Flood_Chat.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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        "from sklearn.model_selection import train_test_split"
      ],
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          "name": "stdout",
          "text": [
            "Saving all_data.csv to all_data.csv\n"
          ]
        }
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        "from google.colab import files\n",
        "uploaded = files.upload()"
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    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "7ea966e9"
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      "outputs": [],
      "source": [
        "flood_df = pd.read_csv('all_data.csv')"
      ],
      "id": "7ea966e9"
    },
    {
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              "   Unnamed: 0  Year Disaster Subtype  Country Continent       Location  \\\n",
              "0           0  1927      Flash Flood  Algeria    Africa     Mostaganem   \n",
              "1           1  1952      Flash Flood  Algeria    Africa   Not Recorded   \n",
              "2           2  1954   Riverine flood    Egypt    Africa   Not Recorded   \n",
              "3           3  1963      Flash Flood  Algeria    Africa       Southern   \n",
              "4           4  1966      Flash Flood  Algeria    Africa  West El-Asnam   \n",
              "\n",
              "  River Basin  Start Year  Start Month  Start Day  End Year  End Month  \\\n",
              "0        None        1927         11.0          1      1927       11.0   \n",
              "1        None        1952          9.0          2      1952        9.0   \n",
              "2        None        1954          3.0          4      1954        3.0   \n",
              "3        None        1963         12.0          5      1963       12.0   \n",
              "4        None        1966         10.0          6      1966       10.0   \n",
              "\n",
              "   End Day  Total Deaths  No Injured  No Affected  No Homeless  \\\n",
              "0        1          3000          12        45400        15900   \n",
              "1        2            25          12        45400        15900   \n",
              "2        4            36          12        45400        15900   \n",
              "3        5            46          12        45400        15900   \n",
              "4        6            57          12        20000        15900   \n",
              "\n",
              "   Total Affected Start Date   End Date  \n",
              "0           45400  11/1/1927  11/1/1927  \n",
              "1           45400   9/2/1952   9/2/1952  \n",
              "2           45400   3/4/1954   3/4/1954  \n",
              "3           45400  12/5/1963  12/5/1963  \n",
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              "\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "flood_df.head()"
      ],
      "id": "a5c20e01"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dd02030f"
      },
      "outputs": [],
      "source": [
        "flood_df = flood_df.drop('Unnamed: 0', axis=1)"
      ],
      "id": "dd02030f"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17f79c5c",
        "outputId": "d8ac8ecc-e746-440e-92db-6f637a3d4bc2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                0\n",
              "Disaster Subtype    0\n",
              "Country             0\n",
              "Continent           0\n",
              "Location            0\n",
              "River Basin         0\n",
              "Start Year          0\n",
              "Start Month         0\n",
              "Start Day           0\n",
              "End Year            0\n",
              "End Month           0\n",
              "End Day             0\n",
              "Total Deaths        0\n",
              "No Injured          0\n",
              "No Affected         0\n",
              "No Homeless         0\n",
              "Total Affected      0\n",
              "Start Date          0\n",
              "End Date            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "flood_df.isna().sum()"
      ],
      "id": "17f79c5c"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a5540a0",
        "outputId": "0d2150a9-f5fc-461d-da79-1c2d15ac60ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                  int64\n",
              "Disaster Subtype     object\n",
              "Country              object\n",
              "Continent            object\n",
              "Location             object\n",
              "River Basin          object\n",
              "Start Year            int64\n",
              "Start Month         float64\n",
              "Start Day             int64\n",
              "End Year              int64\n",
              "End Month           float64\n",
              "End Day               int64\n",
              "Total Deaths          int64\n",
              "No Injured            int64\n",
              "No Affected           int64\n",
              "No Homeless           int64\n",
              "Total Affected        int64\n",
              "Start Date           object\n",
              "End Date             object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "flood_df.dtypes"
      ],
      "id": "3a5540a0"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8fab5ec9"
      },
      "outputs": [],
      "source": [
        "flood_df['Start Date'] = pd.to_datetime(flood_df['Start Date'])\n",
        "flood_df['End Date'] = pd.to_datetime(flood_df['End Date'])"
      ],
      "id": "8fab5ec9"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "0572a047",
        "outputId": "166eb792-1b26-416b-d4fb-137eb7706fb3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Year   Start Year  Start Month    Start Day     End Year  \\\n",
              "count  3383.000000  3383.000000  3383.000000  3383.000000  3383.000000   \n",
              "mean   2004.853680  2004.852793     6.603311    14.622820  2004.875850   \n",
              "std      12.255227    12.254601     3.301732     8.551834    12.264697   \n",
              "min    1927.000000  1927.000000     1.000000     1.000000  1927.000000   \n",
              "25%    2000.000000  2000.000000     4.000000     8.000000  2000.000000   \n",
              "50%    2007.000000  2007.000000     7.000000    15.000000  2007.000000   \n",
              "75%    2013.000000  2013.000000     9.000000    22.000000  2013.000000   \n",
              "max    2021.000000  2021.000000    12.000000    31.000000  2021.000000   \n",
              "\n",
              "         End Month      End Day  Total Deaths    No Injured   No Affected  \\\n",
              "count  3383.000000  3383.000000   3383.000000   3383.000000  3.383000e+03   \n",
              "mean      6.735146    15.855158     38.013302    361.918416  1.015521e+05   \n",
              "std       3.314381     8.493449    125.962784   1805.369994  3.230881e+05   \n",
              "min       1.000000     1.000000      1.000000      1.000000  7.000000e+01   \n",
              "25%       4.000000     9.000000      6.000000     15.000000  5.000000e+03   \n",
              "50%       8.000000    16.000000     15.000000     35.000000  2.000000e+04   \n",
              "75%       9.000000    23.000000     34.000000     88.000000  8.200000e+04   \n",
              "max      12.000000    31.000000   3000.000000  18500.000000  7.000000e+06   \n",
              "\n",
              "          No Homeless  Total Affected  \n",
              "count     3383.000000    3.383000e+03  \n",
              "mean     25588.707360    9.197008e+04  \n",
              "std      71196.353002    3.256403e+05  \n",
              "min         50.000000    4.000000e+00  \n",
              "25%       1976.500000    3.000000e+03  \n",
              "50%       6293.000000    1.500000e+04  \n",
              "75%      18346.500000    6.000000e+04  \n",
              "max    1000000.000000    7.000867e+06  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Start Year</th>\n",
              "      <th>Start Month</th>\n",
              "      <th>Start Day</th>\n",
              "      <th>End Year</th>\n",
              "      <th>End Month</th>\n",
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              "      <th>Total Deaths</th>\n",
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              "      <th>No Affected</th>\n",
              "      <th>No Homeless</th>\n",
              "      <th>Total Affected</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3383.000000</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>3383.000000</td>\n",
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              "      <td>3.383000e+03</td>\n",
              "      <td>3383.000000</td>\n",
              "      <td>3.383000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2004.853680</td>\n",
              "      <td>2004.852793</td>\n",
              "      <td>6.603311</td>\n",
              "      <td>14.622820</td>\n",
              "      <td>2004.875850</td>\n",
              "      <td>6.735146</td>\n",
              "      <td>15.855158</td>\n",
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              "      <td>361.918416</td>\n",
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              "      <td>25588.707360</td>\n",
              "      <td>9.197008e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.255227</td>\n",
              "      <td>12.254601</td>\n",
              "      <td>3.301732</td>\n",
              "      <td>8.551834</td>\n",
              "      <td>12.264697</td>\n",
              "      <td>3.314381</td>\n",
              "      <td>8.493449</td>\n",
              "      <td>125.962784</td>\n",
              "      <td>1805.369994</td>\n",
              "      <td>3.230881e+05</td>\n",
              "      <td>71196.353002</td>\n",
              "      <td>3.256403e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1927.000000</td>\n",
              "      <td>1927.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1927.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000e+01</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "      <td>1976.500000</td>\n",
              "      <td>3.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>2.000000e+04</td>\n",
              "      <td>6293.000000</td>\n",
              "      <td>1.500000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>2013.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2013.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>8.200000e+04</td>\n",
              "      <td>18346.500000</td>\n",
              "      <td>6.000000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>18500.000000</td>\n",
              "      <td>7.000000e+06</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>7.000867e+06</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </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-33bc693a-3d61-4d8a-baca-1d38d4740cfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "flood_df.describe()"
      ],
      "id": "0572a047"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed6ad529",
        "outputId": "73774757-8e3c-4ecb-d437-0cb47a00b72b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3383 entries, 0 to 3382\n",
            "Data columns (total 19 columns):\n",
            " #   Column            Non-Null Count  Dtype         \n",
            "---  ------            --------------  -----         \n",
            " 0   Year              3383 non-null   int64         \n",
            " 1   Disaster Subtype  3383 non-null   object        \n",
            " 2   Country           3383 non-null   object        \n",
            " 3   Continent         3383 non-null   object        \n",
            " 4   Location          3383 non-null   object        \n",
            " 5   River Basin       3383 non-null   object        \n",
            " 6   Start Year        3383 non-null   int64         \n",
            " 7   Start Month       3383 non-null   float64       \n",
            " 8   Start Day         3383 non-null   int64         \n",
            " 9   End Year          3383 non-null   int64         \n",
            " 10  End Month         3383 non-null   float64       \n",
            " 11  End Day           3383 non-null   int64         \n",
            " 12  Total Deaths      3383 non-null   int64         \n",
            " 13  No Injured        3383 non-null   int64         \n",
            " 14  No Affected       3383 non-null   int64         \n",
            " 15  No Homeless       3383 non-null   int64         \n",
            " 16  Total Affected    3383 non-null   int64         \n",
            " 17  Start Date        3383 non-null   datetime64[ns]\n",
            " 18  End Date          3383 non-null   datetime64[ns]\n",
            "dtypes: datetime64[ns](2), float64(2), int64(10), object(5)\n",
            "memory usage: 502.3+ KB\n"
          ]
        }
      ],
      "source": [
        "flood_df.info()"
      ],
      "id": "ed6ad529"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "1558903a"
      },
      "outputs": [],
      "source": [
        "X = flood_df.drop(['Total Deaths'], axis=1)\n",
        "y = flood_df['Total Deaths']"
      ],
      "id": "1558903a"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "e6a16951"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "id": "e6a16951"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7f4aec85",
        "outputId": "327ef42d-d58c-4cf6-e456-b2def5010631"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.bar(flood_df['Year'], flood_df['Total Deaths'])\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Total Deaths')\n",
        "plt.title('Total Deaths by Year')\n",
        "plt.show()"
      ],
      "id": "7f4aec85"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "64d59218"
      },
      "outputs": [],
      "source": [
        "country_deaths = flood_df.groupby('Country')['Total Deaths'].sum().reset_index()"
      ],
      "id": "64d59218"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "1334c492"
      },
      "outputs": [],
      "source": [
        "top_countries = country_deaths.sort_values(by='Total Deaths', ascending=False).head(10)"
      ],
      "id": "1334c492"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0b154311",
        "outputId": "5e65f245-4b33-428f-95b9-3cd29077da2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Country  Total Deaths\n",
              "42       Somalia         14514\n",
              "0        Algeria         11745\n",
              "18      Ethiopia          8437\n",
              "34    Mozambique          8029\n",
              "24         Kenya          6927\n",
              "37       Nigeria          6677\n",
              "29        Malawi          6324\n",
              "45   Sudan (the)          5591\n",
              "33       Morocco          5529\n",
              "43  South Africa          4723"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Total Deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>14514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>11745</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>8437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>8029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>6927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>6677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>6324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Sudan (the)</td>\n",
              "      <td>5591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>5529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>4723</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-cf2a7dca-aedb-4121-9734-ae118f72c4c8')\"\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",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-cf2a7dca-aedb-4121-9734-ae118f72c4c8 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-cf2a7dca-aedb-4121-9734-ae118f72c4c8');\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-fcb67300-2139-4385-a704-2f70e8a0bf46\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fcb67300-2139-4385-a704-2f70e8a0bf46')\"\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-fcb67300-2139-4385-a704-2f70e8a0bf46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_953f6e56-8f16-4e72-9567-b8205f08cc24\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top_countries')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_953f6e56-8f16-4e72-9567-b8205f08cc24 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top_countries');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "top_countries"
      ],
      "id": "0b154311"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "c85d05a6",
