Telco_customer_churn_prediction_with_ML.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Telco customer data prediction\n",
        "### Mohon Dash Tanu-19301184\n",
        "### Md. Atif Hossain-19201016\n",
        "### Anindya Mitra-23141063\n",
        "### Pial Ghosh Id-19201069\n"
      ],
      "metadata": {
        "id": "OPYC_51pbIzq"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JelO9zaEThLP"
      },
      "source": [
        "### Importing necessary modules for data extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "P_0BHKV8j-_B"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VN8q7TuSlLc6"
      },
      "source": [
        "### Loaded the dataset as dataframe using pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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          "height": 342
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        "id": "5piFJR-1kui3",
        "outputId": "78ae70bc-6802-42cf-983c-d04a804a1f83"
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          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "      <th></th>\n",
              "      <th>customerID</th>\n",
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              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
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              "      <td>One year</td>\n",
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              "      <td>3668-QPYBK</td>\n",
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              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>One year</td>\n",
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              "      <td>9237-HQITU</td>\n",
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              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "telco"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "telco = pd.read_csv('/content/dataset.csv')\n",
        "telco.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xX_gf0lVlLap"
      },
      "source": [
        "### Checking the shape and missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWo3DeLilZ2a",
        "outputId": "04fc381e-2ba4-4a44-c3ce-b98beebdd398"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "telco.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9Vuw7yvmY1j",
        "outputId": "b42e609c-c6c4-481d-b0bf-8551b37962c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID          0\n",
              "gender              0\n",
              "SeniorCitizen       0\n",
              "Partner             0\n",
              "Dependents          0\n",
              "tenure              0\n",
              "PhoneService        0\n",
              "MultipleLines       0\n",
              "InternetService     0\n",
              "OnlineSecurity      0\n",
              "OnlineBackup        0\n",
              "DeviceProtection    0\n",
              "TechSupport         0\n",
              "StreamingTV         0\n",
              "StreamingMovies     0\n",
              "Contract            0\n",
              "PaperlessBilling    0\n",
              "PaymentMethod       0\n",
              "MonthlyCharges      0\n",
              "TotalCharges        0\n",
              "Churn               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "telco.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eoN60rnWThLt"
      },
      "source": [
        "### Dropping the CustomerID\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "id": "3qx5y6LImzZM",
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          "height": 296
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  Female              0     Yes         No       1           No   \n",
              "1    Male              0      No         No      34          Yes   \n",
              "2    Male              0      No         No       2          Yes   \n",
              "3    Male              0      No         No      45           No   \n",
              "4  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
              "0  No phone service             DSL             No          Yes   \n",
              "1                No             DSL            Yes           No   \n",
              "2                No             DSL            Yes          Yes   \n",
              "3  No phone service             DSL            Yes           No   \n",
              "4                No     Fiber optic             No           No   \n",
              "\n",
              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "0               No          No          No              No  Month-to-month   \n",
              "1              Yes          No          No              No        One year   \n",
              "2               No          No          No              No  Month-to-month   \n",
              "3              Yes         Yes          No              No        One year   \n",
              "4               No          No          No              No  Month-to-month   \n",
              "\n",
              "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
              "0              Yes           Electronic check           29.85        29.85   \n",
              "1               No               Mailed check           56.95       1889.5   \n",
              "2              Yes               Mailed check           53.85       108.15   \n",
              "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
              "4              Yes           Electronic check           70.70       151.65   \n",
              "\n",
              "  Churn  \n",
              "0    No  \n",
              "1    No  \n",
              "2   Yes  \n",
              "3    No  \n",
              "4   Yes  "
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            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "telco.drop('customerID', inplace=True,axis=1)\n",
        "telco.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxpiIEltThL0"
      },
      "source": [
        "### Checking the data type of the features of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_lupwPWolT9",
        "outputId": "0efc0161-840f-4cde-8778-2fe5a20b1f8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "telco.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tEgjIv4dThL2"
      },
      "source": [
        "### Encoding Features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "qLEkMj5polbH",
        "outputId": "af98fd41-62f1-4f01-c841-2452ff4ac5fa"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
              "0       0              0        1           0       1             0   \n",
              "1       1              0        0           0      34             1   \n",
              "2       1              0        0           0       2             1   \n",
              "3       1              0        0           0      45             0   \n",
              "4       0              0        0           0       2             1   \n",
              "\n",
              "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
              "0              1                0               0             2   \n",
              "1              0                0               2             0   \n",
              "2              0                0               2             2   \n",
              "3              1                0               2             0   \n",
              "4              0                1               0             0   \n",
              "\n",
              "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
              "0                 0            0            0                0         0   \n",
              "1                 2            0            0                0         1   \n",
              "2                 0            0            0                0         0   \n",
              "3                 2            2            0                0         1   \n",
              "4                 0            0            0                0         0   \n",
              "\n",
              "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
              "0                 1              2           29.85         29.85      0  \n",
              "1                 0              3           56.95       1889.50      0  \n",
              "2                 1              3           53.85        108.15      1  \n",
              "3                 0              0           42.30       1840.75      0  \n",
              "4                 1              2           70.70        151.65      1  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "telco",
              "summary": "{\n  \"name\": \"telco\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PhoneService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultipleLines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InternetService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineSecurity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineBackup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DeviceProtection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TechSupport\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingTV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingMovies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaperlessBilling\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaymentMethod\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.090047097678493,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          48.85,\n          20.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2266.771361883145,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 6530,\n        \"samples\": [\n          5594.0,\n          6840.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "enc = LabelEncoder()\n",
        "\n",
        "telco['gender']         = enc.fit_transform(telco['gender'])\n",
        "telco['Dependents']     = enc.fit_transform(telco['Dependents'])\n",
        "telco['Partner']        = enc.fit_transform(telco['Partner'])\n",
        "telco['PhoneService']   = enc.fit_transform(telco['PhoneService'])\n",
        "telco['MultipleLines']  = enc.fit_transform(telco['MultipleLines'])\n",
        "telco['InternetService'] = enc.fit_transform(telco['InternetService'])\n",
        "telco['OnlineSecurity'] = enc.fit_transform(telco['OnlineSecurity'])\n",
        "telco['OnlineBackup']   = enc.fit_transform(telco['OnlineBackup'])\n",
        "telco['DeviceProtection'] = enc.fit_transform(telco['DeviceProtection'])\n",
        "telco['TechSupport']    = enc.fit_transform(telco['TechSupport'])\n",
        "telco['StreamingTV']    = enc.fit_transform(telco['StreamingTV'])\n",
        "telco['StreamingMovies'] = enc.fit_transform(telco['StreamingMovies'])\n",
        "telco['Contract']       = enc.fit_transform(telco['Contract'])\n",
        "telco['PaperlessBilling'] = enc.fit_transform(telco['PaperlessBilling'])\n",
        "telco['PaymentMethod']  = enc.fit_transform(telco['PaymentMethod'])\n",
        "telco['Churn']          = enc.fit_transform(telco['Churn'])\n",
        "telco['TotalCharges']   = pd.to_numeric(telco['TotalCharges'],errors='coerce')\n",
        "telco['Churn']          = enc.fit_transform(telco['Churn'])\n",
        "\n",
        "telco.head()   ### Showing the result of change\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparision of Churn"
      ],
      "metadata": {
        "id": "BzdS0kQbXjep"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "churn_counts = telco['Churn'].value_counts()\n",
        "\n",
        "# Create a bar chart of the counts\n",
        "plt.bar(['0', '1'], [churn_counts[0], churn_counts[1]])\n",
        "\n",
        "# Add labels to the x-axis and y-axis\n",
