thx0G1XNHPTs+B+XJsmXLYG9vDx0dHdSrVw/nzp0rMm9oaChEIpHC9vZ3f0EQMHnyZFhbW0NXVxd+fn64devWR20DB3QAgoKCMHv2bAQHB+P69evYuHEjLC0tAQCGhoYIDQ3F9evX8dNPP2HlypVYuHChQvnbt29j27Zt2L59OyIjI995rq5duyIvLw+7du2SpyUkJGDPnj345ptvAORPt+nTpw++++47XL9+Hb/88gtCQ0Plg0wAEIvFWLx4Ma5du4a1a9fiyJEjGDdunMK5MjIyMGfOHKxatQrXrl1DxYoV/0s3lQkmBoChrgh3415HG7NygEeJAmwrvHvgZWYEBHbWwMgOGujUUAIjPcX9D58JqFpFDJ1XP6BWrSKChgS4H8cpl6T+4uPiIJUmw8urljxNX18frm7uiL5xXWmZnJwc3L59EzW9vOVpYrEYXl7eiIlWXiYz8yUOHdwPSysrWFiU7y9FOTk5uHX7Nry8vORp+f3nhRvR0UrL3IiOhletWgpptWvXluePi4uDVCpFrTeOqa+vDzc3N0TfuFFkXdLT02FoYKCQFhkZiZMnT2LosGEf2LLyw9pSB+ZmWjgflSJPS8/Iw42bL1DVzVBpGYlEBA2JCNk5ip8dWdky1PAwAgBovoruZee8/iwTBCA7V0AND2MVt+LTk5MnQ3RCCuq+MfASi0Soa1cBV+KSiyz3MicXbVfvw+e/7UPg7jO4k5RaEtUtn8Si0ts+wObNmxEYGIgpU6bg4sWLqFmzJvz9/ZGQkFBkGSMjIzx9+lS+PXjwQGH/3LlzsXjxYqxYsQJnz56Fvr4+/P39kZn58aZTl/sB3YsXL/DTTz9h7ty56Nu3L5ycnNCoUSMMGDAAADBp0iQ0aNAA9vb2aN++PcaMGYM//vhD4RjZ2dkICwtDrVq1UKNGjXeeT1dXF7169cKaNWvkaevXr4ednR2aNWsGAJg2bRomTJiAvn37wtHRES1btsSMGTPwyy+/yMt8//33+Oyzz2Bvb4/mzZtj5syZheqVk5ODn3/+GQ0aNICbmxv09N4awaghA538F2r6W6+J9ExAX6foF/HjRAE7T+dh/ZFc7DmXB1MDoF8rDWi9cRXplhN5EIuB8d00MamXBtrVk2DzsTxI04o8LJHakErzv+SYmCrOITYxMYVUKlVWBKmpzyGTyWCqrEyyYpk9f+5C187t0bXzF7hw/h/MmDVH6fVa5UlqaqrS/jM1MYE0WfmXTqlUClMTk8L5Xz1HBf8qPWYRz+OTJ0+wa/dutPn8c4W6LVi4EIGjRkH/E/hs+FjMTfP/hqUpitOSk1OyYWaqpawIXr7Mw5XoVPTtZgtzMy2IxUCrphVQ1c0I5q+mXD549BJxCZkY3NseBvoa0NAQoVfnyrC00JbnoaKlvMxCniDAXE9bId1cTweJ6cqnXNqbGmByS28saF8fM/x9IBOAfn8cQ/yLlyVRZSqjFixYgIEDB6Jfv37w9PTEihUroKenh9WrVxdZRiQSwcrKSr4VBIGA/OjcokWLMGnSJHTo0AE1atRAWFgYnjx5gvDw8I/WjnK/KMqNGzeQlZWFFi1aKN2/efNmLF68GHfu3EFaWhpyc3NhZGSkkKdKlSqoUKH4v0QPHDgQderUwePHj1GpUiWEhoYiICAAIlH+gCQqKgqnTp1SiMjl5eUhMzMTGRkZ0NPTw6FDhxASEoLo6GikpqYiNzdXYT8AaGlpvXeAmZWVVWj6Z26OGBqa2kWUKFnV7UVoV+/1YiYbj/67a3JuP3n9S2lCioBHiXn4vpMGqlYR4dKd/H3Na+ZH58IO5SIjU4C7rRhdG0uw5kAuElL+UzOISlzE0cNYtmSR/PHkaTM/6vmafdYCtWp5Izk5GTu2b8GckJmYO28RtLT45bQ0JSYmYlJwMBo3aoQ2rVvL039avBjNmjVD9erVS7F2ZU/LphUwZoiL/PH4Gdf+1XFmLoxB0AhXhK+ph9w8ATfvpOHwiWdwdcqPkublCZg4+wYmDHfBXxt9kZsn4EKUFGfOJ0PEWf4fRQ1rc9SwNn/jsRm6rDuEbVfvYaivZynWjFRN2XdbbW1taGsrfrfNzs7GhQsXEBQUJE8Ti8Xw8/PDmVdT2JVJS0tDlSpVIJPJ4O3tjR9//FF+HfK9e/cQFxcHPz8/eX5jY2PUq1cPZ86cQY8ePVTRxELK/YBOV1e3yH1nzpzBV199hWnTpsHf3x/GxsbYtGkT5s+fr5BPX1//g85Zq1Yt1KxZE2FhYWjVqhWuXbuGPXv2yPenpaVh2rRp6Ny5c6GyOjo6uH//Ptq1a4chQ4Zg1qxZMDMzw8mTJ9G/f39kZ2fLB3S6urryQWJRQkJCMG2a4uIUTTtNwmedJ39Qmz6WmEcCHiW+vnZQ49XYTl8HSHvjRzV9HSBeWvypkVk5QNILAWaGIgACTA2Auu4S/Lw7B8+e5+eJT5HBrqIIdVzF2HPu01hQhsqPuvV8FVaiLLj2N0UqhZnZ6y81KSlSODo6KT2GkZExxGJxochPSooUpmaKESJ9fX3o6+vDplJluLl7oGe3zjhz+iSaNmuuqiapHSMjI6X9J01JgamZ8pX4TE1NIX1rwRRpSoo8Ilfwb8HqlW/mcXJ0VCiXlJSECRMmwNPDAyNHjlTYFxUVhb///hvbtm2Tp8lkMrRt1w4jR46Ef6tWH9bYT8TJc8m4HvN6JURNzfyJTKYmWkiSvr5+3sxEC7fuFT1940lcJkZMvAwdbTH09SRIkuZg6lh3PI1/Pb3k5p00fDPqEvT1JNDUECMlNQe//K+mwkqYpJyJrjYkIhGS3loAJSkjExb6xftBWlMihlsFYzxKSf8YVSz3RKW4OImy77ZTpkzB1KlTFdISExORl5enEGEDAEtLS0QXMS3ezc0Nq1evRo0aNfD8+XPMmzcPDRo0wLVr11C5cmXExcXJj/H2MQv2fQzlfkDn4uICXV1dHD58WD7NssDp06dRpUoVTJw4UZ729jzZf2vAgAFYtGgRHj9+DD8/P9ja2sr3eXt7IyYmBs7OzkrLXrhwATKZDPPnz4dYnP+CeXu6ZXEFBQUhMDBQIe1/28rOTNzsXCD7rc+2Fy8FOFqJES/NH2RpaQKVLUQ4f7P4gy5NDcDMQITLLwX5YyD/GoY3yQS8d1BMVBbp6ekpTLMWBAGmpmaIiroER6f895aMjHTcjInG523bKz2GpqYmnJ1dcTnqEnwbNASQ/6U/KvIS2rbvoLTMq7NBgKCwgFR5pKmpCRdnZ0RGRaFBgwYA8vsvMjISX7RX3uce7u6IjIxEp44d5WmXLl2Ch3v+4NzKygqmpqaIjIqCk1P+QDw9IwMxMTFo27atvExiYiImTJgAZxcXjBo1Sv5ZUWDB/PkKKx+f+ftvbNmyBQvmz4e5uTnKq5cv8/D4peJMkKTkbNSuYYLb9/K/+OvpSuDhaojwfU/fe7zMLBkys2Qw0NdAXS9TLF97r1Ce9Iw8AHmobK0DNydDrNqgmu8ZnzJNiRjuFU3wz8Nn+MzJBgAgEwT88/AZutVwfE/pfHkyAbeTUtHI3vL9mUmtKPtu+3Z07t/y9fWFr6+v/HGDBg3g4eGBX375BTNmzFDJOf6Ncj+g09HRwfjx4zFu3DhoaWmhYcOGePbsGa5duwYXFxfExsZi06ZNqFOnDvbs2YMdO3ao5Ly9evXCmDFjsHLlSoSFhSnsmzx5Mtq1awc7Ozt06dIFYrEYUVFRuHr1KmbOnAlnZ2fk5ORgyZIlaN++PU6dOoUVK1b8q3ooC0FraJbtL2Fnb8jQuJoYSS8EpKQJ+KymBC8ygOg37hfXu4UE0Q8F/PNqkNfSW4ybjwSkpAsw1BWhWU0xZAJw9X7+/sTnQFKqgHb1JDhwUYaXWflTLp2sRf96micpJ9HXg77z6/uT6TlUhlFNd2QnP0fmw/d/QaJ/RyQS4YuOnbB500bY2FSCpaU11q8LhZm5Oer7NpTnmxg0Fr4NGqJd+44AgI6dvsTCBXPh7OIKV1c37Ny5A5lZmfBrmb/cd9zTpzhxPAK1vGvDyNgESYnPsHXLJmhracGnTt3SaGqZ0qlTJ8xfsAAuLi5wc3VF+M6dyMrKQsuWLQEA8+bNg7m5Ofr16wcA6NChA8aNH49t27ejbp06OHbsGG7duoWRI0YAyH8eO3bsiE2bNqGSjQ0sLS2xbt06mJubo8GrLxmJiYkYP2ECKlasiAH9++P58+fy+hRE9ezsFO8ReOvWLYjFYtjb23/sLlE7f+x+jL7dbPHo6Us8jc/EgF5VkJSchRN/J8rzLJpeHcf/TsT2vfnvYXVrmQAQ4eHjDFSy1sXQAAfEPs7A3sOvV35t1sACKak5iH+WBacqehg5wAknzibhn8iUkm2gmvra2xlTDlyAR0UTVLMyxcZLd/AyJw9feFYBAEzefx4VDHQxomH+VLhfz0ajupUpbE0M8CIrB+su3EJcagY6VrWXH/N5ZjbiXmTgWVp+JPXBq4vozfXyV8WkD/CBi5OokrLvtspYWFhAIpEgPl5xReb4+HhYWVkV61yampqoVasWbt++DQDycvHx8bC2tlY45psLZKlauR/QAUBwcDA0NDQwefJkPHnyBNbW1vj222/Rv39/jBo1CsOHD0dWVhbatm2L4ODgQiHbf8PY2Bhffvkl9uzZg45v/BILAP7+/vjzzz8xffp0zJmTv7CAu7u7PIJYs2ZNLFiwAHPmzEFQUBCaNGmCkJAQ9OnT5z/XSx2cui6DpgbQvp4EOlpAbIKA9UdykfdGgM7MUAQ9ndcDPCM9Eb5sJIauNpCRCcQ+E/DbvlwUzNaQCcDGo7loUUuCns0k0NIEkl/k3xLhzevv6L8zrl0NvofXyR97zvsBAPAwbDsu9w8qqhipwJdduiMzMxNLlyxCeloaPKtWw7TpIQrXucU9fYrU569XfmvctBmep6Zgw7q1+TfGdnTCtOk/yqf+aWpp4tq1K9i1czvS0tJgYmKKqtWqY+78n2Biwps4Nm3aFM9TU7F+3TokS6VwcnTEjOnT5f2X8OwZRG9Ezzw9PTF+3DisDQtDaGgoKlWqhODgYIWBVtcuXZCZmYnFS5YgLS0NVatWxYzp0+XP46VLl/DkyRM8efIEvd/6XPhr796P3+hPzMbtj6CrI8HYoS4w0NfAlRvPMWbaNYVVLG2sdGBs9HoRIH09DQzubY8KFtp48SIXEWcSsXL9feTlvS5jbqaF4f0dYWasiSRpNvYdTcDaPz6N2wuVhFaulSF9mYUVf99AUkYWXC2MsaRjA/mtCOJevFSYYfMiMxszD19CUkYWjLQ14V7RBKu7NYWj+et1EY7dfapw8/Ggv/4BAAyq547B9T1KqGVUUrS0tFC7dm0cPnxY/l1cJpPh8OHDGD58eLGOkZeXhytXruDzV4tOOTg4wMrKCocPH5YP4FJTU3H27FkMGTLkYzQDACAShLcnmVFJadGiBapWrYrFixeXdlUUTFtftiN05Z1PP97wtKxyiT5Y2lWgImiA72tlWd/AJ6VdBSrCX/573p+JSoXB0NmlXYUiZfxWemsx6PWfXuy8mzdvRt++ffHLL7+gbt26WLRoEf744w9ER0fD0tISffr0QaVKlRASEgIAmD59OurXrw9nZ2ekpKTgf//7H8LDw3HhwgV4euYvrjNnzhzMnj0ba9euhYODA4KDg3H58mVcv379vfer/rcYoSsFUqkUERERiIiIwM8//1za1SEiIiIiKne6d++OZ8+eYfLkyYiLi4OXlxf27dsnX9QkNjZW4RpkqVSKgQMHIi4uDqampqhduzZOnz4tH8wBwLhx45Ceno5BgwYhJSUFjRo1wr59+z7aYA5ghE7lYmNjFZ7Ut12/fh1NmjSBVCpFcHAwxowZU4K1Kx5G6Mo2RujKLkboyi5G6Mo2RujKLkboyi5G6JT7kAjdp4IROhWzsbFBZGTkO/ffv3+/xOpDRERERFSiuEJ4ieKATsU0NDSKvN0AERERERGRKnFAR0REREREqiMuO/c0Lg/Y20RERERERGqKAzoiIiIiIiI1xSmXRERERESkOlwUpUQxQkdERERERKSmGKEjIiIiIiKVEXFRlBLF3iYiIiIiIlJTjNAREREREZHqiBgzKknsbSIiIiIiIjXFAR0REREREZGa4pRLIiIiIiJSHTFvW1CSGKEjIiIiIiJSU4zQERERERGRyoi4KEqJYm8TERERERGpKQ7oiIiIiIiI1BSnXBIRERERkepwUZQSxQgdERERERGRmmKEjoiIiIiIVIeLopQo9jYREREREZGa4oCOiIiIiIhITXHKJRERERERqY6Ii6KUJEboiIiIiIiI1BQjdEREREREpDpixoxKEnubiIiIiIhITTFCR0REREREqsPbFpQo9jYREREREZGa4oCOiIiIiIhITXHKJRERERERqY6Yty0oSYzQERERERERqSlG6IiIiIiISHW4KEqJYm8TERERERGpKQ7oiIiIiIiI1BSnXBIRERERkeqIuChKSWKEjoiIiIiISE0xQkdERERERKojZsyoJLG3iYiIiIiI1BQjdEREREREpDq8hq5EMUJHRERERESkpjigIyIiIiIiUlOccklERERERKojYsyoJLG3iYiIiIiI1BQjdEREREREpDq8bUGJ4oCOChnsc7W0q0DvkBp9sLSrQEW45d6ytKtARfCI3lvaVaB32DDPorSrQEWQ/G1Q2lUgovfg8JmIiIiIiEhNMUJHRERERESqw/vQlShG6IiIiIiIiNQUI3RERERERKQ6vG1BiWJvExERERERqSlG6IiIiIiISHV4DV2JYoSOiIiIiIhITXFAR0REREREpKY45ZKIiIiIiFRHzJhRSWJvExERERERqSlG6IiIiIiISGUELopSohihIyIiIiIiUlMc0BEREREREakpTrkkIiIiIiLVETFmVJLY20RERERERGqKEToiIiIiIlIdRuhKFHubiIiIiIhITTFCR0REREREKsPbFpQsRuiIiIiIiIjUFAd0REREREREaopTLomIiIiISHW4KEqJYm8TERERERGpKQ7oiIiIiIhIdUSi0ts+0LJly2Bvbw8dHR3Uq1cP586dKzLvypUr0bhxY5iamsLU1BR+fn6F8gcEBEAkEilsrVu3/uB6fQgO6IiIiIiIqNzZvHkzAgMDMWXKFFy8eBE1a9aEv78/EhISlOaPiIhAz549cfToUZw5cwa2trZo1aoVHj9+rJCvdevWePr0qXz7/fffP2o7OKAjIiIiIqJyZ8GCBRg4cCD69esHT09PrFixAnp6eli9erXS/Bs2bMDQoUPh5eUFd3d3rFq1CjKZDIcPH1bIp62tDSsrK/lmamr6UdvBAR0REREREamOWFxqW1ZWFlJTUxW2rKysQlXMzs7GhQsX4Ofn90a1xfDz88OZM2eK1cyMjAzk5OTAzMxMIT0iIgIVK1aEm5sbhgwZgqSkpP/Wn+/BAR0REREREX0SQkJCYGxsrLCFhIQUypeYmIi8vDxYWloqpFtaWiIuLq5Y5xo/fjxsbGwUBoWtW7dGWFgYDh8+jDlz5uDYsWNo06YN8vLy/lvD3oG3LSAiIiIiIpUR/sXiJKoSFBSEwMBAhTRtbW2Vn2f27NnYtGkTIiIioKOjI0/v0aOH/P/Vq1dHjRo14OTkhIiICLRo0ULl9QAYoSMiIiIiok+EtrY2jIyMFDZlAzoLCwtIJBLEx8crpMfHx8PKyuqd55g3bx5mz56NAwcOoEaNGu/M6+joCAsLC9y+ffvDG1NMHNAREREREZHqiMSltxWTlpYWateurbCgScECJ76+vkWWmzt3LmbMmIF9+/bBx8fnved59OgRkpKSYG1tXey6fSgO6IiIiIiIqNwJDAzEypUrsXbtWty4cQNDhgxBeno6+vXrBwDo06cPgoKC5PnnzJmD4OBgrF69Gvb29oiLi0NcXBzS0tIAAGlpaRg7diz+/vtv3L9/H4cPH0aHDh3g7OwMf3//j9YOXkNHRERERETlTvfu3fHs2TNMnjwZcXFx8PLywr59++QLpcTGxkIsfh3/Wr58ObKzs9GlSxeF40yZMgVTp06FRCLB5cuXsXbtWqSkpMDGxgatWrXCjBkzPsp1fAVEgiAIH+3opJbioi+VdhXoHVI1zUu7ClSEW+4tS7sKVASP6L2lXQV6Bw1ZdmlXgYpQ4e9tpV0FKoJu70mlXYUipf29q9TObVD/i1I7d2nhlEsiIiIiIiI1xSmXRERERESkOqV424LyiBE6IiIiIiIiNcUBHRERERERkZrilEsiIiIiIlIZ4QPuB0f/HXubiIiIiIhITTFCR0REREREqsNFUUoUI3RERERERERqihE6IiIiIiJSHV5DV6LY20RERERERGqKAzoiIiIiIiI1xSmXZVhAQABSUlIQHh5e2lUhIiIiIioWgYuilCgO6Mqwn376CYIglHY1StWOPfuxKXw3kqXP4WRvh+8G9YOHq3OR+Y+e+hurN/yBuIRnqGRjhW/79EJ9n1ry/U079FBa7tu+X6Fn5/YAgO4DhyMuIVFh/6DePfFVlw4qaNGnTRAEbFi/Fgf2/YX09DR4eFbF0GEjYVOp8jvL7dm9E9u3bYFUmgwHBycMHjIMrm7u8v1LlyxC1KWLSE5Ogo6OLjw8PdG33wDY2tp97CaVO2aNfOA4uj+MvatBx6Yizn85FPG7Dpd2tdTa7t27sXXbNkilUjg6OGDIkCFwc3MrMv+JEycQtm4d4uPjUcnGBv2++QZ169SR7xcEAevWr8e+ffuQnp4OT09PDB82DJUqVQIAxMfHY+PvvyMqKgpSqRRmZmZo3rw5enTvDk1NTQDAo0ePsGTpUsTGxiI9PR3m5uZo1rQpvvrqK2holO+vBjv/3Ist23cgWZoCJwd7DBs8EO5urkXmP3byFNau34i4+ARUsrHGgIA+qFfHR74/bMPviDhxEs+eJUJDQwMuzk7o1+dreLw6Zlx8PDZs+gORl68gWZoCczNTtPisGXp16yJ/vki5TedjsPbMNSSlvYSrpSnG+9dF9UoW7y2379o9TNhxEs1cK2NRt8/k6cG7TmH35bsKeRs42uDnXi1UXnciVSrf79pKZGdnQ0tLq7SrAQAwNjYu7SqUqiMnTmPZ6nUIHDIAnq7O2LJ7L8ZMDcH6nxfA1KRw31y9EYMZ8xZjYO+e8K3jjcPHT2JiyDysXDAbjlVsAQDbQ1colDl7IRJzl/6Cpg3qKqR/06sr2rV6/Qaup6vzEVr46dm2dTP+3BWO7wPHwdLKChvWhWJycBB+XvFbka+rE8cisGrlLxg2fCRc3T2wK3w7JgcHYcWvq2FiYgoAcHZ2QbNmzVGhYkW8ePECv28Iw+RJE7Bq9TpIJJKSbOInT6Kvh9TLMXgYug0+W5eVdnXU3rFjx/DrypUYMXw43NzdER4ejknBwVj5668wMTEplP/69euYPWcO+gUEoG7duoiIiMCMGTOwZPFi2NvbAwC2bN2KXbt2YXRgIKysrBC2bh0mBQfjlxUroKWlhYcPH0KQyTBixAjYWFvjwYMH+GnxYmRmZmLggAEAAIlEghbNm8PZ2Rn6+vq4d+8eflq8GIIgICAgoOQ6qIyJOH4Sv6xajZHDhsDDzRXbd+5C0ORpWP3LMpgqeb6u3YjGj3Pno3/f3qhX1wdHI45j6qzZ+HnRfDjYVwEAVK5kg+HfDoK1lSWysrKxbecuTAieirUrl8PE2BgPHz2GTBDw3bAhqGRjjXsPYrFwyTJkZmZicP9+JdwD6mP/tfuYf/A8Jraph+qVLLDh3A0M/f0wdg75Amb6ukWWe5yShgWHLsLbtqLS/Q2dbDCtfQP5Yy0Jr076V7goSon6JHpbJpNh7ty5cHZ2hra2Nuzs7DBr1iwAwPjx4+Hq6go9PT04OjoiODgYOTk58rJTp06Fl5cXVq1aBQcHB+jovP+Lu0wmQ0hICBwcHKCrq4uaNWti69at8v0REREQiUQ4fPgwfHx8oKenhwYNGiAmJkbhODNnzkTFihVhaGiIAQMGYMKECfDy8pLvDwgIQMeOHeWPmzVrhpEjR2LcuHEwMzODlZUVpk6dqnDMlJQUDBgwABUqVICRkRGaN2+OqKioD+jNsuOPnXvQrlVzfO7XDPZ2lTF6yADoaGth76EIpfm37v4Ldb1romfn9rC3rYT+X3WHq6MDduzZL89jbmqisJ06dx61qnvCxspS4Vh6uroK+XSL8XdR3gmCgF3hO9Ctx1eo79sADg6OGDV6PJKTkvD3mVNFlgvfsQ3+rdvAr1Vr2NlVwdDh30FbWxsHD7x+3lq3aYtq1WvA0tIKzs4u+LpPPyQ+e4aEhPiSaFq58mz/cdycsgjxOw+VdlU+CTt27ECb1q3RqlUrVLGzw4jhw6GtrY0DBw4ozb9z50741K6NLl26wM7ODn369IGTkxN2794NIP91Fh4ejh49esDX1xcODg4YM3o0kpKScPrMGQCAj48PAgMDUdvbG9bW1qhfvz6+7NwZp0+flp/H2toarVq1gqOjIywtLVG/fn181qwZrl679vE7pQzbFr4TbfxboXXLFqhiZ4vvhg2BtrY29h9UHqXesWs36tT2RrcvO6GKrS0Cen8FZydH7PxzrzxP82ZN4e1VE9ZWVrCvYodvB3yDjIwM3L13HwBQp7Y3xn4/Ej7etWBtZYUG9eqia6eOOHn675Jostpad/Y6OtdyQUcvZzhVMMGkz+tDR1OC8Mg7RZbJk8nwQ/hJDGlSA5VMDZTm0ZSIYWGgK9+MdLU/VhOIVOaTGNAFBQVh9uzZCA4OxvXr17Fx40ZYWuZ/QTc0NERoaCiuX7+On376CStXrsTChQsVyt++fRvbtm3D9u3bERkZ+d7zhYSEICwsDCtWrMC1a9cwatQofP311zh27JhCvokTJ2L+/Pk4f/48NDQ08M0338j3bdiwAbNmzcKcOXNw4cIF2NnZYfny5e8999q1a6Gvr4+zZ89i7ty5mD59Og4ePCjf37VrVyQkJOCvv/7ChQsX4O3tjRYtWiA5Ofm9xy5LcnJycfPOPdSuWV2eJhaLUbtmdVyLuam0zLWYWwr5AaBOrZpF5k9OScGZ85fwud9nhfZt3LYT7b8egP7fT8Dv23cjNy/vP7SmfIiPi4NUmgwvr9dTXPX19eHq5o7oG9eVlsnJycHt2zdR08tbniYWi+Hl5Y2YaOVlMjNf4tDB/bC0soKFRQXVNoJIhXJycnDr9m2FH+ry/769cCM6WmmZG9HR8KpVSyGtdu3a8vxxcXGQSqWo9cYx9fX14ebmhugbN4qsS3p6OgwNlH+BBYAnT57g/IULqF6tWjFa9mnKycnBzdt34O1VQ54mFovh7VUT16NjlJa5Hh2jkB8AfLxr4UYR+XNycrB33wHo6+vBycGhyLqkZ2TA0LDo56u8y8nLw42nyajnYCVPE4tEqGdvjcuPnxVZ7pcTV2Cmr4NOtVyKzHP+QTw+W/AHOvy8E7P2nkVKRpZK6070Maj9lMsXL17gp59+wtKlS9G3b18AgJOTExo1agQAmDRpkjyvvb09xowZg02bNmHcuHHy9OzsbISFhaFChfd/OczKysKPP/6IQ4cOwdfXFwDg6OiIkydP4pdffkHTpk3leWfNmiV/PGHCBLRt2xaZmZnQ0dHBkiVL0L9/f/Trlz+dYvLkyThw4ADS0tLeef4aNWpgypQpAAAXFxcsXboUhw8fRsuWLXHy5EmcO3cOCQkJ0NbO/0Vp3rx5CA8Px9atWzFo0CCl7cnKUnyzysrOhnYpTzt9npqKPJms0NRKUxNjxD56rLRMckqK0vzJ0udK8+87chx6ujpo4qs43bJzuzZwdbSHkaEBrt64iV/XbUKSVIrh/fv8hxZ9+qTS/B8NTExNFdJNTEwhlUqVlklNfQ6ZTAZTJWUePXyokLbnz10IXb0SmZmZqFTZFjNmzeH1JVSmpaamKv37NjUxKfT3XUAqlRaa2mdqYiJ/DRX8q+yYRb3Onjx5gl27d2PAq+mWbwocPRq3b99GTk4O2rRpg969exerbZ+i56kv8p+vQv1vjIePHiktI5WmFJo6a2pijOQUxefi73P/YNbc+cjKyoKZqSnmzJgGY2Mjpcd8/OQpwnfvweBvAv5tUz550ows5AkCzN+aWmluoIP7Sco/8y/FJiA88jY2D2xb5HEbOtmghbsdKpkY4KH0BZYejcSwTYcRFtAaEvEnEQMpMQK4KEpJUvsB3Y0bN5CVlYUWLZRfsLp582YsXrwYd+7cQVpaGnJzc2FkpPgmWqVKlWIN5oD8aF5GRgZatmypkJ6dnY1ab/2qWqPG61/trK2tAQAJCQmws7NDTEwMhg4dqpC/bt26OHLkyDvP/+YxC46bkJAAAIiKikJaWhrMzc0V8rx8+RJ37iifghASEoJp06YppI0eNghjhn/7znp8Cv46FAG/po0KDV67d3j9Zu9kXwUamhqY//MqDOrTE1ocQMhFHD2MZUsWyR9Pnjbzo56v2WctUKuWN5KTk7Fj+xbMCZmJufMWlZlrXonKosTEREwKDkbjRo3QpnXrQvuDJkxAxsuXuHf3Llb99hu2WVmha9eupVDTT1vNGtWxYvFCPE9NxV/7D2DmnP9h8fy5hQaPiYlJ+GHKNDRp1ACft25VOpX9BKVn5WDizpOY3LY+TPWKvoSiddXXUVOXiqZwrWiKdsvCcf5BPOo5WJdEVYn+FbUf0OnqFn3h65kzZ/DVV19h2rRp8Pf3h7GxMTZt2oT58+cr5NPX1y/2+QoiaHv27JGvKFagICpW4M3ogejV8q0ymazY51Lm7YiESCSSHzMtLQ3W1taIiIgoVE7ZxfdA/nTVwMBAhTTp/aKn7ZQUYyMjSMRiSFMUf2mTpjyHmamJ0jJmJiZF5C+8gErUtRuIffwEU8Z+9966eLo6Iy8vD3Hxz2BX2ab4jfjE1a3nq7ASZcG1qSlSKczMXv+okJIihaOjk9JjGBkZQywWF4ospKRIYWqmGIHQ19eHvr4+bCpVhpu7B3p264wzp0+iabPmqmoSkUoZGRkp/fuWpqTA1MxMaRlTU1NIU1IK538VkSv4t2D1yjfzODk6KpRLSkrChAkT4OnhgZEjRyo9X8GPmVXs7CCTybB4yRJ07ty5XC42ZGxkmP98Fer/54UiogVMTU2QoiS/mYlifl0dHVSysUYlG2t4uruh78Ah2HfgEHp26yLPk5iUjDE/BMPT3R2jhiv+4EuKTPW0IRGJkJT+UiE9KS0TFgaFvxc+lL7Ak+fp+G7zUXma7NUq4rVnrUf4kA6wNTMsVK6yqSFM9bTxMPkFB3QfSOCiKCVK7XvbxcUFurq6OHy48AXLp0+fRpUqVTBx4kT4+PjAxcUFDx48+E/n8/T0hLa2NmJjY+Hs7Kyw2draFvs4bm5u+OeffxTS3n78oby9vREXFwcNDY1CdbOwUL6Mr7a2NoyMjBS20p5uCQCamhpwdXLAhctX5WkymQwXL19F1SKWj67q5qKQHwDOR15Wmn/voaNwc3KEs0OV99bl9t0HEItFMDVRPj2mvNLT04ONTSX5ZmdXBaamZoiKuiTPk5GRjpsx0XD38FR6DE1NTTg7u+LyG2VkMhmiIi/BzV15mXwCBAgKCxwRlTWamppwcXZG5BsLU8lkMkRGRsLD3V1pGQ9390LXcl+6dEme38rKCqampgrHTM/IQExMDNw9PORpiYmJGD9+PJxdXDBq1CiIizFdTCYIyM3NLbe3y9HU1ISrsxMuRV2Wp8lkMlyKugxPd+W3mfB0d8OlyMsKaRcvRcKjiPwFBEGm8P6VmJiEMUGT4OLshDHfjyjW81WeaUok8LA2w7l7cfI0mSDg3P041KhUeMaVg4Uxtg5qh80D28q3pq6VUcfeCpsHtoWVsZ7S88SnpiMlIwsWhkUHD4jKArWP0Ono6GD8+PEYN24ctLS00LBhQzx79gzXrl2Di4sLYmNjsWnTJtSpUwd79uzBjh07/tP5DA0NMWbMGIwaNQoymQyNGjXC8+fPcerUKRgZGcmv43ufESNGYODAgfDx8UGDBg2wefNmXL58GY5v/cL6Ifz8/ODr64uOHTti7ty5cHV1xZMnT7Bnzx506tQJPj4+7z9IGdKtQ1uE/LQc7s6OcHdxxtbde/EyMwtt/PKvS5y1cBkqmJthUJ+eAIAu7dtg5MTp2Bz+J+r71MKRE6cRc+cuxgxTvHYwPSMDEafOYmi/rwud82r0Tdy4eRu1qleFnq4OrkXfwtLVYWjZtPE7FxSg/GjxFx07YfOmjbCxqQRLS2usXxcKM3Nz1PdtKM83MWgsfBs0RLv2HQEAHTt9iYUL5sLZxRWurm7YuXMHMrMy4dfSHwAQ9/QpThyPQC3v2jAyNkFS4jNs3bIJ2lpa8KlTV1lV6D+Q6OtB3/n1/f30HCrDqKY7spOfI/Ph01KsmXrq1KkT5i9YABcXF7i5uiJ8505kZWXJp+3PmzcP5ubm8uupO3TogHHjx2Pb9u2oW6cOjh07hlu3bmHkiBEA8l9nHTt2xKZNm1DJxgaWlpZYt24dzM3N0eDVdd2JiYkYP2ECKlasiAH9++P589czFwqiekeOHoWGRAJ7e3toamri1q1bCA0NRZMmTcr1fei+7NgBcxf+BFcXZ7i5umDHzt3IzMyEv1/+ZR1z5i+Chbk5+gfkX2vY6Yv2GD1hIrZsD0e9Oj6IOH4CN2/fwfevImwvMzOxcfMW+NarC3MzUzxPTcWuP/9CYlIymjTKf19MTEzC6KBJsKxYAYO/CcDz1FR5fcyKiAwS0LueJ4J3nYKntTmqVbLAhrM38DInFx1q5s8ImbTzFCoa6mJkc29oa0jgXFGxLw118n+8LkjPyM7BiuOX4eduB3MDXTySvsCiwxdha2aIBo6cnUNl2yfxrh0cHAwNDQ1MnjwZT548gbW1Nb799lv0798fo0aNwvDhw5GVlYW2bdsiODi40FL/H2rGjBmoUKECQkJCcPfuXZiYmMDb2xs//PBDsY/x1Vdf4e7duxgzZgwyMzPRrVs3BAQE4Ny5c/+6XiKRCHv37sXEiRPRr18/PHv2DFZWVmjSpIl81U910rxxA6SkpmL1xi1IlqbA2aEK/jdlAsxeTR9NSEyEWPz6ottqHm4IHj0Cv63fjJXrNqGyjRVmBY2R34OuwOETpyEIAlo0aYi3aWlq4siJ0wjdtBXZOTmwrlgRXb/4HN06FH0RNb32ZZfuyMzMxNIli5CelgbPqtUwbXqIwnVucU+fIvX56y8sjZs2w/PUFGxYtzb/xsuOTpg2/Uf5FCdNLU1cu3YFu3ZuR1paGkxMTFG1WnXMnf+T/D51pDrGtavB9/A6+WPPefnvaw/DtuNy/6DSqpbaatq0KZ6npmL9unVIlkrh5OiIGdOny/++E549g+iNaIynpyfGjxuHtWFhCA0NRaVKlRAcHCy/Bx0AdO3SBZmZmVi8ZAnS0tJQtWpVzJg+Xf46u3TpEp48eYInT56gdx/FxZz+2pu/nL5ELMaWrVvx+PFjCIKAihUron27dujUqdNH7pGyrVmTRkh5/hxr1/8OqVQKJ0cH/Dh9CkxfTfXPf75ef+5U9XBH0NhAhK7bgDVh61HJxgZTJ06Q34NOIhbj4aPHOHh4DlJTU2FoZAg3FxcsnPMj7Kvk/3ByITIST54+xZOnT9EzoL9CfQ7+GV4i7VZH/lXtIc3IxPJjUUhMfwk3S1P83LM5zF9NuXz6PB2iD1iXQywS4VaCFLsv38GLzBxUMNSFr6M1hjX1gpZG+ZuC/J9xymWJEgnldW5FGdSyZUtYWVlh3bp178/8EcVFX3p/Jio1qZrm789EpeKWe8v3Z6JS4RG99/2ZqNRoyLJLuwpUhAp/byvtKlARdHtPen+mUpISGVFq5zbxalZq5y4tn0SETh1lZGRgxYoV8Pf3h0Qiwe+//45Dhw4p3FOOiIiIiEjdCB8SHqX/jAO6t8TGxsLTs+jFGK5fvw47O7si9xdXwfTIWbNmITMzE25ubti2bRv8/Pz+87GJiIiIiKh84IDuLTY2NoVWGHt7vyro6uri0KFDKjkWEREREVFZwdsWlCwO6N5SsOQ/ERERERFRWcfhMxERERERkZpihI6IiIiIiFSHi6KUKEboiIiIiIiI1BQjdEREREREpDJcFKVksbeJiIiIiIjUFAd0REREREREaopTLomIiIiISGUEcFGUksQIHRERERERkZpihI6IiIiIiFSGi6KULPY2ERERERGRmmKEjoiIiIiIVIc3Fi9RjNARERERERGpKQ7oiIiIiIiI1BSnXBIRERERkcoIjBmVKPY2ERERERGRmmKEjoiIiIiIVEbgoiglihE6IiIiIiIiNcUBHRERERERkZrilEsiIiIiIlIZQcSYUUlibxMREREREakpRuiIiIiIiEhlBHBRlJLECB0REREREZGaYoSOiIiIiIhUhtfQlSz2NhERERERkZrigI6IiIiIiEhNccolERERERGpjCDioigliRE6IiIiIiIiNcUIHRERERERqQxvW1CyGKEjIiIiIiJSUxzQERERERERqSlOuSQiIiIiIpXhfehKFnubiIiIiIhITTFCR0REREREKsNFUUoWI3RERERERERqihE6IiIiIiJSGV5DV7LY20RERERERGqKAzoiIiIiIiI1xSmXRERERESkMlwUpWQxQkdERERERKSmOKAjIiIiIiKVEUTiUts+1LJly2Bvbw8dHR3Uq1cP586de2f+LVu2wN3dHTo6OqhevTr27t2r2HZBwOTJk2FtbQ1dXV34+fnh1q1bH1yvD8EBHRERERERlTubN29GYGAgpkyZgosXL6JmzZrw9/dHQkKC0vynT59Gz5490b9/f1y6dAkdO3ZEx44dcfXqVXmeuXPnYvHixVixYgXOnj0LfX19+Pv7IzMz86O1QyQIgvDRjk5qKS76UmlXgd4hVdO8tKtARbjl3rK0q0BF8Ije+/5MVGo0ZNmlXQUqQoW/t5V2FagIur0nlXYVinTvzu1SO7eDk3Ox89arVw916tTB0qVLAQAymQy2trYYMWIEJkyYUCh/9+7dkZ6ejj///FOeVr9+fXh5eWHFihUQBAE2NjYYPXo0xowZAwB4/vw5LC0tERoaih49evzH1inHRVGokAxNo9KuAr2DBnJKuwpUBA4ayq4b7p+XdhXoHard2FXaVaAiPG3Qs7SrQEVwLO0KvENpLoqSlZWFrKwshTRtbW1oa2srpGVnZ+PChQsICgqSp4nFYvj5+eHMmTNKj33mzBkEBgYqpPn7+yM8PBwAcO/ePcTFxcHPz0++39jYGPXq1cOZM2c+2oCOUy6JiIiIiOiTEBISAmNjY4UtJCSkUL7ExETk5eXB0tJSId3S0hJxcXFKjx0XF/fO/AX/fsgxVYEROiIiIiIiUhlBVHoRuqCgoEJRtLejc58aDuiIiIiIiOiToGx6pTIWFhaQSCSIj49XSI+Pj4eVlZXSMlZWVu/MX/BvfHw8rK2tFfJ4eXl9SDM+CKdcEhERERGRygiCqNS24tLS0kLt2rVx+PBheZpMJsPhw4fh6+urtIyvr69CfgA4ePCgPL+DgwOsrKwU8qSmpuLs2bNFHlMVGKEjIiIiIqJyJzAwEH379oWPjw/q1q2LRYsWIT09Hf369QMA9OnTB5UqVZJfg/fdd9+hadOmmD9/Ptq2bYtNmzbh/Pnz+PXXXwEAIpEI33//PWbOnAkXFxc4ODggODgYNjY26Nix40drBwd0RERERERU7nTv3h3Pnj3D5MmTERcXBy8vL+zbt0++qElsbCzE4tcTGhs0aICNGzdi0qRJ+OGHH+Di4oLw8HBUq1ZNnmfcuHFIT0/HoEGDkJKSgkaNGmHfvn3Q0dH5aO3gfeiokLt37pR2FYiIVIq3LSjbeNuCsitPxN/+yypHJ6fSrkKRbt15UGrndnGqUmrnLi28ho6IiIiIiEhN8WcXIiIiIiJSmdK8sXh5xAgdERERERGRmuKAjoiIiIiISE1xyiUREREREakMp1yWLEboiIiIiIiI1BQjdEREREREpDKM0JUsRuiIiIiIiIjUFCN0RERERESkMozQlSxG6IiIiIiIiNQUB3RERERERERqilMuiYiIiIhIZQSBUy5LEiN0REREREREaooROiIiIiIiUhkuilKyGKEjIiIiIiJSUxzQERERERERqSlOuSQiIiIiIpXhlMuSxQgdERERERGRmmKEjoiIiIiIVIYRupLFCB0REREREZGa4oCOiIiIiIhITXHKJRERERERqYwgcMplSWKEjoiIiIiISE0xQkdERERERCoj46IoJYoROiIiIiIiIjXFCB0REREREakMb1tQshihIyIiIiIiUlMc0BEREREREakpTrkkIiIiIiKV4W0LShYjdERERERERGqKEToiIiIiIlIZLopSshihIyIiIiIiUlMc0BEREREREakpTrkkIiIiIiKV4aIoJYsROiIiIiIiIjXFCB0REREREakMF0UpWYzQERERERERqSmVDOiaNWuG77//XhWHojewX4mIiIhI3QiCqNS28ohTLj9hAQEBSElJQXh4eGlXpUiCIGDd+vXYt28f0tPT4enpieHDhqFSpUrvLLd7925s3bYNUqkUjg4OGDJkCNzc3OT7s7OzsXLlShw7fhw5OTmo7e2NYcOGwdTUVJ4nISEBS5ctw+XLl6GjowM/Pz/0CwiARCJ5fZycHGzcuBFHjxxBslQKMzMz9OrVC/6tWgEAcnNzsfmPP3Do0CEkJSWhcuXK+KZfP/j4+Ki4p0rf+/r8bSdOnEDYunWIj49HJRsb9PvmG9StU0e+/33PfXx8PDb+/juioqIgfdX3zZs3R4/u3aGpqVnofE+ePMHwESMgFouxdcsW1XdAGVYWn5tHjx5hydKliI2NRXp6OszNzdGsaVN89dVX0NDgR8+/YdbIB46j+8PYuxp0bCri/JdDEb/rcGlX65Oz68892LJtB5JfvZ6GfTsI7m6uReY/fuIkQtdvQHx8AirZ2GBAv76oWyf/MyA3NxehYetx7vwFPI2Lg76+Pry9aqJ/QB+Ym5sDAKIuX8HYoIlKj71k4Xy4ubqovpFqoKTf1wDgxYsX+Hn5cpw9exZisRgNGzbEt4MHQ1dXt9D5yvNnDpU9nHJZBmVnZ5d2FUrMlq1bsWvXLowYPhyLFi6Ejo4OJgUHv7MPjh07hl9XrsRXvXphyZIlcHB0xKTgYKSkpMjz/PLrrzh77hx+CArC3DlzkJScjJkzZ8r35+XlYcqUKcjNycH8efMwOjAQBw8exLp16xTOFRISgsjISHz//fdYtXIlJowfj8pvvPmvDQvDX3/9hSFDhuCXFSvw+eefY8bMmbh9547qOqkMKE6fv+n69euYPWcO/Fu1wtIlS+Dr64sZM2bg/v378jzve+4fPnwIQSbDiBEjsGL5cgweNAh79+5F6Nq1hc6Xm5uL2XPmoGrVqh+j+WVaWX1uJBIJWjRvjlkzZ2Llr79i8KBB2Ld/P9avX/8xu+OTJtHXQ+rlGFwdOa20q/LJijh+Ar+s/A1f9+qBnxcvhKODPX4IngJpEa+na9dv4Me589C6VUssX7wIDXzrYerMH3Hv/gMAQFZWFm7duYOvenbHz4sXYsrECXj46DEmT58lP4anhzs2rVursLXxbwUrS0u4ujiXRLPLnNJ4XwOAuXPnIjY2Fj/OmoWpU6fi6tWrWLx4caHzlefPHCqbPnhAl56ejj59+sDAwADW1taYP3++wv5169bBx8cHhoaGsLKyQq9evZCQkAAg/9cRZ2dnzJs3T6FMZGQkRCIRbt++DUEQMHXqVNjZ2UFbWxs2NjYYOXJkseomlUrRp08fmJqaQk9PD23atMGtW7fk+0NDQ2FiYoL9+/fDw8MDBgYGaN26NZ4+fVqs4wcEBKBjx4748ccfYWlpCRMTE0yfPh25ubkYO3YszMzMULlyZaxZs0ah3Pjx4+Hq6go9PT04OjoiODgYOTk58v1Tp06Fl5cXVq1aBQcHB+jo6Cg9/549e2BsbIwNGzYAyP9S1a1bN5iYmMDMzAwdOnSQv3lNnToVa9euxc6dOyESiSASiRAREVGsdpYUQRAQHh6OHj16wNfXFw4ODhgzejSSkpJw+syZIsvt2LEDbVq3RqtWrVDFzg4jhg+HtrY2Dhw4ACD/b/TAgQMYOHAgvLy84OLigsBRo3D9xg3ciI4GAFy8eBGxDx9i7NixcHJyQp06ddCnd2/s/vNP+XNz/vx5XLlyBTOmT0etWrVgaWkJDw8PhTfwI0eOoHu3bqhbpw6sra3Rrm1b1PHxwfbt2z9iz5W89/X523bu3Amf2rXRpUsX2NnZoU+fPnBycsLu3bsBFO+59/HxQWBgIGp7e8Pa2hr169fHl5074/Tp04XOtzYsDLaVK6NJ48YfrxPKqLL63FhbW6NVq1ZwdHSEpaUl6tevj8+aNcPVa9c+fqd8op7tP46bUxYhfueh0q7KJ2vbjp1o07oV/Fv6oYqdHb4bPhTaOtrYf0B5n4fv2o06tb3R7cvOsLOzRUDvr+Hs5Ihdf+4BAOjr62POrBlo2rgRbCtXhoe7O4YPGYxbt28jIeEZAEBTUxNmZqbyzcjIEKf/PotWLVtAJCqf08dK430tNjYW5y9cwHcjR8Ld3R3VqlbFkG+/xbHjx5GUlKRwvvL8mVNcslLcyqMPHtCNHTsWx44dw86dO3HgwAFERETg4sWL8v05OTmYMWMGoqKiEB4ejvv37yMgIAAAIBKJ8M033xQa8KxZswZNmjSBs7Mztm3bhoULF+KXX37BrVu3EB4ejurVqxerbgEBATh//jx27dqFM2fOQBAEfP755wqDp4yMDMybNw/r1q3D8ePHERsbizFjxhS7/UeOHMGTJ09w/PhxLFiwAFOmTEG7du1gamqKs2fP4ttvv8XgwYPx6NEjeRlDQ0OEhobi+vXr+Omnn7By5UosXLhQ4bi3b9/Gtm3bsH37dkRGRhY678aNG9GzZ09s2LABX331FXJycuDv7w9DQ0OcOHECp06dkg9Qs7OzMWbMGHTr1k0+YH369CkaNGhQ7HaWhLi4OEilUtTy8pKn6evrw83NDdE3bigtk5OTg1u3b8PrjTJisRheXl7ywdqtW7eQm5urcFxbW1tUrFBBftwb0dGwt7dXmIJZu3ZtZGRk4EFsLADg77Nn4eLigi1bt+Lr3r0xYMAArFy1CllZWQr10dLSUqijlrY2rn1CX1qL0+dvuxEdDa9atRTSateuLc//b557IH+wbmhgoJAWGRmJkydPYuiwYR/YMvVX1p+bNz158gTnL1xA9WrVitEyopJX8Hqq9dbrqZZXzSJfT9ejo1HLq6ZCmo+3d5H5gfzXikgkgr6BvtL9Z86ew4sXL+Df0u/DG/EJKK33tRvR0TAwMICr6+vptbVq1YJIJEJ0TIw8rTx/5lDZ9UEXMqSlpeG3337D+vXr0aJFCwDA2rVrUblyZXmeb775Rv5/R0dHLF68GHXq1EFaWhoMDAwQEBCAyZMn49y5c6hbty5yXl2jVBC1i42NhZWVFfz8/KCpqQk7OzvUrVv3vXW7desWdu3ahVOnTskHLhs2bICtrS3Cw8PRtWtXAPlvFCtWrICTkxMAYPjw4Zg+fXqx+8DMzAyLFy+GWCyGm5sb5s6di4yMDPzwww8AgKCgIMyePRsnT55Ejx49AACTJk2Sl7e3t8eYMWOwadMmjBs3Tp6enZ2NsLAwVKhQodA5ly1bhokTJ2L37t1o2rQpAGDz5s2QyWRYtWqV/Be8NWvWwMTEBBEREWjVqhV0dXWRlZUFKyurItuTlZWlMEApSNPW1i52n/xbUqkUABQGVQBgamIi3/e21NRUyGQypWUePXwoP66GhgYM3vpyaWJqiuRXx5VKpTAxMVHc/+qxNDkZcHJCXFwcrl27Bi1NTQRPmoTnqalYtmwZXqSmIjAwEABQ29sb23fsQLVq1WBtbY3IyEicPn0aeXl5H94hZVRx+vxtUqkUpm/175vP67957p88eYJdu3djwIABCnVbsHAhxo4ZA309vQ9q16egLD83BQJHj8bt27eRk5ODNm3aoHfv3sVqG1FJk7+elLw+Hj58rLSMVJpSKL+JiYn8s+Zt2dnZWLVmLZo1bVLke9a+AwdR27sWKlhYfHAbPgWl9b4mlUphbGyssF8ikcDQ0FCep7x/5nyI8ro4SWn5oAjdnTt3kJ2djXr16snTzMzMFC5SvXDhAtq3bw87OzsYGhrKByCxr6IeNjY2aNu2LVavXg0g/6LXrKws+YCra9euePnyJRwdHTFw4EDs2LEDubm5763bjRs3oKGhoVA3c3NzuLm54cYbvyrr6enJB3NA/rSggimhxVG1alWIxa+7zdLSUiGCKJFIYG5urnDMzZs3o2HDhrCysoKBgQEmTZok748CVapUUTqY27p1K0aNGoWDBw/K+xIAoqKicPv2bRgaGsLAwAAGBgYwMzNDZmYm7nzA9VshISEwNjZW2FasWFHs8h/iyNGj6NS5s3zLLeODHplMBpFIhHHjxsHNzQ1169TBoIEDcejwYfkgePC336KSjQ0GDR6M9l98gZ+XL0dLPz+FvxH67xITEzEpOBiNGzVCm9at5ek/LV6MZs2aFTuKT6pX1HNTIGjCBCxZsgTjx43DuXPnsG3btlKoJVHpy83NxcyQuQAEjBw2RGmeZ4mJuHDxElq3almylaNi4WcOlVUqXWosPT0d/v7+8Pf3x4YNG1ChQgXExsbC399f4aLTAQMGoHfv3li4cCHWrFmD7t27Q+/VLx22traIiYnBoUOHcPDgQQwdOhT/+9//cOzYMaUr232ot48hEokgCMJ/Kq8sTSbLn8V75swZfPXVV5g2bRr8/f1hbGyMTZs2Fbr2UF9f+dSLWrVq4eLFi1i9ejV8fHzk0bi0tDTUrl1bfj3dm5QNDIsSFBQkjzYVePzGdFFVql+vHtzfGPwXTIUtWCWvgDQlBU6OjkqPYWRkBLFYXChSIE1JgemrY5iamiI3N1ceFS6QIpXC7NWvc6amprh586bCMQouti44jpmZGczNzRWeG1tbWwiCgMTERFSqVAkmxsaYPHkysrOzkZqaCnNzc6xes+adUVF1U5w+f5upqWmhRQSkKSnyX0cL/i3Oc5+UlIQJEybA08Oj0PW0UVFR+PvvvxUGCTKZDG3btcPIkSPlq5F+qsryc1Og4P2oip0dZDIZFi9Zgs6dOyusJktUFshfT0peH2amJkrLmJqaFMqfkpIi/6wpkJubi5mz5yLhWQLm/jizyOjO/oOHYGhoCN9675+Z9Kkqrfc1U1NTPH/+XOEYeXl5ePHihbx8ef/MobLrg8IITk5O0NTUxNmzZ+VpUqlU/sU4OjoaSUlJmD17Nho3bgx3d3el0a/PP/8c+vr6WL58Ofbt26cwTRMAdHV10b59eyxevBgRERE4c+YMrly58s66eXh4IDc3V6FuSUlJiImJgaen54c0U6VOnz6NKlWqYOLEifDx8YGLiwsePHhQ7PJOTk44evQodu7ciREjRsjTvb29cevWLVSsWBHOzs4KW8GUAS0trfdO/dPW1oaRkZHC9rGmW+rp6cHGxka+2dnZwdTUFJFRUfI86RkZiImJgbuHh9JjaGpqwsXZWaGMTCZDZGQkPNzdAQAuLi7Q0NBQuBbx0aNHSHj2TH5cD3d33L9/X2HFrIuXLkFPTw92dnYAAE9PTyQnJ+Ply5fyPI8fP4ZYLIbFW1NhtLS0YGFhgby8PJw6dQq+9ev/u04qg4rT52/zcHcvdC3opUuX5PmtrKyK9dwnJiZi/PjxcHZxwahRowpFPhfMn49lS5fKt6+//hq6urpYtnQpGvj6/teml3ll+blRRiYIyM3N/aAf0YhKivz1FPn26+lyka8nT3d3XIq6rJB28ZLi669gMPf4yRPMnjUDRkZGSo8lCAIOHDyMls0/K9e39iit9zUPd3ekpaUpLKYXGRUFQRDkP0aX98+cDyFAVGpbefRBAzoDAwP0798fY8eOxZEjR3D16lUEBATIP8jt7OygpaWFJUuW4O7du9i1axdmzJhR6DgSiQQBAQEICgqCi4sLfN94EYSGhuK3337D1atXcffuXaxfvx66urqoUqXKO+vm4uKCDh06YODAgTh58iSioqLw9ddfo1KlSujQocOHNFOlXFxcEBsbi02bNuHOnTtYvHgxduzY8UHHcHV1xdGjR7Ft2zb5jca/+uorWFhYoEOHDjhx4gTu3buHiIgIjBw5Ur4gi729PS5fvoyYmBgkJiYqLA5TFohEInTs2BGbNm3C33//jXv37mH+vHkwNzdXeGOcEBSEXa9WqgKATp06Yd++fTh46BBiY2OxdNkyZGVloWXL/Ckq+vr6aNWqFVauXImoqCjcunULCxYuhIeHh/zN3dvbG3a2tvjfvHm4e/cuLly4gLCwMLRv1w5aryKunzVrBkNDQyxYuBAPYmNx5coV/Pbbb2jVsqV80BsdHY1Tp07h6dOnuHr1KiYFB0MQBHTp0qWkurFEvK/P582bp7DYUYcOHXDhwgVs274dDx8+xPr163Hr1i20b98eQPGe+8TERIyfMAEVKlbEgP798fz5cyQnJyM5OVl+Hjs7O9jb28s3C3NziMVi2Nvbw9DQsAR7qPSU1efmyNGj8oWnnj59iuPHjyM0NBRNmjQp119W/wuJvh6MarrDqGb++5ieQ2UY1XSHjq11Kdfs0/Flpw7Yu/8ADhw6jNjYh1i8bDkyMzPh3zJ/3YC58xfit9DXt+fo+EV7nL9wEVu370Dsw0cI27ARN2/fxhft2gLIH8zN+HE2bt66jQljRkOWJ0NyshTJydJCn8mRUZcRFx+P1v6M8pTG+5qdnR18atfGT4sXIyYmBteuXcPyn39G0yZN5PcM5GcOlVUf/Kn6v//9D2lpaWjfvj0MDQ0xevRoeYi6QoUKCA0NxQ8//IDFixfD29sb8+bNwxdffFHoOP3798ePP/6Ifv36KaSbmJhg9uzZCAwMRF5eHqpXr47du3fLX0zvsmbNGnz33Xdo164dsrOz0aRJE+z9f3v3HRbF1bYB/N6l996lgxRRARV7N8YeS9TEXqLRiCZ2ScT6KmpswZhoYm9Rk9jjZ9fE3kGNiIIiimChdxbY7w/i6kqRmHVnF+7fde0V9syZ4RknsDzznDnn4EGFDNV8V926dcP48eMRHByM/Px8dO7cGaGhoZg1a9a/Oo6XlxdOnDiBVq1aQUNDA0uWLMFff/2FqVOnomfPnsjMzISDgwPatm0ru/s3YsQInDp1CvXr10dWVhZOnjyJVq1aKf4k/4PeH3+MvLw8hK9YgaysLNSqVQtz58yRmzkyMTERGa8Ng2jZsiXSMzKwZfNmpKSmwt3NDXPnzJF72PnzkSMhFonwv3nzShYWr1cPY774QrZdQ0MDs2bNwvcrV2LCxInQ0dFBu3bt5CZs0NPTw/x58/Djjz/iyy+/hJGREVo0b45BgwbJ+hRIJNi4aROSkpKgp6eHBvXrY/KkSaUmZFF3b/s3f/b8OUSvVWh8fX0xdcoUbNy0CRs2bICDgwNCQ0Ph4uIi6/O2a3/9+nU8efIET548wcDX/s0B4P8OHnz/J60mVPXaaIjF+PW335CQkACpVApra2t07dIFPXr0eM//IlWXST0/ND7+aq1M38Ulk3E92rQLN4aHCBVWldKqRXOkp6dj05ZtJQtau7lh3pxZ8j9Pry0lUMvXByGTJ2LD5q1Yv3Ez7B3sMWv613B1KbkJ/SI5GecvXgIAjB77pdz3+jZsHurWefUs1qEjR+Hr4w0nxxqo7oT4vQYAU6ZMwQ8//ICQr7+GSCRC06ZNMXrUKKWdd1XCSVGUSyQVaOzL6dOn0bZtWzx69Ag2NjZChEDluF/FFsUmIory7iR0CFQBv6h9QodA5SgSsaKuqtxem+RP1Zy9nSXY927qW7VuqleG0n9K8/Pz8fz5c8yaNQu9e/dmMkdEREREVIVU12fZhKL0udV/+eUXODs7Iy0tDYsWLar0fqdPn5ZNz1/WSxEqOv7p06cV8j2IiIiIiIgURekVuiFDhmDIkCH/er/69euXmsFI0So6voODw3v93kRERERERP+W2gyM1tPTg4eHx3v9Hu/7+EREREREVV0xV6dRKqUPuSQiIiIiIiLFUJsKHRERERERqT5OiqJcrNARERERERGpKSZ0REREREREaopDLomIiIiISGGkUg65VCZW6IiIiIiIiNQUK3RERERERKQwUi5boFSs0BEREREREakpVuiIiIiIiEhhirlsgVKxQkdERERERKSmmNARERERERGpKQ65JCIiIiIiheGyBcrFCh0REREREZGaYkJHREREREQKI5UK93pfUlJS0L9/fxgbG8PU1BTDhw9HVlZWhf3Hjh0LLy8v6OnpwcnJCePGjUN6erpcP5FIVOq1ffv2fxUbh1wSERERERFVoH///khMTMTRo0chkUgwdOhQjBw5Etu2bSuz/5MnT/DkyRMsXrwYvr6+ePjwIUaNGoUnT57gt99+k+u7fv16dOjQQfbe1NT0X8XGhI6IiIiIiKgcUVFROHToEC5fvoz69esDAFasWIFOnTph8eLFsLe3L7WPn58ffv/9d9l7d3d3zJs3DwMGDEBhYSE0NV+lYaamprC1tX3n+DjkkoiIiIiIFEYKkWCv/Px8ZGRkyL3y8/P/0/mcP38epqamsmQOANq1awexWIyLFy9W+jjp6ekwNjaWS+YAYMyYMbC0tERQUBDWrVsH6b8cO8qEjoiIiIiIqoSwsDCYmJjIvcLCwv7TMZOSkmBtbS3XpqmpCXNzcyQlJVXqGC9evMDcuXMxcuRIufY5c+Zg586dOHr0KHr16oUvvvgCK1as+FfxccglEREREREpTPF7nJzkbUJCQjBhwgS5Nh0dnTL7Tps2DQsXLqzweFFRUf85poyMDHTu3Bm+vr6YNWuW3LbQ0FDZ1wEBAcjOzsa3336LcePGVfr4TOiIiIiIiKhK0NHRKTeBe9PEiRMxZMiQCvu4ubnB1tYWz549k2svLCxESkrKW599y8zMRIcOHWBkZITdu3dDS0urwv4NGzbE3LlzkZ+fX+nzYEJHREREREQKoy4Li1tZWcHKyuqt/Ro3boy0tDRcvXoV9erVAwCcOHECxcXFaNiwYbn7ZWRk4MMPP4SOjg727dsHXV3dt36viIgImJmZVTqZA5jQERERERERlcvHxwcdOnTAiBEjsGrVKkgkEgQHB+OTTz6RzXCZkJCAtm3bYtOmTQgKCkJGRgbat2+PnJwcbNmyRTZBC1CSSGpoaGD//v14+vQpGjVqBF1dXRw9ehTz58/HpEmT/lV8TOiIiIiIiIgqsHXrVgQHB6Nt27YQi8Xo1asXwsPDZdslEgmio6ORk5MDALh27ZpsBkwPDw+5Yz148AAuLi7Q0tLCypUrMX78eEilUnh4eGDp0qUYMWLEv4pNJP2382JSlXc/NlboEIiIFCrKu5PQIVAF/KL2CR0ClaNIxHv/qsrN3V3oEMp18JpEsO/dKbDiZ9SqIi5bQEREREREpKZ424WIiIiIiBSmGOoxKUpVwQodERERERGRmmJCR0REREREpKY45JKIiIiIiBSGUy4qFyt0REREREREaooVOiIiIiIiUhiplJOiKBMrdERERERERGqKFToiIiIiIlKYYj5Dp1Ss0BEREREREakpJnRERERERERqikMuiYiIiIhIYbhsgXKxQkdERERERKSmWKEjIiIiIiKFkYLLFigTK3RERERERERqigkdERERERGRmuKQSyIiIiIiUhiuQ6dcTOioFPPflgkdAlWg67m+QodA5di62FLoEKgcflH7hA6BKnDLp5vQIVA57vx6R+gQqBwT3YWOgFQFEzoiIiIiIlIYLlugXHyGjoiIiIiISE0xoSMiIiIiIlJTHHJJREREREQKwyGXysUKHRERERERkZpihY6IiIiIiBSmWCoSOoRqhRU6IiIiIiIiNcUKHRERERERKQyfoVMuVuiIiIiIiIjUFBM6IiIiIiIiNcUhl0REREREpDAccqlcrNARERERERGpKVboiIiIiIhIYYpZoVMqVuiIiIiIiIjUFBM6IiIiIiIiNcUhl0REREREpDBSqUjoEKoVVuiIiIiIiIjUFCt0RERERESkMFy2QLlYoSMiIiIiIlJTrNAREREREZHCcNkC5WKFjoiIiIiISE0xoSMiIiIiIlJTHHJJREREREQKw0lRlIsVOiIiIiIiIjXFCh0RERERESkMK3TKxQodERERERGRmmJCR0REREREpKY45JKIiIiIiBSG69ApFyt0REREREREaooVOiIiIiIiUhhOiqJcrNARERERERGpKVboiIiIiIhIYYqLhY6gemGFjoiIiIiISE0xoSMiIiIiIlJTHHJJREREREQKw0lRlIsVOiIiIiIiIjXFCh0RERERESkMK3TKxQodERERERGRmmJCR0REREREpKY45JKIiIiIiBSmmEMulYoVOiIiIiIiIjXFCh0RERERESmMVNBZUUQCfm9hsEJHRERERESkplihIyIiIiIiheGyBcrFCp2ac3FxwfLly4UOg4iIiIiIBMAKnQLFxcXB1dUV169fh7+/v9DhVFnaAS2g27AtRAbGKHqWgNxjv6Io8WHZff0aQr/zQLk2aaEE6UvGy96bTv2+zH1zT+5G/qXjigu8mhjezxldP7CFoYEGbt7JwJIfY/A4Ma/c/np6GvisnzNaNLKAmYkW7j7IRvjPsbgTkyXrY2aihdGDXdEgwBSGBpqI/Dsdy3+KrfC41d3eAwfx667dSElNg7urC8Z8PgLeXjXL7f/nmbPYuGUbkp4+g4O9HT4bMggNG9SXbd+09RecOn0Gz5+/gKamJjw93DF00AD4/HPMpKdPsXX7TkTcuImU1DRYmJuhbetW6NfnY2hpab3v01Ur+w78gV9/342U1FS4ubpizKiRFV6bv06fwYYtW/H06TM42Nvjs6GDEfTPtSksLMSGTVtw6cpVJCYlwcDAAIH+dTF8yCBYWFgAACJv3MTkkG/KPPaKZUvgVdNT8SdZzZg3qw+3icNhEugHXXtrXOn1BZ7u4+fH+yaVSnH16ApEXfoVBbkZsHUJRLMeM2Fi6VLuPleOrsC1Yyvl2kysXNF30v/J3kdd3IGYiAN4kXAbkvxsDJ51CTp6xu/rNIj+M5VI6AoKCqCtrS10GKQGtLwDodemB3KP7EDhkzjo1G8Ngz5jkPnzHEhzssrcR5qfi4yf57zWIL89/fsQ+e/hVgt6HftBEh2h4Oirvn49a6BXZ3vM/y4aiU/zMLy/C5bM8sPA4KsokJQ9/mJqsCfcnPTxv2XReJFSgPatrLFsTm0MDL6KFykFAID5X/uisEiKkHm3kZ1bhL7dHGR98vKLlXmKauHUX2ewes06jBszGj5eNbFr7z6EzJiNdatXwszUtFT/v6PuYP6iJRg+eCAaBtXHyVN/Yda8Bfhh+RK4ujgDAGo42CN41EjY2dogP78Av+/dh2mhs7Dx5x9hamKCR48TUCyV4ssxo+Fgb4cHD+OxbMVK5OXl4fPhQ5X8L6C6Tv11Gqt/XotxwV/A26smdu3Zh69DZ2LtTz+WfW1uR2H+osUYNmQQGjVogBN//olZ/5uPld8tg6uLM/Lz83EvNhb9P+0LN1cXZGVl4YfVazBjzjys/G4pAMDXxxvbN2+UO+7GLVtxPSISNT09lHHaVZ6GgT4ybkTj0YbfUf+3lW/fgRQi8s81uHV2M1r1WQAj8xq4cuQ7HFz7GXpP+AOaWjrl7mdm44nOI9bJ3ovF8n8OFxbkwbFmczjWbI5Lh5a+t/irsmJ+NCvVOw+5LC4uxqJFi+Dh4QEdHR04OTlh3rx5AICpU6eiZs2a0NfXh5ubG0JDQyGRSGT7zpo1C/7+/lizZg1cXV2hq6v71u936NAhNGvWDKamprCwsECXLl0QGxsr1+fcuXPw9/eHrq4u6tevjz179kAkEiEiIkLW59atW+jYsSMMDQ1hY2ODgQMH4sWLF5U657fF4OrqCgAICAiASCRCq1at3nrMU6dOISgoCAYGBjA1NUXTpk3x8GFJtSk2NhYfffQRbGxsYGhoiAYNGuDYsWMVHi8tLQ2fffYZrKysYGxsjDZt2iAyMrJS56cOdBq0QUHkORTcvIDi5CTkHt4OSAqgXbtx+TtJpZBmZ7565WTKb359W3YmtDxqo/DhPRSnJ7/ns6l6+nR1wKZf43HmUgpiH+Zg3vJoWJjroHkjyzL7a2uL0bKxJX7c8ACRtzOQkJSH9dvjkZCYi+4d7QAAjvZ68PM2xpIfY3AnJguPEnKxZFUMdLTFaNfCSpmnpzZ+37MXHT9sjw4ftIWzkyO+HDMaOjo6OHy07IrB7n370aBeIPr06gFnR0cMGdgfHu5u2HvgoKxPm1YtEehfF3a2tnBxdsKoz4YhJycH9x/EAQAa1AvE5K/GoX5gAOxsbdGkYRB69+iOM+cuKOOU1cbvu/eiY4f2+PCDdnB2csKXwV9AR1cHh4+U/bt9j+za9ISTkyOGDBwAD3c37DvwBwDAwMAAC+fNRcvmzeBYowZ8vL0RPPpz3IuJwbNnzwEAWlpaMDc3k72MjY1w7sJFtP+gLUSi6jcb3Pvw/PBfuDtzOZ7urfgzmhRHKpXi5plNCGgzCi612sLCzgut+yxETsYzxP1d8XUQizWgb2Qle+kamMltr918MPxbj4S1U933eQpECvPOCV1ISAgWLFiA0NBQ3L59G9u2bYONjQ0AwMjICBs2bMDt27fx3Xff4eeff8ayZcvk9o+JicHvv/+OXbt2ySVc5cnOzsaECRNw5coVHD9+HGKxGD169EDxP7cAMjIy0LVrV9SuXRvXrl3D3LlzMXXqVLljpKWloU2bNggICMCVK1dw6NAhPH36FH369KnUOb8thkuXLgEAjh07hsTEROzatavC4xUWFqJ79+5o2bIlbty4gfPnz2PkyJGyD9isrCx06tQJx48fx/Xr19GhQwd07doV8fHx5R6zd+/eePbsGf7v//4PV69eRWBgINq2bYuUlJRKnaNKE2tAw9YRhQ+jX2uUojAuGpoOruXvp60D41FzYDx6Lgx6joTY0rbcriJ9I2i6+6HgxnnFxV1N2NnowsJcG1ci02Rt2TlFiLqbiVpeRmXuo6EhgqaGqFT1Lr+gGHV8Soa3aGmV/DwUSF7d7pNKgYJCKer4mCj4LNSfRCLB3ZhYBPrXkbWJxWIE+tfF7TvRZe5z+060XH8AqB8YgKhy+kskEhw8dAQGBvpwdy3/Zy87JwdGRobvcBZVk0Qiwb2YGAS8NiRfLBYjwL8uou7cKXOf23fuIMBf/o/K+oGB5fYHSj6rRCIRDAwNytx+/uIlZGZm4sMP2v37kyBSEZkpj5Gb+RwOnk1kbdp6RrB2rINn8REV7pv+4iG2/K85flnYDid+mYSs1CfvOdrqRyoV7lUdvdOQy8zMTHz33Xf4/vvvMXjwYACAu7s7mjVrBgCYPn26rK+LiwsmTZqE7du3Y8qUKbL2goICbNq0CVZWlbvD3qtXL7n369atg5WVFW7fvg0/Pz9s27YNIpEIP//8M3R1deHr64uEhASMGDFCts/333+PgIAAzJ8/X+44jo6OuHv3LmrWLP8ZhsrE8PJcLCwsYGtbftLwUkZGBtLT09GlSxe4u7sDAHx8fGTb69ati7p1X32Qz507F7t378a+ffsQHBxc6nhnzpzBpUuX8OzZM+jolAw1WLx4Mfbs2YPffvsNI0eOfGtMqkykbwiRWAPF2fIVtuKcDGha2JS5T1HKM+Qc3Iri5wmAjh50g9rCaMBEZKydB2lmWqn+2n4NIS3Ig+RuxHs4g6rNwqzkOanUtAK59pS0ApiblT2kOje3CDfvZGBwH0fEPc5BaloB2jW3Qi0vYyQk5QIAHj7ORdKzPHw+0AXf/hCDvPwi9OnmABtLHViYc6j2m9IzMlFcXFxq+J6ZqQkePX5c5j6pqWkwLaN/SlqqXNuFS5cxb9ES5Ofnw9zMDAvnzoaJSdnPlSQ8ScSe/X/g82FD3vVUqpyMjIxyro0pHj1KKHOf1NS0Uv1NTU2RkppaZv+CggKsWb8RrVq2gIG+fpl9Dh05inqBAbCyLLtyTqQOcjJLKtD6hhZy7XqGlsjJLH/klbVjXbTqEwYTK1fkZDzDtWMrsW/VAHw8YR+0dXgDisqXkpKCsWPHYv/+/RCLxejVqxe+++47GBqW//9Nq1at8Oeff8q1ff7551i1apXsfXx8PEaPHo2TJ0/C0NAQgwcPRlhYGDQ1K5+mvVNCFxUVhfz8fLRt27bM7Tt27EB4eDhiY2ORlZWFwsJCGBvLf+g7OztXOpkDgHv37mHGjBm4ePEiXrx4IauKxcfHw8/PD9HR0ahTp47c8M2goCC5Y0RGRsr+sd4UGxv71oTubTH8W+bm5hgyZAg+/PBDfPDBB2jXrh369OkDO7uSoWZZWVmYNWsW/vjjDyQmJqKwsBC5ubnlVugiIyORlZUlexD+pdzc3FLDU1/Kz89Hfn6+fFthEXQ0Nf71+aiioicPUPTkgex9dsJ9GH0WCh3/psg7/Uep/tp1GkFy+wpQVKjMMNXSBy2tMGn0q8kUps79+52O879l0QgZWxN71jdEYZEUd2OzcPz0c9R0L/k5LSqS4psFUZgW7In/29YYhUVSXI1MxfkrKeBoMeWqW6c2VoUvQ3pGBv7v8BH8b+G3CF+yqFTC8eJFMr6eORstmjVBpw7thQm2GiosLMT/whYBkGLcmNFl9nn+4gWuXruOb6ZNKXM7kaq6d30/Tu+aKXvfYeiqCnqXz8m7hexrCzsvWDvVxbawNrgfeQjeQR//5zip6urfvz8SExNx9OhRSCQSDB06FCNHjsS2bdsq3G/EiBGYM+fVXA76r91sKyoqQufOnWFra4tz584hMTERgwYNgpaWllwB6m3eKaHT09Mrd9v58+fRv39/zJ49Gx9++CFMTEywfft2LFmyRK6fgUHZQ0HK07VrVzg7O+Pnn3+Gvb09iouL4efnh4KCgrfv/I+srCx07doVCxcuLLXtZRL1vmN40/r16zFu3DgcOnQIO3bswPTp03H06FE0atQIkyZNwtGjR7F48WJ4eHhAT08PH3/8cbnfLysrC3Z2djh16lSpbW/efX8pLCwMs2fPlmub2rYBpn0QVGZ/IUlzsiAtLoLYwAhFr7WL9Y0hzc6o3EGKi1H09BHEpqVvJmjUcIeGhS2y965XTMBV3JlLKbgdfU32XkurZAS3mak2klNfPTNrbqqNew/KnrAGAJ4k5WHsNzegqyOGgb4GklMlmDXZG4lPX81geTc2C8PGX4eBvga0NMVIy5Bg9bd15WbCpBImxkYQi8VITUuTa09NS4eZmVmZ+5iZmSKtjP7mpvL99XR14WBvBwd7O/h6e2HwiNE4dOQYPu3z6o+gF8kpmPR1KHy9vTE++AuFnFNVYWxsXM61SYO5mWmZ+5iZmZbqn5aWBvM3rmVhYSH+t2ARnj1/hkXz/1dude7w0WMwMjJC44aq9zueqCLOvq1h7fhqaHhRYcnfQjlZydA3tpa152a9gIW9T6n9y6OjZwxTKxdkJJc9Wza9m+IqNvQxKioKhw4dwuXLl1G/fskswytWrECnTp2wePFi2Nvbl7uvvr5+uSP3jhw5gtu3b+PYsWOwsbGBv7+/7LGxWbNmVXrSyHd6hs7T0xN6eno4frz0A/bnzp2Ds7MzvvnmG9SvXx+enp6yST7eVXJyMqKjozF9+nS0bdsWPj4+SH1juImXlxdu3rwpV226fPmyXJ/AwED8/fffcHFxgYeHh9zrbQlmZWJ4+Y9eVFRU1iHKFRAQgJCQEJw7d042fBQAzp49iyFDhqBHjx6oXbs2bG1tERcXV+5xAgMDkZSUBE1NzVLnZ1nO0JqQkBCkp6fLvca3rvev4lea4iIUJT2CprPXa40iaLrURGHCg3J3kyMSQcPKHsVlJIA6dRqjMDG+ZHgmvVVubhESkvJkr7hHOUhOKUC9OqayPvp6GvCpaYS/ozPLP9A/8vKLkZwqgaGBJoL8zXD6YulJabJzipCWIUENO114uRvhTBl9qjstLS3U9HDH9cgbsrbi4mJcj7wBX2+vMvfx9fbC9Ygbcm3XrkfAp5z+L0mlxXITXr14kYxJIdPh6eGOSV+NhVjMpU5fp6WlBU8PD0REvJqoqri4GBERN+Dj7V3mPr7e3nLXEnh5bV71f5nMJTx5ggXz5pYaEfOSVCrFkaPH8UGb1v9qKA+RKtDWMYSJpbPsZWbjAT0jKzyJefXMe0FeFp49ugFrJ/9KH1eSn42M5EfQN+YkW1VFfn4+MjIy5F5vjkb7t86fPw9TU1NZMgcA7dq1g1gsxsWLFyvcd+vWrbC0tISfnx9CQkKQk5Mjd9zatWvL5iEBgA8//BAZGRn4++/Kj3x6p09bXV1dTJ06FVOmTMGmTZsQGxuLCxcuYO3atfD09ER8fDy2b9+O2NhYhIeHY/fu3e/ybWTMzMxgYWGBn376CTExMThx4gQmTJgg16dfv34oLi7GyJEjERUVhcOHD2Px4sUAIJtkZMyYMUhJScGnn36Ky5cvIzY2FocPH8bQoUPfmoRVJgZra2vo6enJJltJT0+v8JgPHjxASEgIzp8/j4cPH+LIkSO4d++e7Dk6T09P2aQxkZGRsnMsT7t27dC4cWN0794dR44cQVxcHM6dO4dvvvkGV65cKXMfHR0dGBsby71Uebhl/uUT0K7bBFp+DSG2sIHeh30BLR0U3CyZSU+/80Dotugm66/TpAM0XbwhNrGAhk0N6HcZDLGxOQoiz8kfWFsXWl4BKLjxRjv9Kzv3J2BwH0c0DTKHm7M+pn9VE8kp+Th94dXzDMvn1EbPTq8q4kEBpggKMIOdtQ7q1zVF+P9qIz4hBwePP5X1adXEEv5+JrCz0UWzIHMsnV0bpy8m43JEmjJPT2306v4RDh4+iiPHT+Dho0cI/2EV8vLy8GG7kmHyC5csx9oNm2X9e3TrisvXruPXXXsQ/+gxNm39BXdjYvFRl04AgNy8PKzduBm370Tj6bNnuBsTg8XLV+BFcgpaNGsKoCSZmxgyHdZWlvh82BCkZ2QgJTW13Ge9qqtePT7CwcNHcOTYccTHP0L4yh9Lrs0HJddm0ZJlWLvh1RID3bt1xZWr1/Dbrt3/XJttuBsTg25dOgMoSebmzl+Au/diMG3SRBQXFSMlJRUpKalyyTYARETeQNLTp+jwIYfBKpqGgT6M63rDuG5Joq3vWgPGdb2h6/j20T/0bkQiEWo3G4RrJ1Yh7vYJpCRG4+SOqdA3toZLrVcT/hz4aQhundsie3/hwEI8uX8JmSmPkRR3DUc2jYVILIZ73S6yPjmZz/HiSRQykksecUlJuosXT6KQl5OmtPNTd0JOihIWFgYTExO5V1hY2H86n6SkJFhbW8u1aWpqwtzcHElJSeXu169fP2zZsgUnT55ESEgINm/ejAEDBsgd9/VkDoDsfUXHfdM736ILDQ2FpqYmZsyYgSdPnsDOzg6jRo3C8OHDMX78eAQHByM/Px+dO3dGaGgoZs2a9a7fCmKxGNu3b8e4cePg5+cHLy8vhIeHyy0LYGxsjP3792P06NHw9/dH7dq1MWPGDPTr10/2XJ29vT3Onj2LqVOnon379sjPz4ezszM6dOjw1jvJlYlBU1MT4eHhmDNnDmbMmIHmzZuXOfzxJX19fdy5cwcbN25EcnIy7OzsMGbMGHz++ecAgKVLl2LYsGFo0qQJLC0tMXXqVGRklD+0UCQS4eDBg/jmm28wdOhQPH/+HLa2tmjRokWp/1nUleTONeTqG0KvWWeIDIxQ9CwB2TtXypYiEBuby01xJNLVh36HfhAZGEGal4uip/HI2rIUxcnyPyTaPvUAkQgFt8tOfKlytu16DD1dDUz+whOGBpq4GZWOSbP/lpvF0t5WFybGrxaaNtDXxOcDXWBlqYPMzEKcOv8CP2+JQ1HRq30szLURPNwN5iZaSE4twKGTz7BxZ/mzvVZ3rVo0Q1p6OjZu+QWpqalwd3PF/DkzYfbPsL5nz59DJH71AGItH2+ETJ6ADZu3Yv2mLXCwt8esb6bJ1qDTEIvx6HECjh5fiIyMDBgZG8HL0xPLFs6Hi7MTAOBqRASeJCbiSWIiPh0yXC6eowf2KOW81UGrFs2Rnp6OTVu2ITU1FW5ubpg3Z5ZsOOyz58/llhKo5euDkMkTS67Nxs2wd7DHrOlfy67Ni+RknL9YMsPy6LFfyn2vb8PmoW6d2rL3h44cha+PN5wca7zns6x+TOr5ofHxVzdJfBd/DQB4tGkXbgwPKW83+o/qtvwMhQW5OP37DBTkZcDWpR46DvtZbg26jJR45GW/urGUlf4UJ7ZNRF5OGvQMzGHjUg/dx+yAnqG5rM/tC9vlFh/fv6rkD/CWvefDq35PJZwZ/RchISGlii4vJwt807Rp08p8FOt1UVFR7xzL6xMS1q5dG3Z2dmjbti1iY2NlEyIqgkgqrboTfG7duhVDhw5Fenp6hc/9kby0haVn0CTV0fVcX6FDoHJsXcxZA1WVVMThn6rslk+3t3ciQdz5tfwlMkhYE7ur7sxgi3cJt7L4pJ6V/33//PlzJCdX/PiGm5sbtmzZgokTJ8o9blVYWAhdXV38+uuv6NGjR6W+X3Z2NgwNDXHo0CF8+OGHmDFjBvbt2ye3hNuDBw/g5uaGa9euISAgoFLHrVKD6Ddt2gQ3Nzc4ODggMjISU6dORZ8+fZjMERERERGRHCsrq0rNut+4cWOkpaXh6tWrqFevZK6JEydOoLi4GA0bNqz093uZuL2cjLFx48aYN28enj17JhvSefToURgbG8PX17fSx1WJW5bx8fEwNDQs91XRQtqvS0pKwoABA+Dj44Px48ejd+/e+Omnn5Qaw5sqOubp06ff6ZhERERERKQcPj4+6NChA0aMGIFLly7h7NmzCA4OxieffCKb4TIhIQHe3t64dKlkGHxsbCzmzp2Lq1evIi4uDvv27cOgQYPQokUL1KlTMmNr+/bt4evri4EDByIyMhKHDx/G9OnTMWbMmHKHiZZFJSp09vb2cqXGsrZXxpQpU+QWLxcihjdVdEwHB4d3OiYRERERkaqqassWACWPcgUHB6Nt27ayhcXDw8Nl2yUSCaKjo2WzWGpra+PYsWNYvnw5srOz4ejoiF69emH69OmyfTQ0NHDgwAGMHj0ajRs3hoGBAQYPHiy3bl1lqERC93Ka/aoYg9DnRURERERE/425uXmFi4i7uLjg9alJHB0d8eeff771uM7Ozjh48OB/ik0lEjoiIiIiIqoaqu6Ui6pJJZ6hIyIiIiIion+PCR0REREREZGa4pBLIiIiIiJSmOKqOCuKCmOFjoiIiIiISE2xQkdERERERArDSVGUixU6IiIiIiIiNcUKHRERERERKQwrdMrFCh0REREREZGaYkJHRERERESkpjjkkoiIiIiIFKaYYy6VihU6IiIiIiIiNcUKHRERERERKYy0WOgIqhdW6IiIiIiIiNQUEzoiIiIiIiI1xSGXRERERESkMFJOiqJUrNARERERERGpKVboiIiIiIhIYYo5KYpSsUJHRERERESkppjQERERERERqSkOuSQiIiIiIoXhpCjKxQodERERERGRmmKFjoiIiIiIFKaYBTqlYoWOiIiIiIhITbFCR0RERERECiNliU6pWKEjIiIiIiJSU0zoiIiIiIiI1BSHXBIRERERkcJw1QLlYoWOiIiIiIhITbFCR0REREREClPMSVGUihU6IiIiIiIiNcWEjoiIiIiISE1xyCURERERESmMlLOiKBUrdERERERERGqKFToiIiIiIlIYabHQEVQvrNARERERERGpKVboiIiIiIhIYYr5DJ1SsUJHRERERESkppjQERERERERqSkOuSQiIiIiIoXhsgXKxQodERERERGRmmKFjoiIiIiIFKa4mBU6ZWJCR6UcbLpC6BCoAv9nFCJ0CFQOjQuGQodA5Uhs8qnQIVAF7vx6R+gQqBzevb2FDoHKI4kWOgJSERxySUREREREpKZYoSMiIiIiIoXhnCjKxQodERERERGRmmKFjoiIiIiIFEbKSVGUihU6IiIiIiIiNcUKHRERERERKUwxH6JTKlboiIiIiIiI1BQTOiIiIiIiIjXFIZdERERERKQwnBRFuVihIyIiIiIiUlOs0BERERERkcKwQqdcrNARERERERGpKSZ0REREREREaopDLomIiIiISGE44lK5WKEjIiIiIiJSU6zQERERERGRwnBSFOVihY6IiIiIiEhNsUJHREREREQKI5WyQqdMrNARERERERGpKSZ0REREREREaopDLomIiIiISGGKOSmKUrFCR0REREREpKZYoSMiIiIiIoXhpCjKxQodERERERFRBVJSUtC/f38YGxvD1NQUw4cPR1ZWVrn94+LiIBKJynz9+uuvsn5lbd++ffu/io0VOiIiIiIiogr0798fiYmJOHr0KCQSCYYOHYqRI0di27ZtZfZ3dHREYmKiXNtPP/2Eb7/9Fh07dpRrX79+PTp06CB7b2pq+q9iY0JHREREREQKI61ik6JERUXh0KFDuHz5MurXrw8AWLFiBTp16oTFixfD3t6+1D4aGhqwtbWVa9u9ezf69OkDQ0NDuXZTU9NSff8NDrkkIiIiIqIqIT8/HxkZGXKv/Pz8/3TM8+fPw9TUVJbMAUC7du0gFotx8eLFSh3j6tWriIiIwPDhw0ttGzNmDCwtLREUFIR169b962cQmdAREREREZHCSIulgr3CwsJgYmIi9woLC/tP55OUlARra2u5Nk1NTZibmyMpKalSx1i7di18fHzQpEkTufY5c+Zg586dOHr0KHr16oUvvvgCK1as+FfxccglERERERFVCSEhIZgwYYJcm46OTpl9p02bhoULF1Z4vKioqP8cU25uLrZt24bQ0NBS215vCwgIQHZ2Nr799luMGzeu0sdnQkdERERERApTLOCyBTo6OuUmcG+aOHEihgwZUmEfNzc32Nra4tmzZ3LthYWFSElJqdSzb7/99htycnIwaNCgt/Zt2LAh5s6di/z8/EqfBxM6IiIiIiKqdqysrGBlZfXWfo0bN0ZaWhquXr2KevXqAQBOnDiB4uJiNGzY8K37r127Ft26davU94qIiICZmVmlkzmACR0REREREVG5fHx80KFDB4wYMQKrVq2CRCJBcHAwPvnkE9kMlwkJCWjbti02bdqEoKAg2b4xMTH466+/cPDgwVLH3b9/P54+fYpGjRpBV1cXR48exfz58zFp0qR/FR8TOiIiIiIiUpiqtmwBAGzduhXBwcFo27YtxGIxevXqhfDwcNl2iUSC6Oho5OTkyO23bt061KhRA+3bty91TC0tLaxcuRLjx4+HVCqFh4cHli5dihEjRvyr2ETSfzsvJlV5287wfwlV1u1GiNAhUDk0jAzf3okEkdjkU6FDoArsvukmdAhUDu/e3kKHQOXoLIkWOoRyDZ5RuZkf34eNc959PTd1xQodEREREREpDOtFysV16IiIiIiIiNQUEzoiIiIiIiI1xSGXRERERESkMMVVcFIUVcYKHRERERERkZpihY6IiIiIiBSmKi5boMpYoSMiIiIiIlJTrNAREREREZHCcNkC5WKFjoiIiIiISE0xoSMiIiIiIlJTTOhUWFxcHEQiESIiIoQOhYiIiIioUqTFxYK9qiM+Q6fCHB0dkZiYCEtLS6FDUSlSqRSn9q7Atb9+RV5OBhw9AtF54ExY2LiUu8/lk7/gyqlfkPYiAQBgbe+BFt3GwLN2C7l+j2Ku48Tu5Ui4fwMisRi2jj4YMGENtLR13+cpVRk7I+9j09V7SM7Jg6elCaa0qgM/W/My++67/RCzj16Ta9PWEON88Eey9ydiEvDbzTjceZaK9DwJtvVrDS8r0/d5ClXW9ivR2Hj+byRn5aKmjRmmfhiE2g5v/91y6O8HmLb7DFrVrIHlfVrL2kP3ncX+G/fl+jZxs8cP/doqPHZ1t3//fvz2++9ITU2Fm6srRo8eDS8vr3L7nz59Gps2b8bTp0/hYG+PocOGIahBA9l2qVSKzVu24NChQ8jOzoavry+Cx4yBg4ODrE9mZiZ++PFHXLx4EWKxGE2bNsWozz+Hnp5eqe/35MkTBI8dC7FYjN9+/VWxJ18FSKVSXD26AlGXfkVBbgZsXQLRrMdMmFi6lLvPlaMrcO3YSrk2EytX9J30f7L3URd3ICbiAF4k3IYkPxuDZ12Cjp7x+zqNas28WX24TRwOk0A/6Npb40qvL/B033GhwyJSGCZ0KqqgoADa2tqwtbUVOhSVc/b/1uDisc3oPnwBzCxr4OSe77Bl6WcY878/oKmlU+Y+xmY2aNdrIsxtnAGpFBHn9mD7ijH4fOYuWDt4AihJ5rYuH4FmnUaiY7/pEGto4OmjaIhELGRXxpG7j7H09E183doffrZm2BYRi+A957Br0Acw1y/7uhhoa2LXoA9k70VvbM+VFMHf3gIfeDrgf8evv8foq7bDf8dhydEr+KZjQ9R2sMTWS1H44pfj2Du6G8wNSv+B/1JCWhaWHruGQEfrMrc3dbfH7K5NZO+1Nfiz8qY///wTP/38M8YGB8PL2xt79uzB9NBQ/PzTTzA1NS3V//bt21iwcCGGDhmCoKAgnDp1CnPnzsWK8HC4uLgAAH797Tfs27cPEydMgK2tLTZt3ozpoaFYvWoVtLW1AQCLFi1CSmoq5s+bh8KiIixbtgzh4eGYOnWq3PcrLCzEgoULUatWLURFRb3vfw61FPnnGtw6uxmt+iyAkXkNXDnyHQ6u/Qy9J5T/mQMAZjae6Dxiney9WCz/J1dhQR4cazaHY83muHRo6XuLnwANA31k3IjGow2/o/5vK9++A/1nXFhcufjpqyStWrVCcHAwgoODYWJiAktLS4SGhspmAXJxccHcuXMxaNAgGBsbY+TIkWUOufz777/RpUsXGBsbw8jICM2bN0dsbKxs+5o1a+Dj4wNdXV14e3vjhx9+UPapvldSqRQXj21Ciy6j4B3QFjaOXug+fCEy057hzrVj5e7n5d8GnnVawsLGBRa2rmjbczy0dfTx+H6krM/hHQsQ1HYgmnUaCWsHT1jauqFWg47Q1NJWxqmpvS3XYtCjlgu61XKGm4Uxvm7jD11NDez9O67cfUQQwdJAV/ayMJCvhHb2ccLIht5o6GT1nqOv2jZfvI2eAZ7o7u8BdytTTO/UCLpaGtgTEVvuPkXFxfh6zxmMblEHDmaGZfbR0hDD0lBP9jLWK/+P2+pq9+7d6NihA9q3bw9nJyeMDQ6Gjo4Ojhw5Umb/vXv3on69evj444/h5OSEQYMGwd3dHfv37wdQ8jtwz549+OSTT9C4cWO4urpi0sSJSE5Oxrnz5wEA8fHxuHL1Kr4cNw7e3t7wq1ULo0eNwp9//YXk5GS577dx0yY41qiBFs2bv99/CDUllUpx88wmBLQZBZdabWFh54XWfRYiJ+MZ4v4u/zMHAMRiDegbWcleugZmcttrNx8M/9YjYe1U932eAgF4fvgv3J25HE/3VnzNiNQVEzol2rhxIzQ1NXHp0iV89913WLp0KdasWSPbvnjxYtStWxfXr19HaGhoqf0TEhLQokUL6Ojo4MSJE7h69SqGDRuGwsJCAMDWrVsxY8YMzJs3D1FRUZg/fz5CQ0OxceNGpZ3j+5b24jGy0p/DzfdVVUBX3wg13OrgUWxEpY5RXFyEWxf/gKQgB47u/gCA7IxkJNyPhIGxOdbO/wSLxzfFhoUDEH/v6ns4i6pHUlSMO8/SEPRa4iUWiRDkZIWbSSnl7pcrKUTndYfQae0hTNh/HrHJGcoIt1qRFBUhKjEFDV1fVfvFIhEautjhRsLzcvdbffomzA100SPAs9w+Vx4+ReulO/HRD3sx7+BFpOXkKzR2dSeRSHAvJgb+/v6yNrFYDH9/f0TduVPmPlF37sA/IECurV69erL+SUlJSE1NRcBrxzQwMICXlxfu/FNhi7pzB4aGhqhZs6asT0BAAEQiEe5ER8vaIiIicObMGXwxZsx/PdUqKzPlMXIzn8PB87VKtJ4RrB3r4Fl8RIX7pr94iC3/a45fFrbDiV8mISv1yXuOloiqKw65VCJHR0csW7YMIpEIXl5euHnzJpYtW4YRI0YAANq0aYOJEyfK+sfFxcntv3LlSpiYmGD79u3Q0tICALkP7JkzZ2LJkiXo2bMnAMDV1RW3b9/G6tWrMXjw4DJjys/PR36+/B9hkgJtaGmr5p32rPSSP0ANjC3k2g2MLZGd8aLCfZ8+jsba+Z+iUJIPbR199B3zPazsPQAAqc8fAQD+3Ps9PugzBbaOPog8vxebFg/B6Dn7K3w+j4C03HwUSaWweGNopYW+LuJSssrcx8XMEDM+CISnpTGy8gux+do9DN35J34d0A42RuUPA6R/JzXnn2vzxtBKC0NdxCWnl7nP9fhn2BMRgx0jOpd73Kbu9mjr7QQHU0M8Ss3E9ycjMGb7cWwa0gEaYt4rBICMjAwUFxfDzEy+MmNmaorHjx6VuU9qairM3hiKaWZqitTUVNl2AGUe8/U+JiYmcts1NDRgZGQk65ORkYGly5Zh8qRJMNDXf7cTrAZyMks+c/QN5T9z9AwtkZNZ/meOtWNdtOoTBhMrV+RkPMO1Yyuxb9UAfDxhH7R1yq54E1UlXIdOuZjQKVGjRo0gEr16Sqhx48ZYsmQJioqKAAD169evcP+IiAg0b95clsy9Ljs7G7GxsRg+fLgsQQRKno9484P9dWFhYZg9e7ZcW8+hM9Br2KxKnNH7d+PCfhzYNFP2vt+Xq975WJa2rhg1czfycjNx++ph7Fk7DUOmboaVvQek0pJZkeq17IuAZr0AAHbOvngQdR7Xz/yOdr0mVnRoegd17CxQx87itffm+HjzMfx+6wG+aOwrYGTVW3a+BN/sPYMZnRvBTL/8yYA61HKVfe1pbYaa1mbosnIPrjx8ioaudsoIlf6D78LD0apVK9SuXVvoUFTKvev7cXrXq8+cDkPf7TPHyfvVhFsWdl6wdqqLbWFtcD/yELyDPv7PcRIRvY4JnQoxMDCocHtZs5O9lJVVUgX5+eef0bBhQ7ltGhoa5e4XEhKCCRMmyLXtvqI6z4x51W2NGjPryN4XFhYAKBkiaWT6aqKG7IwXsHH0qfBYGpraJZOiALB38cOTB7dw4dgmdB00B4YmJcd6WbF7ycrOHRnJiQo5l6rMVE8HGiIRkt8YcpeckwdLg8pVe7U0xPCyMsHjtOz3EWK1Zab/z7XJzpVrT87Kg6Vh6d8pj1Iz8SQ9G1/uOClrK/7nTmu9eVuwZ/RHcDQ3KrVfDTMjmOnr4FFKJhO6fxgbG0MsFsuqYi+lpqXBzLzs2V/NzMyQmpZWuv8/FbmX/01NTYX5a8dITUuDu5ubrE96unz1taioCJmZmbL9IyMjceHCBfz++++yPsXFxejcpQvGjRuHD9u3f4czVn/Ovq1h7fjqM6fon8+cnKxk6Bu/+szJzXoBC/uKP3Nep6NnDFMrF2QkP1RcsEQqTMpJUZSKCZ0SXbx4Ue79hQsX4OnpWWHC9bo6depg48aNkEgkpap0NjY2sLe3x/3799G/f/9Kx6SjowMdHfk/uLW0VeeHUEfPEDp6r4anSKVSGJpY4X7Uedg6lXyY5udm4fH9G6jf6tN/dWyptBhFkpIPa1NLBxiZWuNF0gO5PslJcfCozckC3kZLQwxva1NcfvQcrd3tAZQkAZcfPUefOm6VOkZRsRQxyRlo5mLzPkOtdrQ0NOBjZ45LD5LQxssJQMm1uRSXhE/ql54639XSBL+N7CLX9v2pCOQUFGJK+/qwNSl7eN7TjGyk5eTDksNlZbS0tODp4YGIyEg0aVLyDFZxcTEiIiLQrWvXMvfx8fZGREQEenTvLmu7fv06fLy9AQC2trYwMzNDRGQk3N3dAQDZOTmIjo5G586dZcfIysrCvXv34OlZ8gxkRGQkpFIpvP9ZLmHpkiUofm29pvMXLuDXX3/F0iVLYGEhP7ywOtHWMZQbEimVSqFnZIUnMedh+U8CV5CXhWePbsCnUeU/cyT52chIfgTPwG4Kj5mIiAmdEsXHx2PChAn4/PPPce3aNaxYsQJLliyp9P7BwcFYsWIFPvnkE4SEhMDExAQXLlxAUFAQvLy8MHv2bIwbNw4mJibo0KED8vPzceXKFaSmppaqwqkrkUiEhu0G4fSBVbCwcYGppQNO7g6Hkak1vAPbyfpt+nYIvAPbIajtAADAsd+XwNOvBUws7JCfl42bFw8gLvoSBoxfIztukw7DcWrvCtg6esHW0QcR5/bgRdJ99P7iO0HOVd0MCPTAzCNX4WNtWrJswfVY5EqK0M23pCo64/AVWBnqYWzTWgCAny7eQW1bMziaGiIzX4LNV+8hKSMH3Wu5yI6ZnleApMwcPM/KAwA8TC2pRFvol8yKSZUzsKEvQvedha+dBfwcLLH1YhRyJYX4qG5JQjB971lYG+lhXJtA6GhqwMNa/vksI92Sqv3L9pwCCVb9dQPtvJ1gYaiHx6mZWH78GhzNjdDEzV65J6fievTogSVLl8LT0xNeNWtiz969yM/PxwcflCzXsXjxYlhYWGDo0KEAgI8++ghTpk7F77t2IahBA/z555+4d+8exo0dC6Dkd1X37t2xfft2ONjbw8bGBps3b4aFhQWaNG4MAHByckL9evXwXXg4xgYHo7CwED/+8ANatmghS9acnJzk4rx37x7EYrFsaQQqIRKJULvZIFw7sQrGli4wNnPA5SPh0De2hkutV585B34aAhe/dvBrUvKZc+HAQjj5toaRqT2yM57h6tHvIRKL4V731c2SnMznyMl8gYzkeABAStJdaOkYwNDUDrr6pko9z6pOw0AfBh6v/p/Xd60B47reKEhJR94jjsIh9ceETokGDRqE3NxcBAUFQUNDA19++SVGjhxZ6f0tLCxw4sQJTJ48GS1btoSGhgb8/f3RtGlTAMBnn30GfX19fPvtt5g8eTIMDAxQu3ZtfPXVV+/pjITRtONnkBTkYv/GGcjLyYCTZz0MGP+z3HpAKc/jkZP1aphTdkYKdq+diqz059DRM4JNDS8MGL8G7rWayvo0+mAwCiX5OLx9AXKz02Hj6IWBE9fB3Fr+Dx8qW/uaNZCam49VF6KQnJOPmpYmWNG9iWwpgqTMXLlnSDPzCvC/49eRnJMPYx0teFubYl2flnCzeLWw7p/3E+UWHw/5v8sAgJENvfF5o8oPd6ruPqzlgtScPPz4ZyReZOfCy8YMP3zaBhb/DLlMTM+G6M1FACsgFolw71kq9t+IRWaeBFZGemjsZocxLf2hrVm5EQfVRcuWLZGekYEtmzcjJTUV7m5umDtnjmzo47PnzyF6bRIZX19fTJ0yBRs3bcKGDRvg4OCA0NBQuUSr98cfIy8vD+ErViArKwu1atXC3DlzZGvQAcCUKVPwww8/IOTrryESidC0aVOMHjVKaeddldRt+RkKC3Jx+vcZKMjLgK1LPXQcJv+Zk5ESj7zsV585WelPcWLbROTlpEHPwBw2LvXQfcwO6Bm+GiZ7+8J2ucXH968qSQZb9p4Pr/o9lXBm1YdJPT80Pr5Z9t538dcAgEebduHG8BChwqrSOORSuURSTkOjFK1atYK/vz+WL18udChvte0M/5dQZd1u8MNHVWkYcfY6VZXY5N8NySbl2n2zckOzSfm8e3sLHQKVo7Mk+u2dBPLxl/cF+96/fVf9fp+wQkdERERERApTLC1+eydSGC4WREREREREpKZYoVOSU6dOCR0CEREREdF7x2folIsVOiIiIiIiIjXFhI6IiIiIiEhNccglEREREREpDIdcKhcrdERERERERGqKFToiIiIiIlIYLnOtXKzQERERERERqSkmdERERERERGqKQy6JiIiIiEhhiouLhQ6hWmGFjoiIiIiISE2xQkdERERERArDZQuUixU6IiIiIiIiNcUKHRERERERKYxUymfolIkVOiIiIiIiIjXFhI6IiIiIiEhNccglEREREREpDCdFUS5W6IiIiIiIiNQUK3RERERERKQwrNApFyt0REREREREaooJHRERERERkZrikEsiIiIiIlKYYq5Dp1Ss0BEREREREakpVuiIiIiIiEhhOCmKcrFCR0REREREpKZYoSMiIiIiIoWRFvMZOmVihY6IiIiIiEhNMaEjIiIiIiJSUxxySURERERECsNJUZSLFToiIiIiIiI1xQodEREREREpjJQLiysVK3RERERERERqigkdERERERGRmuKQSyIiIiIiUphiToqiVKzQERERERERqSlW6IiIiIiISGGkxZwURZlYoSMiIiIiIlJTrNAREREREZHCcGFx5WKFjoiIiIiISE0xoSMiIiIiIlJTHHJJREREREQKI5VyUhRlYoWOiIiIiIhITbFCR0RERERECsNJUZSLFToiIiIiIiI1xYSOiIiIiIhITYmkUilrolRl5efnIywsDCEhIdDR0RE6HHoNr41q4/VRXbw2qovXRrXx+lBVxYSOqrSMjAyYmJggPT0dxsbGQodDr+G1UW28PqqL10Z18dqoNl4fqqo45JKIiIiIiEhNMaEjIiIiIiJSU0zoiIiIiIiI1BQTOqrSdHR0MHPmTD78rIJ4bVQbr4/q4rVRXbw2qo3Xh6oqTopCRERERESkplihIyIiIiIiUlNM6IiIiIiIiNQUEzoiIiIiIiI1xYSOiIiIiIhITTGhIyKlkEqliI+PR15entChEBEREVUZTOiISCmkUik8PDzw6NEjoUOhCqSlpWHNmjUICQlBSkoKAODatWtISEgQOLLq7a+//kJhYWGp9sLCQvz1118CRESv488NEQmJyxZQlZSWlobffvsNsbGxmDx5MszNzXHt2jXY2NjAwcFB6PCqrVq1amHt2rVo1KiR0KFQGW7cuIF27drBxMQEcXFxiI6OhpubG6ZPn474+Hhs2rRJ6BCrLQ0NDSQmJsLa2lquPTk5GdbW1igqKhIoMuLPDREJjRU6qnJu3LiBmjVrYuHChVi8eDHS0tIAALt27UJISIiwwVVzCxYswOTJk3Hr1i2hQ6EyTJgwAUOGDMG9e/egq6sra+/UqROrQAKTSqUQiUSl2pOTk2FgYCBARPQSf25U3+bNm9G0aVPY29vj4cOHAIDly5dj7969AkdGpBiaQgdApGgvP1wXLVoEIyMjWXunTp3Qr18/ASOjQYMGIScnB3Xr1oW2tjb09PTktr8cqkTCuHz5MlavXl2q3cHBAUlJSQJERD179gQAiEQiDBkyBDo6OrJtRUVFuHHjBpo0aSJUeAT+3Ki6H3/8ETNmzMBXX32FefPmyarZpqamWL58OT766COBIyT675jQUZXDD1fVtXz5cqFDoAro6OggIyOjVPvdu3dhZWUlQERkYmICoKRCZ2RkJHcTRFtbG40aNcKIESOECo/AnxtVt2LFCvz888/o3r07FixYIGuvX78+Jk2aJGBkRIrDhI6qHH64qq7BgwcLHQJVoFu3bpgzZw527twJoKQqFB8fj6lTp6JXr14CR1c9rV+/HgDg4uKCSZMmcXilCuLPjWp78OABAgICSrXr6OggOztbgIiIFI/P0FGV8/LDVSKRAOCHq6rKy8tDRkaG3IuEtWTJEmRlZcHa2hq5ublo2bIlPDw8YGRkhHnz5gkdXrU2c+ZM6Ojo4NixY1i9ejUyMzMBAE+ePEFWVpbA0VVv/LlRba6uroiIiCjVfujQIfj4+Cg/IKL3gLNcUpWTnp6Ojz/+GFeuXEFmZibs7e2RlJSExo0b4+DBg7zDLaDs7GxMnToVO3fuRHJycqntnKlPNZw5cwY3btxAVlYWAgMD0a5dO6FDqvYePnyIDh06ID4+Hvn5+bh79y7c3Nzw5ZdfIj8/H6tWrRI6xGqPPzeqac2aNZg1axaWLFmC4cOHY82aNYiNjUVYWBjWrFmDTz75ROgQif4zJnRUZfHDVfWMGTMGJ0+exNy5czFw4ECsXLkSCQkJWL16NRYsWID+/fsLHSKRSurevTuMjIywdu1aWFhYIDIyEm5ubjh16hRGjBiBe/fuCR0ikcraunUrZs2ahdjYWACAvb09Zs+ejeHDhwscGZFiMKEjIqVxcnLCpk2b0KpVKxgbG+PatWvw8PDA5s2b8csvv+DgwYNCh1jtHT9+HMePH8ezZ89QXFwst23dunUCRUUWFhY4d+4cvLy8YGRkJEvo4uLi4Ovri5ycHKFDrFbCw8Mr3XfcuHHvMRL6N3JycmTDY4mqEk6KQlVOeR+0IpEIurq68PDwQIsWLaChoaHkyCglJQVubm4AAGNjY9kyBc2aNcPo0aOFDI0AzJ49G3PmzEH9+vVhZ2dX5rpnJIzi4uIyhyQ/fvxYbnkWUo5ly5ZVqp9IJGJCJ7AHDx6gsLAQnp6e0NfXh76+PgDg3r170NLSgouLi7ABEikAEzqqcpYtW4bnz58jJycHZmZmAIDU1FTo6+vD0NAQz549g5ubG06ePAlHR0eBo61e3Nzc8ODBAzg5OcHb2xs7d+5EUFAQ9u/fD1NTU6HDq/ZWrVqFDRs2YODAgUKHQm9o3749li9fjp9++glASaKQlZWFmTNnolOnTgJHV/08ePBA6BCokoYMGYJhw4bB09NTrv3ixYtYs2YNTp06JUxgRArEWS6pypk/fz4aNGiAe/fuITk5GcnJybh79y4aNmyI7777DvHx8bC1tcX48eOFDrXaGTp0KCIjIwEA06ZNw8qVK6Grq4vx48dj8uTJAkdHBQUFXKRaRS1ZsgRnz56Fr68v8vLy0K9fP7i4uCAhIQELFy4UOjwilXX9+nU0bdq0VHujRo3KnP2SSB3xGTqqctzd3fH777/D399frv369evo1asX7t+/j3PnzqFXr15ITEwUJkgCUDJz39WrV+Hh4YE6deoIHU61N3XqVBgaGiI0NFToUKgMhYWF2LFjByIjI2WTPfXv319usXESxuPHj7Fv3z7Ex8ejoKBAbtvSpUsFiooAwMTEBKdOnSq1Ft3Vq1fRqlUr2RIgROqMQy6pyklMTERhYWGp9sLCQiQlJQEomeGKv8SFlZeXB2dnZzg7OwsdCv0jLy8PP/30E44dO4Y6depAS0tLbjv/MBXOL7/8gk8//RT9+/cvNRvs5MmT8e233woUGR0/fhzdunWDm5sb7ty5Az8/P8TFxUEqlSIwMFDo8Kq9Fi1aICwsDL/88ovs2fmioiKEhYWhWbNmAkdHpBis0FGV07lzZyQlJWHNmjWyO3LXr1/HiBEjYGtriwMHDmD//v34+uuvcfPmTYGjrV6Kioowf/58rFq1Ck+fPpWtpRUaGgoXFxdOIS2w1q1bl7tNJBLhxIkTSoyGXmdqaopffvkFHTt2lGsfP348tm/fztEGAgoKCkLHjh0xe/Zs2Qyk1tbW6N+/Pzp06MAJnwR2+/ZttGjRAqampmjevDkA4PTp08jIyMCJEyfg5+cncIRE/x0TOqpykpKSMHDgQBw/flxWYSgsLETbtm2xefNm2NjY4OTJk5BIJGjfvr3A0VYvc+bMwcaNGzFnzhyMGDECt27dgpubG3bs2IHly5fj/PnzQodIpJL++OMP9O/fHwcOHJBVFcaOHYtdu3bh+PHj8Pb2FjjC6svIyAgRERFwd3eHmZkZzpw5g1q1aiEyMhIfffQR4uLihA6x2nvy5Am+//57REZGQk9PD3Xq1EFwcDDMzc2FDo1IIZjQUZV1584d3L17FwDg5eUFLy8vgSMiDw8PrF69Gm3btpVbS+vOnTto3LgxUlNThQ6RSGVt27YNwcHBOHr0KNauXYu9e/fi5MmTqFmzptChVWu2trY4efIkfHx84OvriwULFqBbt26IjIxE06ZNkZWVJXSIRFTF8Rk6qrK8vb1511rFJCQkwMPDo1R7cXExJBKJABFRz549sWHDBhgbG6Nnz54V9t21a5eSoqKy9OvXD2lpaWjatCmsrKzw559/lvnzRMrVqFEjnDlzBj4+PujUqRMmTpyImzdvYteuXWjUqJHQ4VVLN27cgJ+fH8RiMW7cuFFhX07IRVUBEzqqkjjjmGry9fXF6dOnS02E8ttvv5WagYyUw8TERLaAuImJicDR0OsmTJhQZruVlRUCAwPxww8/yNr4e004S5culVXhZs+ejaysLOzYsQOenp68LgLx9/dHUlISrK2t4e/vD5FIhLIGpIlEIhQVFQkQIZFiMaGjKoczjqmuGTNmYPDgwUhISEBxcTF27dqF6OhobNq0CQcOHBA6vGpp/fr1ZX5Nwrt+/XqZ7R4eHsjIyJBtf5mQkzDc3NxkXxsYGGDVqlUCRkNAycLvVlZWsq+Jqjo+Q0dVDmccU22nT5/GnDlz5NbSmjFjBieoISK19OjRI4hEItSoUQMAcOnSJWzbtg2+vr4YOXKkwNFVbxKJBJ9//jlCQ0Ph6uoqdDhE7w0TOqpyOOMY0bsJCAgos9ojEomgq6sLDw8PDBkypMLlDUjxJBIJ9PT0EBERwSnWVVDz5s0xcuRIDBw4EElJSahZsyb8/Pxw7949jB07FjNmzBA6xGrNxMQEERERTOioShMLHQCRohkYGMiem7Ozs0NsbKxs24sXL4QKiwAMHjwYf/31l9BhUDk6dOiA+/fvw8DAAK1bt0br1q1haGiI2NhYNGjQAImJiWjXrh327t0rdKjVipaWFpycnPisj4q6desWgoKCAAA7d+5E7dq1ce7cOWzduhUbNmwQNjhC9+7dsWfPHqHDIHqv+AwdVTmccUx1paeno127dnB2dsbQoUMxePBgODg4CB0W/ePFixeYOHEiQkND5dr/97//4eHDhzhy5AhmzpyJuXPn4qOPPhIoyurpm2++wddff43Nmzdz7SwVI5FIoKOjAwA4duwYunXrBqBkpmUu+C48T09PzJkzB2fPnkW9evVgYGAgt33cuHECRUakOBxySVXO/fv3kZWVhTp16iA7OxsTJ07EuXPnZDOOvTnDIinX8+fPsXnzZmzcuBG3b99Gu3btMHz4cHz00UeyheBJGCYmJrh69WqpqfBjYmJQr149pKen486dO2jQoAEyMzMFirJ6CggIQExMDCQSCZydnUv9UXrt2jWBIqOGDRuidevW6Ny5M9q3b48LFy6gbt26uHDhAj7++GM8fvxY6BCrtYqGWopEIty/f1+J0RC9H6zQUZXDGcdUm5WVFSZMmIAJEybg2rVrWL9+PQYOHAhDQ0MMGDAAX3zxBTw9PYUOs1rS1dXFuXPnSiV0586dg66uLoCSNQNffk3K0717d6FDoHIsXLgQPXr0wLfffovBgwejbt26AIB9+/bJhmKScF6f5fJlDYMzw1JVw4SOiASRmJiIo0eP4ujRo9DQ0ECnTp1w8+ZN+Pr6YtGiRRg/frzQIVY7Y8eOxahRo3D16lU0aNAAAHD58mWsWbMGX3/9NQDg8OHD8Pf3FzDK6mnmzJlCh0DlaNWqFV68eIGMjAyYmZnJ2keOHAl9fX3Z+7Nnz6J+/fqy4ZmkPGvXrsWyZctw7949ACXDML/66it89tlnAkdGpBgccklVgpmZWaXvuKWkpLznaKg8EokE+/btw/r163HkyBHUqVMHn332Gfr16wdjY2MAwO7duzFs2DCkpqYKHG31tHXrVnz//feIjo4GAHh5eWHs2LHo168fACA3N1c26yURVZ6xsTEiIiLkRpHQ+