        "outputId": "30d82a4a-fbbd-4fcd-b915-bc338cb28573"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(flood_df['Total Affected'], flood_df['Total Deaths'], color='r', marker='x')\n",
        "plt.xlabel('Total Affected')\n",
        "plt.ylabel('Total Deaths')\n",
        "plt.title('Total Deaths vs Total Affected')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "id": "c85d05a6"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "a13bcccf",
        "outputId": "592f157d-b904-470e-f98c-00d9ec76d2c6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for country in top_countries['Country']:\n",
        "    country_data = flood_df[flood_df['Country'] == country]\n",
        "    plt.scatter(country_data['Total Affected'], country_data['Total Deaths'], label=country, marker='o')\n",
        "\n",
        "plt.xlabel('Total Affected')\n",
        "plt.ylabel('Total Deaths')\n",
        "plt.title('Total Deaths vs Total Affected for Top 10 Countries')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "id": "a13bcccf"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "28d8ee97"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost import XGBRegressor\n",
        "#from tensorflow import keras\n",
        "#from tensorflow.keras.models import Sequential\n",
        "#from tensorflow.keras.layers import Dense\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from datetime import datetime"
      ],
      "id": "28d8ee97"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "0d107ce0",
        "outputId": "13157105-c9c8-48f5-ba19-7a5a218f56fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year Disaster Subtype                                 Country Continent  \\\n",
              "0     1927      Flash Flood                                 Algeria    Africa   \n",
              "1     1952      Flash Flood                                 Algeria    Africa   \n",
              "2     1954   Riverine flood                                   Egypt    Africa   \n",
              "3     1963      Flash Flood                                 Algeria    Africa   \n",
              "4     1966      Flash Flood                                 Algeria    Africa   \n",
              "...    ...              ...                                     ...       ...   \n",
              "3378  2020      Flash Flood  Congo (the Democratic Republic of the)    Africa   \n",
              "3379  2020      Flash Flood  Congo (the Democratic Republic of the)    Africa   \n",
              "3380  2020      Flash Flood                                  Zambia    Africa   \n",
              "3381  2020      Flash Flood                                  Zambia    Africa   \n",
              "3382  2020      Flash Flood                             South Sudan    Africa   \n",
              "\n",
              "                                               Location  \\\n",
              "0                                            Mostaganem   \n",
              "1                                          Not Recorded   \n",
              "2                                          Not Recorded   \n",
              "3                                              Southern   \n",
              "4                                         West El-Asnam   \n",
              "...                                                 ...   \n",
              "3378                   Djugu territory (Ituri province)   \n",
              "3379                         Bukavu, Uvira (South Kivu)   \n",
              "3380      Gwembe, Siavonga, Mambwe and Lumezi districts   \n",
              "3381  Samfya, Mushindamo, Nakonde districts (Luapula...   \n",
              "3382  Bor South, Twic East, Duk, Ayod Countie (Jongl...   \n",
              "\n",
              "                  River Basin  Start Year  Start Month  Start Day  End Year  \\\n",
              "0                        None        1927         11.0          1      1927   \n",
              "1                        None        1952          9.0          2      1952   \n",
              "2                        None        1954          3.0          4      1954   \n",
              "3                        None        1963         12.0          5      1963   \n",
              "4                        None        1966         10.0          6      1966   \n",
              "...                       ...         ...          ...        ...       ...   \n",
              "3378                     None        2020          5.0          5      2020   \n",
              "3379           Mulongwe river        2020          4.0         16      2020   \n",
              "3380                     None        2020          1.0         18      2020   \n",
              "3381                     None        2020          3.0         20      2020   \n",
              "3382  White Nile, Akobo River        2020          7.0         20      2020   \n",
              "\n",
              "      End Month  End Day  Total Deaths  No Injured  No Affected  No Homeless  \\\n",
              "0          11.0        1          3000          12        45400        15900   \n",
              "1           9.0        2            25          12        45400        15900   \n",
              "2           3.0        4            36          12        45400        15900   \n",
              "3          12.0        5            46          12        45400        15900   \n",
              "4          10.0        6            57          12        20000        15900   \n",
              "...         ...      ...           ...         ...          ...          ...   \n",
              "3378        5.0        5            42          69          500        17500   \n",
              "3379        5.0       18            52          80        92500        17500   \n",
              "3380        1.0       22            52          80         1500        17500   \n",
              "3381        3.0       26            52          80       700000        17500   \n",
              "3382       12.0       26            52          80      1042000        17500   \n",
              "\n",
              "      Total Affected Start Date   End Date  \n",
              "0              45400 1927-11-01 1927-11-01  \n",
              "1              45400 1952-09-02 1952-09-02  \n",
              "2              45400 1954-03-04 1954-03-04  \n",
              "3              45400 1963-12-05 1963-12-05  \n",
              "4              35912 1966-10-06 1966-10-06  \n",
              "...              ...        ...        ...  \n",
              "3378             500 2020-05-05 2020-05-05  \n",
              "3379           92580 2020-04-16 2020-05-18  \n",
              "3380            1500 2020-01-18 2020-01-22  \n",
              "3381          700000 2020-03-20 2020-03-26  \n",
              "3382         1042000 2020-07-20 2020-12-26  \n",
              "\n",
              "[3383 rows x 19 columns]"
            ],
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              "\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>Year</th>\n",
              "      <th>Disaster Subtype</th>\n",
              "      <th>Country</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Location</th>\n",
              "      <th>River Basin</th>\n",
              "      <th>Start Year</th>\n",
              "      <th>Start Month</th>\n",
              "      <th>Start Day</th>\n",
              "      <th>End Year</th>\n",
              "      <th>End Month</th>\n",
              "      <th>End Day</th>\n",
              "      <th>Total Deaths</th>\n",
              "      <th>No Injured</th>\n",
              "      <th>No Affected</th>\n",
              "      <th>No Homeless</th>\n",
              "      <th>Total Affected</th>\n",
              "      <th>Start Date</th>\n",
              "      <th>End Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1927</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Mostaganem</td>\n",
              "      <td>None</td>\n",
              "      <td>1927</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1927</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3000</td>\n",
              "      <td>12</td>\n",
              "      <td>45400</td>\n",
              "      <td>15900</td>\n",
              "      <td>45400</td>\n",
              "      <td>1927-11-01</td>\n",
              "      <td>1927-11-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1952</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Not Recorded</td>\n",
              "      <td>None</td>\n",
              "      <td>1952</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1952</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>45400</td>\n",
              "      <td>15900</td>\n",
              "      <td>45400</td>\n",
              "      <td>1952-09-02</td>\n",
              "      <td>1952-09-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1954</td>\n",
              "      <td>Riverine flood</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Not Recorded</td>\n",
              "      <td>None</td>\n",
              "      <td>1954</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1954</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>36</td>\n",
              "      <td>12</td>\n",
              "      <td>45400</td>\n",
              "      <td>15900</td>\n",
              "      <td>45400</td>\n",
              "      <td>1954-03-04</td>\n",
              "      <td>1954-03-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1963</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Southern</td>\n",
              "      <td>None</td>\n",
              "      <td>1963</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1963</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5</td>\n",
              "      <td>46</td>\n",
              "      <td>12</td>\n",
              "      <td>45400</td>\n",
              "      <td>15900</td>\n",
              "      <td>45400</td>\n",
              "      <td>1963-12-05</td>\n",
              "      <td>1963-12-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1966</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>West El-Asnam</td>\n",
              "      <td>None</td>\n",
              "      <td>1966</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1966</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "      <td>57</td>\n",
              "      <td>12</td>\n",
              "      <td>20000</td>\n",
              "      <td>15900</td>\n",
              "      <td>35912</td>\n",
              "      <td>1966-10-06</td>\n",
              "      <td>1966-10-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3378</th>\n",
              "      <td>2020</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Congo (the Democratic Republic of the)</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Djugu territory (Ituri province)</td>\n",
              "      <td>None</td>\n",
              "      <td>2020</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>42</td>\n",
              "      <td>69</td>\n",
              "      <td>500</td>\n",
              "      <td>17500</td>\n",
              "      <td>500</td>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>2020-05-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3379</th>\n",
              "      <td>2020</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Congo (the Democratic Republic of the)</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Bukavu, Uvira (South Kivu)</td>\n",
              "      <td>Mulongwe river</td>\n",
              "      <td>2020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18</td>\n",
              "      <td>52</td>\n",
              "      <td>80</td>\n",
              "      <td>92500</td>\n",
              "      <td>17500</td>\n",
              "      <td>92580</td>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>2020-05-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3380</th>\n",
              "      <td>2020</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Gwembe, Siavonga, Mambwe and Lumezi districts</td>\n",
              "      <td>None</td>\n",
              "      <td>2020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18</td>\n",
              "      <td>2020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22</td>\n",
              "      <td>52</td>\n",
              "      <td>80</td>\n",
              "      <td>1500</td>\n",
              "      <td>17500</td>\n",
              "      <td>1500</td>\n",
              "      <td>2020-01-18</td>\n",
              "      <td>2020-01-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3381</th>\n",
              "      <td>2020</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Samfya, Mushindamo, Nakonde districts (Luapula...</td>\n",
              "      <td>None</td>\n",
              "      <td>2020</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20</td>\n",
              "      <td>2020</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26</td>\n",
              "      <td>52</td>\n",
              "      <td>80</td>\n",
              "      <td>700000</td>\n",
              "      <td>17500</td>\n",
              "      <td>700000</td>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>2020-03-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3382</th>\n",
              "      <td>2020</td>\n",
              "      <td>Flash Flood</td>\n",
              "      <td>South Sudan</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Bor South, Twic East, Duk, Ayod Countie (Jongl...</td>\n",
              "      <td>White Nile, Akobo River</td>\n",
              "      <td>2020</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20</td>\n",
              "      <td>2020</td>\n",
              "      <td>12.0</td>\n",
              "      <td>26</td>\n",
              "      <td>52</td>\n",
              "      <td>80</td>\n",
              "      <td>1042000</td>\n",
              "      <td>17500</td>\n",
              "      <td>1042000</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>2020-12-26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3383 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51ebff36-a9d7-4e34-95bc-86fc8d35fc20')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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",
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              "      display: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-51ebff36-a9d7-4e34-95bc-86fc8d35fc20 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-51ebff36-a9d7-4e34-95bc-86fc8d35fc20');\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",
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              "\n",
              "\n",
              "<div id=\"df-91e84a28-c501-4154-affb-be062817a8ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-91e84a28-c501-4154-affb-be062817a8ba')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </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",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    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",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    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-91e84a28-c501-4154-affb-be062817a8ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_39c629b4-d3a3-4aa5-a865-27280e3bb540\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('flood_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('flood_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        " flood_df"
      ],
      "id": "0d107ce0"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "513ede70"
      },
      "outputs": [],
      "source": [
        "label_encoder = LabelEncoder()\n",
        "flood_df['Disaster Subtype'] = label_encoder.fit_transform(flood_df['Disaster Subtype'])\n",
        "flood_df['Country'] = label_encoder.fit_transform(flood_df['Country'])\n",
        "flood_df['Continent'] = label_encoder.fit_transform(flood_df['Continent'])\n",
        "flood_df['Location'] = label_encoder.fit_transform(flood_df['Location'])\n",
        "flood_df['River Basin'] = label_encoder.fit_transform(flood_df['River Basin'])"
      ],
      "id": "513ede70"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "eb333e4b"
      },
      "outputs": [],
      "source": [
        "flood_df['Start Year'] = flood_df['Start Date'].dt.year\n",
        "flood_df['Start Month'] = flood_df['Start Date'].dt.month\n",
        "flood_df['Start Day'] = flood_df['Start Date'].dt.day\n",
        "flood_df['End Year'] = flood_df['End Date'].dt.year\n",
        "flood_df['End Month'] = flood_df['End Date'].dt.month\n",