        "plt.xlabel('Churn');plt.ylabel('Count');\n",
        "\n",
        "# Add a title to the chart\n",
        "plt.title('Churn Count')\n",
        "\n",
        "# Show the chart\n",
        "plt.show()\n",
        "print(\" 0's→ {}    1's→{}\".format (churn_counts[0],churn_counts[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "4X6Yd3whXq27",
        "outputId": "566d57f0-6427-422c-bbf0-a64a1fb9b661"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 0's→ 5174    1's→1869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlation of the features\n"
      ],
      "metadata": {
        "id": "PeL0mGfPWkR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import seaborn as sns\n",
        "corr_matrix = telco.corr()\n",
        "# sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "sns.heatmap(corr_matrix, cmap = 'YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "NXwN-AXrWtYB",
        "outputId": "4115d257-c613-4fbf-ba63-d692f7a72089"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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qVJGu6eLiQrdu3Rg2bBhHjx4lNjaWd999FwcHB7p165bvubNnz8bR0ZHmzZuzfv16Ll68yNWrV1m3bh2enp6kpqbmep6zszORkZEkJSXx+++/5zielJREjx496Nu3L76+viQlJZGUlMSdO3cAGDlyJPfu3aNfv3788MMPxMfHs3fvXgYNGvTC5FEIIYQoq6S7Nn+l+l1ptVqaN2/O4sWLad26NQ0bNmTy5MkMGzaM5cuXoygKu3btonXr1gwaNIg6derQt29ffvrpJ2xtbYt83ZCQEBo3bkyXLl3w9vYmOzubXbt25ejufJ6NjQ3ff/897777LrNmzcLT05NWrVrx5Zdf8q9//QsrK6tcz1u4cCH79+/H0dERT0/PHMcvXbrE7du3CQsLw97eXrc1bdoUgGrVqhEdHU1mZiavv/46bm5ujB07FmtrawwMSvW3WAghhCiy3KYXK+pWHinZ/9QTA6Jc2Hlrt6rx3mq5XtV4mw4PUC3Wa3YZLy5UCEeT1O3qNlD5d9L3d0xUjdf5lT9VjWdrpt6vqoRUdf/5MVL5e1HTQt0W+O+T1f3spWWp+4Zfs01XLdaaS+aqxQLo5vSHqvHWXFJ3kvuGFdW7dwB309T92ZjXtIOq8Z7n6D5DtVi3zk1RLVZpUSbH5AkhhBBClNduVrVIkieEEEKIMkmers2f3B0hhBBCiHJIWvKEEEIIUSZJd23+JMkTJUrNByUA3mmt3oMc1YKGqxYLwMpK3cHqk1/NfUqeolL7V+WDDHUj7ril7oMhaspQ+UEEK5OXm6z9eU/UDaf6Qz//jbdRLdZEt/uqxQJwqKDuzfOwUfdBiW8TKqgaL7TtvRcXKkUkycuf3B0hhBBCiHJIWvKEEEIIUSbJgxf5kyRPCCGEEGWTdNfmS+6OCgICAnQzZpuYmFC7dm1mzJjBkydPXipm9+7d1aukEEIIUc7Ismb5k5Y8lfj5+RESEkJaWhq7du1i5MiRGBsbExQUVKg4mZmZ//jyKunp6ZiYlN5B7UIIIYQovPKZupYAjUaDnZ0dTk5ODB8+HB8fHyIiIli0aBFubm6Ym5vj6OjIiBEjSE3966nI0NBQrK2tiYiIoH79+mg0GgYPHkxYWBjffvutroUwKiqKhIQEFEVh+/bttGvXjgoVKuDh4cHx48f16nL06FFatWqFmZkZjo6OjB49mkePHumOOzs7M3PmTAYMGIClpSXvvffeP3afhBBCCLXI2rX5kySvmJiZmZGeno6BgQHLli3jwoULhIWF8d133zFhwgS9so8fP2bevHmsXbuWCxcusGzZMt5++238/PxITEwkMTGRV199VVd+0qRJjB8/npiYGOrUqUO/fv10XcPx8fH4+fnRq1cvzp07x+bNmzl69CijRo3Su+aCBQvw8PDg7NmzTJ48ufhviBBCCKEyBQPVtvJIumtVlp2dTWRkJHv37uXDDz9k7NixumPOzs7MmjWLDz74gJUrV+r2Z2RksHLlSjw8PHT7zMzMSEtLw87OLsc1xo8fzxtvvAHA9OnTadCgAdeuXaNevXoEBwfj7++vu66LiwvLli2jTZs2rFq1ClNTUwDat2/PRx99lO97SUtLIy0tTW9fRloGxhp1FzsXQgghhPrKZ+paAnbs2IFWq8XU1JROnTrRp08fpk2bxoEDB+jQoQMODg5YWFjQv39/7t69y+PHj3XnmpiY4O7uXuBr/b2svb09AMnJyQDExsYSGhqKVqvVbb6+vmRlZXHjxg3deU2aNHnhdYKDg7GystLbtqzYXOB6CiGEEMVJHrzIn7TkqaRdu3asWrUKExMTqlWrhpGREQkJCXTp0oXhw4cze/ZsbGxsOHr0KEOGDCE9PZ0KFZ7OVG5mZlao8QDGxn+1pD07Lyvr6azsqampvP/++4wePTrHedWrV9d9bW5u/sLrBAUFERgYqLfvu+SoAtdTCCGEKFbldCydWiTJU4m5uTm1a9fW23f69GmysrJYuHAhBgZP/0vYsmVLgeKZmJiQmZlZ6Hp4eXlx8eLFHHUpCo1Gg0aj0dtnfF+6aoUQQoiyoHy2T5YStWvXJiMjg88//5zr16+zYcMGVq9eXaBznZ2dOXfuHJcvX+a3334jIyOjQOdNnDiRY8eOMWrUKGJiYrh69SrffvttjgcvhBBCiDLPQMWtCFasWIGzszOmpqY0b96ckydP5lt+yZIl1K1bVzf7xbhx4/jzzz+LdvECkCSvGHl4eLBo0SLmzZtHw4YN2bhxI8HBwQU6d9iwYdStW5cmTZpQpUoVoqOjC3Seu7s7hw4d4sqVK7Rq1QpPT0+mTJlCtWrVXuatCCGEEKWPoqi3FdLmzZsJDAxk6tSpnDlzBg8PD3x9fXVj5J+3adMmPvnkE6ZOnUpcXBz/+c9/2Lx5M59++unL3oU8KdnZ2dnFFl2UOztv7VY1Xnrhe6Tz9U7r9arFqhY0XLVYAFZW6o4dmfxq6osLFcKp39SdEPtV23RV4x1OKr0Tdmdkqfu9tTLJUjXeE3XDYaDyMKhjyWaqxZrodl+1WAC1LdX9JbUtwVTVeN8mVFA1Xmjbe6rGq2nRVdV4z6vjvUq1WOejBueYUSK3YUvPNG/enKZNm7J8+XLg6dh4R0dHPvzwQz755JMc5UeNGkVcXByRkZG6fR999BEnTpzg6NGjqr2Pv5OWPCGEEEKUTSq25OU2o0RevW/p6emcPn0aHx8f3T4DAwN8fHxyLFDwzKuvvsrp06d1XbrXr19n165ddO7cWf378v/JgxdCCCGEKJtUbKrKbUaJvFrxfvvtNzIzM7G1tdXbb2try6VLl3I955133uG3337jtddeIzs7mydPnvDBBx8Ua3ettOQJIYQQokzKVhTVNo1Gg6Wlpd6WV5JXFFFRUcyZM4eVK1dy5swZtm/fzs6dO5k5c6Zq13ietOSJQlF7bM9rdgV7arig1BxH92uwemM9AHptHaZqPBNDVcPRoVraiwsVwi+PVa6giuzM1P0gGxmoO7Q5+Q91//9We1yZuZG67zfuvnr1q2au7vf2l8fqfi86O6r7c2ZhrPJnWaadK5DKlStjaGjI7du39fbfvn0715WqACZPnkz//v0ZOnQoAG5ubjx69Ij33nuPSZMm6aZaU5O05AkhhBCibFJU3ArBxMSExo0b6z1EkZWVRWRkJN7e3rme8/jx4xyJnKHh03+Gi+sZWGnJE0IIIUTZpPaj3oUQGBjIwIEDadKkCc2aNWPJkiU8evSIQYMGATBgwAAcHBx0D2907dqVRYsW4enpSfPmzbl27RqTJ0+ma9euumRPbZLklbDQ0FDGjh1LSkpKSVdFCCGEEAXUp08f7ty5w5QpU0hKSqJRo0bs2bNH9zDGzZs39VruPvvsMxRF4bPPPuOXX36hSpUqdO3aldmzZxdbHct0d21AQACKoqAoCsbGxtja2tKxY0fWrVunW8v1f1FUVBSKokjiKIQQonwrwcmQ4encdz/99BNpaWmcOHGC5s2b645FRUURGhqqe21kZMTUqVO5du0af/zxBzdv3mTFihVYW1u/5E3IW5lO8gD8/PxITEwkISGB3bt3065dO8aMGUOXLl148uRJSVdPCCGEEMWlhMbklRVlPsnTaDTY2dnh4OCAl5cXn376Kd9++y27d+/WZdApKSkMHTqUKlWqYGlpSfv27YmNjdXFmDZtGo0aNWLNmjU4OjpSoUIF3n77be7f1585fe3atbi6umJqakq9evVYuXKl7lhCQgKKorB9+3batWtHhQoV8PDwyDEpYmhoKNWrV6dChQr06NGDu3fv5nhP3377LV5eXpiamlKzZk2mT5+ul7AqisLatWvp0aMHFSpUwMXFhYiICF092rVrB0DFihVRFIWAgAAAtm7dipubG2ZmZlSqVAkfHx8ePXpU9JsvhBBCiFKrzCd5uWnfvj0eHh5s374dgN69e5OcnMzu3bs5ffo0Xl5edOjQgXv3/lq+5dq1a2zZsoX/+7//Y8+ePZw9e5YRI0bojm/cuJEpU6Ywe/Zs4uLimDNnDpMnTyYsLEzv2pMmTWL8+PHExMRQp04d+vXrp0vQTpw4wZAhQxg1ahQxMTG0a9eOWbNm6Z1/5MgRBgwYwJgxY7h48SJr1qwhNDQ0R5/99OnTefvttzl37hydO3fG39+fe/fu4ejoyLZt2wC4fPkyiYmJLF26lMTERPr168fgwYOJi4sjKiqKnj17FtsTPUIIIUSxM1DU28qhcvvgRb169Th37hxHjx7l5MmTJCcn6yY1XLBgAeHh4WzdupX33nsPgD///JP169fj4OAAwOeff84bb7zBwoULsbOzY+rUqSxcuJCePXsCUKNGDV0SNnDgQN11x48fzxtvvAE8TcQaNGjAtWvXqFevHkuXLsXPz48JEyYAUKdOHY4dO8aePXt050+fPp1PPvlEF7NmzZrMnDmTCRMmMHXqVF25gIAA+vXrB8CcOXNYtmwZJ0+exM/PDxsbGwCqVq2q6+uPj4/nyZMn9OzZEycnJ+DpHD35SUtLy7GOX0ZaBsYa4wJ9D4QQQohiVcSxdP8rymVLHjydc0ZRFGJjY0lNTaVSpUpotVrdduPGDeLj43Xlq1evrkvwALy9vcnKyuLy5cs8evSI+Ph4hgwZohdj1qxZejEA3N3ddV/b29sDkJycDEBcXJzeoMxn1/m72NhYZsyYoXedYcOGkZiYyOPHj3O9jrm5OZaWlrrr5MbDw4MOHTrg5uZG7969+eKLL/j999/zvYe5reO3deXmfM8RQgghROlQblvy4uLiqFGjBqmpqdjb2xMVFZWjTEGfaElNTQXgiy++yJGkPT+3jbHxX61cyv//D6MwT/qmpqYyffp0XYvh35mamuZ6nWfXyu86hoaG7N+/n2PHjrFv3z4+//xzJk2axIkTJ6hRo0au5+S2jt++pKgCvxchhBCiWElDXr7KZZL33Xffcf78ecaNG8crr7xCUlISRkZGODs753nOzZs3+fXXX6lWrRoA33//PQYGBtStWxdbW1uqVavG9evX8ff3L3K9XF1dOXHihN6+77//Xu+1l5cXly9fpnbt2kW+jomJCQCZmfpLBSmKQsuWLWnZsiVTpkzBycmJb775Jkci94xGo8mxbp/x79JVK4QQopQop2Pp1FLmk7y0tDSSkpLIzMzk9u3b7Nmzh+DgYLp06cKAAQMwMDDA29ub7t27M3/+fOrUqcOvv/7Kzp076dGjB02aNAGetpINHDiQBQsW8ODBA0aPHs3bb7+tW4Nu+vTpjB49GisrK/z8/EhLS+PUqVP8/vvveSZJzxs9ejQtW7ZkwYIFdOvWjb179+qNxwOYMmUKXbp0oXr16rz11lsYGBgQGxvLjz/+mOMhjbw4OTmhKAo7duygc+fOmJmZceHCBSIjI3n99depWrUqJ06c4M6dO7i6uhbibgshhBCliOR4+SrzY/L27NmDvb09zs7O+Pn5cfDgQZYtW8a3336LoaEhiqKwa9cuWrduzaBBg6hTpw59+/blp59+0s1KDVC7dm169uxJ586def3113F3d9ebImXo0KGsXbuWkJAQ3NzcaNOmDaGhoXl2deamRYsWfPHFFyxduhQPDw/27dvHZ599plfG19eXHTt2sG/fPpo2bUqLFi1YvHix7mGJgnBwcNA9wGFra8uoUaOwtLTk8OHDdO7cmTp16vDZZ5+xcOFCOnXqVOC4QgghhCg7lGyZQ4Np06YRHh5OTExMSVel1Pv2p92qxnvVNkPVeC1CrFWL9WvwKtViAXy4dZiq8drYp724UCGYGar7q+CXx+quxfjj7+p1PNiZqbsijpGBuvcu+Q91//+ubZn54kKFYG6k7vvdklBBtVjTPB+oFgvg9zR1m4psNOreuyNJ6g6h8XslXdV41bVdVY33vNqdQ1SLdW3XINVilRZlvrtWCCGEEP+jZExevsp8d60QQgghhMhJkjyedtdKV60QQghRxsjatfmS7lpRKGq3jB9VeTyJlZV6Feyl8hi6z9/6QtV4LY8MUDXeZz9Yqhov2FvdsVH/vmiuWqw/HpfuocgVzNX9//vkb+q+3ydZ6v4iSNhwU7VY8XOtVYsF8IZjdVXjvX0w53rlLyN2QuyLCxXCoJOtVI1X7GTFi3xJS54QQgghRDkkLXlCCCGEKJvkwYt8SZInhBBCiLJJcrx8SXetEEIIIUQ5JEleMWnbti1jx44t6WoIIYQQ5ZeiqLeVQ9JdW85lZ2eTmZmJkZF8q4UQQpQz5TQ5U4u05BWDgIAADh06xNKlS1EUBUVRSEhI4Mcff6RTp05otVpsbW3p378/v/32m+68tm3bMnr0aCZMmICNjQ12dnZMmzZNdzwhIQFFUfTm9EtJSUFRFKKiogCIiopCURR2795N48aN0Wg0HD16lKysLIKDg6lRowZmZmZ4eHiwdevWf+iOCCGEEMXAQMWtHCqnb6tkLV26FG9vb4YNG0ZiYiKJiYlYWFjQvn17PD09OXXqFHv27OH27du8/fbbeueGhYVhbm7OiRMnmD9/PjNmzGD//v2FrsMnn3zC3LlziYuLw93dneDgYNavX8/q1au5cOEC48aN49133+XQoUNqvW0hhBBClCLSh1cMrKysMDExoUKFCtjZ2QEwa9YsPD09mTNnjq7cunXrcHR05MqVK9SpUwcAd3d3pk6dCoCLiwvLly8nMjKSjh07FqoOM2bM0J2TlpbGnDlzOHDgAN7e3gDUrFmTo0ePsmbNGtq0aZNrjLS0NNLS0vT2ZaRlYKxRdwJjIYQQokikuzZf0pL3D4mNjeXgwYNotVrdVq9ePQDi4+N15dzd3fXOs7e3Jzk5udDXa9Kkie7ra9eu8fjxYzp27Kh3/fXr1+td+3nBwcFYWVnpbV+v3FzougghhBDFQpY1y5e05P1DUlNT6dq1K/PmzctxzN7eXve1sbF+K5miKGRlZQFgYPA0J8/O/muJooyMjFyvZ27+1xJQqampAOzcuRMHBwe9chqNJs86BwUFERgYqLfvwO2oPMsLIYQQovSQJK+YmJiYkJmZqXvt5eXFtm3bcHZ2LvKTrlWqVAEgMTERT09PAL2HMPJSv359NBoNN2/ezLNrNjcajSZHEmicIl21QgghSodsWfEiX5LkFRNnZ2dOnDhBQkICWq2WkSNH8sUXX9CvXz/d07PXrl3jq6++Yu3atRgaGr4wppmZGS1atGDu3LnUqFGD5ORkPvvssxeeZ2Fhwfjx4xk3bhxZWVm89tpr3L9/n+joaCwtLRk4cKAab1kIIYT4Z8mYvHzJmLxiMn78eAwNDalfvz5VqlQhPT2d6OhoMjMzef3113Fzc2Ps2LFYW1vrumELYt26dTx58oTGjRszduxYZs2aVaDzZs6cyeTJkwkODsbV1RU/Pz927txJjRo1ivoWhRBCCFGKSUteMalTpw7Hjx/PsX/79u15nvNsrru/Cw8P13vt6urKsWPH9Pb9fYxe27Zt9V4/oygKY8aMYcyYMS+ouRBCCFFGSENeviTJE0IIIUTZJGPy8iXdtUIIIYQQ5ZC05AkhhBCibJIHL/KlZOc2gEuIPOz5ebeq8dIyX1ymMLJU/DSbvPiB50J5kqVuvL6t1qsab+f3/VWNF3jQQtV4C9o+VC3WvTR1OzHMjNT9NWpmqG68W4/U/TAbqdwHZG2i3g+H2j9nZio3hay9Yv7iQoXgZZP24kKF0KDiE1XjdXPqpGq859UaqN4E/fFhfVSLVVpIS54QQgghyiYZk5cvGZMnhBBCCFEOlbkkLzQ0FGtr65KuRokLCAige/fuJV0NIYQQouQYKOpt5VCpTPICAgJQFAVFUTAxMaF27drMmDGDJ0/UHSvwsm7cuME777xDtWrVMDU15ZVXXqFbt25cunSp2K+9dOlSQkNDi/06QgghRGmVrai3lUeldkyen58fISEhpKWlsWvXLkaOHImxsTH29vYlXTUAMjIy6NixI3Xr1mX79u3Y29vz888/s3v3blJSUoocNz09HRMTkxeWs7KyKvI1hBBCCFH+lcqWPACNRoOdnR1OTk4MHz4cHx8fIiIidMf37t2Lq6srWq0WPz8/EhMTdceysrKYMWMGr7zyChqNhkaNGrFnzx7d8YSEBBRFYfv27bRr144KFSrg4eGRY4WKo0eP0qpVK8zMzHB0dGT06NE8evQIgAsXLhAfH8/KlStp0aIFTk5OtGzZklmzZtGiRQtdjFu3bvH2229jbW2NjY0N3bp1IyEhQXf8Wbfr7NmzqVatGnXr1uXTTz+lefPmOe6Jh4cHM2bM0Dvv7+95/vz51K5dG41GQ/Xq1Zk9e3aB6yGEEEKUOdJdm69Sm+Q9z8zMjPT0dAAeP37MggUL2LBhA4cPH+bmzZuMHz9eV3bp0qUsXLiQBQsWcO7cOXx9fXnzzTe5evWqXsxJkyYxfvx4YmJiqFOnDv369dN1CcfHx+Pn50evXr04d+4cmzdv5ujRo4waNQqAKlWqYGBgwNatW8nMzH0ekIyMDHx9fbGwsODIkSNER0frktJn7wUgMjKSy5cvs3//fnbs2IG/vz8nT54kPj5eV+bChQucO3eOd955J9drBQUFMXfuXCZPnszFixfZtGkTtra2haqHEEIIUaYoinpbOVTqk7zs7GwOHDjA3r17ad++PfA0aVm9ejVNmjTBy8uLUaNGERkZqTtnwYIFTJw4kb59+1K3bl3mzZtHo0aNWLJkiV7s8ePH88Ybb1CnTh2mT5/OTz/9xLVr1wAIDg7G39+fsWPH4uLiwquvvsqyZctYv349f/75Jw4ODixbtowpU6ZQsWJF2rdvz8yZM7l+/bou/ubNm8nKymLt2rW4ubnh6upKSEgIN2/e1Fun1tzcnLVr19KgQQPd5uHhwaZNm3RlNm7cSPPmzaldu3aOe/Tw4UOWLl3K/PnzGThwILVq1eK1115j6NChharH89LS0njw4IHelp6WUeDvnRBCCCFKTqlN8nbs2IFWq8XU1JROnTrRp08fpk2bBkCFChWoVauWrqy9vT3JyckAPHjwgF9//ZWWLVvqxWvZsiVxcXF6+9zd3fViALo4sbGxhIaGotVqdZuvry9ZWVncuHEDgJEjR5KUlMTGjRvx9vbm66+/pkGDBuzfv18X49q1a1hYWOhi2NjY8Oeff+q10rm5ueUYh+fv769L8rKzs/nyyy/x9/fP9V7FxcWRlpZGhw4dcj1e0Ho8Lzg4GCsrK71tywr1Jp4UQgghXop01+ar1D540a5dO1atWoWJiQnVqlXDyOivqhobG+uVVRSFoizc8fc4yv9vqs3KejpdempqKu+//z6jR4/OcV716tV1X1tYWNC1a1e6du3KrFmz8PX1ZdasWXTs2JHU1FQaN27Mxo0bc8SoUqWK7mtz85wzoPfr14+JEydy5swZ/vjjD27dukWfPrnPxm1mZpbv+yxoPZ4XFBREYGCg3r6oO1H5XksIIYT4x5RwU9WKFSv417/+RVJSEh4eHnz++ec0a9Ysz/IpKSlMmjSJ7du3c+/ePZycnFiyZAmdO3culvqV2iTP3Nw8167JF7G0tKRatWpER0fTpk0b3f7o6Oh8b/zzvLy8uHjxYqHqoCgK9erV49ixY7oYmzdvpmrVqlhaWhb8TQCvvPIKbdq0YePGjfzxxx907NiRqlWr5lrWxcUFMzMzIiMjdV20z7+XotRDo9Gg0Wj09pk8MM6jtBBCCPG/Y/PmzQQGBrJ69WqaN2/OkiVL8PX15fLly7n+vU5PT9f9Ld+6dSsODg789NNPxTr3b6ntrn0ZH3/8MfPmzWPz5s1cvnyZTz75hJiYGMaMGVPgGBMnTuTYsWOMGjWKmJgYrl69yrfffqt78CImJoZu3bqxdetWLl68yLVr1/jPf/7DunXr6NatG/C0y7Vy5cp069aNI0eOcOPGDaKiohg9ejQ///zzC+vg7+/PV199xddff51nVy2AqakpEydOZMKECaxfv574+Hi+//57/vOf/6hSDyGEEKJUUvHBi9zGoael5b028KJFixg2bBiDBg2ifv36rF69mgoVKrBu3bpcy69bt4579+4RHh5Oy5YtcXZ2pk2bNnh4eBTX3SmfSd7o0aMJDAzko48+ws3NjT179hAREYGLi0uBY7i7u3Po0CGuXLlCq1at8PT0ZMqUKVSrVg142tLm7OzM9OnTad68OV5eXixdupTp06czadIk4OnYwcOHD1O9enV69uyJq6srQ4YM4c8//yxQi9pbb73F3bt3efz48QtXt5g8eTIfffQRU6ZMwdXVlT59+ujGF75sPYQQQohSScUxebmNQw8ODs71sunp6Zw+fRofH5+/qmJggI+PT47p2J6JiIjA29ubkSNHYmtrS8OGDZkzZ06eM3SoQckuymA28T9rz8+7VY2XpvJnO0vFT7OJoXqxAJ5kqRuvb6v1qsbb+X1/VeMFHrRQNd6Ctg9Vi3UvTd3/b82M1P01amaobrxbj9T9MBup3DxgbaLeD4faP2dmKg9qWnsl5xjsl+Flk3dLU1E0qKjuylLdnDqpGu95NcZ8q1qsS/P9crTc5TZsCeDXX3/FwcGBY8eO4e3trds/YcIEDh06xIkTJ3KcU69ePRISEvD392fEiBFcu3aNESNGMHr0aKZOnara+/i7UjsmTwghhBDin5JXQqeWrKwsqlatyr///W8MDQ1p3Lgxv/zyC//6178kyRNCCCGE0FNCg84qV66MoaEht2/f1tt/+/Zt7Ozscj3H3t4eY2NjDA3/all3dXUlKSmpwEuaFla5HJMnhBBCiP8BJTRPnomJCY0bN9ZbiCErK4vIyEi97tu/a