zdjxgwsXboUY8eORePGjQEA58+fx/fff4/x48djzpw5AkdI9N8xoaMqYePGjZXuO3jw4PcYCVXE0tISxcXF+PTTTzFixIgyKz1paWkICAjgYrBErykqKsKyZcuwc+dOxMfHy2byfYk3qlTfy3VRmdApl5WVFcLDw/Hpp5/Ktf/yyy8YO3YsZ7+mKoFDLqlKYJKmHpYtW4bevXtXWN0xNTXF6dOnUVxcDLGYK6sIoaCgAM+ePUNxcbFcu5OTk0AR0ezZs7FmzRpMnDgR06dPxzfffIO4uDjs2bOH65wRVUAikaB+/fql2uvVq4fCwkIBIiJSPFboqErIyMiQDdnLyMiosO/LfqS6ODRJGPfu3cOwYcNw7tw5uXapVAqRSMR10ATk7u6O8PBwdO7cGUZGRoiIiJC1XbhwAdu2bRM6RHoLVuiEMXbsWGhpaWHp0qVy7ZMmTUJubi5WrlwpUGREisMKHVUJZmZmSExMhLW1NUxNTct8no5/lKoP3mcSxpAhQ6CpqYkDBw7Azs6OM8GpkKSkJNSuXRsAYGhoiPT0dABAly5dSq0bSETy1q5diyNHjsjWor148SLi4+MxaNAgTJgwQdbvzaSPSF0woaMq4cSJE7LFdk+ePClwNETqKSIiAlevXoW3t7fQodAbatSogcTERDg5OcHd3R1HjhxBYGAgLl++zFkT1QRvkAjj1q1bCAwMBADExsYCKHme29LSErdu3ZL14/UhdcaEjqqEli1byn2dl5eHGzdulPkcEBGVzdfXlxMEqKgePXrg+PHjaNiwIcaOHYsBAwZg7dq1iI+P5xIfaoIjD4TBm7xUHfAZOqpyDh06hEGDBpX5hymHXKoHPmsijBMnTmD69OmYP38+ateuDS0tLbntfP5UdZw/fx7nz5+Hp6cnunbtKnQ41VqbNm2wa9cumJqayrVnZGSge/fuOHHihDCBEVG1wYSOqhxPT0+0b98eM2bMgI2NjdDh0DvgpCjCeDmr6JtDj/j8KVH5xGIxkpKSYG1tLdf+7NkzODg4QCKRCBQZEVUXHHJJVc7Tp08xYcIEJnNqjPeZhMGhSartyZMnOHPmTJlDyceNGydQVNXXjRs3ZF/fvn0bSUlJsvdFRUU4dOgQHBwchAiNiKoZVuioyhk2bBiaNm2K4cOHCx0KvUYikUBPTw8RERHw8/OrsO+jR49gb28PDQ0NJUVHpNo2bNiAzz//HNra2rCwsJCroopEIty/f1/A6KonsVgsuw5l/Smlp6eHFStWYNiwYcoOjYiqGSZ0VOXk5OSgd+/esLKyKvM5IN7JFo6bmxt2796NunXrCh0KleP06dNYvXo17t+/j19//RUODg7YvHkzXF1d0axZM6HDq7YcHR0xatQohISEyIbGkrAePnwIqVQKNzc3XLp0CVZWVrJt2trasLa25k0pIlIKJnRU5axduxajRo2Crq4u72SrmLVr12LXrl3YvHmzbJkJUh2///47Bg4ciP79+2Pz5s24ffs23Nzc8P333+PgwYM4ePCg0CFWWxYWFrh06RLc3d2FDoWIiFQMEzqqcmxtbTFu3DhMmzaNd7JVTEBAAGJiYiCRSODs7AwDAwO57deuXRMoMgJKrs/48eMxaNAguZlGr1+/jo4dO8o9I0TKNWXKFJibm2PatGlCh0LluH37NuLj41FQUCDX3q1bN4EiIqLqgpOiUJVTUFCAvn37MplTQd27dxc6BKpAdHQ0WrRoUardxMQEaWlpyg+IZMLCwtClSxccOnSozKHkS5cuFSgyun//Pnr06IGbN29CJBLJnqd7OTqEs8MS0fvGhI6qnMGDB2PHjh34+uuvhQ6F3jBz5kyhQ6AK2NraIiYmBi4uLnLtZ86c4RISAgsLC8Phw4fh5eUFAKWGkpNwvvzyS7i6uuL48eNwdXXFpUuXkJycjIkTJ2Lx4sVCh0dE1QATOqpyioqKsGjRIhw+fBh16tThnWyiShoxYgS+/PJLrFu3DiKRCE+ePMH58+cxadIkhIaGCh1etbZkyRKsW7cOQ4YMEToUesP58+dx4sQJWFpaQiwWQywWo1mzZggLC8O4ceNw/fp1oUMkoiqOCR1VOTdv3kRAQAAA4NatW3LbeCdbWEVFRVi2bBl27txZ5rMmKSkpAkVGADBt2jQUFxejbdu2yMnJQYsWLaCjo4NJkyZh7NixQodXreno6KBp06ZCh0FlKCoqgpGREQDA0tIST548gZeXF5ydnREdHS1wdERUHXBSFCJSmhkzZmDNmjWYOHEipk+fjm+++QZxcXHYs2cPZsyYwSUlVERBQQFiYmKQlZUFX19fGBoaym1//Pgx7O3t+ZyqEoWFhSExMRHh4eFCh0JvaN68OSZOnIju3bujX79+SE1NxfTp0/HTTz/h6tWrpW4sEhEpGhM6IlIad3d3hIeHo3PnzjAyMkJERISs7cKFC9i2bZvQIVIlGBsbIyIigs/VKVGPHj1w4sQJWFhYoFatWqWGku/atUugyOjw4cPIzs5Gz549ERMTgy5duuDu3buwsLDAjh070KZNG6FDJKIqjkMuiUhpkpKSULt2bQCAoaEh0tPTAQBdunThM1pqhPcBlc/U1BQ9e/YUOgwqw4cffij72sPDA3fu3EFKSgrMzMzkhvmzsk1E7wsTOiJSmho1aiAxMRFOTk5wd3fHkSNHEBgYiMuXL0NHR0fo8IhU1vr16yvV7+zZs6hfvz5/ngRmbm5eqs3X15eVbSJ6L3ibiIiUpkePHjh+/DgAYOzYsQgNDYWnpycGDRqEYcOGCRwdkfrr2LEjEhIShA6DysDKNhG9L6zQEZHSLFiwQPZ137594ezsjHPnzsHT0xNdu3YVMDKiqoFJAxFR9cMKHREpTVhYGNatWyd736hRI0yYMAHPnz/HwoULBYyM/g0u/0FERKQ6mNARkdKsXr0a3t7epdpr1aqFVatWCRARvQtWgYiIiFQHEzoiUpqkpCTY2dmVareyskJiYqIAEdFLEokEmpqalVoz6/bt23B2dlZCVERVByvbRPS+MKEjIqVxdHTE2bNnS7WfPXsW9vb2AkREL2lpacHJyQlFRUVv7evo6AgNDQ0lREX/FpMG1cXKNhG9L0zoiEhpRowYga+++grr16/Hw4cP8fDhQ6xbtw7jx4/HiBEjhA6v2vvmm2/w9ddfIyUlRehQ6B0xaRBOTEwMDh8+jNzcXAClrwUr20T0voik/O1PREoilUoxbdo0hIeHo6CgAACgq6uLqVOnYsaMGQJHRwEBAYiJiYFEIoGzszMMDAzktl+7dk2gyIhUV3JyMvr27YsTJ05AJBLh3r17cHNzw7Bhw2BmZoYlS5YIHSIRVXFctoCIlEYkEmHhwoUIDQ1FVFQU9PT04OnpyUWQVUT37t2FDoHK8fTpU0yaNAnHjx/Hs2fPSlV/KjNUlt6P8ePHQ1NTE/Hx8fDx8ZG19+3bFxMmTGBCR0TvHSt0REREKq5jx46Ij49HcHAw7OzsSj0r99FHHwkUGdna2uLw4cOoW7cujIyMEBkZCTc3N9y/fx916tRBVlaW0CESURXHCh0REZGKO3PmDE6fPg1/f3+hQ6E3ZGdnQ19fv1R7SkoKRx8QkVJwUhQiIgJQMmxv8eLFCAoKgq2tLczNzeVeJBxHR0dOeKKimjdvjk2bNsnei0QiFBcXY9GiRWjdurWAkRFRdcGEjoiIAACzZ8/G0qVL0bdvX6Snp2PChAno2bMnxGIxZs2aJXR41dry5csxbdo0xMXFCR0KvWHRokX46aef0LFjRxQUFGDKlCnw8/PDX3/9hYULFwodHhFVA3yGjoiIAADu7u4IDw9H586dYWRkhIiICFnbhQsXsG3bNqFDrLbMzMyQk5ODwsJC6OvrQ0tLS247l5oQVnp6Or7//ntERkYiKysLgYGBGDNmDOzs7IQOjYiqASZ0REQEADAwMEBUVBScnJxgZ2eHP/74A4GBgbh//z4CAgKQnp4udIjV1saNGyvcPnjwYCVFQkREqoaTohAREQCgRo0aSExMhJOTE9zd3XHkyBEEBgbi8uXLnNxBYEzYVNeNGzfKbBeJRNDV1YWTkxN/fojovWJCR0REAIAePXrg+PHjaNiwIcaOHYsBAwZg7dq1iI+Px/jx44UOr9orKirCnj17EBUVBQCoVasWunXrBg0NDYEjq978/f1ly0i8HPT0+rISWlpa6Nu3L1avXg1dXV1BYiSiqo1DLomIqEwXLlzAuXPn4Onpia5duwodTrUWExODTp06ISEhAV5eXgCA6OhoODo64o8//oC7u7vAEVZfe/fuxdSpUzF58mQEBQUBAC5duoQlS5Zg5syZKCwsxLRp09C3b18sXrxY4GiJqCpiQkdERACAsLAw2NjYYNiwYXLt69atw/PnzzF16lSBIqNOnTpBKpVi69atsiUkkpOTMWDAAIjFYvzxxx8CR1h9BQUFYe7cufjwww/l2g8fPozQ0FBcunQJe/bswcSJExEbGytQlERUlTGhIyIiAICLiwu2bduGJk2ayLVfvHgRn3zyCR48eCBQZGRgYIALFy6gdu3acu2RkZFo2rQpsrKyBIqM9PT0cP36dXh7e8u137lzBwEBAcjNzUVcXBx8fX2Rk5MjUJREVJVxHToiIgIAJCUllTnNupWVFRITEwWIiF7S0dFBZmZmqfasrCxoa2sLEBG95O3tjQULFqCgoEDWJpFIsGDBAlmSl5CQABsbG6FCJKIqjpOiEBERAMDR0RFnz56Fq6urXPvZs2dhb28vUFQEAF26dMHIkSOxdu1a2XNaFy9exKhRo9CtWzeBo6veVq5ciW7duqFGjRqoU6cOAODmzZsoKirCgQMHAAD379/HF198IWSYRFSFccglEREBABYtWoRFixbh22+/RZs2bQAAx48fx5QpUzBx4kSEhIQIHGH1lZaWhsGDB2P//v2yRcULCwvRrVs3bNiwASYmJgJHWL1lZmZi69atuHv3LgDAy8sL/fr1g5GRkcCREVF1wISOiIgAlEy5Pm3aNISHh8uGj+nq6mLq1KmYMWOGwNERUDLb5ctlC3x8fODh4SFwREREJDQmdEREJCcrKwtRUVHQ09ODp6cnF0VWI8bGxoiIiICbm5vQoVQ7t2/fRnx8vNyzdAA4JJaI3js+Q0dERHIMDQ3RoEEDocOgd8B7tMp3//599OjRAzdv3oRIJCq1uHhRUZGQ4RFRNcBZLomIiIje0ZdffglXV1c8e/YM+vr6+Pvvv/HXX3+hfv36OHXqlNDhEVE1wAodERER0Ts6f/48Tpw4AUtLS4jFYojFYjRr1gxhYWEYN24crl+/LnSIRFTFsUJHRERE9I6Kiopks1laWlriyZMnAABnZ2dER0cLGRoRVROs0BEREVURL5/bIuXx8/NDZGQkXF1d0bBhQyxatAja2tr46aefODkNESkFEzoiIqIqgpOiKN/06dORnZ0NAJgzZw66dOmC5s2bw8LCAjt27BA4OiKqDrhsARERkZopKirCzZs34ezsDDMzM1n7mTNn0KBBAy41IbCUlBSYmZnJVUwfP34Me3t7iMV82oWIFIu/VYiIiFTcV199hbVr1wIoSeZatmyJwMBAODo6ys2k2KxZMyZzKsDc3LzU8FdfX1/ExcUJExARVWlM6IiIiFTcb7/9hrp16wIA9u/fjwcPHuDOnTsYP348vvnmG4Gjo8rggCgiel+Y0BEREam4Fy9ewNbWFgBw8OBB9O7dGzVr1sSwYcNw8+ZNgaMjIiIhMaEjIiJScTY2Nrh9+zaKiopw6NAhfPDBBwCAnJwcaGhoCBwdEREJibNcEhERqbihQ4eiT58+sLOzg0gkQrt27QAAFy9ehLe3t8DRERGRkJjQERERqbhZs2bBz88Pjx49Qu/evWUTn2hoaGDatGkCR0eVwTUCieh94bIFREREKu7+/ftcpFrNGRkZITIykteRiBSOz9ARERGpOA8PD7Ru3RpbtmxBXl6e0OHQPyQSCTQ1NXHr1q239r19+zacnZ2VEBURVTdM6IiIiFTctWvXUKdOHUyYMAG2trb4/PPPcenSJaHDqva0tLTg5OSEoqKit/Z1dHTkBDZE9F5wyCUREZGaKCwsxL59+7BhwwYcOnRItnTBwIEDYWVlJXR41dLatWuxa9cubN68Gebm5kKHQ0TVEBM6IiIiNZOfn48ffvgBISEhKCgogLa2Nvr06YOFCxfCzs5O6PCqlYCAAMTExEAikcDZ2RkGBgZy269duyZQZERUXXCWSyIiIjVx5coVrFu3Dtu3b4eBgQEmTZqE4cOH4/Hjx5g9ezY++ugjDsVUsu7duwsdAhFVc6zQERERqbilS5di/fr1iI6ORqdOnfDZZ5+hU6dOEItfPQr/+PFjuLi4oLCwUMBIiYhI2VihIyIiUnE//vgjhg0bhiFDhpQ7pNLa2hpr165VcmRERCQ0VuiIiIiI3lFRURGWLVuGnTt3Ij4+HgUFBXLbU1JSBIqMiKoLLltARESkJnJycnDnzh3cuHFD7kXCmT17NpYuXYq+ffsiPT0dEyZMQM+ePSEWizFr1iyhwyOiaoAVOiIiIhX3/PlzDBkyBIcOHSpze2XWQaP3w93dHeHh4ejcuTOMjIwQEREha7tw4QK2bdsmdIhEVMWxQkdERKTivvrqK6Snp+PixYvQ09PDoUOHsHHjRnh6emLfvn1Ch1etJSUloXbt2gAAQ0NDpKenAwC6dOmCP/74Q8jQiKia4KQoREREKu7EiRPYu3cv6tevD7FYDGdnZ3zwwQcwNjZGWFgYOnfuLHSI1VaNGjWQmJgIJycnuLu748iRIwgMDMTly5eho6MjdHhEVA2wQkdERKTisrOzYW1tDQAwMzPD8+fPAQC1a9fmwtUC69GjB44fPw4AGDt2LEJDQ+Hp6YlBgwZh2LBhAkdHRNUBK3REREQqzsvLC9HR0XBxcUHdunWxevVquLi4YNWqVeUuY0DKsWDBAtnXffv2hbOzM86dOwdPT0907dpVwMiIqLrgpChEREQqbsuWLSgsLMSQIUNw9epVdOjQAcnJydDW1sbGjRvRt29foUOstsLCwmBjY1OqGrdu3To8f/4cU6dOFSgyIqoumNARERGpmZfLFzg5OcHS0lLocKo1FxcXbNu2DU2aNJFrv3jxIj755BM8ePBAoMiIqLrgkEsiIiIVNGHChEr3Xbp06XuMhCqSlJRU5rBXKysrJCYmChAREVU3TOiIiIhU0PXr1+XeX7t2DYWFhfDy8gIA3L17FxoaGqhXr54Q4dE/HB0dcfbsWbi6usq1nz17Fvb29gJFRUTVCRM6IiIiFXTy5EnZ10uXLoWRkRE2btwIMzMzAEBqaiqGDh2K5s2bCxUiARgxYgS++uorSCQStGnTBgBw/PhxTJkyBRMnThQ4OiKqDvgMHRERkYpzcHDAkSNHUKtWLbn2W7duoX379njy5IlAkZFUKsW0adMQHh6OgoICAICuri6mTp2KGTNmCBwdEVUHrNARERGpuIyMDNnac697/vw5MjMzBYiIXhKJRFi4cCFCQ0MRFRUFPT09eHp6clFxIlIaVuiIiIhU3KBBg3D69GksWbIEQUFBAEpmUZw8eTKaN2+OjRs3ChwhEREJhQkdERGRisvJycGkSZOwbt06SCQSAICmpiaGDx+Ob7/9FgYGBgJHSEREQmFCR0REpCays7MRGxsLAHB3d2ciR0RETOiIiIiIiIjUlVjoAIiIiIiIiOjdMKEjIiIiIiJSU0zoiIiIiIiI1BQTOiIiIiIiIjXFhI6IiIiIiEhNMaEjIiIiIiJSU0zoiIiIiIiI1NT/A/yGZRoiC5fEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Understanding Correlation Values**:\n",
        "   - **Positive Correlation**: When the value is close to 1, it indicates a strong positive relationship between the two variables (as one increases, the other tends to increase).\n",
        "   - **Negative Correlation**: When the value is close to -1, it indicates a strong negative relationship (as one increases, the other tends to decrease).\n",
        "   - **No Correlation**: Values close to 0 suggest little to no linear relationship between the variables.\n",
        "\n",
        "2. **Key Findings**:\n",
        "   - **Strong Negative Correlation**:\n",
        "     - There is a very strong negative correlation (-0.99) between `car_year` and `car_age_at_sale`. This makes sense since a newer car (`car_year` closer to the present) typically has a lower age at the time of sale.\n",
        "   - **Moderate Positive Correlation**:\n",
        "     - `car_engine` has a moderate positive correlation with `price` (0.45), meaning cars with larger engines tend to have higher prices.\n",
        "     - `car_year` also shows a moderate positive correlation with `price` (0.51), indicating that newer cars tend to be more expensive.\n",
        "   - **Weak Correlation**:\n",
        "     - `days_on_market` shows almost no correlation with `price` (0.0004), suggesting the number of days a car spends on the market has little to no effect on its price.\n",
        "     - `milage` has a weak negative correlation with `price` (-0.36), suggesting that cars with higher mileage tend to have lower prices, although this relationship is not very strong.\n",
        "\n",
        "3. **Implications**:\n",
        "   - **For Pricing Strategy**:\n",
        "     - Stakeholders should consider that both the car's engine size and year significantly influence the price. Efforts to price cars should take these factors into account.\n",
        "   - **Further Analysis**:\n",
        "     - The high negative correlation between `car_year` and `car_age_at_sale` suggests potential multicollinearity, which can be explored further in model development to ensure it doesn’t adversely affect predictive performance.\n",
        "   - **Insights for Sales Strategy**:\n",
        "     - Since `days_on_market` doesn't significantly affect the price, the time taken to sell a car might be more influenced by other factors, such as market conditions or sales tactics, rather than the intrinsic features of the car itself."
