DataFlow_Solubility.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **My first Machine Learning Project**\n",
        "\n"
      ],
      "metadata": {
        "id": "XHDd54ATe9sj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Load data***"
      ],
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        "id": "xkKaBlF3fVzR"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')\n",
        "df\n",
        ""
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion   logS\n",
              "0     2.59540  167.850                0.0            0.000000 -2.180\n",
              "1     2.37650  133.405                0.0            0.000000 -2.000\n",
              "2     2.59380  167.850                1.0            0.000000 -1.740\n",
              "3     2.02890  133.405                1.0            0.000000 -1.480\n",
              "4     2.91890  187.375                1.0            0.000000 -3.040\n",
              "...       ...      ...                ...                 ...    ...\n",
              "1139  1.98820  287.343                8.0            0.000000  1.144\n",
              "1140  3.42130  286.114                2.0            0.333333 -4.925\n",
              "1141  3.60960  308.333                4.0            0.695652 -3.893\n",
              "1142  2.56214  354.815                3.0            0.521739 -3.790\n",
              "1143  2.02164  179.219                1.0            0.461538 -2.581\n",
              "\n",
              "[1144 rows x 5 columns]"
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              "      <th>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
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              "      <td>2.59540</td>\n",
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              "      <td>133.405</td>\n",
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              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
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              "      <th>3</th>\n",
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              "      <td>133.405</td>\n",
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              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>-4.925</td>\n",
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              "      <td>-3.893</td>\n",
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              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
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              "      <td>-3.790</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775324,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.6204995656741,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656514,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.343305175882067,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0965019290656026,\n        \"min\": -11.6,\n        \"max\": 1.58,\n        \"num_unique_values\": 743,\n        \"samples\": [\n          -1.7,\n          -6.62,\n          -3.324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data preparation**"
      ],
      "metadata": {
        "id": "jTTMcElMfmHq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***Data separation as X and Y***\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "25gdh3JOfriz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['logS']\n",
        "y\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0      -2.180\n",
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              "1140   -4.925\n",
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              "1142   -3.790\n",
              "1143   -2.581\n",
              "Name: logS, Length: 1144, dtype: float64"
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          "metadata": {},
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    {
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        "X = df.drop('logS', axis=1)\n",
        "X\n",
        ""
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
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              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775324,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.6204995656741,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656514,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.343305175882067,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data splitting**"
      ],
      "metadata": {
        "id": "jX3czPdigWbN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**sklearn.model_selection**"
      ],
      "metadata": {
        "id": "95EbYqnlgYYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
      ],
      "metadata": {
        "id": "ba_TCN5ZgVBe"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yropkLFkgtMS",
        "outputId": "cba4fa5f-cde9-4b83-d0f3-51318691c2f3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "107  3.14280  112.216                5.0            0.000000\n",
              "378 -2.07850  142.070                0.0            0.000000\n",
              "529 -0.47730  168.152                0.0            0.000000\n",
              "546 -0.86740  154.125                0.0            0.000000\n",
              "320  1.62150  100.161                2.0            0.000000\n",
              "..       ...      ...                ...                 ...\n",
              "802  3.00254  250.301                1.0            0.842105\n",
              "53   2.13860   82.146                3.0            0.000000\n",
              "350  5.76304  256.348                0.0            0.900000\n",
              "79   3.89960  186.339               10.0            0.000000\n",
              "792  2.52334  310.297                3.0            0.300000\n",
              "\n",
              "[915 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bad41061-77c9-4269-8868-1d9e10cc5dfb\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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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>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>3.14280</td>\n",
              "      <td>112.216</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>-2.07850</td>\n",
              "      <td>142.070</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>-0.47730</td>\n",
              "      <td>168.152</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>-0.86740</td>\n",
              "      <td>154.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>1.62150</td>\n",
              "      <td>100.161</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>802</th>\n",
              "      <td>3.00254</td>\n",
              "      <td>250.301</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.842105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2.13860</td>\n",
              "      <td>82.146</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>5.76304</td>\n",
              "      <td>256.348</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>3.89960</td>\n",
              "      <td>186.339</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>2.52334</td>\n",
              "      <td>310.297</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>915 rows × 4 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-bad41061-77c9-4269-8868-1d9e10cc5dfb 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-bad41061-77c9-4269-8868-1d9e10cc5dfb');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    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",
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              "    20% {\n",
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              "      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-e0d33e0a-957a-4c20-9384-b6d3828f6629 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e147612b-9504-4e28-97ff-5a48616304a5\">\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('X_train')\"\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_e147612b-9504-4e28-97ff-5a48616304a5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 915,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8379735599136915,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 767,\n        \"samples\": [\n          2.585,\n          4.515300000000004,\n          1.8437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.84523286107176,\n        \"min\": 26.038,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 679,\n        \"samples\": [\n          214.294,\n          227.085,\n          429.77200000000016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7012928712183357,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.0,\n          4.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34322299404825873,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          0.4615384615384615,\n          0.0,\n          0.3571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
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          "height": 424
        },
        "id": "VCtkyjyviNIu",
        "outputId": "655543d2-04bb-49ea-896e-4d4767534e9d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "822   2.91000  172.268                7.0            0.000000\n",
              "118   7.27400  360.882                1.0            0.666667\n",
              "347   1.94040  145.161                0.0            0.909091\n",
              "1123  1.98640  119.378                0.0            0.000000\n",
              "924   1.70062  108.140                0.0            0.750000\n",
              "...       ...      ...                ...                 ...\n",
              "1114  1.76210  478.513                4.0            0.000000\n",
              "427   6.32820  276.338                0.0            1.000000\n",
              "711   0.04430  218.205                5.0            0.000000\n",
              "4     2.91890  187.375                1.0            0.000000\n",
              "948   3.56010  318.328                2.0            0.750000\n",
              "\n",
              "[229 rows x 4 columns]"
            ],
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              "      <th></th>\n",
              "      <th>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>822</th>\n",
              "      <td>2.91000</td>\n",
              "      <td>172.268</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>7.27400</td>\n",
              "      <td>360.882</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>1.94040</td>\n",
              "      <td>145.161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.909091</td>\n",
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              "    <tr>\n",
              "      <th>1123</th>\n",
              "      <td>1.98640</td>\n",
              "      <td>119.378</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>1.70062</td>\n",
              "      <td>108.140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>1.76210</td>\n",
              "      <td>478.513</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>6.32820</td>\n",
              "      <td>276.338</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>0.04430</td>\n",
              "      <td>218.205</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>3.56010</td>\n",
              "      <td>318.328</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>229 rows × 4 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
              "  <div id=\"id_df473ffb-dd33-401c-b19e-78067ac1489f\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 229,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9781161342582056,\n        \"min\": -5.397199999999993,\n        \"max\": 9.8876,\n        \"num_unique_values\": 213,\n        \"samples\": [\n          3.386800000000001,\n          4.102000000000002,\n          2.935900000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.78845308303764,\n        \"min\": 16.043,\n        \"max\": 764.9499999999999,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          139.11,\n          232.32700000000003,\n          104.15199999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3074347142542226,\n        \"min\": 0.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          14.0,\n          6.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34322081551818723,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          0.4166666666666667,\n          0.2608695652173913,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Building**"
      ],
      "metadata": {
        "id": "FeMXhL1QiTKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Linear Regression***"
      ],
      "metadata": {
        "id": "5QBwmJIhiU0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***1.   Training the model***\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1JtRSNJKiYav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "i8rZjmMsihzx",
        "outputId": "c461f9a6-8844-490a-8e33-0dfea3fee714"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
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              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
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              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***2.  Applying the model to make a prediction***\n",
        "\n"
      ],
      "metadata": {
        "id": "363ZL1eeitqu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_train_pred = lr.predict(X_train)\n",
        "y_lr_test_pred = lr.predict(X_test)\n",
        ""
      ],
      "metadata": {
        "id": "B24jqGLHis50"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_train_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUumD9UEi5tl",
        "outputId": "b4667fcf-9e7d-4815-88be-adde89e53def"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.83668969,  0.84208401, -0.50930302, -0.13058053, -1.6269208 ,\n",
              "       -2.86103037, -2.32208333, -4.16684323, -5.04235077, -2.29737009,\n",
              "       -3.15082845, -4.33037208, -1.43700682, -2.59060064, -7.09025955,\n",
              "       -2.32734557, -4.19073421, -2.63904005, -3.58104074, -3.75929058,\n",
              "       -2.5501665 , -3.26968347, -2.78911367, -2.69251051, -0.77437547,\n",
              "       -6.61955792, -2.99084623, -2.36975061, -7.18167736, -1.52816491,\n",
              "       -4.81736265, -0.86996608, -7.09025955, -1.87404275, -2.58013247,\n",
              "       -4.80953284, -4.17454084, -1.95444152, -3.50503339, -2.7411418 ,\n",
              "       -1.87131835, -2.45270806, -3.07252433, -1.48988982, -3.98778754,\n",
              "       -1.35216679, -0.16932277, -6.05237002, -3.29550658,  0.30513174,\n",