9myJdeuXdNN1QZw5coV7O3tiyXBA2nJEyXs+zvqfrDV/K+lQzV1x7p89oO6D7moPYbujRYbVI13+lzeT4QXxdX76o0ru3Jf3V99ag+bbmSj7soysffUnfooK1vdiWPtzNQbB3YvTd3xh2MbPlI13rm8558vkrOX1e1enPl68T0EUN4EBgYycOBAmjRpQrNmzViyZAmPHj1i0KBBAAwYMAAHBwfdwxvDhw9n+fLljBkzhg8//JCrV68yZ86cXOfjVYskeUIIIYQom0pwzdk+ffpw584dpkyZQlJSEo0aNWLPnj26deNv3ryJgcFfTQ+Ojo7s3buXcePG4e7ujoODA2PGjGHixInFVkdJ8oQQQghRNpXwcmSjRo3SzZ/7vNzWhvf29ub7778v5lr9RcbkCSGEEEKUQ/9zSZ6iKISHh+dbJiAg4IWTDz/P2dmZJUuWFLlez2vbti1jx45VLZ4QQghR7igqbuVQqU/yAgICUBSFDz74IMexkSNHoigKAQEBRYqdkJCAoijExMTo7V+6dCmhoaFFillQoaGh+a5Xt337dmbOnFmsdRBCCCHKsmwDRbWtPCr1SR48Haz41Vdf8ccff+j2/fnnn2zatInq1aurfj0rK6tiXTC4IGxsbLCwUHfFACGEEKJcKaEpVMqKMpHkeXl54ejoyPbt23X7tm/fTvXq1fH09NTty63LtFGjRkybNi3XuDVq1ADA09MTRVFo27YtkLO7tm3btrrBlVZWVlSuXJnJkyeT34pwKSkpDB06lCpVqmBpaUn79u2JjY0t8Ht+vrvW2dmZOXPmMHjwYCwsLKhevTr//ve/9c65desWb7/9NtbW1tjY2NCtWzcSEhJ0x6OiomjWrBnm5uZYW1vTsmVLfvrppwLXSQghhBBlR5lI8gAGDx5MSEiI7vW6det0c9EU1cmTJwE4cOAAiYmJeknk88LCwjAyMuLkyZMsXbqURYsWsXbt2jzL9+7dm+TkZHbv3s3p06fx8vKiQ4cO3Lt3r8j1XbhwIU2aNOHs2bOMGDGC4cOHc/nyZQAyMjLw9fXFwsKCI0eOEB0djVarxc/Pj/T0dJ48eUL37t1p06YN586d4/jx47z33nsoJfj4uRBCCPFSFEW9rRwqM1OovPvuuwQFBelanqKjo/nqq69yfUS5oKpUqQJApUqV8lyG5BlHR0cWL16MoijUrVuX8+fPs3jxYoYNG5aj7NGjRzl58iTJycm6dfAWLFhAeHg4W7du5b333itSfTt37syIESMAmDhxIosXL+bgwYPUrVuXzZs3k5WVxdq1a3WJW0hICNbW1kRFRdGkSRPu379Ply5dqFWrFvB0OZX8pKWl5VisOT0tAxONuhOrCiGEEEVSZpqqSkaZuT1VqlThjTfeIDQ0lJCQEN544w0qV678j12/RYsWeq1e3t7eXL16lczMnLODx8bGkpqaSqVKldBqtbrtxo0bxMcXfbpzd3d33deKomBnZ0dycrLumteuXcPCwkJ3PRsbG/7880/i4+OxsbEhICAAX19funbtytKlS0lMTMz3esHBwVhZWeltW1ZsLnL9hRBCCPHPKTMtefC0y/bZpIMrVqzIcdzAwCDHOLmMDHWXByqI1NRU7O3tc21lfJkHOoyN9VvQFEXRrYGXmppK48aN2bhxY47znrVYhoSEMHr0aPbs2cPmzZv57LPP2L9/Py1atMj1ekFBQQQGBurti7oTVeT6CyGEEKoqp92sailTSd6z8WWKouDr65vjeJUqVfRapx48eMCNGzfyjPdsQeDcWuOed+LECb3X33//PS4uLhga5lwn0cvLi6SkJIyMjHB2dn5hbDV4eXmxefNmqlatiqVl3mukenp64unpSVBQEN7e3mzatCnPJE+j0ei6m58xeSBdtUIIIUqJcvpUrFrKTHctgKGhIXFxcVy8eDHX5Kp9+/Zs2LCBI0eOcP78eQYOHJhruWeqVq2KmZkZe/bs4fbt29y/fz/Psjdv3iQwMJDLly/z5Zdf8vnnnzNmzJhcy/r4+ODt7U337t3Zt28fCQkJHDt2jEmTJnHq1ClduczMTGJiYvS2uLi4QtyRv/j7+1O5cmW6devGkSNHuHHjBlFRUYwePZqff/6ZGzduEBQUxPHjx/npp5/Yt28fV69efeG4PCGEEEKUTWWqJQ/It5UqKCiIGzdu0KVLF6ysrJg5c2a+LXlGRkYsW7aMGTNmMGXKFFq1apXngxwDBgzgjz/+oFmzZhgaGjJmzJg8H6BQFIVdu3YxadIkBg0axJ07d7Czs6N169a6hYvhaRfr36eAAahVqxbXrl3L5w7krkKFChw+fJiJEyfSs2dPHj58iIODAx06dMDS0pI//viDS5cuERYWxt27d7G3t2fkyJG8//77hb6WEEIIUSpIS16+lOz8JnsTwNM56xo1aqTqsmVl1Z6fd6sa71CSiarx1Gya7lAt7cWFCuGz7/P+B6UoZrV4oGq8N1psUDXe6XP+qsa7ej/vVvnCOn9P3WEHLx7wUTiNbNQdS3wwUfPiQoWQla3uH1Y7syeqxbqXpt7nBGBsw0eqxmu/yUrVeJnq3ToAZr7+WNV479TyUzXe85xm7Vct1k+fdVQtVmlRprprhRBCCCFEwZS57lohhBBCCECaql5AkrwCeJkJl4UQQghRTGQKlXxJkicKJVPlEZydX/lT1XgPMtT7t+6Xx+qO7Qn2VncM3ZjvLFSNp/YYusbuOedsfBl7TvRXLVZNS3UHMmWqPEbN2kTdHzQ7M3VHDRqX4taTD1zVHUO3LUHd8YznB5uqGu/On7dVjXfrUSn+5uZGHrzIVxn7bgohhBBCiIKQljwhhBBClE3Skpcvackro6ZNm0ajRo1KuhpCCCFEyVFU3MqhfyTJCwgIoHv37gUurygK4eHhxVafwoqKikJRFFJSUvT237lzh+HDh1O9enU0Gg12dnb4+voSHR1d7HUaP348kZGRxX4dIYQQQpRN5bq7NiMjA2Pj4ltrtVevXqSnpxMWFkbNmjW5ffs2kZGR3L17t8gx09PTdWvq5ker1aLVaot8HSGEEKKsy5bu2nz94921bdu2ZfTo0UyYMAEbGxvs7OyYNm2a7rizszMAPXr0QFEU3WuAb7/9Fi8vL0xNTalZsybTp0/nyZO/npJTFIVVq1bx5ptvYm5uzuzZs3Xdmhs2bMDZ2RkrKyv69u3Lw4cPdedlZWURHBxMjRo1MDMzw8PDg61btwKQkJBAu3btAKhYsSKKohAQEEBKSgpHjhxh3rx5tGvXDicnJ5o1a0ZQUBBvvvmmLnZKSgpDhw6lSpUqWFpa0r59e2JjY3XHn9Vv7dq11KhRA1NTU/79739TrVo1srKy9O5dt27dGDx4sN55f7du3ToaNGiARqPB3t6eUaNGFbgeQgghRJmjKOpt5VCJjMkLCwvD3NycEydOMH/+fGbMmMH+/U+XJvnhhx8ACAkJITExUff6yJEjDBgwgDFjxnDx4kXWrFlDaGgos2fP1os9bdo0evTowfnz53UJUXx8POHh4ezYsYMdO3Zw6NAh5s6dqzsnODiY9evXs3r1ai5cuMC4ceN49913OXToEI6Ojmzbtg2Ay5cvk5iYyNKlS3UtaeHh4aSl5b38Ve/evUlOTmb37t2cPn0aLy8vOnTowL1793Rlrl27xrZt29i+fTsxMTH07t2bu3fvcvDgQV2Ze/fusWfPHvz9c5/mYtWqVYwcOZL33nuP8+fPExERQe3atQtVDyGEEEKUHyXSXevu7s7UqVMBcHFxYfny5URGRtKxY0eqVKkCgLW1NXZ2drpzpk+fzieffMLAgQMBqFmzJjNnzmTChAm6WADvvPMOgwYN0rteVlYWoaGhWFg8nVesf//+REZGMnv2bNLS0pgzZw4HDhzA29tbF/vo0aOsWbOGNm3aYGNjA0DVqlWxtrbWxQ0NDWXYsGGsXr0aLy8v2rRpQ9++fXF3dwfg6NGjnDx5kuTkZDSap3MtLViwgPDwcLZu3cp7770HPO2iXb9+ve69A3Tq1IlNmzbRoUMHALZu3UrlypV1rYrPmzVrFh999BFjxozR7WvatGmh6vG8tLS0HAlsRloGxpri6wIXQgghCky6a/NVIi15z5KgZ+zt7UlOTs73nNjYWGbMmKFrQdNqtQwbNozExEQeP/5rQeUmTZrkONfZ2VmX4D1/vWvXrvH48WM6duyoF3v9+vXEx8fnW6devXrx66+/EhERgZ+fH1FRUXh5eREaGqqrc2pqKpUqVdKLfePGDb3YTk5OegkegL+/P9u2bdMlWRs3bqRv374YGOT8liUnJ/Prr7/qEsLc7l1B6vG84OBgrKys9LYtKzbne0+EEEKIf4w8XZuvEmnJe/5hCEVRcow/e15qairTp0+nZ8+eOY6Zmv41g7i5uXmhrpeamgrAzp07cXBw0Cv3rNUrP6ampnTs2JGOHTsyefJkhg4dytSpUwkICCA1NRV7e/tcl0X7e4tgbnXu2rUr2dnZ7Ny5k6ZNm3LkyBEWL16cax3MzMzyrWNB6/G8oKAgAgMD9fZ9l5wzhhBCCCFKn1L5dK2xsTGZmfrL8Hh5eXH58mW9cWZqqF+/PhqNhps3b9KmTZtcyzx72vX5OuUV79n0L15eXiQlJWFkZKT3AElBmJqa0rNnTzZu3Mi1a9eoW7cuXl5euZa1sLDA2dmZyMjIXLtzi1oPjUaTI9E1vi9dtUIIIUqHXDq3xN+UyiTvWcLSsmVLNBoNFStWZMqUKXTp0oXq1avz1ltvYWBgQGxsLD/++COzZs0q8rUsLCwYP34848aNIysri9dee4379+8THR2NpaUlAwcOxMnJCUVR2LFjB507d8bMzIy0tDR69+7N4MGDcXd3x8LCglOnTjF//ny6desGgI+PD97e3nTv3p358+dTp04dfv31V3bu3EmPHj1y7Vr+O39/f7p06cKFCxd499138y07bdo0PvjgA6pWrUqnTp14+PAh0dHRfPjhhy9dDyGEEKI0KqcPxaqmVObACxcuZP/+/Tg6OuLp6QmAr68vO3bsYN++fTRt2pQWLVqwePFinJycXvp6M2fOZPLkyQQHB+Pq6oqfnx87d+6kRo0aADg4OOge/LC1tWXUqFFotVqaN2/O4sWLad26NQ0bNmTy5MkMGzaM5cuXA0+7hXft2kXr1q0ZNGgQderUoW/fvvz000/Y2tq+sF7t27fHxsaGy5cv88477+RbduDAgSxZsoSVK1fSoEEDunTpwtWrV1WphxBCCFEayQwq+VOys7OzS7oSouzYeWu3qvEsjfMfi1lYDzLU+7/lXpq6/wNVN39xd39hjPnO4sWFCmHTG/dVjdfYfaOq8fac6K9arF8eq/u9zcxW9y+E2p+V48nqDrMwLpXNA091c/pT1Xg7b714bHZhfFDPWtV4d/68rWq8W4/U/eZ6V31D1XjPq7nykGqxro/IfchWWVYqu2uFEEIIIV5EKa9NcCqRJE8IIYQQZZLkePkrxY3uQgghhBCiqKQlTxSKxkDdIZy2ZurG23HLRNV4avr3xZzzIb6MBW0fqBrv6n1DVeOpOYYOwK/5BtViDdmc+yovRWWkqPs53nRd3c/KOzUfqRrvibpDaVl+uoJqsd6qoe6YvA7V0lWNF3vvjqrx5p2zVjXe+jZla5i+tOTlT5I8IYQQQpRJivRH5ktujxBCCCFEOSQteUIIIYQok6S7Nn9lviUvISEBRVGIiYkBICoqCkVRSElJKdF6/ZPatm3L2LFjS7oaQgghxD/KQFFvK49KNMm7desWgwcPplq1apiYmODk5MSYMWO4e/dukWO++uqrJCYmYmVlpWJN4dChQ7oVKCpUqICLiwsDBw4kPV3dQblFsX37dmbOnKl77ezszJIlS0quQkIIIYQocSWW5F2/fp0mTZpw9epVvvzyS65du8bq1auJjIzE29ube/fuFSmuiYkJdnZ2qk6QePHiRfz8/GjSpAmHDx/m/PnzfP7555iYmJCZqe7M9IXxLMG0sbHBwkLd1Q+EEEKI0k6WNctfiSV5I0eOxMTEhH379tGmTRuqV69Op06dOHDgAL/88guTJk0CnrZKzZkzh8GDB2NhYUH16tX597//nWfc57trQ0NDsba2Zu/evbi6uqLVavHz8yMxMVHvvLVr1+Lq6oqpqSn16tVj5cqVumP79u3Dzs6O+fPn07BhQ2rVqoWfnx9ffPEFZmZmunJHjx6lVatWmJmZ4ejoyOjRo3n06K+pC9LS0pg4cSKOjo5oNBpq167Nf/7zH716/l14eLhesjpt2jQaNWrE2rVrqVGjBqampoB+d23btm356aefGDduHIqioCgKjx49wtLSkq1bt+aIb25uzsOHD/P7VgkhhBClkiR5+SuRJO/evXvs3buXESNG6CVJAHZ2dvj7+7N582aeLau7cOFCmjRpwtmzZxkxYgTDhw/n8uXLBb7e48ePWbBgARs2bODw4cPcvHmT8ePH645v3LiRKVOmMHv2bOLi4pgzZw6TJ08mLCxMV6fExEQOHz6c5zXi4+Px8/OjV69enDt3js2bN3P06FFGjRqlKzNgwAC+/PJLli1bRlxcHGvWrEGr1Rb4fQBcu3aNbdu2sX37dt04xL/bvn07r7zyCjNmzCAxMZHExETMzc3p27cvISEhemVDQkJ46623pBVQCCFEmfSsMUONrShWrFiBs7MzpqamNG/enJMnTxbovK+++gpFUejevXuRrltQJfJ07dWrV8nOzsbV1TXX466urvz+++/cufN00sjOnTszYsQIACZOnMjixYs5ePAgdevWLdD1MjIyWL16NbVq1QJg1KhRzJgxQ3d86tSpLFy4kJ49ewJQo0YNLl68yJo1axg4cCC9e/dm7969tGnTBjs7O1q0aEGHDh0YMGAAlpaWAAQHB+Pv769rUXNxcWHZsmW0adOGVatWcfPmTbZs2cL+/fvx8fEBoGbNmoW8c0+7aNevX0+VKlVyPW5jY4OhoSEWFhbY2dnp9g8dOlQ3XtHe3p7k5GR27drFgQMH8rxWWloaaWlp+tdPy8BEo+5i50IIIURZs3nzZgIDA1m9ejXNmzdnyZIl+Pr6cvnyZapWrZrneQkJCYwfP55WrVoVex1L9MGLZy11L+Lu7q77WlEU7OzsSE5OLvB1KlSooEvwAF2SA/Do0SPi4+MZMmQIWq1Wt82aNYv4+HgADA0NCQkJ4eeff2b+/Pk4ODgwZ84cGjRooOv2jY2NJTQ0VC+Gr68vWVlZ3Lhxg5iYGAwNDWnTpk2B650bJyenPBO8/DRr1owGDRroWif/+9//4uTkROvWrfM8Jzg4GCsrK73tq+Vbilx3IYQQQk2KgXpbWloaDx480Nueb+j4u0WLFjFs2DAGDRpE/fr1Wb16NRUqVGDdunV5npOZmYm/vz/Tp08vUkNPYZVIkle7dm0URSEuLi7X43FxcVSsWFGXzBgb67ccKYpCVlbB19XJ7fxnCWZqaioAX3zxBTExMbrtxx9/5Pvvv9c7z8HBgf79+7N8+XIuXLjAn3/+yerVq3Vx3n//fb0YsbGxXL16lVq1auXoln6egYFBjqQ3IyMjRzlz86IvdzR06FBCQ0OBp121gwYNyreJOigoiPv37+ttfUe9XeTrCyGEEGpSc0xebg0bwcHBuV43PT2d06dP63rm4OnfcR8fH44fP55nfWfMmEHVqlUZMmSI6vciNyXSXVupUiU6duzIypUrGTdunF4ClJSUxMaNGxkwYICqT8jmxdbWlmrVqnH9+nX8/f0LfF7FihWxt7fXPVjh5eXFxYsXqV27dq7l3dzcyMrK4tChQ3ofimeqVKnCw4cPefTokS6Ry23MXUHk9dTvu+++y4QJE1i2bBkXL15k4MCB+cbRaDRoNBr92A+lq1YIIUT5ExQURGBgoN6+5/8GPvPbb7+RmZmJra2t3n5bW1suXbqU6zlHjx7lP//5T5H/thdFiXXXLl++nLS0NHx9fTl8+DC3bt1iz549dOzYEQcHB2bPnv2P1WX69OkEBwezbNkyrly5wvnz5wkJCWHRokUArFmzhuHDh7Nv3z7i4+O5cOECEydO5MKFC3Tt2hV4Olbw2LFjjBo1ipiYGK5evcq3336re/DC2dmZgQMHMnjwYMLDw7lx4wZRUVFs2fK0+7N58+ZUqFCBTz/9lPj4eDZt2qRrdSssZ2dnDh8+zC+//MJvv/2m21+xYkV69uzJxx9/zOuvv84rr7zyEndNCCGEKFlqtuRpNBosLS31trySvMJ6+PAh/fv354svvqBy5cqqxCyIEkvyXFxcOHXqFDVr1uTtt9+mVq1avPfee7Rr147jx49jY2Pzj9Vl6NChrF27lpCQENzc3GjTpg2hoaHUqFEDeDqeLTU1lQ8++IAGDRrQpk0bvv/+e8LDw3Vj7Nzd3Tl06BBXrlyhVatWeHp6MmXKFKpVq6a7zqpVq3jrrbcYMWIE9erVY9iwYbqWQBsbG/773/+ya9cu3Nzc+PLLL5k2bVqR3s+MGTNISEigVq1aOcbvDRkyhPT0dAYPHlyk2EIIIURpUVJTqFSuXBlDQ0Nu376tt//27dt6Dz0+Ex8fT0JCAl27dsXIyAgjIyPWr19PREQERkZGumcA1KZkF/TpB1EubNiwgXHjxvHrr79iYmJS6PMP/LJL1fpU1xZ8bGVB/OdK/mMfS9KxRHX+I3xmauMHqsZ79ETd4RHWJur+avFrvkG1WEM2v6daLAAjRd33evlB4X828/NOzUcvLlQIT9T9sWX56QqqxdreNUW1WAAPM9T9uXis8s/ZvHOWqsZb30bdz7K5Ud4P96mh0cYjqsWK8S/c067NmzenWbNmfP755wBkZWVRvXp1Ro0axSeffKJX9s8//+TatWt6+z777DMePnzI0qVLqVOnTpH+Jr9IiYzJE/+8x48fk5iYyNy5c3n//feL5cMkhBBC/JNKcs3ZwMBABg4cSJMmTWjWrBlLlizh0aNHDBo0CHg6N66DgwPBwcGYmprSsGFDvfOfLYDw/H41SZL3P2L+/PnMnj2b1q1bExQUVNLVEUIIIV5aSa5U0adPH+7cucOUKVNISkqiUaNG7NmzR/cwxs2bNzEwKNGZ6iTJ+18xbdq0Io/xE0IIIUROo0aN0lvZ6u+ioqLyPbeoD1cWhiR5olBUHopDQmrJ/peTHzszdd/tH4/VHetyL03de3flvrq/DmpaPlE1nprj6P7TJ+/1r4ti0FfqjvFzMs85R+bLuJii7vdW7TGIVSup1xxz+b6harEAnFUeNxz+s7pjcxMKvi5AgaRmpKgaz7yYs4zyuuasWiTJE0IIIUSZpJTkoLwyQJI8IYQQQpRJ0pKXv9LbV/aSEhISUBRFN7N0VFQUiqKQkpJSovUqirJcdyGEEEKUjFKZ5N26dYvBgwdTrVo1TExMcHJyYsyYMdy9e7fIMV999VUSExOxsrJSsaZP18F9thkZGVG9enUCAwPzXdRYCCGEEC+vpCZDLitKXZJ3/fp1mjRpwtWrV/nyyy+5du0aq1evJjIyEm9vb+7du1ekuCYmJtjZ2RXLerghISEkJiZy48YNVq5cyYYNG5g1a5bq1xFCCCHEXyTJy1+pS/JGjhyJiYkJ+/bto02bNlSvXp1OnTpx4MABfvnlFyZNmgQ8XZ91zpw5DB48GAsLC6pXr86//533E3PPd3mGhoZibW3N3r17cXV1RavV4ufnR2Jiot55a9euxdXVFVNTU+rVq8fKlStzxLa2tsbOzg5HR0e6dOlCt27dOHPmjO54fHw83bp1w9bWFq1WS9OmTTlw4IBejLS0NCZOnIijoyMajYbatWvzn//8J9f38vjxYzp16kTLli1JSUkhICCA7t2765UZO3Ysbdu21b1u27at7lFvKysrKleuzOTJk5EFT4QQQojyqVQleffu3WPv3r2MGDECMzP95ans7Ozw9/dn8+bNusRk4cKFNGnShLNnzzJixAiGDx/O5cuXC3y9x48fs2DBAjZs2MDhw4e5efMm48eP1x3fuHEjU6ZMYfbs2cTFxTFnzhwmT55MWFhYnjGvXLnCd999R/PmzXX7UlNT6dy5M5GRkZw9exY/Pz+6du3KzZs3dWUGDBjAl19+ybJly4iLi2PNmjVotdoc8VNSUujYsSNZWVns379fN2N2QYSFhWFkZMTJkydZunQpixYtYu3atQU+XwghhChNDBT1tvKoVD1de/XqVbKzs3F1dc31uKurK7///jt37twBoHPnzowYMQKAiRMnsnjxYg4ePEjdunULdL2MjAxWr15NrVq1gKeTGs6YMUN3fOrUqSxcuJCePXsCUKNGDS5evMiaNWsYOHCgrly/fv0wNDTkyZMnpKWl0aVLF71VJTw8PPDw8NC9njlzJt988w0RERGMGjWKK1eusGXLFvbv34+Pjw8ANWvWzFHfpKQk+vTpg4uLC5s2bSr00mSOjo4sXrwYRVGoW7cu58+fZ/HixQwbNizX8mlpaTnGFqanZWCiMS7UdYUQQojiUF67WdVSqlryniloF6K7u7vua0VRsLOzIzm54DNDVqhQQZfgAdjb2+vOf/ToEfHx8QwZMgStVqvbZs2aRXx8vF6cxYsXExMTQ2xsLDt27ODKlSv0799fdzw1NZXx48fj6uqKtbU1Wq2WuLg4XUteTEwMhoaGtGnTJt/6duzYkdq1a7N58+YirT3bokULvTGJ3t7eXL16lczMzFzLBwcHY2VlpbdtXr6l0NcVQgghxD+vVLXk1a5dG0VRiIuLo0ePHjmOx8XFUbFiRapUqQKAsbF+i5KiKGRlFXx28tzOf5ZgpqamAvDFF1/odb0CGBrqz6huZ2dH7dq1Aahbty4PHz6kX79+zJo1i9q1azN+/Hj279/PggULqF27NmZmZrz11lukp6cD5Oiazssbb7zBtm3buHjxIm5ubrr9BgYGORLjjIyXnzE/KCiIwMBAvX2Hfzv40nGFEEIINSilsqmq9ChVSV6lSpXo2LEjK1euZNy4cXrJT1JSEhs3bmTAgAHF8oTs82xtbalWrRrXr1/H39+/UOc+SwL/+OMPAKKjowkICNAlrqmpqSQkJOjKu7m5kZWVxaFDh3TdtbmZO3cuWq2WDh06EBUVRf369QGoUqUKP/74o17ZmJiYHEnsiRMn9F5///33uLi45Ehan9FoNGg0+kvwmDyUrlohhBClg3TX5q/U5cDLly8nLS0NX19fDh8+zK1bt9izZw8dO3bEwcGB2bNn/2N1mT59OsHBwSxbtowrV65w/vx5QkJCWLRokV65lJQUkpKS+PXXXzl06BAzZsygTp06urGFLi4ubN++Xdel+8477+i1ODo7OzNw4EAGDx5MeHg4N27cICoqii1bcnaNLliwAH9/f9q3b8+lS5cAaN++PadOnWL9+vVcvXqVqVOn5kj6AG7evElgYCCXL1/myy+/5PPPP2fMmDFq3jIhhBBClBKlLslzcXHh1KlT1KxZk7fffptatWrx3nvv0a5dO44fP46Njc0/VpehQ4eydu1aQkJCcHNzo02bNoSGhlKjRg29coMGDcLe3p5XXnmFfv360aBBA3bv3o2R0dOG0kWLFlGxYkVeffVVunbtiq+vL15eXnoxVq1axVtvvcWIESOoV68ew4YN49GjR7nWa/Hixbz99tu0b9+eK1eu4Ovry+TJk5kwYQJNmzbl4cOHDBgwIMd5AwYM4I8//qBZs2aMHDmSMWPG8N576i6sLoQQQvxT/r4gwctu5ZGSLROl/U9o27YtjRo1YsmSJS8VZ98vu9SpUDGJ/FXz4kIFZGdW8PGdBbHxR1NV401olvs/AUV15b66ozdqWj5RNd6xZPW+t//pk/ecmkUx6Ct1/1nKUvm3ckWNup9lI0XdCp66q97PxrgGD1SLBeCsVffebbmu7u+Bby6p93MBsKv776rGszV7U9V4z2uzI1q1WIe6tFQtVmlRqsbkCSGEEEIUVDltgFNNqeuuFUIIIYQQL09a8v5HREVFlXQVhBBCCFVJS17+JMkThZKRpe5PlJmhumN71KyfkUHpHq5qZqRu/XKfEvsl4mWr+1lRcxyY2mPoQvqqO8avW9hwVePdeqTu1EdPVP7RuJ+qXixzlX8uDFQef5iSrm4H2hOVf3DV/h1f3MrrcmRqke5aIYQQQohySFryhBBCCFEmSUte/iTJE0IIIUSZpHZ3enlT6rtro6KiUBSFlJSUkq5KqaIoCuHh4SVdDSGEEEKUUkVO8gICAnSzRBsbG2Nra0vHjh1Zt26d3pJdL+vVV18lMTERKysrVeIlJCTozXBdqVIlXn/9dc6ePatK3JiYGFXq+cy0adNo1KhRjv2JiYl06tRJ1WsJIYQQZYmBot5WHr1US56fnx+JiYkkJCSwe/du2rVrx5gxY+jSpQtPnqgz272JiQl2dnaqLzly4MABEhMT2bt3L6mpqXTq1CnP1sKMjAxVr60GOzs7NBp1ZzoXQgghyhIDFbfy6KXel0ajwc7ODgcHB7y8vPj000/59ttv2b17N6GhoQCkpKQwdOhQqlSpgqWlJe3btyc2NhaAK1euoCgKly5d0ou7ePFiatWqBeTeXRsdHU3btm2pUKECFStWxNfXl99/f7oUS1ZWFsHBwdSoUQMzMzM8PDzYunVrjrpXqlQJOzs7mjRpwoIFC7h9+zYnTpzQtcht3ryZNm3aYGpqysaNG8nKymLGjBm88soraDQaGjVqxJ49e3Txnq1n6+npiaIotG3bVnds7dq1uLq6YmpqSr169Vi5cqVeXX7++Wf69euHjY0N5ubmNGnShBMnThAaGsr06dOJjY3VtTw+u6/Pd9eeP3+e9u3bY2ZmRqVKlXjvvfdITf1rXoKAgAC6d+/OggULsLe3p1KlSowcObJUJrBCCCGEeHmqJ6/t27fHw8OD7du3A9C7d2+Sk5PZvXs3p0+fxsvLiw4dOnDv3j3q1KlDkyZN2Lhxo16MjRs38s477+QaPyYmhg4dOlC/fn2OHz/O0aNH6dq1K5mZTycLCg4OZv369axevZoLFy4wbtw43n33XQ4dOpRnnc3MzABIT0/X7fvkk08YM2YMcXFx+Pr6snTpUhYuXMiCBQs4d+4cvr6+vPnmm1y9ehWAkydPAn+1ED57/xs3bmTKlCnMnj2buLg45syZw+TJkwkLCwMgNTWVNm3a8MsvvxAREUFsbCwTJkwgKyuLPn368NFHH9GgQQMSExNJTEykT58+Oer/6NEjfH19qVixIj/88ANff/01Bw4cYNSoUXrlDh48SHx8PAcPHiQsLIzQ0FBd0iiEEEKUNQZKtmpbeVQsT9fWq1ePc+fOcfToUU6ePElycrKua3HBggWEh4ezdetW3nvvPfz9/Vm+fDkzZ84EnrbunT59mv/+97+5xp4/fz5NmjTRaw1r0KABAGlpacyZM4cDBw7g7e0NQM2aNTl69Chr1qyhTZs2OeKlpKQwc+ZMtFotzZo1448//gBg7Nix9OzZU1duwYIFTJw4kb59+wIwb948Dh48yJIlS1ixYgVVqlQB/mohfGbq1KksXLhQF6tGjRpcvHiRNWvWMHDgQDZt2sSdO3f44YcfsLGxAaB27dq687VaLUZGRnoxn7dp0yb+/PNP1q9fj7m5OQDLly+na9euzJs3D1tbWwAqVqzI8uXLMTQ0pF69erzxxhtERkYybNiwXOOmpaWRlpamty8jLQNjjboTqwohhBBFUV7H0qmlWLqhs7OzURSF2NhYUlNTqVSpElqtVrfduHGD+Ph4APr27UtCQgLff/898LTly8vLi3r16uUa+1lLXm6uXbvG48eP6dixo9711q9fr7veM6+++iparZaKFSsSGxvL5s2bdckQQJMmTXRfP3jwgF9//ZWWLVvqxWjZsiVxcXF53odHjx4RHx/PkCFD9Ooza9YsXX1iYmLw9PTUJXhFERcXh4eHhy7Be1a3rKwsLl++rNvXoEEDDA0Nda/t7e1JTk7OM25wcDBWVlZ625YVm4tcTyGEEEJNMiYvf8XSkhcXF0eNGjVITU3F3t4+13VTra2tgacPELRv355NmzbRokULNm3axPDheS/p86xrNTfPxqDt3LkTBwcHvWPPP6SwefNm6tevT6VKlXR1+bu/J0xF9aw+X3zxBc2bN9c79izZyu/9qM3YWL8FTlGUfJ+EDgoKIjAwUG/fd8lRxVE1IYQQQqhM9STvu+++4/z584wbN45XXnmFpKQkjIyMcHZ2zvMcf39/JkyYQL9+/bh+/bquSzQ37u7uREZGMn369BzH6tevj0aj4ebNm7l2zf6do6Oj7uGOF7G0tKRatWpER0frxY2OjqZZs2bA06eAAd3YQABbW1uqVavG9evX8ff3z/P9rF27lnv37uXammdiYqIXMzeurq6Ehoby6NEjXXIaHR2NgYEBdevWLdB7zI1Go8mRHBvfl65aIYQQpYN01+bvpVoo09LSSEpK4pdffuHMmTPMmTOHbt260aVLFwYMGICPjw/e3t50796dffv2kZCQwLFjx5g0aRKnTp3SxenZsycPHz5k+PDhtGvXjmrVquV5zaCgIH744QdGjBjBuXPnuHTpEqtWreK3337DwsKC8ePHM27cOMLCwoiPj+fMmTN8/vnnugcdiurjjz9m3rx5bN68mcuXL/PJJ58QExPDmDFjAKhatSpmZmbs2bOH27dvc//+fQCmT59OcHAwy5Yt48qVK5w/f56QkBAWLVoEQL9+/bCzs6N79+5ER0dz/fp1tm3bxvHjxwFwdnbmxo0bxMTE8Ntvv+UYIwdPk2RTU1MGDhzIjz/+yMGDB/nwww/p37+/Xhe0EEIIUZ4oSrZqW3n0Uknenj17sLe3x9nZGT8/Pw4ePMiyZcv49ttvMTQ0RFEUdu3aRevWrRk0aBB16tShb9++/PTTT3rJh4WFBV27diU2NjbPFq9n6tSpw759+4iNjaVZs2Z4e3vz7bffYmT0tFFy5syZTJ48meDgYFxdXfHz82Pnzp26KU6KavTo0QQGBvLRRx/h5ubGnj17iIiIwMXFBQAjIyOWLVvGmjVrqFatGt26dQNg6NChrF27lpCQENzc3GjTpg2hoaG6+piYmLBv3z6qVq1K586dcXNzY+7cubru3F69euHn50e7du2oUqUKX375ZY66VahQgb1793Lv3j2aNm3KW2+9RYcOHVi+fPlLvWchhBBClF1KdnZ2+UxfRbHYeWu3qvHMDNX9+O24ZaparBoW6kzo/UzYOXXHX059NfXFhQrhzF11u+JrWuQ/zKCwTv+mXv3Ss9Tt4wnp+29V43ULy3tcclE8zFB3WPkTlf9q3Ffxo7zwtfvqBQNszdRbwQlgddzLj/f+u/1X1f253dkjRdV4r5h3VTXe894+eFi1WFvatVYtVmlRLA9eCCGEEEIUt/L6VKxa5P4IIYQQQpRD0pInhBBCiDKpvK5UoRZJ8kShGKr8uLra47asTNQbP5P8h7oN3RXM1Y2n9njGRjbqrmNsbaJu/TZdV28sk5O5uu9V7TF03w5cpWq8kV/nvqpNabHzZ/X+FNlo1P3cpWWq+0vPxyHnDAkvI+6+iarxKmosVY1X3GQKlfxJd60QQgghRDkkLXlCCCGEKJOkpSp/cn/+Ztq0aTRq1KikqyGEEEKIAjBQ1NvKozKX5CmKku82bdq0Yr3+nTt3GD58ONWrV0ej0WBnZ4evry/R0dHFel01RUVFoSgKKSkpJV0VIYQQosgMlGzVtvKozCV5iYmJum3JkiVYWlrq7Rs/fnyxXr9Xr16cPXuWsLAwrly5QkREBG3btuXu3bvFel21ZGSoO+BcCCGE+F+1YsUKnJ2dMTU1pXnz5pw8eTLPsl988QWtWrWiYsWKVKxYER8fn3zLq6HMJXl2dna6zcrKCkVR9PZ99dVXuLq6YmpqSr169Vi5cqXe+T///DP9+vXDxsYGc3NzmjRpwokTJ/TKbNiwAWdnZ6ysrOjbty8PHz4EICUlhSNHjjBv3jzatWuHk5MTzZo1IygoiDfffBOAhIQEFEUhJiZGFy8lJQVFUYiKigL+aknbuXMn7u7umJqa0qJFC3788UfdOaGhoVhbWxMeHo6Liwumpqb4+vpy69YtvbquWrWKWrVqYWJiQt26ddmwYYPecUVRWLVqFW+++Sbm5uYMGzaMdu3aAVCxYkUURSEgIKDI3w8hhBCipJRkd+3mzZsJDAxk6tSpnDlzBg8PD3x9fUlOTs61fFRUFP369ePgwYMcP34cR0dHXn/9dX755ZeXvAt5K3NJXn42btzIlClTmD17NnFxccyZM4fJkycTFhYGQGpqKm3atOGXX34hIiKC2NhYJkyYQFbWX9NuxMfHEx4ezo4dO9ixYweHDh1i7ty5AGi1WrRaLeHh4aSlvfxj8B9//DELFy7khx9+oEqVKnTt2lWvpe3x48fMnj2b9evXEx0dTUpKCn379tUd/+abbxgzZgwfffQRP/74I++//z6DBg3i4MGDeteZNm0aPXr04Pz580yfPp1t27YBcPnyZRITE1m6dOlLvxchhBDin2ag4paWlsaDBw/0tvz+1i9atIhhw4YxaNAg6tevz+rVq6lQoQLr1q3LtfzGjRsZMWIEjRo1ol69eqxdu5asrCwiIyNVuRe5KVdJ3tSpU1m4cCE9e/akRo0a9OzZk3HjxrFmzRoANm3axJ07dwgPD+e1116jdu3avP3223h7e+tiZGVlERoaSsOGDWnVqhX9+/fXfQOMjIwIDQ0lLCwMa2trWrZsyaeffsq5c+eKXN+OHTvi5uZGWFgYt2/f5ptvvtEdz8jIYPny5Xh7e9O4cWPCwsI4duyYrnl3wYIFBAQEMGLECOrUqUNgYCA9e/ZkwYIFetd55513GDRoEDVr1sTJyQkbGxsAqlatqmsRzU1uH/j0NOnuFUIIUf4EBwdjZWWltwUHB+daNj09ndOnT+Pj46PbZ2BggI+PD8ePHy/Q9R4/fkxGRobub3JxKDdJ3qNHj4iPj2fIkCG6FjetVsusWbOIj48HICYmBk9Pz3xvqLOzMxYWFrrX9vb2ek2vvXr14tdffyUiIgI/Pz+ioqLw8vIiNDS00HX+e3JpY2ND3bp1iYuL0+0zMjKiadOmutf16tXD2tpaVyYuLo6WLVvqxWzZsqVeDIAmTZoUum6Q+wd+y4rNRYolhBBCqE3NBy+CgoK4f/++3hYUFJTrdX/77TcyMzOxtbXV229ra0tSUlKB6j5x4kSqVaumlyiqrdzMk5eamgo8HdjYvHlzvWOGhoYAmJmZvTCOsbGx3mtFUfS6cwFMTU3p2LEjHTt2ZPLkyQwdOpSpU6cSEBCAgcHTvDk7+68ndUr6YQdz86KtFBAUFERgYKDevqg7USrUSAghhHh5ak59otFo0Gg06gXMx9y5c/nqq6+IiorC1NS02K5TblrybG1tqVatGtevX6d27dp6W40aNQBwd3cnJiaGe/fuqXrt+vXr8+jRIwCqVKkCPH0K+Jm/P4Txd99//73u699//50rV67g6uqq2/fkyRNOnTqle3358mVSUlJ0ZVxdXXNM3RIdHU39+vXzra+JydNlcDIz819STKPRYGlpqbeZaIzzPUcIIYQo7ypXroyhoSG3b9/W23/79m3s7OzyPXfBggXMnTuXffv24e7uXpzVLD8teQDTp09n9OjRWFlZ4efnR1paGqdOneL3338nMDCQfv36MWfOHLp3705wcDD29vacPXuWatWq6XWd5uXu3bv07t2bwYMH4+7ujoWFBadOnWL+/Pl069YNeNpa2KJFC+bOnUuNGjVITk7ms88+yzXejBkzqFSpEra2tkyaNInKlSvTvXt33XFjY2M+/PBDli1bhpGREaNGjaJFixY0a9YMePrgxttvv42npyc+Pj783//9H9u3b+fAgQP5vg8nJycURWHHjh107twZMzMztFptAe+yEEIIUTqU1CTGJiYmNG7cmMjISN3f7WcPUYwaNSrP8+bPn8/s2bPZu3dvkYdSFUa5ackDGDp0KGvXriUkJAQ3NzfatGlDaGioriXPxMSEffv2UbVqVTp37oybmxtz587Vdee+iFarpXnz5ixevJjWrVvTsGFDJk+ezLBhw1i+fLmu3Lp163jy5AmNGzdm7NixzJo1K9d4c+fOZcyYMTRu3JikpCT+7//+T9fKBlChQgUmTpzIO++8Q8uWLdFqtWze/NeYuO7du7N06VIWLFhAgwYNWLNmDSEhIbRt2zbf9+Hg4MD06dP55JNPsLW1zfcDKYQQQpRWaj5dW1iBgYF88cUXhIWFERcXx/Dhw3n06BGDBg0CYMCAAXpj+ubNm8fkyZNZt24dzs7OJCUlkZSUpBtuVhyU7L8PHhP/iKioKNq1a8fvv/+OtbV1rmVCQ0MZO3ZsqVuVYs/Pu1WNV8/qiarxwq69eNxlQT3JenGZwjj0q3p1A5jidV/VeI+eqPsvsbWJur9a5p63VC2Wk7m642RT0gv2j2JBfTtwlarxRn49TNV4atv5o3qdStt7PVAtFkCWyn8hbz1S97Py+UWLFxcqhC3t8x/GU1jmRm1Ujfe80ccPvrhQAS3zblfoc5YvX86//vUvkpKSaNSoEcuWLdM9F9C2bVucnZ11D2Y6Ozvz008/5YgxderUYlutq1x11wohhBDif0dJL0c2atSoPHvDni2A8ExCQkLxV+g5kuQJIYQQokwqqTF5ZUW5GpNXVrRt25bs7Ow8u2oBAgICSl1XrRBCCFGalOSYvLJAWvJEifo+Wd0pWdQcR1fbUt2xKSd/U7dbQe2xPbH31P1e2Jmpe//eqflItVgXU9T91Xfrkbr3Tu0xdCt6f6FqPMfJw1WN16yOej8bu26pO8+Z3ysvv4Tl3838Qd0xdKmP1P29cuY3dR8CaJX/bCKimEmSJ4QQQogySbpr8ydJnhBCCCHKJKWEH7wo7cprN3SpFBoamu84PCGEEEIItZSZJO/OnTsMHz6c6tWro9FosLOzw9fXV7esl6IohIeHl2wlX6BPnz5cuXJFtXgJCQkoipLvtnDhQgwNDfnll19yjeHi4pJjfVohhBCiLDBQ1NvKozLTXdurVy/S09MJCwujZs2a3L59m8jISO7evVvgGOnp6XorSvzTzMzMMDNTb0JcR0dHvTVyFyxYwJ49e/SWNTM3N2fevHmEhYXx6aef6p1/+PBhrl27xpAhQ1SrkxBCCPFPKTMtVSWkTNyflJQUjhw5wrx582jXrh1OTk40a9aMoKAg3nzzTZydnQHo0aMHiqLoXk+bNo1GjRqxdu1aatSogampqS7e0KFDqVKlCpaWlrRv357Y2Fjd9eLj4+nWrRu2trZotVqaNm2aYz1YZ2dnZs2axYABA9BqtTg5OREREcGdO3fo1q0bWq0Wd3d3Tp06pTvn+e7aZ/XbsGEDzs7OWFlZ0bdvXx4+fKgr8/DhQ/z9/TE3N8fe3p7FixfTtm1bxo4di6GhIXZ2drpNq9ViZGSkt8/CwoL+/fvrZtz+u3Xr1tG8eXMaNGjwkt8hIYQQQpQ2ZSLJ02q1aLVawsPDSUvL+Tj7Dz/8AEBISAiJiYm61wDXrl1j27ZtbN++nZiYGAB69+5NcnIyu3fv5vTp03h5edGhQwfu3bsHQGpqKp07dyYyMpKzZ8/i5+dH165duXnzpt51Fy9eTMuWLTl79ixvvPEG/fv3Z8CAAbz77rucOXOGWrVqMWDAAPJbOS4+Pp7w8HB27NjBjh07OHToEHPnztUdDwwMJDo6moiICPbv38+RI0c4c+ZMoe7fkCFDuHr1KocPH9btS01NZevWrdKKJ4QQoswyULJV28qjMpHkGRkZERoaSlhYGNbW1rRs2ZJPP/2Uc+fOAVClShUArK2tsbOz072Gp12069evx9PTE3d3d44ePcrJkyf5+uuvadKkCS4uLixYsABra2u2bt0KgIeHB++//z4NGzbExcWFmTNnUqtWLSIiIvTq1blzZ95//31cXFyYMmUKDx48oGnTpvTu3Zs6deowceJE4uLiuH37dp7vLSsri9DQUBo2bEirVq3o378/kZGRwNNWvLCwMBYsWECHDh1o2LAhISEhZGYWbv6x+vXr06JFC9atW6fbt2XLFrKzs+nbt2+hYgkhhBClhYzJy1+ZSPLg6Zi8X3/9lYiICPz8/IiKisLLyyvXbsi/c3Jy0kv6YmNjSU1NpVKlSroWQq1Wy40bN4iPjweetnKNHz8eV1dXrK2t0Wq1xMXF5WjJc3d3131ta2sLgJubW459ycnJedbP2dkZC4u/Jse0t7fXlb9+/ToZGRk0a9ZMd9zKyoq6devm+55zM3jwYLZu3arrCl63bh29e/fWu/bz0tLSePDggd6Wnqbuwu5CCCGEKB5lJskDMDU1pWPHjkyePJljx44REBDA1KlT8z3H3Nxc73Vqair29vbExMTobZcvX+bjjz8GYPz48XzzzTfMmTOHI0eOEBMTg5ubG+np6XqxjI3/muVeUZQ892Vl5b0Mw9/LPzsnv/JF9azFbsuWLVy9epXo6OgXdtUGBwdjZWWlt21ZsVn1ugkhhBBFIS15+SszT9fmpn79+rppU4yNjQvUjenl5UVSUhJGRka6BzSeFx0dTUBAAD169ACeJoYJCQkq1brgatasibGxMT/88APVq1cH4P79+1y5coXWrVsXKpaFhQW9e/dm3bp1xMfHU6dOHVq1apXvOUFBQTmmV4m6E1Wo6wohhBDFRd3FHcufMpHk3b17l969ezN48GDc3d2xsLDg1KlTzJ8/n27dugFPuz0jIyNp2bIlGo2GihUr5hrLx8cHb29vunfvzvz586lTpw6//vorO3fupEePHrpxetu3b6dr164oisLkyZOLpXXtRSwsLBg4cCAff/wxNjY2VK1alalTp2JgYKBrJSyMIUOG0KpVK+Li4pg4ceILy2s0GjQa/XUgTR6ou0anEEIIUVTl9YEJtZSJ7lqtVkvz5s1ZvHgxrVu3pmHDhkyePJlhw4axfPlyABYuXMj+/ftxdHTE09Mzz1iKorBr1y5at27NoEGDqFOnDn379uWnn37SjaFbtGgRFStW5NVXX6Vr1674+vri5eX1j7zX5y1atAhvb2+6dOmCj48PLVu2xNXVVTcdTGG89tpr1K1blwcPHjBgwIBiqK0QQgghSgslO7/5PUSp8+jRIxwcHFi4cGGJTH+y5+fdqsZLSVN3IMSFFPUap2tbFu4p5hfZdN38xYUKoW+NR6rGi72nbiutnZm698/BXL3W9Isqfk4Azt3TvLhQIbhapb+4UCGs6P2FqvEcJw9XNV6zOur9GfK0Uffe+b2Sc9qul/FelLWq8VIfqfsnfLnPA1XjtbJ7Q9V4z5sbu1+1WJ94dFQtVmlRJrpr/5edPXuWS5cu0axZM+7fv8+MGTMAdN3UQgghxP+q8vrAhFokySsDFixYwOXLlzExMaFx48YcOXKEypUrl3S1hBBCCFGKSZJXynl6enL69OmSroYQQghR6hhKS16+JMkTQgghRJkk3bX5kyRPlKi0LHV/QtX8gTc3UndA8xOV36uRys/GZ2WrWz9jlev3RMVZjIxUnnbhSSl/fE3tByVuzVylarwm6z9QLZaRgbrfjCLMVvWPUvvRSUmayhdJ8oQQQghRJsk8efmTJE8IIYQQZZK0POavTEyGrLbQ0FCsra1LuhovLSEhAUVRiImJKemqCCGEEKKUKXSSd+fOHYYPH0716tXRaDTY2dnh6+tLdHQ08HRFiWfryZZWffr04cqVK6rHbdu2LYqiMHfu3BzH3njjDRRFYdq0aapdz9HRkcTERBo2bKhaTCGEEKKsMFRxK48K3V3bq1cv0tPTCQsLo2bNmty+fZvIyEju3r1b4Bjp6emYmJgU9tKqMTMzw8zMrFhiOzo6EhoayieffKLb98svvxAZGYm9vb2q1zI0NMTOzk7VmEIIIURZId21+StUS15KSgpHjhxh3rx5tGvXDicnJ5o1a0ZQUBBvvvkmzs7OAPTo0QNFUXSvp02bRqNGjVi7di01atTQrbuakpLC0KFDqVKlCpaWlrRv357Y2Fjd9eLj4+nWrRu2trZotVqaNm3KgQMH9Ork7OzMrFmzGDBgAFqtFicnJyIiIrhz5w7dunVDq9Xi7u7OqVOndOc83137rH4bNmzA2dkZKysr+vbty8OHD3VlHj58iL+/P+bm5tjb27N48WLatm3L2LFj9erTpUsXfvvtN13LJkBYWBivv/46VatW1Sv7+++/M2DAACpWrEiFChXo1KkTV69eBeDBgweYmZmxe7f+MmLffPMNFhYWPH78ONfu2h9//JFOnTqh1WqxtbWlf//+/Pbbb7rjW7duxc3NDTMzMypVqoSPjw+PHqm7PJYQQgjxTzBQslXbyqNCJXlarRatVkt4eDhpaTnX8/vhhx8ACAkJITExUfca4Nq1a2zbto3t27frkpLevXuTnJzM7t27OX36NF5eXnTo0IF79+4BkJqaSufOnYmMjOTs2bP4+fnRtWtXbt68qXfdxYsX07JlS86ePcsbb7xB//79GTBgAO+++y5nzpyhVq1aDBgwgPyW6Y2Pjyc8PJwdO3awY8cODh06pNftGhgYSHR0NBEREezfv58jR45w5syZHHFMTEzw9/cnJCREty80NJTBgwfnKBsQEMCpU6eIiIjg+PHjZGdn07lzZzIyMrC0tKRLly5s2rRJ75yNGzfSvXt3KlSokCNeSkoK7du3x9PTk1OnTrFnzx5u377N22+/DUBiYiL9+vVj8ODBxMXFERUVRc+ePfO9L0IIIYQomwrVXWtkZERoaCjDhg1j9erVeHl50aZNG/r27Yu7uztVqlQBwNraOkc3Ynp6OuvXr9eVOXr0KCdPniQ5ORmN5uni3gsWLCA8PJytW7fy3nvv4eHhgYeHhy7GzJkz+eabb4iIiGDUqFG6/Z07d+b9998HYMqUKaxatYqmTZvSu3dvACZOnIi3tze3b9/Os3szKyuL0NBQLCwsAOjfvz+RkZHMnj2bhw8fEhYWxqZNm+jQoQPwNJGtVq1arrEGDx5Mq1atWLp0KadPn+b+/ft06dJFbzze1atXiYiIIDo6mldffRV4msA5OjoSHh5O79698ff3p3///jx+/JgKFSrw4MEDdu7cyTfffJPrdZcvX46npydz5szR7Vu3bh2Ojo5cuXKF1NRUnjx5Qs+ePXFycgLAzc0t11gAaWlpOZL59LQMTDTqLmQvhBBCFIWseJG/Qj940atXL3799VciIiLw8/MjKioKLy8vQkND8z3PyclJl+ABxMbGkpqaSqVKlXQthFqtlhs3bhAfHw88bckbP348rq6uWFtbo9VqiYuLy9GS5+7urvva1tYW0E9enu1LTk7Os37Ozs66BA/A3t5eV/769etkZGTQrFkz3XErKyvq1q2baywPDw9cXFzYunUr69ato3///hgZ6efTcXFxGBkZ0bx5c92+SpUqUbduXeLi4oCnyauxsTEREREAbNu2DUtLS3x8fHK9bmxsLAcPHtS7n/Xq1QOetlR6eHjQoUMH3Nzc6N27N1988QW///57nvckODgYKysrvW3Lis15lhdCCCH+SQaKelt5VKR58kxNTenYsSMdO3Zk8uTJDB06lKlTpxIQEJDnOebm5nqvU1NTsbe3JyoqKkfZZ+Plxo8fz/79+1mwYAG1a9fGzMyMt956i/T0dL3yxsZ/tSwp/3968tz2ZWXlPWX+38s/Oye/8i8yePBgVqxYwcWLFzl58mSRYpiYmPDWW2+xadMm+vbty6ZNm+jTp0+OhPGZ1NRUunbtyrx583Ics7e3