        "flood_df['End Day'] = flood_df['End Date'].dt.day"
      ],
      "id": "eb333e4b"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "d7cd79f0"
      },
      "outputs": [],
      "source": [
        "flood_df['Season'] = flood_df['Start Date'].dt.month.map({12: 'Dry', 1: 'Dry', 2: 'Dry', 3: 'Wet', 4: 'Wet', 5: 'Wet', 6: 'Dry', 7: 'Dry', 8: 'Dry', 9: 'Wet', 10: 'Wet', 11: 'Wet'})"
      ],
      "id": "d7cd79f0"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "b5a32e84"
      },
      "outputs": [],
      "source": [
        "flood_df['Flood Duration'] = (flood_df['End Date'] - flood_df['Start Date']).dt.days"
      ],
      "id": "b5a32e84"
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "58628f64"
      },
      "outputs": [],
      "source": [
        "flood_df['Total Affected Normalized'] = (flood_df['Total Affected'] - flood_df['Total Affected'].min()) / (flood_df['Total Affected'].max() - flood_df['Total Affected'].min())"
      ],
      "id": "58628f64"
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "95686d4b"
      },
      "outputs": [],
      "source": [
        "#encoding"
      ],
      "id": "95686d4b"
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "da1d6cc9"
      },
      "outputs": [],
      "source": [
        "flood_data = flood_df.copy()"
      ],
      "id": "da1d6cc9"
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fb504fea"
      },
      "outputs": [],
      "source": [
        "#onehot_encoder = OneHotEncoder(sparse=False, drop='first')  # 'drop' removes one level to avoid multicollinearity\n",
        "#encoded_columns = pd.DataFrame(onehot_encoder.fit_transform(flood_df[['Disaster Subtype', 'Country', 'Continent', 'Location', 'River Basin']]))"
      ],
      "id": "fb504fea"
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "7b0e5df6"
      },
      "outputs": [],
      "source": [
        "#categorical_features = ['Country', 'Continent', 'Location', 'River Basin']\n",
        "#onehot_encoder = OneHotEncoder(sparse=False, drop='first')\n",
        "#encoded_columns = pd.DataFrame(onehot_encoder.fit_transform(flood_data[categorical_features]))\n",
        "#flood_data_encoded = pd.concat([flood_data, encoded_columns], axis=1)\n",
        "#flood_data_encoded = flood_data_encoded.drop(categorical_features, axis=1)"
      ],
      "id": "7b0e5df6"
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "d3f5818a"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "numerical_features = ['Total Deaths', 'No Injured', 'No Affected', 'No Homeless', 'Total Affected', 'Flood Duration']\n",
        "scaler = StandardScaler()\n",
        "flood_data[numerical_features] = scaler.fit_transform(flood_data[numerical_features])"
      ],
      "id": "d3f5818a"
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48c18233",
        "outputId": "292dd182-9c0f-4e5d-8dc5-7ba9a66c5bc0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Year', 'Disaster Subtype', 'Country', 'Continent', 'Location',\n",
              "       'River Basin', 'Start Year', 'Start Month', 'Start Day', 'End Year',\n",
              "       'End Month', 'End Day', 'Total Deaths', 'No Injured', 'No Affected',\n",
              "       'No Homeless', 'Total Affected', 'Start Date', 'End Date', 'Season',\n",
              "       'Flood Duration', 'Total Affected Normalized'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "flood_data.columns"
      ],
      "id": "48c18233"
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "17f2e526",
        "outputId": "cee4c99a-d81c-467b-8331-8852ed7aa0d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Disaster Subtype  Country  Continent  Location  River Basin  \\\n",
              "0  1927                 1        0          0       780          151   \n",
              "1  1952                 1        0          0       884          151   \n",
              "2  1954                 3       16          0       884          151   \n",
              "3  1963                 1        0          0      1034          151   \n",
              "4  1966                 1        0          0      1127          151   \n",
              "\n",
              "   Start Year  Start Month  Start Day  End Year  ...  Total Deaths  \\\n",
              "0        1927           11          1      1927  ...     23.518253   \n",
              "1        1952            9          2      1952  ...     -0.103326   \n",
              "2        1954            3          4      1954  ...     -0.015986   \n",
              "3        1963           12          5      1963  ...      0.063415   \n",
              "4        1966           10          6      1966  ...      0.150755   \n",
              "\n",
              "   No Injured  No Affected  No Homeless  Total Affected  Start Date  \\\n",
              "0    -0.19385    -0.173824    -0.136104       -0.143032  1927-11-01   \n",
              "1    -0.19385    -0.173824    -0.136104       -0.143032  1952-09-02   \n",
              "2    -0.19385    -0.173824    -0.136104       -0.143032  1954-03-04   \n",
              "3    -0.19385    -0.173824    -0.136104       -0.143032  1963-12-05   \n",
              "4    -0.19385    -0.252452    -0.136104       -0.172173  1966-10-06   \n",
              "\n",
              "    End Date Season Flood Duration Total Affected Normalized  \n",
              "0 1927-11-01    Wet      -0.506977                  0.006484  \n",
              "1 1952-09-02    Wet      -0.506977                  0.006484  \n",
              "2 1954-03-04    Wet      -0.506977                  0.006484  \n",
              "3 1963-12-05    Dry      -0.506977                  0.006484  \n",
              "4 1966-10-06    Wet      -0.506977                  0.005129  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53fa0581-4766-42af-b1f6-b88692d25d5a\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Disaster Subtype</th>\n",
              "      <th>Country</th>\n",
              "      <th>Continent</th>\n",
              "      <th>Location</th>\n",
              "      <th>River Basin</th>\n",
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              "      <th>Start Day</th>\n",
              "      <th>End Year</th>\n",
              "      <th>...</th>\n",
              "      <th>Total Deaths</th>\n",
              "      <th>No Injured</th>\n",
              "      <th>No Affected</th>\n",
              "      <th>No Homeless</th>\n",
              "      <th>Total Affected</th>\n",
              "      <th>Start Date</th>\n",
              "      <th>End Date</th>\n",
              "      <th>Season</th>\n",
              "      <th>Flood Duration</th>\n",
              "      <th>Total Affected Normalized</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1927</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>780</td>\n",
              "      <td>151</td>\n",
              "      <td>1927</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1927</td>\n",
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              "      <td>23.518253</td>\n",
              "      <td>-0.19385</td>\n",
              "      <td>-0.173824</td>\n",
              "      <td>-0.136104</td>\n",
              "      <td>-0.143032</td>\n",
              "      <td>1927-11-01</td>\n",
              "      <td>1927-11-01</td>\n",
              "      <td>Wet</td>\n",
              "      <td>-0.506977</td>\n",
              "      <td>0.006484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1952</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>884</td>\n",
              "      <td>151</td>\n",
              "      <td>1952</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1952</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.103326</td>\n",
              "      <td>-0.19385</td>\n",
              "      <td>-0.173824</td>\n",
              "      <td>-0.136104</td>\n",
              "      <td>-0.143032</td>\n",
              "      <td>1952-09-02</td>\n",
              "      <td>1952-09-02</td>\n",
              "      <td>Wet</td>\n",
              "      <td>-0.506977</td>\n",
              "      <td>0.006484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1954</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>884</td>\n",
              "      <td>151</td>\n",
              "      <td>1954</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1954</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.015986</td>\n",
              "      <td>-0.19385</td>\n",
              "      <td>-0.173824</td>\n",
              "      <td>-0.136104</td>\n",
              "      <td>-0.143032</td>\n",
              "      <td>1954-03-04</td>\n",
              "      <td>1954-03-04</td>\n",
              "      <td>Wet</td>\n",
              "      <td>-0.506977</td>\n",
              "      <td>0.006484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1963</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1034</td>\n",
              "      <td>151</td>\n",
              "      <td>1963</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>1963</td>\n",
              "      <td>...</td>\n",
              "      <td>0.063415</td>\n",
              "      <td>-0.19385</td>\n",
              "      <td>-0.173824</td>\n",
              "      <td>-0.136104</td>\n",
              "      <td>-0.143032</td>\n",
              "      <td>1963-12-05</td>\n",
              "      <td>1963-12-05</td>\n",
              "      <td>Dry</td>\n",
              "      <td>-0.506977</td>\n",
              "      <td>0.006484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1966</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1127</td>\n",
              "      <td>151</td>\n",
              "      <td>1966</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>1966</td>\n",
              "      <td>...</td>\n",
              "      <td>0.150755</td>\n",
              "      <td>-0.19385</td>\n",
              "      <td>-0.252452</td>\n",
              "      <td>-0.136104</td>\n",
              "      <td>-0.172173</td>\n",
              "      <td>1966-10-06</td>\n",
              "      <td>1966-10-06</td>\n",
              "      <td>Wet</td>\n",
              "      <td>-0.506977</td>\n",
              "      <td>0.005129</td>\n",
              "    </tr>\n",
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              "\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-afefda35-530e-4f47-a6ca-e4fb4d3ef3df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "flood_data.head()"
      ],
      "id": "17f2e526"
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "e2f94ff9"
      },
      "outputs": [],
      "source": [
        "X = flood_data[['Year', 'Country', 'Continent', 'Location', 'River Basin', 'Flood Duration']]\n",
        "y = flood_data['Total Affected']"
      ],
      "id": "e2f94ff9"
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "e08a5263"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
      ],
      "id": "e08a5263"
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "cdccb98d",
        "outputId": "b395c9c5-bd2f-46ee-a29a-2e2bb1370a74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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>DecisionTreeRegressor()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "Dt = DecisionTreeRegressor()\n",
        "Dt.fit(X_train, y_train)"
      ],
      "id": "cdccb98d"
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3d34cb8b",
        "outputId": "56995080-3405-496c-cd4b-ab57a1bc8c59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Mean Squared Error: 0.21130175266235426\n"
          ]
        }
      ],
      "source": [
        "predictions_decision_tree = Dt.predict(X_test)\n",
        "mse_decision_tree = mean_squared_error(y_test, predictions_decision_tree)\n",
        "print(f\"Decision Tree Mean Squared Error: {mse_decision_tree}\")"
      ],
      "id": "3d34cb8b"
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "2b00f34e",
        "outputId": "c9d07b3d-0422-4fe1-fe85-480cebf272e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#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()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "Rf = RandomForestRegressor()\n",
        "Rf.fit(X_train, y_train)"
      ],
      "id": "2b00f34e"
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4281462",
        "outputId": "1d9df781-cadc-4ac0-e4f9-cd781782abfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.1048422102382894\n"
          ]
        }
      ],
      "source": [
        "predictions = Rf.predict(X_test)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(f\"Mean Squared Error: {mse}\")"
      ],
      "id": "a4281462"
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "99a15240",
        "outputId": "55f774f9-624a-4b52-e3f8-bdf2e2121c51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "model_xgboost = XGBRegressor()\n",
        "model_xgboost.fit(X_train, y_train)"
      ],
      "id": "99a15240"
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e79639d",
        "outputId": "ed7d5c93-8834-4906-ea5f-c6597b49b338"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Mean Squared Error: 0.025169439798689817\n"
          ]
        }
      ],
      "source": [
        "predictions_xgboost = model_xgboost.predict(X_test)\n",
        "mse_xgboost = mean_squared_error(y_test, predictions_xgboost)\n",
        "print(f\"XGBoost Mean Squared Error: {mse_xgboost}\")"
      ],
      "id": "2e79639d"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "2d85e54a"
      },
      "outputs": [],
      "source": [
        "def plot_actual_vs_predicted(actual, predicted, model_name):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.scatterplot(x=actual, y=predicted)\n",
        "    plt.title(f\"{model_name} - Actual vs. Predicted\")\n",
        "    plt.xlabel(\"Actual Values\")\n",
        "    plt.ylabel(\"Predicted Values\")\n",
        "    plt.show()"
      ],
      "id": "2d85e54a"
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "510cda91",
        "outputId": "21e9e95f-7245-4e18-ddec-627e6bd79591"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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NG9fy5s2bl1rX6Q4fPux23263l3uiz3vvvafAwEC1aNFCiYmJkk6G/mbNmun999/X4MGDJZ38itvLy0sXX3xxheo4F7///rsefPBBrV27tkSAdzgcld7fiBEjtHLlSm3dulXdunXTnj179NNPP7m+yi7e5kyva2X5+fnp008/lXTyc6N58+alzk8u7bVNSEjQjh07zvh+3L9/vxo0aKCgoCC39aW9t053rq9pTdSICwvBFReEgwcPyuFwuEJK8WjJ3XffXWIUo1hZgaaipk6dqqFDh2rlypX68ssv9dBDD2n27Nlau3atOnbseE77LlbW6Nqpo0Zny2q1lvhlXFRUpIiICL3//vulPqb4l1Pxz/fdd98tNahX9Kzldu3aqV+/fpJOdkPIysrShAkT1KNHDzVu3Nh1nFGjRpU5F+5sW6CVFuSKiorUrl07zZ07t9THNG7c2PXYjRs3at26dfr888+1atUqLVmyRFdeeaW++uoreXt7q3Xr1tq9e7c+++wzrVq1SsuXL9crr7yihx9+WI8++miV1VyaBg0auN1fsGBBmW2MDMPQ4sWLlZmZWWp4OXr0qDIyMkoEjrNR1gje6SdcpaWlqXfv3rLZbHrssccUHR0tPz8//fzzz7rvvvvOajR06NChCggI0NKlS9WtWzctXbpUXl5e+vvf/+7apiKva2V5e3u73uPlKev9eNVVV5XZYeOiiy6qdD1VzQw1wlwIrrggvPvuu5LkCqnFX736+Pic8ZdGdHS0fvvtt7M6bnR0tO666y7dddddSkhI0CWXXKLnnnuuRHeDYk2bNtWOHTtUVFTkFhqLv/ps2rTpWdVRVaKjo/X111+re/fu5Yak6OhoSVJERESFfilX1FNPPaUVK1boiSee0Pz58xUeHq7g4GAVFhae8ThNmzbVH3/8IcMw3AJS8QhiRURHR2v79u3q27fvGb8m9fLyUt++fdW3b1/NnTtXTz75pB544AGtW7fOVWtgYKBGjBihESNGKC8vT9dee62eeOIJzZgxQ+Hh4QoICNDu3btL7HvXrl3y8vJyBeXKWr16tdv9U0dtT7dhwwYdPHhQjz32mGvkv9iJEyd0yy23aOXKlRo1apSio6NVVFSkP/74Q5dcckmZ+yzrZ1f8jUFaWprra3mp5DcN69evV2pqqj766CP16tXLtXzv3r1lHvNMik88W7ZsmebOnaslS5aoZ8+eJdpRVeR1rSnR0dHKyMio0Ht/zZo1Jf7AKO29VdoxzuU1rYkacWFhjivOe2vXrtWsWbPUvHlzV0uniIgI9enTR6+99poOHTpU4jHHjh1z/Ts+Pl7bt28vMedMKntkMysrSzk5OW7LoqOjFRwcXO4VcQYNGqTDhw9ryZIlrmUFBQV68cUXFRQUpN69e5f/ZKvZ9ddfr8LCQs2aNavEuoKCAlfrmri4ONlsNj355JPKz88vse2pP9/KiI6OVnx8vBYuXKjDhw/L29tb8fHxWr58eal/XJx6nLi4OP31119u7c5ycnL073//u8LHv/766/XXX3+V+pjs7GzXmfzHjx8vsb74l37x65+amuq23tfXVxdffLEMw1B+fr68vb3Vv39/ffzxx25toI4cOaJFixapR48eFZ5ycbp+/fq53U4fgT1V8TSBe+65R9ddd53bbcKECYqNjXWNwA8bNkxeXl567LHHSox6nvp/JTAwsNQ2R8V/8BTPe5ROzgE9vR1c8cjmqfvMy8vTK6+8UsGfQOlGjBih5ORkvfHGG9q+fbtGjBjhtr4ir6t08g+LpKSkc6qlIq6//npt3brVbZ5xsbS0NBUUFEg6+blSUFCgV1991bW+sLBQL7744hmPca6vaU3UiAsLI644r3zxxRfatWuXCgoKdOTIEa1du1arV69W06ZN9cknn7hdJODll19Wjx491K5dO02YMEEtWrTQkSNHtHXrVh08eFDbt2+XJN1zzz368MMP9fe//13jxo1Tp06ddPz4cX3yySeaP3++OnToUKKOP//8U3379tX111+viy++WHXq1NGKFSt05MgR3XDDDWXWf8stt+i1117TmDFj9NNPP6lZs2b68MMPtWXLFs2bN0/BwcFV/0OrhN69e+vWW2/V7Nmz9csvv6h///7y8fFRQkKCli1bpueff17XXXedbDabXn31Vd1000269NJLdcMNNyg8PFxJSUn6/PPP1b17d7300ktnVcM999yjpUuXat68eXrqqaf01FNPad26derSpYsmTJigiy++WMePH9fPP/+sr7/+2hU2br31Vr300ksaOXKk7rjjDjVo0EDvv/++6z1RkRNNbrrpJi1dulT//Oc/tW7dOnXv3l2FhYXatWuXli5d6uqz+dhjj2njxo0aPHiwmjZtqqNHj+qVV15Ro0aN1KNHD0lS//79FRkZqe7du6t+/frauXOnXnrpJQ0ePNj1Oj/++OOuvqG333676tSpo9dee025ubl6+umnz+rnVxm5ublavny5rrrqqjIvsHH11Vfr+eef19GjRxUTE6MHHnhAs2bNUs+ePXXttdfKarXqhx9+UFRUlGbPni1J6tSpk1599VU9/vjjiomJUUREhK688kr