              "       -3.25501463, -5.31993586, -0.4733928 , -4.79180882, -5.82486323,\n",
              "       -2.70343539, -3.70838271, -2.4987341 , -2.12301018, -3.11393974,\n",
              "       -2.79842053, -1.38274662, -4.3267103 , -3.57778946, -3.42356884,\n",
              "       -9.15826626, -6.52588866, -1.75832085, -4.97842312, -1.59255758,\n",
              "       -1.91317252, -2.81075595, -1.14056747, -1.6326702 , -1.6269208 ,\n",
              "       -5.33227425, -3.88805074,  0.70432882, -3.99288096, -2.22595859,\n",
              "       -2.63405871, -1.80643013, -4.18615661, -0.32535905, -3.36770115,\n",
              "       -2.94105086, -3.02039211, -2.01312164, -2.97735566, -2.58917919,\n",
              "       -6.78423811, -2.34521849, -3.11841945, -0.8177211 , -1.69000394,\n",
              "       -1.20887457, -3.4996968 , -4.98602607, -4.36391839, -1.59023154,\n",
              "       -1.36030606, -1.97047357, -2.63177729, -2.88308299, -5.15950486,\n",
              "       -5.67559475, -3.04604495, -5.17135083, -1.38069437, -4.57622144,\n",
              "       -3.72245121, -5.49591484, -3.39797976, -6.025302  , -3.80350106,\n",
              "       -5.72200956, -0.20160041, -0.54441899, -1.4937231 , -4.17747099,\n",
              "       -5.31993586, -5.30332414, -4.68554881, -2.54472004, -1.91317252,\n",
              "       -5.0001379 , -1.29322696, -4.9018288 , -5.72200956, -1.63254894,\n",
              "       -0.50844757, -2.79777831, -3.40409244, -3.75108143, -2.13430517,\n",
              "       -2.3552617 , -4.47491213, -1.37020598, -2.98467788, -1.1097015 ,\n",
              "       -2.65582153, -2.03469435, -3.82343254, -3.40409244, -3.86002039,\n",
              "       -1.6454705 , -1.67610138, -2.25004084, -2.35293566, -3.97998511,\n",
              "       -0.45142958, -3.70838271, -3.21804175, -3.26826004, -3.5011185 ,\n",
              "       -0.69767632, -4.61318325, -2.53209123, -1.58352433,  0.11192491,\n",
              "       -2.071356  , -2.52212098, -5.16155398, -3.79386016, -2.05580278,\n",
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              "       -1.51156575, -7.7015858 , -2.99084623, -2.90410699, -4.2232049 ,\n",
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              "       -3.59939381, -2.79154825, -3.25842453, -2.25768799, -2.14996491,\n",
              "       -4.95790808, -1.4036531 , -4.64502179, -2.31758244, -3.01521178,\n",
              "       -5.1577739 , -4.68504851, -2.98727379, -2.16925807, -2.65875355,\n",
              "       -4.53388983, -3.39620209, -2.97542583, -2.63426164, -7.77785827,\n",
              "       -2.34848929, -3.95395012, -3.14810449, -2.99620551, -4.19807234,\n",
              "       -2.57905657, -5.72200956, -1.86861231, -1.48463069, -2.03428932,\n",
              "       -7.44538031, -6.08246673, -3.2086445 , -2.137733  , -8.85737405,\n",
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              "        1.43453026, -3.29138689, -2.41564138, -3.88412944, -5.4954495 ,\n",
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              "       -4.00428682, -4.29865745, -1.3893508 , -0.39302341, -1.51156575,\n",
              "       -2.34521849, -1.29089314, -0.97595542, -2.41038224, -4.0790451 ,\n",
              "       -2.6818627 , -7.27035604, -3.46876907, -0.83629487, -3.25056894,\n",
              "       -2.77794109, -2.70606495, -0.88802552,  0.33102924, -8.73475591,\n",
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              "       -3.67078501, -3.11422447, -1.35216679, -2.33929584, -3.735629  ,\n",
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              "       -2.75272546, -6.40483191, -3.43858076, -1.33564461, -7.39989571,\n",
              "       -2.3552617 , -1.97390964, -3.31913336, -0.99788515, -1.49117202,\n",
              "        2.54283347, -2.57935977, -1.5372532 , -1.46969589, -5.50256395,\n",
              "       -2.84523685, -5.79050585, -0.72972899, -4.50417895, -3.41743324,\n",
              "       -1.89701519, -3.04694269, -1.10327863, -1.48988982, -2.81732645,\n",
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              "       -1.94336083, -1.87633593, -9.15826626, -3.65922507, -2.29683578,\n",
              "       -0.5325366 , -7.5914365 , -2.15587374, -1.90396018, -1.78645244,\n",
              "       -4.36890615, -1.91112045, -5.28950294, -3.70838271, -3.34373141,\n",
              "       -3.82343254, -3.84290966, -1.75395277, -2.82944893, -1.92755736,\n",
              "       -3.95353483, -2.51949142, -4.16972153, -0.47784193, -2.80004662,\n",
              "       -2.08049675, -1.57187693, -3.79234971, -1.27341971, -1.10845148,\n",
              "       -3.87977779, -1.08911411, -2.73044354, -1.76722676, -2.22965253,\n",
              "       -3.56321848, -6.67044148, -1.51156575, -0.24983949, -1.72672119,\n",
              "       -2.14897281, -4.05818764, -2.37140338, -1.897265  , -1.42797042,\n",
              "       -3.08706826, -4.24521459, -6.52588866, -4.60929775, -4.67022068,\n",
              "       -2.03122625, -3.11841945, -5.04235077,  0.60553271, -8.46728525,\n",
              "       -4.23480474, -2.75684478, -2.14339849, -1.5231707 , -2.67587664,\n",
              "       -3.59418929, -5.68837433, -5.02111613, -0.94099449, -3.51200413,\n",
              "       -1.41666505, -1.9765392 , -2.67587664, -5.87753962, -3.73205662,\n",
              "       -2.65599022, -2.46014399, -3.93774254, -8.46728525, -0.43381828,\n",
              "       -3.81993282, -3.25038467, -5.64350181, -4.18419436, -6.47162256,\n",
              "       -2.29452458, -3.70387135, -6.42099768, -1.53182046, -2.45533763,\n",
              "       -1.91317252, -2.137733  , -1.31128145, -3.16496704, -2.22556914,\n",
              "       -3.81016932, -2.44235234, -2.76845189, -2.73924332, -1.59715854,\n",
              "       -2.90610261, -7.77785827, -2.74034339, -7.77785827, -6.53218341,\n",
              "       -2.55678026, -2.59432621, -6.06602826, -2.73144347, -2.39319255,\n",
              "       -3.79386016, -6.40483191, -1.32841939, -1.62955037, -4.07880131,\n",
              "       -2.42494059, -6.04136471, -1.63103729, -5.2602532 , -3.86312053,\n",
              "       -2.12601292, -4.28342693, -2.6178098 , -7.14847318, -1.53350725,\n",
              "       -1.08911411, -4.24790282, -4.63837202, -3.79691901, -2.13813936,\n",
              "       -3.86772233, -1.2281273 , -3.7672691 , -4.28538142, -1.43139099,\n",
              "       -3.40980857, -2.6111225 , -1.41272408, -3.07730828, -3.90594959,\n",
              "       -2.35283034, -0.7271857 , -4.19010154, -0.69374352, -1.10472591,\n",
              "       -1.62166167, -3.90123327, -2.78518496, -3.43585411, -3.92684454,\n",
              "       -2.54406861, -1.91317252, -1.77769503, -0.79480376, -4.23915408,\n",
              "       -1.51148185, -1.14665655, -2.00855418, -7.03862139, -2.74305517,\n",
              "       -2.11738204, -3.42356884, -2.49648251, -2.1372074 , -1.88151626,\n",
              "       -1.62653948, -4.00428682, -2.04784256, -4.47943132, -2.61216962,\n",
              "       -1.69531648, -5.54561991, -7.77785827, -2.13315833, -6.08238994,\n",
              "       -0.73579117, -1.90396018, -3.66566374, -3.26337102, -3.5528319 ,\n",
              "       -0.88717007, -4.50417895, -3.93774254, -2.31911758, -1.79407614,\n",
              "       -2.73044354, -1.62464083, -3.36099002, -1.68087565, -4.17747099,\n",
              "       -4.36658559, -2.68703435, -5.54667039, -2.40682831, -5.07481336,\n",
              "       -3.80377549, -2.93847429, -3.69840797, -3.63698515, -2.77930842,\n",
              "       -0.77056362, -2.37730267,  1.68379176, -3.2158222 , -3.70838271,\n",
              "       -3.17308636, -4.36923501, -0.44844001, -5.24894879, -6.52225128,\n",
              "       -1.80406335, -1.80643013, -1.90447849, -5.52193556, -2.07398557,\n",
              "       -3.22132977, -6.70156561, -5.44962012, -1.25610101, -3.35223802,\n",
              "       -3.98606913, -1.89546347, -6.08789325, -3.88711135, -3.78407726])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_test_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kS_hr27i-I9",
        "outputId": "7519fa17-12ea-482b-92d3-abe5a681edd4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.05722870e+00, -7.77785827e+00, -2.55016650e+00, -2.01523582e+00,\n",
              "       -2.06375990e+00, -9.99672215e-01, -5.94603364e-01, -5.53626003e-01,\n",
              "       -5.72200956e+00, -3.94006681e+00, -3.95496755e+00, -2.29737009e+00,\n",
              "       -1.48980354e+00, -1.48988982e+00, -4.64510806e+00, -1.90396018e+00,\n",
              "       -1.51566313e+00, -3.16424605e+00, -3.70863920e+00, -5.58105660e+00,\n",
              "       -3.25038467e+00, -5.04235077e+00, -5.69194881e+00, -2.14339849e+00,\n",
              "       -4.35689341e+00, -5.03964756e+00, -3.10383618e+00, -4.40286964e+00,\n",
              "       -4.21276272e+00,  5.56508349e-01, -1.45537678e+00, -4.41027396e+00,\n",
              "       -2.59668773e+00, -1.53336276e+00, -5.55749874e-01, -1.67111795e+00,\n",
              "       -2.78163675e+00, -3.15395565e+00, -5.27083361e+00, -1.75321446e+00,\n",
              "       -1.53350725e+00, -2.01255666e+00, -6.57559167e+00, -7.89433046e+00,\n",
              "       -5.76437127e+00, -4.16422068e+00, -3.43694663e+00,  1.43834212e+00,\n",
              "       -1.12679105e-02, -2.34521849e+00, -1.86480046e+00, -5.03964756e+00,\n",
              "        8.55886378e-01, -3.17679292e+00, -5.06764094e+00, -1.99464442e+00,\n",
              "       -7.77785827e+00, -1.21764693e+00, -9.09541075e-01, -5.04235077e+00,\n",
              "       -2.43898748e+00, -2.84034045e+00, -2.53403538e+00, -2.36170311e+00,\n",
              "       -1.63103729e+00, -1.53182046e+00, -3.23931568e+00, -2.88008616e+00,\n",
              "       -1.88300518e+00, -3.21582220e+00, -3.40245202e+00, -9.01813905e-01,\n",
              "       -4.82308940e+00, -7.69116343e-01, -7.12894308e+00, -1.05440427e+01,\n",
              "       -1.95444152e+00, -3.50194744e+00, -7.18167736e+00, -6.01555673e+00,\n",
              "       -2.08189806e+00, -2.31652280e+00, -3.44556948e+00, -2.05480142e+00,\n",
              "       -6.01555673e+00, -2.88308299e+00, -4.84867198e+00, -3.51006495e-01,\n",
              "       -3.54726250e+00, -1.21057919e+00, -4.36658559e+00, -4.21815903e-01,\n",
              "       -1.63103729e+00, -2.51604291e+00, -2.16707077e+00, -1.48726025e+00,\n",
              "       -3.20864450e+00, -1.51411141e+00, -1.65033691e+00, -3.66287663e+00,\n",
              "       -3.26968347e+00, -3.94492313e+00, -4.22509088e+00, -3.68794650e+00,\n",
              "       -5.98734972e+00, -1.43710934e+00, -1.97653920e+00, -1.85076729e+00,\n",
              "       -1.14179382e+00, -3.07730828e+00, -4.84867198e+00, -2.19679345e+00,\n",
              "       -1.68737438e+00, -2.20390218e+00,  1.89408269e+00, -3.61322115e+00,\n",
              "       -2.79173430e+00, -2.41564138e+00, -7.53910534e-01, -8.54744860e-01,\n",
              "       -9.20407401e-02, -6.14209981e+00, -3.79386016e+00, -7.77785827e+00,\n",
              "       -1.79874130e+00, -2.50544035e+00, -3.77102985e+00, -2.25250766e+00,\n",
              "       -2.57788713e+00, -2.06375990e+00, -3.33843958e+00, -1.03912484e+00,\n",
              "       -6.68946164e+00, -1.91112045e+00, -2.58735850e+00, -2.19902800e+00,\n",
              "       -1.90219551e+00, -2.81396751e+00, -4.16972153e+00, -5.72200956e+00,\n",
              "       -1.60816482e+00, -3.68121117e+00, -4.60929775e+00, -2.45888480e+00,\n",
              "        1.13185484e+00, -1.69279625e+00, -7.09025955e+00, -3.79386016e+00,\n",
              "       -2.99712058e+00, -5.79600137e+00, -2.44845780e+00, -5.29399242e+00,\n",
              "       -5.29389899e-01, -3.53652118e+00, -3.51200413e+00, -2.02419300e+00,\n",
              "       -4.47466933e+00, -3.63836536e+00, -4.56596939e+00, -5.57842703e+00,\n",
              "       -5.30676136e+00, -2.39225449e+00, -4.88290144e+00, -2.61359308e+00,\n",
              "       -3.11841945e+00, -2.05580278e+00, -1.64987419e+00, -4.91881901e+00,\n",
              "       -3.93774254e+00, -4.26411548e+00, -3.15082845e+00, -3.49352203e+00,\n",
              "       -3.81768831e+00, -3.77197350e+00, -2.55016650e+00, -1.97653920e+00,\n",
              "       -2.59432621e+00, -5.38480406e+00, -5.44932525e+00, -3.04107137e+00,\n",
              "       -1.87252408e+00, -2.25124657e+00, -2.89215707e+00, -3.46087334e+00,\n",
              "       -6.06861986e+00, -1.89916369e+00, -1.98035105e+00, -2.45036038e+00,\n",
              "       -2.79393037e+00, -4.76010415e+00, -1.72379306e+00, -7.09025955e+00,\n",
              "       -2.86880150e+00, -2.70674744e+00, -4.36825704e+00, -3.11841945e+00,\n",
              "       -3.85805633e+00,  9.78662246e-03, -4.47466933e+00, -6.08708502e+00,\n",
              "       -5.19970454e+00, -6.40483191e+00, -4.10155218e+00, -1.07044752e+00,\n",
              "        1.99249372e+00, -3.63698515e+00, -8.50933009e-01, -3.26770298e+00,\n",
              "       -4.78343575e+00, -1.48988982e+00, -2.24629276e+00, -4.13247222e+00,\n",
              "       -4.36873484e+00, -1.89724815e+00, -1.50596465e+00, -1.16713539e-02,\n",
              "       -1.73602998e+00, -2.34521849e+00, -4.54942814e-01, -4.18812419e+00,\n",
              "       -1.87770440e+00, -3.70838271e+00, -1.59899899e+00, -3.26143822e+00,\n",
              "       -4.17623614e+00, -6.67872053e+00, -1.23069039e+00, -3.14545964e+00,\n",
              "       -4.79863925e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***3. Evaluate model performance***\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "bW04GLMLjDeK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "lr_train_mse = mean_squared_error(y_train, y_lr_train_pred)\n",
        "lr_train_r2 = r2_score(y_train, y_lr_train_pred)\n",
        "\n",
        "lr_test_mse = mean_squared_error(y_test, y_lr_test_pred)\n",
        "lr_test_r2 = r2_score(y_test, y_lr_test_pred)"
      ],
      "metadata": {
        "id": "SDEeuqHCjRbb"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('LR MSE (Train): ', lr_train_mse)\n",
        "print('LR R2 (Train): ', lr_train_r2)\n",
        "print('LR MSE (Test): ', lr_test_mse)\n",
        "print('LR R2 (Test): ', lr_test_r2)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbWq5gT5jVMt",
        "outputId": "a846f742-64c2-468d-8402-af955918b3c2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR MSE (Train):  1.0075362951093687\n",
            "LR R2 (Train):  0.7645051774663391\n",
            "LR MSE (Test):  1.0206953660861033\n",