x9DQkP3793Ps2DH27dvH559/zqRJkzhx4gQ1atTIcU5QUBCBgYF6+6LuRBXpvQghhBDin6XKPHn169fXDd43NjYmMzPzhed4eXmRlJSEkZERtWvX1tsqV64MQHR0NAEBAfTo0QM3Nzfs7OxISEhQo8qFUrNmTYyNjfXGGN6/fz/faVjeeecdzp8/T8OGDalfv36O466urjx58oQTJ07o9t29e5fLly/rlff392fPnj1cuHCB7777Dn9//zyv6eXlxYULF3B2ds5xT58l2Yqi0LJlS6ZPn87Zs2cxMTHJs/tXo9FgaWmpt0lXrRBCiNJCWvLyV6gk7+7du7Rv357//ve/nDt3jhs3bvD1118zf/58unXrBjzt9oyMjCQpKSnfrkAfHx+8vb3p3r07+/btIyEhgWPHjjFp0iTdk7AuLi66BzViY2N55513Xqp1ragsLCwYOHAgH3/8MQcPHuTChQsMGTIEAwMDXSvh8ypWrEhiYiKRkZG5HndxcaFbt24MGzaMo0ePEhsby7vvvouDg4PuXgK0bt0aOzs7/P39qVGjhl737vNGjhzJvXv36NevHz/88APx8fHs3buXQYMGkZmZyYkTJ5gzZw6nTp3i5s2bbN++nTt37uDq6vpyN0gIIYQoAZLk5a/QT9c2b96cxYsX07p1axo2bMjkyZMZNmwYy5cvB2DhwoXs378fR0dHPD0984ylKAq7du2idevWDBo0iDp16tC3b19++ukn3Ri6RYsWUbFiRV599VW6du2Kr68vXl5eL/F2i27RokV4e3vTpUsXfHx8aNmyJa6urrrpYHJjbW2do5v670JCQmjcuDFdunTB29ub7Oxsdu3alaOruV+/fsTGxubbigdQrVo1oqOjyczM5PXXX8fNzY2xY8dibW2NgYEBlpaWHD58mM6dO1OnTh0+++wzFi5cSKdOnQp/Q4QQQghRqinZMn9GkTx69AgHBwcWLlzIkCFDSro6/5g9P+9+caFCuP2HuivrXX+o3rzlbhWfqBYLYNUlixcXKoQAl1RV4/1wR90Jyp206t4/G416rfhqfk4ATv6W9z97ReFmnf7iQoUQcU7dZcpvzVylarwe6z9QLVbzKjmn93oZHR3U/V4MO2itaryHqer+CV/1+gNV47W0fUPVeM/bFL9HtVjv1PJTLVZpoe5Pfjl29uxZLl26RLNmzbh//z4zZswA0OtaFUIIIcQ/R91mgvJHkrxCWLBgAZcvX8bExITGjRtz5MgR3UMiQgghhBCliSR5BeTp6cnp06dLuhpCCCGE+P/K6wMTapEkTxRKpsojOF+zVXe8y3/jbVSLFXf/xVMBFUbChpsvLlQI1nOtVY1nZ6buGDq1LT+dcym/oqpaSd2/DPfVHR7Jzp/V/dXcrI66P7hNVBxDB/DNgNWqxZof945qsQAqqPxX8vNW91WN1/trdcf6trR1UTVecZMkL3+S5AkhhBCiTDJU5NnR/MiYRSGEEEKIckiSPCGEEEKUSSU9GfKKFStwdnbG1NSU5s2bv3AZ06+//pp69ephamqKm5sbu3btKtqFC0iSvAJISkriww8/pGbNmmg0GhwdHenatWueq1kURdu2bRk7dqxq8fITFRWFoiikpKT8I9cTQgghikNJJnmbN28mMDCQqVOncubMGTw8PPD19SU5OTnX8seOHaNfv34MGTKEs2fP0r17d7p3786PP/74knchb5LkvUBCQgKNGzfmu+++41//+hfnz59nz549tGvXjpEjR/6jdcnOzubJk9I9OF4IIYQoi9LS0njw4IHelpaW9+TaixYtYtiwYQwaNIj69euzevVqKlSowLp163Itv3TpUvz8/Pj4449xdXVl5syZeHl56VYMKw6S5L3AiBEjUBSFkydP0qtXL+rUqUODBg0IDAzk+++/B+DmzZt069YNrVaLpaUlb7/9Nrdv39bFmDZtGo0aNWLDhg04OztjZWVF3759efjwIQABAQEcOnSIpUuXoigKiqKQkJCga3HbvXs3jRs3RqPRcPToUeLj4+nWrRu2trZotVqaNm3KgQMH9OqdlpbGxIkTcXR0RKPRULt2bf7zn/+QkJBAu3btgKfr6yqKQkBAwD9zM4UQQggVqdmSFxwcjJWVld4WHByc63XT09M5ffo0Pj4+f9XFwAAfHx+OHz+e6znHjx/XKw/g6+ubZ3k1SJKXj3v37rFnzx5GjhyZ6xq01tbWZGVl0a1bN+7du8ehQ4fYv38/169fp0+fPnpl4+PjCQ8PZ8eOHezYsYNDhw4xd+5c4Gl27+3tzbBhw0hMTCQxMRFHR0fduZ988glz584lLi4Od3d3UlNT6dy5M5GRkZw9exY/Pz+6du3KzZt/TdExYMAAvvzyS5YtW0ZcXBxr1qxBq9Xi6OjItm3bALh8+TKJiYksXbq0OG6fEEIIUawMFfW2oKAg7t+/r7cFBQXlet3ffvuNzMxMbG1t9fbb2tqSlJSU6zlJSUmFKq8GmUIlH9euXSM7O5t69erlWSYyMpLz589z48YNXWK2fv16GjRowA8//EDTpk0ByMrKIjQ0FAuLp3Ma9e/fn8jISGbPno2VlRUmJiZUqFABOzu7HNeYMWMGHTt21L22sbHBw8ND93rmzJl88803REREMGrUKK5cucKWLVvYv3+/7r+GmjVr6p0PULVqVaytrYt4d4QQQojyQ6PRoNFoSroaqpKWvHxkZ794/p24uDgcHR31Wt7q16+PtbU1cXFxun3Ozs66BA/A3t4+z8GZz2vSpIne69TUVMaPH4+rqyvW1tZotVri4uJ0LXkxMTEYGhrSpk2bAsXPS27jEzLSMl4qphBCCKEWAyVbta0wKleujKGhod7QLIDbt2/n2lgDYGdnV6jyapAkLx8uLi4oisKlS5deOpaxsbHea0VRyMrKKtC5z3cVjx8/nm+++YY5c+Zw5MgRYmJicHNzIz396eoRZmZmL11fyH18wpYVm1WJLYQQQrwsAxW3wni2hv3fZ9nIysoiMjISb2/vXM/x9vbOMSvH/v378yyvBkny8mFjY4Ovry8rVqzg0aNHOY6npKTg6urKrVu3uHXrlm7/xYsXSUlJoX79+gW+lomJCZmZBVtGKzo6moCAAHr06IGbmxt2dnYkJCTojru5uZGVlcWhQ4fyvBbwwuvlNj7h7ZF98j1HCCGE+F8QGBjIF198QVhYGHFxcQwfPpxHjx4xaNAg4OnY+L+P6RszZgx79uxh4cKFXLp0iWnTpnHq1ClGjRpVbHWUJO8FVqxYQWZmJs2aNWPbtm1cvXqVuLg4li1bhre3Nz4+Pri5ueHv78+ZM2c4efIkAwYMoE2bNjm6WfPj7OzMiRMnSEhI4Lfffsu3lc/FxYXt27cTExNDbGws77zzjl55Z2dnBg4cyODBgwkPD+fGjRtERUWxZcsWAJycnFAUhR07dnDnzh1SU3NfeFOj0WBpaam3GWuMcy0rhBBC/NNKcp68Pn36sGDBAqZMmUKjRo2IiYlhz549uocrbt68SWJioq78q6++yqZNm/j3v/+Nh4cHW7duJTw8nIYNG6p1O3KQJO8FatasyZkzZ2jXrh0fffQRDRs2pGPHjkRGRrJq1SoUReHbb7+lYsWKtG7dGh8fH2rWrMnmzYXr1hw/fjyGhobUr1+fKlWq6D0p+7xFixZRsWJFXn31Vbp27Yqvry9eXl56ZVatWsVbb73FiBEjqFevHsOGDdO1Rjo4ODB9+nQ++eQTbG1ti/W/CCGEEKK4qPl0bVGMGjWKn376ibS0NE6cOEHz5s11x6KioggNDdUr37t3by5fvkxaWho//vgjnTt3fol3/2JKdkGeLhDi/9t5a7eq8epZqTu58wfHbFSLVVFTsO7zgjr9xS+qxlsy11rVeOfvqfuwvcZQ1XBs+tFUtVhVKxXxN3oefn+oajjupxRsvG5BNa6t7q/5LJX/anwzYLVqsa7GvaNaLIAKKs9BkfBQ3baV3l9bvLhQIcQPV/shgDoqx9N3JGmnarFa2b2hWqzSQlryhBBCCCHKIZknTwghhBBlUlHG0v0vkSRPCCGEEGWSJHn5k+5aIYQQQohySB68EIWy75ddqsY78It6g+kBXnf4U7VY1czVHfwe/0DdJxGeqFs9om+ru5zPB64555Z8GcYq/kt6+b663wtzI3V/jdpo1I2365a631sjA3Xr18s5TbVYLq6bVIsFcPj0u6rGG3XIStV4baqnqxov9Ym6TWMrX22narznnUhW78GL5lXL34MX0l0rhBBCiDJJke7afEl3rRBCCCFEOfQ/meRFRUWhKAopKSklXRUdRVEIDw8HICEhAUVRiImJAXLWNzQ0FGtr6xKppxBCCFFaKCpu5VGxJnkBAQEoioKiKJiYmFC7dm1mzJjBkyfqToBbmk2bNk13DxRFwcrKilatWuVYVzYxMZFOnToVKGafPn24cuVKcVRXCCGEKDMURb2tPCr2ljw/Pz8SExO5evUqH330EdOmTeNf//pXcV82TxkZGf/4NRs0aEBiYiKJiYkcP34cFxcXunTpwv3793Vl7Ozs0GgKNjjazMyMqlWrFld1hRBCCFEOFHuSp9FosLOzw8nJieHDh+Pj40NERASLFi3Czc0Nc3NzHB0dGTFiBKmpqbrznnVJhoeH4+LigqmpKb6+vty6dUsv/rfffouXlxempqbUrFmT6dOn67UUKorCqlWrePPNNzE3N2f27Nm51vPo0aO0atUKMzMzHB0dGT16tG6tV4CVK1fq6mFra8tbb72lO7Z161bc3NwwMzOjUqVK+Pj46J1rZGSEnZ0ddnZ21K9fnxkzZpCamqrXGvf37toXeb67dtq0aTRq1IgNGzbg7OyMlZUVffv25eHDv9ZaevjwIf7+/pibm2Nvb8/ixYtp27YtY8eOLdA1hRBCiNLGQMWtPPrH35eZmRnp6ekYGBiwbNkyLly4QFhYGN999x0TJkzQK/v48WNmz57N+vXriY6OJiUlhb59++qOHzlyhAEDBjBmzBguXrzImjVrCA0NzZHITZs2jR49enD+/HkGDx6co07x8fH4+fnRq1cvzp07x+bNmzl69CijRo0C4NSpU4wePZoZM2Zw+fJl9uzZQ+vWrYGn3az9+vVj8ODBxMXFERUVRc+ePclrZpq0tDRCQkKwtrambt26L3Uvn38P4eHh7Nixgx07dnDo0CHmzp2rOx4YGEh0dDQRERHs37+fI0eOcObMGdWuL4QQQvzTFCVbta08+semUMnOziYyMpK9e/fy4Ycf6rUgOTs7M2vWLD744ANWrlyp25+RkcHy5ctp3rw5AGFhYbi6unLy5EmaNWvG9OnT+eSTTxg4cCAANWvWZObMmUyYMIGpU6fq4rzzzjsMGjRI9/r69et6dQsODsbf319XJxcXF5YtW0abNm1YtWoVN2/exNzcnC5dumBhYYGTkxOenp7A0yTvyZMn9OzZEycnJwDc3Nz04p8/fx6tVgs8TVwtLCzYvHkzlpaWL3NL9WRlZREaGoqFxdPFqvv3709kZCSzZ8/m4cOHhIWFsWnTJjp06ABASEgI1apVyzdmWloaaWn681elp2VgojFWrd5CCCFEUZXToXSqKfaWvB07dqDVajE1NaVTp0706dOHadOmceDAATp06ICDgwMWFhb079+fu3fv8vjxY925RkZGNG3aVPe6Xr16WFtbExcXB0BsbCwzZsxAq9XqtmHDhpGYmKgXp0mTJvnWMTY2ltDQUL04vr6+ZGVlcePGDTp27IiTkxM1a9akf//+bNy4URffw8ODDh064ObmRu/evfniiy/4/fff9eLXrVuXmJgYYmJiOH36NMOHD6d3796cOnXqpe/vM87OzroED8De3p7k5GTgaVKbkZFBs2bNdMetrKxe2JIYHByMlZWV3rZ5+RbV6iyEEEKI4lPsSV67du2IiYnh6tWr/PHHH4SFhXHnzh26dOmCu7s727Zt4/Tp06xYsQKA9PSCz96dmprK9OnTdQlUTEwM58+f5+rVq5ia/rWSgrm5+QvjvP/++3pxYmNjuXr1KrVq1cLCwoIzZ87w5ZdfYm9vz5QpU/Dw8CAlJQVDQ0P279/P7t27qV+/Pp9//jl169blxo0buvjPniyuXbs2np6ezJ07FwcHB5YsWVK4m5kPY2P91jVFUcjKerklEYKCgrh//77e1mfU2y8VUwghhFCLPF2bv2LvrjU3N6d27dp6+06fPk1WVhYLFy7EwOBpnrllS84WoidPnnDq1CldC9Tly5dJSUnB1dUVAC8vLy5fvpwjfmF5eXlx8eLFfOMYGRnh4+ODj48PU6dOxdramu+++46ePXuiKAotW7akZcuWTJkyBScnJ7755hsCAwPzjGdoaMgff/zxUvUuqJo1a2JsbMwPP/xA9erVAbh//z5XrlzRjS3MjUajyfHEr8lD6aoVQghROpTT3Ew1JbKsWe3atcnIyODzzz+na9euREdHs3r16hzljI2N+fDDD1m2bBlGRkaMGjWKFi1a6JK+KVOm0KVLF6pXr85bb72FgYEBsbGx/Pjjj8yaNavA9Zk4cSItWrRg1KhRDB06FHNzcy5evMj+/ftZvnw5O3bs4Pr167Ru3ZqKFSuya9cusrKyqFu3LidOnCAyMpLXX3+dqlWrcuLECe7cuaNLROFpspqUlAQ8fcp18+bNXLx4kYkTJ77knSwYCwsLBg4cyMcff4yNjQ1Vq1Zl6tSpGBgYoJTXf1+EEEL8P/bOOyyKs2vj9y5KryoqKEUEpdhALNg7QgyWGGNERQGTaBR7ixFFsUYUW4RYALEXbNEIiopdsYAFkCKIBbsgRZFyvj+4mI+VBXaWQdD3+eWaK+7Mzr1nCztnz3MK43+caqkabt26NVavXo0VK1agRYsW2LlzJ5YtW1bqfqqqqpg9ezZGjBiBzp07Q11dHXv37uWO29vb499//0VYWBjatWuHjh07Ys2aNVwBhKy0atUKERERiI+PR9euXWFtbQ1PT0+uMEFbWxshISHo1asXLCws4Ofnh927d8PKygqampo4f/48HB0d0axZM/z555/w8fGRaGx8//596OnpQU9PD23atMG+ffuwadMmjB49Ws5XkD+rV6+GnZ0dBgwYgD59+qBz586wsLCQWNZmMBgMBuNrQiwSbvsWEVFZvT6qmcDAQEyZMqVGjR77lsjOzkajRo3g4+MDNzc3mc8Le3pCUDtOPxXWyezX6KNgWvpqlctp/Jyk9wqC6uULax4uvZCtGbes/GaRXfGdeFBbwJ+kDzKEfS/Uagn7NVpHSVi9E4+FfW9riYW17wfj3IrvJCNmFrsE0wKA8zdHCqo3MUJLUL3uhrLnsctCVr6w3s7fnXoKqvc599/9K5iWlc4AwbRqCtWyXMv48ty+fRtxcXFo3749MjIysGjRIgDAwIEDq9kyBoPBYDAYVQFz8v6HWLVqFR48eABFRUW0bdsWFy5cQL169arbLAaDwWAw5IKllZdPjXXyxowZgzFjxlS3Gd8M1tbWuHnzZnWbwWAwGAyGYDAfr3xqrJPHqJlk5wn7JzXQSNg2Mo1UhUtUe5ojbF3SdwaGguqFPU0VVG9KC2Fz6A6mCJsH1ltfuNwjY3VhExrFAo9Eyi0Q9u+sf2Phct4A4aMnqgJeiYTOoevWdoegeskPfhZUT0dRT1C9NffSBdVjVC/MyWMwGAwGg/FVwiJ55cOcPAaDwWAwGF8l32rrE6Golj55DNkxNjYWdPxZMWPGjMGgQYME12UwGAwG40shEnD7FhHMyRszZgxEIhFEIhE3q3XRokXIz88X6iGqhcDAQGhra5fa36NHD4hEIixfvrzUse+++w4ikQgLFy6s9OMwGAwGg8FgyIOgkbz+/fsjLS0NCQkJmD59OhYuXIi//vpLyIeoURgYGCAwMFBi39OnTxEeHg49PWGTYRkMBoPBYEgiEpFg27eIoE6ekpISGjZsCCMjI4wfPx59+vTB0aNHsXr1arRs2RJqamowMDDAhAkTkJWVBaBo8oKmpiYOHDggoXX48GGoqakhMzMTKSkpEIlE2LdvH7p27QoVFRW0a9cO8fHxiIyMhK2tLdTV1eHg4IBXr15J6GzZsoUb32Vubo6///6bO1asGxISgp49e0JVVRWtW7fGlStXAADnzp3D2LFjkZGRwUUpS0bnBgwYgNevX+PSpUvcvqCgIG6ObUlyc3MxY8YMNGrUCGpqaujQoQPOnTsn0+Pk5OTA1dUVGhoaMDQ0xD///COhfffuXfTq1QsqKiqoW7cufvnlF+71BYCCggJMmzYN2traqFu3LmbNmoUaOuiEwWAwGAyZYcu15VOlOXkqKir49OkTxGIx1q1bh/v37yMoKAhnzpzBrFmzAABqamoYPnw4AgICJM4NCAjA0KFDoaGhwe1bsGAB/vzzT9y6dQu1atXCiBEjMGvWLKxduxYXLlxAYmIiPD09ufvv3LkTnp6eWLJkCWJjY7F06VLMnz8fQUFBEo81b948zJgxA1FRUWjWrBl+/vln5Ofno1OnTvD19YWmpibS0tKQlpaGGTNmcOcpKirC2dlZwvbAwEC4urqWei0mTpyIK1euYM+ePbhz5w5+/PFH9O/fHwkJCRU+jo+PD2xtbXH79m1MmDAB48ePx4MHDwAUOcn29vbQ0dFBZGQk9u/fj9OnT2PixIkS5wcGBmLbtm24ePEi3r59i0OHDvF6LxkMBoPBYHxdVImTR0Q4ffo0QkND0atXL0yZMgU9e/aEsbExevXqBW9vb+zbt4+7v7u7O0JDQ5GWlgYAePnyJU6cOFHKWZoxYwbs7e1hYWGByZMn4+bNm5g/fz46d+4Ma2truLm54ezZs9z9FyxYAB8fHwwZMgRNmjTBkCFDMHXqVPj7+5fS/e6779CsWTN4eXnh0aNHSExMhKKiIrS0tCASidCwYUM0bNgQ6urqEue6urpi3759yM7Oxvnz55GRkYEBAyTn36WmpiIgIAD79+9H165d0bRpU8yYMQNdunRBQEBAhY/j6OiICRMmwNTUFLNnz0a9evW457lr1y58/PgR27dvR4sWLdCrVy9s2LABwcHBePHiBQDA19cXc+fOxZAhQ2BhYQE/Pz9oaVU8PzE3Nxfv37+X2PJy8yo8j8FgMBiML4FIJNz2LSKok/fvv/9CXV0dysrKcHBwwE8//YSFCxfi9OnT6N27Nxo1agQNDQ2MGjUKb968QU5ODgCgffv2sLKy4iJsO3bsgJGREbp16yah36pVK+7fDRo0AAC0bNlSYt/Lly8BFEW4kpKS4ObmBnV1dW7z9vZGUlJSmbrFuXTFOhXRunVrmJmZ4cCBA9i2bRtGjRqFWrUkO9PcvXsXBQUFaNasmYQtERERpWyRRkn7ih3BYvtiY2PRunVrqKmpcffp3LkzCgsL8eDBA2RkZCAtLQ0dOnTgjteqVQu2trYVPu6yZcugpaUlsR3ctLfC8xgMBoPB+BKIBdy+RQTtk9ezZ09s2rQJioqK0NfXR61atZCSkoIBAwZg/PjxWLJkCerUqYOLFy/Czc0Nnz59gqqqKoCiaN7GjRsxZ84cBAQEYOzYsRB95lrXrl2b+3fxsc/3FRYWdbIvzknbvHmzhIMDAAoKChXqFuvIgqurKzZu3IiYmBhcv3691PGsrCwoKCjg5s2bpR7788igNEraV2wjH/vkZe7cuZg2bZrEvpNp56r8cRkMBoPBYFQeQZ1XNTU1mJqawtDQkItm3bx5E4WFhfDx8UHHjh3RrFkzPHv2rNS5I0eOxKNHj7Bu3TrExMTAxcWlUrY0aNAA+vr6ePjwIUxNTSW2Jk2ayKyjqKiIgoKCcu8zYsQI3L17Fy1atIClpWWp49bW1igoKMDLly9L2dKwYUOZH0caFhYWiI6ORnb2/4+kunTpEsRiMZo3bw4tLS3o6enh2rVr3PH8/HyZ5tgqKSlBU1NTYqutVLvC8xgMBoPB+BKw5dryqfIIpampKfLy8rB+/Xo8fPgQwcHB8PPzK3U/HR0dDBkyBDNnzkS/fv3QuHHjSj+2l5cXli1bhnXr1iE+Ph53795FQEAAVq9eLbOGsbExsrKyEB4ejtevX3NLzJ/bnpaWhvDwcKkazZo1g7OzM0aPHo2QkBAkJyfj+vXrWLZsGY4fPy7z40jD2dkZysrKcHFxwb1793D27FlMmjQJo0aN4pa0J0+ejOXLl+Pw4cOIi4vDhAkTkJ6eLvNrwGAwGAxGTYRV15ZPlTt5rVu3xurVq7FixQq0aNECO3fuxLJly6Tet3gJV1p1qjy4u7tjy5YtCAgIQMuWLdG9e3cEBgbyiuR16tQJv/32G3766Sfo6upi5cqVUu+nra0tkRf3OQEBARg9ejSmT5+O5s2bY9CgQYiMjIShoSGvx/kcVVVVhIaG4u3bt2jXrh2GDh2K3r17Y8OGDdx9pk+fjlGjRsHFxQV2dnbQ0NDA4MGDZX4NGAwGg8FgfH2IqAY1TAsODsbUqVPx7NkzKCoqVrc5DCkcSvlPUL36KsLmFjZSFU7vaY6wv4Hs6hsLqhf2NFVQvRY6wk6nOZiiJKheb/1PgmkpCvzzVixwI9XcgpodVxB6aauhinCvX9J7Yd/cbm13CKqX/OBnQfV0FIVtvL/mXrqgenNa9xVU73MeZx8TTMtA7XvBtGoKghZeyEtOTg7S0tKwfPly/Prrr8zBYzAYDAaDUSE1++dQ9VMjqoZXrlwJc3NzNGzYEHPnzq1ucxgMBoPBYHwFiEXCbd8iNcLJW7hwIfLy8hAeHi5TSxEGg8FgMBgMRvnUiOVaBoPBYDAYDL58owE4wWBOHoMXarWFTTD3jxM2ctu6jnDJ+Y4GuYJpAcCws28E1QPKruaWhzsVD1/hxV1XZUH1ot++Ekzr8BNhi0LSPwm7KNKnkbCfvcWRGhXfqRpZ3zVDMK2JERWPbOSD0IUSTZrvFlRPv8cgQfX6/lpfUL2qRiRw0VNV8PbtW0yaNAnHjh2DWCzGDz/8gLVr15a5cvn27VssWLAAYWFhSE1Nha6uLgYNGoTFixfLNJK0JDViuZbBYDAYDAbjW8TZ2Rn379/HqVOn8O+//+L8+fP45Zdfyrz/s2fP8OzZM6xatQr37t1DYGAgTp48CTc3N96PzSJ5DAaDwWAwvkqEXK7Nzc1Fbq5kFF1JSQlKSvJH/mNjY3Hy5ElERkZyM+PXr18PR0dHrFq1Cvr6+qXOadGiBQ4ePMjdbtq0KZYsWYKRI0ciPz+fmygmC99kJE8kEuHw4cNlHj937hxEIpHgUx8CAwOhra0tqCaDwWAwGAzpCDnWbNmyZdDS0pLYyhreICtXrlyBtrY25+ABQJ8+fSAWiyXGjVZERkYGNDU1eTl4gMBO3pgxYyASifDbb7+VOvb7779DJBJhzJgxgj3ewoUL0aZNG8H0KuLs2bNwdHRE3bp1oaqqCktLS0yfPh1Pnz79YjYwGAwGg8EQnrlz5yIjI0Niq2xbt+fPn6N+fck8x1q1aqFOnTp4/vy5TBqvX7/G4sWLy13iLQvBI3kGBgbYs2cPPnz4wO37+PEjdu3axY3w+hrx9/dHnz590LBhQxw8eBAxMTHw8/NDRkYGfHx8qvSx8/LyqlSfwWAwGIyvESFn1yopKUFTU1NiK2upds6cORCJROVucXFxlX5+79+/x3fffQdLS0ssXLiQ9/mCO3k2NjYwMDBASEgIty8kJASGhoawtrbm9uXm5sLDwwP169eHsrIyunTpgsjISO548ZJqeHg4bG1toaqqik6dOuHBgwcAipZGvby8EB0dzb2ggYGB3PmvX7/G4MGDoaqqCjMzMxw9elSqvdnZ2dDU1MSBAwck9h8+fBhqamrIzMzEkydP4OHhAQ8PD2zbtg09evSAsbExunXrhi1btsDT01Pi3NDQUFhYWEBdXR39+/dHWloadywyMhJ9+/ZFvXr1oKWlhe7du+PWrVsS54tEImzatAlOTk5QU1PDkiVLAADe3t6oX78+NDQ04O7ujjlz5pSKZG7ZsgUWFhZQVlaGubk5/v77b+7Yp0+fMHHiROjp6UFZWRlGRkaVDkUzGAwGg1FdiAXc+DB9+nTExsaWu5mYmKBhw4Z4+fKlxLn5+fl4+/YtGjZsWO5jZGZmon///tDQ0MChQ4dQu3ZtnlZWUU6eq6srAgICuNvbtm3D2LFjJe4za9YsHDx4EEFBQbh16xZMTU1hb2+Pt2/fStxv3rx58PHxwY0bN1CrVi24uroCAH766SdMnz4dVlZWSEtLQ1paGn766SfuPC8vLwwbNgx37tyBo6MjnJ2dS2kDgJqaGoYPHy5hLwAEBARg6NCh0NDQwP79+/Hp0yfMmjVL6vMtmYeXk5ODVatWITg4GOfPn0dqaipmzJjBHc/MzISLiwsuXryIq1evwszMDI6OjsjMzJTQXLhwIQYPHoy7d+/C1dUVO3fuxJIlS7BixQrcvHkThoaG2LRpk8Q5O3fuhKenJ5YsWYLY2FgsXboU8+fPR1BQEABg3bp1OHr0KPbt24cHDx5g586dMDY2lvqcGAwGg8FgSEdXVxfm5ublboqKirCzs0N6ejpu3rzJnXvmzBkUFhaiQ4cOZeq/f/8e/fr1g6KiIo4ePQplZflaUlVJde3IkSMxd+5cPHr0CABw6dIl7NmzB+fOnQNQFD3btGkTAgMD4eDgAADYvHkzTp06ha1bt2LmzJmc1pIlS9C9e3cAReHR7777Dh8/foSKigrU1dVRq1Ytqd7wmDFj8PPPRf2Nli5dinXr1uH69evo379/qfu6u7ujU6dOSEtLg56eHl6+fIkTJ07g9OnTAICEhARoampCT6/iQdB5eXnw8/ND06ZNAQATJ07EokWLuOO9evWSuP8///wDbW1tREREYMCAAdz+ESNGSDjG69evh5ubG7fP09MTYWFhyMrK4u6zYMEC+Pj4YMiQIQCAJk2aICYmBv7+/nBxcUFqairMzMzQpUsXiEQiGBkZlftcpFUafcrNg6IS/18TDAaDwWAIjaiGd0O2sLBA//79MW7cOPj5+SEvLw8TJ07E8OHDucrap0+fonfv3ti+fTvat2/POXg5OTnYsWMH3r9/j/fv3wMoci4VFBRkfvwqieTp6uriu+++Q2BgIAICAvDdd9+hXr163PGkpCTk5eWhc+fO3L7atWujffv2iI2NldBq1aoV9+9iJ+vz0Kc0Sp6npqYGTU3NMs9r3749rKysuIjXjh07YGRkhG7dugEAiAgiGT9JqqqqnINXbHPJx33x4gXGjRsHMzMzaGlpQVNTE1lZWUhNTZXQKVmJAwAPHjxA+/btS9ldTHZ2NpKSkuDm5gZ1dXVu8/b2RlJSUZfbMWPGICoqCs2bN4eHhwfCwsLKfS7SKo32btgn0+vAYDAYDEbVI2RWXtWwc+dOmJubo3fv3nB0dESXLl3wzz//cMfz8vLw4MED5OTkAABu3bqFa9eu4e7duzA1NYWenh63PX78mNdjV1mfPFdXV0ycOBEAsHHjRrl1Sq5BFztahYWFvM4rPre889zd3bFx40bMmTMHAQEBGDt2LPd4zZo1Q0ZGBhfp4/u4RP/fkdvFxQVv3rzB2rVrYWRkBCUlJdjZ2eHTJ8lJDWpq/KYZFEf0Nm/eXCoEXOz129jYIDk5Gf/99x9Onz6NYcOGoU+fPqXyEYuZO3cupk2bJrHv/OuzvOxiMBgMBqOqEH0Fg83q1KmDXbt2lXnc2NhYwk/o0aOHxO3KUGV98vr3749Pnz4hLy8P9vb2EseaNm0KRUVFXLp0iduXl5eHyMhIWFpayvwYioqKKCgoEMTekSNH4tGjR1i3bh1iYmLg4uLCHRs6dCgUFRWxcuVKqefy6bd36dIleHh4wNHREVZWVlBSUsLr168rPK958+YShSkAJG43aNAA+vr6ePjwIUxNTSW2Jk2acPfT1NTETz/9hM2bN2Pv3r04ePCg1FxFQHqlEVuqZTAYDAbj66DKInkKCgrc0uvn68dqamoYP348Zs6ciTp16sDQ0BArV65ETk4Or7EdxsbGSE5ORlRUFBo3bgwNDQ25O1Pr6OhgyJAhmDlzJvr164fGjRtzxwwMDLBmzRpMnDgR79+/x+jRo2FsbIwnT55g+/btUFdXl7mNipmZGYKDg2Fra4v3799j5syZUFFRqfC8SZMmYdy4cbC1tUWnTp2wd+9e3LlzByYmJtx9vLy84OHhAS0tLfTv3x+5ubm4ceMG3r17h2nTpmH16tXQ09ODtbU1xGIx9u/fj4YNG7IGzgwGg8H4KhGJvsmZDoJRpa9OcfRHGsuXL8cPP/yAUaNGwcbGBomJiQgNDYWOjo7M+j/88AP69++Pnj17QldXF7t3V27ws5ubGz59+sRV8JZkwoQJCAsLw9OnTzF48GCYm5vD3d0dmpqaEtWzFbF161a8e/cONjY2GDVqFNdGpiKcnZ0xd+5czJgxg1t2HTNmjETFjbu7O7Zs2YKAgAC0bNkS3bt3R2BgIBfJ09DQwMqVK2Fra4t27dohJSUFJ06cgFjM/kgYDAaD8TVS83PyqhMRCbXw+w0QHByMqVOn4tmzZ1BUVKxucyqkb9++aNiwIYKDg7/YY4Y9PSGo3o5EfrmHFdG6zqeK7yQjjga5Fd+JB/NvSf/BU1O4kySs3l1X+Ur+yyL67SvBtMKeyD+LUhrpn4T9odSnkbCfvcWRGoLqCc36rhmCaY0/pyWYFgAc+U56Oou8NGleuWDE5+j3GCSoXt9fKw468MGvc09B9T4n/dN/gmlpKzoIplVTqLLl2q+JnJwcpKWlYfny5fj1119rpIOXk5MDPz8/2NvbQ0FBAbt378bp06dx6tSp6jaNwWAwGIxq4WsovKhO2DodgJUrV8Lc3BwNGzas9Jy6qkIkEuHEiRPo1q0b2rZti2PHjuHgwYPo06dPdZvGYDAYDEY1wZZry4NF8lA0XUKemXBfEhUVFa45M4PBYDAYDEZFMCePwYu8QmF/7bTQES6HDgCOpKgKpqVRu+J+jHyInhUtqN7YLeaC6t1+IGye2quPLwTVW3FHWzCtlIr7qfMiX5hOThyxGcKmjGRlC5t6LXQm94/7hcsZ/KGtsN8pOooVTzrig9A5dM/OHRZUz8v/Z0H1qhpWXVs+zMljMBgMBoPxlfJtLrMKBXOBGQwGg8FgML5BmJNXBiKRCIcPH+Z1TmBgIGsszGAwGAzGF0Ik4H/fIjXeyROJROVu5RVMpKSkQCQSISoqShBbzp49C0dHR9StWxeqqqqwtLTE9OnT8fTpU0H0GQwGg8FgyA5z8sqnxjt5aWlp3Obr6wtNTU2JfXymTVQGf39/9OnTBw0bNsTBgwcRExMDPz8/ZGRkyDzSTF7y8vKqVJ/BYDAYjK8TsYDbt0eNf1YNGzbkNi0tLYhEIu52/fr1sXr1ajRu3BhKSkpo06YNTp48yZ1bPM7L2toaIpEIPXr0AABERkaib9++qFevHrS0tNC9e3fcunWrTBuePHkCDw8PeHh4YNu2bejRoweMjY3RrVs3bNmyBZ6enhL3Dw0NhYWFBdTV1dG/f3+kpaVxx2R5bJFIhE2bNsHJyQlqampYsmQJAMDb2xv169eHhoYG3N3dMWfOHLRp00bi3C1btsDCwgLKysowNzfH33//zR379OkTJk6cCD09PSgrK8PIyAjLli2T/c1gMBgMBoPx1VDjnbzyWLt2LXx8fLBq1SrcuXMH9vb2cHJyQkJCAgDg+vXrAIDTp08jLS0NISEhAIDMzEy4uLjg4sWLuHr1KszMzODo6IjMzEypj7N//358+vQJs2bNknq8ZB5eTk4OVq1aheDgYJw/fx6pqakS0UZZH3vhwoUYPHgw7t69C1dXV+zcuRNLlizBihUrcPPmTRgaGmLTpk0S5+zcuROenp5YsmQJYmNjsXTpUsyfPx9BQUEAgHXr1uHo0aPYt28fHjx4gJ07d8LY2Fj2F5zBYDAYjBpERSldfLZvka+6hcqqVaswe/ZsDB8+HACwYsUKnD17Fr6+vti4cSN0dXUBAHXr1kXDhg2583r16iWh888//0BbWxsREREYMGBAqcdJSEiApqYm9PQq7peUl5cHPz8/NG3aFAAwceJELFq0iPdjjxgxAmPHjuVur1+/Hm5ubtw+T09PhIWFISsri7vPggUL4OPjgyFDhgAoimTGxMTA398fLi4uSE1NhZmZGbp06QKRSAQjI6Nyn0tubi5ycyVnaObl5qG2Uu0KXwcGg8FgMKqeb9M5E4qvNpL3/v17PHv2DJ07d5bY37lzZ8TGxpZ77osXLzBu3DiYmZlBS0sLmpqayMrKQmpqqtT7E5HMXr6qqirn4AGAnp4eXr78/86rsj62ra2txO0HDx6gffv2EvtK3s7OzkZSUhLc3Nygrq7Obd7e3khKKpo8P2bMGERFRaF58+bw8PBAWFhYuc9l2bJl0NLSktj2bdwr0+vAYDAYDAajevmqI3ny4uLigjdv3mDt2rUwMjKCkpIS7Ozs8OmT9E7pzZo1Q0ZGBtLS0iqM5tWuLRnlEolEoBLt4WV9bDU1NV7PqTiit3nzZnTo0EHimIKCAgDAxsYGycnJ+O+//3D69GkMGzYMffr0wYEDB6Rqzp07F9OmTZPYd+blOV52MRgMBoNRVXyrVbFC8dVG8jQ1NaGvr49Lly5J7L906RIsLS0BAIqKRaOBCgoKSt3Hw8MDjo6OsLKygpKSEl6/fl3mYw0dOhSKiopYuXKl1OPp6eky2833sYtp3rw5IiMjJfaVvN2gQQPo6+vj4cOHMDU1ldiKC1CAotftp59+wubNm7F3714cPHgQb9++lfqYSkpK0NTUlNjYUi2DwWAwag6surY8vupI3syZM7FgwQI0bdoUbdq0QUBAAKKiorBz504AQP369aGiooKTJ0+icePGUFZWhpaWFszMzBAcHAxbW1u8f/8eM2fOhIqKSpmPY2BggDVr1mDixIl4//49Ro8eDWNjYzx58gTbt2+Hurq6zG1U+D52MZMmTcK4ceNga2uLTp06Ye/evbhz5w5MTEy4+3h5ecHDwwNaWlro378/cnNzcePGDbx79w7Tpk3D6tWroaenB2tra4jFYuzfvx8NGzZkDZwZDAaDwfgG+apdVw8PD0ybNg3Tp09Hy5YtcfLkSRw9ehRmZmYAgFq1amHdunXw9/eHvr4+Bg4cCADYunUr3r17BxsbG4waNQoeHh6oX79+uY81YcIEhIWF4enTpxg8eDDMzc3h7u4OTU1NXr365HlsAHB2dsbcuXMxY8YMbtl1zJgxUFZW5u7j7u6OLVu2ICAgAC1btkT37t0RGBjIRfI0NDSwcuVK2Nraol27dkhJScGJEycgFn/VHwMGg8Fg/I/CmiGXj4hKJowxvir69u2Lhg0bIjg4+Is95vHH/wmqd/+dsMHkfx+pCqY10iyr4jvx4K+R8YLqjd1iLqje1itKgupFjM4QVG/yVW3BtFJeVnwfPuQXVHwfPjSuL+wF58lLYb/mhb5qZGcJJ/hD23zBtABgcVsNQfUsxz4UVO/ZucOC6qU8+FlQvQYqToLqfc7HgiuCaSkr2AmmVVP4qpdr/5fIycmBn58f7O3toaCggN27d+P06dM4depUdZvGYDAYDEY18W1G4ISCOXlfCSKRCCdOnMCSJUvw8eNHNG/eHAcPHkSfPn2q2zQGg8FgMBg1EObkfSWoqKjg9OnT1W0Gg8FgMBg1BtHXXVpQ5TAnj8FgMBgMxlcKW64tD1Z4weDFySfCFl6cTVMUVO9X8xzBtGoJ/N2hp2ogqN6Jx2mC6mXnC/uEm2gIW43Qqo5wCfBZeemCaQFAXqGwr52OkqagerdeC1tEJBb4b6NzAzPBtCZcfiqYFgAYqglbyJGSJWxsxctG+sx1eTFuvltQvQ+pwup9Tm5BZMV3khElhXaCadUUWCSPwWAwGAzGV4msI0f/V2FOHoPBYDAYjK8U5uSVB8tYrEGIRCIcPny4us1gMBgMBoPxDcCcvC/I8+fPMWnSJJiYmEBJSQkGBgb4/vvvER4eXt2mMRgMBoPx1SGCWLDtW4Qt134hUlJS0LlzZ2hra+Ovv/5Cy5YtkZeXh9DQUPz++++Ii4urksf99OkTFBWFLW5gMBgMBqNmwJZry+PbdF1rIBMmTIBIJML169fxww8/oFmzZrCyssK0adNw9epV7n6vX7/G4MGDoaqqCjMzMxw9epQ7FhgYCG1tbQndw4cPSySeLly4EG3atMGWLVvQpEkTbratSCTCli1bytRmMBgMBoPxbcGcvC/A27dvcfLkSfz+++9QU1Mrdbyk4+bl5YVhw4bhzp07cHR0hLOzM96+fcvr8RITE3Hw4EGEhIQgKipKUG0Gg8FgMGoKIgH/+xZhTt4XIDExEUQEc/OKB8qPGTMGP//8M0xNTbF06VJkZWXh+vXrvB7v06dP2L59O6ytrdGqVSu5tXNzc/H+/XuJ7VNuHi9bGAwGg8GoKkQikWDbtwhz8r4AfPpNl3TK1NTUoKmpiZcvX/J6PCMjI+jq6lZae9myZdDS0pLY9m3cy8sWBoPBYDCqDrGA27fHt/msahhmZmYQiUQyFVfUrl1b4rZIJEJhYSEAQCwWl3IY8/JKR9akLQlXpC2NuXPnIiMjQ2Ib9vtPFT4HBoPBYDAY1Q9z8r4AderUgb29PTZu3Ijs7OxSx9PT02XS0dXVRWZmpoRGyZw7oVFSUoKmpqbEpqhUu+ITGQwGg8H4ArCcvPJhTt4XYuPGjSgoKED79u1x8OBBJCQkIDY2FuvWrYOdnZ1MGh06dICqqir++OMPJCUlYdeuXQgMDKxawxkMBoPBqLGIBNy+PZiT94UwMTHBrVu30LNnT0yfPh0tWrRA3759ER4ejk2bNsmkUadOHezYsQMnTpxAy5YtsXv3bixcuLBqDWcwGAwGg/FVIiI+VQGM/3lOPvlPUL2zacI2av7VPEcwrVoC/7DTUzUQVO/E4zRB9bLzhX3CTTQKBNVrVUdDMK2svHTBtAAgr1DY105HSVNQvVuvswTVEwv8t9G5gZlgWhMuPxVMCwAM1fIF1UvJEnYGgZdNpqB6xs13C6r3IVVYvc8hxAqmJYKFYFo1BTbxgsFgMBgMxlcKW5AsD/bqMBgMBoPBYHyDsEgeg8FgMBiMr5JvtSpWMIjBEJiPHz/SggUL6OPHjzVOrybbxvRqjhbT+7b1arJt/4t6jKqDFV4wBOf9+/fQ0tJCRkYGNDUrn0AupF5Nto3psfeW6X0ZvZps2/+iHqPqYDl5DAaDwWAwGN8gzMljMBgMBoPB+AZhTh6DwWAwGAzGNwhz8hiCo6SkhAULFkBJSanG6dVk25hezdFiet+2Xk227X9Rj1F1sMILBoPBYDAYjG8QFsljMBgMBoPB+AZhTh6DwWAwGAzGNwhz8hgMBoPBYDC+QZiTx2AwGAwGg/ENwpw8BoPBYDAYjG8Q5uQxKk1BQQHOnz+P9PT06jaF8T9GdnZ2dZvwxXB1dUVmZmap/dnZ2XB1da0GixhfE+z7+X8T5uQxKo2CggL69euHd+/eVbcpUsnLy4OrqyuSk5Or25QyuXDhAkaOHAk7Ozs8ffoUABAcHIyLFy9Ws2U1mwYNGsDV1bVKXiciQk3qMBUUFIQPHz6U2v/hwwds376dl9aMGTMQFxcnlGkStuTk5HC3Hz16BF9fX4SFhfHWYk6t/KxYsQJ79+7lbg8bNgx169ZFo0aNEB0dLZdmeno6wsLCsGPHDmzfvl1iY9RgiMEQgLZt29Lp06cF0zt9+jTNnTuX3NzcaOzYsRKbPGhqatLDhw8Fsy8nJ4eys7O52ykpKbRmzRoKDQ3lrXXgwAFSUVEhd3d3UlJSoqSkJCIiWr9+PTk4OMhlX15eHp06dYr8/Pzo/fv3RET09OlTyszM5K2VmppKjx8/5m5fu3aNJk+eTP7+/nLZRkT07t072rx5M82ZM4fevHlDREQ3b96kJ0+e8NI5dOgQDRw4kGrXrk1mZma0bNkyevr0qdx2ERFt2bKFrKysSFFRkRQVFcnKyoo2b95cKc3KkJGRQenp6SQSiSgxMZEyMjK47e3btxQUFER6enq8NE1NTUksFpOdnR1t3bqVsrKyBLG1b9++tGnTJiIqeo8bNGhAjRs3JmVlZfr77795aYnFYnrx4kWp/a9evSIFBQVB7K3pvHv3Tq7zjI2N6dKlS0REFBYWRtra2hQaGkpubm7Ut29f3npHjx4lDQ0NEolEpKWlRdra2tymo6Mjl42MLwNrhswQhJMnT2Lu3LlYvHgx2rZtCzU1NYnjmpqaMmt5eXlh0aJFsLW1hZ6eHkQikcTxQ4cO8bbPxcUFbdq0wdSpU3mfK41+/fphyJAh+O2335Ceng5zc3PUrl0br1+/xurVqzF+/HiZtaytrTF16lSMHj0aGhoaiI6OhomJCW7fvg0HBwc8f/6cl22PHj1C//79kZqaitzcXMTHx8PExASTJ09Gbm4u/Pz8eOl17doVv/zyC0aNGoXnz5+jefPmsLKyQkJCAiZNmgRPT09eenfu3EGfPn2gpaWFlJQUPHjwACYmJvjzzz+RmpoqV2Tg1atXCA4ORmBgIGJjY2Fvbw9XV1c4OTmhVq1aMut4enpi9erVmDRpEuzs7AAAV65cwYYNGzB16lQsWrSIt20FBQUIDAxEeHg4Xr58icLCQonjZ86cKfd8sVhc6m+gJCKRCF5eXpg3bx4vu86fP49t27bh4MGDAIAff/wR7u7u6NSpEy+dktSrVw8RERGwsrLCli1bsH79ety+fRsHDx6Ep6cnYmNjK9R4//49iAg6OjpISEiArq4ud6ygoADHjh3DnDlz8OzZM972TZs2Tep+kUgEZWVlmJqaYuDAgahTp06ZGkOGDJH58UJCQmS+74oVK2BsbIyffvoJQFH07eDBg2jYsCFOnDiB1q1by6yloqKC+Ph4GBgYYPLkyfj48SP8/f0RHx+PDh068F51adasGRwdHbF06VKoqqryOpdRzVSzk8n4RhCJRNwmFou5rfg2Hxo2bEjbt28X1L7FixeTtrY2/fDDD7R06VJau3atxMaXunXr0r1794iIaPPmzdSqVSsqKCigffv2kbm5OS8tFRUVSk5OJiIidXV1LpKXlJRESkpKvG0bOHAgjRw5knJzcyX0zp49S6amprz1tLW1KS4ujoiI1q5dS506dSIiotDQUGrSpAlvvd69e9PMmTOJSPL5Xrp0iYyMjHjrfc66detISUmJRCIR6erq0vz58yWiruVRr1492rVrV6n9u3btorp168plz++//05qamo0bNgwmjx5Mk2ZMkViq4hz587R2bNnSSQSUUhICJ07d47bLl++XOnIZVZWFm3dupW6dOlCIpGIzM3N6a+//qLnz5/z1lJRUaFHjx4REdGPP/5ICxcuJKKiaLCKiopMGp9/h3y+KSgokLe3N2/biIh69OhBmpqapKamRjY2NmRjY0Pq6uqkpaVFHTp04CJT9+/fL1NjzJgx3Obi4kKamppkYGBAgwcPpsGDB5OhoSFpamrSmDFjeNkmZPRNT0+P02rWrBnt27ePiIji4uJIQ0ODlxYRkaqqKvd3yvi6kP0nLoNRDmfPnhVM69OnT5WKJkhj69at0NbWxs2bN3Hz5k2JYyKRCB4eHrz0cnJyoKGhAQAICwvDkCFDIBaL0bFjRzx69IiXVsOGDZGYmAhjY2OJ/RcvXoSJiQkvLaAov+/y5ctQVFSU2G9sbMzl+/EhLy+Pm1F5+vRpODk5AQDMzc2RlpbGWy8yMhL+/v6l9jdq1Ih31LKYFy9eICgoCIGBgXj06BGGDh0KNzc3PHnyBCtWrMDVq1dlygvLy8uDra1tqf1t27ZFfn6+XLbt2bMH+/btg6Ojo1znd+/eHfn5+XBxcYGtrS0MDAzk0ikLNTU1uLq6wtXVFYmJiQgICMCyZcswb9485Obm8tIyNTXF4cOHMXjwYISGhnKR85cvX8oczT979iyICL169cLBgwclomqKioowMjKCvr4+L7uKKY7SBQQEcPZkZGTA3d0dXbp0wbhx4zBixAhMnToVoaGhUjUCAgK4f8+ePRvDhg2Dn58fFBQUABRFGydMmMBr9QIAnj9/zr23//77L4YNG4Z+/frB2NgYHTp04KU1ZMgQjBgxAmZmZnjz5g0cHBwAALdv34apqSkvLQCwt7fHjRs35Po+YlQz1e1lMhifM2vWLFq0aFF1m1EuLVu2pLVr11JqaippamrS5cuXiYjoxo0b1KBBA15aS5cuJUtLS7p69SppaGjQhQsXaMeOHaSrq0vr1q3jbZu2tjYXiSgZKbtw4QLVr1+ft1779u1p9uzZdP78eVJWVqaoqCgiIrpy5Qo1atSIt56uri7dunWrlH1hYWHUuHFjXloHDx6kAQMGUO3atal169a0fv36UnlMiYmJVLt2bZn0Jk6cSFOnTi21f/r06TRhwgRethWjp6dHDx48kOvckqirq3MR36ogKyuLtm3bRp07d+YienzZv38/1a5dm8RiMfXp04fbv3TpUurfvz8vrZSUFCosLORtQ3no6+tLjdLdu3eP9PX1iagoN1TWqG29evW4KHdJ4uLiqE6dOrxsEzL69unTJ/rrr7/Iw8OD+1sjIlq9erVc+aVbtmwhQ0NDWrBgAR04cICOHDkisTFqLszJYwjG+fPnydnZmezs7LgE+u3bt9OFCxd46Xh4eJC2tjZ169aNu+iW3CpDbm4uxcXFUV5eXqV0Sl7MSi6lyHMxKywsJG9vb1JTU+OWvJWVlenPP/+Uy7Zhw4bRuHHjiKjIMXj48CFlZmZSr169eC8hERUt82pra5NYLJYofJk7dy4NHjyYt56bmxsNGjSIPn36xNn36NEjsra2psmTJ/PS0tTUpF9++YWuX79e5n1ycnK4ZcOKmDhxImlqapKVlRW5ubmRm5sbtWjRgjQ1NUt9FmVl1apVNGHChEo7LE5OThQYGFgpDWlcuHCBxo4dSxoaGqSurk5jx46lixcvyq2XlpZGt27dooKCAm7ftWvXKDY2lpfOtm3bOEenJPv27ZP7dVBTU6OzZ8+W2n/27FlSV1cnoqI0CVmdKm1tbTp8+HCp/YcPHyZtbW1etv3+++9kZGREffr0obp163JFUrt37yZra2teWkJTMh3n841vOg7jy8IKLxiCcPDgQYwaNQrOzs4IDg5GTEwMTExMsGHDBpw4cQInTpyQWatnz55lHhOJRBUmqksjJycHkyZNQlBQEABwxQiTJk1Co0aNMGfOHN6az58/R1paGlq3bg2xuKgb0fXr16GlpYXmzZvLpFFQUIBLly6hVatWUFVVRWJiIrKysmBpaQl1dXXeNgHAkydPYG9vDyJCQkICbG1tkZCQgHr16uH8+fOoX78+b82CggK8f/8eOjo63L6UlBSoqqry1svIyMDQoUNx48YNZGZmQl9fH8+fP4ednR1OnDhRqminPHJycgRNBC/vs1cSPp/DwYMH4+zZs6hTpw6srKxQu3ZtieOyJuf7+fnBy8sLzs7OUoubipfRZSEtLY1b3o6Pj0fHjh3h6uqK4cOHy/25K0liYiKSkpLQrVs3qKiogIjKLR6RRrNmzeDv71/qPYmIiMAvv/yCBw8e8LbL2dkZV65cgY+PD9q1awegKH1gxowZ6NSpE4KDg7Fnzx6sWrUKN27cqFBv2rRp2L59O/744w+0b98eAHDt2jUsX74co0aNwurVq2W2LS8vD2vXrsXjx48xZswYWFtbAwDWrFkDDQ0NuLu783quwcHB8Pf3x8OHD3HlyhUYGRnB19cXTZo0wcCBA3lpMb5iqtfHZHwrtGnThoKCgohIcgnu1q1bvJcvqwIPDw9q27YtXbhwgdTU1Dj7Dh8+TG3atOGtN3bsWK41SUmysrJ4t3lRUlIStL0LUVELlR07dtDMmTNp/PjxtHnzZsrJyamUnlAtWYq5ePEibdy4kVasWEGnTp2SS6OsNhuvX7+uMRGGkon60jZZETKaoqCgQA0aNKDp06dTTEwM36dUJq9fv6ZevXpxNhX/nY0dO5amTZvGS0tJSUnq8nRycjIpKyvLZV9mZia5u7uToqIiV8ihqKhI48aN49rI3L59m27fvi2TXkFBAa1YsYL09fW590JfX59WrFhB+fn5ctkoBH///TfVq1ePvL29SUVFhXsfAgICqEePHry0Pn36RAoKCnT37t2qMJVRxTAnjyEIQleIEhElJCTQyZMnOeekMstdhoaGdOXKlVL2JSQkyFVtJmQPL6F7DApNSkoKmZubk6qqKikoKHCvnYeHB/3666/VaptIJJL6Pjx9+lRuR+B/AR8fn0o56GUxatQosre3p8ePH0v8nZ08eZIsLS15aRkYGEjN9zp8+LBcuaAlyczMpOjoaIqOjhbsdSjuXVgZtm/fTp07dyY9PT1KSUkhIqI1a9ZIXRIuDwsLCzp06BARSX7f3b17V64q8SZNmnC5uIyvC1ZdyxAEIStE37x5g2HDhuHs2bMQiURISEiAiYkJ3NzcoKOjAx8fH972vXr1SuqyYnZ2Nq9lpOIeXkSEzMxMKCsrc8cKCgpw4sQJ3suX3t7emDFjhiA9BgFg2bJl3CSIkmzbtg2vXr3C7NmzeelNnjwZtra2iI6ORt26dbn9gwcPxrhx43hpAYCHhwdMTU1LVTRv2LABiYmJ8PX1rVBj3bp1AIqWTbds2SKxxFg8Zs/c3Jy3bT179iz38yBPqkAxr1694pYYmzdvLtH/7Uszc+ZMjBw5UpCl2ZKEhYUhNDQUjRs3lthvZmbGu+r8559/hoeHBzQ0NNCtWzcARUu1kydPxvDhwytlp7q6Olq1alUpjZKUfG/Nzc1Rr1493hqbNm2Cp6cnpkyZgiVLlqCgoAAAoK2tDV9fX15LrMnJydxyb0mUlJTkGgU4b948/PHHHwgODi63hyCjBlLdXibj20DIClEhowHFdO3albOjONmfqCjR3t7eXmadqujhJWSPQSIiIyMjrkqvJFevXiVjY2PeenXq1OEqCEu+H8nJyTL3PiuJvr4+3bhxo9T+mzdvyhyhMTY2JmNjYxKJRGRgYMDdNjY2pmbNmlG/fv3o6tWrvG37vIfd77//Tp07dyYtLS3y8PDgrUf0/0v4CgoK3Ptcq1YtcnV1lbl/XzHnzp2jAQMGUNOmTalp06b0/fff0/nz53nbVFYEtLKoq6tTfHw89+/iz0pkZCTvatPc3FwaNmwYiUQiql27NtWuXZsUFBRo7NixlJubK5d9WVlZ9Oeff5KdnR01bdqUmjRpIrHJoyfUeytk9M3CwoKL/pXUWrdunVxFHG3atCF1dXVSUlKiZs2akbW1tcTGqLmwSB5DEObMmYPCwkL07t0bOTk56NatG5SUlDBjxgxMmjSJl5aQ0YBili5dCgcHB8TExCA/Px9r165FTEwMLl++jIiICJl1qqKHl5A9BoGighA9Pb1S+3V1deXqa1dYWMhFFUry5MkTrlcgH968eQMtLa1S+zU1NfH69WuZNIrnEPfs2RMhISESBSGVYc2aNVL3L1y4EFlZWXJpTps2DRERETh27Bg6d+4MoCjC7eHhgenTp2PTpk0y6ezYsQNjx47FkCFDuCjopUuX0Lt3bwQGBmLEiBG87OJbCCELXbt2xfbt27F48WLuMQoLC7Fy5UqZi1qKUVRUxN69e7F48WJER0dDRUUFLVu2hJGRkdz2ubu7IyIiAqNGjZI6TYcvQr23gLDRt2nTpuH333/Hx48fQUS4fv06du/ejWXLlmHLli28tABg0KBBvM9h1BCq28tkfFvk5ubS/fv36dq1a3LnuggZDShJYmIiubu7U7t27cjCwoKcnZ3pzp07cmmlpKRItIioSZiamlJwcHCp/du3b5crWiF0SxYrKytav359qf3r1q0jCwsL3npfgoSEBLlndNatW1dq244zZ85QvXr1ZNYxNzen1atXl9rv4+PDu6edSCSili1blorIVDZCc/fuXapfvz7179+fFBUVaejQoWRhYUENGjSgxMRE3npCo6WlVan2MJ8j1HtLJHz0bceOHWRqaspFGBs1akRbtmzhrcP4umGRPIagKCoqwtLSslIaQkYDStK0aVNs3ry5UrYVY2RkhPT0dFy/fl3qPNLRo0fLrHX+/PlyjxfnI8nKuHHjMGXKFOTl5aFXr14AgPDwcMyaNQvTp0/npQUAPj4+sLe3h6WlJT5+/IgRI0ZwLVl2797NW2/atGmYOHEiXr16JWGfj4+PTPl406ZNw+LFi6GmplbmLNJi+LSwKI8rV65I5F/yIScnBw0aNCi1v379+sjJyZFZ5+HDh/j+++9L7XdycsIff/zB2y57e3vBc/JatGiB+Ph4bNiwARoaGsjKysKQIUPw+++/S40uV8STJ09w9OhRpKam4tOnTxLH5HlvdXR0BM0pE+q9BYSPvjk7O8PZ2Rk5OTnIysqSq3US4+uH9cljyE1VDeq+d+8eevfuDRsbG5w5cwZOTk64f/8+3r59i0uXLqFp06bymIvCwkIkJiZKdcr4OlLHjh2Ds7MzsrKyoKmpKbHsIxKJ8PbtW5m1invslaSknrSl0vIgIsyZMwfr1q3jLozKysqYPXs2PD09eWkVk5+fjz179uDOnTvIysqCjY0NnJ2doaKiIpfepk2bsGTJEm7IvLGxMRYuXCiTc9yzZ08cOnQI2tra6NGjR5lLbvL0VPz8M01ESEtLw40bNzB//nwsWLCAlx4A9O7dG3Xr1sX27ds5R/HDhw9wcXHB27dvcfr0aZl0TE1NMXPmTPz6668S+/38/ODj44OEhASZbRKLxXj+/HmNvvCHh4fDyckJJiYmiIuLQ4sWLZCSkgIi4r4b+LJjxw4cOXIEQUFBgvRXFOq9LWbnzp1YuHAhkpKSAAD6+vrw8vKCm5tbpW2tDGKxuNylbb7fUYwvB3PyGHIzduxY7t9EhEOHDkFLS4ub/Xnz5k2kp6djyJAhEvMeZSEjIwMbNmxAdHQ051TIGw0AgKtXr2LEiBF49OgRPv/Ii0Qi3l9SzZo1g6OjI5YuXVrpi0VGRobE7by8PNy+fRvz58/HkiVL0Lt3b7l0s7KyEBsbCxUVFZiZmXHzZ2sSr169goqKiuARJXkp+ZkGii5uurq66NWrF/r16yeX5r1792Bvb4/c3Fy0bt0aABAdHQ1lZWWEhobCyspKJp1NmzZhypQpcHV15WY7X7p0CYGBgVi7dm0p5688FBQUkJaWJoiTd+fOHbRo0QJisRh37twp9758Klrbt28PBwcHeHl5QUNDA9HR0ahfvz6cnZ3Rv39/jB8/nret1tbWSEpKAhHB2Ni4VGPqW7du8dIT6r39nMpG36ytraU6ZSKRCMrKyjA1NcWYMWNkXhk5cuSIxO3i76igoKAa4YQyyoY5eQxBmD17Nt6+fVvmoO6//vqrWu1r06YNmjVrBi8vL6kJ19IKAcpDTU0Nd+/erdKB3REREZg2bRpu3rxZZY8hKwkJCTh79qzUKKi80cHKkpeXBxUVFURFRaFFixaCaL5//77MljWJiYlyDXcHii7aO3fuRFxcHADAwsJCrkjooUOH4OPjg9jYWE5n5syZvCcYCBnJK6lVHPGRdlnh+2NKQ0MDUVFRaNq0KXR0dHDx4kVYWVkhOjoaAwcOREpKCm9bvby8yj0uT6RWqPdWSObOnYtNmzahZcuW3CSOyMhI3LlzB2PGjEFMTAzCw8MREhJSqekXu3btwt69e0s5gYyaA3PyGIKgq6uLixcvlhrn9eDBA3Tq1Alv3ryRWcvExATdu3eHn5+fRPTp9evXaN++PR4+fMjbPjU1NURHR8t9kf6cIUOGYPjw4Rg2bJggetKIi4uDra2tTFWdQ4YMQWBgIDQ1NStcRuezdA4Amzdvxvjx41GvXj00bNiw1NK0LNEPGxsbhIeHQ0dHp8woQzF8oikmJiY4dOgQF0WpLF27dsXp06dLRT0fPHiA3r1748mTJ4I8TnXz6NEjGBoaClJhW1Kroup3PpWxDRs2xNmzZ2FhYQFLS0ssX74cTk5OiI6ORufOneWudq6pCBl9GzduHAwNDTF//nyJ/d7e3nj06BE2b96MBQsW4Pjx4zKNbyuLhw8folWrVt/ce/EtwQovGIKQn5+PuLi4Uk5eXFxcqchPRaSkpKBWrVro2rUrjh49ioYNGwIoigzK20KlQ4cOlYrEfM53332HmTNnIiYmBi1btiy17MNnjujnS1zFeWDLly9HmzZtZNLQ0tLiLhB8o5IV4e3tjSVLlvBuolySgQMHco7TwIEDBWvfIXSTVnV1dQwePBhHjx5FrVpFX4+xsbHo1asXL4f+6NGjcHBwQO3atXH06NFy78vnswIAN27c4CJ5lpaWaNu2La/zgf93tsoqXCnpWAwcOLDc17ak49agQQO5C1Q+p2PHjrh48SIsLCzg6OiI6dOn4+7duwgJCUHHjh0FeQwhECrK3b9//wqjb3369JEp+rZv3z6pKwDDhw9H27ZtsXnzZvz888+VKkz68OED1q1bh0aNGsmtwfgCfPF6XsY3ydSpU6lu3brk4+NDFy5coAsXLtCqVauoXr16NHXqVF5axTMvBw8eTPr6+nT9+nUiInr+/Lnc80hDQkLI0tKSAgIC6MaNG9xIo+KNL0LOES0+53MdOzs7io2N5W2b0GhoaHDtHGoaQjdpzcnJoU6dOtGwYcOosLCQawnC9zNcstmwUJ+Vx48fU5cuXUgkEpGOjg7p6OiQSCSizp070+PHj3nZV0yPHj1IU1OT1NTUyMbGhmxsbEhdXZ20tLSoQ4cOpK2tTTo6OnT//n2Z9DQ0NGj06NEUFhZW6RZDSUlJ3N9mVlYW/frrr9SyZUsaMmQIN/JLFnR0dOjVq1dERNzzKWvjyz///MPNAW7dujW1adOG2/h+/tzd3WnRokWl9i9evJjc3d2JiMjT05Patm1boVb9+vW5WeIlCQoKovr16xMR0f3792Vu8/L566atrU0KCgqkoaEhdfQco+bAlmsZglBYWIhVq1Zh7dq1XMNdPT09TJ48GdOnT+fy9GShZI7P3LlzsXbtWvzzzz/o27cv9PX15arkKquClYjkKrwQks+jk8XJ/vJGRLy9veHs7IwmTZoIYR7c3NzQrl07/Pbbb4Loubu7Y+TIkejRo0eltaoixyo9PR09evSAmZkZzp8/j9GjR1d7TilQFOlJT09HUFAQFzF/8OABxo4dC01NTZw8eZK3pq+vLy5cuICAgAAuFzEjIwPu7u7o0qULxo0bhxEjRuDDhw8IDQ2tUO/QoUPYtWsXjh8/Di0tLfz0008YOXIkV4wlKwUFBbh06RJatWoFbW1t3s+rJEFBQRg+fDiUlJQQFBRU7n1dXFx4aRsZGWHChAmVinIXo6WlhZs3b5ZabUhMTETbtm2RkZGBuLg4tGvXDpmZmeVqeXt7Y+nSpRg3bhzatWsHoCgquGXLFvzxxx+YN28e1qxZgxMnTuDUqVMV2vb561b8HdWhQwfBGpEzqohqdjIZ3yCVHdQtFoslRi4FBweTsrIyjR07Vu5IXkpKSrlbZfjw4UOlzheaVq1akVgsJjs7O9q4cSMXxZCXpUuXUr169cjFxYVWrVpFa9euldj44uTkREpKStS4cWOaMWNGtQ8+L/68ltzi4uLIwMCAxo8fL7FfHoKCgujjx4+l9ufm5kqNtpSFsrIy3bp1q9T+GzduyDVejqhoxJy0KN29e/dIX1+fiIrGzfEdq/X+/Xvatm0b9e3blxQUFMjMzIy8vLx4aSgpKXHjB2sqQka5hY6+7dixgzp27MhF3zp27Eg7d+7kjufk5NS47y6G8LBIHqPGIa3y78qVKxg8eDBevXolV9Tt/Pnz6NSpE5dnVUx+fj4uX77Mu09eQUEBli5dCj8/P7x48QLx8fEwMTHB/PnzYWxszLulQHh4OMLDw6Xm9Wzbto2XFgDcv38fO3fuxJ49e/DkyRP07dsXzs7OGDRoEO+WL+VFBEUikVyFMO/evcP+/fuxa9cuXLhwAebm5nB2dsaIESNgbGzMW68ylNUDrPirsbIR37Lalbx58wb169eXWbNZs2bYsWMHl69VzPXr1zFixAgkJibytk1dXR3//vtvqajquXPn8P333yMzMxMPHz5EmzZt8P79e976ABATEwNnZ2fcuXOH1+tna2uLFStWyN1CqBg+dpdVWV0WQka5hYq+5efnY+nSpXB1dS01GrIyCNX8nfFlYU4eQxBevHiBGTNmcI7K5x8rIZZDX7x4gbi4OHTv3p33uUJdaItZtGgRgoKCsGjRIowbNw737t2DiYkJ9u7dC19fX1y5ckVmLS8vLyxatAi2trZS27scOnSIl22fc+nSJezatQv79+/Hx48f5b5YVxVPnjzB7t27sW3bNiQkJCA/P1/mc4Vo0spndrE8nz2xWIwXL15AV1dXYn90dDR69uwpc+PsI0eOYOnSpdi4cSO3/Hnjxg1MmjQJs2fPlmu+qLOzM65cuQIfHx8Jx2LGjBno1KkTgoODsWfPHqxatYpXFebHjx9x9OhR7Nq1CydPnkSDBg3w888/Y/ny5TJrnDx5EnPnzsXixYvRtm1bqKmpSRyX1SGr6DMCgJcTv27dOu7f2dnZWL16Nb777jupBVjFM4ZlZefOndiwYQMePHgAAGjevDkmTZrEzSX+8OEDVxRTHurq6rh3755gP5iEbP7O+LIwJ48hCA4ODkhNTcXEiROlOiqV6cUkBGVdaOPj42Fra8vb8TE1NYW/vz969+7NNWot7sxvZ2eHd+/eyaylp6eHlStXYtSoUbxskJWoqCjs2LEDe/bswZs3b/Dhw4cqeRx5yMvLw/Hjx7Fjxw4cP34cderUwdOnT2U+vyY3aS1uiREdHQ0rKyuJKHJBQQGSk5PRv39/7Nu3TyY9HR0d5OTkID8/n9Mq/vfnDpCsF92srCxMnToV27dv55zrWrVqwcXFBWvWrIGamhqioqIAQKZK79DQUOzatQuHDx9GrVq1MHToUDg7O/OOlAOSebQlv0/4RlWFduJlzXXlE+UWOvo2cOBADBkyhHeOYVkI2fyd8WVhTh5DEDQ0NHDhwgWZW358TlX1USvuGXfkyBH0799fov9ZQUEB7ty5g+bNm/NOWldRUUFcXByMjIwknLyYmBi0b9+eV9+ounXr4vr163KPa5NGcnIydu3ahV27duHBgwfo3r07RowYgaFDh8rUYqWq58OePXsWu3btwsGDB1FYWIghQ4bA2dkZvXr1EqS9irxNWgMCAqCuro4ff/xRYv/+/fuRk5PD66JZXBTi5eWF6dOnS0z1UFRUhLGxMX744QcoKirKpFdR0UBJ+F7cs7KyOIfExMRE7gkkqqqqGDBgAJydneHo6FgqssWHipwzeaKqNRkho29+fn7w8vKCs7Oz1Cgo37Y9X6L5O6NqYH3yGIJgYGAgtcu9rFRVH7Vih4aIoKGhIdGFXlFRER07dsS4ceN461paWuLChQulmrseOHAA1tbWvLTc3d2xa9euUo1L5aVjx46IjIxEq1atMHbsWPz888+8e1ndvn0beXl53L+FpFGjRnj79i369++Pf/75B99//73gI9c6duyIX375hfd5y5Ytg7+/f6n99evXxy+//MLLeSqu7DU2NuaqOyuDUFEZaairq/MaOVYWL168gIaGhgAWFUXMDAwMSn0XEBEeP34ss05Fo9ZKwvc1WLRoEWbMmFEquvXhwwf89ddfvPrk9e7dGxEREYI4eRMmTAAg/QeYPLml9vb2uHHjBnPyvkJYJI8hCGFhYfDx8YG/v/8XT5yvCCKCq6sr1q9fL9iM1CNHjsDFxQVz587FokWL4OXlhQcPHmD79u34999/0bdvX5m1Jk+ejO3bt6NVq1Zo1apVqegH30jZvHnz4OzsDEtLS17nfSk2b96MH3/8sdKtMcriw4cPmDt3Lv777z8ut0lWlJWVERcXV+oznJKSAgsLC7mWuiMjI1FYWIgOHTpI7L927RoUFBR4tRcpKCjAoUOHJJohDxw4sFRBkaxkZ2dj+fLlZRb9yFNUU1BQgMOHD5eykU8bJUC4PNryRq2VRB7nR8hcX6Gjb5WlZBPvV69eYdGiRRg7dmylm78zvizMyWMIQsl8IVVV1VJfAnwSc01MTBAZGYm6detK7E9PT4eNjQ3vC09hYSGUlZVx//59mJmZ8Tq3PC5cuIBFixYhOjoaWVlZsLGxgaenJ+9B9uWNKRKJRDhz5ozMWnl5eTA3N8e///4LCwsLXnbwJS4uDk5OToiPj5f5HKHnzero6JTK18rMzISqqip27NjB++JjaGiIDRs2lDrvyJEj+P333+Uaa9a+fXvMmjULQ4cOldgfEhKCFStW4Nq1azLp3L9/H05OTnj+/DnXJy8+Ph66uro4duyYXK/nzz//jIiICIwaNUpqLu3kyZN56SUmJsLR0RFPnz6V6OVnYGCA48eP80pJKCuP9tGjR7C0tER2drZMOnym5PAZu1aejWfOnMFPP/2EV69e8dIqi+ro5VmePSWp7j6jjPJhy7UMQfD19RVMKyUlReqXRm5urlwXWbFYDDMzM7x580ZQJ69r164yNRKtiLNnzwpgTRG1a9fGx48fBdMrj9zcXCQlJfE6p3bt2jA0NBTsorBmzRoJx6SyTVp//vlneHh4QENDgysWiIiIwOTJkzF8+HC5bIyJiYGNjU2p/dbW1oiJiZFZx93dHVZWVrhx4wb33N69e4cxY8bgl19+weXLl3nb9t9//+H48ePo3Lkz73Ol4eHhgaZNm+Lq1avcKLQ3b95g5MiR8PDwwPHjxyvUKM4BFYlEmD9/vsRSaEFBAa5du8Yr95ev4yYLxT8uRCIRmjVrJvEZLCgoQFZWFu+2KnzHP1ZEdnY2IiIikJqaik+fPkkck6XqV2h7GNUDi+QxagzFywODBg1CUFCQRIFAQUEBwsPDcerUKd5LcEBRC4CVK1di06ZNgkSQqoLExEQkJSWhW7duUFFR4aoI+bJ06VLEx8djy5Ytci/jyUJ0dDRsbGx4O2xbt25FSEiIYPNmheTTp08YNWoU9u/fz712hYWFGD16NPz8/GQukihJ3bp18e+//8LOzk5i/+XLl/Hdd9/JXImtoqKCGzduwMrKSmL/vXv30K5dO7mWkps0aYITJ04IFvVVU1PD1atX0bJlS4n90dHR6Ny5s0wFScWR7YiICNjZ2Um85sUFKzNmzJD5B1tVzBEOCgri0kB8fX0lvquKbfz8/f6S3L59G46OjsjJyUF2djbq1KmD169fQ1VVFfXr15d5NeTMmTOYOHEirl69WqplTUZGBjp16gQ/Pz907dq1Kp4GQwCYk8cQjKSkJAQEBCApKQlr165F/fr18d9//8HQ0LDUhUkaxcsD0vJnateuDWNjY/j4+GDAgAG8bSu5nKyoqChRgAHItpz8+dJgefBZnn7z5g2GDRuGs2fPQiQSISEhASYmJnB1dYWOjg58fHxk1gKAwYMHIzw8HOrq6mjZsmWp3J6QkBBeemUhr5NnbW2NxMRE5OXlwcjIqJR9fKqnhayGLUl8fDyio6OhoqKCli1bVioa9PPPPyMtLQ1HjhzhnIH09HQMGjQI9evXl7mFSuvWrbFmzRr06tVLYv+ZM2cwefJk3L17l7dtO3bswJEjRxAUFCRIa4w6derg33//RadOnST2X7p0Cd9//z2vv4uxY8di7dq1vBsUf07J5upCL4lGRESgc+fOgv2Yqmz0rZgePXqgWbNm8PPzg5aWFqKjo1G7dm2MHDkSkydP5roOVISTkxN69uyJqVOnSj2+bt06nD17ttK9PBlVB3PyGIIQEREBBwcHdO7cGefPn0dsbCxMTEywfPly3LhxAwcOHJBZq0mTJoiMjES9evUEs0+ImZUlNd68eQNvb2/Y29tzv9ivXLmC0NBQzJ8/v8wvRWmMHj0aL1++xJYtW2BhYcG1YwkNDcW0adNw//59mbWAootjeQQEBPDSKwt5nTwh5802a9YM/v7+pfIaIyIi8Msvv8gV9RWap0+folu3bnjz5g1XeR0VFYUGDRrg1KlTMDAwkEnnxIkTmDVrFhYuXIiOHTsCAK5evYpFixZh+fLl6NKlC3dfWR0ja2trJCUlgYhgbGxcKpeWj8MNFH2Wb926ha1bt3KTOa5du4Zx48ahbdu2CAwM5KX3NVDZH7fFCBV9AwBtbW1cu3YNzZs3h7a2Nq5cuQILCwtcu3YNLi4uiIuLk0nHyMgIJ0+eLDPSGxcXh379+iE1NVVm2xhfFubkMQTBzs4OP/74I6ZNmybRN+769esYMmSIXLl0NZkffvgBPXv2xMSJEyX2b9iwAadPn8bhw4dl1mrYsCFCQ0PRunVridfu4cOHaNWqFa+ee0JSUeQyPz8f2dnZ1Zp0XRXVsE+ePMHRo0elRlPk6QkIFEVodu7cyUUHW7VqhZ9//plXHzlpzYFLjl4rvs0nIiWkww0URShdXFxw7Ngx7rnl5+fDyckJgYGBMvVoLKYqKn+FRsgft0JF3wBAV1cXly9fhpmZGZo1a4b169fD3t4ecXFxaNu2rcxFK8rKyrh37x5MTU2lHk9MTETLli1rVIN1hiSs8IIhCHfv3sWuXbtK7a9fvz5ev35d4fnr1q3DL7/8AmVlZYmxQdLgOyrocz5+/Fjq4s13SSg0NBQrVqwotb9///6YM2cOL63s7GypS2Vv376Vu7dafn4+zp07h6SkJIwYMQIaGhp49uwZNDU1ZW4jI2Qxzeekp6fjwIEDSEpKwsyZM1GnTh3cunULDRo04NXTr379+rhz504pJy86OrpUdbYshIeHw8nJiZte0qJFC6SkpICIpBZPyIqamppcfftKImSBTjF8nbiK0NbWxpEjR5CQkMBFiywsLMp0EsrD3d293MpfvmRmZiI+Ph7NmzeHuro6bt26BV9fX3z48AGDBg2Cs7Mzb805c+bA29ub+3FbTK9evbBhwwZeWlFRUfD394dYLIaCggJyc3NhYmKClStXwsXFhZeTZ21tjcjISJiZmaF79+7w9PTE69evERwczCsnuVGjRuU6eXfu3IGenp7MeoxqgBgMAWjUqBFdunSJiIjU1dUpKSmJiIhCQkLIxMSkwvONjY3p9evX3L/L2po0aSKXfVlZWfT777+Trq4uicXiUhtfDA0NadWqVaX2r1q1igwNDXlpOTg40J9//klERa/dw4cPqaCggH788Uf64YcfeNuWkpJC5ubmpKqqSgoKCtx74eHhQb/++itvPaGJjo4mXV1dMjU1pVq1anH2zZs3j0aNGsVLa9asWWRkZERnzpyh/Px8ys/Pp/DwcDIyMqLp06fztq1du3bk6elJRP//Oc7MzCQnJyf6+++/eesVs337durcuTPp6elRSkoKERGtXr2aDh8+LLfmt46WlhZdvHhREK2IiAjS0NAgkUhEderUodDQUNLQ0CBzc3OysrIisVhM//zzD29dNTU1evjwIRFJfu8lJyeTkpISL6169epRfHw8ERGZmZnRyZMniYgoNjaWVFVVeWlFRkbSmTNniIjoxYsXZG9vTxoaGmRjY0NRUVEy60ycOJFatGhBHz58KHUsJyeHWrRoQZMmTeJlG+PLwiJ5DEEYPnw4Zs+ejf3790MkEqGwsBCXLl3CjBkzMHr06ArPT05OlvpvoZg1axbOnj2LTZs2YdSoUdi4cSOePn0Kf39/XkPTi/Hy8oK7uzvOnTvHNbm9