1799fTZo00fjx43XPPffI29tbb731luu9U6xbt26qW7euRo8erSlTpshisejdd9895ykyxT2L7777btcfRaeqyOsqnZyD3Lt3b7fewNXhnnvu0SeffKIhQ4ZozJgx6tSpkzIzM/Xrr7/qww8/1L59+xQWFqahQ4eqe/fumj59uvbt26eLL75YH330UYXmAp/ra1oTNeIC45FeBkAVK26XU3zz9fU1IiMjjauuusp4/vnnXW2kTrdnzx7j5ptvNiIjIw0fHx+jYcOGxpAhQ4wPP/zQbbvU1FRj0qRJRsOGDQ1fX1+jUaNGxujRo10tmE5vh5WSkmJMnDjRaNWqlREYGGjY7XajS5cuxtKlS932e3o7LMMwjCNHjhhjx441wsLCDF9fX6Ndu3YlWsEUH6+0dluSjJkzZ1b4Z1dWO6zAwMAyH/P6668bnTp1Mvz9/Y3g4GCjXbt2xr333mskJye7bbdu3TojLi7OsNvthp+fnxEdHW2MGTPG+PHHH8utqbgd1rJly0pd36dPH8NmsxlpaWmGYZz8mU2cONFo3Lix4ePjY0RGRhp9+/Y1Xn/9dbfH/fe//zUGDx5s+Pv7G+Hh4cZdd91lLF++3JBkfPvtt67tevfuXWarqry8PGPOnDlGmzZtDKvVatStW9fo1KmT8eijjxoOh8MwDMNYs2aNcc011xhRUVGGr6+vERUVZYwcOdKtJdBrr71m9OrVywgNDTWsVqsRHR1t3HPPPa59FPv555+NuLg4IygoyAgICDD+9re/Gd98843bNhVpB3c2in82b775ZpnbrF+/3pBkPP/8865lb731ltGxY0fXz6d3797G6tWrXesPHz5sDB482AgODjYkuf0f+Omnn4wuXboYvr6+RpMmTYy5c+eW2g5ry5YtxhVXXGH4+/sbUVFRxr333mt8+eWXJVqbVaQd1qluvPFGQ5LRr1+/Eusq8roahlHiOZXlTP/PijVt2tQYPHhwqevS09ONGTNmGDExMYavr68RFhZmdOvWzXj22WeNvLw813apqanGTTfdZNhsNsNutxs33XSTsW3btjO2wyp2Lq9pVdeIC5vFMKrgLA4AMKl58+bpzjvv1MGDB9WwYUNPlwMAKAfBFcAFIzs72+2kspycHHXs2FGFhYX6888/PVgZAKAimOMK4IJx7bXXqkmTJrrkkkvkcDj03nvvadeuXWW29wIA1C4EVwAXjLi4OL3xxht6//33VVhYqIsvvlgffPBBibPHAQC1E1MFAAAAYAr0cQUAAIApEFwBAABgCuf9HNeioiIlJycrODi4Qg3GAQAAULMMw1B6erqioqLcLnl+uvM+uCYnJ5/19bwBAABQcw4cOKBGjRqVuf68D67Fl048cODAWV/XGwAAANXH6XSqcePGZ7y0+XkfXIunB9hsNoIrAABALXamaZ2cnAUAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFOp4ugAAAADUHo6sPKVk5MmZky+bv4/CAn1lD/D1dFmSCK4AAAD4n+S0bN23fIc2JaS4lvWKDdNT8e0VFeLvwcpOYqoAAAAA5MjKKxFaJWljQoqmL98hR1aehyr7/wiuAAAAUEpGXonQWmxjQopSMgiuAAAAqAWcOfnlrk8/w/qaQHAFAACAbH4+5a4PPsP6mkBwBQAAgMKCfNUrNqzUdb1iwxQW5PnOAgRXAAAAyB7gq6fi25cIr71iwzQnvn2taIlFOywAAABIkqJC/PXiyI5KychTek6+gv18FBZEH1cAAADUQvaA2hNUT8dUAQAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACm4NHgOnv2bF122WUKDg5WRESEhg0bpt27d7ttk5OTo4kTJyo0NFRBQUGKj4/XkSNHPFQxAAAAPMWjwXXDhg2aOHGivv32W61evVr5+fnq37+/MjMzXdvceeed+vTTT7Vs2TJt2LBBycnJuvbaaz1YNQAAADzBYhiG4ekiih07dkwRERHasGGDevXqJYfDofDwcC1atEjXXXedJGnXrl1q3bq1tm7dqiuuuOKM+3Q6nbLb7XI4HLLZbNX9FAAAAFBJFc1rtWqOq8PhkCTVq1dPkvTTTz8pPz9f/fr1c23TqlUrNWnSRFu3bi11H7m5uXI6nW43AAAAmF+tCa5FRUWaOnWqunfvrrZt20qSDh8+LF9fX4WEhLhtW79+fR0+fLjU/cyePVt2u911a9y4cXWXDgAAgBpQa4LrxIkT9dtvv+mDDz44p/3MmDFDDofDdTtw4EAVVQgAAABPquPpAiRp0qRJ+uyzz7Rx40Y1atTItTwyMlJ5eXlKS0tzG3U9cuSIIiMjS92X1WqV1Wqt7pIBAABQwzw64moYhiZNmqQVK1Zo7dq1at68udv6Tp06ycfHR2vWrHEt2717t5KSktS1a9eaLhcAAAAe5NER14kTJ2rRokX6+OOPFRwc7Jq3arfb5e/vL7vdrvHjx2vatGmqV6+ebDabJk+erK5du1aoowAAAADOHx5th2WxWEpdvmDBAo0ZM0bSyQsQ3HXXXVq8eLFyc3MVFxenV155pcypAqejHRYAAEDtVtG8Vqv6uFYHgisAAEDtZso+rgAAAEBZCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBY8G140bN2ro0KGKioqSxWLRypUr3daPGTNGFovF7TZgwADPFAsAAACP8mhwzczMVIcOHfTyyy+Xuc2AAQN06NAh123x4sU1WCEAAABqizqePPjAgQM1cODAcrexWq2KjIysoYoAAABQW9X6Oa7r169XRESEWrZsqdtuu02pqanlbp+bmyun0+l2AwAAgPnV6uA6YMAAvfPOO1qzZo3mzJmjDRs2aODAgSosLCzzMbNnz5bdbnfdGjduXIMVAwAAoLpYDMMwPF2EJFksFq1YsULDhg0rc5v//ve/io6O1tdff62+ffuWuk1ubq5yc3Nd951Opxo3biyHwyGbzVbVZQMAAOAcOZ1O2e32M+a1Wj3ieroWLVooLCxMiYmJZW5jtVpls9ncbgAAADA/UwXXgwcPKjU1VQ0aNPB0KQAAAKhhHu0qkJGR4TZ6unfvXv3yyy+qV6+e6tWrp0cffVTx8fGKjIzUnj17dO+99yomJkZxcXEerBoAAACe4NHg+uOPP+pvf/ub6/60adMkSaNHj9arr76qHTt26O2331ZaWpqioqLUv39/zZo1S1ar1VMlAwAAwENqzclZ1aWik30BAADgGeflyVkAAAC4cBFcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAp1PF0AAAA1xZGVp5SMPDlz8mXz91FYoK/sAb6eLgtABRFcAQAXhOS0bN23fIc2JaS4lvWKDdNT8e0VFeLvwcoAVBRTBQAA5z1HVl6J0CpJGxNSNH35Djmy8jxUGYDKILgCAM57KRl5JUJrsY0JKUrJILgCZkBwBQCc95w5+eWuTz/DegC1A8EVAHDes/n5lLs++AzrAdQOBFcAwHkvLMhXvWLDSl3XKzZMYUF0FgDMgOAKADjv2QN89VR8+xLhtVdsmObEt6clFmAS59wOy+l0au3atWrZsqVat25dFTUBAFDlokL89eLIjkrJyFN6Tr6C/XwUFkQfV8BMKh1cr7/+evXq1UuTJk1Sdna2OnfurH379skwDH3wwQeKj4+vjjoBADhn9gCCKmBmlZ4qsHHjRvXs2VOStGLFChmGobS0NL3wwgt6/PHHq7xAAAAAQDqL4OpwOFSvXj1J0qpVqxQfH6+AgAANHjxYCQkJVV4gAAAAIJ1FcG3cuLG2bt2qzMxMrVq1Sv3795cknThxQn5+flVeIAAAACCdxRzXqVOn6sYbb1RQUJCaNGmiPn36SDo5haBdu3ZVXR8AAAAg6SyC6+23367LL79cBw4c0FVXXSUvr5ODti1atGCOKwAAAKqNxTAM42wemJeXp7179yo6Olp16pxzV61q43Q6Zbfb5XA4ZLPZPF0OAAAATlPRvFbpOa5ZWVkaP368AgIC1KZNGyUlJUmSJk+erKeeeursKwYAAADKUengOmPGDG3fvl3r1693OxmrX79+WrJkSZUWBwAAABSr9Hf8K1eu1JIlS3TFFVfIYrG4lrdp00Z79uyp0uIAAACAYpUecT127JgiIiJKLM/MzHQLsgAAAEBVqnRw7dy5sz7//HPX/eKw+sYbb6hr165VVxkAAABwikpPFXjyySc1cOBA/fHHHyooKNDzzz+vP/74Q9988402bNhQHTUCAAAAlR9x7dGjh3755RcVFBSoXbt2+uqrrxQREaGtW7eqU6dO1VEjAAAAcPZ9XM2CPq4AAAC1W0XzWqWnChT3bS1LkyZNKrtLAAAA4IwqHVybNWtWbveAwsLCcyoIAAAAKE2lg+u2bdvc7ufn52vbtm2aO3eunnjiiSorDAAAADhVpYNrhw4dSizr3LmzoqKi9Mwzz+jaa6+tksIAAACAU1W6q0BZWrZsqR9++KGqdgcAAAC4qfSIq9PpdLtvGIYOHTqkRx55RLGxsVVWGAAAAHCqSgfXkJCQEidnGYahxo0b64MPPqiywgAAAIBTVTq4rlu3zu2+l5eXwsPDFRMTozp1Kr07AAAAoEIqnTR79+5dHXUAAAAA5apQcP3kk08qvMOrr776rIsBAAAAylKh4Dps2LAK7cxisXABAgAAAFSLCgXXoqKi6q4DAAAAKFeV9XEFAAAAqtNZtQHIzMzUhg0blJSUpLy8PLd1U6ZMqZLCAAAAgFNVOrhu27ZNgwYNUlZWljIzM1WvXj2lpKQoICBAERERBFcAAABUi0pPFbjzzjs1dOhQnThxQv7+/vr222+1f/9+derUSc8++2x11AgAAABUPrj+8ssvuuuuu+Tl5SVvb2/l5uaqcePGevrpp3X//fdXR40AAABA5YOrj4+PvLxOPiwiIkJJSUmSJLvdrgMHDlRtdQAAAMD/VHqOa8eOHfXDDz8oNjZWvXv31sMPP6yUlBS9++67atu2bXXUCAAAAFR8xLX4wgJPPvmkGjRoIEl64oknVLduXd122206duyYXn/99eqpEgAAABe8Co+4NmzYUGPGjNG4cePUuXNnSSenCqxataraigMAAACKVXjEdeLEifrwww/VunVr9ezZUwsXLlRWVlZ11gYAAAC4VDi4PvTQQ0pMTNSaNWvUokULTZo0SQ0aNNCECRP03XffVWeNAAAAQOW7CvTp00dvv/22Dh8+rOeee047d+5U165d1aZNG82dO7c6agQAAABkMQzDONedfP7557r55puVlpbmOomrtnA6nbLb7XI4HLLZbJ4uBwAAAKepaF6r9IhrsaysLC1cuFC9e/fW1VdfrdDQUD3xxBNnuzsAAACgXJXu4/rNN9/orbfe0rJly1RQUKDrrrtOs2bNUq9evaqjPgAAAEBSJYLr008/rQULFujPP/9U586d9cwzz2jkyJEKDg6uzvoAAAAASZUIrs8884xGjRqlZcuWcYUsAAAA1LgKB9fk5GT5+PhUZy0AAABAmSp8chahFQAAAJ501l0FAAAAgJpEcAUAAIApEFwBAABgChU6OcvpdFZ4h1ydCgAAANWhQsE1JCREFoulQjusbZd8BQAAwPmhQsF13bp1rn/v27dP06dP15gxY9S1a1dJ0tatW/X2229r9uzZ1VMlAAAALngWwzCMyjygb9+++r//+z+NHDnSbfmiRYv0+uuva/369VVZ3zlzOp2y2+1yOBxMYwAAAKiFKprXKn1y1tatW9W5c+cSyzt37qzvv/++srsDAAAAKqTSwbVx48b697//XWL5G2+8ocaNG1dJUQAAAMDpKnzJ12L/+te/FB8fry+++EJdunSRJH3//fdKSEjQ8uXLq7xAAAAAQDqLEddBgwbpzz//1NChQ3X8+HEdP35cQ4cO1Z9//qlBgwZVR40AAABA5U/OMhtOzgIAAKjdqu3kLEnatGmTRo0apW7duumvv/6SJL377rvavHnz2VULAAAAnEGlg+vy5csVFxcnf39//fzzz8rNzZUkORwOPfnkk1VeIAAAACCdRXB9/PHHNX/+fP373/+Wj4+Pa3n37t31888/V2pfGzdu1NChQxUVFSWLxaKVK1e6rTcMQw8//LAaNGggf39/9evXTwkJCZUtGQAAAOeBSgfX3bt3q1evXiWW2+12paWlVWpfmZmZ6tChg15++eVS1z/99NN64YUXNH/+fH333XcKDAxUXFyccnJyKls2AAAATK7S7bAiIyOVmJioZs2auS3fvHmzWrRoUal9DRw4UAMHDix1nWEYmjdvnh588EFdc801kqR33nlH9evX18qVK3XDDTdUtnQAAACYWKVHXCdMmKA77rhD3333nSwWi5KTk/X+++/r7rvv1m233VZlhe3du1eHDx9Wv379XMvsdru6dOmirVu3lvm43NxcOZ1OtxsAAADMr9IjrtOnT1dRUZH69u2rrKws9erVS1arVXfffbcmT55cZYUdPnxYklS/fn235fXr13etK83s2bP16KOPVlkdAAAAqB0qPeJqsVj0wAMP6Pjx4/rtt9/07bff6tixY5o1a1Z11FdpM2bMkMPhcN0OHDjg6ZIAAABQBSodXMeNG6f09HT5+vrq4osv1uWXX66goCBlZmZq3LhxVVZYZGSkJOnIkSNuy48cOeJaVxqr1SqbzeZ2AwAAgPlVOri+/fbbys7OLrE8Oztb77zzTpUUJUnNmzdXZGSk1qxZ41rmdDr13XffqWvXrlV2HAAAAJhDhee4Op1OGYYhwzCUnp4uPz8/17rCwkL95z//UURERKUOnpGRocTERNf9vXv36pdfflG9evXUpEkTTZ06VY8//rhiY2PVvHlzPfTQQ4qKitKwYcMqdRwAAACYX4WDa0hIiCwWiywWiy666KIS6y0WS6VPivrxxx/1t7/9zXV/2rRpkqTRo0dr4cKFuvfee5WZmalbbrlFaWlp6tGjh1atWuUWmgEAAHBhsBiGYVRkww0bNsgwDF155ZVavny56tWr51rn6+urpk2bKioqqtoKPVtOp1N2u10Oh4P5rgAAALVQRfNahUdce/fuLenk1/lNmjSRxWI59yoBAACACqr0yVlr167Vhx9+WGL5smXL9Pbbb1dJUQAAAMDpKh1cZ8+erbCwsBLLIyIi9OSTT1ZJUQAAAMDpKh1ck5KS1Lx58xLLmzZtqqSkpCopCgAAADhdpYNrRESEduzYUWL59u3bFRoaWiVFAQAAAKerdHAdOXKkpkyZonXr1qmwsFCFhYVau3at7rjjDt1www3VUSMAAABQ8a4CxWbNmqV9+/apb9++qlPn5MOLiop08803M8cVAAAA1abCfVxP9+eff2r79u3y9/dXu3bt1LRp06qurUrQxxUAAKB2q/I+rqe76KKLSr2CFgAAAFAdKhRcp02bplmzZikwMNB1WdayzJ07t0oKAwAAAE5VoeC6bds25efnu/5dFq6mBQAAgOpy1nNczYI5rgAAALVbRfNapdthAQAAAJ5QoakC1157bYV3+NFHH511MQAAAEBZKjTiarfbXTebzaY1a9boxx9/dK3/6aeftGbNGtnt9morFAAAABe2Co24LliwwPXv++67T9dff73mz58vb29vSVJhYaFuv/125pACAACg2lT65Kzw8HBt3rxZLVu2dFu+e/dudevWTampqVVa4Lni5CwAAIDardpOziooKNCuXbtKLN+1a5eKiooquzsAAACgQip95ayxY8dq/Pjx2rNnjy6//HJJ0nfffaennnpKY8eOrfICAQAAAOksguuzzz6ryMhIPffcczp06JAkqUGDBrrnnnt01113VXmBAAAAgHSOFyBwOp2SVKvnjjLHFQAAoHar1gsQFBQU6Ouvv9bixYtdl3lNTk5WRkbG2VULAAAAnEGlpwrs379fAwYMUFJSknJzc3XVVVcpODhYc+bMUW5urubPn18ddQIAAOACV+kR1zvuuEOdO3fWiRMn5O/v71o+fPhwrVmzpkqLAwAAAIpVesR106ZN+uabb+Tr6+u2vFmzZvrrr7+qrDAAAADgVJUecS0qKlJhYWGJ5QcPHlRwcHCVFAUAAACcrtLBtX///po3b57rvsViUUZGhmbOnKlBgwZVZW0AAACAS6XbYR04cEADBgyQYRhKSEhQ586dlZCQoLCwMG3cuFERERHVVetZoR0WAABA7VbRvHZWfVwLCgq0ZMkSbd++XRkZGbr00kt14403up2sVVsQXAEAAGq3agmu+fn5atWqlT777DO1bt26SgqtbgRXAACA2q1aLkDg4+OjnJyccy4OAAAAqKxKn5w1ceJEzZkzRwUFBdVRDwAAAFCqSvdx/eGHH7RmzRp99dVXateunQIDA93Wf/TRR1VWHAAAAFCs0sE1JCRE8fHx1VELAAAAUKZKB9cFCxZURx0AAABAuSo8x7WoqEhz5sxR9+7dddlll2n69OnKzs6uztoAAAAAlwoH1yeeeEL333+/goKC1LBhQz3//POaOHFiddYGAAAAuFQ4uL7zzjt65ZVX9OWXX2rlypX69NNP9f7776uoqKg66wMAAAAkVSK4JiUladCgQa77/fr1k8ViUXJycrUUBgAAAJyqwsG1oKBAfn5+bst8fHyUn59f5UUBAAAAp6twVwHDMDRmzBhZrVbXspycHP3zn/906+VKH1cAZTnizNGJzDw5cwpk86+jugG+qm/zO/MDAQBQJYLr6NGjSywbNWpUlRYD4PyVlJqpGSt+1ZbEVNeyHjGhenJ4OzUJDSznkQAAnGQxDMPwdBHVyel0ym63y+FwyGazeboc4IJ0xJmjaUt/cQutxXrEhOq56y9h5BUALmAVzWsVnuMKAGfrRGZeqaFVkjYnpupEZl4NVwQAMCOCK4Bq58wpOKf1AABIBFcANcDmV/50+jOtBwBAIrgCqAF1A33VIya01HU9YkJVN9C3hisCAJgRwRVAtatv89OTw9uVCK/FXQU4MQsAUBF8PwegRjQJDdRz11/y//u4+tVR3UD6uAIAKo7gCqDG1Lf5EVQBAGeNqQIAAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBboKeFByWrYc2flyZufL7u8jm7+PokL8PV0WAABArURw9ZD9qZm6f8Wv2pKY6lrWIyZUTwxvp6ahgR6sDAAAoHZiqoAHJKdllwitkrQ5MVUPrPhVyWnZHqoMAACg9iK4eoAjO79EaC22OTFVjuz8Gq4IAACg9iO4eoDzDME0PYfgCgAAcDqCqwfY/H3KXR/sV/56AACACxHB1QPs/j7qERNa6roeMaGynyHYAgAAXIgIrh4QFeKvJ4a3KxFei7sK0BILAACgJNpheUjT0EA9fV0HObLzlZ6Tr2A/H9np4woAAFAmgqsHRYX4E1QBAAAqiKkCAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWunAWYkCMrTykZeXLm5Mvm76OwQF/ZA3w9XRYAANWK4AqYTHJatu5bvkObElJcy3rFhump+PZcQhgAcF5jqgBgIo6svBKhVZI2JqRo+vIdcmTleagyAACqH8EVMJGUjLwSobXYxoQUpWQQXAEA5y+CK2Aizpz8ctenn2E9AABmRnAFTMTm51Pu+uAzrAcAwMwIroCJhAX5qldsWKnresWGKSyIzgIAgPMXwRUwEXuAr56Kb18ivPaKDdOc+Pa0xAIAnNdohwWYTFSIv14c2VEpGXlKz8lXsJ+PwoLo4woAOP8RXAETsgcQVAEAFx6mCgAAAMAUCK4AAAAwBYIrAAAATKFWB9dHHnlEFovF7daqVStPlwUAAAAPqPUnZ7Vp00Zff/21636dOrW+ZAAAAFSDWp8C69Spo8jISE+XAQAAAA+r1VMFJCkhIUFRUVFq0aKFbrzxRiUlJZW7fW5urpxOp9sNAAAA5lerg2uXLl20cOFCrVq1Sq+++qr27t2rnj17Kj09vczHzJ49W3a73XVr3LhxDVYMAACA6mIxDMPwdBEVlZaWpqZNm2ru3LkaP358qdvk5uYqNzfXdd/pdKpx48ZyOByy2Ww1VSoAAAAqyOl0ym63nzGv1fo5rqcKCQnRRRddpMTExDK3sVqtslqtNVgVAAAAakKtnipwuoyMDO3Zs0cNGjTwdCkAAACoYbU6uN59993asGGD9u3bp2+++UbDhw+Xt7e3Ro4c6enSAAAAUMNq9VSBgwcPauTIkUpNTVV4eLh69Oihb7/9VuHh4Z4uDQAAADWsVgfXDz74wNMlAAAAoJao1VMFAAAAgGIEVwAAAJhCrZ4qANQ2jqw8pWTkyZmTL5u/j8ICfWUP8PV0WQAAXBAIrkAFJadl677lO7QpIcW1rFdsmJ6Kb6+oEH8PVgYAwIWBqQJABTiy8kqEVknamJCi6ct3yJGV56HKAAC4cBBcgQpIycgrEVqLbUxIUUoGwRUAgOpGcAUqwJmTX+769DOsBwAA547gClSAzc+n3PXBZ1gPAADOHcEVOIUjK097jmZoW9IJ7TmW4Zq7Ghbkq16xYaU+pldsmMKC6CwAAEB1o6sA8D9n6hrwVHx7TV++QxtPWz8nvj0tsQAAqAEWwzAMTxdRnZxOp+x2uxwOh2w2m6fLQS3lyMrTpMXbSj0Bq1dsmF4c2VH2AF9XH9f0nHwF+/koLIg+rgAAnKuK5jVGXAFVrGuAPcDXdQMAADWPOa6A6BoAAIAZEFwB0TUAAAAzILgComsAAABmQHAFJNkDfPVUfPsS4ZWuAQAA1B6cnAX8T1SIv14c2ZGuAQAA1FIEV+AUdA0AAKD2YqoAAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFOo4+kCgFM5svKUkpEnZ06+bP4+Cgv0lT3A19NlAQCAWoDgilojOS1b9y3foU0JKa5lvWLD9FR8e0WF+HuwMgAAUBswVQC1giMrr0RolaSNCSmavnyHHFl5HqoMAADUFgRX1AopGXklQmuxjQkpSskguAIAcKEjuKJWcObkl7s+/QzrAQDA+Y/gilrB5udT7vrgM6wHAADnP4IrapQjK097jmZoW9IJ7TmW4Zq7Ghbkq16xYaU+pldsmMKC6CwAAMCFjuCKGnHEmaNdh536cf8J7T+epTW7jmroi5s1efE2Jadlyx7gq6fi25cIr71iwzQnvj0tsQAAgCyGYRieLqI6OZ1O2e12ORwO2Ww2T5dzQUpKzdQDK37VpsRU17LuMaEa2725pizeps5N6+rFkR1lD/B19XFNz8lXsJ+PwoLo4woAwPmuonmNPq6oVkecOZqx4ldtOSW0SnLdH9ejuV5am6iUjDzZA3xdNwAAgNMxVcDDyprzaXbFzyspNUvje7TQpCtjFODr7bbNlsRUdWwcIomuAQAA4MwYcfWg8/VKUaU9r+4xoXphZEdNWbxNWXmFruW5BUWS6BoAAADOjBFXDzkfrxR1xJmjvccydN+H20s8ry2JqVqwZa/G9Wjuttxax4uuAQAAoEIIrh5yvl0pKik1U9OW/qL/pmS6nYR1qlOnBkgnR2GPpufSNQAAAFQIwdVDzqcrRZ16AlbxV/9lKV7fMyZMjw9rq0FtI9XAxNMiAABAzWGOq4ecT1eKOpGZ5+oSYK1T/t9CTUMDtOqOnqob6Kv6Nr+aKA8AAJwnGHH1EDNfKer0Tginjh5vO5Cm7jGhpT6uV2yYGoX4q1UDG6EVAABUGsHVQ8x6pahDadn6z2+HtS81U4ccOdqfmqUg6/8fHX5r816N7d68RHit7c8LAADUflw5y8PMdKWo5LRsObPz9VdatiwWi35OOqG3Nu/VW2M666W1idr8v+kCAb7eGtejuetErMZ1A1TfZq21zwsAAHgWV84yCbNcKaqsy7a+MLKj7lm2QwvGXqZHPvldmxNTlZVXqJfWJqpHTKieHN5OTUIDPVg5AAA4XxBccUZnumzrNR0b6obXv9Xyf3ZTdn6hnDkFsvnV4QQsAABQpZjjijIVn4SVkpFbIrQWK+7NmpKRp2MZuWrVwKbLm9fjBCwAAFDlGHFFqU69bOsrN15a7rbFvVnt/rWvhVfxHGJnTr5s/j4KCzTH1AwAAFASwRUlHHHmaF9KpkZe3kRjuzdXWJCvAny9lZVXWOr21jpe6hkbpohgaw1XWr5Tw3exXrFheiq+vaK46AEAAKbDVAG4HHHmaNdhp377y6GcgiL9ccipSYt+1jOrdunN0Z0V4Otd4jHFl219upa1unJk5ZUIrdLJy+lOX75DjixzXVIXAAAQXD3i9Ab+tSFEHUjN1J6jGfrvsUxXq6s/kh16YWRH/ZSUplfW7dGDg1u7PaY2X7Y1JSOvRGgttjEhRSkZnv+ZAwCAymGqQA2rjV9fJ6Vmluga0D0mVGO7N9ei7/ZrXI/memltou7oF6s3R3eWzc9HwbW8a8CpV/MqTfoZ1gMAgNqHEdcaVNu+vnZk5WnvsQw9UEarqwVb9uriKLvrQgJH03P19jf7dFH9oFrfNcDmV/6JYsFnWA8AAGofgmsNqk1fXyenZWvSom36b0qm20UFTlXc6qq4a0CIv49pLtsaFuRb4nK6xXrFhiksqPY/BwAA4I7gWk1Km8daW76+do38Jqa4QmlZcguKXF0DoiOCat1c1rLYA3z1VHz7EuG1V2yYacI3AABwxxzXalDWPNYHTju56XTV/fV1clq2HNn5Ss/O130DWmlMtxx5e1nKfYzd30dJx7P0dHz7Wj01oDRRIf56cWRHpWTkKT0nX8F+PgoLoo8rAABmRXCtYuXNYx2YlKZesWHaWMp0ger8+tqRladDzhz9dSLb1THgrc17dWmTEM26pq26x4SWemWsHjGhalzXX22jbKYNe/YAgioAAOcLpgpUsfLmsc767A89dk3bGv36ungu64B5mzT+7R81buEP2pZ0Qi+M7Kifk9L0+Od/aOaQNuoeE+r2uJ6xYXpyeDs1CQ0k+AEAgFqBEdcqVt481qy8Qjmz82rs6+sjzhzd9+H2EidfFY+uFre5mtb/InVsUlfjujeXJIUGWRUe5KuGdQOqvCYAAICzRXCtYmdqwxRo9amRr6+T07K17wwdA4qDqjO7UC+tTVTP2DDNuqat6gb4MMoKAABqHYJrFXJk5anIMPTm6M5uc0mz8gol1UwbJkdWnpzZ+Trwv/msb425rEQdxVxtrgJ8tGZab05cAgAAtRrBtYqU1kmge0yoXhjZUVMWb1PnpnWrvQ3TX8ezlJ1fqEc//d1tpPXUOk4Nr9Y6XuoREyq7v4/HrtoFAABQUQTXKlBWJ4Etianyslj0xZSeCqnmr9/3p2ZqS2KKPv/1UKlXwZL+/5xW6WSYPerM0RPD2xFaAQCAKdBVoAqU10lgU0KKCoqMagutjqw8/Xk4XYlHM9SuUUipba2k/38VLOlkx4DHh7VVn5YRahoaWC11AQAAVDVGXKuAp66IlXQ8Sw98tMM1LeCVGy8td/sA3zpa9H9dFB0RZLqLCQAAABBcq8CZOglU9RWxTj0Ba2SXphrbo4V+TjohPx/vch9nGIaahwUSWgEAgCkRXKtAWJBvjV0R66/jWXLk5Gv2f3aWOAGr/8X1y7wKVs/YMEZaAQCAqTHHtQrYA3z1VHz7ar8i1sHjWXLmFuiQI0dje7TQpCtjFOB7cpR1S2Kq5n39p+6Ja1XqVbCejm9PaAUAAKZmMQzD8HQR1cnpdMput8vhcMhms1XrsRxZedVyRayjzhxl5hXo4ZW/lRhlHdu9uVubq4VjLtOPSScU16a+jjpz1aiuvyJtfvRnBQAAtVZF8xpTBapQdVwRa39qpr5JTNFnFWxzlZVfqO0H0hTfsaEiGvoxygoAAM4bTBWopRxZeTqQmqkHV/yqCJtfhdpcSZLd30dPDG+n5uHMZwUAAOcXgmstlJyWrZ2HnXLmFGhTYqrr0qxlKV7fMyZMTer6q0m9gJooEwAAoEYRXGuZgyeydCIzT3Z/XzlzCiSdvDRreax1vNQzNkxPDG+rxlxQAAAAnKeY41pLOLLydMiZo79OZMtisSjQ11tBfic7Bmw7kFZum6sWYYF69u8dmBoAAADOawTXWuBAaqZSM/OVk1+oiGCrJIsCrd7al5qpnjGhemvzXr0wsqMkuYXXnjGhenxYWy7bCgAALgi0w/IgR1aeTmTl66GVv5ZoczX5b7Hy8TZk87fqsU//0E9JJzSuR3PXiViRNj/Z/OowNQAAAJheRfMawbWKJadly5GdL2d2vuz+PrL5+ygqxL/EdodSM5UvKTOv0LWtj7eXbnvvJ/15NEPdY0I1pH2UrN4W1Quyqr7NTxk5BQqweivAx1u+XhY1JLQCAIDzAH1cPWD//9pXbTrt6/zHhrWVMztfQX4+Cgv0VXp2vgoMQ3+l5ciRnS8/H29tSkzRzmSHXr+5s25550dtSUzVuO7N1TDEX/lFRa6LGgT6esvfx1vhzGcFAAAXGIJrFTmQmqn7V/xa4gSqTYmpenDlb+rYpK427j6qV/5xqXIKizTzk9/dti2+Ctasz37Xy6Mu1VVzNyq3oEhp2fl6eW2i7hnQUkccObooMpjQCgAALkimaIf18ssvq1mzZvLz81OXLl30/fffe7okN8lp2TqQlu0KogG+3pp0ZYzeHN1Zr9x4qW7p2UIjOzXSi/+4VM7cAj16WmiVTp50tWDLXrWOsqug8OTsDWsdL/nV8das4W01buEPKjQMTV++Q46svBp/jgAAAJ5W64PrkiVLNG3aNM2cOVM///yzOnTooLi4OB09etTTpbk4c/LlJYukk6H1hZEdtS3phMa//aPuXrZdu484VSQpI6dAGbmFGtujhSZdGaMAX2+3/RRfBSs9O1/dY0J1ND1XoYE+mrLoZ7WMDNa2A2namJCilAyCKwAAuPDU+qkCc+fO1YQJEzR27FhJ0vz58/X555/rrbfe0vTp0z1c3f8YUuH/znEb16O5FmzZqy2JqWrf0KZX/nGpCgxp+mnTCLrHhOqFkR01ZfE2ZeUVupbnFhQpKsRHk6+MVWSwVePf/lHhNqvGdm+uKYu3SZLSc/Jr9vkBAADUArU6uObl5emnn37SjBkzXMu8vLzUr18/bd26tdTH5ObmKjc313Xf6XRWe52GpF8OnNDs4W3VtpFdFzewafLfotUwJEB5RYZmfvxbqVMDpJNB96W1ia7ldn8fBfl6S4ah3UczdN/AVtp2IM0t4Ab7+VT7cwIAAKhtanVwTUlJUWFhoerXr++2vH79+tq1a1epj5k9e7YeffTRmijPJTe/QFddHKlZn/6uV9bv0fxRnVTH26JkR46C/XzcugycqrhzQLEeMaFqUtdfNn8fPbDyN21MSCnxmF6xYQoL8q225wIAAFBb1fo5rpU1Y8YMORwO1+3AgQPVfszQQKteXZegh65urUX/10WSdDwzX4HWOjIMo8Rc1lPlFhRJOnnp1ieGt5OfpLyCIj0V3169YsPctu0VG6Y58e1lD/CVIytPe45maFvSCe05lsEJWwAA4LxXq0dcw8LC5O3trSNHjrgtP3LkiCIjI0t9jNVqldVqrYnyXHKLCnVPv4uUb7Ho/hJ9XMNKnctarGm9AK2a2lNWb4tGvfGdmoYG6JGr28jf20svjuyolIw8Vw/XsCBf2QN8lZyWrfuW79CmU0Zke8WG6an49qVe7AAAAOB8UKtHXH19fdWpUyetWbPGtayoqEhr1qxR165dPViZu0AvbxWWElolaVNiihZu2atxPZqXeFyPmFDZ/Oroic9+14Mf/65rOjbU5sRUPfLJ7zrgyFZOQZGiI4J0SZO6io4Ico20nh5aJWljQgqtsgAAwHmtVgdXSZo2bZr+/e9/6+2339bOnTt12223KTMz09VloDYokHTgRHaJE7CKbU5MVbcWoW7LesSE6vHh7XT/ih3alHjc1QqrePtAax2dyCwZQlMy8kqE1mK0ygIAAOezWj1VQJJGjBihY8eO6eGHH9bhw4d1ySWXaNWqVSVO2PKkjLxCpWWX36KqjreXPp3cXVm5hbL5+yg5LVt7j2VqU+Jx1zbF810lKSOnUN5eRSX24zxDKyxaZQEAgPNVrQ+ukjRp0iRNmjTJ02WUyZmdL2ud8gevA329teNgmq6IDtXf529VSkae3hzd2W2bU/cR5OctH6+S+7SdoRUWrbIAAMD5qtZPFTADm7+Pth1IU/eY0FLX94wJU5C1ji5rXk83v/m9UjLy1D0mVNsOpLm2OfV+j5hQZeYWqG5gybZXYUG+JboNFKNVFgAAOJ8RXKtAsF8d7Ux2aGz35iXC68m5rG21bf9xXf3SFh04ka0eMaEa27253tq8V5LU85T7PWJC9cjVbdXI7q/6Nr8Sx7IH+J6xVRYAAMD5yGIY/7tW6XnK6XTKbrfL4XDIZrNV23H2p2bqsU9/V+souzo2DlFuQZFC/H3UuK6//AxDxwuKdDwzT8F+PvKt46X8wkI5sgoUGuQrX28vpWbmKtDqo0Bfb/n7eCu8lNB6KkdWXqmtsgAAAMymonmN4FqFDp7IUnpOgStMBvl6y0+G8r28FFU3QEecOTqRmSdnToFsfnVUN9C31FFVAACAC0lF85opTs4yi0Z1A8pdX9/mR1AFAAA4S8xxBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKdTxdQHUzDEOS5HQ6PVwJAAAASlOc04pzW1nO++Canp4uSWrcuLGHKwEAAEB50tPTZbfby1xvMc4UbU2uqKhIycnJCg4OlsVi8WgtTqdTjRs31oEDB2Sz2TxaCzyP9wOK8V5AMd4LKHahvRcMw1B6erqioqLk5VX2TNbzfsTVy8tLjRo18nQZbmw22wXxJkTF8H5AMd4LKMZ7AcUupPdCeSOtxTg5CwAAAKZAcAUAAIApEFxrkNVq1cyZM2W1Wj1dCmoB3g8oxnsBxXgvoBjvhdKd9ydnAQAA4PzAiCsAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgmsNevnll9WsWTP5+fmpS5cu+v777z1dEmrYI488IovF4nZr1aqVp8tCDdi4caOGDh2qqKgoWSwWrVy50m29YRh6+OGH1aBBA/n7+6tfv35KSEjwTLGoVmd6L4wZM6bE58SAAQM8Uyyq1ezZs3XZZZcpODhYERERGjZsmHbv3u22TU5OjiZOnKjQ0FAFBQUpPj5eR44c8VDFnkdwrSFLlizRtGnTNHPmTP3888/q0KGD4uLidPToUU+XhhrWpk0bHTp0yHXbvHmzp0tCDcjMzFSHDh308ssvl7r+6aef1gsvvKD58+fru+++U2BgoOLi4pSTk1PDlaK6nem9IEkDBgxw+5xYvHhxDVaImrJhwwZNnDhR3377rVavXq38/Hz1799fmZmZrm3uvPNOffrpp1q2bJk2bNig5ORkXXvttR6s2sMM1IjLL7/cmDhxout+YWGhERUVZcyePduDVaGmzZw50+jQoYOny4CHSTJWrFjhul9UVGRERkYazzzzjGtZWlqaYbVajcWLF3ugQtSU098LhmEYo0ePNq655hqP1APPOnr0qCHJ2LBhg2EYJz8HfHx8jGXLlrm22blzpyHJ2Lp1q6fK9ChGXGtAXl6efvrpJ/Xr18+1zMvLS/369dPWrVs9WBk8ISEhQVFRUWrRooVuvPFGJSUlebokeNjevXt1+PBht88Iu92uLl268BlxgVq/fr0iIiLUsmVL3XbbbUpNTfV0SagBDodDklSvXj1J0k8//aT8/Hy3z4ZWrVqpSZMmF+xnA8G1BqSkpKiwsFD169d3W16/fn0dPnzYQ1XBE7p06aKFCxdq1apVevXVV7V371717NlT6enpni4NHlT8OcBnBKST0wTeeecdrVmzRnPmzNGGDRs0cOBAFRYWero0VKOioiJNnTpV3bt3V9u2bSWd/Gzw9fVVSEiI27YX8mdDHU8XAFxIBg4c6Pp3+/bt1aVLFzVt2lRLly7V+PHjPVgZgNrihhtucP27Xbt2at++vaKjo7V+/Xr17dvXg5WhOk2cOFG//fYb5z2cASOuNSAsLEze3t4lzgI8cuSIIiMjPVQVaoOQkBBddNFFSkxM9HQp8KDizwE+I1CaFi1aKCwsjM+J89ikSZP02Wefad26dWrUqJFreWRkpPLy8pSWlua2/YX82UBwrQG+vr7q1KmT1qxZ41pWVFSkNWvWqGvXrh6sDJ6WkZGhPXv2qEGDBp4uBR7UvHlzRUZGun1GOJ1Offfdd3xGQAcPHlRqaiqfE+chwzA0adIkrVixQmvXrlXz5s3d1nfq1Ek+Pj5unw27d+9WUlLSBfvZwFSBGjJt2jSNHj1anTt31uWXX6558+YpMzNTY8eO9XRpqEF33323hg4dqqZNmyo5OVkzZ86Ut7e3Ro4c6enSUM0yMjLcRsz27t2rX375RfXq1VOTJk00depUPf7444qNjVXz5s310EMPKSoqSsOGDfNc0agW5b0X6tWrp0cffVTx8fGKjIzUnj17dO+99yomJkZxcXEerBrVYeLEiVq0aJE+/vhjBQcHu+at2u12+fv7y263a/z48Zo2bZrq1asnm82myZMnq2vXrrriiis8XL2HeLqtwYXkxRdfNJo0aWL4+voal19+ufHtt996uiTUsBEjRhgNGjQwfH19jYYNGxojRowwEhMTPV0WasC6desMSSVuo0ePNgzjZEushx56yKhfv75htVqNvn37Grt37/Zs0agW5b0XsrKyjP79+xvh4eGGj4+P0bRpU2PChAnG4cOHPV02qkFp7wNJxoIFC1zbZGdnG7fffrtRt25dIyAgwBg+fLhx6NAhzxXtYRbDMIyaj8sAAABA5TDHFQAAAKZAcAUAAIApEFwBAABgCgRXAAAAmALBFQAAAKZAcAUAAIApEFwBAABgCgRXAAAAmALBFQBqMYvFopUrV1brMfr06aOpU6dW6zEAoCoQXAFA0tatW+Xt7a3BgwdX+rHNmjXTvHnzqr6oMxg6dKgGDBhQ6rpNmzbJYrFox44dNVwVAFQfgisASHrzzTc1efJkbdy4UcnJyZ4up0LGjx+v1atX6+DBgyXWLViwQJ07d1b79u09UBkAVA+CK4ALXkZGhpYsWaLbbrtNgwcP1sKFC0ts8+mnn+qyyy6Tn5+fwsLCNHz4cEknv2bfv3+/7rzzTlksFlksFknSI488oksuucRtH/PmzVOzZs1c93/44QddddVVCgsLk91uV+/evfXzzz9XuO4hQ4YoPDy8RL0ZGRlatmyZxo8fr9TUVI0cOVINGzZUQECA2rVrp8WLF5e739KmJ4SEhLgd58CBA7r++usVEhKievXq6ZprrtG+fftc69evX6/LL79cgYGBCgkJUffu3bV///4KPzcAKA3BFcAFb+nSpWrVqpVatmypUaNG6a233pJhGK71n3/+uYYPH65BgwZp27ZtWrNmjS6//HJJ0kcffaRGjRrpscce06FDh3To0KEKHzc9PV2jR4/W5s2b9e233yo2NlaDBg1Senp6hR5fp04d3XzzzVq4cKFbvcuWLVNhYaFGjhypnJwcderUSZ9//rl+++033XLLLbrpppv0/fffV7jO0+Xn5ysuLk7BwcHatGmTtmzZoqCgIA0YMEB5eXkqKCjQsGHD1Lt3b+3YsUNbt27VLbfc4gr1AHC26ni6AADwtDfffFOjRo2SJA0YMEAOh0MbNmxQnz59JElPPPGEbrjhBj366KOux3To0EGSVK9ePXl7eys4OFiRkZGVOu6VV17pdv/1119XSEiINmzYoCFDhlRoH+PGjdMzzzzjVu+CBQsUHx8vu90uu92uu+++27X95MmT9eWXX2rp0qWu8F1ZS5YsUVFRkd544w1XGF2wYIFCQkK0fv16de7cWQ6HQ0OGDFF0dLQkqXXr1md1LAA4FSOuAC5ou3fv1vfff6+RI0dKOjmKOWLECL355puubX755Rf17du3yo995MgRTZgwQbGxsbLb7bLZbMrIyFBSUlKF99GqVSt169ZNb731liQpMTFRmzZt0vjx4yVJhYWFmjVrltq1a6d69eopKChIX375ZaWOcbrt27crMTFRwcHBCgoKUlBQkOrVq6ecnBzt2bNH9erV05gxYxQXF6ehQ4fq+eefr9RINACUhRFXABe0N998UwUFBYqKinItMwxDVqtVL730kux2u/z9/Su9Xy8vL7ev76WTX7GfavTo0UpNTdXzzz+vpk2bymq1qmvXrsrLy6vUscaPH6/Jkyfr5Zdf1oIFCxQdHa3evXtLkp555hk9//zzmjdvntq1a6fAwEBNnTq13GNYLJZya8/IyFCnTp30/vvvl3hseHi4pJMjsFOmTNGqVau0ZMkSPfjgg1q9erWuuOKKSj03ADgVI64ALlgFBQV655139Nxzz+mXX35x3bZv366oqCjXSUzt27fXmjVrytyPr6+vCgsL3ZaFh4fr8OHDbgHwl19+cdtmy5YtmjJligYNGqQ2bdrIarUqJSWl0s/j+uuvl5eXlxYtWqR33nlH48aNc32Fv2XLFl1zzTUaNWqUOnTooBYtWujPP/8sd3/h4eFuI6QJCQnKyspy3b/00kuVkJCgiIgIxcTEuN3sdrtru44dO2rGjBn65ptv1LZtWy1atKjSzw0ATkVwBXDB+uyzz3TixAmNHz9ebdu2dbvFx8e7pgvMnDlTixcv1syZM7Vz5079+uuvmjNnjms/zZo108aNG/XXX3+5gmefPn107NgxPf3009qzZ49efvllffHFF27Hj42N1bvvvqudO3fqu+++04033nhWo7tBQUEaMWKEZsyYoUOHDmnMmDFux1i9erW++eYb7dy5U7feequOHDlS7v6uvPJKvfTSS9q2bZt+/PFH/fOf/5SPj49r/Y033qiwsDBdc8012rRpk/bu3av169drypQpOnjwoPbu3asZM2Zo69at2r9/v7766islJCQwzxXAOSO4Arhgvfnmm+rXr5/bKGGx+Ph4/fjjj9qxY4f69OmjZcuW6ZNPPtEll1yiK6+80u2s/Mcee0z79u1TdHS066vy1q1b65VXXtHLL7+sDh066Pvvv3c7Sar4+CdOnNCll16qm266SVOmTFFERMRZPZfx48frxIkTiouLc5v28OCDD+rSSy9VXFyc+vTpo8jISA0bNqzcfT333HNq3LixevbsqX/84x+6++67FRAQ4FofEBCgjRs3qkmTJrr22mvVunVrjR8/Xjk5ObLZbAoICNCuXbsUHx+viy66SLfccosmTpyoW2+99ayeGwAUsxinT2QCAAAAaiFGXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApvD/AFgqEdOMIfmLAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#DT\n",
        "plot_actual_vs_predicted(y_test, predictions_decision_tree, \"Decision Tree Regressor\")"
      ],
      "id": "510cda91"
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "382927d6",
        "outputId": "3960ca42-b697-4295-bffc-88600819585c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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ddpvuvvtuq384VMQDDzygu+66S1lZWVq7dq3ef/99Sy/l+XXu27dPNWrUKHYf//77r6Rz35NatWoVudEnLCys2O1cXFyKtLJER0crOTlZQUFBpR6re/fuioiI0EsvvaR3331XPXr00MCBAzVs2DDL5/iRRx7R4sWL1a9fP9WuXVu9e/fW4MGD1bdvX8v+jh49avlH0PnO/5ydPytHcZ/74pw+fdrq++jj41NqW8GiRYuUm5ur1q1bKyYmxjLeoUMHzZ8/3/KPhsI/xV+OmULi4uL04osvauXKlUX+eynsHS+PIUOGaMaMGVq5cqWGDRumtLQ0/fDDD5YWKKlsP9eKWLp0qfz8/OTq6qprrrmm2Haf2rVrF2lRiY6O1sGDB8v02Xdyciqy3+J+P1zoUn+ml6NGOCYCL64K5we5gQMHqkuXLho2bJgOHTpk+R/zl19+qZEjR2rgwIF66qmnFBQUJGdnZ0VFRRW5qUlSiXceGxfcZFYVSjq2LWu60IVXBgsKCizzrRZX5/kB6e2339bIkSP1zTff6Oeff9b48eMVFRWlbdu2FQmN5REeHq5evXpJkm699VY5Oztr0qRJuvHGGy2fj4KCAt188816+umni93HtddeW6Fju7u7F5nhoKCgQEFBQZo/f36x2xT+T91kMunrr7/Wtm3b9O233+qnn37SqFGj9Pbbb2vbtm3y8fFRUFCQ9uzZo59++kk//vijfvzxR82ZM0f33nuv5s2bV6Gay3p1t127dlb/KJsyZUqpE/sXnm/nzp2LXf7333+rQYMGZS+0BCVdMbzwHzn5+fm6+eabdebMGT3zzDNq1KiRvL299c8//2jkyJFFblYsixtuuEH16tXT4sWLNWzYMH377bfKzMzUkCFDrOq72M+1Irp162bpvS9JcT/bgoICNW/eXO+8806x21z4D2pbuBJqhH0i8OKqUxhib7zxRs2cOdPyYIWvv/5aDRo00LJly6z+RzllypQKHadu3bqSzl2RuNChQ4eKrPvLL78oNTXV6irvn3/+abUvWyk8/oV1S+dqDAwMvOh0Ug0bNpRhGKpfv36ZQmPz5s3VvHlzPf/889qyZYs6d+6s2bNn69VXX5VUOX/Sfu655/TJJ5/o+eef16pVqyx1pqWlWYJxSerWrat169YVmc7p/CuWF9OwYUP98ssv6ty5c5nC5Q033KAbbrhBr732mhYsWKDhw4frq6++0pgxYyRJbm5uGjBggAYMGKCCggI98sgj+s9//qMXXnhBYWFhqlu3bok/w8Jzqoj58+dbzWZQWliNjY3Vli1bNG7cOHXv3t1qWUFBge655x4tWLBAzz//vOXq3IEDB0q8ci6V/Fko/KtLUlKS1fiFfzHZv3+//vrrL82bN0/33nuvZby09pCyGDx4sN577z2lpKRo0aJFqlevnm644YYi613s53q5NGzYUHv37lXPnj1L/e+rbt26Kigo0OHDh62umBb32SruGFLFf6aXo0Y4Jnp4cVXq0aOH2rdvrxkzZigrK0vS/10dPf9q6Pbt2yv8JK5atWqpVatWmjdvntWfRFevXl2kV7LwYQgzZ860Gn/33XdlMpkqPCF/ZTn/XM4PDwcOHNDPP/+s/v37X3Qfd9xxh5ydnfXSSy8VueJsGIYSExMlnZstIy8vz2p58+bN5eTkZDXtkLe3d5EgU17+/v568MEH9dNPP2nPnj2SzoWUrVu36qeffiqyflJSkqW2Pn36KDc3V5988olleUFBgWbNmlXm4w8ePFj5+fl65ZVXiizLy8uznN/Zs2eLfM8KZw0p/J4Ufv8KOTk5qUWLFlbr9O/fXzt27LD6TKenp+vjjz9WvXr1ivSWl1Xnzp3Vq1cvy6u0wFt4dffpp5/WnXfeafUaPHiwunfvblmnd+/e8vX1VVRUlOW/00Lnfz+8vb2LbTsoDFeFfdfSuau5H3/8sdV6xf23bxhGkSndymvIkCHKzs7WvHnztGrVqiJzfpfl5yqdawMo7q9MlW3w4MH6559/rD7ThTIzM5Weni5Jlt9H77//vtU6ZXky2qX+TC9HjXBMXOHFVeupp57SXXfdpblz5+qhhx7SrbfeqmXLlmnQoEG65ZZbFBsbq9mzZ6tJkyZWN56UR1RUlG655RZ16dJFo0aN0pkzZ/TBBx+oadOmVvscMGCAbrzxRj333HM6cuSIWrZsqZ9//lnffPONHn/88QpNuVXZpk+frn79+qljx44aPXq0ZVoys9lcpufSN2zYUK+++qomT56sI0eOaODAgfL19VVsbKyWL1+uBx54QBMnTtTatWs1btw43XXXXbr22muVl5en//73v3J2dlZERIRlf23atNEvv/yid955RyEhIapfv36x/akX89hjj2nGjBl6/fXX9dVXX+mpp57SypUrdeutt2rkyJFq06aN0tPTtX//fn399dc6cuSIAgMDNXDgQLVv315PPvmkYmJi1KhRI61cuVJnzpyRVLYr0N27d9eDDz6oqKgo7dmzR71795arq6uio6O1ZMkSvffee7rzzjs1b948ffjhhxo0aJAaNmyo1NRUffLJJ/Lz87P8Y2PMmDE6c+aMbrrpJl1zzTU6evSoPvjgA7Vq1crSoztp0iQtXLhQ/fr10/jx41W9enXNmzdPsbGxWrp0aaU+VKIk8+fPV6tWrUr80/Ntt92mRx99VLt27dL111+vd999V2PGjFG7du0s8zrv3btXGRkZllaNNm3aaNGiRZowYYLatWsnHx8fDRgwQE2bNtUNN9ygyZMn68yZM6pevbq++uqrIv+gatSokRo2bKiJEyfqn3/+kZ+fn5YuXVrm3veSXH/99QoLC9Nzzz2n7Oxsq3YGSWX6uUrnblKVVKlzahfnnnvu0eLFi/XQQw9p3bp16ty5s/Lz8/Xnn39q8eLF+umnn9S2bVu1atVKkZGR+vDDD5WcnKxOnTppzZo1Zfrrhp+f3yX9TC9HjXBQNpgZArhsCqfEKW7an/z8fKNhw4ZGw4YNjby8PKOgoMCYNm2aUbduXcPd3d1o3bq18d133xkjRoywmh6ncIqr6dOnF9mnipnuZunSpUbjxo0Nd3d3o0mTJsayZcuK7NMwzk0P9cQTTxghISGGq6urER4ebkyfPt1qqp7CY4wdO9ZqrKSaCqdfutgUX6V9n873yy+/GJ07dzY8PT0NPz8/Y8CAAcYff/xhtU7h1EinT58udh9Lly41unTpYnh7exve3t5Go0aNjLFjxxqHDh0yDMMw/v77b2PUqFFGw4YNDQ8PD6N69erGjTfeaPzyyy9W+/nzzz+Nbt26GZ6enoakUqcoK+1nZhiGMXLkSMPZ2dky/VtqaqoxefJkIywszHBzczMCAwONTp06GW+99ZaRk5Nj2e706dPGsGHDDF9fX8NsNhsjR440Nm/ebEgyvvrqK8t6I0aMMLy9vUus7+OPPzbatGljeHp6Gr6+vkbz5s2Np59+2jhx4oRhGIaxa9cuIzIy0qhTp47h7u5uBAUFGbfeequxc+dOyz6+/vpro3fv3kZQUJDh5uZm1KlTx3jwwQeNkydPWh3r8OHDxp133mn4+/sbHh4eRvv27Y3vvvvOap2yfm7K6/fffzckGS+88EKJ6xw5csSQZDzxxBOWsZUrVxqdOnWyfO7at29vLFy40LI8LS3NGDZsmOHv719kyr/Dhw8bvXr1skzN9eyzzxqrV68uMi3ZH3/8YfTq1cvw8fExAgMDjfvvv9/Yu3dvkSnmyjIt2fmee+45Q5IRFhZWZFlZfq6GYRh169YtdoquC13sv71C3bt3N5o2bVrsspycHOONN94wmjZtari7uxvVqlUz2rRpY7z00ktGcnKyZb3MzExj/PjxRkBAgOHt7W0MGDDAOHbs2EWnJSt0KT/Tyq4RVweTYdjgbhYAcFArVqzQoEGDtGnTphJvygIAXF4EXgCooMzMTKubzfLz89W7d2/t3LlT8fHxVTKHLQCg/OjhBYAKevTRR5WZmamOHTsqOztby5Yt05YtWzRt2jTCLgDYEa7wAkAFLViwQG+//bZiYmKUlZWlsLAwPfzwwxo3bpytSwMAnIfACwAAAIdm03l4o6Ki1K5dO/n6+iooKEgDBw4sMil0VlaWxo4dq4CAAPn4+CgiIkKnTp0qdb+GYejFF19UrVq15OnpqV69ehU7+T8AAAAcn00D7/r16zV27Fht27ZNq1evVm5urnr37m2ZOFqSnnjiCX377bdasmSJ1q9frxMnTuiOO+4odb9vvvmm3n//fc2ePVvbt2+Xt7e3+vTpU2SSawAAADg+u2ppOH36tIKCgrR+/Xp169ZNycnJqlGjhhYsWKA777xT0rlHYDZu3Fhbt24t9hGNhmEoJCRETz75pCZOnChJSk5OVs2aNTV37lwNHTr0onUUFBToxIkT8vX1rZTHlwIAAKByGYah1NRUhYSEXPTBOXY1S0PhYwSrV68uSfr999+Vm5tr9Uz7Ro0aqU6dOiUG3tjYWMXHx1ttYzab1aFDB23durXYwJudnW31GMd//vmnwo/YBAAAwOVz7NgxXXPNNaWuYzeBt6CgQI8//rg6d+6sZs2aSZLi4+Pl5uYmf39/q3Vr1qyp+Pj4YvdTOF6zZs0ybxMVFaWXXnqpyPixY8fk5+dX3lMBAABAFUtJSVFoaKh8fX0vuq7dBN6xY8fqwIED2rRp02U/9uTJkzVhwgTL+8JvoJ+fH4EXAADAjpWl/dSmN60VGjdunL777jutW7fO6pJ0cHCwcnJylJSUZLX+qVOnFBwcXOy+CscvnMmhtG3c3d0t4ZaQCwAA4FhsGngNw9C4ceO0fPlyrV27VvXr17da3qZNG7m6umrNmjWWsUOHDikuLk4dO3Ysdp/169dXcHCw1TYpKSnavn17idsAAADAcdk08I4dO1ZffvmlFixYIF9fX8XHxys+Pl6ZmZmSzt1sNnr0aE2YMEHr1q3T77//rvvuu08dO3a0umGtUaNGWr58uaRzl7Uff/xxvfrqq1q5cqX279+ve++9VyEhIRo4cKAtThMAAAA2ZNMe3o8++kiS1KNHD6vxOXPmaOTIkZKkd999V05OToqIiFB2drb69OmjDz/80Gr9Q4cOWWZ4kKSnn35a6enpeuCBB5SUlKQuXbpo1apV8vDwqNLzAQAAgP2xq3l47UVKSorMZrOSk5Pp5wUAALBD5clrdnHTGgAAAFBVCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Gz6aGEAAABc+ZIzcpSQlqOUrFz5eboq0NtNZi83W5dlQeAFAABAhZ1IytQzS/dpY3SCZaxbeKBej2ihEH9PG1b2f2hpAAAAQIUkZ+QUCbuStCE6QZOW7lNyRo6NKrNG4AUAAECFJKTlFAm7hTZEJyghjcALAACAK1hKVm6py1MvsvxyIfACAACgQvw8XEtd7nuR5ZcLgRcAAAAVEujjpm7hgcUu6xYeqEAf+5ipgcALAACACjF7uen1iBZFQm+38EC9EdHCbqYmY1oyAAAAVFiIv6c+iGythLQcpWblytfDVYE+zMMLAAAAB2L2sq+AeyFaGgAAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKHZNPBu2LBBAwYMUEhIiEwmk1asWGG13GQyFfuaPn16ifucOnVqkfUbNWpUxWcCAAAAe2XTwJuenq6WLVtq1qxZxS4/efKk1evzzz+XyWRSREREqftt2rSp1XabNm2qivIBAABwBXCx5cH79eunfv36lbg8ODjY6v0333yjG2+8UQ0aNCh1vy4uLkW2BQAAwNXpiunhPXXqlL7//nuNHj36outGR0crJCREDRo00PDhwxUXF1fq+tnZ2UpJSbF6AQAAwDFcMYF33rx58vX11R133FHqeh06dNDcuXO1atUqffTRR4qNjVXXrl2Vmppa4jZRUVEym82WV2hoaGWXDwAAABsxGYZh2LoI6dwNasuXL9fAgQOLXd6oUSPdfPPN+uCDD8q136SkJNWtW1fvvPNOiVeHs7OzlZ2dbXmfkpKi0NBQJScny8/Pr1zHAwAAQNVLSUmR2WwuU16zaQ9vWW3cuFGHDh3SokWLyr2tv7+/rr32WsXExJS4jru7u9zd3S+lRAAAANipK6Kl4bPPPlObNm3UsmXLcm+blpamw4cPq1atWlVQGQAAAOydTQNvWlqa9uzZoz179kiSYmNjtWfPHqubzFJSUrRkyRKNGTOm2H307NlTM2fOtLyfOHGi1q9fryNHjmjLli0aNGiQnJ2dFRkZWaXnAgAAAPtk05aGnTt36sYbb7S8nzBhgiRpxIgRmjt3riTpq6++kmEYJQbWw4cPKyEhwfL++PHjioyMVGJiomrUqKEuXbpo27ZtqlGjRtWdCAAAAOyW3dy0Zk/K0wQNAACAy688ee2K6OEFAAAAKorACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEOzaeDdsGGDBgwYoJCQEJlMJq1YscJq+ciRI2Uymaxeffv2veh+Z82apXr16snDw0MdOnTQjh07qugMAAAAYO9sGnjT09PVsmVLzZo1q8R1+vbtq5MnT1peCxcuLHWfixYt0oQJEzRlyhTt2rVLLVu2VJ8+ffTvv/9WdvkAAAC4ArjY8uD9+vVTv379Sl3H3d1dwcHBZd7nO++8o/vvv1/33XefJGn27Nn6/vvv9fnnn2vSpEmXVC8AAACuPHbfw/vrr78qKChI1113nR5++GElJiaWuG5OTo5+//139erVyzLm5OSkXr16aevWrSVul52drZSUFKsXAAAAHINdB96+ffvqiy++0Jo1a/TGG29o/fr16tevn/Lz84tdPyEhQfn5+apZs6bVeM2aNRUfH1/icaKiomQ2my2v0NDQSj0PAAAA2I5NWxouZujQoZavmzdvrhYtWqhhw4b69ddf1bNnz0o7zuTJkzVhwgTL+5SUFEIvAACAg7DrK7wXatCggQIDAxUTE1Ps8sDAQDk7O+vUqVNW46dOnSq1D9jd3V1+fn5WLwAAADiGKyrwHj9+XImJiapVq1axy93c3NSmTRutWbPGMlZQUKA1a9aoY8eOl6tMAAAA2BGbBt60tDTt2bNHe/bskSTFxsZqz549iouLU1pamp566ilt27ZNR44c0Zo1a3T77bcrLCxMffr0seyjZ8+emjlzpuX9hAkT9Mknn2jevHk6ePCgHn74YaWnp1tmbQAAAMDVxaY9vDt37tSNN95oeV/YRztixAh99NFH2rdvn+bNm6ekpCSFhISod+/eeuWVV+Tu7m7Z5vDhw0pISLC8HzJkiE6fPq0XX3xR8fHxatWqlVatWlXkRjYAAABcHUyGYRi2LsLepKSkyGw2Kzk5mX5eAAAAO1SevHZF9fACAAAA5UXgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQbBp4N2zYoAEDBigkJEQmk0krVqywLMvNzdUzzzyj5s2by9vbWyEhIbr33nt14sSJUvc5depUmUwmq1ejRo2q+EwAAABgr2waeNPT09WyZUvNmjWryLKMjAzt2rVLL7zwgnbt2qVly5bp0KFDuu222y6636ZNm+rkyZOW16ZNm6qifAAAAFwBXGx58H79+qlfv37FLjObzVq9erXV2MyZM9W+fXvFxcWpTp06Je7XxcVFwcHBZa4jOztb2dnZlvcpKSll3hYAAAD27Yrq4U1OTpbJZJK/v3+p60VHRyskJEQNGjTQ8OHDFRcXV+r6UVFRMpvNlldoaGglVg0AAABbumICb1ZWlp555hlFRkbKz8+vxPU6dOiguXPnatWqVfroo48UGxurrl27KjU1tcRtJk+erOTkZMvr2LFjVXEKAAAAsAGbtjSUVW5urgYPHizDMPTRRx+Vuu75LRItWrRQhw4dVLduXS1evFijR48udht3d3e5u7tXas0AAACwD3YfeAvD7tGjR7V27dpSr+4Wx9/fX9dee61iYmKqqEIAAADYM7tuaSgMu9HR0frll18UEBBQ7n2kpaXp8OHDqlWrVhVUCAAAAHtn08CblpamPXv2aM+ePZKk2NhY7dmzR3FxccrNzdWdd96pnTt3av78+crPz1d8fLzi4+OVk5Nj2UfPnj01c+ZMy/uJEydq/fr1OnLkiLZs2aJBgwbJ2dlZkZGRl/v0AAAAYAds2tKwc+dO3XjjjZb3EyZMkCSNGDFCU6dO1cqVKyVJrVq1stpu3bp16tGjhyTp8OHDSkhIsCw7fvy4IiMjlZiYqBo1aqhLly7atm2batSoUbUnAwAAALtkMgzDsHUR9iYlJUVms1nJycnl7hkGAABA1StPXrPrHl4AAADgUhF4AQAA4NAIvAAAAHBoBF4AAAA4tEsOvCkpKVqxYoUOHjxYGfUAAAAAlarcgXfw4MGWeW8zMzPVtm1bDR48WC1atNDSpUsrvUAAAADgUpQ78G7YsEFdu3aVJC1fvlyGYSgpKUnvv/++Xn311UovEAAAALgU5Q68ycnJql69uiRp1apVioiIkJeXl2655RZFR0dXeoEAAADApSh34A0NDdXWrVuVnp6uVatWqXfv3pKks2fPysPDo9ILBAAAAC5FuR8t/Pjjj2v48OHy8fFRnTp1LI/43bBhg5o3b17Z9QEAAACXpNyB95FHHlH79u117Ngx3XzzzXJyOneRuEGDBvTwAgAcUnJGjhLScpSSlSs/T1cFervJ7OVm67IAlJHJMAyjIhvm5OQoNjZWDRs2lItLuXOzXSvPs5kBAI7tRFKmnlm6TxujEyxj3cID9XpEC4X4e9qwMuDqVp68Vu4e3oyMDI0ePVpeXl5q2rSp4uLiJEmPPvqoXn/99YpVDACAHUrOyCkSdiVpQ3SCJi3dp+SMHBtVBqA8yh14J0+erL179+rXX3+1ukmtV69eWrRoUaUWBwCALSWk5RQJu4U2RCcoIY3AC1wJyt2LsGLFCi1atEg33HCDTCaTZbxp06Y6fPhwpRYHAIAtpWTllro89SLLAdiHcl/hPX36tIKCgoqMp6enWwVgAACudH4erqUu973IcgD2odyBt23btvr+++8t7wtD7qeffqqOHTtWXmUAANhYoI+buoUHFrusW3igAn2YqQG4EpS7pWHatGnq16+f/vjjD+Xl5em9997TH3/8oS1btmj9+vVVUSMAADZh9nLT6xEtNGnpPm24YJaGNyJaMDUZcIWo0LRkhw8f1uuvv669e/cqLS1N119/vZ555hmHefAE05IBAM5XOA9valaufD1cFejDPLyArZUnr1V4Hl5HRuAFAACwb+XJa+VuaSicd7ckderUKe8uAQAAgCpT7sBbr169UmdjyM/Pv6SCAAAAgMpU7sC7e/duq/e5ubnavXu33nnnHb322muVVhgAAABQGcodeFu2bFlkrG3btgoJCdH06dN1xx13VEphAAAAQGUo9zy8Jbnuuuv022+/VdbuAAAAgEpR7iu8KSkpVu8Nw9DJkyc1depUhYeHV1phAAAAQGUod+D19/cvctOaYRgKDQ3VV199VWmFAQAAAJWh3IF33bp1Vu+dnJxUo0YNhYWFycWl3LsDAAAAqlS5E2r37t2rog4AAACgSpQp8K5cubLMO7ztttsqXAwAAABQ2coUeAcOHFimnZlMJh48AQAAALtSpsBbUFBQ1XUAAAAAVaLS5uEFAAAA7FGFplVIT0/X+vXrFRcXp5ycHKtl48ePr5TCAAAAgMpQ7sC7e/du9e/fXxkZGUpPT1f16tWVkJAgLy8vBQUFEXgBAABgV8rd0vDEE09owIABOnv2rDw9PbVt2zYdPXpUbdq00VtvvVUVNQIAAAAVVu7Au2fPHj355JNycnKSs7OzsrOzFRoaqjfffFPPPvtsVdQIAAAAVFi5A6+rq6ucnM5tFhQUpLi4OEmS2WzWsWPHKrc6AAAA4BKVu4e3devW+u233xQeHq7u3bvrxRdfVEJCgv773/+qWbNmVVEjAAAAUGFlvsJb+ECJadOmqVatWpKk1157TdWqVdPDDz+s06dP6+OPP66aKgEAAIAKKvMV3tq1a2vkyJEaNWqU2rZtK+lcS8OqVauqrDgAAADgUpX5Cu/YsWP19ddfq3Hjxuratavmzp2rjIyMqqwNAAAAuGRlDrwvvPCCYmJitGbNGjVo0EDjxo1TrVq1dP/992v79u1VWSMAAABQYeWepaFHjx6aN2+e4uPj9fbbb+vgwYPq2LGjmjZtqnfeeadc+9qwYYMGDBigkJAQmUwmrVixwmq5YRh68cUXVatWLXl6eqpXr16Kjo6+6H5nzZqlevXqycPDQx06dNCOHTvKVRcAAAAcR7kDbyEfHx+NGTNGmzZt0rfffqv4+Hg99dRT5dpHenq6WrZsqVmzZhW7/M0339T777+v2bNna/v27fL29lafPn2UlZVV4j4XLVqkCRMmaMqUKdq1a5datmypPn366N9//y1XbQAAAHAMJsMwjIpsmJGRocWLF2vOnDnatGmTGjZsqFGjRmnSpEkVK8Rk0vLlyzVw4EBJ567uhoSE6Mknn9TEiRMlScnJyapZs6bmzp2roUOHFrufDh06qF27dpo5c6YkqaCgQKGhoXr00UfLXFtKSorMZrOSk5Pl5+dXofMBAABA1SlPXiv3Fd4tW7ZozJgxqlWrlsaOHat69epp3bp1+uuvvyocdosTGxur+Ph49erVyzJmNpvVoUMHbd26tdhtcnJy9Pvvv1tt4+TkpF69epW4jSRlZ2crJSXF6gUAAADHUObA++abb1pmaNi/f7+mT5+u+Ph4zZs3T926dav0wuLj4yVJNWvWtBqvWbOmZdmFEhISlJ+fX65tJCkqKkpms9nyCg0NvcTqAQAAYC/KHHinT5+uvn37au/evdq+fbseeOAB+fr6VmVtl83kyZOVnJxsefGIZAAAAMdR5gdPnDhxQq6urlVZi5Xg4GBJ0qlTpyxPdit836pVq2K3CQwMlLOzs06dOmU1furUKcv+iuPu7i53d/dLLxoAAAB2p8xXeC9n2JWk+vXrKzg4WGvWrLGMpaSkaPv27erYsWOx27i5ualNmzZW2xQUFGjNmjUlbgMAAADHVuYrvFUhLS1NMTExlvexsbHas2ePqlevrjp16ujxxx/Xq6++qvDwcNWvX18vvPCCQkJCLDM5SFLPnj01aNAgjRs3TpI0YcIEjRgxQm3btlX79u01Y8YMpaen67777rvcpwcAAAA7YNPAu3PnTt14442W9xMmTJAkjRgxQnPnztXTTz+t9PR0PfDAA0pKSlKXLl20atUqeXh4WLY5fPiwEhISLO+HDBmi06dP68UXX1R8fLxatWqlVatWFbmRDQAAAFeHCs/D68iYhxcAAMC+lSevlekKb3nmpSUgAgAAwJ6UKfD6+/vLZDKVaYf5+fmXVBAAAABQmcoUeNetW2f5+siRI5o0aZJGjhxpmflg69atmjdvnqKioqqmSgAAAKCCyt3D27NnT40ZM0aRkZFW4wsWLNDHH3+sX3/9tTLrswl6eAEAAOxbefJamefhLbR161a1bdu2yHjbtm21Y8eO8u4OAAAAqFLlDryhoaH65JNPiox/+umnCg0NrZSiAAAAgMpS7nl43333XUVEROjHH39Uhw4dJEk7duxQdHS0li5dWukFAgAAAJei3Fd4+/fvr7/++ksDBgzQmTNndObMGQ0YMEB//fWX+vfvXxU1AgAAABXGgyeKwU1rAAAA9q1Kb1qTpI0bN+ruu+9Wp06d9M8//0iS/vvf/2rTpk0V2R0AAABQZcodeJcuXao+ffrI09NTu3btUnZ2tiQpOTlZ06ZNq/QCAQAAgEtR7sD76quvavbs2frkk0/k6upqGe/cubN27dpVqcUBAAAAl6rcgffQoUPq1q1bkXGz2aykpKTKqAkAAACoNOUOvMHBwYqJiSkyvmnTJjVo0KBSigIAAAAqS7kD7/3336/HHntM27dvl8lk0okTJzR//nxNnDhRDz/8cFXUCAAAAFRYuR88MWnSJBUUFKhnz57KyMhQt27d5O7urokTJ+rRRx+tihoBAACACqvwPLw5OTmKiYlRWlqamjRpIh8fn8quzWaYhxcAAMC+Vek8vKNGjVJqaqrc3NzUpEkTtW/fXj4+PkpPT9eoUaMqXDQAAABQFcodeOfNm6fMzMwi45mZmfriiy8qpSgAAACgspS5hzclJUWGYcgwDKWmpsrDw8OyLD8/Xz/88IOCgoKqpEgAAACgosoceP39/WUymWQymXTttdcWWW4ymfTSSy9VanEAAADApSpz4F23bp0Mw9BNN92kpUuXqnr16pZlbm5uqlu3rkJCQqqkSAAAAKCiyhx4u3fvLkmKjY1VnTp1ZDKZqqwoAAAAoLKU+6a1tWvX6uuvvy4yvmTJEs2bN69SigIAAAAqS7kDb1RUlAIDA4uMBwUFadq0aZVSFAAAAFBZyh144+LiVL9+/SLjdevWVVxcXKUUBQAAAFSWcgfeoKAg7du3r8j43r17FRAQUClFAQAAAJWl3IE3MjJS48eP17p165Sfn6/8/HytXbtWjz32mIYOHVoVNQIAAAAVVuZZGgq98sorOnLkiHr27CkXl3ObFxQU6N5776WHFwAAAHbHZBiGUZEN//rrL+3du1eenp5q3ry56tatW9m12UxKSorMZrOSk5Pl5+dn63IAAABwgfLktXJf4S107bXXFvvENQAAAMCelCnwTpgwQa+88oq8vb01YcKEUtd95513KqUwAAAAoDKUKfDu3r1bubm5lq9LwtPXAAAAYG8q3MPryOjhBQAAsG/lyWvlnpYMAAAAuJKUqaXhjjvuKPMOly1bVuFiAAAAgMpWpiu8ZrPZ8vLz89OaNWu0c+dOy/Lff/9da9askdlsrrJCAQAAgIoo0xXeOXPmWL5+5plnNHjwYM2ePVvOzs6SpPz8fD3yyCP0uwIAAMDulPumtRo1amjTpk267rrrrMYPHTqkTp06KTExsVILtAVuWgMAALBvVXrTWl5env78888i43/++acKCgrKuzsAAACgSpX7SWv33XefRo8ercOHD6t9+/aSpO3bt+v111/XfffdV+kFAgAAAJei3IH3rbfeUnBwsN5++22dPHlSklSrVi099dRTevLJJyu9QAAAAOBSXNKDJ1JSUiTJ4fpc6eEFAACwb1X+4Im8vDz98ssvWrhwoeVxwidOnFBaWlpFdleqevXqyWQyFXmNHTu22PXnzp1bZF0PD49KrwsAAABXhnK3NBw9elR9+/ZVXFycsrOzdfPNN8vX11dvvPGGsrOzNXv27Eot8LffflN+fr7l/YEDB3TzzTfrrrvuKnEbPz8/HTp0yPK+MJQDAADg6lPuwPvYY4+pbdu22rt3rwICAizjgwYN0v3331+pxUnnpkE73+uvv66GDRuqe/fuJW5jMpkUHBxc6bUAAADgylPuwLtx40Zt2bJFbm5uVuP16tXTP//8U2mFFScnJ0dffvmlJkyYUOpV27S0NNWtW1cFBQW6/vrrNW3aNDVt2rTE9bOzs5WdnW15X9ibDAAAgCtfuXt4CwoKrFoMCh0/fly+vr6VUlRJVqxYoaSkJI0cObLEda677jp9/vnn+uabb/Tll1+qoKBAnTp10vHjx0vcJioqyurxyaGhoVVQPQAAAGyh3LM0DBkyRGazWR9//LF8fX21b98+1ahRQ7fffrvq1Klj9RjiytanTx+5ubnp22+/LfM2ubm5aty4sSIjI/XKK68Uu05xV3hDQ0OZpQEAAMBOlWeWhgrNw9u3b181adJEWVlZGjZsmKKjoxUYGKiFCxdWuOiLOXr0qH755RctW7asXNu5urqqdevWiomJKXEdd3d3ubu7X2qJAAAAsEPlDryhoaHau3evFi1apL179yotLU2jR4/W8OHD5enpWRU1SpLmzJmjoKAg3XLLLeXaLj8/X/v371f//v2rqDIAAADYs3IF3tzcXDVq1Ejfffedhg8fruHDh1dVXVYKCgo0Z84cjRgxQi4u1iXfe++9ql27tqKioiRJL7/8sm644QaFhYUpKSlJ06dP19GjRzVmzJjLUisAAADsS7kCr6urq7KysqqqlhL98ssviouL06hRo4osi4uLk5PT/917d/bsWd1///2Kj49XtWrV1KZNG23ZskVNmjS5nCUDAADATpT7prVp06bpr7/+0qefflrkaquj4NHCAAAA9q1Kb1r77bfftGbNGv38889q3ry5vL29rZaX96YyAAAAoCqVO/D6+/srIiKiKmoBAAAAKl25A29VzrMLAAAAVLYyP2mtoKBAb7zxhjp37qx27dpp0qRJyszMrMraAAAAgEtW5sD72muv6dlnn5WPj49q166t9957T2PHjq3K2gAAAIBLVubA+8UXX+jDDz/UTz/9pBUrVujbb7/V/PnzVVBQUJX1AQAAAJekzIE3Li7O6mllvXr1kslk0okTJ6qkMAAAAKAylDnw5uXlycPDw2rM1dVVubm5lV4UAAAAUFnKPEuDYRgaOXKk3N3dLWNZWVl66KGHrObiZR5eAAAA2JMyB94RI0YUGbv77rsrtRgAAACgspU58DL/LgAAAK5EZe7hBQAAAK5EBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDc7F1AQBQmlMpWTqbnqOUrDz5ebqompebavp52LosAMAVhMALwG7FJaZr8vL92hyTaBnrEhagaYOaq06Atw0rAwBcSWhpAGCXTqVkFQm7krQpJlHPLt+vUylZNqoMAHClIfACsEtn03OKhN1Cm2ISdTY95zJXBAC4UhF4AdillKy8S1oOAEAhAi8Au+TnUfotBhdbDgBAIQIvALtUzdtNXcICil3WJSxA1bzdLnNFAIArFYEXgF2q6eehaYOaFwm9hbM0MDUZAKCs+JsgALtVJ8Bbbw9u9X/z8Hq4qJo38/ACAMqHwAvArtX08yDgAgAuiV23NEydOlUmk8nq1ahRo1K3WbJkiRo1aiQPDw81b95cP/zww2WqFgAAAPbIrgOvJDVt2lQnT560vDZt2lTiulu2bFFkZKRGjx6t3bt3a+DAgRo4cKAOHDhwGSsGAACAPbH7wOvi4qLg4GDLKzAwsMR133vvPfXt21dPPfWUGjdurFdeeUXXX3+9Zs6ceRkrBgAAgD2x+8AbHR2tkJAQNWjQQMOHD1dcXFyJ627dulW9evWyGuvTp4+2bt1a6jGys7OVkpJi9QIAAIBjsOvA26FDB82dO1erVq3SRx99pNjYWHXt2lWpqanFrh8fH6+aNWtajdWsWVPx8fGlHicqKkpms9nyCg0NrbRzAOxFckaODv+bpt1xZ3X4dJqSM3g0LwDg6mDXszT069fP8nWLFi3UoUMH1a1bV4sXL9bo0aMr7TiTJ0/WhAkTLO9TUlIIvXAoJ5Iy9czSfdoYnWAZ6xYeqNcjWijE39OGlQEAUPXs+grvhfz9/XXttdcqJiam2OXBwcE6deqU1dipU6cUHBxc6n7d3d3l5+dn9QIcRXJGTpGwK0kbohM0aek+rvQCABzeFRV409LSdPjwYdWqVavY5R07dtSaNWusxlavXq2OHTtejvIAu5SQllMk7BbaEJ2ghDQCLwDAsdl14J04caLWr1+vI0eOaMuWLRo0aJCcnZ0VGRkpSbr33ns1efJky/qPPfaYVq1apbffflt//vmnpk6dqp07d2rcuHG2OgXA5lKycktdnnqR5QAAXOnsuof3+PHjioyMVGJiomrUqKEuXbpo27ZtqlGjhiQpLi5OTk7/l9k7deqkBQsW6Pnnn9ezzz6r8PBwrVixQs2aNbPVKQA25+fhWupy34ssBwDgSmcyDMOwdRH2JiUlRWazWcnJyfTz4oqXnJGjRxfu1oZi2hq6hQfqg8jWMnu52aAyAAAqrjx5za5bGgBcOrOXm16PaKFu4dYPbekWHqg3IloQdgEADs+uWxoAVI4Qf099ENlaCWk5Ss3Kla+HqwJ93Ai7AICrAoEXuEqYvQi4AICrEy0NAAAAcGgEXgAAADg0Ai8AAAAcGoEXAAAADo3ACwAAAIdG4AUAAIBDI/ACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQ3OxdQGAo0vOyFFCWo5SsnLl5+mqQG83mb3cbF0WAABXDQIvUIVOJGXqmaX7tDE6wTLWLTxQr0e0UIi/pw0rAwDg6kFLA1BFkjNyioRdSdoQnaBJS/cpOSPHRpUBAHB1IfACVSQhLadI2C20ITpBCWkEXgAALgdaGoBLVFKPbkpWbqnbpV5kOQAAqBwEXuASlNaj6+fhWuq2vhdZDgAAKgctDUAFXaxH18fDRd3CA4vdtlt4oAJ9mKkBAIDLgcALVNDFenTTs/P0ekSLIqG3W3ig3ohowdRkAABcJrQ0ABV0sR7dlMxcNajhow8iWyshLUepWbny9XBVoA/z8AIAcDkReIEKKmuPrtmLgAsAgC3R0gBUUKCPGz26AABcAQi8QAWZvdzo0QUA4ApASwNwCUL8PenRBQDAzhF4gUtEjy4AAPaNlgYAAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaDx44gpzIilTyZm5SsnMldnTVX6ergrx97R1WQAAAHaLwHsFOZqYrmeX79fmmETLWJewAL02qLnqBnjbsDIAAAD7RUvDFeJEUmaRsCtJm2IS9dzy/TqRlGmjygAAAOwbgfcKkZyZWyTsFtoUk6jkzNzLXBEAAMCVwa4Db1RUlNq1aydfX18FBQVp4MCBOnToUKnbzJ07VyaTyerl4eFxmSquOikXCbSpWQReAACA4th14F2/fr3Gjh2rbdu2afXq1crNzVXv3r2Vnp5e6nZ+fn46efKk5XX06NHLVHHV8fN0LXW5r0fpywEAAK5Wdn3T2qpVq6zez507V0FBQfr999/VrVu3ErczmUwKDg6u6vIuK7Onq7qEBWhTMW0NXcICZL5IIAYAALha2fUV3gslJydLkqpXr17qemlpaapbt65CQ0N1++2363//+1+p62dnZyslJcXqZW9C/D312qDm6hIWYDVeOEvD1Tw1WXJGjg7/m6bdcWd1+HSakjNybF0SAACwIybDMAxbF1EWBQUFuu2225SUlKRNmzaVuN7WrVsVHR2tFi1aKDk5WW+99ZY2bNig//3vf7rmmmuK3Wbq1Kl66aWXiownJyfLz8+v0s6hMhTOw5ualStfD1eZHWge3uSMHCWk5SglK1d+nq4K9HaT2cut1G1OJGXqmaX7tDE6wTLWLTxQr0e0cJjvCwAAKColJUVms7lMee2KCbwPP/ywfvzxR23atKnE4Fqc3NxcNW7cWJGRkXrllVeKXSc7O1vZ2dmW9ykpKQoNDbXLwOuoKhJckzNyNG7hbqttzt/2g8jWFw3MAADgylSewHtFtDSMGzdO3333ndatW1eusCtJrq6uat26tWJiYkpcx93dXX5+flYvXD7JGTlFwq4kbYhO0KSl+0psUUhIyyk27BZum5BGawMAALDzwGsYhsaNG6fly5dr7dq1ql+/frn3kZ+fr/3796tWrVpVUCEqw8WC67+p2cX26KZcZCo2pmoDAACSnc/SMHbsWC1YsEDffPONfH19FR8fL0kym83y9Dz3Z+57771XtWvXVlRUlCTp5Zdf1g033KCwsDAlJSVp+vTpOnr0qMaMGWOz80DpLhZc485kaPS8nZb3ha0OF5uZgqnaAACAZOdXeD/66CMlJyerR48eqlWrluW1aNEiyzpxcXE6efKk5f3Zs2d1//33q3Hjxurfv79SUlK0ZcsWNWnSxBangDLwK2cwLWx18HZ3UbfwwGLX6RYeqEAf+ncBAMAVdNPa5VSeJmhcuuSMHD26cLc2FNPW0DksQK3rVNPMtUV7sNdM6C5PN2dNWrrPattu4YF6I6KFajFLAwAADqs8ec2uWxpwdTB7uen1iBZFgmvX8ECN6FRP4xfuLna71KxcNQzy0QeRrZWQlmOZqi3Q5+LTmQEAgKsHgRd2IcTfs0hwdXEyqd/7G5WRk1/sNoU9umYvAi4AACgZgRd248LgmpyRo7Z1qxXb6kCPLgAAKCu7vmkNV7fCVocLb0wr7NHlqi4AACgLrvDCrhXX6kCPLgAAKA8CL+wePboAAOBSEHgdVHJGjhLScpSSlSs/T1cFehMaAQDA1YnA64BOJGXqmaX7rB7XW/h0spBLnJuWIA0AAK40BF4Hk5yRUyTsSv/3dLIPIltXOKBWZZAGAACoKszS4GAS0nKKhN1CG6ITlJCWU6H9XixIJ2eUvt/kjBwd/jdNu+PO6vDptIuuDwAAUFm4wmunKto6kJKVW+ry1IssL0lZgnRhfefXbvZ0lZuzkyYv26+NMeW7MnwqJUtn03OUkpUnP08XVfNyU00/jwrVXx60bQAA4FgIvDZ2frjycXeRm7OTEtOzlZ1XoM2HE/X5plhl5OSXuXXA7/8/fawkvhdZXpKyBukL2x7G3RSm3XFntTkm0Wr9DdEJembpPs0socUiLjFdk5fvt9quS1iApg1qrjoB3hU6h7KgbQMAAMdDS4MNnUjK1LiFu9XznfUa9OEW3fzuBj27Yr/OZORq9Lyd2h13Vu9HtpaXm3OZWwcCfdyKPKih0KU8nawsQbq4tofWof5Fwm6hjdEJ+jc1u8j4qZSsImFXkjbFJOrZ5ft1KiWrAmdwcZfatgEAAOwTgddGSgpXm2MSNWdzrEZ1qW/1tVS2HtyqejpZWYJ0cW0P2XkFpe43ObPoleOz6TklhuRNMYk6m141wbOq+p8BAIBt0dJgI6WFq80xiRrVuX6Rr6Wy9eBWxdPJCoP0pKX7tOGCP/e/GdFCkpSdl68Ph18vD1dn7Yo7q883xcrL1VnjbgpT61B/ZecVWC3LyMmXl5tzkWOlZOWVWsvFlldUVfU/AwAA2yLw2sjFwtX5V0bP/7qwB/diN1ZVxdPJzg/S6dm5Mnu6KSe/QMeTMpWbb91z3DksQDOHtVZ1HzftjjurmWtjLPvpHBag9yNba8H2o/J2K/oR9PMo/WN5seUVVVX9zwAAwLYIvDZysXDl7uJU5OvC1oHibqzqGh6oV25vpmperlU6o0BhkD5+JkOTl+3TxvNaDwqD7PiFu7U5JlFOkvo1r1WkPWFzTKJMkl6+rZn8vVyLzMbg6+Gino1qaM2fp4scv0tYgKp5V835FbZtbCjmyvul9D8DAADbIvDaSGnhqnNYgHYfS7L6urAHV1Kxvb8boxP03Ir9urVFiHpcW0NOTqYqm9Lrn7MZembZvmKDrCSN6lJfM9fGaGNMokae145xvk0xicozCpScmVvsbAyvDWou6X9WobdwloaqmpqstLaNS+l/BgAAtkXgtZGSwlXnsADd17m+xi/cra7hgXrptqaSpPu71JfZy02H/00rtfd3dJcGOnomQ7PWRltdfe0aFqiptzWVySQFlGNe2QtbJ3zcXRR3JqPEm8ou7Dku7aY1VyenEmdjeG75fr0R0UJP9ck7F9o9XFTNu+rn4a2K/mcAAGBbBF4bujBcef//eXiTM3P07bguxQati/X+ujib9MHa6CIhcmNMgl5ceUCt61TTvmNJF51XNjkjR/EpWTp+NlMmk8lyo1mbutU09sawUms4P+Se35pxoay8glJnY0jNylOjWn6lHqsqVEX/MwAAsB0Cr40VH67OPVih8HG859+YdrHe32perhe9+jpzbYwmLd2nD0p46MOJpEw98/U+qyejnd+fW1BglFpDYcjtGh6of0uYM7dLWMBFZz2oqtkYAADA1YXAa6dKeuJX1B3NS+39zcsvPYwWXn298HHAhSzzA8cUnR9YOtefu+XvRHUNDyy2teLCnuO8/AJ1CQvQpmKemJaRk19qrVU1GwMAALi6kCjsUGlP/Jq68n+aNqi5nl2+v9je3+SM0q+ant9ikJ6dW6RHNz/fuOj8wI8u3K2V47poysoDVleTu4YFaurtTWXS//UcS9Lbg1v93w105/XinkrJKhKGC1XlbAwAAODqQuC1Q6U9lGL1wX81uX9jfRDZ2tJjK0m7jyVp/MLdeuHWJuoaFljkCq1kPfuDl5uz/DzdNG7hbqtjfTaibam1ZecVKCMnX9l5eerUMECT+jVSZk6BvN2c5eXmLG93FwVdcGNZTT+PYm82q+nnYQnvxV0Bruob1AAAwNWBwGuHLnZjWkpmrhrU8JHZy03Bfh5KSMtRgLebBrWqrUAfN3VuGFAkRJ4/+4MkvXBrE72w4oA2xiTIy81Zo7rUV+tQf3m7uejzke2snoZ2PncXJ3UOC5C3q4tuaR6i51YUnVJs2qDmqhPgXaZzrRPgXeIVYAAAgMpgMgyj9KbPq1BKSorMZrOSk5Pl53d5Zgk4v7XA081ZfWdsLHHdNRO6q2GQT6n7O5WSpbMZOUrLylNOXoG2/J2or3bEaWj7OurUIEBmL1fd8v4mebk56/3I1pqzOdYquJ4fkAtDb+ewAN3SvJbqB/oo1N9DzxQzpZh0LvS+PbgVoRUAAFSZ8uQ1rvDagQtvUBt3U1iJva1lfeLX+W0EyRk5CjZ7aFCr2pq68oBmro3Rh8Ovl3TuJrQLw65U9CESXcMC9cKAJtoTd1ZZuflKzckvdUqxs+k5BF4AAGAXSp4kFZdFcTeofb4pViM711eXsACrdSv6xC+zl5u83V304soDlodRFN681jrUv9RpzHo3qanPRrTVi7c10YmzmYo7m6mxC3YxpRgAALhicIXXxpIycjWyUz1Ftq8jD1dnS+/s+IW7NapLfT1/SxNl5eZf8hO/zqbnaHNMoqVf1+zpqs9GtJW3e+kfgeNnMzV/+1G1rlNNM9fGSDoXvC82HzBTigEAAHtBKrGhE0mZen7FfqtHAJ//gIeZa2PUq1GQWtWpdsnHSs/J0+O9wtWzcZD+TclWSlaedh9L0vUX2bfZ09XqZrfCq8xOTiamFAMAAFcEAq+N/N8DHkrvnfW9yJXUsjiZlCmzh6t2HjmjGb9EW8Y7hwWod5Oa6hwWUGxbQ9fwQNUP9FZWbr4WjOlQ5CozU4oBAIArAYHXRkqba7fwAQ/dwgPl4mTS7rizlkcLl7elITkjR78eOq3v958ocjV2c0yiZvzyl57q00jSn0VmaXjl9mYK8fcscd9MKQYAAK4EBF4budhcu5L0yI1h6vf+Rsu0YN3CA/V6RItSQ2ihwmnOzqTnqNk1Zk1evr/Y9db+eVr33lBPretU0zN9G+n42Uy5uzhp97EkpWTmSCp9Pt2SHioBAABgLwi8NnKxm74CfNw17JNtVg9+2BCdoElL9+mDyNaWK73nz9/r4+4id2cn5RuGtv2dqCA/D2XnFSi0mqfG3RRW7IMkJCkjN18z18aodai/Hpm/yzI+qFXtSjpbAAAA2yHw2kigj5u6hQdqQzFtDV3DA/XLwVPFhtMN0QlKSMuR2cutyPy9Xm7O+mJUW3m5ueqH/Sf1e1ySRnWpL3cXJ7UO9dfiBzvql4On9PGGv632Xfj0tMLHDktln+8XAADA3vGktWJcrietHU1M13PF3PT12qDmuvvT7Tp2NtNq/cIpxfo3C1ZWbr6y8wq0PfaMTCapeW1/+bg5q6bZQ88u36/dcUnFPkGta1iARpz3BLXOYQG6tXkt1fDzsIwVzsRQqwytEwAAALZQnrxG4C3G5Qi8p1KyNHnZPjUJMat1qL+y8wosvbMHTyRrWIe6Gj1vp2X9kh4B3DU8UC/e2kR/J6QrxOyplKxcDf90u8bdFKbdcWeLnX2hc1iAWteppr3HkvTy7U3l7eaitOw8pWTmXvJ8vwAAAJcDjxa+ApxNz9HaP09r7Z+ni10+4ebrrN6X9AjgjdEJevnbPzSycz0lpmUrI/dcq0LrUH/LgyIutDkmUc/f0kTD2tex3AAXdKknBAAAYKcIvDaSkpVnaVEovMJ7/pPW0nPyrHp8SwuwG2MS9MiNDRXk56FjZzIkSdl5BaUePy07T41rVV27BgAAgL0g8NqI2ctFM4e11uebYq2CbNewAM0c1lpmL1d9ENlaCWk5Ss/OlZursz4b0bZIMC68+cxkMmna939oUv/G6hwWIHcXp1KP73ORRwoDAAA4ClKPjfi4uWjOptgiD4PYGJMomUx6/Y7mOpmcpbTsPAX4uOnFFQdKfARxRk6+8gsMNQ4xKyE1W6/c3kzb/04s+QlqYYFyMlX5KQIAANgFAq+NpGbnFXmscKGN0QlKzc7T3Z9t19031NXvR86U+gji3XFntfXvRLUO9dfZjFxV83bTv2nZmnJrU73y3f+KBOWXbm9adScGAABgZwi8NpKamVfq8n/OZmrpQx2VmVegGb9El9jv27lhgFqF+mv8wt16666WcndxUnJGrt5dHa3/rP9bo7rU18jO9a1mgUhIy9Z1NX0v05kCAADYFoHXRrzcnUu9aa2mn4cMSWn//+a2winJzu/37RwWoNtbhujR//9ENrOnq7b+nahbmteSJGXk5Bd7o9stzWsx7RgAALhqlH5nk52YNWuW6tWrJw8PD3Xo0EE7duwodf0lS5aoUaNG8vDwUPPmzfXDDz9cpkrLzs/dRZ+NaKvdcWc1et5OPTJ/l0bN/U27487qsxFtZXZ3UYEhebu7aPbdbeTl5qzWdarJy83Zso/NMYl6+ds/NLR9HXUND5Svh4v+PJEsL1dndQkLKPa4XcICVN2bsAsAAK4edh94Fy1apAkTJmjKlCnatWuXWrZsqT59+ujff/8tdv0tW7YoMjJSo0eP1u7duzVw4EANHDhQBw4cuMyVly7PMPThupgiN5VtjknUZ5tilWcYmvLNAfV7b6Pu/XyHhn2yXbvjzur9yNZWoXdjTII6NQjQS7c11X/WH9borg3108GTmnpb0yKht0tYgKYNaq6afh6X5RwBAADsgd0/aa1Dhw5q166dZs6cKUkqKChQaGioHn30UU2aNKnI+kOGDFF6erq+++47y9gNN9ygVq1aafbs2WU65uV40tr/TiTrrtlbi21p8HR10rbDicXe1Fb4lLTzWxUWP9hRTibJ291Z8cnZmr/tqJ64OVyn03IU4OOmtKx8+Xu5qrq3G2EXAAA4BId50lpOTo5+//13TZ482TLm5OSkXr16aevWrcVus3XrVk2YMMFqrE+fPlqxYkWJx8nOzlZ2drblfUpKyqUVXgZZOfn6aPj1OpGUKUlycTaptr+napk9VGAYmv7TX8VutzkmUaM617caczJJPh4ucnKSXJxMahTip7v+s80yR68krZnQnbALAACuSnbd0pCQkKD8/HzVrFnTarxmzZqKj48vdpv4+PhyrS9JUVFRMpvNlldoaOilF38RAd5u8nR1krOTSf5ervL3dFNufoHy8g2dTc8pddvzn6LWNSxQsafTVFBg6I0f/tT2I2c0c22MVdjtFh6oQB/6dgEAwNXJrgPv5TJ58mQlJydbXseOHbs8BzaZ9PP/4rXu0GmlZuUq7kyGUrNzdU01z1IDauFT1LqGB2rcTWFqW6+67v18h9b8eVqdGlj37XYLD9QbES1k9nJTckaODv+bpt1xZ3X4dJqSM0oP1gAAAI7ArlsaAgMD5ezsrFOnTlmNnzp1SsHBwcVuExwcXK71Jcnd3V3u7u6XXnA5FEj6eP1hDe1Qt8h0Y13CAvTFqPa6c/ZWqyu10rkruiH+nlowpoOuqeYpk6Rhn25XQtq58Orq4qQ1E7orNStXvh6uCvRxk9nLTSeSMvXM0n3aGJ1g2Ve38EC9HtFCIf6el+OUAQAAbMKur/C6ubmpTZs2WrNmjWWsoKBAa9asUceOHYvdpmPHjlbrS9Lq1atLXN9WsvMK1CjErDmbY4vM1LApJlFRP/6pF25pYjXeOSxAY28M04F/khXi76GdR85o2Z5/dOxspmUdX3cXNQzyUas61dQwyMdyZffCsCtJG6ITNGnpPq70AgAAh2bXV3glacKECRoxYoTatm2r9u3ba8aMGUpPT9d9990nSbr33ntVu3ZtRUVFSZIee+wxde/eXW+//bZuueUWffXVV9q5c6c+/vhjW55GEWnZeWod6l/sgyGkc48XntyvkT4b0Va+Hq7ydHPS/uPJCvJzl6+Hi6avOqRBba7Rf775n2WbLmEBqlbMHLsJaTlFwm6hDdEJSkjL4UEUAADAYdl94B0yZIhOnz6tF198UfHx8WrVqpVWrVpluTEtLi5OTk7/d6G6U6dOWrBggZ5//nk9++yzCg8P14oVK9SsWTNbnUKxfNxddDo1u9R1UrLyNGdzrMbdFC43ZyedSM5Sdl6BUrNydU+nuho1d6el5aG0OXZTsnJLPU7qRZYDAABcyex+Hl5buBzz8P55MkVn0nM07NPtJa6z4pHO8vFwkYtJuvM/W3VdsK9ubV5LncMC5eHqrDPpOUrJypOfh4uqlTLH7uF/09TznfUlHmfNhO5qGORzyecEAABwuTjMPLyOzOQk+Xq4qEtYgDYV84CJruGBCvB2VU5Bge75/Dc1ruWnl29vKm83FwX9/2AbVMZ5dQN93NQtPFAbimlrYMoyAADg6Oz6pjVH5uPmotnrD+upPo3UNTzQalnXsAC9OrCZTDKUkpmrufe118zI1qof6FPmkHs+s5ebXo9ooW4XHOf8KcsAAAAcFS0NxbgcLQ2SdDQxXa98+z81CjFbHi/s7+mq0Gqe8nAyqUY1r0o9XnJGjhLScopMWQYAAHCloaXhClE3wFtTb2+m1Kw8pWblKsTDVb4eLrqmkoNuIbMXARcAAFx9CLw2VlXhFgAAAOfQwwsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaAReAAAAODQCLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0F1sXYI8Mw5AkpaSk2LgSAAAAFKcwpxXmttIQeIuRmpoqSQoNDbVxJQAAAChNamqqzGZzqeuYjLLE4qtMQUGBTpw4IV9fX5lMJluXo5SUFIWGhurYsWPy8/OzdTmwIT4LKMRnAYX4LKDQ1fZZMAxDqampCgkJkZNT6V26XOEthpOTk6655hpbl1GEn5/fVfEBxsXxWUAhPgsoxGcBha6mz8LFruwW4qY1AAAAODQCLwAAABwagfcK4O7urilTpsjd3d3WpcDG+CygEJ8FFOKzgEJ8FkrGTWsAAABwaFzhBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXjs3a9Ys1atXTx4eHurQoYN27Nhh65JwmU2dOlUmk8nq1ahRI1uXhctgw4YNGjBggEJCQmQymbRixQqr5YZh6MUXX1StWrXk6empXr16KTo62jbFospd7PMwcuTIIr8r+vbta5tiUWWioqLUrl07+fr6KigoSAMHDtShQ4es1snKytLYsWMVEBAgHx8fRURE6NSpUzaq2D4QeO3YokWLNGHCBE2ZMkW7du1Sy5Yt1adPH/3777+2Lg2XWdOmTXXy5EnLa9OmTbYuCZdBenq6WrZsqVmzZhW7/M0339T777+v2bNna/v27fL29lafPn2UlZV1mSvF5XCxz4Mk9e3b1+p3xcKFCy9jhbgc1q9fr7Fjx2rbtm1avXq1cnNz1bt3b6Wnp1vWeeKJJ/Ttt99qyZIlWr9+vU6cOKE77rjDhlXbAQN2q3379sbYsWMt7/Pz842QkBAjKirKhlXhcpsyZYrRsmVLW5cBG5NkLF++3PK+oKDACA4ONqZPn24ZS0pKMtzd3Y2FCxfaoEJcThd+HgzDMEaMGGHcfvvtNqkHtvPvv/8akoz169cbhnHu94Crq6uxZMkSyzoHDx40JBlbt261VZk2xxVeO5WTk6Pff/9dvXr1sow5OTmpV69e2rp1qw0rgy1ER0crJCREDRo00PDhwxUXF2frkmBjsbGxio+Pt/odYTab1aFDB35HXMV+/fVXBQUF6brrrtPDDz+sxMREW5eEKpacnCxJql69uiTp999/V25urtXvhkaNGqlOnTpX9e8GAq+dSkhIUH5+vmrWrGk1XrNmTcXHx9uoKthChw4dNHfuXK1atUofffSRYmNj1bVrV6Wmptq6NNhQ4e8BfkegUN++ffXFF19ozZo1euONN7R+/Xr169dP+fn5ti4NVaSgoECPP/64OnfurGbNmkk697vBzc1N/v7+Vute7b8bXGxdAIDS9evXz/J1ixYt1KFDB9WtW1eLFy/W6NGjbVgZAHsydOhQy9fNmzdXixYt1LBhQ/3666/q2bOnDStDVRk7dqwOHDjAfR1lwBVeOxUYGChnZ+cid1WeOnVKwcHBNqoK9sDf31/XXnutYmJibF0KbKjw9wC/I1CSBg0aKDAwkN8VDmrcuHH67rvvtG7dOl1zzTWW8eDgYOXk5CgpKclq/av9dwOB1065ubmpTZs2WrNmjWWsoKBAa9asUceOHW1YGWwtLS1Nhw8fVq1atWxdCmyofv36Cg4OtvodkZKSou3bt/M7ApKk48ePKzExkd8VDsYwDI0bN07Lly/X2rVrVb9+favlbdq0kaurq9XvhkOHDikuLu6q/t1AS4MdmzBhgkaMGKG2bduqffv2mjFjhtLT03XffffZujRcRhMnTtSAAQNUt25dnThxQlOmTJGzs7MiIyNtXRqqWFpamtXVudjYWO3Zs0fVq1dXnTp19Pjjj+vVV19VeHi46tevrxdeeEEhISEaOHCg7YpGlSnt81C9enW99NJLioiIUHBwsA4fPqynn35aYWFh6tOnjw2rRmUbO3asFixYoG+++Ua+vr6Wvlyz2SxPT0+ZzWaNHj1aEyZMUPXq1eXn56dHH31UHTt21A033GDj6m3I1tNEoHQffPCBUadOHcPNzc1o3769sW3bNluXhMtsyJAhRq1atQw3Nzejdu3axpAhQ4yYmBhbl4XLYN26dYakIq8RI0YYhnFuarIXXnjBqFmzpuHu7m707NnTOHTokG2LRpUp7fOQkZFh9O7d26hRo4bh6upq1K1b17j//vuN+Ph4W5eNSlbcZ0CSMWfOHMs6mZmZxiOPPGJUq1bN8PLyMgYNGmScPHnSdkXbAZNhGMblj9kAAADA5UEPLwAAABwagRcAAAAOjcALAAAAh0bgBQAAgEMj8AIAAMChEXgBAADg0Ai8AAAAcGgEXgAAADg0Ai8AOBiTyaQVK1ZU6TF69Oihxx9/vEqPAQCVhcALABW0detWOTs765Zbbin3tvXq1dOMGTMqv6iLGDBggPr27Vvsso0bN8pkMmnfvn2XuSoAqFoEXgCooM8++0yPPvqoNmzYoBMnTti6nDIZPXq0Vq9erePHjxdZNmfOHLVt21YtWrSwQWUAUHUIvABQAWlpaVq0aJEefvhh3XLLLZo7d26Rdb799lu1a9dOHh4eCgwM1KBBgySdawc4evSonnjiCZlMJplMJknS1KlT1apVK6t9zJgxQ/Xq1bO8/+2333TzzTcrMDBQZrNZ3bt3165du8pc96233qoaNWoUqTctLU1LlizR6NGjlZiYqMjISNWuXVteXl5q3ry5Fi5cWOp+i2uj8Pf3tzrOsWPHNHjwYPn7+6t69eq6/fbbdeTIEcvyX3/9Ve3bt5e3t7f8/f3VuXNnHT16tMznBgAlIfACQAUsXrxYjRo10nXXXae7775bn3/+uQzDsCz//vvvNWjQIPXv31+7d+/WmjVr1L59e0nSsmXLdM011+jll1/WyZMndfLkyTIfNzU1VSNGjNCmTZu0bds2hYeHq3///kpNTS3T9i4uLrr33ns1d+5cq3qXLFmi/Px8RUZGKisrS23atNH333+vAwcO6IEHHtA999yjHTt2lLnOC+Xm5qpPnz7y9fXVxo0btXnzZvn4+Khv377KyclRXl6eBg4cqO7du2vfvn3aunWrHnjgAcs/BgDgUrjYugAAuBJ99tlnuvvuuyVJffv2VXJystavX68ePXpIkl577TUNHTpUL730kmWbli1bSpKqV68uZ2dn+fr6Kjg4uFzHvemmm6zef/zxx/L399f69et16623lmkfo0aN0vTp063qnTNnjiIiImQ2m2U2mzVx4kTL+o8++qh++uknLV682BLay2vRokUqKCjQp59+agmxc+bMkb+/v3799Ve1bdtWycnJuvXWW9WwYUNJUuPGjSt0LAC4EFd4AaCcDh06pB07digyMlLSuaumQ4YM0WeffWZZZ8+ePerZs2elH/vUqVO6//77FR4eLrPZLD8/P6WlpSkuLq7M+2jUqJE6deqkzz//XJIUExOjjRs3avTo0ZKk/Px8vfLKK2revLmqV68uHx8f/fTTT+U6xoX27t2rmJgY+fr6ysfHRz4+PqpevbqysrJ0+PBhVa9eXSNHjlSfPn00YMAAvffee+W68g0ApeEKLwCU02effaa8vDyFhIRYxgzDkLu7u2bOnCmz2SxPT89y79fJycmqzUA61wpwvhEjRigxMVHvvfee6tatK3d3d3Xs2FE5OTnlOtbo0aP16KOPatasWZozZ44aNmyo7t27S5KmT5+u9957TzNmzFDz5s3l7e2txx9/vNRjmEymUmtPS0tTmzZtNH/+/CLb1qhRQ9K5K77jx4/XqlWrtGjRIj3//PNavXq1brjhhnKdGwBciCu8AFAOeXl5+uKLL/T2229rz549ltfevXsVEhJiubmrRYsWWrNmTYn7cXNzU35+vtVYjRo1FB8fbxUc9+zZY7XO5s2bNX78ePXv319NmzaVu7u7EhISyn0egwcPlpOTkxYsWKAvvvhCo0aNsrQabN68WbfffrvuvvtutWzZUg0aNNBff/1V6v5q1KhhdUU2OjpaGRkZlvfXX3+9oqOjFRQUpLCwMKuX2Wy2rNe6dWtNnjxZW7ZsUbNmzbRgwYJynxsAXIjACwDl8N133+ns2bMaPXq0mjVrZvWKiIiwtDVMmTJFCxcu1JQpU3Tw4EHt379fb7zxhmU/9erV04YNG/TPP/9YAmuPHj10+vRpvfnmmzp8+LBmzZqlH3/80er44eHh+u9//6uDBw9q+/btGj58eIWuJvv4+GjIkCGaPHmyTp48qZEjR1odY/Xq1dqyZYsOHjyoBx98UKdOnSp1fzfddJNmzpyp3bt3a+fOnXrooYfk6upqWT58+HAFBgbq9ttv18aNGxUbG6tff/1V48eP1/HjxxUbG6vJkydr69atOnr0qH7++WdFR0fTxwugUhB4AaAcPvvsM/Xq1cvqqmShiIgI7dy5U/v27VOPHj20ZMkSrVy5Uq1atdJNN91kNcvByy+/rCNHjqhhw4aWP+k3btxYH374oWbNmqWWLVtqx44dVjePFR7/7Nmzuv7663XPPfdo/PjxCgoKqtC5jB49WmfPnlWfPn2s2jOef/55XX/99erTp4969Oih4OBgDRw4sNR9vf322woNDVXXrl01bNgwTZw4UV5eXpblXl5e2rBhg+rUqaM77rhDjRs31ujRo5WVlSU/Pz95eXnpzz//VEREhK699lo98MADGjt2rB588MEKnRsAnM9kXNh0BQAAADgQrvACAADAoRF4AQAA4NAIvAAAAHBoBF4AAAA4NAIvAAAAHBqBFwAAAA6NwAsAAACHRuAFAACAQyPwAgAAwKEReAEAAODQCLwAAABwaP8PSxL2NWcSYnUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#RF\n",