      ],
      "metadata": {
        "id": "QGT2uLYOQaOM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One-Way ANOVA for Categorical Features"
      ],
      "metadata": {
        "id": "peUAT0MfO34j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from scipy.stats import f_oneway\n",
        "\n",
        "# Select categorical features for ANOVA\n",
        "categorical_features = [\n",
        "    'color',\n",
        "    'car_brand',\n",
        "    'car_model',\n",
        "    'car_variant'\n",
        "]\n",
        "\n",
        "# Create an empty DataFrame to store ANOVA results\n",
        "anova_results = pd.DataFrame(columns=['Feature', 'F-Statistic', 'P-Value'])\n",
        "\n",
        "# Perform One-Way ANOVA for each categorical feature\n",
        "results_list = []\n",
        "for feature in categorical_features:\n",
        "    # Group the data by the feature and extract price for each group\n",
        "    groups = [df[df[feature] == value]['price'] for value in df[feature].unique()]\n",
        "\n",
        "    # Perform the ANOVA test\n",
        "    f_statistic, p_value = f_oneway(*groups)\n",
        "\n",
        "    # Store the result in a dictionary\n",
        "    result = {'Feature': feature, 'F-Statistic': f_statistic, 'P-Value': p_value}\n",
        "\n",
        "    # Append the result to the list\n",
        "    results_list.append(result)\n",
        "\n",
        "# Convert the list of dictionaries to a DataFrame\n",
        "anova_results = pd.concat([anova_results, pd.DataFrame(results_list)], ignore_index=True)\n",
        "\n",
        "# Sort results by F-Statistic\n",
        "anova_results = anova_results.sort_values(by='F-Statistic', ascending=False)\n",
        "\n",
        "# Display ANOVA results\n",
        "print(anova_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-NKmV2YQy1_",
        "outputId": "f864e14e-9970-435e-a7b1-0e6640c53dda"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Feature  F-Statistic  P-Value\n",
            "1    car_brand  1891.835806      0.0\n",
            "2    car_model   637.021178      0.0\n",
            "0        color   256.495205      0.0\n",
            "3  car_variant   131.615168      0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Simplified Summary\n",
        "\n",
        "1. **F-Statistic Values**:\n",
        "   - **Car Brand: 1891.84**\n",
        "   - **Car Model: 637.02**\n",
        "   - **Color: 256.50**\n",
        "   - **Car Variant: 131.62**\n",
        "\n",
        "   These F-Statistic values are considered \"very high\" because they are significantly larger than typical values seen in most ANOVA tests, indicating a strong effect.\n",
        "\n",
        "2. **Statistical Significance**:\n",
        "   - For all these features, the p-value is zero. This means that the differences between the group means (like those for car brands, models, colors, and variants) are extremely unlikely to have occurred by chance.\n",
        "   - A p-value of zero indicates very strong statistical significance, showing that the observed results are highly unlikely to be due to random variation.\n",
        "\n",
        "3. **Conclusion**:\n",
        "   - The high F-Statistic values and the zero p-values both confirm that `car_brand`, `car_model`, `color`, and `car_variant` all have a significant impact on the outcome. The results are statistically significant, and these features play a crucial role in explaining the variance in the outcome variable."
      ],
      "metadata": {
        "id": "bocVImbUUIzK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multicolinnearity Analysis"
      ],
      "metadata": {
        "id": "aeByYvg9MREM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "VDYvw_TVMzuT"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the continuous features\n",
        "cont_features = ['milage', 'days_on_market', 'car_age_at_sale', 'price']\n",
        "\n",
        "# Subset the DataFrame to include only the continuous features\n",
        "df_cont = df[cont_features]"
      ],
      "metadata": {
        "id": "JStyeG9uM1N4"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a constant column for VIF calculation\n",
        "df_cont = df_cont.assign(Intercept=1)\n",
        "\n",
        "# Calculate VIF for each feature\n",
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"Feature\"] = df_cont.columns\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(df_cont.values, i) for i in range(len(df_cont.columns))]\n",
        "\n",
        "# Exclude the intercept from the VIF DataFrame\n",
        "vif_data = vif_data[vif_data[\"Feature\"] != \"Intercept\"]\n",
        "\n",
        "# Display the VIF values\n",
        "print(vif_data.sort_values(by=\"VIF\", ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R4kj3KbM9Bc",
        "outputId": "9e869aa2-79d7-4cdb-88bd-6a217a064495"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Feature       VIF\n",
            "2  car_age_at_sale  1.760924\n",
            "0           milage  1.507836\n",
            "3            price  1.361246\n",
            "1   days_on_market  1.001288\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Multicollinearity Analysis Summary**\n",
        "\n",
        "**Context:**\n",
        "In our analysis of factors influencing car prices, we examined the relationship between key continuous features like mileage, car age at sale, and the number of days a car spends on the market. One important aspect of this analysis is to ensure that these features are not overly correlated with each other, which could distort the results of our predictive models.\n",
        "\n",
        "**Key Findings:**\n",
        "We conducted a multicollinearity analysis using the Variance Inflation Factor (VIF) to measure how much each feature is correlated with the others:\n",
        "- **Car Age at Sale**: VIF = 1.76\n",
        "- **Mileage**: VIF = 1.51\n",
        "- **Price**: VIF = 1.36\n",
        "- **Days on Market**: VIF = 1.00\n",
        "\n",
        "**Interpretation:**\n",
        "- **VIF Values**: A VIF value below 5 generally indicates that there is no significant multicollinearity between the features. In our case, all VIF values are well below this threshold, meaning that each of these features provides unique information about the cars without being overly redundant.\n",
        "- **Conclusion**: The low VIF values indicate that we can confidently include these features in our predictive models without worrying about multicollinearity. This enhances the reliability and interpretability of our results.\n",
        "\n",
        "**Implications:**\n",
        "- **For Model Development**: These findings suggest that our model will have a stable foundation, as the features are not excessively interdependent.\n",
        "- **For Decision-Making**: The distinctiveness of each feature allows us to understand how different factors like mileage and car age at sale independently impact car prices, helping us to make more informed pricing and sales strategy decisions.\n"
      ],
      "metadata": {
        "id": "Zpz4blntNdox"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Development"
      ],
      "metadata": {
        "id": "NcZUjCyzWTwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_data = df.drop(\"price\", axis=1)\n",
        "y_data = df[\"price\"]\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
        "\n",
        "print('Train set:', x_train.shape,  y_train.shape)\n",
        "print('Test set:', x_test.shape,  y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_1s8sr2WcYg",
        "outputId": "b700b393-83f2-457b-a2dd-6b49b724ae03"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (85883, 14) (85883,)\n",
            "Test set: (9543, 14) (9543,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import numpy as np\n",
        "\n",
        "def evaluation_train(y, predictions):\n",
        "    r2_train = r2_score(y, predictions)\n",
        "    return r2_train\n",
        "\n",
        "def evaluation_test(y, predictions):\n",
        "    rmse_test = np.sqrt(mean_squared_error(y, predictions))\n",
        "    r2_test = r2_score(y, predictions)\n",
        "    return rmse_test, r2_test\n",
        "\n",
        "def R2_train_adjusted(r_squared, n, k):\n",
        "    adjusted_r2 = r_squared - (k - 1) / (n - k) * (1 - r_squared)\n",
        "    return adjusted_r2\n",
        "\n",
        "def R2_test_adjusted(r_squared, n, k):\n",
        "    adjusted_r2 = r_squared - (k - 1) / (n - k) * (1 - r_squared)\n",
        "    return adjusted_r2\n",
        "\n",
        "def cross_fold(pipeline, x_data, y_data):\n",
        "    Rcross = cross_val_score(pipeline, x_data, y_data, cv=10).mean()\n",
        "    RMSEcross = np.sqrt(-cross_val_score(pipeline, x_data, y_data, scoring=\"neg_mean_squared_error\", cv=10)).mean()\n",
        "    return Rcross, RMSEcross\n"
      ],
      "metadata": {
        "id": "3AJ2JPG1WueX"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# Define the initial models_table DataFrame\n",
        "models_table = pd.DataFrame(columns=[\"Model\", \"Model Alias\", \"Detail\",\n",
        "                                     \"R2 Score (Train)\", \"Adjusted R2 Score (Train)\",\n",
        "                                     \"RMSE (Test)\", \"R2 Score (Test)\",\n",
        "                                     \"Adjusted R2 Score (Test)\",\n",
        "                                     \"Cross-Validated RMSE\", \"Cross-Validated R2 Score\"])\n",
        "\n",
        "# Ensure to define only those columns that are genuinely categorical\n",
        "# and avoid columns that are just unique identifiers or hashes.\n",
        "categorical_features = ['color']  # Use valid categorical features\n",
        "numerical_features = ['milage', 'car_year', 'car_engine', 'days_on_market', 'car_age_at_sale']  # Numerical features\n",
        "\n",
        "# Create the transformer to handle categorical and numerical data\n",
        "transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numerical_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
        "    ],\n",
        "    remainder='drop'  # Drop any leftover columns that are not being transformed\n",
        ")\n",
        "\n",
        "# Set up the model and pipeline\n",
        "mlr1 = LinearRegression()\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('ColumnTransformer', transformer),\n",
        "    ('Model', mlr1)\n",
        "])\n",
        "\n",
        "# Fit the model\n",
        "pipe.fit(x_train, y_train)\n",
        "\n",
        "# Model Prediction\n",
        "predictions_train = pipe.predict(x_train)\n",
        "predictions_test = pipe.predict(x_test)\n",
        "\n",
        "# Validate Model Performance\n",
        "n = x_train.shape[0]\n",
        "k = len(x_train.columns)\n",
        "\n",
        "r2_train = evaluation_train(y_train, predictions_train)\n",
        "rmse_test, r2_test = evaluation_test(y_test, predictions_test)\n",
        "adjusted_r2_train = R2_train_adjusted(r2_train, n, k)\n",
        "adjusted_r2_test = R2_test_adjusted(r2_test, n, k)\n",
        "\n",
        "Rcross, RMSEcross = cross_fold(pipe, x_data, y_data)\n",
        "\n",
        "new_row = {\n",
        "    \"Model\": \"Multiple Linear Regression\",\n",
        "    \"Model Alias\": \"mlr1\",\n",
        "    \"Detail\": \"Default, Pre-Process\",\n",
        "    \"R2 Score (Train)\": r2_train,\n",
        "    \"Adjusted R2 Score (Train)\": adjusted_r2_train,\n",
        "    \"RMSE (Test)\": rmse_test,\n",
        "    \"R2 Score (Test)\": r2_test,\n",
        "    \"Adjusted R2 Score (Test)\": adjusted_r2_test,\n",
        "    \"Cross-Validated RMSE\": RMSEcross,\n",
        "    \"Cross-Validated R2 Score\": Rcross\n",
        "}\n",
        "\n",
        "# Append the new row to models_table\n",
        "models_table = pd.concat([models_table, pd.DataFrame([new_row])], ignore_index=True)\n",
        "models_table\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "0IQB4v4ZW3y3",
        "outputId": "5551e934-eba3-4045-b622-6485a4ba0610"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Model Model Alias                Detail  \\\n",
              "0  Multiple Linear Regression        mlr1  Default, Pre-Process   \n",
              "\n",
              "   R2 Score (Train)  Adjusted R2 Score (Train)   RMSE (Test)  R2 Score (Test)  \\\n",
              "0          0.499764                   0.499689  21350.256037         0.496181   \n",
              "\n",
              "   Adjusted R2 Score (Test)  Cross-Validated RMSE  Cross-Validated R2 Score  \n",
              "0                  0.496105          20896.818463                  0.467893  "
            ],
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              "summary": "{\n  \"name\": \"models_table\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Multiple Linear Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model Alias\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"mlr1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Detail\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Default, Pre-Process\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score (Train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.4997644980470842,\n        \"max\": 0.4997644980470842,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4997644980470842\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R2 Score (Train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.4996887656928541,\n        \"max\": 0.4996887656928541,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4996887656928541\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 21350.256037217696,\n        \"max\": 21350.256037217696,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          21350.256037217696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.4961807834266676,\n        \"max\": 0.4961807834266676,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4961807834266676\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R2 Score (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.4961045085216908,\n        \"max\": 0.4961045085216908,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4961045085216908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cross-Validated RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 20896.818463491087,\n        \"max\": 20896.818463491087,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20896.818463491087\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cross-Validated R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.46789265729088614,\n        \"max\": 0.46789265729088614,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.46789265729088614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# Define the hyperparameter grid with very narrow ranges and reduced candidates\n",
        "param_grid = {\n",
        "    'max_features': ['sqrt', 'log2'],  # Use valid options\n",
        "    'max_depth': [10, 20],  # Very narrow range\n",
        "    'min_samples_split': [2, 6],  # Narrow range\n",
        "    'min_samples_leaf': [1, 2],  # Narrow range\n",
        "    'bootstrap': [True]  # Fixed to a single option\n",
        "}\n",
        "\n",
        "# Set up the RandomizedSearchCV with minimal iterations and reduced cv folds\n",
        "rf2 = RandomizedSearchCV(\n",
        "    estimator=RandomForestRegressor(),\n",
        "    param_distributions=param_grid,\n",
        "    scoring='neg_mean_squared_error',\n",
        "    cv=2,  # Reduced from 3 to 2\n",
        "    n_iter=3,  # Very low number of iterations\n",
        "    n_jobs=-1,\n",
        "    verbose=2,\n",
        "    random_state=1\n",
        ")\n",
        "\n",
        "pipe2 = Pipeline([\n",
        "    ('ColumnTransformer', transformer),\n",
        "    ('Model', rf2)\n",
        "])\n",
        "\n",
        "# Fit the model on a smaller subset of data (e.g., 30% of the training data)\n",
        "x_train_subset = x_train.sample(frac=0.3, random_state=1)\n",
        "y_train_subset = y_train.loc[x_train_subset.index]\n",
        "\n",
        "pipe2.fit(x_train_subset, y_train_subset)\n",
        "\n",
        "# Get best parameters\n",
        "best_params = pipe2.named_steps['Model'].best_estimator_.get_params()\n",
        "\n",
        "# Re-train the model with the best parameters on the full dataset\n",
        "rf3 = RandomForestRegressor(**best_params)\n",
        "\n",
        "pipe3 = Pipeline([\n",
        "    ('ColumnTransformer', transformer),\n",
        "    ('Model', rf3)\n",
        "])\n",
        "\n",
        "pipe3.fit(x_train, y_train)\n",
        "\n",
        "# Model Prediction\n",
        "predictions_train = pipe3.predict(x_train)\n",
        "predictions_test = pipe3.predict(x_test)\n",
        "\n",
        "# Validate Model Performance\n",
        "r2_train = evaluation_train(y_train, predictions_train)\n",
        "rmse_test, r2_test = evaluation_test(y_test, predictions_test)\n",
        "adjusted_r2_train = R2_train_adjusted(r2_train, n, k)\n",
        "adjusted_r2_test = R2_test_adjusted(r2_test, n, k)\n",
        "\n",
        "Rcross, RMSEcross = cross_fold(pipe3, x_data, y_data)\n",
        "\n",
        "new_row = {\n",
        "    \"Model\": \"Random Forest Regressor\",\n",
        "    \"Model Alias\": \"rf3\",\n",
        "    \"Detail\": str(best_params),\n",
        "    \"R2 Score (Train)\": r2_train,\n",
        "    \"Adjusted R2 Score (Train)\": adjusted_r2_train,\n",
        "    \"RMSE (Test)\": rmse_test,\n",
        "    \"R2 Score (Test)\": r2_test,\n",
        "    \"Adjusted R2 Score (Test)\": adjusted_r2_test,\n",
        "    \"Cross-Validated RMSE\": RMSEcross,\n",
        "    \"Cross-Validated R2 Score\": Rcross\n",
        "}\n",
        "\n",
        "models_table = pd.concat([models_table, pd.DataFrame([new_row])], ignore_index=True)\n",
        "models_table\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrzBr5dCdvD2",
        "outputId": "61bf9e05-1e07-4455-8956-fa3ab6eddf35"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 3 candidates, totalling 6 fits\n",
            "                        Model Model Alias  \\\n",
            "0  Multiple Linear Regression        mlr1   \n",
            "1     Random Forest Regressor         rf3   \n",
            "\n",
            "                                              Detail  R2 Score (Train)  \\\n",
            "0                               Default, Pre-Process          0.499764   \n",
            "1  {'bootstrap': True, 'ccp_alpha': 0.0, 'criteri...          0.854154   \n",
            "\n",
            "   Adjusted R2 Score (Train)   RMSE (Test)  R2 Score (Test)  \\\n",
            "0                   0.499689  21350.256037         0.496181   \n",
            "1                   0.854132  15724.383354         0.726715   \n",
            "\n",
            "   Adjusted R2 Score (Test)  Cross-Validated RMSE  Cross-Validated R2 Score  \n",
            "0                  0.496105          20896.818463                  0.467893  \n",
            "1                  0.726674          16209.472299                  0.680659  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "LXzLWwBHzl5r",
        "outputId": "88023eaf-ab28-4ade-aa7f-73273089c3e7"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Model Model Alias           Detail  R2 Score (Train)  \\\n",
              "0  Linear Regression         lin  Default, Scaled          0.877362   \n",
              "\n",
              "   Adjusted R2 Score (Train)   RMSE (Test)  R2 Score (Test)  \\\n",
              "0                   0.877343  11558.270116         0.852343   \n",
              "\n",
              "   Adjusted R2 Score (Test)  Cross-Validated RMSE  Cross-Validated R2 Score  \n",
              "0                  0.852321          12199.344562                  0.816807  "
            ],
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              "      <th>Model</th>\n",
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              "type": "dataframe",
              "variable_name": "models_table",
              "summary": "{\n  \"name\": \"models_table\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Linear Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model Alias\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"lin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Detail\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Default, Scaled\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score (Train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8773619842791247,\n        \"max\": 0.8773619842791247,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8773619842791247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R2 Score (Train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8773434176927621,\n        \"max\": 0.8773434176927621,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8773434176927621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 11558.27011630164,\n        \"max\": 11558.27011630164,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          11558.27011630164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8523429346960196,\n        \"max\": 0.8523429346960196,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8523429346960196\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R2 Score (Test)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8523205803906364,\n        \"max\": 0.8523205803906364,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8523205803906364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cross-Validated RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 12199.344561766544,\n        \"max\": 12199.344561766544,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12199.344561766544\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cross-Validated R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8168065802673959,\n        \"max\": 0.8168065802673959,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8168065802673959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcZcTexku9go"
      },
      "source": [
        "1. Baseline Model\n",
        "- Logistics Regression\n",
        "- KNN\n",
        "- Support Vector Machine\n",
        "---\n",
        "\n",
        "2. Ensemble model\n",
        "- Random Forest\n",
        "- Gradient Boost\n",
        "- Histogram Gradient Boosting Classifier\n",
        "- XGboost\n",
        "- Light Gradient Boosting Machine\n",
        "---\n",
        "\n",
        "3. Ensemble\n",
        "- Voting Classier\n",
        "- Stacking"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some of the code below requires high computing power and longer time , so I decided to take Random Forest Model for Now noting that it is much more accurate than other two models that we have run before. But will let this code below for future improvement."