            "LR R2 (Test):  0.7891616188563282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_results = pd.DataFrame(['Linear regression', lr_train_mse, lr_train_r2, lr_test_mse, lr_test_r2]).transpose()\n",
        "lr_results.columns = ['Method', 'Training MSE', 'Training R2', 'Test MSE', 'Test R2']"
      ],
      "metadata": {
        "id": "b1V5DVfSjYHT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "gzTT8yVajdsD",
        "outputId": "0ba533cb-db57-4716-debd-8d13fe0e6f0a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Linear regression     1.007536    0.764505  1.020695  0.789162"
            ],
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              "      <th>Method</th>\n",
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              "      <td>Linear regression</td>\n",
              "      <td>1.007536</td>\n",
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              "          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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lr_results')\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lr_results');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lr_results",
              "summary": "{\n  \"name\": \"lr_results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Linear regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0075362951093687,\n        \"max\": 1.0075362951093687,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0075362951093687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7645051774663391,\n        \"max\": 0.7645051774663391,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7645051774663391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0206953660861033,\n        \"max\": 1.0206953660861033,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0206953660861033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7891616188563282,\n        \"max\": 0.7891616188563282,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7891616188563282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Random Forest**"
      ],
      "metadata": {
        "id": "FboGOm7yjh3N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***1.   Training the model***\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "lJKTXrsHjkzI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf = RandomForestRegressor(max_depth=2, random_state=100)\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "PhfFkMorjkRo",
        "outputId": "2360d537-88dd-43bc-ec42-aca3ce073f18"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=2, random_state=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=2, random_state=100)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=2, random_state=100)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***2.   Applying the model to make a prediction***\n",
        "\n"
      ],
      "metadata": {
        "id": "3LpIT-L2j1Xe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_rf_train_pred = rf.predict(X_train)\n",
        "y_rf_test_pred = rf.predict(X_test)\n",
        ""
      ],
      "metadata": {
        "id": "v1uPtJvljyZ5"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***3.   Evaluate model performance***\n"
      ],
      "metadata": {
        "id": "1j4mSm4dkB5a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "rf_train_mse = mean_squared_error(y_train, y_rf_train_pred)\n",
        "rf_train_r2 = r2_score(y_train, y_rf_train_pred)\n",
        "\n",
        "rf_test_mse = mean_squared_error(y_test, y_rf_test_pred)\n",
        "rf_test_r2 = r2_score(y_test, y_rf_test_pred)"
      ],
      "metadata": {
        "id": "ZTDQ-FbzkKF6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_results = pd.DataFrame(['Random forest', rf_train_mse, rf_train_r2, rf_test_mse, rf_test_r2]).transpose()\n",
        "rf_results.columns = ['Method', 'Training MSE', 'Training R2', 'Test MSE', 'Test R2']\n",
        "rf_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "lZx4D3rIkO6b",
        "outputId": "65ab1f7a-cc17-44bb-87f1-696f0b7e5636"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Random forest     1.028228    0.759669  1.407688  0.709223"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dccc8e90-b61b-4afa-9fd0-3dbf2268f497\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Method</th>\n",
              "      <th>Training MSE</th>\n",
              "      <th>Training R2</th>\n",
              "      <th>Test MSE</th>\n",
              "      <th>Test R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random forest</td>\n",
              "      <td>1.028228</td>\n",
              "      <td>0.759669</td>\n",
              "      <td>1.407688</td>\n",
              "      <td>0.709223</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-dccc8e90-b61b-4afa-9fd0-3dbf2268f497')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dccc8e90-b61b-4afa-9fd0-3dbf2268f497 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-dccc8e90-b61b-4afa-9fd0-3dbf2268f497');\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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              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_21fdd808-ed76-47b6-aa53-0a46b662d209\">\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('rf_results')\"\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",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('rf_results');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rf_results",
              "summary": "{\n  \"name\": \"rf_results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Random forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.028227802112806,\n        \"max\": 1.028227802112806,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.028227802112806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7596688824431413,\n        \"max\": 0.7596688824431413,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7596688824431413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.407688264904896,\n        \"max\": 1.407688264904896,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.407688264904896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7092230211002489,\n        \"max\": 0.7092230211002489,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7092230211002489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model comparison**"
      ],
      "metadata": {
        "id": "rWIG2bIakSps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_models = pd.concat([lr_results, rf_results], axis=0)"
      ],
      "metadata": {
        "id": "A0qCCUMJkX3L"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_models.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lNe69nh4kbd0",
        "outputId": "fa55e663-c343-4416-c524-02cc22f40c6b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Linear regression     1.007536    0.764505  1.020695  0.789162\n",
              "1      Random forest     1.028228    0.759669  1.407688  0.709223"
            ],
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              "      <th></th>\n",
              "      <th>Method</th>\n",
              "      <th>Training MSE</th>\n",
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              "      <th>Test MSE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear regression</td>\n",
              "      <td>1.007536</td>\n",
              "      <td>0.764505</td>\n",
              "      <td>1.020695</td>\n",
              "      <td>0.789162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random forest</td>\n",
              "      <td>1.028228</td>\n",
              "      <td>0.759669</td>\n",
              "      <td>1.407688</td>\n",
              "      <td>0.709223</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dc37d70a-22c4-48f3-bb2e-71601fb63b9c');\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",
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              "\n",
              "<style>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      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",
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              "      border-bottom-color: var(--fill-color);\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a6884a5b-89b4-45ed-847b-78e916e8b6b5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_models\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random forest\",\n          \"Linear regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0075362951093687,\n        \"max\": 1.028227802112806,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.028227802112806,\n          1.0075362951093687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Training R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7596688824431413,\n        \"max\": 0.7645051774663391,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7596688824431413,\n          0.7645051774663391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test MSE\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0206953660861033,\n        \"max\": 1.407688264904896,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.407688264904896,\n          1.0206953660861033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test R2\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.7092230211002489,\n        \"max\": 0.7891616188563282,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7092230211002489,\n          0.7891616188563282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Data visualization of prediction **"
      ],
      "metadata": {
        "id": "KankMRZSkfnJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(x=y_train, y=y_lr_train_pred, c=\"#7CAE00\" ,alpha=0.3)\n",
        "\n",
        "z = np.polyfit(y_train, y_lr_train_pred, 1)\n",
        "p = np.poly1d(z)\n",
        "\n",
        "plt.plot(y_train, p(y_train), '#F8766D')\n",
        "plt.ylabel('Predict LogS')\n",
        "plt.xlabel('Experimental LogS')\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "oJj5EF8wkkkU",
        "outputId": "b42092bc-8b10-4702-e1ed-523e23c6e334"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Experimental LogS')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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OC0WhYudusn/4HlaXf4eqc3s2vd1RQjo9PY1lWayurm67fXV1lfn5+Qs+xvM8PO/GOV4YDLcDNzMtditwpWNAN3ps6OWIdC9HIKve/GW9zrvn3kGUtl/yb+dCUajzo/9P5F37icIlchVjS5847Y27bl27hhDiZbMhvFruKCF1XZeHH36YP/uzP+M7vuM7gKLZ6M/+7M94//vff3MPZzDc4tystNitwJWOAd3IsaGXwyDjcj8I7Jx80yVf52rvSeK0R5h1Lij6Os9J/sO/hnbr7GN37sZ53wfGc6G29EnziFOtL5Jk/W2m85OVfUjp3nAbwmvhjhJSgA984AO8+93v5vWvfz1vfOMb+fmf/3mCIOC9733vzT6awWC4RXmpMSCtNUqlBPEq/WiZkjt5XYzcL5S6BV4Wg4zL/SDQjPa85OvM8pCV7pdJ84jp2r0vEv2D/YM4//u3t1+bURS67XlUzCBaoR0cY7J6gJLlj03nw6RFyZ1i58QbbtmmtztOSL/3e7+X9fV1fuqnfoqVlRVe+9rX8vGPf/xFDUgGg8GwxcWM2cOkRSs4Sic8RZL2cK0KneEJJip7r8jIfUs00zwkzUPirMfm4HnCpE2uk3EUN1He+7IYZFzuBwHgoq9Ta816/ytkKmWqcvf4fs+u4pb2seO/fw6n/8TZ8+/YifP+/9eL3Im01ix3Hsd3GzT1buK0gxCFzZ/vNGkNDiOkxULzdbdsqeGOE1KA97///SaVazAYLpsLjQGFSYulzqMk6QClE6Zr91Lzd7AxOMQgWsGzqwyi5UuODW3VO1d7T7IZHCWI14iSDq5dZbZ+PzO1+7CtEhuDQ6z3D5Hk/Yumbq9XrfBSG12SbEiuUrTWlOwmvfAMbvXuba8zTnu0giPU/B3kOiVOe7h2jfKJTeb/8PHt5/5HP47cd+CCZ9mKjqer96LKCZuDI3SjU+R5hGX5TFUPUvEmseWt28tyRwqpwWAwXAnnjwFVvHk2Bi8wTFrYwqXsTjFdvRvPqeHaVVrBYcpyBt9uvuTY0CBe4YWVj9EOTzAIl1F5ilI5aT7Esny64WlyFbPYfJjJygFWul9mEK+RZEN8p/aic16vlWUvNT88jDc5sfl5LOlyZP1T5CqiFy4ziFdplvfg2TWEsDnTfZQg3sSRFU62/hJbO3ztRz1KAzV+rmDKxX7fP8Gr7Bzfdn5KO83DcXQcqwTE2UUtArAtj0wlt2yjERghNRgMtzHXc0Tk3DGgtf6zbPSfw7PrNMo7mazsH9fntlKsUdrhrqmvoz08vm1saKpykMnqXSiVc2LjLwiSFloplM4peZMEySr10g6SLEBrTZwNaAVHWGy+nsnKfvrRMu3gCPONBy8a6frOBMN485pMIy40P9wLz3B847MIAXubX0+ttEgvPEOQPsWgt8xa/1kc6ePaFZTKsaSDEJLZFYfXf9YBzoroF946pL+Q8+asS51CSC/UjVyym2R5SD88w8bgOZIsoOROjTe4dIbHEQiitEO9tOOqfrc3GiOkBoPhtuRKRkQuV3C3xoCa5T2kWcBEZR++0xx/r9aaJOuTqogw2cRzGtvGhqKkQ2t4jGMbnyNMNlntPU3ZnSFOu1T8WZRKyXWOIx1cp0qSdSl7UwTxOknWx7ErVP05XKt80Ui3XtrBC6sfu+DrrnrzL3qdg3jlotfp3PnhQbTCZnAYz66xZ+prxt3JG4PncIRPrbRIzZujWbqLY5ufIYjWQSm+7s8cGqEzvobrtT6ffus6vtfAyeFU6xFKXvEh5ELdyL3wDP1wheXkKRxZ/Jyt621LH0u4CCFpDY4xU7v/lqyTGiE1GAxXzM22xbuSEZErnckUQlDzF6j4c0jpjF9X0Xh0hCBeJ84GKJ1yYuMvuGv2rdT8BfrRMqfbj4zPZEmX9f6hcbev705gSRtLWCidYQmHRAdIIUlVTK5TyEPK7gwLzQdpDQ7TC08jsNDkVL1ZmuU9nGk/SpR1X/S6NwaHKDnTBMkqaR7gWBWq3hxhsoGGi16nrQ8C/WgZvZpR83fgOTW01rSCoyRZQK20SKYi0iwgVUOGcYupFcm3Pv66bdfu9177BVaneqggZ7f7VeyZ+hrCtMVS+1HQXLgbuXo3g3iVQbxMzVsc/U0V0WiYtPGcGlPVe+hGp4whg8FguDO42bZ4V2KGsFWjvNKZzPNriFHa5kznUZIswHOapHlEzV+gF53hhZWPcWDu7Sx3Ht9+Jq2KRh5doxeephueYqZ6H57TYJhs4liVkagqLOEgsdnoHwI0K50vk6mYJO0R5wGuVUFgcar1CLlK2DP1Ndu6ZHNnkmfP/D5ZHlMr70AAGjiy+iksy+NVi39ne1ftedep7E2RqQgpHRy7DECS9QniNUruBEIIbOkRqQ6t3gt8+2cOUg/P1mnXq11+6/WfJdMJtvKR0saxypTcSSzLY6P/FTRQL+28YDdys7yHtd4zVP050iwY7xNtlHYyWdmH5zToDI/fsnVSI6QGg+GyeTnMAi7F5c5ADuPNq57JPLf5aDN4gUG0Qpz28d1JorSD79SZq78K35mgFRzm+PpnCdP2tjO5do2KN0M3PE3FmyOIVpgo76XqzxNnAf3oDHV/J2kWUPFm2AxeoBueolHehe9OkOUh64PnGIQr1Pw5Kt4MWR6R6YQz3cfY0XyYkjuJ1pqVXtGkJIRkSpbw3SZR0iHOeuhMs9z9MjuaD6PIxo5B54/SnN/Jm+uUXKeURt2ymYqZWoE3fa687Vp96s0nOFY7QcWaRamMVCWU3Uly0nG6OskCEFx03MazazhWiYnyPsruxIucjeJscEsbMtxx+0gNBsON4fxI0LOrSGHh2VUmKwcI0w5n2o9xozczXu4O1UG8ctkzmRdiq/mo7i/SDU+R64wsH9Io7WSx+dA4nV31F2gNj9ELl0jzIXHaQ2uNEILJyn48u4olHbTW9IanUSrDtcqU3SlyFZOrmJIzAUCjvIvFxsPjeVWlMmZrryLJAla6T5KrhIa/iyQLaAVHi2altMd6/xC29HCsyigdLUfRZRGFntj8Cw6vf4oTm5/nxObnOdN5lGzULbsV5W1F4YNoGaUUWR6Rq4Qo6aCznDf9YbRNRNerXf7bN/0Fm3MpUlogJLZVQukE364jsch1SpqHuHYFx6qQ5uEFr7UUNhVvlihtFyM07hSeUx/bMQ6iZZql3caQwWAw3N7cSFu8K+FSM5BbIyLAZbsPXazmW/MX2DP1tbSCo1T9+VHHam3b68/ykI3+c0Rpl054Es+uUvFmxp2+i82HWe09XaTBLY9hsoHvNJhvvJbJ8l6m6/fgWjWOrH0Sf5RGjdMeQbyGLRza4VGCeIMk6yGERaqGVN1ZgniNJOsTZh3irINnNxCAFBaM/qt1RpIHJFkfoaHqzY0dg/rxChPlu8ZR3lYUvjE4xKGVP0KpnH60jLO8zNc9eh/nxl1f/LqAx+zP4+gyEgfHKhOmbdBgS4+KN4Mli3T1IFpmunYfaNgMnt82brP1QWAzeIG5xgNYuLflFiIjpAaD4bK4HrZ414PL3aFa9ecvS3CjpMNy54mL1nwdq0TJncSxyi96njBpcar9CHE+oFHeTZINsK1yIVTRCrP1+yk701TcWabmDjLfeBAool3XLo8FuxcuketkfG1znRKlXaK0S64SXLuCACxh0wuXyPII32mS6xShNVoVJgr10gL26Dls6aN0TpoG2JaPZRXdr45VwvI81npPU/Pm8Z2J8QeJfrhMnA6KaDXP+NufOUAtONuRG075/NW3KNYHx5lU+8d/EwKJ0JDphMnSfjRFansQr1L2Jtkx8TAAw2RtLJRZHrLef45WcARbujiWT720E8uaIUo7t9UWIiOkBoPhsrjcSPBq61iX2wl8uTtUy+7UJQW37M5wqvUIUXbhmu+BubdjSbcYRxkcZq7+GuTI4k5rzebgCINwhfnGa5is7OdM9zH60RmyPKYfLbMxeAHXKiOFzWz9VaQqKIQ62b0twjr/2kpsorRLkgVU/dmiZigdmuU9iNCmHy6TqxS0REoXIUCphKo3P37OTEVIYSGkBQiEcNBakamYKGlT8+dxLJ+NwVdoB8dpD09wavMLtIZH2dua562P7Nx23T/7NesEO6pMevuY5C6EsEmyPnE2oCo9SqO6rhCSTEWU3ElmavduE8GtcZuV3lOsdJ8g1ymTlX1jd6dBtIxvN7lr6uvw3eZts4XICKnBYLgsLjcSvJo61pV2Al/ODlWt9Xhc5MTGXzBRPUDVmyVTUfGG7TQRAqLsws1IZzqP8TfHfoWqN0eQrNMaHKYzPMFC8yHqpUUG8Rpr/Weo+fNMVQ9QcieZrOynHRxjmGxiSZssD5BIpLAZpptMVQ+M7QDPbc46/9oCaDSI0exqOqDsTVFyp5G4hEmbKOuxMXiOmr+DnZNfxXr/GfrRMkqleE5jlNINKDkT1PxF0DmDeBWJTcmdoOotECRrPL/8MYSU5DqnPzzFd37uARrB2Q9DvYbikbcL6tWHcPKExebrUTqjF50iiDbohMeJsx5lb4bJ8j4mKnuZrt5Ds7znRSJY8xe4e+4dxFmPLA8Lj95RLRQYN4G1h8c5WL82Y/6XEyOkBoPhsrjcSPBK3/yuthP4pXao9qNlDq9+ilPtv6IzPE6UdDjV/msa5R3M1V/DfP3VTFT2cmzjcxes+UZpm368zDDZoO7vYL7xIBV3hjOdxzjV+iJT1QNI6VJyJ9k5+aZx92yYtCi7M0xV7yZXKau9pyh700xWDhTNT8MTYzvA87uGz722lvTwnTpKZ3TDE5ScCaruLMNkg9bgKFLY2NJDo5BS0CztoTM8QWd4gu7wJI5dwbNrWNJmoryf/bNvw5IOQbJOLzxNnA1oB4/QDU9S93eyd+rrEC8c4gc+9+C26/D5r22T791NEi2z2nmSzvAEJ1ufH2cemuXd3Dv/nVjSRumEsjvDYvNhLMu66O87StuEaYfp2r0vymzcDku8L4QRUoPBcNlcKhKsevNXbF13LWvDLrRDtR8t89Sp/8PJ1iNI6TBdvQc0dMNTxGmAVikLzdcB+oI13y0jgjxP8O0mluUihUWjvIuav4PV3pM0y3vYOfFmjq5/alyT3Jq7LHuTOFaJMG6hdU7Zm0ZKScmdGDcIeU79RYJxvkWhUhllZ5KyO4UtHYJkg360jJQOE5V9gEIKi8Orf4olPRabDzNdvZf28BhhsoEUNjsnvwrPrlFyJ4nSNq3gKEG8TpZHdMPC4CAMW7ztTxs0h5XxNejWEz75t1bJVUQjLdMeHmOt9zRCSCbK+6mXFnGsMqfbj3B49ZNU/Vkcu4Qtfaar93D/4ney0HztBX/nt0qt/XpihNRgMFwRF4sEB/EKz6989IqMGq53J7DWmqXWo6z2nkEKi7IzhUBg2yWma/fQD8/QHp7gTOcxdk2++YI13y1BdO0KWudY4myzjZSSyeoBorRD2ZukWd4zTseeO3eptWaYbhYNSladNB+S5ylx1idTCR4XFoyta7tz4k1UvTk6wxPM1l5Nmg9Y6j4KCErOFBuD57CkTS9cJs76eHaVYbLG3qm3Mle/j0wl9MLTNEp70Dpjc/ACg3iVIFodpYW7WMJhd3uOdz3xhm3X8COv+2s25hQ1PUeSDdnsH6YdHCdXCROVg1T8WaK0yyBeI4jXidIWluUwX38tqQpY7jxBNzzFV+3/8QuK6Y2utd8MjJAaDIYr5vxI8GrTs9c7OgmTFqfbj9AZngAgSDawhIXnNKh585S8ScK4xUb/K+yaeDPN0m7WB8+h3bmxWUGmktEKsYhGeReuvX0Ly9aZchVvS8c6o7naKO2S5SGeXUfpnI3gEHkekuYRSmes959BilcjhEOuUsKkvS16F0JQ8ac5MPdNvLDyMTrhURy7SpYPsaXHSq9YUVYvHSgaprxJkjRgufM4k5UDNMu78QDb8sfG+ivdJ1lq/w39eI1cxTT8vbztUxM0Bu74dW2UO/zhW55ASJc47YFSoBVaQJIPcOwqVW8K2/KwhFPMoqqIqr+DNBuQ5H3K3hSe3WCt9zTPnvmDbc1ZW9zIWvvNwgipwWC4Jq4lPXu9o5NOeILV3peJs17hdytc0nxIL1wiTNpM1w6CgCQLyHVMvbSDw2uf5GTrCziyXHTYSod+vEzDL7a+nH/mc89U9qbG6dj28ARaa/rJMrPV+yl7Uxxd+zMG8SpVbxFBQsWbYRi3OLbx2fEIy+G1T2Jb/oui93NTvcu9J+lHq+R5hC1LzNbvB6AXLVGy6jiyRDc8RWtwmEZpF0KIseD7bpM901/Lau9phLRZ3Gzwho9tr2F+9PXP8ELlEFbqMFm9Gykc+tEZbMvDs+uj8yziOfXRNQjIVIyUDpawSXVOnHax5CgNXtrFxuAQreAI07W7t/2sG1Vrv5kYITUYDNfEtaRnr2d0orVmo3eIXCtcu06ex4R5izQfolROkKwTZz0apd24dmU0P/oYvtPEki5x1iXJhiRxgMTCtav4I8ehlzrTVidqKzjCZGUfy53HEcKmF57Bscr4dpNBXJjW10s7yfKE0+2/wrNr7J58C/XyjotG71up3qnqQbIsYHN4lJq3iGuXSLMhlijcg6CY24zSzrgGe/6HEFdW+FufaFDrnxXRTjXi/7zxz0cm/Dm5SuiGJynZTcruFL47gWP5xFkP36nD6PeTqwytNRJJmvXJ8pBueJpBsl50KUuHJOvTDU8yVT3wor+Ly+m6vp0wQmowGK6Ja0nPXs/oJExaDNM2M9X7ON1+hH68hBQujlPCsUrI3KIXLoHWHJz7VlrDY4Rph8XmQ0BRG811isRmc/ACUdZlM3iBmr/4kmc6f3QHIRgma2wOnse169RK85TUZBF5Z0P68TK+PUGzvIuS20QgQSt8Z4JueJql9qPcM/+t4+cXQjBVPcBU9SCrvaewS0U61rZKeE6DINoAAWV3CoQkUwk6Kc4+U723MFw48jxv/h8rwFkR/dSbjnK0fgKdg5ASBxeBRJETZT0q3gwlp8FEeR9SFA5FJWcSISWWtBFCkOZxUaO16nhOE1AEyQZBvIZSKc+vfpQsjy4oji/VdX27YYTUYDBcE9eanr1e0UmmIpROmKndy2r3CTIV4VoWaE2mU7I8Qgob16mT5APS4WBbFL2VtgSYrt9LZ3iCur9I+BIuOxerDafZECEsFpqvpebN49pVkmxAmLbRnRy/MkGc9wji9VEkuk6uU7RS9KNlJit3MVt/1fg8Qgh2NB/m8Non6YQnqfnz2KOl2D19GrTAscrkKmGp8xiD6EzhFiQ8gv/wL3Bbwfi5urWEv/wWxcn2UYJoDYGDY7nYlk+jvJuZyv1sBM8xU72XHRNvZBAv0yjt5NDKH9MNT1D2ZrCkjxSSYdrCs2pMVvcjhKYfrZJlEWiN70yilWB98NxF6+QX6rq+HTFCajAYronrkZ69HtHJ1hLoKO3ijSKpKOsQpT0EGsvymfDm2D/7TURZF3ROrbS47axbUalAYkuPPVNfi2OVLniml6oNT1cPstZ/hkG0wlTlboQQeE69eG5hgYA8TzjTeZRcpZS8KSpOYRbRCo7w/PLHQbPN3Wemfj8HZr+Z4xufJTln1dhi8w2kWcDa4FnSdEjJK4whDvTv4u7fO7PtGvX+3rfwufT/sNR6jDBrI3GwLAcpXUCQ5wlBukqzsodqaZ7F5oOcbkeEaYd9M29juftlusMTJHkfx6rgWRm+20BKh2HcIs76aJXj2TXmGq8hUwFVd44gWXvJMaZzUUrRCo4QZ108u8FkZf+LGpZuNYyQGgyGa+J6pWevNTrJVMwgXmWp/SWCZIOS3aTu78Kzq9h2mSQdFCnSygHW+88isMZRdJi02AyO0AtPkY62nrh2hZ0Tb2T31Fdf8OwXqw1vbb+perOs97/CXK3oXM11SpbHSGzaw+MM4w2USnHtaiEaTgPfaeLbDZa7j9MKjzJZ3o8mp+LOMFW7m5nafXSHpwjTFmVvBlt4rHS/zGq/aEZSKidJ+7zzz/cyMTgromHT4djfexDf04gzFtKysXIH2ylhCw/LcnCtKkonAOxsvpEk7+O7zW3Zgp0Tb2S2di++06RZLgwt+tEZusPjDOIVLOlT9eeZqd5DxZ9lEK+iyC57jGm58wTPnvkDNgaHxh9eLjWXeitghNRgMFySS/ng3uzmkcLJ6OOAoOxNE6abaDGKMFWIzyRVf5apamG07jsTlN0J+tEyuTPJic2/oDM8RaZCoqRLlHWxLZ84Dbh/cIQdkw/jO9u9Xy9UGw6T1sj0YI0wbdMZnuTRE/+VamkR1yqjNbSDI4TJJpb0qJbmcawySmcE8Qat4Cglu0HZm2UYbaC1ohsuMYiWUCqnNIpQNbrw842XSdI+Qthkecjuzjzf8/Tbtl2bR74+RO1bYH3jk8VibSx2T75lZPqQ4No1Jip7i/VzOkOrvJifPacz+ULZgjBp0Y+XWWw+TD88xan2F6l6C5TcKaSURUpfOFjCuawxpuXOE3zxyC8yjDdplHfh2TXirH/JudRbASOkBoPhJblcH9yb1Txybop1sfkQE+W95CqlMzyOa1WIs2Ih90LjdeNF3NPVe1hovo4XVj7O4bU/pR0cAyRx1iLNY1y7gmdP0B0e50snfo3Da3/KVO1uKt7s+LWfXxsOkxZLnUcLf1t3Aq0VtvRJ8oB+uES9tAPbKqMBhUaQI4WNQGy9ENIswBmlqJf7TyAHFrlKUColyYdEaZeJ8l04dpkgOkGcdXGsCrbw+YG/fivTYXN8XVrlPr/75i8hLRt35RAITbO0l1wnVNw56qUFgmi92N0arTJZ2Y9vVxhEq2wMnmeiso9eeAaNpuxOvWTHdb20k1q4iGNXkFKitSZK2tRLO3DtGkkevGSdXCnFs2f+gGG8yWz9gXEqt+ROXHIu9VbACKnBYLgoV2q0cDOaR85PsZa9Ke6e/WZOtP6SOO3TsPcgkeQqYXPwApblUistYEuPHRNv4Okzv0uihiRJn1QFOFa52OVJSph2SPKAijdDrlJ8u7FtM8yWkDjl/bSCoyRZUFwPrVkJnsR3miw2HqYbnaTqzTJVPYglXPpRsWYtyQJSEWIJC9+tk6kYjWa58yhx1qfqzQJb6WKN1oo0H5KqIXEeYEufXZ05vv3x12+7Jr//6r/geHMFUkUWReQ6xpYlhvEmtuWRZMNxs1CcdelHy6N9pzZBtIbn1FgffIVj65+m4s2ye+qr2D/7jS/6XW+l9AfxKq5dI4jXKbmTxGkH164yWdkPcMk6eSs4wsbgEI3yrhcJpZSSRvnic6m3AkZIDQbDBblWH9zLef7rEb1eKMVa9qbYO/U1tIKjDOIVBvEq7eAYluXiaJ/jG5/Dkl6xViyL8GWNTA6ougs4VgWt8mIdWhZh2x4SmzDZLKIzZ5bV/pdJ8pC9U1/HIFpltfcknfAUJadZbJcJl1E6Zbp2L5ZtUystkGYBihxNxlTlIErluHaFWmkHlrDJdUaU9OnHy8Rpl4o3h9YKS7qkeYBnF93GmQpJ8xBbOHzv33wt00Fj/Lo3Sz3+9xs+SaqionsWEMIuomOrjFIpQdpjEK0BIGUREad6OBpb2UBrKPsz1PwdY6emQ8t/TBCv85pd3/eiLMRWSj/XKd3wJJ3wOJPlYjWalMWi7kvVyeOsS6YivPNcpLbw7Bq96DRx1r3iv4+XAyOkBoPhglxvH9xzudK1aS/FhcZvtNZIYdMs78VzGnj2BJ5TL6LRc+ZCl9uP0o1O4tl1bFnFlh5pFhBnXYZxG61T4hxWek/guxPEWY8w7TCM1sn1pzjTfpSFidfhO3WStIcAbOlR8WZHKdHJ0Rk9It1BaF1494oibelYPmk2wHInkFoWkWbaxbJKlNwJhskmQhcGCFIUrynLYxZadb7zia/edh3+4DVf4MTEGXKVkWZDFBpLWGiVwyji9awqYdAmVwlSOpTtKVy7SpR2iNIBlnCR0iJK2nQ4hiVtXLuBQLDWe/ZFM65wNqW/a/LNdIYn2BgcIkzaxao0FV9WndyzG9jSJ876lNyJF90fZ31s6ePZjQs8+uZjhNRgMFyQqzVauFSkebW+vBfj/PGbs1tO1shVyjBpYVs+6B3M1O4jTnvkeYQlXZrlfWitiNIOaIiyFkkWjGZScwQ2UgiSbMgwXqcfnqFeWqTizxFnPaKsw8mNzzNTexUz9fuoevOUnCZaw8nW58lG1y9TMZZw8J0JKt4sm8ERPLvOfOPVhZl8vE6WJyiVYFs+Vbf48JLlIXmekGYDFApH+Hz337xpWxTaLg/48Ov+CMvxcGWVodokJwEEmVZIrCLizRNC1UFIG1RSzLbmQzQ5rlMjSrooneLYZRQZjlVCSIsw3SxMI4CN/lfYPflVL/rgtJVSL3tTLDRfd8WZhmbpLsruDKvdJ5ipPUDZm0SI4mcqpegOT7HQfO04VXyrYYTUYDBckKsxWrhUpHk90sVbQp3mRYrTsUpMlPcyiFY503mMfrxMniej7S2FCcPm4AVag8Oc7vz1KM1Zou7P4zuTeE6NXngGKSyUylAqGwlHjiLFkVX06H9ylSCwkMLCtco0y3uJkg6d4Qka5Z2FCcQoPVnxZuiFS1ieR5i0aZR24jl1Jsp3sd5/DiUzfGeCqr/IMF6nFy1RK81xpvUE3egUvXhAkvbJdAJasKe7wN975pu3XYs/fu3jHJs4hVQeSimivEuaDYvfHx5aKECigUG8gtY5Aodcx/haIYVNzd9Bo7SL0+1HSPI+FeGBLq69JV18Z6JwjUo2idPBJRcIXGmdfGvkZXPwPO3wBJvBUer+InP1B3DsCt3hKSreFPcvfuct2WgERkgNBsNFuFKjhcuJNC3hXlO6eEuoV3tPshkcLXxl7ToTlX3UvEWSvM8w2cC3m2id47sTJNmQXEXEaYCTl2mW9qB00akaZX2yrBBbxyoRqhZKZwhhIYREa4VSGVpk2NJHWg5x2isEqDSPLUvYdsww2iDNBpScyfEsbbO8l0G0wlrvaSr+HCV3arwDdNfkm6h4M0RZlyxeRemMyco+GqXd9KNVevGZQtS1wtIO737incwMz6Y8W36PDz/8URynjCvLeJZDPyoWdmtG4ik1aLCEg9aaXCcoMgQ5GojygJJdo+rNEKWdUaSeECTrOLZPrhNsSgghsC2PMG4V//d1XG927sjLVPVu6v4OVnpP0wtPM4hXma3dz87JN5k5UoPBcHtyJUYLlxtpzjcevGpf3i2hbocnGITLaJVRdqdJsmC0zLrFIFxlV/PNVPwZpHBYan+JQbRClHRH6dqU0N6k4s2Q5RFaqSJlapWYrNzN+uA5Yt1G60KMBJpcp1gqw7Jc0JokH1CVc7h2jfbwCFHaIUxaJPmAemk3ZbcQplzFTFTuwrFrDJNNTmx8DmDcBbtv5m1ESZvTnS/RDo7Ryo5ybOOzpHnIbO1+1gfPsn9wF9/26Gu3XYffuv8TnGiuFtcLj0zFQEyttEgpT7BliX50ijgPsIRbiKcoMgxpHqHRWFhIJZC4dMNTSOFiWR6ZKnaq2splmGxiicI6MMtCNJpm5a7rtt7sQiMvJXeCqr/AMNlkvfcsk9UDfP3Bf4lt39pSdWufzmAw3FQu12jhchuTpqoHr8qXd0uoh2m7ED+dUyvtQAiB7zQLYwWVEGU9oqzLtHMP3fAkK90nyEmR0sLGRwuIsx5K5yPR3kSNRFJKh4o7TZ7HCEBIa2zinucRSdbHsUuU/Amq3jyDaIVMFbVWx6piWz698BRJPmTXxJuo+sXoyqnNR+jHZyi5k7hWlTQfcmLjizxz+g9I8gCtFWVvCikk7eAYQlp4ssJ3ffH1TPbL42vQLvX58EN/jOvWKOtJtICyM02mIpKsiyPLTFbupurNstSx6YfLRFkXpVKkdIvXBAgsHLuMJR0yHRHELaYq+7GEjSM9EBJpuaRZyIA1bMsjzxMapZ3snHj9dZsLvtjIi5SSqj+DJV/DMNmgEx67JUdezsUIqcFgeEkux2jhchuTHKt0Vb68W0LtWTVayWF8d2LbdpSSO0EQbyCFw2b/Ber+DjYHh0nz4ShC7JERjBZ2a7rDooabqRghJI70Cew6cTpquJE+ntPAkSU0Cq0EnegYaEGzdHdh96ei0Yq0FeJsAMCZpMcw3eDY+mfYOfFG0jxASofdk28hzjqc2PxLjq1/hs7wNIqtqFvg200myntJ8yHzrSbf8fg9217/H77mr3iu8hVUnpHGEQIbS0oEgjjtIqVLN1oqUsV5gGtVmK4eYLX/LLHOAF10/koHR5aR0qLqzjJI1tAK+tEKrl2l4s1hS58oLZqu0nzIRPku6pUdHJx/BzO1+6/qb+hCDWi3+8jLuRghNRgMl+RSDSSX25jkWKUr9uXVWtOPlgmiVXx3ojB5l962589VyiBcJlcxw3QDjSZKW0jhgNA4lotSKUhBEK+NjA8UtvCx7RJpPiRM2khp4TtNQKMVZBRv9EpoXKuC0hlRtoHIJLbl0YuWGCYbuFYFtAap8ew63fAEYbJJmoeUvSmitEMQr3Gq/TeE8QaK7NyrS5R1We0+wz98+ruZDc5+iGj5PT780B+TqGC0d1SjVY4tNVq7hGkxyuKgce0GGk17eBK0purPU/XmkMIqxkaExneaSOEQRKvEeZ80H+LZderlRSbL++mEJxjGGzhWFUdWUWRM1Q6yc+IN7J/9xquKRi/WgLZluXi7jrycixFSg8FwzVxJY5IQ4rJ9ebfehNf6z7LaexrLcgmTDkrn+E4dKW1ylbHRf44k79Mo70Gjx8KohUTFLZRSCCFHHbc2ruWS5gNK3hS25eKqKmk+wNIOdX8nmS6akzKVIvLCtKDqzZGM5jyjtIPvFK+lGHmZIM0DBBbDdL2IfC1dCDmC062/IYjXiZI2Go3EQZECAolgZ2+eH3r2O7dd09+872Ocnixqr4ocyEf3yGI0RygYzcvmKiXOeriUEEjibECUdXBkBSkclE4pu1N4doN+tITr1PCsOplOmSwfwLfrbAyew7WrVEsLZPmwiEizELRisXl1fskv1YDmWQ1q/g5ag8N4dmNbevd2GHk5FyOkBoPhmrmcxqTz5wvvnnsHUdp+yXnT55c/Ri9awncmaJR30w6O0wvPsDk4RN3fiWOXSNIhcd5jqnJPEfE2H0ZISZx1ibIOKtc4dg0EdIdrSOGQqRBL2Ehp49pVXKvCIF4jGNU9HbvMzok3A7DWf4YkG4yibp+J8l660Ul8ZwJb+HhOjY3BV0jzmCjtkKoAiY1GkekQKSaIsi5J3h91zcpilZrOQMP/9dR3Mz+cHr/ult/lPz/4G8XoSi5hW/QKGtBaoHSGJRxylZDrBKEStM6RwiJXMXHWQ9DCtkqUnRlUnjPIVwBNxZ0lyto0/EWq/hxKK+KsR8mdZrKyn0yF9MIz7CztpOrN0wuXmG88eEUR6YUa0LTW4yXmvfA0zfIewqTFWu/pbUb1t8PIy7kYITUYDNeFl2pMqpd2sNx5/ILzpfXSjhc9l9aaI2uf4mTrC0hhj91yOsMTFFIiGcYt7LxELzqJIytkeUS9NM9C80E8u4kAVnvPEqc9hBD0ok2SbEDRcqPx7aIGKhAj0ZEgBJmKqdpzVL05ltp/TZoPqfk7yPJg7I7k2w2CeI1URThWiU5wlK1I0ZIOjl0lzxOSbFDY7qkMrUGjx00/u3uL/NCz37Htdf/GvR/haPP06DVu/TufDEWGUjaajJx0fE+ucrQo0tJgYwlr1Lmbk+RFHdd3msR5j5IzyVz9AYJ4jY3gBTynSZx1iLIOWR5R8+eYb7waKd2rcrA6vwHt3M04uU5RuuiWvnfh21jpPsnG4BC96DS29FlovvaWH3k5FyOkBoPhunGhxqRMxRxe/fgVORmt95/l8OqfkuQBAEka0IvOMExb2KLosB3EZ1CxIs8TMhnTGh5hx8RD40alemkXS53HCZJ1ys4UVXeONAvROi9GQCyXOOuQ6zKeU0OoDClsorTDSqdDP1olyQPq/g6yfDhulHLsKlHaLmYvsz6e3UBoCykt0jwAnaOkh0KT5zFDvUGuijSuQKM1vOfJb98Whba9Lv/ptf8LLYoPCYWAXir6y8hH0arEQ5OT6QihNQKJFIKSO4VrV2iUd6FURpR2ilVqAlyrSpR1SPMhKo/JhUusYipuj4nKPiYr+yi5kyidX3IF2gVPd04D2vmbcUqjeno7OEI7OMHr9/4IaT68rZZ5n4sRUoPBcF05tzFJa82h5T+hGy5RL+0ErRDIl3Qy0loX85/JGhKrqDeiyPIhEoswaZGrBMvymCjtJSdDa0WS9zmy/mks6VD1F+iFp/CdBlHSRpEVS7OdOnE6QFoOuUrJVYZtlVCqEAtblqj6cyTZgCjpkKmIQBQrxiYrd+E6tSI9KSSuVQYtSLIB0nbI8xQpXJTOSLMAIT08p0mWh1jCJVV9dvUWXlQL/Y17/38cbZ4YfSXP+a/gbF30RVd59N9CcBU5tnBROqeIexUCmzjrIWVxvefqr+Xk5uexpINrVyi5k9jSwxYe3WgJhaJR2snuqbdQ8xfHv4+LjSRdiq0GtCQbbtuMc7bbWlL1FkjzgJXuE1e9/OBWwAipwWC4Yaz3nuX5tY+R5iGd4QkUOb7TZKqyn0Zp9wWdjMKkxSBaRaucRMV4To1ueGo0cylQo3SmUJIo71OyGyihEAh64SmeOfN7TFfvpRueRGuF7zRplHcRJR3K3jRpHhWbWHRSRGVJl0CvIxCU3Ukq3gxTlbtZ6z+NLecRaGy7hDPqRk7zIXHaAWHRKO9kmLaQwhr58a6RKk2aR0idkmSqqGXi8t6n3sV8cG4U2uM/vfY30SIe3VIYJIBCoeGclO35SIomJkXMVqpao0DoUUOVgwAylRBnfXrRMvtmvqVIsaZtJisHxhGf7zSpurOsD75CxZ2h4s5v+1BzqRVoF2OrAe1M5zEG8Rqlc0aWzt1XOlnZf9XLD24VjJAaDIYbQj9a5vnVT9APT1Px5sYOQOvZs6x0nmCh+Trm6g+M5jnPpg0zFZGpuGgKIiVM2qR5iNJ5YdenFAILISyyfEgESOkSpx1sq0KU9EmygCjtIrFJGI46dqHizmBLj+7wFIN4lSQbjsRH4tl10jwsFoFbTSzh47l1sjwkTjpkfogipz04Sjc8TaYSXKuMbzewLR9b9gmTTdA5qY4RSgAWu3rz/P1n//a2a/N/7vkjjkwcpxBBiStrlP0p8ixhmKwDySWursKWZTI1Wh6uhmitEUJiCQspncIdWMVIbKKkQxCtYFke1dHsa8mdQOUJ3eg0/WgJpXI2Bs9zZOPP2NF8HbZVuuhI0uWw1YC20X+OfnSGycr+Ym2diomS9nhfqWOXCZK1K04d30oYITUYDNedrY7NNA9w7Qad8CRaFVtGfGeCIFlntfs0cTZgsrJvW9rQln7hfWuXUeR0ghOjTSxF5KXIkcLBli6OVSbLI4QqrPFs4TNM12gFR9Aj96JevESaDaj484Rpe9TkUhmZy4vCP1b4IHWxIi3doDU4gmvXKLkTuFaFYbLBIFojTFsE8TpxOsB3GtTLu+gPTxOnPSbKe8nymK4+RZ4pLG3xQ0/9bRbOiUK7XsD/etNn8NwJ5kVRv7Skg+cUYykbyfMgJOitFK8678oKJDae3aDmLzJIlnFlgyw+XWx5sZyRvWExWyuxsa0yoOglS1S8WXZNvLnYGzo8Ris4htI59fJuZuuvpj08Tic4SpisM994LfP1V1/Varstiga0t7MZHCFKOsSyhyWccSRaGDMMrip1fCthhNRgMFx3tjo2Jyv7WOs/U6Tx/N0onZIR4doV8jyjNzxF3V/Ad84O5JfcSSYqe1nqfomKO8sgXCZMO8WCagqvWLQYjYDk5DpBYtEo7yFK2ljSZbJyALQiGM10xmkXRl6uNhaRSpAI4myIY/kkYoCNi2cXm17CpIXSKUFcwXcbOKLKZnCIfrRGlscoFRPqlKXWl3CkT6qHZComTgJynbC3v4vvf/pbtl2T37v/zzk+dQbPqlP2Jpiu34tWOZ5TJYg2WUm+TK4ihBBIbY9mR4sPD1sILGxZxnebgKLqzgPgZCUkNoq0iNx1ihQ2tvRwLA+EoOxM4jt1HLvMQuMh0nxImifUS4vFBxIV4Vg+c/UHGcTLTJT3cPfcO6656Wemdj8H59/Jcudx6qWd2NLFtWvjcZirTR3fShghNRgM152tjk2l8yJylCVawxdgSwRRZCpirvZqHMsnStvj+pgQgr3TX8+p1iNs9A/h2g3ivE+eJSitAEFOiMrTQkSFBVIQJm2GyRqeXSdKW+S6MCnQ5OQ6J056OJY/SoFaCGmjdE6c9bGkM962gi6MDrI8oh8tMYhXEcIhzXtorYquX63J84QoO45EIqXHULTQKucHv/yO7VGoH/A/3vAJwryHynIcq9gDKhDkZDiyyvrg8wTxGlrpkYCCRI7iUcWWmBb7US3SfFjUM0tz5GkIFE5AmpxcJUXTkdY4VnW0zHwHr9n5/XTDk2wMDlFxZ0myAY3yThyrtK1mWS8t4rsNwrSz7fdytQgh2DHxMMN4jTBtU/UX0CiS7OJuVrcbRkgNBsN1p0jPuvSjZaK0hxqNfQixZSiQF9GV5ZGp5EX1sXppkYf2vIdHj/86R9b+jCBeRwiJa5VACzIdAarwxbUqZGpIZ3gcxyrh2VWSrE+qQoZJizjtI5Ak+YDeMMW1K2PbueLnhwghyVU8GrdRo32kevS1RGKNG3m0KM4OGksUkWOuYnb0F/mBp9+57XX8wQOf4/jUKipPAI3WCWHSwpYureAYSTbgdOsR+tEKWR5xNvoUCCxKVtFIVSz2Lra4pGqIJ6rsaD7Ejsk3EcRrnOl8iY3gBWxRpMWVzgBByZ1ECsn9i9/JXOMBKv40QbzKZvACcTbAd5qkebitZimEeMktPFfD5S4/uF0xQmowGK47mYoJ4lWW2o/SCg6jUVTcWTy7ipTFTs+KNw8IBvEalvBe9BwLzdfyNXf/MzYHh+lHZ9jaXeI5ZUqiQZT2yVVEmg0QwgY0tiiR62wkziFpGqFIAGvkQlRHa0iyPkoptM7QOj+nBltEf3o8drKVWrXROicnRmgLQQ4CbOHhiirf/+Q3sTA4G4X2vIAPv/5jZDJDpTE5+Xh+NVcJmUpY7j5BnHZGM5/2yKxBAmpktJAR5ikCgcDGlVXK7jQVb5adk29kqrqf/bNvQwhBxZslW/5D+uFpcp3j2lXK7hSuXWWu8SoOzH0TQoixoB1b+3M2Bofohqfx7Oq2miVc/cjLFhcyqb+c5Qe3K3eMkB4/fpx//a//NZ/+9KdZWVlhcXGRH/zBH+Rf/It/geu6N/t4BsMdwYXeIM9/I+xHyxxe/TgIKLtTtAYvAMX6slQFuFaNktOgXlogiNfJspB+vDza4nL+VpkQ36lz1/RbaQ1foB8VkYwlXOp+lVSFaJVRK++kOzxFlgckmYVAFD65UiNypzArUDGuVUZKl3ZwjDSPKeY0i4gzJxvJ5tbP3/pKjtyDihnNs+IqWexO8YPPvGvb6/+D+/+co1PLMIrCc52jyVC6aBQSUhKlXfI8AZGPPgTIIkWt820/X2CNbBxScg310g4e2PldNMt30QoOs9x5nIPz7+S1u3+QHc2HON3+Ep3hMTQa164yU72PHZMPb4v4av4CD+z8XhCw3n+OqcrdeE79mkZezv27iJIOreExuuGpF7lY1fyF23bE5aW4Y4T0ueeeQynFr/zKr3DgwAGefvppfviHf5ggCPi5n/u5m308g+G252JbPM5NzZ3rr7rYeJiSM0FneIxBvFakWKXEFmV8d4r28BhZVjQPPXbiw9T9BaZr97Fj4uwbf5x1yXTETP0+yt4Up9uPsJX+tKRLWU4TZx1KzgSJO6AfhmR5hG35KJUghYMlFLlO0RrCtIdrV9Fiy6iv+N+pGhZNTMWrOOe/enT72YYfjQIteO9T38liMDu+vesO+NWHfhfbKWNJF8+uMwhXUDpGIwrrQFkmJ0NioSQoBVpkxY5Qq0IyqukKXDQpmhwpXGxZw3MqlNwJGqU9JFkfS3qs959l5+SbqHjTzDZexUz9/suK+KSU3DXzVrI8JEjWENK65Baec8nznDOdRxkm68RpnyQPCdONkfnCYWzpsdh8iGZ57yVdrO4E7hghffvb387b3/728df79u3j0KFD/Of//J+NkBoM18hLbfE49w3yfH/VYoyliq8SElGs7eqGJ0fLtePRjkwP164QJR3W+4fYHDzPa3Z9HzV/Ac9ujFdt+U6Dur8DIS0kEiEtsjxBkWFbPo4oUpGeWyv8ZVVGpiKEAKEBNFk+xJYulpAgbHKtEbKoj17c33b717t6s7z72b+77bbfvucTHJ1awrebOE6ZJO1hSx8hHQQOrvApudNIKYv1bbi0hkeLtLOyUJZdRKvI8ZhPEfsKqt4ME5V9OFaZQbzCkbVPjSPsIkU+y4G5bx67Bl1uxHe1dcvDq5/k0RMfZq33NGHaIsmGuFaZmer9lLxJcp1gCWe0TaZwULqYi9Wdwh0jpBei2+0yOfnSqYk4jonjePx1r9e70ccyGG4rtqLMYdKm6s0VoqMVjlWl4s6yGbzAsbU/54Gd3/sif9WN/iGUTrGlT7k8RWd4gkwVG1+2NMuS9tjyL89jTrceoeLN8OCuH2Sysp/p6j0sd55gtvYqPKfJIFrGtnx0phimG0xV7qHqLbDWexbbKuNaNZK8X9RIVYTQcrRLVJHmEZlaJVUJILGFRc1bIEg2yfJgtCf0fCEdpXI1vPfp72FHMDe+p+v2+c+v/W1ymeJQwncaONJDyZRhsokUolikLWwY+RUBuE69SOlqiZAOriyjRDaqkwos4SGEhS086v4efKdBrlKCZJ1ueJKp2kFsXUIDneFJXlj52FVFe1datzy8+kk+89xPM4jXkcJGUJjip3nEau9JSu4kjfIumqXdJFmfVnCERadwNLqQi9Wdwh0rpIcPH+aXfumXLhmNfuhDH+Knf/qnX6ZTGQy3H2HSYqX31Gh7x2FynZLnCbnOsKWN0pqNwSEQMF9/zXZ/1XzIbPV+WsOjdIen0TqnZE8wzDZAF8byNX+eLI+I0vZ4ufTJzS9y99w7qHjT3L/4nXTDUyx1v0SS9ukEJ0hVCChcu0rN30EnPEauUsJko3AGUpARo8mw8NBCI0Wxi1Rqi0xFaDIyXXj3WrjkxLzYUagQ0Z39Bd7zzPYo9Dfv+SMOT5wa1TALK8AgXgEhyfOMTIVo1Mj3FpROqPmLpHkwGqmxikYj7MIUcBQ1q1FjktAOSOjGJ0jzPokK0CobzWL69KNlmqVdzNVfQ3t45KqjvcuNYvM859ETH2YYtyk7M8RZBylsfNdHaEGQrDJMWlS8OQbxCjV/J0G8TpL18Zz6de8EvpW45e31f/InfxIhxEv+e+6557Y9Zmlpibe//e1893d/Nz/8wz/8ks//wQ9+kG63O/536