        "plot_actual_vs_predicted(y_test, predictions, \"Random Forest Regressor\")\n"
      ],
      "id": "382927d6"
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7e6def96",
        "outputId": "9b0fadba-44c0-4a32-8863-9893d7d39b83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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++GN98cUXeuSRRzRr1ix9/fXX6tChw1nvt0GDBurdu7ckacCAAQoLC9P48eN11VVXOU5cKykpUZs2bfTcc8+Vu4/Sn9nZOHlUr/RYFotFK1askLu7e5n+J4+SP/vssxo1apQ++eQTffnll5o4caJmzZqlzZs3q0GDBvL19VVSUpLWrFmjzz//XCtXrtTixYt19dVX68svvyx3/1WttyJDhw51Gq0dOXLkaZcqKx1VHTZsWLnb161bp6uuuqryhVagohG88tYhHjZsmL755hvdf//9at++vQICAlRSUqJ+/fpVOIJ9OtHR0bryyiu1ZMkSPfTQQ9q8ebP27dun2bNnO/U70+t6Njp37uxYVaAi3t7eZf6AKSkpKTPqfbKaGH2vKjPUiAsbwRWowPLly/Wf//xHzz//vBo0aKA5c+boiy++UGJiolasWOHoN3DgQD355JNauHChU3A9W0VFRZL+fwS3UaNGkqRdu3Y5BWa73a60tDRHUDy5X+nX7qV27drl2F6qWbNmuvfee3XvvfcqJSVF7du317PPPqv3339fUvV8VXznnXfq+eef18MPP6whQ4bIYrGoWbNm+uGHH9SrV6/THqNRo0YqKSlRWlqa06hOampqpY/frFkzGYahJk2aOEZ0T6dNmzZq06aNHn74YX3zzTeKj4/XK6+8ohkzZkg6sfRQr1691KtXLz333HN64okn9K9//Utr1qxR79691ahRI+3YsUMlJSVOoeXXX391PKez8eyzzzpN64iOjq6wb05Ojj755BMNHz5cN9xwQ5ntEydO1MKFC3XVVVc5Tpr76aefHO+j8lT0OpWOrJ26usWpI8vHjh3T6tWrNX36dE2dOtXRnpKSUuExK2P48OG6++67tWvXLi1evFh+fn4aNGhQmX5nel1rS7NmzfTVV18pPj7+tH+0lL5PUlJSnH7njxw5csbpPef6mtZGjcC5YKoAUI7jx49r4sSJ6tChg2NOZXR0tB5//HGtXLlSS5cudfSNj4/XNddco9dee02ffPJJufsz/neGe2WUrlLQrl07SSfm03l5eenFF1902s+bb76prKwsx1n2nTp1UkREhF555RWnZaVWrFihnTt3Ovrl5uYqPz/f6ZjNmjVTYGCg0+P8/f3LXW6rKjw8PHTvvfdq586djp/NsGHD9Oeff+r1118v0z8vL085OTmS5Pjq9aWXXnLqM3fu3Eoff+jQoXJ3d9f06dPLvAaGYTiWYrLZbI4/GEq1adNGbm5ujp/JX3/9VWb/7du3lyRHnwEDBujgwYOOr6+lE3+IzJ07VwEBAerRo0elaz9Zx44dHVNKevfurUsuuaTCvsuXL1dOTo4SExN1ww03lLkNHDhQy5YtU0FBgS699FI1adJEc+bMKfNan/zzKl1/9NQ+jRo1kru7u5KSkpzaT33NSkejT30N5syZU5mnX6GEhAS5u7tr0aJFWrp0qQYOHOi0VmplXldJ2rdvn+OPi5o0bNgwFRcX6/HHHy+zraioyPHz7d27tzw9PTV37lynn1llfl7n+prWRo3AuWDEFSjHww8/rPT0dH300UdOXwEnJibqnXfe0aRJk9SvXz/H/Nb3339f/fr10+DBg9W/f3/17t1b9erV08GDB/XVV18pKSlJ/fv3L3Oc7du3O0Y4jx8/rtWrV2vZsmW64oor1KdPH0knvpqbMmWKpk+frn79+um6667Trl279NJLL+myyy5zLMPl6emp2bNna/To0erRo4dGjBjhWA6rcePG+sc//iFJ2r17t3r16qVhw4bpkksukYeHh5YvX65Dhw7ppptuctTWsWNHvfzyy5oxY4ZiY2MVERFRZiS3MkaNGqWpU6dq9uzZGjx4sG677TYtWbJEf//737VmzRrFx8eruLhYv/76q5YsWaIvvvhCnTp1UseOHZWQkKA5c+bo6NGjjuWwSi/DW5kR4WbNmmnGjBmaMmWK9u7dq8GDByswMFBpaWlavny5xo0bp/vuu09ff/21xo8frxtvvFHNmzdXUVGR3nvvPbm7uyshIUHSiaslJSUl6dprr1WjRo10+PBhvfTSS2rQoIHjxLtx48bp1Vdf1ahRo/Tdd9+pcePG+ve//62NGzdqzpw5jvdLTVq4cKFCQ0N1xRVXlLv9uuuu0+uvv67PP/9cQ4cO1csvv6xBgwapffv2Gj16tOrXr69ff/1VP//8s+OKZx07dpR0YrS2b9++cnd310033SSr1aobb7xRc+fOdYymf/bZZ455kKWCgoIcc6kLCwt10UUX6csvv3Sse3q2IiIidNVVV+m5557T8ePHNXz4cKftlXldJelvf/ub1q1bV6U/MM9Gjx49dOedd2rWrFn6/vvv1adPH3l6eiolJUVLly7VCy+8oBtuuEHh4eG67777NGvWLA0cOFADBgxQcnKyVqxY4ThZtCJubm7n9JrWRo3AOXHJWgZAHbZt2zbD3d3dGD9+fLnbt27dari5uRkTJ050as/LyzPmzJljdO3a1QgKCjI8PDyMqKgoY+DAgcbChQuNoqIiR9/ylsPy8PAwmjZtatx///3G8ePHyxx33rx5RosWLQxPT08jMjLSuOuuu8osd2MYhrF48WKjQ4cOhre3txESEmLccsstjmV6DMMwMjIyjMTERKNFixaGv7+/YbVajS5duhhLlixx2s/BgweNa6+91ggMDDQknXFpLElGYmJiudseffRRpyV77Ha7MXv2bKNVq1aGt7e3Ua9ePaNjx47G9OnTjaysLMfjcnJyjMTERCMkJMQICAgwBg8ebOzatcuQZDz55JOOfqXLYVW0VNWyZcuMbt26Gf7+/oa/v7/RokULIzEx0di1a5dhGIbx22+/GWPGjDGaNWtm+Pj4GCEhIcZVV11lfPXVV459rF692rj++uuN6Ohow8vLy4iOjjZGjBhh7N692+lYhw4dMkaPHm2EhYUZXl5eRps2bZyWhTKM/3/9z7QkWVUdOnTI8PDwMG677bYK++Tm5hp+fn7GkCFDHG0bNmwwrrnmGiMwMNDw9/c32rZta8ydO9exvaioyJgwYYIRHh5uWCwWp2WUjhw5YiQkJBh+fn5GvXr1jDvvvNP46aefyiyH9ccffxhDhgwxgoODDavVatx4441Genp6maXNKrscVqnXX3/dkGQEBgY6LQNnGJV7XQ3DMHr06FHu0lCnKq3t22+/PW2/kSNHGv7+/hVuf+2114yOHTsavr6+RmBgoNGmTRvjgQceMNLT0x19iouLjenTpxv169c3fH19jZ49exo//fST0ahRo9Muh1XqXF7T6q4RqE4Ww6jhPzEBoBp9//336tChg95///1KXY0MAHD+YI4rgDorLy+vTNucOXPk5uam7t27u6AiAIArMccVQJ311FNP6bvvvtNVV10lDw8PrVixQitWrNC4cePOadksAIA5MVUAQJ21atUqTZ8+Xb/88ouys7PVsGFD3XbbbfrXv/4lDw/+7gaACw3BFQAAAKbAHFcAAACYAsEVAAAApnDeTxIrKSlRenq6AgMDq+USlgAAAKhehmHo+PHjio6Odrpk9qnO++Canp7O2ccAAAAmsH//fjVo0KDC7ed9cC29xOL+/fsVFBTk4moAAABwKpvNppiYmDNeGvu8D66l0wOCgoIIrgAAAHXYmaZ1cnIWAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFPwcHUBAAAAqDuycu3KyLbLll+oIF9Phfl7yern5eqyJBFcAQAA8D/pmXn657IdWp+S4WjrHhemJxPaKjrY14WVncBUAQAAACgr114mtEpSUkqGHly2Q1m5dhdV9v8IrgAAAFBGtr1MaC2VlJKhjGyCKwAAAOoAW37habcfP8P22kBwBQAAgIJ8PE+7PfAM22sDwRUAAAAKC/BS97iwcrd1jwtTWIDrVxYguAIAAEBWPy89mdC2THjtHhem2Qlt68SSWCyHBQAAAElSdLCv5o7ooIxsu47nFyrQx1NhAazjCgAAgDrI6ld3guqpmCoAAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMwaXBddasWbrssssUGBioiIgIDR48WLt27XLqk5+fr8TERIWGhiogIEAJCQk6dOiQiyoGAACAq7g0uK5bt06JiYnavHmzVq1apcLCQvXp00c5OTmOPv/4xz/06aefaunSpVq3bp3S09M1dOhQF1YNAAAAV7AYhmG4uohSR44cUUREhNatW6fu3bsrKytL4eHh+uCDD3TDDTdIkn799Ve1bNlSmzZt0uWXX37GfdpsNlmtVmVlZSkoKKimnwIAAACqqLJ5rU7Ncc3KypIkhYSESJK+++47FRYWqnfv3o4+LVq0UMOGDbVp06Zy91FQUCCbzeZ0AwAAgPnVmeBaUlKiSZMmKT4+Xq1bt5YkHTx4UF5eXgoODnbqGxkZqYMHD5a7n1mzZslqtTpuMTExNV06AAAAakGdCa6JiYn66aef9OGHH57TfqZMmaKsrCzHbf/+/dVUIQAAAFzJw9UFSNL48eP12WefKSkpSQ0aNHC0R0VFyW63KzMz02nU9dChQ4qKiip3X97e3vL29q7pkgEAAFDLXDriahiGxo8fr+XLl+vrr79WkyZNnLZ37NhRnp6eWr16taNt165d2rdvn7p27Vrb5QIAAMCFXDrimpiYqA8++ECffPKJAgMDHfNWrVarfH19ZbVaNXbsWE2ePFkhISEKCgrShAkT1LVr10qtKAAAAIDzh0uXw7JYLOW2L1iwQKNGjZJ04gIE9957rxYtWqSCggL17dtXL730UoVTBU7FclgAAAB1W2XzWp1ax7UmEFwBAADqNlOu4woAAABUhOAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAU3BpcE1KStKgQYMUHR0ti8Wijz/+2Gn7qFGjZLFYnG79+vVzTbEAAABwKZcG15ycHLVr107z58+vsE+/fv104MABx23RokW1WCEAAADqCg9XHrx///7q37//aft4e3srKiqqlioCAABAXVXn57iuXbtWERERuvjii3XXXXfp6NGjp+1fUFAgm83mdAMAAID51eng2q9fP7377rtavXq1Zs+erXXr1ql///4qLi6u8DGzZs2S1Wp13GJiYmqxYgAAANQUi2EYhquLkCSLxaLly5dr8ODBFfb57bff1KxZM3311Vfq1atXuX0KCgpUUFDguG+z2RQTE6OsrCwFBQVVd9kAAAA4RzabTVar9Yx5rU6PuJ6qadOmCgsLU2pqaoV9vL29FRQU5HQDAACA+ZkquP7xxx86evSo6tev7+pSAAAAUMtcuqpAdna20+hpWlqavv/+e4WEhCgkJETTp09XQkKCoqKitGfPHj3wwAOKjY1V3759XVg1AAAAXMGlwXXbtm266qqrHPcnT54sSRo5cqRefvll7dixQ++8844yMzMVHR2tPn366PHHH5e3t7erSgYAAICL1JmTs2pKZSf7AgAAwDXOy5OzAAAAcOEiuAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUCK4AAAAwBYIrAAAATIHgCgAAAFMguAIAAMAUPFxdAAAAtSUr166MbLts+YUK8vVUmL+XrH5eri4LQCURXAEAF4T0zDz9c9kOrU/JcLR1jwvTkwltFR3s68LKAFQWUwUAAOe9rFx7mdAqSUkpGXpw2Q5l5dpdVBmAqiC4AgDOexnZ9jKhtVRSSoYysgmugBkQXAEA5z1bfuFptx8/w3YAdQPBFQBw3gvy8Tzt9sAzbAdQNxBcAQDnvbAAL3WPCyt3W/e4MIUFsLIAYAYEVwDAec/q56UnE9qWCa/d48I0O6EtS2IBJsFyWACAC0J0sK/mjuigjGy7jucXKtDHU2EBrOMKmAnBFQBwwbD6EVQBMzvnqQI2m00ff/yxdu7cWR31AAAAAOWqcnAdNmyY5s2bJ0nKy8tTp06dNGzYMLVt21bLli2r9gIBAAAA6SyCa1JSkq688kpJ0vLly2UYhjIzM/Xiiy9qxowZ1V4gAAAAIJ1FcM3KylJISIgkaeXKlUpISJCfn5+uvfZapaSkVHuBAAAAgHQWwTUmJkabNm1STk6OVq5cqT59+kiSjh07Jh8fn2ovEAAAAJDOYlWBSZMm6ZZbblFAQIAaNmyonj17SjoxhaBNmzbVXR8AAAAg6SyC6913363OnTtr//79uuaaa+TmdmLQtmnTpsxxBQAAQI2xGIZhnM0D7Xa70tLS1KxZM3l41N3lYG02m6xWq7KyshQUFOTqcgAAAHCKyua1Ks9xzc3N1dixY+Xn56dWrVpp3759kqQJEyboySefPPuKAQAAgNOocnCdMmWKfvjhB61du9bpZKzevXtr8eLF1VocAAAAUKrK3/F//PHHWrx4sS6//HJZLBZHe6tWrbRnz55qLQ4AAAAoVeUR1yNHjigiIqJMe05OjlOQBQAAAKpTlYNrp06d9Pnnnzvul4bVN954Q127dq2+ygAAAICTVHmqwBNPPKH+/fvrl19+UVFRkV544QX98ssv+uabb7Ru3bqaqBEAAACo+ohrt27d9P3336uoqEht2rTRl19+qYiICG3atEkdO3asiRoBAACAs1/H1SxYxxUAAKBuq2xeq/JUgdJ1WyvSsGHDqu4SAAAAOKMqB9fGjRufdvWA4uLicyoIAAAAKE+Vg2tycrLT/cLCQiUnJ+u5557TzJkzq60wAAAA4GRVDq7t2rUr09apUydFR0fr6aef1tChQ6ulMAAAAOBkVV5VoCIXX3yxvv322+raHQAAAOCkyiOuNpvN6b5hGDpw4IAeffRRxcXFVVthAAAAwMmqHFyDg4PLnJxlGIZiYmL04YcfVlthAAAAwMmqHFzXrFnjdN/NzU3h4eGKjY2Vh0eVdwcAAABUSpWTZo8ePWqiDgAAAOC0KhVc//Of/1R6h9ddd91ZFwMAAABUpFLBdfDgwZXamcVi4QIEAAAAqBGVCq4lJSU1XQcAAABwWtW2jisAAABQk85qGYCcnBytW7dO+/btk91ud9o2ceLEaikMAAAAOFmVg2tycrIGDBig3Nxc5eTkKCQkRBkZGfLz81NERATBFQAAADWiylMF/vGPf2jQoEE6duyYfH19tXnzZv3+++/q2LGjnnnmmZqoEQAAAKh6cP3+++917733ys3NTe7u7iooKFBMTIyeeuopPfTQQzVRIwAAAFD14Orp6Sk3txMPi4iI0L59+yRJVqtV+/fvr97qAAAAgP+p8hzXDh066Ntvv1VcXJx69OihqVOnKiMjQ++9955at25dEzUCAAAAlR9xLb2wwBNPPKH69etLkmbOnKl69erprrvu0pEjR/Taa6/VTJUAAAC44FV6xPWiiy7SqFGjNGbMGHXq1EnSiakCK1eurLHiAAAAgFKVHnFNTEzUv//9b7Vs2VJXXnml3n77beXm5tZkbQAAAIBDpYPrI488otTUVK1evVpNmzbV+PHjVb9+fd1xxx3asmVLTdYIAAAAVH1VgZ49e+qdd97RwYMH9eyzz2rnzp3q2rWrWrVqpeeee64magQAAABkMQzDONedfP755/rb3/6mzMxMx0lcdYXNZpPValVWVpaCgoJcXQ4AAABOUdm8VuUR11K5ubl6++231aNHD1133XUKDQ3VzJkzz3Z3AAAAwGlVeR3Xb775Rm+99ZaWLl2qoqIi3XDDDXr88cfVvXv3mqgPAAAAkFSF4PrUU09pwYIF2r17tzp16qSnn35aI0aMUGBgYE3WBwAAAEiqQnB9+umndeutt2rp0qVcIQsAAAC1rtLBNT09XZ6enjVZCwAAAFChSp+cRWgFAACAK531qgIAAABAbSK4AgAAwBQIrgAAADCFSp2cZbPZKr1Drk4FAACAmlCp4BocHCyLxVKpHda1S74CAADg/FCp4LpmzRrHv/fu3asHH3xQo0aNUteuXSVJmzZt0jvvvKNZs2bVTJUAAAC44FkMwzCq8oBevXrp9ttv14gRI5zaP/jgA7322mtau3ZtddZ3zmw2m6xWq7KyspjGAAAAUAdVNq9V+eSsTZs2qVOnTmXaO3XqpK1bt1Z1dwAAAEClVDm4xsTE6PXXXy/T/sYbbygmJqZaigIAAABOVelLvpZ6/vnnlZCQoBUrVqhLly6SpK1btyolJUXLli2r9gIBAAAA6SxGXAcMGKDdu3dr0KBB+uuvv/TXX39p0KBB2r17twYMGFATNQIAAABndwGCmJgYPfHEE/roo4/00UcfaebMmWc1TSApKUmDBg1SdHS0LBaLPv74Y6fthmFo6tSpql+/vnx9fdW7d2+lpKScTckAAAAwubMKruvXr9ett96qK664Qn/++ack6b333tOGDRuqtJ+cnBy1a9dO8+fPL3f7U089pRdffFGvvPKKtmzZIn9/f/Xt21f5+flnUzYAAABMrMrBddmyZerbt698fX21fft2FRQUSJKysrL0xBNPVGlf/fv314wZMzRkyJAy2wzD0Jw5c/Twww/r+uuvV9u2bfXuu+8qPT29zMgsAAAAzn9VDq4zZszQK6+8otdff12enp6O9vj4eG3fvr3aCktLS9PBgwfVu3dvR5vValWXLl20adOmCh9XUFAgm83mdAMAAID5VTm47tq1S927dy/TbrValZmZWR01SZIOHjwoSYqMjHRqj4yMdGwrz6xZs2S1Wh03lugCAAA4P1Q5uEZFRSk1NbVM+4YNG9S0adNqKepcTJkyRVlZWY7b/v37XV0SAAAAqkGVg+sdd9yhe+65R1u2bJHFYlF6eroWLlyo++67T3fddVe1FRYVFSVJOnTokFP7oUOHHNvK4+3traCgIKcbAAAAzK/KFyB48MEHVVJSol69eik3N1fdu3eXt7e37rvvPk2YMKHaCmvSpImioqK0evVqtW/fXtKJ69hu2bKlWgMyAAAAzKHKwdVisehf//qX7r//fqWmpio7O1uXXHKJAgICqnzw7Oxsp2kHaWlp+v777xUSEqKGDRtq0qRJmjFjhuLi4tSkSRM98sgjio6O1uDBg6t8LAAAAJhblacKjBkzRsePH5eXl5cuueQSde7cWQEBAcrJydGYMWOqtK9t27apQ4cO6tChgyRp8uTJ6tChg6ZOnSpJeuCBBzRhwgSNGzdOl112mbKzs7Vy5Ur5+PhUtWwAAACYnMUwDKMqD3B3d9eBAwcUERHh1J6RkaGoqCgVFRVVa4HnymazyWq1Kisri/muAAAAdVBl81qlpwrYbDYZhiHDMHT8+HGnUc/i4mL997//LRNmAQAAgOpS6eAaHBwsi8Uii8Wi5s2bl9lusVg0ffr0ai0OAAAAKFXp4LpmzRoZhqGrr75ay5YtU0hIiGObl5eXGjVqpOjo6BopEgAAAKh0cO3Ro4ekE2f+N2zYUBaLpcaKAgAAAE5V5VUFvv76a/373/8u07506VK988471VIUAAAAcKoqB9dZs2YpLCysTHtERISeeOKJaikKAAAAOFWVg+u+ffvUpEmTMu2NGjXSvn37qqUoAAAA4FRVDq4RERHasWNHmfYffvhBoaGh1VIUAAAAcKoqB9cRI0Zo4sSJWrNmjYqLi1VcXKyvv/5a99xzj2666aaaqBEAAACo/KoCpR5//HHt3btXvXr1kofHiYeXlJTob3/7G3NcAQAAUGOqfMnXUrt379YPP/wgX19ftWnTRo0aNaru2qoFl3wFAACo26r9kq+nat68eblX0AIAAABqQqWC6+TJk/X444/L399fkydPPm3f5557rloKAwAAAE5WqeCanJyswsJCx78rwtW0AAAAUFPOeo6rWTDHFQAAoG6rbF6r8nJYAAAAgCtUaqrA0KFDK73Djz766KyLAQAAACpSqRFXq9XquAUFBWn16tXatm2bY/t3332n1atXy2q11lihAAAAuLBVasR1wYIFjn//85//1LBhw/TKK6/I3d1dklRcXKy7776bOaQAAACoMVU+OSs8PFwbNmzQxRdf7NS+a9cuXXHFFTp69Gi1FniuODkLAACgbquxk7OKior066+/lmn/9ddfVVJSUtXdAQAAAJVS5StnjR49WmPHjtWePXvUuXNnSdKWLVv05JNPavTo0dVeIAAAACCdRXB95plnFBUVpWeffVYHDhyQJNWvX1/333+/7r333movEAAAAJDO8QIENptNkur03FHmuAIAANRtNXoBgqKiIn311VdatGiR4zKv6enpys7OPrtqAQAAgDOo8lSB33//Xf369dO+fftUUFCga665RoGBgZo9e7YKCgr0yiuv1ESdAAAAuMBVecT1nnvuUadOnXTs2DH5+vo62ocMGaLVq1dXa3EAAABAqSqPuK5fv17ffPONvLy8nNobN26sP//8s9oKAwAAAE5W5RHXkpISFRcXl2n/448/FBgYWC1FAQAAAKeqcnDt06eP5syZ47hvsViUnZ2tadOmacCAAdVZGwAAAOBQ5eWw9u/fr379+skwDKWkpKhTp05KSUlRWFiYkpKSFBERUVO1nhWWwwIAAKjbKpvXzmod16KiIi1evFg//PCDsrOzdemll+qWW25xOlmrriC4AgAA1G01ElwLCwvVokULffbZZ2rZsmW1FFrTCK4AAAB1W41cgMDT01P5+fnnXBwAAABQVVU+OSsxMVGzZ89WUVFRTdQDAAAAlKvK67h+++23Wr16tb788ku1adNG/v7+Tts/+uijaisOAAAAKFXl4BocHKyEhISaqAUAAACoUJWD64IFC2qiDgAAAOC0Kj3HtaSkRLNnz1Z8fLwuu+wyPfjgg8rLy6vJ2gAAAACHSgfXmTNn6qGHHlJAQIAuuugivfDCC0pMTKzJ2gAAAACHSgfXd999Vy+99JK++OILffzxx/r000+1cOFClZSU1GR9AAAAgKQqzHHdt2+fBgwY4Ljfu3dvWSwWpaenq0GDBjVSHIDzyyFbvo7l2GXLL1KQr4fq+XkpMsjH1WUBAEyi0sG1qKhIPj7O/4Px9PRUYWFhtRcF4Pyz72iOpiz/URtTjzrausWG6okhbdQw1P80jwQA4IRKB1fDMDRq1Ch5e3s72vLz8/X3v//daS1X1nEFcKpDtvwyoVWSNqQe1UPLf9Szw9oz8goAOKNKB9eRI0eWabv11lurtRgA56djOfYyobXUhtSjOpZjJ7gCAM6o0sGV9VsBnC1b/ukvEX2m7QAASFVYVQAAzlaQz+n/Rj7TdgAAJIIrgFpQz99L3WJDy93WLTZU9fy9arkiAIAZEVwB1LjIIB89MaRNmfBauqoA81sBAJXB93MAakXDUH89O6z9/6/j6uOhev6s4woAqDyCK4BaExnkQ1AFAJw1pgoAAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4AoAAABTILgCAADAFAiuAAAAMAUPVxcAoOqycu3KyLbLll+oIF9Phfl7yern5eqyAACoUQRXwGTSM/P0z2U7tD4lw9HWPS5MTya0VXSwrwsrAwCgZjFVADCRrFx7mdAqSUkpGXpw2Q5l5dpdVBkAADWP4AqYSEa2vUxoLZWUkqGMbIIrAOD8RXAFTMSWX3ja7cfPsB0AADOr08H10UcflcVicbq1aNHC1WUBLhPk43na7YFn2A4AgJnV+ZOzWrVqpa+++spx38OjzpcM1JiwAC91jwtTUjnTBbrHhSksgJUFAADnrzo94iqdCKpRUVGOW1hYmKtLAlzG6uelJxPaqnuc8+9B97gwzU5oy5JYAIDzWp0fvkxJSVF0dLR8fHzUtWtXzZo1Sw0bNqywf0FBgQoKChz3bTZbbZQJ1JroYF/NHdFBGdl2Hc8vVKCPp8ICWMcVAHD+sxiGYbi6iIqsWLFC2dnZuvjii3XgwAFNnz5df/75p3766ScFBgaW+5hHH31U06dPL9OelZWloKCgmi4ZAAAAVWSz2WS1Ws+Y1+p0cD1VZmamGjVqpOeee05jx44tt095I64xMTEEVwAAgDqqssG1zk8VOFlwcLCaN2+u1NTUCvt4e3vL29u7FqsCAABAbajzJ2edLDs7W3v27FH9+vVdXQoAAABqWZ0Orvfdd5/WrVunvXv36ptvvtGQIUPk7u6uESNGuLo0AAAA1LI6PVXgjz/+0IgRI3T06FGFh4erW7du2rx5s8LDw11dGgAAAGpZnQ6uH374oatLAAAAQB1Rp6cKAAAAAKUIrgAAADAFgisAAABMgeAKAAAAU6jTJ2cBdU1Wrl0Z2XbZ8gsV5OupMH8vWf28XF0WAAAXBIIrUEnpmXn657IdWp+S4WjrHhemJxPaKjrY14WVAQBwYWCqAFAJWbn2MqFVkpJSMvTgsh3KyrW7qDIAAC4cBFegEjKy7WVCa6mklAxlZBNcAQCoaUwVAE5S0RxWW37haR93/AzbAQDAuSO4Av9zujmsQT6ep31s4Bm2AwCAc8dUAUBnnsMa4OOh7nFh5T62e1yYwgJYWQAAgJpGcAV05jmsOQVFejKhbZnw2j0uTLMT2rIkFgAAtYCpAoB0xjmstrxCNQ0P0NwRHZSRbdfx/EIF+ngqLIB1XAEAqC0EV0Cq9BxWqx9BFQAAV2GqACApLMCLOawAANRxBFdAJ0ZSmcMKAEDdxlQB4H+ig32ZwwoAQB1GcAVOwhxWAADqLqYKAAAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMgeAKAAAAUyC4AgAAwBQIrgAAADAFgisAAABMwcPVBQAny8q1KyPbLlt+oYJ8PRXm7yWrn5erywIAAHUAwRV1Rnpmnv65bIfWp2Q42rrHhenJhLaKDvZ1YWUAAKAuYKoA6oSsXHuZ0CpJSSkZenDZDmXl2l1UGQAAqCsIrqgTMrLtZUJrqaSUDGVkE1wBALjQEVxRJ9jyC0+7/fgZtgMAgPMfwRV1QpCP52m3B55hOwAAOP8RXFGrsnLt2nM4W8n7jmnPkWzH3NWwAC91jwsr9zHd48IUFsDKAgAAXOhYVQC15kyrBjyZ0FYPLtuhpFO2z05oy5JYAABAFsMwDFcXUZNsNpusVquysrIUFBTk6nIuWFm5do1flFzuCVjd48I0d0QHWf28HOu4Hs8vVKCPp8ICWMcVAIDzXWXzGiOuqBGnXkjA082isfFNNKJzQ/l4umv7vmN6a0Oacu3FjlUDrH5ejhsAAMCpCK6oduVNCegWG6pR8U1039IflGsvVnxsqF4c0UETFyUr117MqgEAAOCMODkL1aqiCwlsSD2qBRvTNKZbE0nSxlPus2oAAAA4E4IrqtXpLiSwMfWoOsQEl7nPqgEAAKAyCK6oNods+for5/RXuCooKinTxqoBAACgMgiuqBb7juZo8pLvz3gFLG8P57fcRfV8VT/YtyZLAwAA5wmCK87ZIVu+piz/URtTjyp5f6biY0PL7RcfG6rk/ZmO+91iQ1WPkVYAAFBJrCqAKjt1qavCohJtTD0qSXprQ5peHNFBkhxt0v+vKjBxUbLj/hND2igyyKf2nwAAADAlgiuqpLylrj6843LHv3PtxZq4KFljujXRmPgmKigqUaNQP9Xz89Lx/EK9Pbqzgnw8VM/fi9AKAACqhOCKSqtoqSt/H3en+7n2Ys37OtVxf+U9Vyo62FcSc1kBAMDZY44rKq2ipa4O2wrUrYJ5rd1iQ1XPn3msAADg3BFcUWkVrRjwz2U79MjAVmXCK/NYAQBAdWKqACp06klYAd7lv10ysu265Y3NWn7XFcqxF8uWX8Q8VgAAUO0IrigjK9eug7Z8/XEsTxaLRdv3HdOHW/dp3s2X6oPbuygzr1A+nu7avu+Y3tqQplx7sS6pH6QgX0/FhPq7unwAAHCeIrjCSXpmnv757x1an/r/c1njY0O18PbLNXvlTn396xGn9hdHdNDirfv02PWtufoVAACoUQRXODhWDUh1PgFrY+pRPf7Zz2rfsJ5TcN2YelRusuiha1vWdqkAAOACxMlZUFauXXsOZ+vPzLxyVw2QpA2pR9UhJrhM+/rUDBWVlOify3YoK9dew5UCAIALGSOuF7BT57J6uFlO27+gqKTc9uz8YiWlZCgj217npguceoJZmL9XnasRAABUDsH1AnXyXFY/L3clXtVMvVpE6sM7Lpe/t7ski77edUivrvtNufZiSZK3R/kD9AH/uwDB8QqWy3KV8q7y1T0uTE8mtP3fBREAAICZEFwvQCfPZfXzctdLt1yqN9f/pqe/2O3oEx8bqvFXxartRcFK/GC7Lm0YrOT9mWX21S02VIdtBZKkQB/P2noKZ1TRVb6SUjL04LIdmjuiAyOvAACYDMH1AnTyFbDGdGuiN9f/pvWpR536bPzf/Wvb1NcjAy9R12ahmvHpz059usWGauqgVrr59c3qHhemsIC6EwQrusqXpDo7rQEAAJwewfUCcOo8z2LDkJ+Xu3LtxeoQE6x5X6eW+7iNqUc1Jr6JLqrnq8ah/po5tK3uzbErK69Q/t7uOmwr0M2vb9Yl9YM0O6FtnQqCFV3lq1Rdm9YAAADOjOB6nitvnueVcWF6cUQHTVyUXOEJV6UKikpkyyuSJEUG+SgyyMcRhEP9DS0e11VhAXXvhKegM0xbqEvTGgAAQOUQXGtIXTibvaJ5nutTMiRDmjuigyyW068k4O3hpiAf57eJ1a/uBdVThQV4qXtcmJLKmS5Q16Y1AACAymEd1xqQnpmn8YuS1eu5dRry0jfq9ew6TViUrPTMvFqt43TzPNenZijU31vhAd66Mjas3D7xsaE6bMtXPX/zhTyrn5eeTGir7nHOz617XFidm9YAAAAqhxHXaubKs9lPHeXNyjv9BQGO5hQo0MdDjw9upUc++dmp5hOrCsSpQfCJ6QFmFB3sq7kjOigj267j+YUK9PGsk9MaAABA5RBcq5mrzmYvby7rB7d3OePjXlidoicGt9a8ER10yFagrLxC+Xm7y8/TXf7eHoowaWgtZYZpDQAAoHIIrtXMFWeznzzK6+flrjHdmqhDTLCKSgx9cEcXfbPnqN7akOa4kIB0YkQ1eX+mNqYeVba9WI3CCHgAAKBuI7hWM1eczV46yuvn5a4XR3TQgo1pTktcdYsNdawikGsvVnxsqEbHN9HERcmSpNyC4op2DQAAUGcQXKtZbZ/NnpVrV0FRsV665VLF1PPT7JU7HRcPKLUh9agssmjh7V2Uay9WfmGxJvwvxEpSoC9vAwAAUPexqkA1q82z2UtXLxjw4gbdvXC7Dh/P14ZTQmup9akZ+ivHrlve2CJJjtB6ZWyY6jFFAAAAmABDbTWgps5mL101ICvPrnp+Xpr6yU9Ol2qtzMUETv7vlbGhmjmktWlXDQAAABcWgmsNqczZ7OmZecrKK5Qtr1BWX08F+XoqOti3wr4nn4D14bjLNSq+iUZ0aSQfT3dt33dMPp7upz2et8eJAfZGoX5aec+VqufvRWgFAACmQXCtRlW5WtbvR3P00PIfneajdosN1cwhbdQo1N+p7yFbvvZm5GhE54YaE99EoQFeevGr3frq1yOOPvGxoepzSaTiY0PLzHEt3Z68P1Pd48LUINiXFQQAAIDpWAzDMFxdRE2y2WyyWq3KyspSUFBQjR2nvHVUu8eF6cmEtmVGUdMz83T/v38oN2B2iw3VUze0czxm