      ],
      "metadata": {
        "id": "NRWONYkIDwuf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "fp3uPXOXBoSq"
      },
      "outputs": [],
      "source": [
        "# from sklearn.compose import ColumnTransformer\n",
        "# from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "# from sklearn.pipeline import Pipeline\n",
        "# from sklearn.linear_model import LinearRegression\n",
        "# from sklearn.ensemble import RandomForestRegressor\n",
        "# from xgboost import XGBRegressor\n",
        "# import pandas as pd\n",
        "\n",
        "# # Define the preprocessing pipeline\n",
        "# # Make sure categorical_features contains all relevant categorical columns\n",
        "# categorical_features = ['color', 'car_variant']  # Replace with your actual categorical feature names\n",
        "# numerical_features = ['milage', 'car_year', 'car_engine', 'days_on_market', 'car_age_at_sale']\n",
        "\n",
        "# # Create the transformer to handle categorical and numerical data\n",
        "# preprocessing_pipeline = ColumnTransformer(\n",
        "#     transformers=[\n",
        "#         ('num', StandardScaler(), numerical_features),\n",
        "#         ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
        "#     ],\n",
        "#     remainder='drop'  # Drop any leftover columns that are not being transformed\n",
        "# )\n",
        "\n",
        "# # Models to be tested\n",
        "# models = {\n",
        "#     \"Linear Regression\": LinearRegression(),\n",
        "#     \"Random Forest\": RandomForestRegressor(random_state=42),\n",
        "#     \"XGBoost\": XGBRegressor(random_state=42)\n",
        "# }\n",
        "\n",
        "# # Initialize an empty DataFrame to store the results\n",
        "# models_table = pd.DataFrame(columns=[\"Model\", \"Model Alias\", \"Detail\", \"R2 Score (Train)\",\n",
        "#                                      \"Adjusted R2 Score (Train)\", \"RMSE (Test)\",\n",
        "#                                      \"R2 Score (Test)\", \"Adjusted R2 Score (Test)\",\n",
        "#                                      \"Cross-Validated RMSE\", \"Cross-Validated R2 Score\"])\n",
        "\n",
        "# # Loop through the models, train, and evaluate each\n",
        "# for model_name, model in models.items():\n",
        "#     pipeline = Pipeline([\n",
        "#         ('preprocessor', preprocessing_pipeline),\n",
        "#         ('model', model)\n",
        "#     ])\n",
        "\n",
        "#     # Fit the model\n",
        "#     pipeline.fit(x_train, y_train)\n",
        "\n",
        "#     # Model Prediction\n",
        "#     predictions_train = pipeline.predict(x_train)\n",
        "#     predictions_test = pipeline.predict(x_test)\n",
        "\n",
        "#     # Validate Model Performance\n",
        "#     n = x_train.shape[0]\n",
        "#     k = len(x_train.columns)\n",
        "\n",
        "#     r2_train = evaluation_train(y_train, predictions_train)\n",
        "#     rmse_test, r2_test = evaluation_test(y_test, predictions_test)\n",
        "#     adjusted_r2_train = R2_train_adjusted(r2_train, n, k)\n",
        "#     adjusted_r2_test = R2_test_adjusted(r2_test, n, k)\n",
        "\n",
        "#     Rcross, RMSEcross = cross_fold(pipeline, x_data, y_data)\n",
        "\n",
        "#     # Append results to the table\n",
        "#     new_row = {\n",
        "#         \"Model\": model_name,\n",
        "#         \"Model Alias\": model_name[:3].lower(),\n",
        "#         \"Detail\": \"Default, Scaled\",\n",
        "#         \"R2 Score (Train)\": r2_train,\n",
        "#         \"Adjusted R2 Score (Train)\": adjusted_r2_train,\n",
        "#         \"RMSE (Test)\": rmse_test,\n",
        "#         \"R2 Score (Test)\": r2_test,\n",
        "#         \"Adjusted R2 Score (Test)\": adjusted_r2_test,\n",
        "#         \"Cross-Validated RMSE\": RMSEcross,\n",
        "#         \"Cross-Validated R2 Score\": Rcross\n",
        "#     }\n",
        "\n",
        "#     models_table = pd.concat([models_table, pd.DataFrame([new_row])], ignore_index=True)\n",
        "\n",
        "# # Display the table with model comparison\n",
        "# print(models_table)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F83kFn0FxlAG"
      },
      "source": [
        "# 5. Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "axCM1XkaCcVH"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import RandomizedSearchCV\n",
        "# from sklearn.ensemble import RandomForestRegressor\n",
        "# from xgboost import XGBRegressor\n",
        "# from sklearn.pipeline import Pipeline\n",
        "# import numpy as np\n",
        "\n",
        "# # Define a smaller hyperparameter grid with fewer options\n",
        "# rf_param_grid = {\n",
        "#     'model__n_estimators': [100, 200],  # Fewer values to try\n",
        "#     'model__max_depth': [10, 20],  # Narrowed range\n",
        "#     'model__min_samples_split': [2, 5]\n",
        "# }\n",
        "\n",
        "# xgb_param_grid = {\n",
        "#     'model__n_estimators': [100, 200],  # Fewer values to try\n",
        "#     'model__max_depth': [3, 6],  # Narrowed range\n",
        "#     'model__learning_rate': [0.01, 0.1]\n",
        "# }\n",
        "\n",
        "# # Use RandomizedSearchCV instead of GridSearchCV with fewer iterations and CV folds\n",
        "# rf_random_search = RandomizedSearchCV(\n",
        "#     estimator=Pipeline([\n",
        "#         ('preprocessor', preprocessing_pipeline),\n",
        "#         ('model', RandomForestRegressor(random_state=42))\n",
        "#     ]),\n",
        "#     param_distributions=rf_param_grid,\n",
        "#     n_iter=5,  # Fewer iterations\n",
        "#     cv=3,  # Reduced cross-validation folds\n",
        "#     scoring='neg_mean_squared_error',\n",
        "#     n_jobs=-1,\n",
        "#     verbose=2,\n",
        "#     random_state=42\n",
        "# )\n",
        "\n",
        "# xgb_random_search = RandomizedSearchCV(\n",
        "#     estimator=Pipeline([\n",
        "#         ('preprocessor', preprocessing_pipeline),\n",
        "#         ('model', XGBRegressor(random_state=42))\n",
        "#     ]),\n",
        "#     param_distributions=xgb_param_grid,\n",
        "#     n_iter=5,  # Fewer iterations\n",
        "#     cv=3,  # Reduced cross-validation folds\n",
        "#     scoring='neg_mean_squared_error',\n",
        "#     n_jobs=-1,\n",
        "#     verbose=2,\n",
        "#     random_state=42\n",
        "# )\n",
        "\n",
        "# # Fit RandomizedSearchCV on a smaller subset of the training data\n",
        "# x_train_subset = x_train.sample(frac=0.5, random_state=42)\n",
        "# y_train_subset = y_train.loc[x_train_subset.index]\n",
        "\n",
        "# rf_random_search.fit(x_train_subset, y_train_subset)\n",
        "# xgb_random_search.fit(x_train_subset, y_train_subset)\n",
        "\n",
        "# # Get best parameters from each RandomizedSearchCV\n",
        "# best_rf_model = rf_random_search.best_estimator_\n",
        "# best_xgb_model = xgb_random_search.best_estimator_\n",
        "\n",
        "# # Re-train the models on the full training set with best parameters\n",
        "# best_rf_model.fit(x_train, y_train)\n",
        "# best_xgb_model.fit(x_train, y_train)\n",
        "\n",
        "# # Model Predictions and Performance Evaluation for both RandomForest and XGBoost\n",
        "# predictions_rf_test = best_rf_model.predict(x_test)\n",
        "# predictions_xgb_test = best_xgb_model.predict(x_test)\n",
        "\n",
        "# rf_r2_train = evaluation_train(y_train, best_rf_model.predict(x_train))\n",
        "# rf_rmse_test, rf_r2_test = evaluation_test(y_test, predictions_rf_test)\n",
        "# rf_adjusted_r2_train = R2_train_adjusted(rf_r2_train, x_train.shape[0], x_train.shape[1])\n",
        "# rf_adjusted_r2_test = R2_test_adjusted(rf_r2_test, x_train.shape[0], x_train.shape[1])\n",
        "# rf_cross_val_r2, rf_cross_val_rmse = cross_fold(best_rf_model, x_data, y_data)\n",
        "\n",
        "# xgb_r2_train = evaluation_train(y_train, best_xgb_model.predict(x_train))\n",
        "# xgb_rmse_test, xgb_r2_test = evaluation_test(y_test, predictions_xgb_test)\n",
        "# xgb_adjusted_r2_train = R2_train_adjusted(xgb_r2_train, x_train.shape[0], x_train.shape[1])\n",
        "# xgb_adjusted_r2_test = R2_test_adjusted(xgb_r2_test, x_train.shape[0], x_train.shape[1])\n",
        "# xgb_cross_val_r2, xgb_cross_val_rmse = cross_fold(best_xgb_model, x_data, y_data)\n",
        "\n",
        "# # Append the tuned results to the models_table\n",
        "# rf_new_row = {\n",
        "#     \"Model\": \"Tuned Random Forest\",\n",
        "#     \"Model Alias\": \"rf_tuned\",\n",
        "#     \"Detail\": \"Tuned, Scaled\",\n",
        "#     \"R2 Score (Train)\": rf_r2_train,\n",
        "#     \"Adjusted R2 Score (Train)\": rf_adjusted_r2_train,\n",
        "#     \"RMSE (Test)\": rf_rmse_test,\n",
        "#     \"R2 Score (Test)\": rf_r2_test,\n",
        "#     \"Adjusted R2 Score (Test)\": rf_adjusted_r2_test,\n",
        "#     \"Cross-Validated RMSE\": rf_cross_val_rmse,\n",
        "#     \"Cross-Validated R2 Score\": rf_cross_val_r2\n",
        "# }\n",
        "# models_table = pd.concat([models_table, pd.DataFrame([rf_new_row])], ignore_index=True)\n",
        "\n",
        "# xgb_new_row = {\n",
        "#     \"Model\": \"Tuned XGBoost\",\n",
        "#     \"Model Alias\": \"xgb_tuned\",\n",
        "#     \"Detail\": \"Tuned, Scaled\",\n",
        "#     \"R2 Score (Train)\": xgb_r2_train,\n",
        "#     \"Adjusted R2 Score (Train)\": xgb_adjusted_r2_train,\n",
        "#     \"RMSE (Test)\": xgb_rmse_test,\n",
        "#     \"R2 Score (Test)\": xgb_r2_test,\n",
        "#     \"Adjusted R2 Score (Test)\": xgb_adjusted_r2_test,\n",
        "#     \"Cross-Validated RMSE\": xgb_cross_val_rmse,\n",
        "#     \"Cross-Validated R2 Score\": xgb_cross_val_r2\n",
        "# }\n",
        "# models_table = pd.concat([models_table, pd.DataFrame([xgb_new_row])], ignore_index=True)\n",
        "\n",
        "# # Display the final model comparison table\n",
        "# models_table\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fYE69Blyg3y"
      },
      "source": [
        "##Retrain with Tuned Hyperparameters\n",
        "\n",
        "- Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CY4KVIgdyzKX"
      },
      "source": [
        "These are the best parameters obtained with RadomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "IlaBnQChy5li",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "18828df8-0e0a-47a5-f0cd-61b394897f05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=20, max_features='log2', min_samples_split=6)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=20, max_features=&#x27;log2&#x27;, min_samples_split=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=20, max_features=&#x27;log2&#x27;, min_samples_split=6)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "# best_param_rf = rf_random_search.best_params_\n",
        "best_param_rf = rf3.get_params()\n",
        "print(best_param_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "hIDOO5Fe8VVp"
      },
      "outputs": [],
      "source": [
        "# %%time\n",
        "# # Assuming best_params_rf is a dictionary of the best parameters obtained from the GridSearchCV or RandomizedSearchCV\n",
        "# # e.g., best_params_rf = rf_grid_search.best_params_\n",
        "\n",
        "# # Create a model pipeline with the best parameters\n",
        "# rf3 = Pipeline([\n",
        "#     ('ColumnTransformer', transformer),\n",
        "#     ('Model', RandomForestRegressor(**best_params_rf))  # Use the correct variable for best parameters\n",
        "# ])\n",
        "\n",
        "# # Fit the model\n",
        "# rf3.fit(x_train, y_train)\n",
        "\n",
        "# # Model Prediction\n",
        "# predictions_train = rf3.predict(x_train)\n",
        "# predictions_test = rf3.predict(x_test)\n",
        "\n",
        "# # Validate Model Performance\n",
        "# n = x_train.shape[0]\n",
        "# k = len(x_train.columns)\n",
        "\n",
        "# r2_train = evaluation_train(y_train, predictions_train)\n",
        "# rmse_test, r2_test = evaluation_test(y_test, predictions_test)\n",
        "# adjusted_r2_train = R2_train_adjusted(r2_train, n, k)\n",
        "# adjusted_r2_test = R2_test_adjusted(r2_test, n, k)\n",
        "\n",
        "# Rcross, RMSEcross = cross_fold(rf3, x_data, y_data)\n",
        "\n",
        "# # Append results to the table\n",
        "# new_row = {\n",
        "#     \"Model\": \"Random Forest\",\n",
        "#     \"Model Alias\": \"rf3\",\n",
        "#     \"Detail\": \"Tuned, Scaled\",\n",
        "#     \"R2 Score (Train)\": r2_train,\n",
        "#     \"Adjusted R2 Score (Train)\": adjusted_r2_train,\n",
        "#     \"RMSE (Test)\": rmse_test,\n",
        "#     \"R2 Score (Test)\": r2_test,\n",
        "#     \"Adjusted R2 Score (Test)\": adjusted_r2_test,\n",
        "#     \"Cross-Validated RMSE\": RMSEcross,\n",
        "#     \"Cross-Validated R2 Score\": Rcross\n",
        "# }\n",
        "\n",
        "# models_table = pd.concat([models_table, pd.DataFrame([new_row])], ignore_index=True)\n",
        "\n",
        "# # Display the table with model comparison\n",
        "# models_table\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XDHYebczWtS"
      },
      "source": [
        "##XGBC Retrain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "4AQlN_mU7qVu"
      },
      "outputs": [],
      "source": [
        "# best_param_xgb = model_xgb.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "OJTcUUtT6l1K"
      },
      "outputs": [],
      "source": [
        "# model_xgb.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "hHdBju0Gz2qr"
      },
      "outputs": [],
      "source": [
        "# %%time\n",
        "# # Create a model pipeline using the best parameters for XGBRegressor\n",
        "# xgb2 = Pipeline([\n",
        "#     ('ColumnTransformer', transformer),\n",
        "#     ('Model', XGBRegressor(**best_params_xgb))  # Ensure best_params_xgb is defined correctly as a dictionary\n",
        "# ])\n",
        "\n",
        "# # Fit the model\n",
        "# xgb2.fit(x_train, y_train)\n",
        "\n",
        "# # Model Prediction\n",
        "# predictions_train = xgb2.predict(x_train)\n",
        "# predictions_test = xgb2.predict(x_test)\n",
        "\n",
        "# # Validate Model Performance\n",
        "# n = x_train.shape[0]\n",
        "# k = len(x_train.columns)\n",
        "\n",
        "# r2_train = evaluation_train(y_train, predictions_train)\n",
        "# rm\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ouULyFf5xsUA"
      },
      "source": [
        "# 6. Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "fKIP8SMI2Vor"
      },
      "outputs": [],
      "source": [
        "models_list = [mlr1, rf2, rf3,]\n",
        "models_list_title = ['mlr1',  'rf2', 'rf3']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqiqqUO-8ENw",
        "outputId": "79a0207a-2bbe-4241-f90e-a2dc97625c38"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['car_brand', 'car_model', 'car_variant', 'car_year', 'car_engine',\n",
            "       'car_transmission', 'milage', 'accident', 'flood', 'color',\n",
            "       'purchase_date', 'sales_date', 'days_on_market', 'car_age_at_sale',\n",
            "       'price'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qT3TLwpxxr9"
      },
      "source": [
        "#7. Summary"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf3 = Pipeline([\n",
        "    ('ColumnTransformer', transformer),  # Assuming transformer is already defined\n",
        "    ('Model', RandomForestRegressor(**best_params_rf))\n",
        "])\n"
      ],
      "metadata": {
        "id": "wo2NpKwP--rm"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import numpy as np\n",
        "\n",
        "def evaluate_regression(model, data):\n",
        "    # Separate the features and target variable\n",
        "    X = data.drop(\"price\", axis=1)\n",
        "    y = data[\"price\"]\n",
        "\n",
        "    # Split dataset into train and test sets\n",
        "    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)\n",
        "\n",
        "    # Train the model\n",
        "    model.fit(x_train, y_train)\n",
        "\n",
        "    # Make predictions on the test set\n",
        "    predictions_test = model.predict(x_test)\n",
        "    predictions_train = model.predict(x_train)\n",
        "\n",
        "    # Calculate R-squared and RMSE for the test set\n",
        "    r2_test = r2_score(y_test, predictions_test)\n",
        "    rmse_test = np.sqrt(mean_squared_error(y_test, predictions_test))\n",
        "\n",
        "    # Calculate cross-validated RMSE and R-squared\n",
        "    cv_rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=5)).mean()\n",
        "    cv_r2 = cross_val_score(model, X, y, scoring=\"r2\", cv=5).mean()\n",
        "\n",
        "    return r2_test, rmse_test, cv_rmse, cv_r2\n"
      ],
      "metadata": {
        "id": "oDqBH1CX_bHe"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_r2, rmse_test, cv_rmse, cv_r2 = evaluate_regression(rf3, df)\n",
        "print(f\"Test R2: {test_r2}, RMSE (Test): {rmse_test}, Cross-Validated RMSE: {cv_rmse}, Cross-Validated R2: {cv_r2}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUFQWC42_CTY",
        "outputId": "f8655150-93cf-4eb3-a31a-626fc11313c4"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test R2: 0.8551345781737633, RMSE (Test): 11448.486888690642, Cross-Validated RMSE: 12661.30872340905, Cross-Validated R2: 0.808147313192031\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the results from your regression model evaluation, here’s how you can interpret and present the findings to the stakeholders:\n",
        "\n",
        "### Final Results:\n",
        "- **Test R-squared (R2):** `0.8551`\n",
        "- **Test RMSE:** `11,448.49`\n",
        "- **Cross-Validated RMSE:** `12,661.31`\n",
        "- **Cross-Validated R-squared (R2):** `0.8081`\n",
        "\n",
        "### Interpretation:\n",
        "\n",
        "1. **Model Accuracy (R-squared):**\n",
        "   - The **Test R-squared** value of `0.8551` indicates that the model explains approximately 85.51% of the variance in the car prices. This is a good level of accuracy, suggesting that the model is capturing most of the important patterns in the data.\n",
        "\n",
        "2. **Error Metric (RMSE):**\n",
        "   - The **Test RMSE** of `RM 11,448.49` suggests that, on average, the predicted car prices are off by about 11,448.49 units from the actual prices. This error is in the same units as the price (likely dollars, if we're predicting car prices in USD).\n",
        "   - The **Cross-Validated RMSE** of `RM 12,661.31` is slightly higher, indicating some variability in model performance across different subsets of the data, but it's still within a reasonable range.\n",
        "\n",
        "3. **Cross-Validation:**\n",
        "   - The **Cross-Validated R-squared** of `0.8081` shows that when we generalize the model performance across different subsets of the data, it still explains about 80.81% of the variance in car prices, confirming that the model is robust.\n",
        "\n",
        "### Key Takeways:\n",
        "\n",
        "- **Overall Model Performance:**\n",
        "   - \"Our model shows strong predictive power with an R-squared of 0.8551 on the test set, meaning it explains about 85% of the variance in car prices. The model's predictions are reasonably accurate, with an average error (RMSE) of around 11,448 units.\"\n",
        "\n",
        "- **Confidence in Predictions:**\n",
        "   - \"We performed cross-validation to ensure the model's robustness, and the results indicate that the model generalizes well across different data subsets. The cross-validated R-squared is slightly lower at 0.8081, but still reflects strong performance.\"\n",
        "\n",
        "- **Recommendations:**\n",
        "   - \"Given these results, we can confidently use this model to predict car prices. However, it's important to continue monitoring the model's performance over time and consider further improvements or refinements as more data becomes available.\"\n",
        "\n",
        "This summary provides stakeholders with a clear understanding of how well the model performs, highlights the accuracy of the predictions, and sets expectations for the model's application in the real world."
      ],
      "metadata": {
        "id": "UZa5iJLCDH0W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save File"
      ],
      "metadata": {
        "id": "QBOMi-kCFfO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "wtYPGHL8Fg_K"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the pipeline and model to a file\n",
        "with open('rf3_pipeline.pkl', 'wb') as file:\n",
        "    pickle.dump(rf3, file)"
      ],
      "metadata": {
        "id": "CpYYb9DgFka0"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Provide the filename of the saved model\n",
        "files.download('rf3_pipeline.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "DnordCfEFn82",
        "outputId": "34e90572-084a-455f-f943-c6ae8fdea30a"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_046ec24b-8612-4589-98c3-4da41f8cc1d3\", \"rf3_pipeline.pkl\", 11021294)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the saved pipeline and model\n",
        "with open('rf3_pipeline.pkl', 'rb') as file:\n",
        "    loaded_pipeline = pickle.load(file)"
      ],
      "metadata": {
        "id": "VDQBfDcEFruj"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using the loaded model\n",
        "predictions_train = loaded_pipeline.predict(x_train)\n",
        "predictions_test = loaded_pipeline.predict(x_test)\n",
        "\n",
        "# Validate Model Performance\n",
        "r2_train = evaluation_train(y_train, predictions_train)\n",
        "rmse_test, r2_test = evaluation_test(y_test, predictions_test)\n",
        "adjusted_r2_train = R2_train_adjusted(r2_train, n, k)\n",
        "adjusted_r2_test = R2_test_adjusted(r2_test, n, k)\n",
        "\n",
        "# Print the performance metrics\n",
        "print(\"-\" * 30)\n",
        "print(\"RANDOM FOREST REGRESSOR\")  # Changeable\n",
        "print(\"-\" * 30)\n",
        "print(\"R2 Score (Train):\", r2_train)\n",
        "print(\"Adjusted R2 Score (Train):\", adjusted_r2_train)\n",
        "print(\"RMSE (Test):\", rmse_test)\n",
        "print(\"R2 Score (Test):\", r2_test)\n",
        "print(\"Adjusted R2 Score (Test):\", adjusted_r2_test)\n",
        "print(\"-\" * 30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufTjGSKoFwaJ",
        "outputId": "982affde-583a-4950-cded-ccb96a1c3933"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------\n",
            "RANDOM FOREST REGRESSOR\n",
            "------------------------------\n",
            "R2 Score (Train): 0.8680907879534208\n",
            "Adjusted R2 Score (Train): 0.8680708177691097\n",
            "RMSE (Test): 11448.486888690642\n",
            "R2 Score (Test): 0.8551345781737633\n",
            "Adjusted R2 Score (Test): 0.8551126465047821\n",
            "------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Define features and target\n",
        "X = df.drop('price', axis=1)\n",
        "y = df['price']\n",
        "\n",
        "def plot_predictions_vs_actual(model, X, y):\n",
        "    # Predict target\n",
        "    y_pred = model.predict(X)\n",
        "\n",
        "    # Plot\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.scatter(y, y_pred, alpha=0.5)\n",
        "    plt.plot([y.min(), y.max()], [y.min(), y.max()], 'r--', lw=2)\n",
        "    plt.xlabel('Actual Values')\n",
        "    plt.ylabel('Predicted Values')\n",
        "    plt.title('Prediction vs Actual Values')\n",
        "    plt.show()\n",
        "\n",
        "plot_predictions_vs_actual(rf3, X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "vLTltiCLWjL1",
        "outputId": "907b5158-3623-4aa5-d36b-fed23adc14be"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": [],
      "authorship_tag": "ABX9TyNuYqzn3vH1nuLza9QgNCQ9",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}