tSpG/lyDIbbjs7wBCvdJwjidRy7gmNVGIz2jvajVXyngRQO6/3nONn6K9Cale7jdIYn8J0mnlunUdqBlBJFjiJDqxzLKlHxpnDsMq5TJU67ZCqi4s0SxGsMohWg6N49MPvNhPEG6/3niPMBGoUlPaRwWes9RSs4gmuVCsN4NRyJ4sjhh5g0C4uREK1ROhl50QosYZGpkCTvjRZtn/eWqDXvfeq7t4loz+3zs2/8TxydWMLCHS9GE0gcu4IUNpkOUaSFD5HwxntDu+FpgrhNPzpT7A6VNmIUmhf14q2uYV0YFzgVBIIo6zOIVhHYaK3oR8t4dpXJyj6klNuivRvFmc6jbA4OUfFmyXWILV2g+IAipMRxamRqWOxCTbug8+JDly48g6+1E/hW5paPSH/iJ36C97znPS/5Pfv27Rv/32fOnOEbvuEb+Oqv/mp+9Vd/9ZLP73kenvfi1nuDwVCkdTcGh8h1Ss3dgS19euFpNIpmaQ9R2qEfnaHizVFxZzjV+iK5zgiTTTrDE0yU91H1Z0nzwgu25i3i2CWyPMaxykjpAGAJh0QHxRiK2C5m3eESR9c+TaZymqW9xWykFCRpTKL75HlEmHbQOqfmL5BkQ6K0ix4vxFYoYpI8xxElLMvCliVSlWMJHzV6oy86d88axV8oCv2te/6IFyZOUnEWsCyHJOsh8gTHLhVRZ67ISQvTByyktJFCIvDIVYKmSClrnePadVy7Tpx1i1VmIscWJZRKUWg8p0bFmyPJe8RJD0u62HaJbniSxebrmaqeHVd5OaK9YbpRpO09nyjrYEsHIQp3KSksHHwiLUjzIY6qkKoQSzhYwrljHIwuxi0vpDMzM8zMzFzW9y4tLfEN3/ANPPzww3z4wx++rfbZGQy3ImHSIkzaTJb3jaIdTZx2ce1qEYXYFfrRChVvjo3B82QqQkqX3ZNfTZR2We8/w1r/KWxZIsp6ZHmMp+pFpJK0ybMExyqT6xRrlOocxGtU/Dmq3jy98AxfPPILnGp9kUxFuFYZRYJnTWDbFYb99WJmNB8ghIPn1AsT92yA0uc3DmWkOiDPihVlGk2mhyhSJM7ItN5GqYQfeubb2TmYHz+y7w75lYd+l4gBlrDx3Rq2dMnzCMsuGqqUzojSNlt7R4toE6SwsaQLWo1SzDZIl8nKQTy3Qjc4RZi2cOwqnl1jmGwUNWPpjZZxSyr+AjV/npo/j0AwXT2I7zbG53s5or2yM40lPdIsQgoJSCzpkeUh0iqRixTbcrEtnzQPGMabTNfuAQSt4PAd4WB0MW55Ib1clpaWeOtb38qePXv4uZ/7OdbX18f3zc/Pv8QjDYbbk8uZ6bzW5w2TNpmKmandx0r3CfrhGdI8wrEq5CohzQZIYaF1RqoSGqXdBMla0T1r10nsPiBAKzyrTqZC7Nwv/G3TPp3wBBMCcpWNbOnaaJ2xa+LN5DrhmaXfZa33DLZVojCYtxjGLZJsgGfXyVVMFLdGadmUXrhEnA5G68Uu+OpQ42Ts1gJt2JoL3R3s4vuf/MZtj/i9+z7D0anTxb5RbeHadWaq9+HaJVL1N8RJMbLiWsXC71wVrkIgEUgs6YxqiU2UyhBS4to1aqUFGuWdlNxpNvvPs9h8HbnOWOk+QbO0j6o/XRjPC5tMJZScwrYPIUdNUWd/Xy9HtLfYfJip6j2sdL6M70yQqgGuVR3tXx0SZ30q3hxVb6HojrZ8LOkQZd07xsHoYtwxQvrJT36Sw4cPc/jwYXbu3LntPq3P78IzGG5vLmem83o8b65S2sEx5uoPsNh8mNXeU/SiJYJkE9cqnIJK0iHXGWV3amQgb9MaHkZKi7I7wyBaJlURE+W7CJJ1hsk6vt2gUilqoa3hMareHFK4SGGxa/JN7J99G8udxxkmm5ScCaK0wzBeR4vCXjCIA7SAPI/JVDyWw0wNeXHX7bloCmOFwglpq0tXacX3PvH126NQJ+CXXve/UDKHTIwchiRCCwbxKq4qYwufRAakWYi2BZbwsW2J0mnxT+WkWQgiHLsXeVadur+IY/mESQfPriGlRaYSlErw7DoVf5qyVzglpXmIyAImqwdY6T1ZRKx54ad7uXOf1wPLsnh4z3v5THi2a7eo6VrE+WD0YaGB51S5f/E72Tn5ML7TvKMcjC7GNQvpiRMnCIKAe++996amUt/znvdcspZqMNwJXO5M5/V43iQbstE/xPGNz3L33DvYPfm1AHTD01TdBeK8i283CuN34dIeHiNXMYNordiJqbPCfUgrchVTdpoj43hFzV/AscokecBc/dVMVO5iunIvU7UijbzWf5aqv0grOEaaD8l0ApqRtV8hornOzqmFXhlq1Nizsz/Pe5757m33/e49n+T5iVNoNJJidAUKIYzyHhuD54pOXZWh1KiuO0olZyokzWM0W7VXG5Rg3BGsBLE3wM0Cch0BEqVzhvE6883XUfHmiLPuOAAIkzaN0k7qoy7YmjeP0hmd4fGX3a/2wNw3AYznSLfO2SztZkfz9eyZ/hp2NB9mpn7/HS2c53PZQvrrv/7rdDodPvCBD4xv+5Ef+RF+7dd+DYB77rmHT3ziE+zatev6n9JgMADbnYMmKwfGb1aeXR0PvS+1Hx2lRotIFQ25jl8yMrjY8/pOjT1TX8Pzqx/j+MZfsHPyTVT9BYJondbwCDV/nrI3Qy86wyBaIVURrlXs2Cy7UyT5AK0UluXSKO+m6s8icelFp1hsPkzJmaIbnuTg/NtHG2KOcWzjcwTRKqu9p5ms3DPa4AJlZ4p+tIwiG9UPh6OU7lVcR3LQgnc/83fZNTgrQH0n4D+97jfJZQpY+HYDsEiyPlC8jiyPiLIuW7XQYmpFkWcRgq15UUUhvgK0KMz6dRG9aQrRjLM+aR6gtWKqcje18iJSSKaqB1jvP0tneHz0uieoeHO0h0eYqOzhwOy3YEvvpvnVHpj7Ju6a/ltjZyMpXKar94ycl+7syPNiXLaQ/uqv/ir/6B/9o/HXH//4x/nwhz/M//yf/5P77ruP97///fz0T/80/+2//bcbclCDwcCLnIPORQiBLX2eX/ko6/2vkOZDBtEqCEHVm6XizV40/Xv+82qtSbI++Wiv5WTlbk61vsggWRmZC4AQFlHa4UznMQbRMsO0RdmZYbKyHxUmKHIs4WFZLlI65CrFliUyFeHZNSruDIkajhyKYlY6XybKulT9BVyrQmd4ks3geYbxGr7dINcxTlZEyXHWG9VBi9rmhR2JLs6u/k7e/cx3bLvttw7+MS9MnsDCRaGQMNqkkmNLj1SF5Hl6zs9S5/zEwuhejwzmt9LHtnRQKkeNunht4SGlIErblOT0qP4ZkakQR1aJ0g6O5VP2ZoqVbkJQ9qYRQjBVOchk9a7x66z5izdNtCzLYtfUG2/Kz74VuWwhfeGFF3j9618//vojH/kI3/7t384P/MAPAPCzP/uzvPe9773+JzQYDGPOdQ46nzBpsd5/ll60RL20cxT19EZWc5KyO3PR9O/5jkSt4ChBvEaUdhlEKwySdXKVMFu7j0Z5N55VZ71/iFwl7Jh4IyA4tPLHZPmATnAMW3qESRuJhT9y8YmzLmk2JMq6+E6D9f4h1gfPUnIm2By8QJZH7J78alyrgpYK2yoR9I8QJBtUvXlcq44U61gyK0ZWVCFOSiejidHLiE41vPuZ72LXYHF8U1EL/R8oqZA42HYJlRXRpCU9JJAwQGqnSCNrOYo4R084Ek8NWMLGsapk+RDXquHaZaS0CZM2QlhYo07fRAXYWYBtFfaI/WiZzvAYc/VX0yztZc/012Jb/jibsGUEf2zjc9e1Jm64Ply2kIZhSL1eH3/9hS98gX/wD/7B+Ot9+/axsrJyfU9nMBi2YUu/GEHIQzy7Or5da00rOFLUN915+tESw3iTWmkRS/h0wxNsBi+wWH+IIF17kQvO1vP2wjNsDJ4jyYKi4zLtEoyMyV2rUpjDpwM6wXEcq4xj1chVRLN8FxPlPeQqox+dJlMxQhR+uFOle+iHy/TC0whkYbgQrRNnR/CcOlJYrHWfIVcJg2iZur+DYbrB5uB5BtEauYrpDE9R9xfIdIxEUPd3EqZdkryLyi8vEt3Rn+e959VCf/vgH/P85HG2oryt3aKWtNBao3Q2itAzLOkU3rzCQo07c8/+E4gifatVsWLN9hBS4NoV4qywHi25TYbxJlJb2JZP2Z1GCkmYduiGJ/HsOq5d4oD1zWO3oX60zOn2I9e9Jm64fly2kO7Zs4dHH32UPXv2sLGxwTPPPMNb3vKW8f0rKys0Go2XeAaDwXCtlNxJmqXdbAwO4Z5Ty0yyPoNojSQfFind/hqWdAjiVXJdrP/qhMeIkw5lbxqlEnZNvnn8Zl1yJ2n4u/jK8h+itaLqL9AeHiHJA6Swiz2iwiFRITV3gY3Bc1T8eWruDoJ4naq3gGV5uFYNz6mRq4S5+gP0oxU2B4cI002S0Wxhmg1x7DJTlYMkeY8o62JbPjV/kW54gsPrnyTPkyKCE37RnatjetEKUgg8p0bVmwEEcdqBSzUbXTQK/e8oqTibGh6ZxWtGi7EZ7SotPqjYlk2eJ2idjfbGyJH935bRfGEVqPWoGUoVzkZFbXTrLIJUDZHYlJzJkTinOJaPZ9XIVMhmcJQ0D0c//9I18TvVCP524rKF9N3vfjfve9/7eOaZZ/j0pz/Nvffey8MPPzy+/wtf+AIPPPDADTmkwWAoEEKw0HwdreAIZzqPUvd3UPZmCNMO7fAYSuW4dgUpHWxZoh+eJs4HOFYF32kiLZdhvEEvPEUnPDEWUiEEk9W7yFVcdKZmHaK0gxAWSR7g23Uq3gxJ2iWz60jpkeVDQBXGC4NDDKJVwmQT32kCRbpY6wzfbmJVfJqlvdS9RY5tfoaSO1VEemhKzgT9aIUo6RImHdIsIFfZeHsMolhbJlBoLVAKetEyST4YuxJdjB39Od77zPdsu+13Dn6cU7N9ROYDxU5SMdq8AsWy7y2bvijvIVQRWecqQ2s9EsazM6iF1aBDposl3FpoXOkXrrk6Ic3jYhuNzkizAK1zPHcK23ILU4g8wrZK2JaPa9dIsmJLzjDepB8ts95/lpq/44I18TvZCP524rKF9J/9s3/GcDjk93//95mfn+d3fud3tt3/l3/5l3zf933fdT+gwWA4Sz9aZrnzOEk+pBeeYbX3FLb0iyYhLDynykRpDyvdx2kPj5KkAyzLIUw2yVTIrL4P352kMzzORu8QC43XndOh22SyeoBcpXSHJwiTDq5VHjUGTePatcKKTwgc6ZLlMUG8QT9aBgTN8m6EkERphzyPObnxl7hOrZgZdWZYbD6IRlP154mSNqeDo/hOgzQb0h2eJErb53TiCqS2ENJHa13s7yRDo0izPrmOClErKpMUzT3noOGHnv0udvfPRqEDJ+S/vvEjKKFwrBpKp+R5zDg1KyQCa+QNK7CEjcRBWoUfrha6mPfM061e3bOmDqLw7d0SWteZQAtFniXkIqLkTZFmAXE2wLbKONInU+kojexiCQfPaaC1QgjJyc0vkKqQIF5ltfs007V7ma7e/SLDhTvZCP524rKFVErJz/zMz/AzP/MzF7z/fGE1GAzXl3PnPD27TsWbJckDgmSDOB2M/Gt9lM6Jsg5h2sK164Vhus7I8oh2cJyyN8VkZT/DtL0tkrGlT8WbxbPqlJwmSR7i23WSLCBMW2QqwRIWrqzg2BXCsEs3OIlluzTLe5GysIzb6H0FJX368QpVNLO1VzFV3Y/vTLDe/wqd4ChBvEE3Oo1rV0YbyjSZzor9nEBhlqDI1LDYkCJt0KIwOUBBnp2z0cUjV+nYzehCUehvH/wTjkwuUbKnmC4dIFdD+uRE+dm1icXYphgv2XYtHwVFJJ730XkxLzqkjdIxSuXokVMSqrA6dOwKmQ7R5Ph2k3Jpmqo/Q720E61z1nrP0Q6PonVOnHZGvx8P1yohhESpjFzFdMNTTFYP4FoV2sOTdIbHSbJeMTJ0jpjeyUbwtxN3jLORwXAnsWXTl+YhaR5iS5+Tm59nmLQpu1Oc6T5GkgXUS4tMlPeyMXiOOGqjtSKINxBaInHJVUquo2J9mHQI4jUcy2e6es9oZ+fZSKbkTuJaVY5tfAaBRZhs0h0ep+xOjW3o6qUdWNIbj33EeZ+F6msRonhTT7MB07V7qXpznOk+hmdXmarcTZz1OL75eda6z9APl4myDnEejEZDCiN3eHGNT+msqEGKrWXXGk2CxsESEoE72kICOlf8/WffdV4UOuQXX/fhUS20SN92o+MIIbFlIWAoTU5W1ENJsYSLY5dwrAoIzVz9foJkk0G0jIVbzHkODhElRerbc2pIYZGpFK1T6v4u7pn7VvbN/C0a5Z3kKhmlYedJs4i/OvoLLLUfLTbRkOFYZar+HHV/D+3hEXxngrn6a4ol4FrTLO2iOzxFnA627fe8043gbyeuWEgnJiYuWNQWQuD7PgcOHOA973mPGYUxGK6SLZu+ld5TtIOjxFkPSzhEWY+p6kF60RJJFlA7Z5a06i8QxOvYlsMw6eHYVWyrVWwV0QpIkELiO00s6RUznudFMoN4hWGyThCvE6U9lE6I0l7hUmQ5NLwdWNJhY/Asrl3n4Nw72QwOYUmHQbyKJRzqpR1MVvYjhU03PEmQbHCi9Rds9A/RDU+T5SGZzkbNNJpUFabsjFyGXowqolW1dV8Oo/VnxUqxjFzDzsEOfuDJb972yN8++Cc8P3n0nFsyBvE6juXh2jUmKvuJ0haZjHFFsYkmTruANe498uw6VW+eij+LbzfYGDxHlPXwnQYVbxrLKhURfx4xTDfJlaTu78C1K5xofQ67W8Kx/G3jKl994J/yzNLvEsQb+G4Tz66jUQzCFYSwWGg+NHaJE0IwWdlHmLYZJi26w9NMVg4gpfOyWQMaLs0VC+lP/dRP8W//7b/lHe94B298YzGQ+9d//dd8/OMf533vex/Hjh3jx37sx8iy7JK7QA0Gw3a20rft4Un68TK5Sim70wTxGkG0VuymTLtM1+7Z9ubp2w0cqwxCYgmPifLukShIPLtBriMcq4xn1RjEK2z0v8L+mW8cRzJb3aFR1qXsTpHmIRKLqj9HlPaKGVAVUfd30CjtZsfEG5iqHuSZpd8ptptYRZ3Ptask2YBcJWRZzMbgBWxxnGHaZhi3yNRwZOl3rmieV998Efq87ylEVAqLXGd831Pfwq7eWY/cwB7yCw9tRaGwZSBfiHKG1iXyLKEbncS2SuRZ0RSk8rywNVSQq5gk0zRLe4qVaELiu02q/hxBtEmjtItcpWQqJM9j0jyk7E7RLO1FCouNwSGitEO1NM+uiTdhW6Vt4yoP7Pzusadxmg+wpMdEZS8ITb20yLmU3El2NB9mY/ACG/3naAdHqfhzd7wR/O3EFQvp5z//ef7Nv/k3/OiP/ui223/lV36FP/3TP+X3fu/3eM1rXsMv/uIvGiE1GK6ALTEbJkWKVqmMeqno1rSESzc6TZZHJHlAmLQpu9NjMc11QtWbQ+mc7vAEreBYMdCPKLZ02FVq/gJpHhGEywzjFvON144fHyYtOsMTpNkQIQQ7J95ApqJiPyiSIN5gffAs/WiVmr+Tld6ThGkb164xTNaZ9A8QpW3OdB5lEK3Rj1ZZ6T5BnPUKg3W2Fjyri1+Ay0aR6oDdvV380FN/e9s9v3PwTzi0LQrdmvPcinhzNAlKK+KsgyMrSCHIVIYeuThlOgZVzIKGaYsTrc+hVEamYpTKSPMhE5U9TFXvJoxaLPUeJc0DXKtGmLQIkjUa5d3M1h9gEK/QGZ5gsfkwk+eMq9w99w52Tr6JZrQHgKpXfBB4+szvvGhGGAoxnandj+80ODhXzI2+Uu34bkWuWEg/8YlP8O///b9/0e1ve9vb+Imf+AkA3vnOd/KTP/mT1346g+EVxJZNn+vUxvsbt94oHbtMzZunG57GliWGySYNtRPHKo/rqa5dJk4G5CqlNTxcOOtYJUrOJFLIos6W9XDtKmHa5sTm57hLvpWav1CsTEtbREkXy/JJ8gFS2rhWlSQfEGYt0EUtseJNI6XD5uB5oJCpM91HGYTLhYFDvMlmcJgo66KVGjXfFJZ6BRfosr0sLIrOJMXff/bvsKe/Y3xPYIf8wkO/fk4Uei7bb8tVimXZxYoyEnyniUAQZ31ylaIZkqsEKWwG4QpaKLI8KVagCYGiWHUWxKtkeUSWx9hWCSELI/wo7eKnfbI8oOROEMRrJFkfz6lT9RdY6T1FnPUI0842l6KF5usuOCMMxYesIF5htnY/s/VXGQG9xbhiIZ2cnOSP/uiP+Kf/9J9uu/2P/uiPmJws0kRBEFCr1a7PCQ2GVwhbNn2uXSPXKSXpje8TQlAvFds/tFZEaZssTwBBLzxNGLeIsgFp1sV3JhAIXLuKLX1s6ZOrCIRgoryXmdr9RFmX9f5zZHnI3fPvKJZhpwGbwQvYlofSqujQtetkKiLJBpS9aWzholHbDAFK7gzDpEMnPE2S9gmSDWzLp2q5DKMNUvRoTORca70rpYjKFwczvPupb992z+8e/ATPTT5/kcedX3ctdprmSgEZQoNrV7CkT5JHCJHjyipKq2I8RXdGhhQ1Yt2lMCX0SLIh/WgVrRSKDM+pkeYBuUrx7AZCCPrxChPlfUS6M4rGIctDVrpPkOUh07V7X+RStNB8iCBepRUc3uZiZOqhtzZXLKT/8l/+S37sx36Mz3zmM+Ma6d/8zd/w0Y9+lP/yX/4LUOwG/fqv//rre1KD4Tbn/E5cxyrhWKVxim7Lpk/pvBjwP89T15Iuk5W7AJu1/tO0g8N4Tp046RPnA3IV0SjvpeLPstp9CrTCli69eAmBZK7+GurlhUIYyJmq3E2QFHaB843XEmYthukGdWsXvlND6WzUgNTCs6qU3Ulcu4olHOCsIUA/PI1jVZio7KcfLSGli215dILjKJ0jsVGMum+RV7X2TGqH73/2nezpna0fBnbILz70P1AStlyJLo3AtcoIHMJsA5XnqGgNTY5SOZbl4Fgl4ixA6xTXqhV1VJ2itcKSLtWRAcYw3aTuLRJnXZK0D0JQ9xdx7cJcIU67xGkXSzhYwkFrzXr/OXKdMlW5e5y+PfdDSS9c4sDc21nuPE4nPEkQr77sq9IMV84VC+kP//APc//99/PLv/zL/P7v/z5QrFD77Gc/y1d/9VcDjFO8BoOh4EKduK5dY6qyj7n6a1iceIiqN0+ztJv1/nOU3Rl60RK2749HHaKkTaO0B89psHPi9dhWmRObnyOihxQWE5W9NEu7cewqaRbSDo5i22UqegYpLBqlHThWeTzG4jl1hLRoD08QZ73i5/t7CZI1LFmMlVjSJ0q72MJHikJEXPtstqnYWRqQ5CFaJ1TdWZJsgGtVishPJ6O0brEdRWBd4kqdL4iCxcEc/9fT2z1yf+fgR3lhcmm0bWWr61ecs77swkhsHLtCmgUIJIqcVA1GTbo2ltYIaeM5VcK0Ra5zpE5IskHx/DovLP6EAyooLAKFTZz3qLoLTNfuJc66BPEGaBimm0xX78G1a8Rpj1ZwhMnKPjynvu1c57oU7Zp8Mwfn30mYtG7aqjTDlXFVc6RvectbtvnsGgyGi3OxTtw0C2gHx0hVNO7mXJwoUntx1kdKm164hDt647eki5CSijfF3XNvR+IUjT6V/az3v0KjtBspC6Gq+/NEWYcgWcXGQ0qXVA2Joy6uXWWysh8hBI5Vop0eJU67TNfupexOcmTtUwTxGvYotexaVTI1xLY8Jir7tr2hp3mIa1fIdU6SDam401jCKgwMtBqJXDGyMrJdeIkrJdkmpFrxg1/5Dvb2do6/Y2hH/MJDv04uNVVnkTjvonWOECVSFXJxERVIbCxZIstiMp1gWx4oPV6GZgmbnBStMny3OZrfLdagSSSW5YNWxZo0HRfjMU6Dur+TzeAFfKeOa1dxpMcgXmeYrtEo7aBZ3kOSj9Lm0mWmdt8FRfFclyIhhLH8u424KiHN85w//MM/5Ctf+QoAr3rVq3jXu96FZV3q06bB8MpCKcWx9T+nFRwvZjrzdNyJ6ztNBtEyWimGaXtsPn73/Du2Ra/DZGM097h3HL3W/AV64RKWdKh6c/TC0+Q6QVKkgl2nxnT1IKvdmDjtoXTRddos7WKysn889pJmQ7I8JNcpSqU0y3dx/47vYq37NK3hcYbxBgBx1idJB7SDowgEJXfyrCFA7T7ipMd6/1lA4NoNNvqHQGgcu0qWbo6uxqVSr0VcaOEVtdCnz6uF3v0xnps6PPpKMEiXz8quhpfuCJbFum01BDSeU8XCIckDPFlCCIEQNknWK0wslMKRfnFddI6QLpZ0ipYplYMAKRyipIft+zRKO5htvIosH5KrFEf6zNZePYpQe2QqZqZ6L47lY19gBR4Yl6LbmSsW0sOHD/POd76TpaUl7rnnHgA+9KEPsWvXLv7kT/6E/fv3X/dDGgy3I/1omWNrf85XVj6CUopBvEzFm8HP6rhOrRBTd4JhskGjtGtsPl7zFzg4/052Tb75gvVUgGG8SZi0yVUKWJTdGVrBEcruFJZVGNYXNdWDRGkLKR32Tb0N362RZAOGySZJOuBM53EQFN220TqN8k4mK/uZbTxAlPWwpE3dX6AXnUFISSs4Spi0mKndT6YiSu4EOyYeRmvN6c4jbAaHsWWJOO8RZwNA4YoauU7IibiQ2EkcGKVZ0Rnf/5V3sKd3tiP3bBSaj77fHyWL41HauPDI3d7ItFWPBdBYOAhpo3WOJgcFqQ6xpIOQFvZogXfR0dwCnVFyJ+lFSyiVY1sWaEnZbZLmQ9I8o+xMonVKmvXZOflm9s18I8N0g364xHzzQe5f+Ds4lj9Oz/rOBC+sfuyiXbk30qVoqz5vUsU3hisW0h//8R9n//79/NVf/dW4S3dzc5Mf/MEf5Md//Mf5kz/5k+t+SIPhduNsOvcEUjiU/QbDZJ0o6dJSR5is7Md1atjSI9KdYrtINhxb9l0stbdVa+2EJ0feucdY7jyOa1XpR0u0gsO4do2SM4kmp+RMsHvqqwBohS+Q9ofEaY9hslks4LYrHJh9O5ka0h4epzs8XSzklg5a50yU9zOIV1iceBjXqhIk63TCUyitODj/TnZMPDxugLln/tv4qyO/xHr/aaK4Ra4VjuXjumWyXKIzPTKkB1AInFEDksaSLlO9Gv/g6b+z7fV++jUv8OXGE+jUxsIFJFJKlM7Q2kaPYlKBRbEfJgUkjih8b5WKSFWMhYeQoHNBplJiHeBYLo5VJlcJiYpHM7MC2yrTKO8mVUNEBEJISk6z+B3pwlO35u+k5s/Sj88wUbqLemkXvegklvTYOfHGizYGbaXuX86u3HP/ZsxS8BvDFQvpZz/72W0iCjA1NcW/+3f/ztRNDQa275CcqtxNEK+h0WOruCQL6McrTNpVMhUXHZ3qxZZ953OuaX3VX8DxS2R5xFLnS+QqYap6D+icQbzOev8ZPKfOXTNv5YEdf5dBtMpjJz5MNzyNY5VJ8gGuU6fqzREkK0xW9hNnPZJ0QD9aIcn6TFYOMIhX8OwqC40H8ewm/egMJXuCMG3jWXUkRTfqIF6hF55iofm6opM4GxT+vqIwss9lTMnRRGkXrTMUGlu6ZCohz2O+5+lvZO85UWhox/zqmz9CRmG2b0sHS7oorUlVAFoihYPSOYp8tBfUwRHVs18LjZAWnlUnzQJQGmn5SKVQOsWxJ/DsGpblEaf90ZqzPq5dp+YvAoosj/DsJlVvjjQPyFRM4ZCk6IVLCCGZKN/FZGUv09V7aJb3jKO9i3Vpv5xduS/6m7lNloLfbhH0FQup53n0+/0X3T4YDHBd97ocymC4ndkyVqj6C7hWhYo3Syc4iRQew2SzWEeWdBjam0Rpm3ppB1HWY7Z230XTehda8Ky1JlMRk+V9BPEmaT6g6s0Xq8us12LZLhPlu6i4c5xpP1ZY+zXfQJS2WWo/im2XkcKmNzyD0JKp6kGCeI2NwSH60TIVb268uSTJBpzpPMFq98mR12+fpc6XmKvfz13Tb0NKQZh2WGw+TLO0h164RK5SSu4kg3iVOOtQsmdIVUSaZaA1vt1kqlvhex/72m2v9aP3/zXH5jexdJk07Y6iKJ+yN0OaR6TRVresBXrrzVVjCYuSO0GSDchUCAjQAq3TUYQ/R7O0h3ZwhEG8Sq4SUhVT8WZQKiVOB0jpMlnex+7Jr6IbnSLNwpGxvIfrlHBkmTjrFWb7KKarB5mu308/WibLQ2qlwv94Kwpc7T3JZnC0MGSw60xU9jFffzULzdexa/LNN1Qobtel4LdjBH3FQvpt3/Zt/MiP/Ai/9mu/Np4jfeSRR/jRH/1R3vWud133AxoMtxtbxgqOVTSxlNxJljuP04/OEGUDVLiE0hn9cKkYi8j7KHIOzH7jRd/QzhXnre9Jsj5BvEa9vJN6aSdhusli83WUnIliQXQe0A1P0QqO0AlPUist4tnVoo4XL5OHCVHaIclD1vpPM588yHTlHuYbr2WYbCCRREmf1uAv6ITHCZMOaI20PDy7iSVsNvqHGMSruFaFfTNvK+ZhLY9GeTeDeJUsD7GFS5pFCNrko8YmoeFbH3uQPd2zb4yRFfNrX/0p/PIUri5jSR/b8ukEEblKGYzmPfVoeZlSxXUQSCR20YUrBdXSwiiSbDCIltBCUJINpqsHsKWPY5fx8iZaK5KsT5YnuHal2GwqJxDSYpisFVF+7QDJyPHIsSqsD54jyQZMlPfS8BeZqR3Ed2p4dnUsTAvN13F49eO0wxMMwmW0yii70yRZQHt4jCw/26VdL+24wG/7+nChv5ktbtWl4LdrBH3FQvqLv/iLvPvd7+arvuqrcJxiMDvLMt71rnfx8z//89f7fAbDbceWsUKahyiV0AqO4NgV6uXdyGiFdnCcLA8QwqZR3sVk+S58u8Fy5zGq/twF3yjOFectcp1uc0ASqTW+Pcn62FaZXMXEWXf82DBpsdZ7ljDeLDxlkXhWlTjvEYSro5qlRZqHtMPjeHaTbnicfrQ8NrIvyynK/jxVb4EwaZGkhfdvLzxDvbQT165SdWeI0g5CyrGJfZz2cZ0K0606735yu0fu7939SY7MnqHsTI/Wqonx0uvCXDAhyRVytAqt2FaaI6WNhY9n1xFCkGYBnlVlsfEQimJFW5z1qPizSOGQZCFKZTTKu8jyAKVzyt40vtug7u2k7E3Tj05jWT4l6TBff5A469AKjtANT5FmRTSMkCw0XjfOIIyFaXiiqEGn7cL1SOfUzunS7kfLaK0YJu0bHg1e6G/mXG61peC3awQNVyGkzWaTj3zkIxw+fHg8/nLfffdx4MCB6344g+F2pOROjo0VorRLkgVMlPcV3Z5pgOfUCpPy0Q7PwjBd0Q5PXPSN4lxx3nLE2XLMyVQ8qrsuozs5QljjTSxldxLPbhS12TRgtfcUcdpFSJss7VFxZ1DkuFTHox2d4RGEkKAF3fAEaR4hxNa2lZQo69GUexECbNsjjvvkKqE1PEK9tMAgXiVI1oiSFmmekmQDbLuMyCXf/vib2dM9u6klsmJ+4eH/BZZTGEDggtbYlo8UDt3wJAiF1PaoUxektNFKjdyIiu7aktskTFoolWFJm/nmq9noP0eqhqA1Ybw+Hm3JVIxHTrO8F9sqsWfqLVS9WXKVstZ/llZwBKVSSt4MWudMVvax2Hw9FW+WLI/w7SZagCWdbb+jYib3CGHWpeJM0UoO45/jl1xkJyYYJuvUyztveDR4ob+Zc7nVxm1uxwh6i6te7H3gwIFt4vnkk0/y+te/niRJXuJRBsOdjxCCxYmHaAVHWOs/g2fXaQWH6UcrtIMjaK2Qrk3NW8CSNlJa9KMzSGGxKp9k1+SbX/RGMRbnwXNodw5FhsSm7M6wOXh+3ERUcqawLY80j9joP89EZW+xPs2u8vzyR2kNj6B0Rph2QCnCtD3uSnXsCp3hMTQCW7j4XhPXqbI+eAatMwp7vSpCaMK0NfoQMCRM+gg0K50niUaWeGVvmonK3Sy1v0R7eJQDrd1896Ht+0I/cvAzPDd9AiFsLOEWzT5qgOvOoXSGZzXJVYprVSmXpknSAWHWQelk3KUrsRBYZDrBdap4dpOyO4Mmp+LN4FpltCwiSK1ybOkjgDQLccoVHNvHsUrkKmWp/Sit4QvF42WObzfohqcJ0zY7mg9TcibwnSa2VSq22uh02+tJ87AYw9Fq1OG73S8ZGHdpS2GR5MMbGg1u/c3cjHGbq+F2i6DP5aqF9Hy01uT51Wx0MBjuPGr+Anumv4ZTrUfoBEdJVVREkNJBChdL2ERZF1eWsYRDyZ+gHy6xGRwdLb3eTmFav4PDa5/kZOsLOLKMa5UBQWt4jFzFzNUfwLY8MhUTpR2a5T34zgTHNj7NZv8wm8MXGEQr2JY/WpcmSdOIkl0vrPt0hmOVcawSUdal6s7iWGXitEOfUb3T8kmzkEG4gu9OYNs+nl0iy1N60RL96DS+00RKl1wnZGnEB7/4D7e9lshK+IWH/ydYAseq4tpVBBZBvMYwaePIKtKyCJIVlEqoePNUvEkq9VcTJpus9Z5FCEGuFJkaggChwZYuFX+eemkXe6a+lkG0SpT1yfKQsjtdeBhLm2HcYqnzJU63/5qJ8l1Y4tHR2ZfxnQZpPiSI18hURMPfRZwNaAVHWWg8RMWboRUcxbVrY89hOCtME5W9xRo8dWG/5K0u7eIsNzYa3PpAd7uY4N9uEfS5XDchNRgM22n4u/GdBrlOqHmLhRFC2sKxytiWT5R2RpZzsrDrsysMk40LCmk/Wma58xj+/5+9Pw/T7C7r/PHX+Zz12Zfaq/ctnTTZSIIhEiFCCITgCCKMig4wfPmJw4zO4Ijwla/oMBiB0dHx61wyXwcE51IREZBVQAIESFiyNOkk3Unv3dW1P/ty9s/vj/NUpavXqu5aevm8rquvpE4956n7OdV93uf+fO77fZtFdGElRulhh25QRWg6g4Wb0DSNljeJrpkUUuspZ7YiNJNnJ/8ZL2yQd9YTxT5RFJDcO2M0KUhb/fRltyMlxER4fo1YVpJ2DSNN1hnBDRrEsU8Q9FpANIGmGbh+gyjy8MMWMo7RdRsJ+EGHDdN5fn7vQneib1zzOI8MPEEcSxwjjxAmmgZ9ma0Yuk29eyQpXjIypKw+TCOHbeZwzCL59ChCGNDYQzdogtSI8XGDmCCysI08GVsiNMFUfQ9B3GG0eCszrb34YTLSLI58Ov4UujAAO8nCu0eZbj6FqacZKdxAzlkPyF7lsU/BWU/bmyKIWpTSW5lp7iOOk5k2sYwWCNPm/pcwXnuM6dZe0lY/TffEAr/krl8l76zDD5oM5K5d8Www54zMO2Vd6ib4l1sGfTJKSBWKk1jW/rXeaYawsc3E6D2Z7hKAtHu2dtr8z/XDNraRP21p6+QijNHiLUBSsRvJAM9vcGD6X8ja6yimNxDGLqaeImuPIISg41WotPeTcYYYyFwLSJruOIZu4QdtQulR6x7tGcwHFJyNoAl0zZiPLW2VeuLdSgqHZIRAwwsS56KYEJAIdIghDkJ+67u/ctrl+J93fp7IiCFM7PWC2MMSOqAlhvjO0Pxyqm1msI0sXlhLRrjlr0dKSaW9H00z0DSdUHZ71ydZ5A1jl44/y87hV9MJKjTcE1wz9CpsI0OlfZCWN0W1fYCOP4ttFIjipD+17VbRNYuMNUAY+2iaoJjaSBT7tL1phGbgWOX5+aEbyi8kZfXjhfWeKC8UJk3Ten7JyTCBZncM08jgh2103ULTBGm7vGrZ4JxT1qXel3m5ZdAns2ghbTQa5/z+mXpLFYrLieXuX4tij6wzhPAELXc8uYEZRTrBLG1/GktPY4o0QdTBDevoetL3eaqQdv0K1c4RzJ59XVJIlFgM6pqJYaQ4Uf0hLX8c0Ss0ytjjlDNbaXkTBFEHW89R7R7EDWqJ92vYwQ/bSA00tMR83szRDaukjCLF9GYmm08gNJO2N0mtfQQvbPZEMz7JjO+5ySsxIdtmB/nX+161IP7vrX+chzY/mVTZRhZR7CPjGE3ERHGIISLa/hSmcAiiFkITFNIbsI0CUkY03Qkma7uxzQItd7I30mxOvJOfH0kfHTt5vTdGKbODmdY+Ot40udQIo2aJpnuCjjdN3PPStc0iWWeIjl9J9izDOm3PJO+swzJz9GW2owuLZnc8mfoSdRjM7Zqf1HM2YTo5C5wUSR9px59J+kjTWxjO37Dq2eDlYoJ/OWXQJ7NoIS0Wi+d8EpBSXpJPCgrFqZwp62x5E8vev2YIh4w9SMYaoOVNJNmNbqJHFo5RBE0iZUgoPQrOBjRNMJy/4bSlq1rnCBP1x9E0bX5WacYepJzZOp/JtrwTlLJbydj9hLGXFMn4FSIZogkz8coFHKuIqWeYbe8njprIWIIGujAopNZTTG1E0wRdv8p47XHa/jRB2E2ESrPQEETSh/mpLj1JlRrv/f47TrsGf/rCT9KmjkEKW88ihE4Uu4RaiNYzS/CCRq8q2CSWHmmrRDm9DccqkjZLxDKi0R0j7BwkCF0kIbJnC2gZ2d59R0MS4cddqu3DDOZuxDbyNNwxss5wb96rjRs2AIGu62TsfnTNQhcWGXuAtj9N26sSy2RCjWXm6BM7CaMuWwdexk0bfpm03Td/nzuXMJ3PL1ndK8/O5ZJBn8yihfSBBx5YyTgUilXhTFlnwdmAG1aXvX/t5D2fkcKtBFGL/txOphpPEUY+ftQm74wwkH8eQdg643Jf0x3nyOx36ASz5OwRUmYfXlBnprWPRvdEMmhbmKSsAYKwRdjz73XMIpXWfjL2ELaexfUrlDLb0ESyV2fraezUZtr+DLaR4/rRXyDj9BHJgOOz3+fAzL8QxyGGZuPTwhAWmmYSS3puQs8VFm6vbuYX9i3sC31w3Q/51oaHAZ0kc8yQtYfRhCAMfaSU6JqNbeQoZbaStUYYq32fSAZkU+vm20bSzgDDhRvpeDN0gxpab29WkxpSSwazGZpDLENiGUEc0/Fn8cIGpcxWLD09v0zoR138oIkmdNJGiawzjNB0DGGgi3wy4SZs4AUtrJ59Y9OdIGX1s33w5WSc/iX9XbtcssDlYLkt/S63a7doIX3JS16yknEoFCvO2VxTxuuPMdt6hg3lO5a1f+3kPZ9q5wBZZ4RiejO6ZnGi9igmTu/9YgZy1562dDW3NxrJgMHsLmbb+2l7M/hhnTAOqQQHkFJSSG9ktHgLlpml403jyhq6ZtKfuwZds/HDBmHUoetXsK0cUkZJ64bUMHWHrDNI2u4jliEnKo8wXt+NH7QoprfQ9qfQQgNDdzBEirY3TZKFSpDw3u//h9M+9wdf8BcE+lxrSAQIwiig6Y5jGunkfE0DLSaI2kSxRztILPZMI4PGwiITP2qRssp0wxq2kcf1KxiGQyRDothLmmCERRR3Mc104hrljrGp705Gis8/ydd2CiF0dM2hmNmKbeSShwqzQNubwTLyCM0glj4tbxKhmZjCYXP/SxjI71r07/1q43K09FtuVLGR4qrgXK4p+dR6xmo/ouEmzjynium5+tfO9yR+tj2f60ZfkzgaWcWzPsHPNajn7BGiyKPRPU4QtcnaI6TMAlHsMdvaj6GnWV+6jZwzQt5ZlxgbCAtDTzNZ300+vQ7DSNPoHsMPmoRxQBQHCGFQcDaQsQeJYo+x2g8Zrz9O002Gj7vBLGHPzQggkm4yggzYXt3CL+x79YJ4H1z3A7614ftnuPo6Qmj4cZs4CIiJsfRsz7UoIoy8pCqYiLQ9QCz9nqimCaMuXpAMI9d7/aYSiR+2EtMIkqVtQ/gIYaFryR5s2uqfv5HnnMSBqemOE8uIljeJH7bQhTmfvTe6Y0hCytkdrC/fASS/13xqHduHzm7deLVzuVr6LTdKSBVXBedyTTGERcrqo9k9gZ9rYpv5Bd8/W//aYp/EL3TPJ4xd2t4Ujc4Y443H8KMWGoKWN5EIkaZjGxnCqMORynd7Bg8mGXuAcmYbYexiGRlMI0MpvY2Cs566e4ww7M3hRKOU2UYYdzlceZCjs98ljFz8qI3QjF57R1LYE0WJw22Shf7702JdmIXOoaFrNhomlsiTtvrwowYdv4KpO0Sxl5gwRG2y1hBB1MELqlh6KlmmBWIZEcVJ5mkZOUCiC4Mo1oAYKTUkIZEUGJqBLiz6szu5ZviV87+DuWXClFVmY/mFHK/+sGfT91z2Plq8jZY3ScosEsUehu4wWrzlqsqqlsrlbOm33CghVVwVnMs1xTJy5J1RphpPEMY+J3vRnK1/bSlP4ufKWqWUdPxZWu4EAFl7eL6gxQ1qVFr7CSKXMPIopDYCAs9PBnU7Zom2N0MkfYLQxc7k0IROoztG16/iWCUGstcSxQH17lGG888nnxoliLpEsU+tc5zZ9jNogB92iCIfS88iZUwUR7S9qaTAiYg49tlW3cAv7LtvwbX7zrof8s0ND5/himvomoNtZAiiLm5YxY+bRLE3X3VrGllSZnItWv4EujDwwiZGUE36NGWcTGWJWkgZJ4VUkYfQDNywRixDIhliYGEIm2J6ExvLd7J18C4GcqcvxZ681N7xq+TT6xGaTiwj/KDJcOEGNpRvT9yLLoMCl7Xmcrb0W26UkCquCs7lmqJpGjlnlFrnCI3ucQzdOWf/2lKexFvexFmzVoADU1/n6OxDtL0pADL2IEP561lfup2Z1r7e3p+PlEmskhDbKuL5TWbbiZerrefoBImRQ9rowzYKjNcfQ9esxGRdwmz7GY7MPIhtldA1nShOMsEw7KIJrZd1m+i6g6PpdLwZYhkh0DFFmv/83Teedk0/9IL/D18PoDdc+2QEVs96L8kmJRItTiFlMq0llD5a3EHX+klZRVy/2asG1jD0FJ2wSsefAaFj6zmEoWMZGbywSRB10AODKEpM+3Vhk7b6ksKl7CbWlW49qwCeutTuRx10YZ9xj1pxbi5nS7/lZslC+m//7b/lT//0T8nlcguOt9tt/sN/+A989KMfXbbgFIrl4nyuKVHssn3wHlJWiXr32Dn71xb7JD7deIrj1e+fMWudbe2jGzSYaT6NECaF9Eb8sMV08ymO177P3vHPo+sWOXsEL2hT7x5GuAa6sNE0CEKPMO7Sl9mOZWQSWwTpUW0fouVOJu0isUs3qGCKNG5UT9pMujqmSAaCh7GHRvIQEUUuAqNn9OATESSFNtUhXrtnYaHhd0cf4zubd2MaBRzNpO3PEMm5m6WOwEgGZmsW3ajak1jRc1OS6JqBRrI/6oY1bDOPbpj4fgtTpOjrDRkPog6mnmaocD2ahNnOAYrpzbhBFYBWPIXQdLLOAIXURgzdYVPfi84rhpdje8WlyOVs6bfcaFJKef6XPYeu64yPjzM4OLjg+MzMDMPDw4RhuKwBrjaNRoNCoUC9Xiefz5//BMVlw9mWY+eyzh1Drzxno/0cje4Ye8Y+RTG9GaHpp/2cWEZU24dIWWXqnaPkU+sxhDVvohDHMfsm/ola9xh5Zx351DqCsEWlfQA/7BBGXUIZEscBujBp+9OEURcpY0w9jS5MwtjFC9v0ZbcjNINiahOD+euptPcz09xLJ5hFxklFb9efwQuT/dUg7iRWetYAQjOYae/rRa3hGDkkAq8nVP/x2z9/2mf7xMt+RCOaRBcWQdwiCDt4YQdJAOjYepYwdnuTUQRe1AAiDC2LLozE2F3TccxCsvcqA7LOEKZI0fXrpMwC14y8mkJ6w4Jl1yDqJK5G6GiaICYxlc+lRshayeSWWIbcsP5fX/HLiJcKUkqemfgSM619C1Zm5r5Xae/v7VdfnnukS9GCJTkbSSmRUtJsNnGc554yoijiS1/60mniqlBcSizWNeVcN+LEAKGDH3VpeVPk7OHTbhJB1KXjV5hsPAESat0jPROFpAhIaAZ+2KXrVcg5IwRRh4Y7nkxkAdywRsudxgvbmMICoZEy+9GFnoxiizromoNGB89vUsxsBCRj1e+joRPFPpqmY5spTN3G1QSxDNEQgI4X1MnZw4nTu4Qg6pCyykRxgKE7bK6M8po9P7ngM+3eOsa+6wPcTpu2P93zvy2RMvpoepPEsUcoA2IZEUtJFHWQPetAoFcQJNGEQKATRp1ela1JyiiDBqbukbb7GMjuxLEKz11ze5jZ1rMUUpuTEWhGGl2zyKfWzxeGzd20z+bDutx9jorL29JvuVmys5GmaVxzzTWnfV/TNH7/939/WYNTKJabi1nWm6/S7Ryh2j7IieqPGMw9j77stvkbuJSSmeY+Wu44HX+WcmYbpp4ijD0a3THcoEbK7KPhHqcdzFBtH6TRHaPtT0IsiZEEYSJCuqYREWJiE0YtTFFGFzbdoEpEiGVkQJMU05ux9AzHqw9jiBRe2EIXRtKT2dsPTTJhjyByiSKXyeaenuF6O3lA9pP5pr/5wKtO+9x/c88TxIbE9zuJAGo6UeTRjidBSoRuUUxtpuPN0PImiefNGub2TnvzONF7E1OSPtIwctE0HaEZaGhYRoaR4vNPq5rWNA1TT3Gs8jAtbxw3aGD2fm+lzFYyzgCl9Oaz3rRVn+PKcbla+i03S3I2klLy0pe+lE9/+tOUy889+VmWxaZNmxgdHV2RIBWK5eRCXFNOXRbeWL6D45WHmajvpuVPsqF0O4aeotk9gRvWSNml3mQTAWggJaaRpd45xnS0LxG73vBtZEzHq6ChYYpUL6tLpsFEcUAUh0g0gribtKYgQJOkrT7CyKflTZBzRhCaiR+1COIOltFPyiyhiWRvMo5D3LBDHHtIKZOK115lrCRi/WyRX9q70J3o4XV7eHjbPsrGdgxh4WhFKsGzGMJGFw5+2CKMXWTYxvVrSSGRJsga/dhmHjeo0w6SIipds9GFjiYM4iict+EzRaZn5O9jGznyzvoFMUgpqXePcXj6W0w395JPrSdl9eOHzZ6l3xQD+V1sLP9k0mN6ilWp6nNcedSe8wU4Gx06dIiNGzdeVRdJcXVzpipd28iyse9FzLb2M9V6imOVhxku3EzeGSWWPvnUJmZb+5hpPYOUceIuFId0/GmCqEvKLIGRTZZThUXiFSvxwkbiGyR9pNR7GiwJog4SSRS5pK1+dGFjGXmgRRR5dP0qUewTxj4ps4Spp9D1ZIi1qWeoRceJIz+Z3akJ4jhAaIIYwe88/GunfeY/vv0TxKaJJVK4QYWUOUC1sz8RX03DDzvEROhCJ45IWloI0aSOYTiE0sWL6nNXED9uoUsLkxSG7uCFTSQRER519wiWniGSAfsmvsBQ4XpGCjcDMNs6wInaD5lpPYuU8Xybiy4Mun6NSnM/ldYB9ox9ikrrAMX0pvlMSPU5rh6Xm6XfcrPkqt1vfOMbZLNZXv/61y84/qlPfYpOp8Ob3vSmZQtOobgUOFuVbsoqs670AgqpDXT8WbYPvhxTT9E6MYllpElZ5cSyL2zgGEU0JGHkEoRtUmaBUnoTHX8WP2wjNJ0w8giiJnJuPqmeIibGk020nnGe0AykDAjjiJZ7gqw9jG0VKTjrSZklap1j2HqWmc4+Gp3jZOwBTD2LjCMkEZoUoMWAYEtlHa9/+uULPuv3Rh/nW5se6dnnuYSRoB3PEsQuURxi6Xm8qD7fqhJHfi82ARhIIprdsZ7VXpDMWkVDEhPLgCBKDBWKqY0EcRsNg/7sdZQzW6l1DlHvHON49Yc03QlMPU0YuXhBG4nWM3RoUW0fJOsMJy1DQkOXVu/BwFiQaeqapfocFauCWOoJ999/P/39p5s3Dw4O8gd/8AfLEpRCcSlxrn45TdPIOIOYRgrLSCeZoLAJwg5dfxbTyGBoNk33OJX2Qbp+BV1YpO0+SumtrCu+ANvIIGWEH7YIYh9I5paGcVKpmwyh1ohkmGS3kYuUWtJWoIHrV2m5kwRxl24ww2RzDx1vhkrnABP1x2l0j2IaSVyGbhHHkt/+3r89TUQ/dNv/x7c3PQI9VyFLz2LpWegZw2fsAdBI4iKed8SVSGLCXuWuRBKhCxu9Z9mXiJhAaCaOUSDvbMDUs8TE5FOjDOWvxzazFFObSNt9eH6TifrjVFuHMfUMftTEECY5Z5iUVU6WZpv7CKIuGWsAXZhEsY+uW5Qz2+kGNU5UH+0ZT5y7zzGKvauiz1Gxsiw5Iz169Chbtmw57fimTZs4evTosgR1sXiex+23387u3bt57LHHuPnmm9c6JMVlzFL65eb6VU/UHqXaPkQUuUlxS2YLURRQae/H1NPEUUQ3mGHbwCsoZbYSx/9ARRzF9WsYwkQIkyBqJ3me5iC1iDAOkMQgNSQBMRFSxrhBnYZ7AlM4mHqGTHYAL2jS8qZwg6SPs5jajC4shqdSvOKRaxfE/93RR3hg40NJVikT670w7mAZA/Q2WNE1MxHxoAlINASyl2kmBUVzE0qTgqkw9tCFSayFRFGyDxtLiOKgV9U8Pe+8JITAC5u0/EniOCAmoOVOEUY+jpUjbfUTxi6aMJJxaLpNyz1BMbO5Nx8VDN1B18wFmWZf9hrV56hYFZYspIODg/z4xz9m8+bNC47v3r2bvr5LY3nkXe96F6Ojo+zevXutQ1FcAZzPzOFkC8G5loDp5l5mWklhTsYeIO6Zs2fsfoSwkxmb7gRR7NHxp8g4Q3hxG9Fz9oEYx8jjBrVEmmSM0ASGnqaU2dyb6pIU3MhYEsQdnMwWNva9CE3TCMJOUngUutS7R0mZZX7+c+tP+2wfuu1/4xtdEjHU0TUDNNl7/w6x9DGEg6mnqXWOEkqPZCFLO6m95fRW9EgGSRWynJtZ2lv+1STdYJZYRokdotOPFzaptA4Qxi62VUTXU/hhB1O3MfUMhdRmpptP4QfNXnW0RiQjkFpP2AX51IaeF+9zjjqmnlr0702huBiWLKS/+Iu/yK//+q+Ty+V48YtfDMC3vvUtfuM3foNf+IVfWPYAl8qXv/xlvvrVr/LpT3+aL3/5y2sdjuIKYKn9cjlnhA3lF/L0+OcAcMMGuqaTtvrJOSO03HHcsImIBG1/hnrnOEKYDGR20jROEMYBftAikB0sM59IkKYDGhm7n1xvwkskfaI4otrajx+FlNPbEELgB02a3gReUCeSESPTGe7+4UIRfWjkcb6x6QckY84kiTgmgmfpWXQ9qYCNohhNaNhmFstMgZ8IZyKiJ1sD6jw3o1Qk2Wjs95aALWwj1SvSypN3NtDyJhCAH3ZpeROEsYtjJjNIo9jH1B1SZrKFJIRGIbWe2faztLypeROMjjeDricm9X2ZbfPXfy7TNPWU6nNUrApLFtL3v//9HD58mJe97GUYRnJ6HMf8m3/zb9Z8j3RycpK3ve1tfPaznyWdTq9pLIori6X2yxXTm+jP7sQPW2TsAULp4fo1uv4MYezjBjUEOrPt/Xhhg/7ctfRltlNpH2SyuYeUUcQNG0CMF9QJNRNIJOw5NKLIxdQzdIMqhu7gB00q7QMEkYtlZPnXn91w2mf58zs+Q1OroEtBLCUSjbmMUQgNXbcglthmPpkoo1toCIrpbbS8KcLQJ8LlueVcrVdQNPeVgY6F1GIs3SFrj2IICzes05+7lrQ5QNruo+WNU+8cIYjbWEY2ETQpCSMPQ09h6DZZe4gw6jJauhXHLDLd2ku9cywpYtJ0Rou3MVq8eUEf76krBKrPUbHSLFlILcvik5/8JO9///vZvXs3qVSKG264gU2bNq1EfItGSsmb3/xm3v72t3Pbbbdx+PDhRZ3neR6e581/3Wg0VihCxeXOUvrlTD1FObuNaucQbX+arl8hjkM0TU8mmZgDmIaDKVJYqfUUnA2krDLpsI5XaeCFDTL2ABqCKAqIZSUZkWYNEsce3aiDrumkrDK6MOkGFcLIxQ0TT90N1UFe8tBCkdi7vc7D259hY+ZlTLeeJIw8vKhN250gJkJgYulZbCNHGHnYRo6B3HV0/Ome61EGS2QINRdkMgt07o88yYTB0Ex03UJgJGYLmsCPWzhWEdvIkbYLjKZv5NDMt/DCBt2gQs4ZRcrkoSGWEYO56zD1FG5QTSa86A7rSreRskpo5Tvoz11H0z1BJAOEsHrmEmdfIbja+xwVK8sFT3+55pprzuhwtNy8+93v5oMf/OA5X/P000/z1a9+lWazyXve854lvf/999+vHJkUi2ax/XIpq8xw/gaCsIvnN+j6s8kkFOmjazaJu49gtvUMIKl1jzKY3UUQt8nYA6TMPrr+DN2ggmXkKaQ20Q0q+GGD/uwuLCNNEHUJohYpo59Seguz7f24XpW3f+Pu0+L57H1jRJaO7EakrCLl7DZa7jQZBtDQCGM38fmNfbygQdYZYSh/I0HU7u2x9tHxZgilSyQ9nlsOPunaYKKhzxcg6ZqJJMILa9hGnnJ6CwO5nZQzWxHCYkP5DgSCvRNfpO1OIoSBYxbpz13LSOEmACYbe6h3j9FyJ0hZ5QUzQk92LDpfpnm19zkqVpZFmda/853v5P3vfz+ZTIZ3vvOd53ztH//xHy9bcADT09PMzs6e8zVbt27lDW94A5///OcXPGVGUYSu67zxjW/k4x//+BnPPVNGumHDBmVar7homu44e45/imcnv4IXJCsdhp4mjDp0wxo6JimrhGOVsIwsbXcKP2yQdUbxwgYtb4pEkCw6fgUvbKLJGNNIYxgpbD2PYxYRugESzIPHed2euxbE8PDIbh7c9mM2lO5Iql29CYbzN+KHHSabTxCEHaLYT0a1AbaRYbhwE+XMdibrT1DpHCSKXQyRTsathS717pFeBvrcrUPDwBA2lpknijwMkcIQJimrzPrS7fTldpC1B+cLgua8cbcPvpI9Y59kvP4YhdRGUlYZu2fuL6Vktv0seWeUTX0/hamnTsskzzfrVWWhigtl2U3rH3vsMYIgmP//s7ESf0kHBgYYGBg47+v+x//4H/zX//pf578+ceIEr3jFK/jkJz/J7bffftbzbNvGtu2zfl+huFByzggb+17EvokvEskI28ghhA6xTsos9ybNzOKHTTb13cmU9hTPTn2F2dYBDCNpZUGCFzeJpIeUEZEMCIMALWgQ2zEZewgDi5/+ep5yZ/uCn//fbvvfBIbEIkulvQ9DzyI0nfHa7qSiN+r0mlUSy8AwdknbAwxkdzFW+wH17jGyzjBdb4ZIhggs/Kgzb0N/MhoGQrOQcYjAIGsPk00NMlq4CcfswzQyBLGL57XxgyZpu8xo6RZ0XWfr4E8TxS7doIamCSRxUoTkjpO2ymwZuOuse5lnyzSVv65iNVmUkD7wwANn/P9LiY0bNy74OptN+sa2bdvG+vWnl/0rFKuBbeRxzFIyqcTIEcU+1fZBDCOFJjQMw8ELGoSRRxh1QSaDIQrOZvyoSdM9AWiIXluKlGD2+h69oI453eDnHrppwc/84cgevr19D8g0InaJZUDbrYOooGk6huYghE62NwHGCxrEcUA+tZG8M8rRyoO0/Sp92WvI2oOMhQ2IIYpCvKDCXKvMwt7RiCj2iaVPyu5jfflWcqlR1pdeyMHpf+FI5UHiOMQycgzkdrJt8GXzgrbcxufKX1ex2lzwHqlCcSlyqS3nGcLBEBZe0MQxCwjNJCZGYBAEXdr+DHEccGj6mzTdcYKwRSh9Kp1nkTIijHxiGfTeTfRcgywsPc0rfnwTW2eGFvy8P33h3xEaMbGMkgw2Dohw0YWDgZ0IMhFoBmHcmT/PsUps7PtJBjI7eXb6nylmNjKQu643XDtLGFVpuMeI5FzvqMbcVJeEmFiGWCLD+uIL2Nz/YiYbT/L0+D/S9qbRhYWt50hZRQDGa4+SdYYWiOlyFAQpf13FWrAoIf25n/u5Rb/hP/7jP15wMMvJ5s2bWeLMcsVlzqWwnHeykLt+jfHGbtywTtubpuEeJ2WW8MMGbXeSjj9LGLvEcUS9cwQh7DlbBIKgTdhz+dEATTMwdZMwDsg3dN7y2D0Lfu7Xr93NE4PPEkQumtSJZZiIaS9rTCa+JCYPmqYh4+TfRtrsI2WXSVuDCE1D6Aa6ZuDoecLYS/ZO4wA/ahHHAdp8Jhr33ltb0O5imhl0zeJ47UdUWweRSPqyOzB1hzD2cP0q+BXQOE3QlqMg6Gy+yHPvr/x1FSvBooS0UDhpyK6UfOYzn6FQKHDbbbcB8Mgjj1Cr1ZYkuArFcnIpLOctrCKdYra1PxlA7azDEA5RHNDoHKXWPUYc+5hGDl04CC3qmdnXieMIXTeQkvleTYlAyKT45uf2vpxrKs+1mkkkf3T7x8EwCYNuMsBbS6ppE1ehxDRB0+Yqc+vYRgHLTGMZaQbyu3o9mJKWN0kUedhGHiFMpupPAIKmN4kbVNE0HV0KInzA6Fk4JBlqTIxGTNYewA2adIMqmiboz12LZSQ93aaewnAcWu44RuhQ6xxZdkE7ly/yXAxtb1L56yqWlUUJ6cc+9rH5///t3/5t3vCGN/AXf/EX6HriMBJFEf/u3/07VeWqWBMuheW8k4U8Yw/T6I71zBMkUexhG9nEgzZyaflTRNrcxBRJyizSiCaI4zDxpI11bD1LHEfE+ICkr5PnV3/8iwt+5ue3fYOnho4RxQFEPjEBEPd6PBPP3Ln9zCiO0LSkRcUycvhRkzDyiKSfWApGLgKDMPbIpUaZbT2LH3cSn11hYYoUYdQlIoCe027yXyMxRxDJzxSaSSm7iZnWM6T0HKa+0MdW0zQcq4QXJP2jyy1oS/FFViiWiyVPf/noRz/Kf/7P/3leRAF0Xeed73wnH/3oR5c1OIViMSxlOW8lOE3IkXT9WXLOMLlUMuzeNgqkzTLdoIIQJqaeIW33YRo2USx7g657y6SaICbEEAZInZ/fd+9pIvrBF/wv9gwexDLSWEaKmLnsE54T0LmtDYkhTHRhEROQtsqYeoawZ2AQRyFNdxw/amGINFlrAEtPk3fWYVt5QCZTYOZNGHqiKQx0Yc3vmBZSG+jP7KSc3o4uDHTdIow9TkXXLDpBBS9s4YedZd2CmfNFbrnjp73vnOtRsddmo1AsF0suNgrDkL1797Jz584Fx/fu3Uscx2c5S6FYOdZ6Oe9UIY9kQCQDUsKez8CCsM1AbhfVziGEZiA0nb7sTqqdg0SRhxtVkIEETYIm0BCU2wX+r90Lt0u+tP077B54GqFZ6FqybCs0C1vPJv2g0j+pMeUkMdUkjpEniP35NpO03UfLnaEbPIiMYwrpjbT9yXkDBsNw6Popun4FISyEEFQ7x9A0DR0DodsgYzQt6TMtpDag6yaR9LD0HI5ZoutXMRxn/gHHD5pUO0eodg5jCof9U19jtvXMsu1jL9UXWaFYDpYspG95y1t461vfyoEDB/iJn/gJAL7//e/zh3/4h7zlLW9Z9gAVivOx1st5pwq5rpnomknYO2YIG1fWEtN2M0/bm0EIE9vMkrH7qbePowsH6PSKgkJe88xdXDO70Hbzf975eQIRYMlsMtvTzJG2hmj7EwRhB1Mfou2NI2UyFDySQdIrKiUaOkHcJYpDWt44umZiiCxBNIOpp0nb/Zh6ijBKBniHcRcR64ktn1mi1j2GrllEcUQ3qCJlSBh10DSBY5YopbclRoEyJIw8BnLJqLaunyx7p6wSceQz3dpHo3ucQnojm/vvwjTSy76PvdztNArF+ViykP63//bfGB4e5o/+6I8YHx8HYGRkhN/6rd/iN3/zN5c9QIXifCxlzNlKcKqQW0aOjD1IvXscw0mqVXXNTPpJ7fVMN57C0CwMkSJnD9P1q9BNRGiwW+Jtu1+/4P2/cs33eWZ0ElOkyIgSblTDMrIU0psQmqDpjiGEgaVn6WL29jFjbCOPhkxaYKRHEMTJeNEojWWm8cIqftgkljEZexAvrOMGddygQsbqJ4g6tL0phos34YYNqq0DoEk0NEwzTxC20IVFMbWVtFWm1jlMxh6gkF7PaPEWxmuPAhpGlMINasy2nqXjz1JKb2Xb0N1knUGAFdnHVv66itVkyUIqhOBd73oX73rXu+YN3lWRkeJiuNjez+VazpuLI/Gw7WLqqTPa0p0asy5sCs4GZtvPzAt5ObOVblCl0R3DD1vknRH8sI0QglJmC37YodY9QtYeJO9sZLq5l9c9czfXVrYu+DkfeclXaUbTEIJmQChdLD2DrtmEYYcw8rD0DG5YpxtUiGSAJEoGnUUdIhkQExLFSXWt1nMnCqLOvO+vqacIpY8bNNA1m1jGVDoHGczdQNubnh9TdrzyfYKoi6FbmJpDOl0iikM6/hRuWMHUHTb3/xTX9DLLrDPEieqjVDtHaHaP0zInGMxfz7rirQsqdVeqLUX56ypWiwsyZAjDkG9+85scOHCAX/qlXwISS758Pj/vKKRQLIbl6v282OW8uTgmGz9mtn2Arl/BEA59mW2sK9/OutKtZKwhKu0DVNoHqHePJlNZCNCFnSwpS8lEffe8X2zGGmamsQ83rOJHTVr+FP3Zndy66a003QmOVR+m3jlKphbz69/82QXxfO+Go+wZ2k8Q+jhWiZRRpD+7kxP1R2h1J5BaMlrbMnPYRp4g6hBEbWyjwNxYszB2QWpImfR8Gpo97/UbyTBZ6tYiYukThC1SRpFYelhGNilE8sYRmk4QdWn7VXRhkbWHKKW3IInml4G9oI6hp7hhw7/mBZvfjhBi/ncylxVWO4cwJzMM5Hahi9NvO6otRXE5s2QhPXLkCK985Ss5evQonufx8pe/nFwuxwc/+EE8z+Mv/uIvViJOxRXIcvd+LnU5by6rrHWOcGT2O3T8WSrtAzS7E8QyIIi71DtHmW0f4OD0A2gazLb2U+8cRQLlzBY29d2JYxaZaSZ7f7EM8OttgrhLEHbI2sNsG3wZKbuPWEb4QZNG9xjbh17JNUP3Ev/tX2PtPbQgrn941QFmvIMEUZe03UfaKNONGsy09hHHkogQDZ20PUAcR3S8GeI4whTpxIZQhkgZoUuDCA8pk0pZQ3dAhkhihNARwiSMXdreTGLkAJhGFj0OsIwcuqbjR20m6rsZq/0IXRjEMqLpjZO1h0jbg4DENNLYRp6tA3fNi+gcJ2eFKavcy+BVW4riymLJQvobv/Eb3HbbbezevZu+vueWTV772tfytre9bVmDU1y5rFTv52KX8+Yy0GrnCBP1x3tWfRFtbwLbyJGyymQwaPvTVFoHOVp5CMcsUkitwzFLGLpDo3ucZya+xKb+F9P1Z2l70/TlrmFL//UcrzxMtXuItNVP1hl6bvC0PUylvZ+ZAw+y7q+/uyCm8Zduo3vDFrZ3r0OffgDDSGFoNtOtfTS7YwjNQtMkhm4SRxHEESmzSFv6lFOj5J11aJrADWo03aQv1dTTRHGDZAi4DwI0KUBLhoJrmkYsw6SSWLdx/Tpe2OiJdIgmNI5VfkAnmCFvr8PUdbywwWRjDxDjWGUyVh9oMWF0eqvLHGu9j61QrCRLFtIHH3yQ733ve1iWteD45s2bGRsbW7bAFFc2a2nldnImbBpZNE0jZZQ4VvkuEsjaQ+gi+fvtGAWmOseJIhepRwSRi2MVen63GWqdwxyY+jrlzDbK2e2JV27sggYDuefhBjUq7YOMmiU0LTGkv+abTfL7F4rokX/3Mqb9A5QB00hhW3ky1iCTjT3UOoeTWKwcXtDENgoEWodYS0aq5fXEEL+QWk9MzGBuF/unv4YfNrH0Am5QBxIXIiGTftIgdpFEGFoGtBCJRMYxftTBCxvomomILTrBFH7UQNMMfKOFIwoEYTepQNaLFJxR8s56mv4ER2a/Qy41csZVBNWWoriSWbKQxnFMFEWnHT9+/Di5XG5ZglJc+axV7+epmXA3qBDLCKFpidWAZtANquR0J8napIcfNUlZfQRxmyDqkO5lTZoQOFaRavsgfZnET9YL6wRRO+kj1R00rUTbm8IPm2QbgvV//dCCeIyf/0X0F9zBiDtOa2KWSns/lp5FoNN0J5htPYOuGQg9iy5soImmCQzhIDSdjj9NyiwTxR6aphNHHkHUIe+s72XYk2hCoGs6GlbPbQnmzBti2U2qgc1BOt40naCChkbbn6UTVJAyxtKzxIQ0umO4YRNTWBikEsOFyMMNagxmdxHJ4JyrCKotRXGlsmQhveeee/iTP/kT/tf/+l9A8qTZarV43/vex6te9aplD1BxZbJWvZ+nZsKCZN/PD1u9ySrppKI19tF1mzDszu8xxgQITRDJAF1LMtaktzKpiJ1rczH1zHwf6VwP6chX9lE4UFsQS/Q778HOn2GUWOcIMTGN7jE0BIXUJryojiRGQ8MPk/7NOIoI4xpxHCI0QTG1CYSGG9QoZbYQy4iWN0nGHOhV84ImY4LIBS1CkwIpBWm7H6GbBG4HS08jEQRhGyk1JCFe1ETXTKSMiWSAZvWRsrLzQ8HzqXX0ZbcjhHXeVQTVlqK4ErmgPtJXvvKV7Nq1C9d1+aVf+iWeffZZ+vv7+du//duViFFxBbJWe2YnZ8Jdv8Js6wAtd5K2O4EfuoSRj2Vkk0pXKQlliKHbeFGblJknbQ3gBy1SVqmXsfrzVaiuXyWfWkfOGabpDtDojlHuFHjVF9JAbT6Gw3f2wW23cU1ueEFsJ4vMhr4X8sTRT9LojpG4Fxl4QYtQBoRRB1PPIHQTXVoYRooo8hlvPMZw/ibQdJJWFzCEhRAGcRASRV1imcwONYSDqacRmkU5swM3rGNbRQwcGt5xhDAQmkYsk0HbsYyRMsRAEIRdDNEmkj55K+kZTVllYhktahVBtaUorjSWLKQbNmxg9+7dfPKTn2T37t20Wi3e+ta38sY3vpFU6szLdArFqVzMntmF9J3OndP1q8RxQKM7xkxrH37YppjemGR6UYe2N00YuaTNPnTdwtQzWHoON6yRy15HMb2JSvtAYn2np+l6VTL2IG1/mlJ6C+XMNjRNUM5sY/sDVYaPhgviePRNG7Ezfew4y2ebE5lI+hQym0DTqHT2I4SFjENknMwjFZreGwQuSJtlnFSeTlDt2QSGaGjkUqM03TGCyMOxDMLI7mXbOo5ZIGMNUkxvYqT0fCbqu9EQ1DqHkMTYRo44joAw8QHWQENPOlGNNGmrHz9uM1q8hXxqfc+EvkYcB70laIXi6mFJQhoEAddeey1f+MIXeOMb38gb3/jGlYpLcRVwIXtmF9J3evI5YeQy2z5EtXWAlFWinE2yYUN3EJrRM2Po0PKnKDjrSTtlbCNDwx0jxCWWIcX0Jmqdw9Q6BzH1NOuKt6FpOo5VQAgLfabBrv/zCCcPvj78U/3Udg1QXkSP7FwxVBh12dL/Yk7UHkWiEcceHb+Crjn4UYdYBmSdYQqp9ZQyW0nbgwRhG8vIJMYSoUfKLGMZEZaZRcYxXX+WtNWHphl0ghlsM4ulZzH0FIYw8aNW0iojUkSaj4wikBqx9DG0DDIOCSMXL6xTSG2gkFrPidojtNwpWt44udR6js0+zLryrZfEnuelNuhdcWWyJCE1TRPXVQ3TiuVjKXtmF9J3euo5hu3QDeocnX2Qjj+LYxVJW30IzSBt9bG5/6fQpEYnrDCY20XGHmQgex2GbnNk9jvMtPYRxi6mnmXH0L1sG3wZ64q3EUQuR2a/Td8XH6P/0MJ/I9HvvIcRM2bDIvpaO94s+ye/Rr07RjG1BaEJ2t4MLW+CWEIsJbbhkE2NkrOH2dj3k2TtQSwjhySm1jnMxvIdHJn9DuONb5FLraPtT+MFdZCQtkoUUutwoxZC6IwWb8WP2vhBA10kAqqRtMSYwiaOA0IChGZgCAM/SkzxLSPLYP55VNr76QY1JFBIbaQ/dx2z7Wfo+FOrMgP2XFwKg94VVwdLXtp9xzvewQc/+EH+8i//EsO4IGMkhWIBi9kzu5C+01PPcYMqE8291NoH0TUTL2wyVv0R/dlrcMwChfR6ypltWEae6eaT7Bh6JaX0lnnx2zrwMirtA3i94djlzDaEEDS6Jxh/9ivs/JvdC2IOfuZesnfeu6hrMHfTn2o+xaGZbxJFPjHfhDhKzB0iDykDko+msb74E4wUb1ywh+yHSYFWMZ2Y3U829iTTZ4w8cRQSyi6RDOmGdYqprZiGxbUj/wpDOGTtIaaaTxFEHm1vHD/q4EcdNM3AEg6aZiJlSNouc+O6X6Ivv5PjlYdpuGPknFGy9iDlzFZSVhkp5arMgD3f9VzrQe+Kq4clK+EPf/hD/uVf/oWvfvWr3HDDDWQymQXf/8d//MdlC06hmONsfadSSvywiS5sJhtPUkpvRxIAoAuLWucIWWcEN6gyVnsEP2xjmXkyziCpOKYbVtHQGMpfTz61AU3T8MIWKauPUnrLAoEXQtCf27EgrvHa4/h/81G2HFkY70NvLJLNnaB/+jtE0kXXbPpz15Kx+08TlpNv+mHk0/FmieIAN6yjY5Jx+gnCbjLAW0LXn53vDT35OpxaoDVcuBkhDFruOOPR47huDS9s4AX1+Qktpp4ibfexfejlxHFIrXOUKHaxjCJR3CWI2mBkMYWNoae4buRn+alrfpuuX6HtTTKYfx4ps4hl5OY/10r3AZ+PS2HQu+LqYslCWiwWed3rXrcSsSgUZ+VMfaddv0KlfZC2N0U3qFJpHWT/5D9j6E7PcD6NJGJL/0upd4/ih+35LKRrFml7Mzh6gZikTSTnJEUzs+1nGchei2OWzhlT8+iPKf/5wmH2e2/XObipgdedojb7VbywidB0hKaTc0bZMfRKrl//8/NxnHzTd4wSR2a/Q9ubTMapxQGx8Gh0vcSyT1hYRhY3qDJefxRNCDaWX4ihp04r0EpZZUrpTRyY/gYT9cfxwiYZewBTpPCjDrOtffhhk1rnKGm7L1liH7kXXTd5+sRnqXfHMIw0jlnCNvM4Zp7R4i3cuOEXEUIQSQ9dmORT6xCaftq1WUvv3LU0+1BcnSxZSD/2sY+tRBwKxTk5te+061fmM0xDM2l2x2kHU4TSJSuGyVvr6PpVqu2DvZaWFGl7cP7GmnWS8WV+1MQSGSqtQ3T8Kk33BIawMHWHZye/fNb9tOBv/gpr96MLjn3vl/JEpgbdJmPVH+CFTWwjR3/2GtA0Wu4Ejx/7azr+LD+x9VfJOSPzN31dOByrPkSzO45tFmi70xi6gx82iWQL2yxg6A5eUKeU3obQDFruCY5VHma4cPNpBVqapjFcuJkfHvoILXeCUmY7pm4n02DigGJ6CxE+T49/luHCTQghyDkj3LThlxkt3sLxyo+odg4BEsvIMpC9bkEB0VrPgD0Xaz3oXXH1sWghjeOYD3/4w/zTP/0Tvu/zspe9jPe9732q5UWxKpzcd2qmt1FpH8QP22TtYSqt/TS9cRy9SDm9Ay9KljDLmR24fo1Kez9pa4Ccs37+/Sw9S9oq94zUPaZaT5KzRxnM72Igdx2Gnlqwn5a1h+n6FaLJMaw//8sFsT1xq8/x7RJTdtACmKj/mCBsIzQLU0+hCxPDSGPpOWqdwxytfI/R4m1cO3IfYewSRi4dfxYvbGAaWSwydPwKsQyIpQQp8YJGL8OGjNOHwGQofwOSmO2DL6cvu/207CuI2sQyIGcPE0RtwqiLLvQkA7WHiWTITGsflfaB+SVrTdMYzD+PgdyucxaAXcreuZeyyCuuTBYtpB/4wAf4vd/7Pe6++25SqRR/+qd/ytTUFB/96EfPf7JCcZGc3Hc62fgxte4xUmYRN6xR6R5EaAZ5ZxihCywtixfUiVMe5dw2vGqDlneCtjtJNjVMGHu4ftL/OZy/mbH6o+TsUXYO/wz51Oi8KMztpx2Y+jqOUaLwxYcp728siOt/3/UAnu7BtExGm0lBJ5ghjmOEFiKJ0ESy9KkJjYwzQNercKL2Izb13YEhHGIZ0nBPYIoMkkk0DFJGAT/uIGmhoRFEbTRNoAGNzhhoGhlnEMcsYBnpM+67PjPxJRrdE0m7Cxqm7pCySthmEU3TsYRDIz6OFy7cb5273uda9ryUvXMvZZFXXJksWkg/8YlP8D//5//kV3/1VwH4+te/zn333cdf/uVfnjY6SaFYCeb6TvdPfrVnIACRDDFFGtNysMxkwLzQDHwZEcchaauPYmYzLXeCppfM8TSERT61LjFPQKflnmAwv2uBiEIiFoZwmHj26/z0VwsLYvnhjZM8VH4IXdhoUhDKgCjqIqOIMPIQukkUB4RRQBxH6L1/IqZI0SbGCxuEsUvOGcUQFtONp3CMPF2/Rhh10TQdTeoYwkYXJnEcEschOWcYXVg4VjFxZPKmcf0a+dS6+djmipda3iSGkUbXbUBS7xyj2jlIyuzria+Opum9GaYX/vu41LxzL2WRV1yZLFpIjx49usBL9+6770bTNE6cOMH69evPcaZCsXwkfaf30famMPR0Mv4LQb17fN4DN5YhuqYjhEEYe5gizbrirYk7EBHl9HYyziBh7DLT3IshLAZy1512Y5VSMvq1/dx8aKHQfO8XCxxqPIXetROvXRkQxx62nsenTRS2kGGAbZYwdBs3qCX7dZpGEHcRCGwjjyEcWt4EXtAgjgO8qE3WHqTtTtIJq0SRBwjC2AcZkTb6EMLAMjIUUhvxggaaJqh0DjGQ3wVAx59l/+RXqXfHGC38BNXOEaabT6NpBpIYKUmyZEyq7QOUM9sx9fRF/j4uPe/cS1XkFVcmixbSMAxxnIV7CqZpEgTBsgelWD0uR+eXtNXHQG4XM6195NPrKaQ20uiewA+aOGYJP2yRtvrQNYdqz87PNByyZoGWN8lE88dk/cGe2cK1mLqDcUphillps/4T31twbOal1zFzXZ7K9NfRNCimN9HsnkDKANPIJObxCDQNpNSwjAyWkUts+aSPkBZtdxrHKjJavA3HLPHs5Jcx9DSb+l/MVPNJNDRss4AmdNyggYwl3XAWQzjYZoGcM0La6sMPm9hmjv7sTurdY0w3nqLSOcRY9Uccnf0eppHGDer0Z3Yy3XiSjj9JxhpEFzZdv5oMHXdGKOe2MVF//LRsfClcqt65l6rIK648Fi2kUkre/OY3Y9vP+Wi6rsvb3/72Bb2kqo/08uFydX45eemu2jlAzhmmkFrHdHMvHb9C2upHSsmx2e/RCWYpprcwlL+BXGqUIOxQaR/A1DNs6Xsx/bnreHbyywv20wa+8gTZvRMLfuaht98FjknkzyazSYGUWcQPW2ihhi4sIhGgyaR/1Y+6RDLAC+oITU8GZgcNhNDZVH4R68u34gZVat2j5FKjZOx+pAzp+LNkNRMhTPyoS8s9QV6uoy+zHbQYpCCMXByzQDm7g6wzwmTjxzwx9kkmG3todMZouOMYus1Mcx85Z4iM3Y9ppPGCOpFMlojLme1sHXwpGXvwim4FuVRFXnFlsWghfdOb3nTasV/+5V9e1mAUq8fl7vxy6tJdMbMZSUyldYBa+xBTzSeRcTi/PxjLAKHp2GaO4cJNVNr7qXYOM5DfNS/K7bGnueEfFg6n3/38NvELbqVgG/hBAy9sJRM9pSSIk6rQZIqKTuKtK3s2fikiGVFtH8QNamiaoJDauKCPtNEdm2/TEEaWcnY77coMlc5BojhAFyZCMxgp3MiG8h10gwqz7QO4QQ0vbDHV2EO1fZiZ5l6a7gm6YbU3tSYCJFLGVNoHMXSHbf33gBbjhU1iGbJj4FXouiCIOokvb9Rd9d+hQnGlsGghVf2jVw6XivPLxS4rn7p0N9N8hh8c/AiaZuBYZTy/ihAmbW+SveOf59qRn6GU2XpaU37OGeHa74XouxeK6OFfeymm1qTSPkDLnaDjTxPGPm5QpxNUwJMUMhtxgxot9wRCs4mkh6GnGM7fxeb+F3Os+n0cI8emvhczkF/obHRym0Yc+1TbBzBFiqHCjQh0/KjTcznyGav/iDgK8KMOaasPQ9gEkcvx6g+ptJ5FCAPbyGEbebpBJWml0dMIYeEFDardQ4wUbiaKPWyzn0rnWTr+NF7YIpYBR2YfZIu4a77NRy2FKhSLR5nlXoWspfPLnHjWOkeYae2j61d7Mz0vbFl5bukujmN+eOh/EcZdNva9CD9sMhFU0HWLjD5Myx3naOUhiunNaJqYb8qPpsbw/t/f52RvnuC+l+M+/1qG9RSV9n4OTn+Drl+hkN5A1h5Eypha9wh+2ES2IwwjiyZM/LCJpgl0zaQTTDPRfJzB/E52DL3yjJ9prk1jurUX168lzku9vUopJX7UZrhwA0iYaOxGIunL7sAQdtLCE1TRkMn1w8Qysgih45h5JFFiViHSgKDWOUzKKPSWeBu4QQPHLBJGLjlnmIZ7gh8f+9tk3mrUuqyW+hWKtUYJ6VXIWjm/zO3JTjSeYKL+OJEMKKe3ntEAYak37kr7ADOtfRTSGwjiNnX3GB1viq4/iy4s0ATV9iGa7gT51ChB1GXLN2exnl1oruC/552c6D5JdepryeDv9rP4QYtiagux9Gj705i6w7b+lzNW/T5uUEeQtMkYlo1j5Mk6Q8lSqYRtA/egaxaN7thpGd7cXm+lfYCp1lPk7BFAEkQuXb+KbWTpy24jiD0mmrsppTYSRh28sI6umaTNMm1zGtNIE0U+EAOgC5uUWUJDxwuavWFukljG6MIiiLqkrDJeUMM28wzlr0dKybOTX8YycmwZeCmWkb6slvoVirVECellxnJU2eqaTdQbbn2q4TisjPPL3J5sx6/S9SsYIkXOWkfXrzBRf5zR4q2Uz7CsvNjP64V1wthFQ6PSOtBztSngRx2EZiZ7gVGLpjtOuZtnyyceWnB+8KqX03n+Vo7N/gu17jGCqEPbm2S6+TQAUeyzse8OcqlRdM1EQmL+Lj2G8jckWa7moOsWhu4QyYCON8uR2W/TDWpnzfByzgib+u5ksrGHWAa0vEl0zaSQWj8/TSXypuYt/0w91WvzMYlin4Y7jmPkaYbjBGG39y9aoAuLlFkmjF0y9iCbyj9Ff+4aDs08gNBMwqgz30vrmCVO1H6EEEmRk4ZM9pOVybtCsSiUkF5GLEeVbdMdZ6z6CNX2obOOwFpu55eT92Sz9lBi2Wf39YzlE8P1SvsAo8XbFiwrR9Jf9Oe1jQK6ZlNpHyKSHimzhBAGQbeDH7XQpE4chWx84AQbjlYWnPvjt1yDr+9n9kBSfGUKBzQNQ6SwjTyWnqHljXOs8hC7Rl+HbeXp+LNoQoMopu1PEUXuvMBl7MRxaLKxmyh26c9diyEc2v40x6s/oNI+wPPW/Tz51CiQtNHMTWoxhIWumQsebsIoIIoCqp1DFFIbyTnDaJrACxrJvqhepC6P0/TGMcNeb60w0QBN0ymkNrB18KcZzt9Ex59OisuEM/8zvKBB25smYw/ihQ0i+VxLmzJ5VyjOjxLSy4TlqLI9+T2G8tcjNEE3qDEbNun4FQZy1xHF7rI7v5y8JxvFHpEMSImkjUrTNByrRNubxg+bmEaGtjdJrXuEidrji/685cw2iumNPDv5z+ScEVreBEHUISZK+ksbMb/+6GsXxHX0J8tM7cqTdfow4oCJ6Ala7jixDBkp3oKuGRjCRBOCrDNKx5tiqrmHTdaL0TWTIOxQd4+joZNLjZDq7V3WO8c47n4fhKAvs4M49plo7qXtTRHFAZONPXT8WV6w5VfJp0bnJ7XMtPaRO8XSrtI6wL7xL9Hwxqh1juCYRQrp9Wws30ExvQVDT9ENK6SMImHsE8chUouTAiYZUEhtZFPfi1hXuhVds0hZfZh6eoEHbSQDIhlgkELXTHTNXHCdlMm7QnFulLffZcCpVba2kZ1feitnttMNapyoPoqUctHvUUhvYF3pBZQzW7H1LI3uUaaaT9KXueasxTEXysl7snM36jD25r9vCHv+Zh5EXXTNYqaxb0mfVwjBtsGXIzSdmeYzdPxZhKajIfjpp27gbaeI6Fdf0+GR4adJmWVsI4skJpYBurDQNL3nhJOYIPhBC10zEcKm5U3hh00MkaHrV5NJKqlNmHoKTRO9SlyLaucIQoqk4rb2CPXucUwjQ9YZIueMMNncw5Nj/0DTHZ/fK02ZRSrt/b1K2oiJ+h52H/8b2sEUw/kbkoHdGsw2n+XpE59jqrGHljeJZWQYKtzIUOFGMvYAurAQmoFtFFhX+gluWP8L5JyR+eKmlju+4NrpmonAoOVNkbEHsYzcgmulTN4VinOjMtLLgOWosj3Te6SsMqNmCT9s9oZKd9jQ90Iydv+yxn9ym4dl5MjYg9S7xzEcB03TCGPvuZu5O07OGaHjV5f8eUeLt9CX2cGUfBoZR1h1n7c8dNeC8x+5YRr9jhcT13cjIpNq5xApq5xkYZpGFPukzGLPvMAlZw/jh23a/jQCQRxHdIMaXf8gtpkj4wzQ9idxrFKyZ9k9TrV9hCBsUe8e48nxT2PrOconTWdxzAJB1KXjz87vPZ7aF9vsjnNk9jtoaGzuewmOlccLm7TcCbp+jUb3OAenH6Cc2c51I68lil1a7iRpq0wcB6TsMsXUFmwzh3FS9n8mD1rQiGWIlCGl9BZl8q5QLBElpJcBy1Fle7b30DQN28xjGhlqncNEJ2WKy8XJ0zjKme2UM1vpBlWa7jiOWcT1K6Ttflp+IgT92Z0cnv320j+vhLQzwLAwufWREusPL1xw+bt79oDjsEOGxISJr20vw7SMHDlrkEprP7GMiXqm97aZp5zZxmT9SWLpJyLUOkg5k7glFdIbqHUOU+0codo+SBB3khYY3UkEt14lY/VjmTlyzjAAYewlxvnOugUPBCf3xU41n+Zo5TuUiy/AsRIzftvIYWWyhKkuOWeYljtJyiwxmL8ODdGbXRrM77FKYmqdwwuu09k8aLcMvISON003qKDrtjJ5VyiWgBLSy4DlmK+4ljMaz5QJjRRuZrq5l0r7AIawSFtlBrLXMlq6BV2z0KtLjzWSHv1eH7f+o77g+EPXHuLpjRM4VpGMWUTA/D7g3JKypmkMFq5nprWPRncMxyqhaYIg6tL2JtEAN6wjhMls+xncoIokImMPMFq8lSDq4AY1jDCFJEYDOn4FXZi4YZPJ+m4sPYNlZHH9KvnUOtL2APXukQVCN9cXa3YcJDG2uXCZVdM0TD1NzrFoe1PEvf1Q28hi96bfzOGHZ75OZ/OgbXkTyuRdobgAlJBeBizHfMW1ntF4aiYUxR7F9GZGC8+nP7+TYmrTfGuLlPKCYjU/98/c+lhtwbE/uu1jeIaPaJmJP228BclPYhkZKu2D2EYe0ftnkLb62DrwMp488WmCqEPLnUZDo+lO0PGmsIw8Q4UbsYwMTXeSRucorl9j2+DL8cJ2zyZQkjJLxLGHYxbwwzZWz0B+tr2frD2IbeQoZ7YRxu5ZH15so4AhHLywScoqnfZ9L2xiG3n60ltpueOY6W0EUWs+IzX17Dl/p2fyoFUm7wrFhaGE9DJgOeYrXgozGhd7oz5frLZRwDGLnKg9gm0UKHXzhH/0gQWVc9/c/mN+NPQEYGBhEEtwgzrTzb3sOf73ZOxBKp1DmHqKtNXPYH4Xhp5kk7tGX4shHLp+jZnW00SxR8YeZajwvPkMsZzZAjKi4Y5xZPY7xDLCD1vouoUb1nDMIn2Zncy0n0kclOKQjjfNYPZahgo34JglKu39ZxW6cmYb/dmdjNcexzYKC2b+xnFMvXOMkeLNXDN8H0+OfZJ9E58n7mXCEhAIhgrPW/LvVJm8KxRLRwnpZcJyzFe8FGY0LvZGfbZYhTCZaj7FgemvEUQutz3WR+5IZsG5H33JA1TicSw9g67ZiZWf9BGYBIHHbPsZhBCM9AwOqp1DNN3jDBduYih/I6OlW8jaw8y29vP40Y8TEZGzR7GM5/ZsNU0jn16P1AAEQVij48+SsQdIW31knWFsI4dpZphs/BjXr5OzR+jPXYsQFpX2/nM+vAgh2DX6WurdY0w19lBIb8A2cnhhk3rnGBm7j12jr0UIwVz9rSYB7bn/nquKW6FQLB9KSC8jlmPp7XJavjs11tnWszx65ON0/Qr9fj8//eXBBa8/dHueQztcwgmBFWYQmkASYRgOujQJoi5SJGPEYilZV7p1fkD2bPtZiqnN7Bi6dz77s4zE3MAUDqZunxafIWwsPU3eWY9lZDk0802K6c2kT1qiFuiJqYPIoOs2LW+ClOxb1MPLSPFm7tj26zx14jPJ3q17HEM4jBRvZtfoaxku3MQzE18CYOfwz5y2tFvtHFCORArFKqCE9DJjOZbeLqflu1NN6bt+hZ98YhPrDi583cd/+iE0J4VetZGEOEYhGdatSTQEsYxpe5MIzUDTdCw9halneoYQBfrFtXSDGm5Qnb82hnCwjAzIpNL21CriMPaQgG3muGbwXpruGNX2YUw9RSwDGt3jNN0JBDqWkaOY3siOoXsppTcv+uFlpHgzQ/kbqbQP4IV1bKNAObMNIQQdb3a+pUkIgS0WFhspRyKFYnVQQqq4LKi0D9Cd2M/r/3nTguO7b2pwcHsXJxyk480i45gg9jGFjdAEQiR/xaOgmTgdyRANQdufZbr5FEK7npRVPmNLTcoq05+7junmPrpeBaM3mQV6nsdeBaEJBrLXMVh4HrdseguPHvkrZpp76foVpAYZqw9Lz2BbRUw9w2R9N/mT3mcxCCHoz+047fhaDR9QKBQLUc5GissC85++zMv/eWFW9dcvfZjd6w4QRl1MkQIkxdQWBDpB1MGPOkgpk5FjYYMo9ojjGNvIk0+to+3NMlZ7ZH6w9akVtJqmsa50K4P5XQRRm1rnMH7Yxg/bVDuHCeIOQ4Xnsa58K5qmMVK8mTt3/BaD+eeRssr0pbeRT22gP3ctm8o/yWjxlkW5UC2Wk1uazoRyJFIoVgeVkV7GLMckmJV4r+VEzkzjf/j9nCwF3935DPu3NogRhGGTMPawjRy6sBgs7KITTFHvHscLaoSxSyxDvKCJlBq60CmkN1DKbMbSszTdcWZbB3DMAgO5a0+roM05I9y44RfJ2AMcnX2IeucoABlniA2lF7J96O4F+5ym7lBMb6ac3X5GA/rlXG5d65YmhUKRcMUJ6Re/+EX+y3/5L/z4xz/GcRxe8pKX8NnPfnatw1p2lmMSzEq813IS/MPfEP/w4QXHPvHT38M3InTsxFbQMPD8Bm5QY7SQFA9tGfhpiCXPTH2ZavsgXthAFw6F1DrSdj+F1DqEZgASU08x1XySLQN3nbWCNueMcNOGX2bH0L203AkAsvYwabvvtNcnwu2Tt0d7faULOdty64U8yCy1pelSfVhSKC53righ/fSnP83b3vY2/uAP/oCXvvSlhGHInj171jqsJXPqDc8xS7hBdf7rMPbYP/mVBZNR/LDDidqjzDT3smP4lQzkdi3qJrkcU2UulDiOqbT2U3ePzbfhZOx+qMzgf+j9C157/CeHOL5ToE8X8btHEYGLY5SIpI8ftdGFyWD+ejr+JLmeiG0s30nOXke1e4j+zA7Wl2/HNrJUOwdpe9O4soZAJ2WV2dT3ojN+zlN/F4P5553zul6Ig9TFPMgstqXpUn1YUiiuBK4YIQ3DkN/4jd/gwx/+MG9961vnj+/atWsNo1o6p97wgshN9gB1B0NPoWsWLW8S0BgtJtlG169QaR+k5U3RdE8w2z7ANcOvYl3p1nPeJE+dCDMnEKsx0Hm89jiPHf1rxqo/wgtqaJog64zw03tvYOiZhXt+3m//BpOVzzOQ3oxppDk4/QDTzadouGMYeoqcM4xtFomljxeGTDWeohtUMfU0pmHhGDmCuEOl/QyjxVsZLd4270sbRT6xDCmmNp0W44WIz1KXW5fjQeZ8LU1r+bCkUFwNXDFC+uijjzI2NoYQguc///lMTExw88038+EPf5jrr7/+rOd5nofnPWfU3mg0ViPcM3LqDS+Mukw1n6bVnSDnDLG+/ELiOGSyuYe01Y8bVAEYqz2CH7ZJWSXKmW24fo3x2mN0vKlz3iSXY6rMhTBee5xv77uf6dY+TD1DIb0Rqx7wmq9uB54T0cqLd5J92c9haBZ6LcnyypmtFFObmW3tZab1LH7URkNHEpKxhhhvPErbm6ac3Y6pOwSRS6M7TsubRGjm/ABx28wjpaTin9ld6ELFZynLrcv5IHO2lqa1fFhSKK4Wrpiq3YMHk8bC3/u93+O9730vX/jCFyiVStx1111UKpWznnf//fdTKBTm/2zYsGG1Ql7AqTc8S89Q6xwhjkMG89cTy4ha5zC6MHGMIlHkM9s6wGzrAH7YJmsPg5TEMiCSPnln3XkrRBfTPhHF3rK2T8RxzFNjn6HaOUza7KOQWsdtj/fzmm9es+B13/55kwM7XJ6d+DJh7C2YoymEYCC/i2tH/hU7h+6jL7uNa4f/FY5VoBtUKWe3YxlpNE1gGWkG8tehoSUDwzvHcYMaXtg6q7vQyb+LUnobyBg3qIGMKaW3nfe6zi239md34gY1ap3DuEGN/uzOBbNel/Igc6Gsxs9QKK52LvmM9N3vfjcf/OAHz/map59+mjiOAfid3/kdXve61wHwsY99jPXr1/OpT32KX/3VXz3jue95z3t45zvfOf91o9FYEzE99YbnBQ3a3hQpq4QQAscq0famyTnrMHrDpxvdY0gNTM2m2jmAF9QJoqRKdbr5FKXM1nNmlGsxEabSPsBk8wl04VAOStz7xeEF33945yEeW/8sI/L57LSfT9ufYrz2GCPF558xy2v7U5Qymxkp3MTT4/+EKdKY+sJ4HTPPYP551DpH6PozVNsHyThDZ3UXmvtdGMJhvP4IbW963jEoYw+QtYfPm6kvxkFqNfpAVa+pQrHyXPJC+pu/+Zu8+c1vPudrtm7dyvj4OLBwT9S2bbZu3crRo0fPeq5t29j26fZvq82pN7y58V6p3lBmQ9i4soYhLDL2APXOsWT/NO7SiQPC2Mc0MmhxQMYq0QkqhE2XtD1w1pvkWrRPeGGdIOpwx54N7Di68H3/7uU/xjMCpB8TRp1kZmgvY9pQfuE5i2qkjJFEWHr6jC5EaasPL2hRymzheet+npwzctaq1TB2aXtTdLxpgqiLY5VICZsw9mh0x3oCevbrOsf5HKRW40FmLcfnKRRXC5e8kA4MDDAwMHDe1916663Yts2+ffu48847AQiCgMOHD7Np0+mFJJcap97wdM1E18x5UQhjLzkmLMqZbbTcCVr+NH7QBMCxSgRhG0tPUUxtxDSy1DqHiWWMLs78oLAWE2GcJvzCPy1cxn1o5wGe3VYHNMLIQ0Ng6OmeZ+xzGVM+te6sWV7HmyVllnHNOl2/guE4C+Kee+gYyu06b+WtLmxa7iRe2KCY3jz/WlNPYTjOea/rYlmNBxnVa6pQrDyXvJAulnw+z9vf/nbe9773sWHDBjZt2sSHP/xhAF7/+tevcXTn59QbnmXkyNiD1LvH0W17fhi0ZSRjvLKpESyjwPHKwwRRJxkHZveRs4exzBxSymQqiKbBOUx0VnMiTPCPnyT7/e8uOPbZe48xG08gpIlAxwsamEaacjq5Bklry3MZ09myvJRVppjeRMufJIxcWu44jlXCEDZB5FJp7aeU3cLmgZec/8FAApp21su2mOu6GFbjQeZSGJ+nUFzpXDFCCvDhD38YwzD4lV/5FbrdLrfffjvf+MY3KJVOH4x8qXGmG14xvYmmN8FUYw85Z5hiejN+1KbljlNKb2YofyNC02n5k0SRT9YZ7lWqdun6VdJmibTdTyS9c/7slZ4II2dn8D/0XxYce+KGJg+PPIYp02gYdP06cewhhMFgbhf9ue0Ai86YTr5+AEbo4AUNWtEkQdyhlNnCLRvfTD41et54I+mRtQfREDR7YmP0lnaXcl3h/CYIq/EgcymMz1MormQ0qYYWLqDRaFAoFKjX6+Tz+fOfsMyct4/0pF5GXbN4YuzvkVLS9iZpe1MnFcUMkrGH0DSNG9a9YdWnf0gp6XizRJ/9FPZjTy/4nvW++5nwn53vI+340/hhC1NPs750O1sGXoKhp+YzppMrXefe+2yGFW5Qo9I6RL17lG5QQUOnlNnM5v6XLEpEATre7LJc16X0oa6G65ByNlIoFs9StOCKykivBM6UHZ7qbDR3A5RSzi8HjxRuOeM8yrXY/2q64xx59ovs+D8/XvAXbPJFGyjd88vYToaRdG88WM/ZyAsa+GEHN6zjhQ3C2DtjxrSYB41CagNb+l+CYxUXLRgni4wubArOBmbbz1zwdV1qH+qFjLZbqjBeTuPzFIrLCSWklyBnuuGd6QZ48nJmtXOArDOCYxQJoi7VzoE12f9quuPU/u7/ZcfTzQXHv/yvqnjGJOuP6dy44RfJ9WZo9uevoT+fFB+dTxgWY1hh6ClmW8/Mm1EsRjjOlDnaRhYNLui6roYJwsVa/qnsVKFYPpSQXuZcSvtf8ewM1ofuZ/CkYwdvcxh7nk1G5om7J5hqPMVY9RF2Dt93RoOAswnfqeIEMNPcO29Y0fYmqHUOM1q8jfISxOpsmWPLTdqp0tYAblBb0nU9uScYwAsa8xmtZeQu2jHqYi3/lO+uQrG8KCG9AljpYqHFEHzm74kf/s6CY998HcSpCD2KcMMakQwIww5TjafYWL5jSSJyJsOKljeFKRyCqI0hHNreNH7YxDbzixKrxWSOKavENaV7iaS36Os61xMcRl1mmk+fZuhQTG++YMeoi812zyXCLXeCDX0vxDEXvySuUCiUkF4xrNX+l6zM4n/w9xcce/S6SZ7ZUSf2Y4Jum65fI4590DSiKKDtzzBSvJkdQ/cs+uecaljR9qaptg+goREjEQhiYvpzO7HN/KIcexZjn1fvHmNj+Q7yzrpFx2oIhzDqMt14ilhGpxk6tNwJSpktF2SCcDH