31+5+tdHO/TdvkzHMleSFBnko692HtJrSb855qte3SJcE3s119Nf/Oq079JVBBZv3afHrm+t+hWM6gIAALhCZfMawbUaZOXaNX5RcrkXHugeF1bmalk7D9jU/4X1Fe5vxT1XqmX9IO0/mqMHl/+o5H2ZjmWuTg6kV8aGauT/lrUqDa9vj7pM6Vl5ujgqUDn2YtXz85RhSH5e7goP8GakFQAA1DmVzWtMFagGVb1ali3vzBcpOGTL175jedqYelTjr44tE1olaX3qUZVIGtOtiWPd1mLDUESQj257c6vTBQdWT+5BaAUAAKbGcljVoKpXywryPfNFCv7KsZ+4/KqXu3o2Dy93WoEkbUw96pg+IEnBfp5OI7AV1QAAAGA2BNdqUNWrZVl9PdUtNrTcvvGxofr8xwP6K8cuP88TV8LKOsMIbenyVt1iQ+XpXv5LWhNX7AIAAKhNBNdqUHq1rPKUd7Ws6GBfzRzSRt1iQ+Xn5a5JveP077931eJxl+uhAS3V95IoRVm9VSJDCzamnfH43h5uio8N1aj4Jprz1W6N6dbkjDUAAACYDXNcq0Hp1bIeXLbD6VKvFV0tKyvXrqJiQ5N6N1eAt4fsxSU6llOob3//S29tSFOHhsGacHWcIoN8TkwFaFivwmWurowNVXigtzo0rOeYInBLl0ZnrAEAAMBsCK7VpPRqWQdt+bLlFcnPy11+XmUvCFDeslmly1X9mm7TS7dcqrsXbtfcr1N0d89YSdJbG9L04ogOklTuqgI3vbbZaU5rkI+nPr77imq7YhcAAEBdQHCtRpl5hXrss1/KvahAQXGxSoqlmZ//4nSZVun/w2iHhvX05oY0JV7VTE9/sVsP9m8hScq1F2viomSN6dZEY+KbqKCoRN4ebooM8tGwVzeVOREr0MdDLerX3Jq1AAAArsAc12qSnplX5kpYkrQh9age/vgn/ZVdoBJDZUJrqdLVAdanZKh783BJUlGxoStjT8ydzbUXa97XqRr7zjbdvXC7FmxMk385o7rdYkNVz58RVgAAcP4xRXCdP3++GjduLB8fH3Xp0kVbt251dUll2PIKnUKrn5e7xl8dq3dGd9ZDA1ooMtBXhmHopVsu1VujLtP4q2PLhM7S1QHy7Cf+m5VbqMcHty6zAkHpiVgzPv9FsxPaOtq7xYbqiSFtFBnkU1NPEwAAwGXq/FSBxYsXa/LkyXrllVfUpUsXzZkzR3379tWuXbsUERHh6vIcsguKHP8OC/DSW6MuU569SJGBPioqMTT1k5+cRluvjA3Vx3fHK+1ojjzd3bR93zH5eZ4Isv5e7oqPDdUBW77qB/uofcN6Gn3SFIHk/ZmOE7Hu7dtCS+68XEE+nqrn70VoBQAA5606f8nXLl266LLLLtO8efMkSSUlJYqJidGECRP04IMPnvHxtXHJ14OZecoqsMteaMjD3U22vEJZfT3l7+WurXv/0kfJf5a7IkB8bKg6NKyneV+nqltsqO7r20Ivrt6th6+9RIePF8jdIhmShr26ucJjL7rjcoUFeCkuMrBGnhsAAEBNq2xeq9NTBex2u7777jv17t3b0ebm5qbevXtr06ZN5T6moKBANpvN6VbTiguL5evhoR//zFJ6Zp6O5tiVnpWvDalH1bqBtVJXvdqQelTPfPGr7u/TQm6SooO8tXXvX/L1Ov2geJCPhyICvav5GQEAANQ9dXqqQEZGhoqLixUZGenUHhkZqV9//bXcx8yaNUvTp0+vjfL+n5tFf2bm6fMfD5RZruqyJiHy83Ivc+Z/qdJ5rdKJ8GpxkzLz7DqeV6wmYQHy8XRTt9hQbSgn/JZewCAjxy5JLHsFAADOa3U6uJ6NKVOmaPLkyY77NptNMTExNXrMghJD89akKnlfpsZfHasOMcEqKCqRj6e7vt37l+7s0VTPr0op97HeHs6D3lm5hYoI9NarSb9qxU+HFBbgpYW3X67HP/vZKbx2iw3V1EGtdOOrm5SRbVf3uDA9mdBW0cG+NfpcAQAAXKVOB9ewsDC5u7vr0KFDTu2HDh1SVFRUuY/x9vaWt3ftfnWeV1is5H2ZenFEBy3YmKZ5X6c6tsXHhmr6oFZ6dd1vZUZd42NDlbw/06nNz9tdU//zs6YPaiVbfpE2ph7VLW9s1uyEtprSv6Vy7EXy9/LQQVu+bn59szKyT4y2JqVk6MFlOzR3RAdGXgEAwHmpTs9x9fLyUseOHbV69WpHW0lJiVavXq2uXbu6sDJnefZijenWRAs2ppWZz7ox9aimf/qLHrm2pVN76dWy3tqQ5mjrFhuqw7YCrU/JUEFxiQa2qa83R3bSY9e3liTt+CNTwX6eunbuBo19Z5sjtJZKSsko0wYAAHC+qNMjrpI0efJkjRw5Up06dVLnzp01Z84c5eTkaPTo0a4uzcHf20MdYoKdRlpPtj41Q1MGtNR/xscr114sq6+nkvcdcyxpJf3/V/83v35iBYHjeYXq3jxcx/OLZMsvUpCPh1pfZFV6Zt5pazmeX1i9Tw4AAKCOqPPBdfjw4Tpy5IimTp2qgwcPqn379lq5cmWZE7ZcyVKJPll5hcq1F6mgqET59mKlZ+Xrjb91koe7m3y93HTYVuD01X+Ar6eO5xeVuXRrdn5Rebt3CPTxPNunAQAAUKfV+eAqSePHj9f48eNdXUaFDBm66AwnRQX5eGjjngx1bRqqTb8dVfK+YxrcPlrXzdtYZu5rt9hQFRWXKL+wpMx+wgK81D0uTEkpGWW2dY8LU1gA81sBAMD5qU7PcTULHw932fILdeUpl2Yt1S02VP7e7tqZnqWLgn11WaMQDWwTLW93N3VsVK9M38cHt1Hiwu0K8in7d4XVz0tPJrRV97gwp/bucWGandCWE7MAAMB5yxQjrnWdt5tF7hZp2nWt9eh/fiqzbNXMIW308tpU/bN/S9325hY1DvPXIwMv0dh3tqlP6yg9NKClbHmFCvT1VFFxif725hY1CvVTPf/yQ2h0sK/mjuigjGy7jucXKtDHU2EBXoRWAABwXqvzl3w9V7VxydesXLuy8gqVvO+Y2jYIVn5RiSNQBni5K7vArsISiwqLSpSVX6Tt+47prQ1pyrUXq1tsqMZfHaubXtvi2F+32FA9MaSNGob610i9AAAAdUll8xojrtXA6ucle36ROjSsp2x7sY7/b/S0sLhEN7+xTfuPnVgJ4K1RnTTm7W2Ox5UGVF9Pd62850rH6gH1/L0UGeTjqqcDAABQJxFcq0l4iJ92HbRpwAvrK+xzUbBvhQE1nKAKAABwWgTXahTs56UrY0O1/pSLEEgnRleD/RhJBQAAOFusKlCNIoN8NHNIG3U7ZXWB0ikBhFYAAICzx4hrNWsY6q9nh7XXsRw7c1YBAACqEcG1BkQG+RBUAQAAqhlTBQAAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYgoerC6hphmFIkmw2m4srAQAAQHlKc1ppbqvIeR9cjx8/LkmKiYlxcSUAAAA4nePHj8tqtVa43WKcKdqaXElJidLT0xUYGCiLxeLSWmw2m2JiYrR//34FBQW5tBa4Hu8HlOK9gFK8F1DqQnsvGIah48ePKzo6Wm5uFc9kPe9HXN3c3NSgQQNXl+EkKCjogngTonJ4P6AU7wWU4r2AUhfSe+F0I62lODkLAAAApkBwBQAAgCkQXGuRt7e3pk2bJm9vb1eXgjqA9wNK8V5AKd4LKMV7oXzn/clZAAAAOD8w4goAAABTILgCAADAFAiuAAAAMAWCKwAAAEyB4FqL5s+fr8aNG8vHx0ddunTR1q1bXV0Satmjjz4qi8XidGvRooWry0ItSEpK0qBBgxQdHS2LxaKPP/7YabthGJo6darq168vX19f9e7dWykpKa4pFjXqTO+FUaNGlfmc6Nevn2uKRY2aNWuWLrvsMgUGBioiIkKDBw/Wrl27nPrk5+crMTFRoaGhCggIUEJCgg4dOuSiil2P4FpLFi9erMmTJ2vatGnavn272rVrp759++rw4cOuLg21rFWrVjpw4IDjtmHDBleXhFqQk5Ojdu3aaf78+eVuf+qpp/Tiiy/qlVde0ZYtW+Tv76++ffsqPz+/litFTTvTe0GS+vXr5/Q5sWjRolqsELVl3bp1SkxM1ObNm7Vq1SoVFhaqT58+ysnJcfT5xz/+oU8//VRLly7VunXrlJ6erqFDh7qwahczUCs6d+5sJCYmOu4XFxcb0dHRxqxZs1xYFWrbtGnTjHbt2rm6DLiYJGP58uWO+yUlJUZUVJTx9NNPO9oyMzMNb29vY9GiRS6oELXl1PeCYRjGyJEjjeuvv94l9cC1Dh8+bEgy1q1bZxjGic8BT09PY+nSpY4+O3fuNCQZmzZtclWZLsWIay2w2+367rvv1Lt3b0ebm5ubevfurU2bNrmwMrhCSkqKoqOj1bRpU91yyy3at2+fq0uCi6WlpengwYNOnxFWq1VdunThM+ICtXbtWkVEROjiiy/WXXfdpaNHj7q6JNSCrKwsSVJISIgk6bvvvlNhYaHTZ0OLFi3UsGHDC/azgeBaCzIyMlRcXKzIyEin9sjISB08eNBFVcEVunTporffflsrV67Uyy+/rLS0NF155ZU6fvy4q0uDC5V+DvAZAenENIF3331Xq1ev1uzZs7Vu3Tr1799fxcXFri4NNaikpESTJk1SfHy8WrduLenEZ4OXl5eCg4Od+l7Inw0eri4AuJD079/f8e+2bduqS5cuatSokZYsWaKxY8e6sDIAdcVNN93k+HebNm3Utm1bNWvWTGvXrlWvXr1cWBlqUmJion766SfOezgDRlxrQVhYmNzd3cucBXjo0CFFRUW5qCrUBcHBwWrevLlSU1NdXQpcqPRzgM8IlKdp06YKCwvjc+I8Nn78eH322Wdas2aNGjRo4GiPioqS3W5XZmamU/8L+bOB4FoLvLy81LFjR61evdrRVlJSotWrV6tr164urAyulp2drT179qh+/fquLgUu1KRJE0VFRTl9RthsNm3ZsoXPCOiPP/7Q0aNH+Zw4DxmGofHjx2v58uX6+uuv1aRJE6ftHTt2lKenp9Nnw65du7Rv374L9rOBqQK1ZPLkyRo5cqQ6deqkzp07a86cOcrJydHo0aNdXRpq0X333adBgwapUaNGSk9P17Rp0+Tu7q4RI0a4ujTUsOzsbKcRs7S0NH3//fcKCQlRw4YNNWnSJM2YMUNxcXFq0qSJHnnkEUVHR2vw4MGuKxo14nTvhZCQEE2fPl0JCQmKiorSnj179MADDyg2NlZ9+/Z1YdWoCYmJifrggw/0ySefKDAw0DFv1Wq1ytfXV1arVWPHjtXkyZMVEhKioKAgTZgwQV27dtXll1/u4updxNXLGlxI5s6dazRs2NDw8vIyOnfubGzevNnVJaGWDR8+3Khfv77h5eVlXHTRRcbw4cON1NRUV5eFWrBmzRpDUpnbyJEjDcM4sSTWI488YkRGRhre3t5Gr169jF27drm2aNSI070XcnNzjT59+hjh4eGGp6en0ahRI+OOO+4wDh486OqyUQPKex9IMhYsWODok5eXZ9x9991GvXr1DD8/P2PIkCHGgQMHXFe0i1kMwzBqPy4DAAAAVcMcVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwAAAJgCwRUAAACmQHAFAACAKRBcAQAAYAoEVwCowywWiz7++OMaPUbPnj01adKkGj0GAFQHgisASNq0aZPc3d117bXXVvmxjRs31pw5c6q/qDMYNGiQ+vXrV+629evXy2KxaMeOHbVcFQDUHIIrAEh68803NWHCBCUlJSk9Pd3V5VTK2LFjtWrVKv3xxx9lti1YsECdOnVS27ZtXVAZANQMgiuAC152drYWL16su+66S9dee63efvvtMn0+/fRTXXbZZfLx8VFYWJiGDBki6cTX7L///rv+8Y9/yGKxyGKxSJIeffRRtW/f3mkfc+bMUePGjR33v/32W11zzTUKCwuT1WpVjx49tH379krXPXDgQIWHh5epNzs7W0uXLtXYsWN19OhRjRgxQhdddJH8/PzUpk0bLVq06LT7LW96QnBwsNNx9u/fr2HDhik4OFghISG6/vrrtXfvXsf2tWvXqnPnzvL391dwcLDi4+P1+++/V/q5AUB5CK4ALnhLlixRixYtdPHFF+vWW2/VW2+9JcMwHNs///xzDRkyRAMGDFBycrJWr16tzp07S5I++ugjNWjQQI899pgOHDigAwcOVPq4x48f18iRI7VhwwZt3rxZcXFxGjBggI4fP16px3t4eOhvf/ub3n77bad6ly5dquLiYo0YMUL5+fnq2LGjPv/8c/30008aN26cbrvtNm3durXSdZ6qsLBQffv2VWBgoNavX6+NGzcqICBA/fr1k91uV1FRkQYPHqwePXpox44d2rRpk8aNG+cI9QBwtjxcXQAAuNqbb76pW2+9VZLUr18/ZWVlad26derZs6ckaebMmbrppps0ffp0x2PatWsnSQoJCZG7u7sCAwMVFRVVpeNeffXVTvdfe+01BQcHa926dRo4cGCl9jFmzBg9/fTTTvUuWLBACQkJslqtslqtuu+++xz9J0yYoC+++EJLlixxhO+qWrx4sUpKSvTGG284wuiCBQsUHBystWvXqlOnTsrKytLAgQPVrFkzSVLLli3P6lgAcDJGXAFc0Hbt2qWtW7dqxIgRkk6MYg4fPlxvvvmmo8/333+vXr16VfuxDx06pDvuuENxcXGyWq0KCgpSdna29u3bV+l9tGjRQldccYXeeustSVJqaqrWr1+vsWPHSpKKi4v1+OOPq02bNgoJCVFAQIC++OKLKh3jVD/88INSU1MVGBiogIAABQQEKCQkRPn5+dqzZ49CQkI0atQo9e3bV4MGDdILL7xQpZFoAKgII64ALmhvvvmmioqKFB0d7WgzDEPe3t6aN2+erFarfH19q7xfNzc3p6/vpRNfsZ9s5MiROnr0qF544QU1atRI3t7e6tq1q+x2e5WONXbsWE2YMEHz58/XggUL1KxZM/Xo0UOS9PTTT+uFF17QnDlz1KZNG/n7+2vSpEmnPYbFYjlt7dnZ2erYsaMWLlxY5rHh4eGSTozATpw4UStXrtTixYv18MMPa9WqVbr88sur9NwA4GSMuAK4YBUVFendd9/Vs88+q++//95x++GHHxQdHe04ialt27ZavXp1hfvx8vJScXGxU1t4eLgOHjzoFAC///57pz4bN27UxIkTNWDAALVq1Ure3t7KyMio8vMYNmyY3Nzc9MEHH+jdd9/VmDFjHF/hb9y4Uddff71uvfVWtWvXTk2bNtXu3btPu7/w8HCnEdKUlBTl5uY67l966aVKSUlRRESEYmNjnW5Wq9XRr0OHDpoyZYq++eYbtW7dWh988EGVnxsAnIzgCuCC9dlnn+nYsWMaO3asWrdu7XRLSEhwTBeYNm2aFi1apGnTpmnnzp368ccfNXv2bMd+GjdurKSkJP3555+O4NmzZ08dOXJETz31lPbs2aP58+drxYoVTsePi4vTe++9p507d2rLli265ZZbzmp0NyAgQMOHD9eUKVN04MABjRo1yukYq1at0jfffKOdO3fqzjvv1KFDh067v6uvvlrz5s1TcnKytm3bpr///e/y9PR0bL/lllsUFham66+/XuvXr1daWprWrl2riRMn6o8//lBaWpqmTJmiTZs26ffff9eXX36plJQU5rkCOGcEVwAXrDfffFO9e/d2GiUslZCQoG3btmnHjh3q2bOnli5dqv/85z9q3769rr76aqez8h977DHt3btXzZo1c3xV3rJlS7300kuaP3++2rVrp61btzqdJFV6/GPHjunSSy/VbbfdpokTJyoiIuKsnsvYsWN17Ngx9e3b12naw8MPP6xLL71Uffv2Vc+ePRUVFaXBgwefdl/PPvusYmJidOWVV+rmm2/WfffdJz8/P8d2Pz8/JSUlqWHDhho6dKhatmypsWPHKj8/X0FBQfLz89Ovv/6qhIQENW/eXOPGjVNiYqLuvPPOs3puAFDKYpw6kQkAAACogxhxBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYwv8BQ9PIxPPUXhUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_actual_vs_predicted(y_test, predictions_xgboost, \"XGBoost Regressor\")\n"
      ],
      "id": "7e6def96"
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "e362d1a4"
      },
      "outputs": [],
      "source": [
        "# Function for hyperparameter tuning using GridSearchCV\n",
        "def tune_hyperparameters(model, param_grid, X_train, y_train):\n",
        "    grid_search = GridSearchCV(model, param_grid, scoring='neg_mean_squared_error', cv=5)\n",
        "    grid_search.fit(X_train, y_train)\n",
        "    best_model = grid_search.best_estimator_\n",
        "    return best_model"
      ],
      "id": "e362d1a4"
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "3f7a4e67"
      },
      "outputs": [],
      "source": [
        "dt_param_grid = {\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}"
      ],
      "id": "3f7a4e67"
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "c60cd2d0"
      },
      "outputs": [],
      "source": [
        "rf_param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}"
      ],
      "id": "c60cd2d0"
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "80cb97d4"
      },
      "outputs": [],
      "source": [
        "xgb_param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [3, 5, 7],\n",
        "    'learning_rate': [0.01, 0.1, 0.2]\n",
        "}"
      ],
      "id": "80cb97d4"
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "3b9bbc36"
      },
      "outputs": [],
      "source": [
        "best_dt_model = tune_hyperparameters(DecisionTreeRegressor(), dt_param_grid, X_train, y_train)"
      ],
      "id": "3b9bbc36"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e5ffc534"
      },
      "outputs": [],
      "source": [
        "best_rf_model = tune_hyperparameters(RandomForestRegressor(), rf_param_grid, X_train, y_train)"
      ],
      "id": "e5ffc534"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "42401a18"
      },
      "outputs": [],
      "source": [
        "best_xgb_model = tune_hyperparameters(XGBRegressor(), xgb_param_grid, X_train, y_train)"
      ],
      "id": "42401a18"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a74b94bc"
      },
      "outputs": [],
      "source": [
        "# checking for best models\n",
        "best_dt_predictions = best_dt_model.predict(X_test)\n",
        "best_rf_predictions = best_rf_model.predict(X_test)\n",
        "best_xgb_predictions = best_xgb_model.predict(X_test)"
      ],
      "id": "a74b94bc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "30c6411e"
      },
      "outputs": [],
      "source": [
        "def evaluate_model(model, X_test, y_test):\n",
        "    predictions = model.predict(X_test)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "mae = mean_absolute_error(y_test, predictions)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, predictions))"
      ],
      "id": "30c6411e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7e385c29"
      },
      "outputs": [],
      "source": [
        "print(f'R-squared: {r2:.4f}')\n",
        "print(f'Mean Absolute Error: {mae:.4f}')\n",
        "print(f'Root Mean Squared Error: {rmse:.4f}')"
      ],
      "id": "7e385c29"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0bda0cbb"
      },
      "outputs": [],
      "source": [
        "print(\"Decision Tree Regressor:\")\n",
        "evaluate_model(best_dt_model, X_test, y_test)\n",
        "#print(\"\\n\")"
      ],
      "id": "0bda0cbb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ac5a3741"
      },
      "outputs": [],
      "source": [
        "evaluate_model(best_dt_model, X_test, y_test)"
      ],
      "id": "ac5a3741"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11499fab"
      },
      "outputs": [],
      "source": [
        "evaluate_model(best_rf_model, X_test, y_test)\n",
        "#print(\"\\n\")"
      ],
      "id": "11499fab"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cf15f707"
      },
      "outputs": [],
      "source": [
        "# Evaluate XGBoost Regressor\n",
        "print(\"XGBoost Regressor:\")\n",
        "evaluate_model(best_xgb_model, X_test, y_test)"
      ],
      "id": "cf15f707"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4a5369fa"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import joblib"
      ],
      "id": "4a5369fa"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8c242302"
      },
      "outputs": [],
      "source": [
        "numeric_features = ['Year', 'Flood Duration']\n",
        "categorical_features = ['Country', 'Continent', 'Location', 'River Basin']"
      ],
      "id": "8c242302"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1a0684db"
      },
      "outputs": [],
      "source": [
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='mean')),\n",
        "    ('scaler', StandardScaler())\n",
        "])\n",
        "\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])"
      ],
      "id": "1a0684db"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5bab8666"
      },
      "outputs": [],
      "source": [
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', categorical_transformer, categorical_features)\n",
        "    ])"
      ],
      "id": "5bab8666"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bdcf414f"
      },
      "outputs": [],
      "source": [
        "Rf = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('regressor', RandomForestRegressor(n_estimators=100, random_state=101))  # You can adjust hyperparameters\n",
        "])"
      ],
      "id": "bdcf414f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0c0aa7d3"
      },
      "outputs": [],
      "source": [
        "#rf_model = Pipeline(steps=[\n",
        "#    ('preprocessor', preprocessor),\n",
        " #   ('regressor', RandomForestRegressor(n_estimators=100, random_state=101))  # You can adjust hyperparameters\n",
        "#])"
      ],
      "id": "0c0aa7d3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "83dd929c"
      },
      "outputs": [],
      "source": [
        "#rf_model.fit(X_train, y_train)"
      ],
      "id": "83dd929c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "78990cf7"
      },
      "outputs": [],
      "source": [
        "joblib.dump(Rf, 'random_forest_model.joblib')\n"
      ],
      "id": "78990cf7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "78aba77f"
      },
      "outputs": [],
      "source": [
        "from flask import Flask, request, jsonify\n",
        "import joblib\n",
        "import pandas as pd\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "\n",
        "app = Flask(__name__)\n",
        "\n",
        "# Load the trained model\n",
        "model = joblib.load('random_forest_model.joblib')\n",
        "\n",
        "# Assuming you have a preprocessor defined\n",
        "# Note: The preprocessor should match the one used during training\n",
        "preprocessor = ...  # Your preprocessor here\n",
        "\n",
        "@app.route('/predict', methods=['POST'])\n",
        "def predict():\n",
        "    try:\n",
        "        # Get data from the request\n",
        "        data = request.json\n",
        "\n",
        "        # Create a DataFrame from the received data\n",
        "        input_data = pd.DataFrame([data])\n",
        "\n",
        "        # Preprocess the data\n",
        "        processed_data = preprocessor.transform(input_data)\n",
        "\n",
        "        # Make a prediction\n",
        "        prediction = model.predict(processed_data)\n",
        "\n",
        "        # Prepare the response\n",
        "        response = {'prediction': prediction.tolist()}\n",
        "\n",
        "        return jsonify(response)\n",
        "\n",
        "    except Exception as e:\n",
        "        return jsonify({'error': str(e)})\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run(port=5000)\n"
      ],
      "id": "78aba77f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "caac224c"
      },
      "outputs": [],
      "source": [
        "\n",
        "import streamlit as st\n",
        "import joblib\n",
        "\n",
        "\n",
        "model = joblib.load('random_forest_model.joblib')\n",
        "\n",
        "# Streamlit UI\n",
        "st.title('FloodSafety Prediction App')\n",
        "user_input = st.text_input('Flood Chat Model')\n",
        "\n",
        "if st.button('Predict'):\n",
        "    prediction = Rf.predict([user_input])\n",
        "    st.write(f'Prediction: {prediction}')\n"
      ],
      "id": "caac224c"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4ECU76fcJI6P"
      },
      "id": "4ECU76fcJI6P",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}