+yOcS4cgsc2T2O4zVHqEvuwNDd1SWqlAsEiWkigsm+OzfEz+0MAv9wqsnmQoPkdEGkHHYc2DqYBlZHKMIeLS9aR478ldk7UFGijcvOP9se3cnG1bEsc9U80n8sJWIlJ7Bj9r4foOpxlOkzBJCWOd17Fkp+zzHLBFELk13ksH89Qgh5t9Pt22mGnuSfVdz6W1ZFxPz2US461c4UX+UIHaRMiZt9SGEqabDKBSLRAmpYsmcKQvVX/Wz7L+mizb2GexOgTDyaHZPEMuQtJn0XLp+Hdss0J+9Bi9o8dSJzyS9sD2hOXXvTmgWabNEf34nBWcjhdQGZlr7cP0acRyRT22kG8wiRZoo8imkNhLLkNnWfhyryED22nNW1q6UfZ4bVDH0FNnUMC1v4rQ+1GxqGENP4QbVJa8iXEzMZxJhKSWV9kH8sE0xtZG2P4UkVtNhFIoloIRUMc9iKjmDz36K+KEHFxyz3nc/Xd2lPvb3rCvdiqbBeH03bX8a28gitZgoCollQNHcSD49ShyHzLT2UWkfoD+344wVudPNvRxs/wvGhMlw4SZyzvpkj7X1FDl7BMvM4QZVat0jpM0SOXuQmJip1lNs6bvrvBXLK2WfF8Yupu6woXQ7tc4R2t4UrqyhayaF1HqK6U29Fp+l75FeTMxnEmE/bM4PR4ikP29NCSs7Sk+huJJQQqoAzl/JebYs1HjJywAIuzWi2KOY3sxmI0MQdWl0jhHGHlocoAsLRy/Ql9uBbeSI45CGexwvrM/v3bW9CpaRYba1j0rrIBo6xfRmXL9Cx68ghEUURxjCJpYBceyStYdIWX0YwsCPOwhNJ232sanvReddjlwp+7w5wTL0FKPFW+eHiM9V7fpRmzD2LmiP9GJiPpMIzw1HcDSLtje5wIYS1HQYhWIxKCFVnLed4tpHdPQfPLLgHOt370fLZOa/PjnbSVllNve9mEr7ILqwMPUUmiYAiWMWAPDCJoZwsI0CXb/Ckdnv9n7eVFLlGiczVQfEtaTsPvywRdYaIgg72EaR4cJNmLo9b5IwZ5oQxj5xHFJML25QwUq0D50sWOXMdmzzOVeU5TCKv9CYzyTCGoJYxtS7R0lbfZQz204x+1fTYRSK86GE9CrnXJWc6WgdG/9yocn8yVnoyZya7eRSI5Qym6m2D5GxhvCiGmmrD0OkiOOYeucYI8WbKWe2cWDqaxyY/jpIiWOVkikrukPLn8SrNhgt3YamacSElDPbaLrjdLwphgs3PRevyPf2+/YvWaSWu33orFlj2KHSPoBlZCilN1/Qe19szKeKcBi5yQB3BCOF5y+4bmo6jEKxOJSQXuWcrZKz/M29FB4/tuC1p2ahJ3Mm8Vhf+gma3XFmWk+RdYbJ9MwN6p1jZOw+do2+FoAD0/9CGHcpprb2mlnAMjJYeoa2P8108ymG8jcl2aeRJusMYenpZV2OXe72oVMFa7b1DC13EjSNLIMcmv021c7hi2ovudCYTxVh169xrPJ9ukEFXbfVdBiFYokoIb3KObWS02h02fDRhS0tx19QZuC+t2Gnziyic5wqHpom2NT3IiqdwwRRm7p7DEM4jBRvZtfoaxkp3sxM81nqnaMUU5sIow6mkUZoglhG6JqJredpuZOMFvT5/cWMPciW/hdTbR9eczenczEnWNPNp3h24itojqCU2YZlpJfkRLQSnCzC+dQ6Unb5knDHUiguR5SQXuWcvLc58p3jp2Whz7z1NjpGm5FF7pGdacnRNopUOwfxwjq2UaCc2Tbf8uKFdULpUkxvpt45QhC2EcIiDDugQxT7gEbGSYa7zy01DuR2MZDbdVn4xVbbh0HTFi5FX2LtJZeCO5ZCcbmihPQqJ2WV6QsG2PCRby84XnnRdmq3babR3k9/aml7ZGdacuzP7Tjja22jMF/IUs5so+lNELsRnqwTBR6GniJj9GHr+XnLvpOXGi/1loyLcSJabdR0GIXiwlBCepUTff4f2fDdhSJ66P93J54V0TqDcC035cw2+rM7Ga89zmD+esqZ7eSddbhhnbY7Q7VzgIw5iC5sSpnNl91S40q5JykUiksHJaRXKbJawf/D31twrPqiHRy53iCKT6AHq7NHJoRg1+hrqXePMdXYQyG9AdvIYQgHScxQ/kaev+nfsK5422W51LhS7kkKheLSQQnpVUj4T58m+u63FhyzfvcPGEpnyK/BHtlI8Wbu2PbrPHXiM8y09tFwj2MIh9Hi8+eLkpbKpTJvc6XckxQKxaWDEtKriDNloforX43x089NYVmrPbKR4s0M5W+k0j5wxqKkpXApzdtcKfckhUJx6aCE9Coh/Pw/En3nmwuOWb/7B2iZZLnxYjO4c52/2PcWQpy1KGmxLGXo9WplrZfS8HWFQrH8KCG9wjljFvqKV2O89Lks9GIzuHOdDyxw0YllSNYeZF3pNgZyu5ZVuJYy9LrlTaxq1qraSxSKKxclpFcw58tCYWkZ3Jk41/kzrX1ogAR04dDxZ2m4Jzgy+x0OTH2d7UP3sG3wbrL28LIIzJlaTaSU86bxlp6l1jnCdPMpjle+f8Gf+UJR7SUKxZWJEtLLjMUsR8paFf/+9y04dmoWOvdei83gziRs5zrfTG9j38TnAdhYfhHjjcfwwzZpq0zOHqbePcqh6W/RcqfJ2AP4UeuiM8NTW026foVK+0Bigi8DBDoxEVEcIIku6DMrFArFqSghvYxYzBLsGbPQ/+cDaNncae/X8WaZaj6FqafxwyaWkZsXj8WYBZzLbCCIWkgZEyOZbj2JH7bJnfS6rDNMy53iyOy3SVv9bBl46UVb553cahLHPidqj+CHbRyrRErYuEGdRucErt9gy8Bdl7xBgkKhuDxQQrrMrFQBy3mXYFMvwvrjP19wjv6K+zBe+oqzvt/+ya9xvPJ9bDOPIWwy9iDlzNb5VozzmQWcy2wgkgFoEEceLW+KjD244DrovfmXlpFBCBMNidD0i8oM51pNplt7cf0aftieF3kpJUHUpZzZRrV9kKZ7gkJq/WnvrQwSFArFUlFCuoysVNvF+ZZg0//yA6w9jy8452xZ6Fycz058mXp3DNvI45hFNE1Q7x6nG1RZV7yVlFU+r1nAucwGdM0ECZGM0KTAEPaC77thnSDqUE5vnR8uPceFZoZzrSaV9gGmWk+Rs0cASRC5dP0qtpGlL7sdL2jQcE/gh80Fs0JBGSQoFIqlo4R0mbjYop1zcbYlVL3psvF/P7jgtfo992G87MxZKCwU5aH8jcQyoNEdI+uMkHNGaLrjVNoHGTGK5zULOJfZgKln0TSBoZlYepbwpMw1ydpnMfQUuu5A3BPek7jQzDDnjLCp704mG3uIZUDLm0TXTAqp9ZQzW3HMEk13gsnGE4Sxz8nyrgwSFArFhaCEdBm42KKd83GmJdTyt5+h8OiRBa/z/vO/Jz9wzTnf62RRFkJQzmzDDWq03HEcq4RjFqh1jyE0g0J6/TnNAs5nNjCUfx5Sxkw1n6bpTlBMbSSSPq5fxTbz6JpJ25+mL7MNy1iYPV9MZlhMb2K4cDNCGBjCQtfMBfu/eWeUeucIje5xDN1ZVYOES8VxSaFQLB9KSJeBlZ7wcfISarprnJaFTt++gbGbs9yQP/97nyrKKavMaPHW+erWMPbwgwbF9Ea2D91z3iz6fGYDAPsnv87+qa8y3XySlNlHLjVKzhlhvPYYUdSilN6yrNZ5KatMKb2JmdY+cmew5Qtjl+1D9+AYJerusVUzSLiUHJcUCsXyoYR0GVjpCR9zS6jOvzzE8BONBd87/LYXMyOP0p/auCjROdO+ZsoqM2qW8MMm3aBGGHW4Zvg+Mnb/ouI71WxAF3Zvf9TDEA43bXgj64q3MFZ7hLY3iaYZaJpgy8BL6HjTdIMKum4vW2a4GFu+5exfXQwrufSvUCjWFiWky8CKT/io19j8J/+84FDlhVuYum2Ylnt0SaJztn1NTdOwjBwtb5KB3C7S1tIy5zmzgaY7zvHK98+Ydd288VdOE66THYaWMzNcrC3farS4rPTSv0KhWFuUkC4DKznhI/ziZ4m+/Y0Fx/b8mx14doQe1JYsOitpon4hWddKWuddKrZ8l9Nwb4VCsXSUkC4DKyFOZ3Qnevmr0F/2Cq67SGFYCRP1i8m6VtI671Kw5VPDvRWKKxslpMvEcopT+KXPEX3rXxYcO7kv9EKE4dRq0aw9vKzZmsq6zo4a7q1QXNkoIV1GLnYpUdZr+H/wuwuO6S+/F+Puey8qrtWoFlVZ19lRw70ViisbJaTLzIUuJZ4vC71QVqtaVGVdZ0cN91YormyUkK4xZ8xC774X4+UXl4XCc/uWnaBK1hoiij2QMZaRo7zM1aIq6zo3ari3QnHlooR0DQm//E9E3/z6gmPWe/8rWi5/ljOWRtevMNn4MR2/QqW1n0gG6JpJxh6gnNm2rPuWKus6P5dKFbFCoVhelJCuAUvJQk8tEnLMEl2/QsubAJJxZGmr74w341r3CBP13RgiRcruIyVswtij0R3DDWoMF24mir1l2beUUiIwKaY3EzSfptE5hhAmhu6orOskLoUqYoVCsbwoIV1llpKFnlokFEZdWt4MHX+WIGwBkLEH2dh3B9sG714gVFJKZhr7COOArLNuvgjI1FMYjkPLHWe6+TSl9JaL3rdMRrJ9nWPVh2m7k0jAMfMMF25iS9+LGcjvUlmXQqG4YlFCukrIRh3/A//PgmP6y16Jcc+rzvj6U4uEwqjLWPVHTDb2YAiH4cKNWEaWtjfFvvEv0PamuXHDL86Ladev0AmqlDPb6PizmHpqgYuRbRapdA4yWrzlovYtm+44Txz7W45Wvo8QJoX0RgDa3hTHZr+HlBEpu7yobFQZuisUissRJaSrwFL3Qk81NwCYbjxNy50ibfaBpuGFDTL2IMX0ZprdE0w1nmKs+gg7h+9D0zTC2CWWPgO5axmvP06zt09p9JZ3u34FXbPoz+68YLGSUjJWeYTJxlNYeoZcanT+vUz9zHGdDWXorlAoLleUkK4gS81C5zjV3MALGtTdY0hibCsRXy+oE8ZdTD1Nyi7T9SrMNJ9mY/kO0nbffDuKoadYV7yVSvsgbW8KV9bmC45SVplietMFf76uX2G69TQxMWl7YfaoadoZ4zoTytBdoVBczighXSHCr3ye6IGvLTi22IrcU80NIhkQRS4ayQBsicSXEXEcgg6GsEEDP2zPFw6d3I5SzmxntHgrftgkkgECo2dOf+1FLeuGsUsQtdHoxXAKZ4rrVJShu0KhuNxRQroCeL/7W+B581/rL3sFxj33Lfr8U80NdM1E1x0kiagC6JqOEMmvL4w9kGAZmfnCoTO2oxgZ6LWjpO3yRbejGMLB1DPIXgynuhqdKa5TUdaCCoXickesdQDLyTPPPMPP/uzP0t/fTz6f58477+SBBx5Y/UBOElHrvf91SSIKz2WTLXccKSWWkaPgbEBD4PkNvKCJbRYwRCop0PEqgEYutY4g6tLxZpFSzpsA9Gd34gY1ap3DuL2JMTuGXnnRy6Upq8xA9joEgq5XQUo5/725uDRN0J+77qyZ72KsBZerRUehUChWgisqI331q1/Njh07+MY3vkEqleJP/uRPePWrX82BAwcYHh5etTisd/0ucnYGcc21F3T+mbLJUmYz1c7BXtVuinJmK0HUoe1N4YdN0vYgtc5RnvT+4bRCnZUyAdA0jXXlW6m0n+Fo5ftUO4fJ2oNAUrUby4ANpReyrnTrWX+eshZUKBSXO5o8OY24jJmZmWFgYIBvf/vb/NRP/RQAzWaTfD7P1772Ne6+++5FvU+j0aBQKFCv18nnl8dh6EJZTB+pZWQQmkk+tZ7+3M6FbkJmcVUKdU7tI4Wz97eeipSSZya+NL+Xe6q1YKW9n/7sTrVHqlAoVpWlaMEVk5H29fWxc+dOPvGJT3DLLbdg2zYf+chHGBwc5NZbbz3reZ7n4Z20FNtoNFYj3EVxpmzyZGcjKSWTjSdouuP0ZXesWaFOzhnh5o2/zDVD9y7KcelklLWgQqG43LlihFTTNL7+9a/zmte8hlwuhxCCwcFBvvKVr1Aqlc563v3338/v//7vr2KkS+NMlnIZp5+M00/Hm+VI5TsL+jdPPm81C3U0TZuPa6koQ3eFQnE5c8kXG7373e9G07Rz/tm7dy9SSt7xjncwODjIgw8+yA9+8ANe85rX8DM/8zOMj4+f9f3f8573UK/X5/8cO3ZsFT/dxXElFerMZd83rHsD1697PTesewPXDL9KiahCobjkueT3SKenp5mdnT3na7Zu3cqDDz7IPffcQ7VaXbCevWPHDt761rfy7ne/e1E/71LaIz0fHW+WJ8b+HscsnrFQxwtbuEGNG9a9QbWOKBQKxRK4ovZIBwYGGBgYOO/rOp0OAEIsTLKFEMRxvCKxrTVqBqhCoVCsPZf80u5iueOOOyiVSrzpTW9i9+7dPPPMM/zWb/0Whw4d4r77ltbHebkwV6iTMotU2vvxwhaxjPDCFpX2flWoo1AoFKvAFSOk/f39fOUrX6HVavHSl76U2267je985zt87nOf46abblrr8FaMlTZdUCgUCsW5ueT3SFeby2mP9GTUCDKFQqFYPq6oPVLF4jhTm4xCoVAoVh4lpJcAl0I2eb4YLoUYFQqF4lJECekacykMtD5fDJdCjAqFQnGpooR0DbkUBlqfL4aR4i2M1x5VQ7cVCoXiLFwxVbuXG6cOtLaNLELTsY0s5cx2ukGNE9VHWclasPPF0AmqPHXiM3T86pJjlFLS8WZpdMfmx7opFArFlYjKSNeIS2Gg9flisPUcY9UfsmXgpUuKUS0FKxSKqwmVka4Rl4JP7vli0IROGLsITV90jHNLxTOtfThmkWJ6M45ZZKa1j2cnvkzTPbvvsUKhUFyOKCFdI04eaH0mVmOg9flikHGEIRxiGS0qxkthuVqhUChWGyWka8ScT27LHT9NWOZ8coupjSvqk3u+GLyoSX92J37QXFSMS1muVigUiisFJaRrxKXik1tKb0ZKmKjvxguaC2JIW2V2jb6WtFVaVIyXwnK1QqFQrDaq2GgNWcuB1icXBPlRk5Y3RdMdJ+sMkbEHF8SQdYYWFePJS8VnGuu2GsvVCoVCsdooIV1j5gZar6Zr0Km9ozlnBD/sUG0fwNLTbOl/MQO5XfMxLDZGNdZNoVBcjSghvQRYTZ/cUwuC5sTOMXMMF26i0t5PtX2YgdyuJcc4t1zd9iaptPcvMHBoueNqrNsKoiwcFYq1QwnpVcZK96+u5XL11Yrq21Uo1hYlpFcZiykIanuTF1UQtBbL1Vcrl4LNpEJxtaOqdq8yVqt/dW4pOJ9aR9ruUyK6Aqi+XYXi0kAJ6VXGpdC/qlgeVN+uQnFpoIT0KuNS6V9VXDyqb1ehuDRQe6RXIaog6MpA9e0qFJcGSkivUlRB0OWP6ttVKC4NlJBexaxm/6pi+VF9uwrFpYES0lUijmMq7QN4YR3bKFDObEMItUWtuDjUMr1CsfYoIV0FxmuP89SJzzDT2je/jNqf3cmu0dcyUrx5rcNTXOaoZXqFYm1RQrrCjNce56ED/4OON0shvQHbyOGFTcZrj1PvHuOObb+uxFRx0ahleoVi7VBriytIHMc8deIzdLxZBvPXk7JKCGGQskoM5q+n483y1InPEMfxWoeqUCgUigtECekKUmkfYKa1j0J6w2n7oUIICukNzLT2UWkfWKMIFQqFQnGxKCFdQbywThi72EbujN+3jRxh7OKF9VWOTKFQKBTLhRLSFcQ2ChjCwQubZ/y+FzYxhINtFFY5MoVCoVAsF0pIV5ByZhv92Z3UO8dO2weN45h65xj92Z2UM9vWKEKFQqFQXCxKSFcQIQS7Rl9L2u5jqrGHrl8ljkO6fpWpxh4ydh+7Rl+r+kkVCoXiMka1v6wwI8WbuWPbr8/3kTbc4xjCYaR4s+ojVSgUiisAJaSrwEjxZobyNypnI4VCobgCUUK6Sggh6M/tWOswFAqFQrHMqJRIoVAoFIqLQAmpQqFQKBQXgRJShUKhUCguAiWkCoVCoVBcBEpIFQqFQqG4CJSQKhQKhUJxESghVSgUCoXiIlBCqlAoFArFRaAMGa4wpJR0/Qph7GIIh5RVRtO0tQ5LoVAorliUkF5BNN1xTlQfpdY9ShR76MKmmNrIaOkWcs7IWoenUCgUVyRKSK8Qmu44z058mW5QI+uMYOopgqjLTGsfbW+SHcP3KjFVKBSKFUDtkV4BSCk5UX2UblCjnNmObWQRmo5tZClnttMNapyoPoqUcq1DVSgUiiuOy0ZIP/CBD/CTP/mTpNNpisXiGV9z9OhR7rvvPtLpNIODg/zWb/0WYRiubqBrQNevUOseJeuMnLYfqmkaWWeEWvcoXb+yRhEqFArFlctlI6S+7/P617+eX/u1Xzvj96Mo4r777sP3fb73ve/x8Y9/nL/6q7/id3/3d1c50tUnjF2i2MPUU2f8vqmniGKPMHZXOTKFQqG48rlshPT3f//3+U//6T9xww03nPH7X/3qV3nqqaf4P//n/3DzzTdz77338v73v58///M/x/f9VY52dTGEgy5sgqh7xu8HURdd2BjCWeXIFAqF4srnshHS8/HQQw9xww03MDQ0NH/sFa94BY1GgyeffHINI1t5UlaZYmojLXf8tH1QKSUtd5xiaiMpq7xGESoUCsWVyxVTtTsxMbFARIH5rycmJs56nud5eJ43/3Wj0ViZAFcQTdMYLd1C25uk0t6/oGq35Y6TskqMlm5R/aQKhUKxAqxpRvrud78bTdPO+Wfv3r0rGsP9999PoVCY/7Nhw4YV/XkrRc4ZYcfwvfRnd+IGNWqdw7hBjf7sTnYMvVK1vigUCsUKsaYZ6W/+5m/y5je/+Zyv2bp166Lea3h4mB/84AcLjk1OTs5/72y85z3v4Z3vfOf8141G47IW02uGX6WcjRQKhWIVWVMhHRgYYGBgYFne64477uADH/gAU1NTDA4OAvC1r32NfD7Prl27znqebdvYtr0sMVwKaJpG2u5b6zAUCoXiquGy2SM9evQolUqFo0ePEkURjz/+OADbt28nm81yzz33sGvXLn7lV36FD33oQ0xMTPDe976Xd7zjHVeUUCoUCoXi0kKTl4ndzZvf/GY+/vGPn3b8gQce4K677gLgyJEj/Nqv/Rrf/OY3yWQyvOlNb+IP//APMYzFPy80Gg0KhQL1ep18Pr9c4SsUCoXiMmIpWnDZCOlqoYRUoVAoFEvRgiumj1ShUCgUirVACalCoVAoFBeBElKFQqFQKC4CJaQKhUKhUFwESkgVCoVCobgIlJAqFAqFQnERKCFVKBQKheIiuGycjVaLubbay3EKjEKhUCiWhzkNWIzVghLSU2g2mwCXrXG9QqFQKJaPZrNJoVA452uUs9EpxHHMiRMnyOVyV8XUlLlpN8eOHbtqnZyu9mtwtX9+UNfgav/8cPo1kFLSbDYZHR1FiHPvgqqM9BSEEKxfv36tw1h18vn8VfsPaI6r/Rpc7Z8f1DW42j8/LLwG58tE51DFRgqFQqFQXARKSBUKhUKhuAiUkF7l2LbN+973vqt6ZuvVfg2u9s8P6hpc7Z8fLu4aqGIjhUKhUCguApWRKhQKhUJxESghVSgUCoXiIlBCqlAoFArFRaCEVKFQKBSKi0AJqWKeZ555hp/92Z+lv7+ffD7PnXfeyQMPPLDWYa06X/ziF7n99ttJpVKUSiVe85rXrHVIa4Lnedx8881omsbjjz++1uGsCocPH+atb30rW7ZsIZVKsW3bNt73vvfh+/5ah7ai/Pmf/zmbN2/GcRxuv/12fvCDH6x1SKvG/fffzwte8AJyuRyDg4O85jWvYd++fUt6DyWkinle/epXE4Yh3/jGN3jkkUe46aabePWrX83ExMRah7ZqfPrTn+ZXfuVXeMtb3sLu3bv57ne/yy/90i+tdVhrwrve9S5GR0fXOoxVZe/evcRxzEc+8hGefPJJ/vt//+/8xV/8Bf/3//1/r3VoK8YnP/lJ3vnOd/K+972PRx99lJtuuolXvOIVTE1NrXVoq8K3vvUt3vGOd/Dwww/zta99jSAIuOeee2i324t/E6lQSCmnp6clIL/97W/PH2s0GhKQX/va19YwstUjCAK5bt06+Zd/+ZdrHcqa86UvfUlee+218sknn5SAfOyxx9Y6pDXjQx/6kNyyZctah7Fi/MRP/IR8xzveMf91FEVydHRU3n///WsY1doxNTUlAfmtb31r0eeojFQBQF9fHzt37uQTn/gE7XabMAz5yEc+wuDgILfeeutah7cqPProo4yNjSGE4PnPfz4jIyPce++97NmzZ61DW1UmJyd529vexl//9V+TTqfXOpw1p16vUy6X1zqMFcH3fR555BHuvvvu+WNCCO6++24eeuihNYxs7ajX6wBL+p0rIVUAoGkaX//613nsscfI5XI4jsMf//Ef85WvfIVSqbTW4a0KBw8eBOD3fu/3eO9738sXvvAFSqUSd911F5VKZY2jWx2klLz5zW/m7W9/O7fddttah7Pm7N+/nz/7sz/jV3/1V9c6lBVhZmaGKIoYGhpacHxoaOiq2tKZI45j/uN//I+86EUv4vrrr1/0eUpIr3De/e53o2naOf/s3bsXKSXveMc7GBwc5MEHH+QHP/gBr3nNa/iZn/kZxsfH1/pjXBSLvQZxHAPwO7/zO7zuda/j1ltv5WMf+xiapvGpT31qjT/FxbHYa/Bnf/ZnNJtN3vOe96x1yMvKYj//yYyNjfHKV76S17/+9bztbW9bo8gVq8k73vEO9uzZw9/93d8t6TxlEXiFMz09zezs7Dlfs3XrVh588EHuueceqtXqgjFKO3bs4K1vfSvvfve7VzrUFWOx1+C73/0uL33pS3nwwQe588475793++23c/fdd/OBD3xgpUNdMRZ7Dd7whjfw+c9/fsEs3iiK0HWdN77xjXz84x9f6VBXhMV+fsuyADhx4gR33XUXL3zhC/mrv/qr886jvFzxfZ90Os0//MM/LKhOf9Ob3kStVuNzn/vc2gW3yvz7f//v+dznPse3v/1ttmzZsqRz1TzSK5yBgQEGBgbO+7pOp/P/b+/+Y5q62jiAf9s7ga5lWCC0zkjJhpqyKDBGQVxiwdl2c44YBom6UlgmmZGKQ7OGJQwyNQYU0LFkiZAUkrkfcfHHMqZOGkkIE2FTfpmtW5oCCwN0Q2fKSJFy9oev9+W+wMAV6Cs+n+QmnHMvz33uJeHhnFvuAYBJvzDEYjE/UntUzfYexMXFwd/fH3a7nS+k9+7dQ3d3N1Qq1XynOa9mew8+/PBDHDx4kG//9ttv0Ov1+OKLL5CQkDCfKc6r2V4/cH8kmpyczM9ILNYiCgB+fn6Ii4uDzWbjC+n4+DhsNhtyc3N9m9wCYYzBbDbjzJkzaGhoeOgiClAhJf+xbt06yOVymEwmvP/++5BIJKiqqoLT6cTmzZt9nd6CeOqpp/D222+jqKgIK1asgEqlwpEjRwAA6enpPs5uYYSHhwvaMpkMAPDss88+Fgve9/X1QavVQqVS4ejRo7h16xa/T6lU+jCz+ZOfnw+TyYQXXngBGo0Gx44dw/DwMLKzs32d2oLYvXs3Pv30U5w7dw6BgYH8s+GgoCBIJJLZBZmfDxCTR1FrayvT6XQsODiYBQYGssTERPbNN9/4Oq0FNTo6yvbt28fCwsJYYGAge+mll1hXV5ev0/IZp9P5WP37i9VqZQCm3BazyspKFh4ezvz8/JhGo2HNzc2+TmnBTPfztlqts45Bz0gJIYQQLyzeyX9CCCFkAVAhJYQQQrxAhZQQQgjxAhVSQgghxAtUSAkhhBAvUCElhBBCvECFlBBCCPECFVJCHkFZWVmCd6M+iiIiInDs2DFfp0GI16iQEjKFrKysKVcIMRgMvk4NAHD8+HHU1NT4Og0A95fgO3v27JzHLS4uRkxMzJzHnejWrVvYtWsXwsPD4e/vD6VSCb1ej6ampnk9L1lc6F27hEzDYDDAarUK+vz9/X2UzX0ejwcikQhBQUE+zWOxSEtLw+joKGpra/HMM89gcHAQNpttxpViCJmIRqSETOPBCGXi9mCR84aGBvj5+aGxsZE/vrS0FGFhYRgcHAQAaLVa5ObmIjc3F0FBQQgNDUVhYSEmvpXT7XZj//79WL58OaRSKRISEtDQ0MDvr6mpwdKlS/HVV18hKioK/v7+6O3tnTS1q9VqYTabsXfvXsjlcigUClRVVfEvHw8MDERkZCTOnz8vuMauri68/PLLkMlkUCgUMBqN+P333wVx9+zZg3fffRfBwcFQKpUoLi7m90dERAAAtm7dCpFIxLcdDgdSU1OhUCggk8kQHx+P+vp6b34ck3R2diIlJQUSiQQhISHIycmBy+Xi94+NjWHPnj1YunQpQkJCYLFYYDKZ+Pt2584dNDY2oqSkBMnJyVCpVNBoNCgoKMBrr702p7mSxY0KKSH/glarxd69e2E0GvHnn3/i+vXrKCwsRHV1NRQKBX9cbW0tnnjiCbS0tOD48eMoLy9HdXU1vz83NxdXrlzB559/jo6ODqSnp8NgMOCXX37hj/nrr79QUlKC6upq3LhxA2FhYVPmVFtbi9DQULS0tMBsNmPXrl1IT09HUlISrl27Bp1OB6PRyC+Zd+fOHaSkpCA2Nhbff/89Lly4gMHBQWRkZEyKK5VKcfXqVZSWluKDDz7ApUuXAACtra0AAKvViv7+fr7tcrnwyiuvwGaz4fr16zAYDNiyZQt6e3vn4O4Dw8PD0Ov1kMvlaG1txalTp1BfXy9Y+qukpAQnT56E1WpFU1MT7t69K5iClslkkMlkOHv2LNxu95zkRR5T8/RCfUIeaSaTiXEcx6RSqWA7dOgQf4zb7WYxMTEsIyODRUVFsZ07dwpibNiwganVajY+Ps73WSwWplarGWOM9fT0MI7jWF9fn+D7Nm7cyAoKChhj/12NpK2tbVJ+qampgnO9+OKLfHtsbIxJpVJmNBr5vv7+fgaAXblyhTHG2IEDB5hOpxPE/fXXXxkAZrfbp4zLGGPx8fHMYrHwbQDszJkzU9xFoeeee45VVlbybZVKxSoqKqY9vqioiEVHR0+578SJE0wulzOXy8X31dXVMbFYzAYGBhhjjCkUCnbkyBF+/9jYGAsPDxfcty+//JLJ5XIWEBDAkpKSWEFBAWtvb5/xWgiZiJ6REjKN5ORkfPzxx4K+4OBg/ms/Pz+cPHkSa9euhUqlQkVFxaQYiYmJEIlEfHvdunUoKyuDx+NBZ2cnPB4PVq1aJfget9uNkJAQwXnWrl07Y74Tj+E4DiEhIVizZg3f92CkfPPmTQBAe3s7Ll++zK85OpHD4eDz+t9zL1u2jI8xHZfLheLiYtTV1aG/vx9jY2MYGRmZsxHpjz/+iOjoaEilUr5v/fr1GB8fh91uR0BAAAYHB6HRaPj9HMchLi5OsFB9WloaNm/ejMbGRjQ3N+P8+fMoLS1FdXU1srKy5iRXsvhRISVkGlKpFJGRkf94zHfffQcAGBoawtDQkOAX+0xcLhc4jsMPP/wAjuME+yYWN4lEIijG01myZImgLRKJBH0PYjwoJC6XC1u2bEFJScmkWMuWLfvHuBOL0VT279+PS5cu4ejRo4iMjIREIsHrr7+O0dHRGa9joQUEBGDTpk3YtGkTCgsL8dZbb6GoqIgKKZk1ekZKyL/kcDjwzjvvoKqqCgkJCTCZTJMKzNWrVwXt5uZmrFy5EhzHITY2Fh6PBzdv3kRkZKRgUyqV857/888/jxs3biAiImLS+R/mD4IlS5bA4/EI+pqampCVlYWtW7dizZo1UCqV6O7unrPc1Wo12tvbMTw8LDinWCzG6tWrERQUBIVCwT+zBe5/4vnatWszxo6KihLEJWQmVEgJmYbb7cbAwIBge/CJVo/HgzfeeAN6vR7Z2dmwWq3o6OhAWVmZIEZvby/y8/Nht9vx2WefobKyEnl5eQCAVatWYceOHcjMzMTp06fhdDrR0tKCw4cPo66ubt6vb/fu3RgaGsK2bdvQ2toKh8OBixcvIjs7e1Jh/CcRERGw2WwYGBjA7du3AQArV67E6dOn0dbWhvb2dmzfvn3GUexURkZG0NbWJtgcDgd27NiBgIAAmEwmdHV14fLlyzCbzTAajfwUttlsxuHDh3Hu3DnY7Xbk5eXh9u3b/Mj8jz/+QEpKCj755BN0dHTA6XTi1KlTKC0tRWpq6kPnSh5fNLVLyDQuXLggmOIEgNWrV+Onn37CoUOH0NPTg6+//hrA/anQEydOYNu2bdDpdIiOjgYAZGZmYmRkBBqNBhzHIS8vDzk5OXw8q9WKgwcPYt++fejr60NoaCgSExPx6quvzvv1Pf3002hqaoLFYoFOp4Pb7YZKpYLBYIBYPPu/scvKypCfn4+qqiosX74c3d3dKC8vx5tvvomkpCSEhobCYrHg7t27D53jzz//jNjYWEHfxo0bUV9fj4sXLyIvLw/x8fF48sknkZaWhvLycv44i8WCgYEBZGZmguM45OTkQK/X89PoMpkMCQkJqKiogMPhwL1797BixQrs3LkT77333kPnSh5fIsYm/FMbIWTOaLVaxMTE0Gvw/k+Mj49DrVYjIyMDBw4c8HU6ZBGhESkhZFHq6enBt99+iw0bNsDtduOjjz6C0+nE9u3bfZ0aWWToGSkhZFESi8WoqalBfHw81q9fj87OTtTX10OtVvs6NbLI0NQuIYQQ4gUakRJCCCFeoEJKCCGEeIEKKSGEEOIFKqSEEEKIF6iQEkIIIV6gQkoIIYR4gQopIYQQ4gUqpIQQQogXqJASQgghXvgbEW3LXJBChFgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}