du0aTp48ic2bN/PSWrlyJXr37o0bN27g06dPmDVrFu7fv4+3b9/i0qVLvG2bPHkybG1tS0WzBg8eLNd0D0C4yBtQ1CJjzJgxWLlypUT0w9HRkRvELiuLFy9GSkoKevfuXaoadunSpby0ACA2Nha7d+8GUDTD9cOHD1BXV8eiRYswcOBAjB8/nrfmpk2b4OnpiSlTpsDb25tbStXR0YGvr6/Mc53Pnz9f7nFZ58PWqVMH8fHxqFevXoVL8rIWSixatEim+/GZAKGjoyNY9fWff/6JH3/8EYsWLcK2bdvw008/YeLEidxnxNvbGxs3buT996GtrY20tLRS82xv377N++9CqOgbAIkG2/Xr1+c9trGYP//8EyEhIWjWrBkmTpzI9T6Mi4vDxo0bUVBQgHnz5smlzfhCVLeXyfg2yM3NJXd3d6pVqxaJRCKqXbs2iUQiGjlyJOXn58usk5GRQQUFBaX2FxQUUEZGhtz2GRgY0NmzZ4mISENDgxISEoioKMLi4OAgl+bVq1dpxIgRZG1tTdbW1jRixAi6evUqb51Hjx7RmzdvyNvbm3788UdycHCgefPm0bNnz+jRo0e89erUqUNxcXFEVDq6oKKiwltPyMgbEZGmpiYlJiaWsi8lJYV39KOYBw8e0L59++jYsWNcpEweGjRoQDExMUREZGFhQUeOHCEioqioKFJTU5NL08LCgg4dOkREks/37t27VLduXZl1RCJRqU2eaHRgYCB9/PiR+3d5Gx/bGjVqRNbW1tSmTRupm7W1tcx6RETBwcE0dOhQys7O5nWeNLS0tCg2NpaIir6rxGKxREQrISGB1NXVeetOnz6dunTpQmlpadz3ysWLF8nExIQWLlzIS0uo6JvQpKSkkIODA4nFYonPnYODAxfFZNRcmJPHEJTU1FQ6fvw47d27l1t6kJWQkBAyMzOT+qWelZVFzZo1o6NHj8pll5qaGucwNWrUiK5du0ZERA8fPpT74i0UYrGYXrx4UWr/69ev5VpK1tbWpvv37xORpFNx4cIFql+/Pm+93r1708yZM0vpXbp0iYyMjHjr6erq0q1bt0rphYWFUePGjXnrERVduOPi4igvL0+u84sZOHAgt2w3ffp0MjU1JW9vb7KxsaHevXvLpamsrMw5niWfb3x8PCkrK8usk56eLrG9evWKwsLCqEOHDnT69Gm5bBMKR0dHUlZWpoEDB9KRI0ek/lDjS5s2bUhDQ4PU1dWpRYsW3I+p4o0PIpFI4m+s5PtARPT8+XO5/tak/bgVi8W8f9wKzfPnz2nkyJGkp6dHCgoKlU5PISJ6+/YtXb9+na5du0Zv374V2GJGVcGWaxmCUNylviRXr16FSCSCsrIyTE1NMXDgwHKXXzZt2oRZs2ZJLUJQU1PD7NmzsWHDBnz//fe87TMxMUFycjIMDQ1hbm6Offv2oX379jh27JjcM1QLCwuRmJgotfJP1qUzAGXO1MzKyoKysjJvu/r16wdfX1/8888/AIoqL7OysrBgwQI4Ojry1ouMjIS/v3+p/Y0aNcLz58956zk5OWHRokVcfziRSITU1FTMnj0bP/zwAy+tnJwcTJo0iWtvEx8fDxMTE0yaNAmNGjXiXQSzevVqrprZy8sLWVlZ2Lt3L8zMzOSurG3SpAmioqJK9dorrzWFNKRVpvbt2xeKioqYNm0abt68KZd9APDy5Uupn+NWrVrJdP7x48fx7NkzBAUFYebMmfj1118xevRouLq6ckt8fBk0aJBc50mjeDpFWbflRVFREZs3b8b8+fNx7949ZGVlwdraWtDJOvIwZswYpKamYv78+YIUrQBFy+ft2rUTwDrGF6W6vUzGt0GPHj1IU1OT1NTUyMbGhmxsbEhdXZ20tLSoQ4cOpK2tTTo6OlyESRp6enrcMqo0EhISSE9PTy77Vq9eTWvXriUiolOnTpGysjIpKSmRWCwmX19f3npXrlyhJk2aSCxhlFzKkIWpU6fS1KlTSSwW06+//srdnjp1Knl4eFCHDh2oU6dOvG17/PgxWVpakoWFBdWqVYs6duxIdevWpebNm0uNGFaE0JG39PR06tOnD2lra5OCggIZGBhQ7dq1qVu3bpSVlcVLy8PDg9q2bUsXLlwgNTU1zrbDhw9TmzZteNtWFWzevJkaNWpEe/bsITU1Ndq9ezd5e3tz/64ssbGxckejb9y4wRUeyPs5lkZERASNGTOGNDQ0qFOnTpSTkyO3lhCIRCJq2bIlFwVUUFAgKysr7nbLli0r9XyFQMjom7q6Ot2+fbtqDGV8VbBIHkMQiqN0AQEBXDuSjIwMuLu7o0uXLhg3bhxGjBiBqVOnIjQ0VKrGu3fvkJ+fX+Zj5OXlyTwCqpjCwkL89ddfOHr0KD59+oRnz55hwYIFiIuLw82bN2FqaipztKIkv/32G2xtbXH8+HG5fynfvn0bQFEk7+7du6XGN7Vu3RozZszgrdu4cWNER0dj7969iI6ORlZWFtzc3ODs7Fxq0ocsCBl5A4oiUqdOncKlS5c4+2xsbNCnTx/eWocPH8bevXvRsWNHiffAysqK91xdaTx8+BAfPnyAhYWFzAPbP8fd3R0qKir4888/kZOTgxEjRkBfXx9r167lNQ/3zp07EreJCGlpaVi+fDmvWa4lcXV1RbNmzbB161Y0aNBAkIgPALRr1w4pKSmIiYnB7du3kZeXJ9dnDwBu3ryJ2NhYAEXva3FDaT583ipGWrELn89yVRSaCBl9MzAwKHOFgPE/RjU7mYxvBH19falRunv37pG+vj4REd28ebPcRHNzc3MKDg4u8/j27dupefPmvOxatGgRicVi6tevHw0cOJCUlZVp7NixvDSkoaqqWm7UkQ9jxoypVFFJVSNk5E1oVFRUuOhdyShjVFQUaWpqyqzz6dMn8vT0pAEDBpC3tzfl5+fT8OHDuSiKhYUFJScnV9re7OxsuaKpRMRF1z6PuNnZ2XFFBXxRV1cX7HNMRHT58mVyd3cnTU1NsrW1pY0bN9K7d+/k0nrx4gX17NmTRCIR6ejokI6ODolEIurVqxe9fPlSMJvloSoKTYSMvoWGhlK/fv0E+cwyvm6Yk8cQBDU1Na56tSRnz57lqtaSkpJIQ0OjTI0//viDDA0N6fnz56WOpaWlkaGhIf3xxx+87DI1NSU/Pz/u9qlTp0hRUbHSieE9e/ak//77r1IaQvPgwQOuoKSY06dPU48ePahdu3a0ZMmSSulfuHCBNm7cSCtWrKBTp07xPv/y5ct07NgxiX1BQUFkbGxMurq6NG7cOK7qU1a6du1K69atI6L/7zFIVNTfy97eXmadadOmka6uLrm7u5OJiQk5OTlR8+bNac+ePbRv3z5q2bIljRgxgpdtxfTs2VOqo5ORkUE9e/aUWSclJUViS01Nldq/jA8DBw6kAwcOVEqDiGjFihVkYWFBurq6NGXKFIqOjq605rBhw8jW1pardiYiun//Ptna2tLw4cPl0szJyZEo7EpJSaE1a9ZQaGgoL52qKDSxsLDg0iLkoTglpnhTVFQksVhM6urqEvt1dHQqbSvj64GNNWMIgrOzM65cuQIfHx8uOTcyMhIzZsxAp06dEBwcjD179mDVqlW4ceOGVI3MzEzY2dkhNTUVI0eOlOjJtHPnThgYGODq1asSvdUqQklJCYmJiRLzQZWVlZGYmIjGjRvL/XwPHTqEP//8EzNnzkTLli1LjaeSZwm4sgwePBgtW7bklpKSk5NhZWWFrl27wtzcHNu2bcPixYsxZcqUL24bADg4OKBHjx6YPXs2gKIpKTY2NhgzZgwsLCzw119/4ddff8XChQtl1rx48SIcHBwwcuRIBAYG4tdff0VMTAwuX76MiIgItG3bViYdIyMjbNq0CY6OjoiPj4e5uTmOHz8OBwcHAEXjq5ydneUazycWi/H8+XPUr19fYv/Lly/RqFEj5OXl8dYUitevX8PFxQXt27dHixYtSn2OnZycZNIRi8UwNDTEgAEDJNIOPodP8YqWlhZOnz5dKtn/+vXr6NevH9LT02XWKqZfv34YMmQIfvvtN6Snp6N58+ZQVFTE69evsXr1al59EIsLTQIDA/H+/ftKF5qEhYXBx8cH/v7+pSa4yEJF87lLIsusbsa3AXPyGIKQlZWFqVOnYvv27VxeXa1ateDi4oI1a9ZATU0NUVFRAFBu/lBGRgbmzp2LvXv3cvl32traGD58OJYsWQIdHR1edikoKOD58+fQ1dXl9mloaODOnTulGpjyQVp+lkgk4j07VEgMDAywb98+2NnZAShq8HrgwAHudd+6dSvWr1/P3S6PikbLlUTWMXN6eno4duwY16h13rx5iIiIwMWLFwEA+/fvx4IFCxATEyPzYwNFA+KXL18ukd83e/ZstGzZUmaN2rVrIyUlhWtgq6Kigjt37nBVkmlpaTAwMCg3Z/RzinPo2rRpgzNnzkhUlhcUFODkyZPw9/dHSkpKuTpXrlzBmzdvMGDAAG7f9u3bsWDBAmRnZ2PQoEFYv369XCPwjh07hlGjRuH9+/eljvH5HPfo0aPCHDKRSIQzZ87IbJuGhgYuXLhQ6vvi9u3b6N69u1SbK6JevXqIiIiAlZUVtmzZgvXr1+P27ds4ePAgPD09udw/vpw/fx4BAQE4ePAgWrZsidOnT8uUg/h5M+rs7Gzk5+dDVVW1lMMta2NqBqMkzMljCEpWVhY3ONzExETmOaklSU1Nha6uLrKyskBE0NXVlTsJWSwWw8HBQeICeOzYMfTq1QtqamrcvpCQEF66jx49Kvf45+0yvgQqKiqIj4/nopa9e/dGp06dsHjxYgBFzlDbtm1lioDI6gCLRCKZB8UrKysjISGBs69Lly5wcHDgOuanpKSgZcuWyMzMlElPSD6PtmloaCA6OhomJiYAgBcvXkBfX5+X8y4Wi7nPrbSvWRUVFaxfvx6urq7l6lRFBLQYY2NjDBgwAPPnz0eDBg14n1+VDBw4EOnp6di9ezf09fUBAE+fPoWzszN0dHRw6NAh3pqqqqqIi4uDoaEhhg0bBisrKyxYsACPHz9G8+bNkZOTI5etHz58wP79+7Fx40bcvXsXz58/l2kettDRt2fPnmH16tXw9PQs9fgZGRnw9vbGjBkzatx7zag6WHUtQ1DU1dUrtVRZWFgIU1NT3L9/X5BeU9K+GEeOHFlp3epw4iqiTp06XMSpsLAQN27ckOhf+OnTJ5kr7qpitFyDBg2QnJwMAwMDfPr0Cbdu3YKXlxd3PDMzs1T0oizy8/NRUFAg4by/ePECfn5+yM7OhpOTE7p06cLLvtDQUK4XXWFhIcLDw3Hv3j0AkGtpMDk5GUQEExMTXL9+XSKarKioiPr160NBQaFCnaioKM5RB4A9e/agQ4cO3Pg8AwMDLFiwQC4n782bN5g6dWqNvOhv2LABTk5OMDY25n4YPH78GC1atMCOHTvk0jQ1NcXhw4cxePBghIaGYurUqQCKls5lcco+58qVK9i2bRv27duHZs2aYezYsRgxYoTMWkIvm65evRrv37+X+vhaWlrIzMzE6tWrsWLFCkEfl1FzYU4eo0YhFothZmaGN2/eCOLkBQQECGCVdIKDg+Hn54fk5GRcuXIFRkZG8PX1RZMmTWSeRyokPXr0wOLFi/H3339j//79KCwsRI8ePbjjMTExcuX6lKTYSZQnsuro6Ig5c+ZgxYoVOHz4MFRVVdG1a1fu+J07d9C0aVOZtMaNGwdFRUWuSXNmZibatWuHjx8/Qk9PD2vWrMGRI0d4NX/+/IL766+/Stzm+5yLfwh83mCYL+/evZNwwiIiIrhcQaCoXcnjx4/l0h4yZAjOnj0r8+teEdKaogPg1RS9GAMDA9y6dQunT59GXFwcAMDCwkKuVjvFeHp6cq2cevfuzaU2hIWF8WrNsnLlSgQGBuL169dwdnbGhQsX5P5xK2T07eTJk/Dz8yvz+OjRozFu3Djm5P0vUT31HgxG2Rw9epS6dOlCd+/erW5TyuTvv/+mevXqkbe3t0Qbj4CAAOrRo0e12JScnEympqYkEomoVq1a9Pfff0scHzhwIE2ZMkUu7S1btpCVlRUpKiqSoqIiWVlZ0ebNm3lpvHr1irp27UoikYg0NDQoJCRE4nivXr1krp42MzOTqIjcsGED6evrU3p6OhERzZo1q9reB2kkJibSxIkTqXfv3tS7d2+aNGkSN7+3IgwNDSkiIoKIisZoqaioSIwxu3PnjtwVk97e3lSvXj1ycXGhVatW0dq1ayU2vgjRFD08PJwsLCykthVKT08nS0tLOn/+PG/biklLS6Nbt25JVMReu3aNVxsakUhERkZG9Pvvv0s0Mf98k4Xp06fTuHHjyjz+66+/0qxZs2TSUlVVLXfe9aNHj0hVVVUmLca3AXPyGDUObW1trvxfWVm5Rpb/CzV0Xmjy8vIoKiqKnj59WupYVFQUvX79mrfm/PnzSU1NjebMmUNHjhyhI0eO0Jw5c0hdXZ3mz5/PWy89PV3qXM83b97Qp0+fZNJQVVWVGI4+ePBgmjRpEnf7/v37pKury9u2quDkyZOkqKhI7du35y7+7du3JyUlJQoLC6vw/N9++43s7Ozo/PnzNG3aNKpbty7l5uZyx3fs2EG2trZy2WZsbFzm1qRJE956a9asoSFDhkg4aOnp6TR06FDy9fWl7OxsGjhwIPXr169Mje+//55Wr15d5vG1a9fSoEGDeNsmjYyMDDp06JBEmxZZ6N69O/Xo0aPCTRasrKzowoULZR6/dOkSWVpayqRVt25d7geBNCIiIqr1+4nx5WFOHqPGERgYWO5WExBq6HxVkpubS3FxcZSXl1cpnXr16tGuXbtK7d+1a5dcF4yxY8fS+/fvS+3PysqSuVF1nTp1JKJBenp6tGPHDu52UlISqaio8LaNqKjpdqdOnUhPT497j1evXk2HDx+WS69NmzY0e/bsUvtnz54tU8NcISOgVY0QTdENDQ3LdbpiY2PJwMBALvt+/PFHWr9+PREV9cwzMzOj2rVrU61atQTpFygPQkbfHB0dyd3dvczjbm5u5ODgwNtGxtcLc/IYDDmwsLDgLvolnbx169bx7nQvNNnZ2eTq6koKCgqkoKDA2TZx4kRatmwZbz0tLS2Kj48vtf/BgwekpaXFW08sFkud+vDq1StSUFCQSaNXr140Z84cIiI6f/48icVievbsGXc8LCyMmjZtytu2qliGV1JSKvP1U1JSklmnvAhoychedSJEU3QlJaUKZ1jL+0OqQYMGFBUVRUREO3fuJFNTU8rOzqa///5brlnHXl5eEs2Vi8nJySEvLy+ZNISMvp05c4YUFBRo+vTpEk3lnz9/TtOmTSMFBQUKDw+XSYvxbcCcPEaNJD8/nw4cOECLFy+mxYsXU0hIiNQLXHVR1UPnK4OHhwe1bduWLly4QGpqapyjcvjwYbkuZBMnTpSaXzR9+nSaMGGCzDoZGRmUnp5OIpGIEhMTKSMjg9vevn1LQUFBpKenJ5PWuXPnSEVFhUxMTEhFRYVcXV0ljo8fP55Gjx4ts23FVMUyfOPGjWnfvn2l9u/du1fuiJSQPH78mDZu3EizZ8+WK6esJCNGjKAmTZpQSEgIPX78mB4/fkwhISFkYmJCI0eOJCKi3bt3U9u2bcvUMDEx4d4DaRw8eFCupWSiogh8amoqERGNGjWKi7A+evSI1NTUeOuV9YPl9evXJBaLZdIQOvrm5+dHSkpKJBaLuRxIsVhMSkpKpfJ0Gd8+zMlj1DgSEhLIzMyMVFVVydramqytrUlVVZWaN28uc7L6l2DHjh1coUPxLMstW7ZUt1lkaGhIV65cISJJRyUhIaHcCEpZTJw4kTQ1NcnKyorc3NzIzc2NWrRoQZqampwDKItTUDx7taxNQUGBvL29ZbYrJiaGfH19ac+ePaXGSvn7+8s1B7QqluG9vLxIW1ubli9fTufPn6fz58/TsmXLSFtbmxYtWiSzTlZWFv35559kZ2dHTZs2pSZNmkhs8nD69GlSVVWlFi1aUK1atahNmzakra1NWlpavEauFZOZmUnu7u5cTq1YLCZFRUUaN24cN+f49u3b5b43EydOpBYtWkgd2ZaTk0MtWrSQyL/kg5mZGe3du5eysrJIV1eXi2pFRUXJ5cSLRCKpc3TDw8OpXr16MmlURfTtyZMntHr1apowYQKNHz+e1qxZQ48fP+alwfg2YM2QGTUOR0dHEBF27tzJtVp48+YNRo4cCbFYjOPHj1ezhZLk5OQgKyur1Niq6kJVVRX37t2DiYmJRFPf6OhodOvWDRkZGbz0evbsKdP9KppoEBERASJCr169cPDgQYk2GoqKijAyMuKa3lYXlpaWWLZsGQYOHCjx2q1fvx4BAQG4desWb00igq+vL3x8fPDs2TMAgL6+PmbOnAkPDw+ZW7P8/PPPiIiIwKhRo6Cnp1fqvMmTJ/O2rX379nBwcICXlxf3fOvXrw9nZ2f079+f15ivklSmKfqLFy9gY2MDBQUFTJw4UWK84caNG1FQUIBbt27J1dvv77//xuTJk6Gurg5DQ0Pcvn0bYrEY69evR0hICM6ePSuTTvGkioyMDGhqakq8FwUFBcjKysJvv/2GjRs3yqTn7++PyZMnIy8vj9PLyMhA7dq1sWbNGrnfBwaDOXmMGoeamhquXr1aaixVdHQ0OnfujKysrGqyrDQvX77EgwcPAADm5uYSDW+ri27duuHHH3/EpEmTJEa4TZo0CQkJCTh58mS12vfo0SMYGhrKPcXkc4TsV7hlyxYsXLgQPj4+cHNzw5YtW5CUlIRly5Zhy5YtGD58OC+9/Px87Nq1C/b29mjQoAE3zYPP/OVitLW1cfz4cXTu3Jn3uWWhoaGBqKgoNG3aFDo6Orh48SKsrKwQHR2NgQMHVjhyrap49OgRxo8fj9DQUInejPb29ti4cWOlRhLeuHEDjx8/Rt++fTnn8/jx49DW1pb5tQ0KCgIRwdXVFb6+vlwTbaDoB4uxsTHXg09Wnjx5gv379yMxMRFEhGbNmmHo0KEyz9g+evSozI8l60xixjdAdYUQGYyy0NHRoUuXLpXaf/HixRrTQuX9+/c0cuRIUlBQ4JZra9WqRc7OzlyvturiwoULpK6uTr/99hspKyvT5MmTqW/fvqSmpkY3btyoVtuKOX/+PDk7O5OdnR09efKEiIqqWstrJSGNqiiUEHoZXkVFhVsCrgzGxsa8W31URIMGDThNCwsLOnLkCBEVLV/Kk6Mm9JLy27dv6fr163Tt2jV6+/Yt7/PLQqjK83Pnzsnc9qciipez5aX481rRJmuuIOPbgEXyGDWO0aNH49atW9i6dSvat28PALh27RrGjRuHtm3bIjAwsHoNBPDTTz/h9u3bWL9+PfeL/cqVK5g8eTLatGmDPXv2VKt9SUlJWL58OaKjo5GVlQUbGxvMnj27VHS0LIYMGYLAwEBoampiyJAh5d6X79zfgwcPYtSoUXB2dkZwcDBiYmJgYmKCDRs24MSJEzhx4oTMWpaWlli6dCkGDRoksbx679499OjRA69fv+ZlW0mEWobv0aMHpkyZgkGDBlVKZ8eOHThy5AiCgoKgqqpaKa1iBg0ahO+++w7jxo3DjBkzcOTIEYwZMwYhISHQ0dHB6dOneelVxZKykOTk5GDSpEnczNj4+HiYmJhg0qRJaNSoEebMmcNbs7CwEImJiXj58mWp6SbdunWTWUddXR3Dhg2Dq6sr75F8DEZZsLFmjBrHunXr4OLiAjs7O26WaX5+PpycnLB27dpqtq6If//9F6GhoRJfxvb29ti8eTP69+9fjZYV0bRpU262qTxoaWlxF+jPc44qi7e3N/z8/DB69GgJZ7hz587w9vbmpZWcnCx1HJWSkhKys7MrZaeqqqogztSECRMwffp0PHnyBG3btoWamprE8fLGYVlbW0u89omJiWjQoAGMjY1LzfmVJ19w9erVXPqDl5cXsrKysHfvXpiZmWH16tW89f777z/Bl5SFZO7cuYiOjsa5c+ck/k779OmDhQsX8nbyrl69ihEjRuDRo0el5kKLRCIUFBTIrLVjxw4EBgaiV69eMDY2hqurK0aPHl3teaqMrxvm5DFqHNra2jhy5AgSEhIkZlaamppWs2X/T926dSXycIrR0tKCjo5ONVgkSWWjCyVn/godOX3w4IFUG7S0tJCens5Lq0mTJoiKiuLmxBZz8uRJWFhY8LbtxYsXmDFjBsLDw/Hy5ctSF24+F+1iivP4PDw8uH0ikQhEVKEjUNnoX0WYmJhw/1ZTUyt37qks6OjoyDSXtro4fPgw9u7di44dO0o4z1ZWVkhKSuKt99tvv8HW1hbHjx+XGrnkw6BBgzBo0CC8evUKwcHBCAwMxPz582Fvbw9XV1c4OTmhVi3ZL9nZ2dmIiIhAamoqPn36JHGs5GeR8Y1TrYvFDMZXir+/P/Xp04fS0tK4fWlpadSvXz/y8/OrRsuIrly5Qk2aNCGxWCxIPk7Pnj3p3bt3pfZnZGTI1WajSZMmdOrUKSKSbFMSFBREFhYWvLSE7lfYv39/srS0pL///psOHTpEhw8fltjkISUlpdytunn37h1t3ryZ5syZQ2/evCGioqkUxbmSfAgODqahQ4dKbRBcEyiZt1nysxcVFUWampq89VRVVctt3FxZ1q1bR0pKSiQSiUhXV5fmz58v02t769YtatiwIWlqapKCggLp6uqSSCQiNTU1udvtML5OWCSPUSOYNm0aFi9eDDU1NUybNq3c+8qzjCQ0mzZtQmJiIgwNDWFoaAgASE1NhZKSEl69egV/f3/uvvIso1UGIaMLAHDu3LlSkQAA+PjxIy5cuMBbb9y4cZg8eTK2bdsGkUiEZ8+e4cqVK5gxYwbmz5/PS8vd3R0qKir4888/kZOTgxEjRkBfXx9r167lXQkLABcvXsSFCxfQpk0b3udK4/3794iPj8enT5/Qvn37SlVfR0ZGorCwEB06dJDYf+3aNSgoKMDW1pa35p07d9CnTx9oaWkhJSUF48aNQ506dRASEoLU1FRs376dl56Pjw+SkpIEXVIWkuK/i0mTJgEA97exZcsW3tWwANChQwckJiYKusrw4sULBAUFITAwEI8ePcLQoUPh5uaGJ0+eYMWKFbh69SrCwsLK1Zg6dSq+//57+Pn5QUtLC1evXkXt2rUxcuTIas+LZHxZmJPHqBHcvn0beXl53L/LQsjcsMpQ1ctolSEhIQEHDhyo9IXnzp073L9jYmLw/Plz7nZBQQFOnjyJRo0a8dadM2cOCgsL0bt3b+Tk5KBbt25QUlLCjBkzuIsvH5ydneHs7CxIoYSBgUGpJVp5iYqKgqOjI168eAEigoaGBvbt2wd7e3u59H7//XfMmjWrlJP39OlTrFixAteuXeOtOW3aNIwZMwYrV66UaOvi6OiIESNG8NaryX8XALB06VI4ODggJiYG+fn5WLt2LWJiYnD58mVERETw1ps0aRKmT5+O58+fo2XLlqWc2vLyLT8nJCQEAQEBCA0NhaWlJSZMmICRI0dCW1ubu0+nTp1kSkOIioqCv78/xGIxFBQUkJubCxMTE6xcuRIuLi4VFlMxviGqO5TIYDCEpWfPnvTff/9VWqfkhApprRhUVVVp69atcuvn5ubS/fv36dq1a5SZmVlpe4UgNDSU+vXrR8nJyZXW6tevH3Xq1IkuX75Mt27dosGDB5OpqanceiVH1JXk4cOH3FxYvmhqanJTZEouX6akpPCaq/s1kZiYSO7u7tSuXTuysLAgZ2dnunPnjlxaZbUokSc1QlNTk3755Re6fv16mffJycmhhQsXVqhVr149bl6ymZkZnTx5koiIYmNjSVVVlZddjK8bFsljMOQkPT0dBw4cQFJSEmbOnIk6depwnfjliXAJhVDRheTkZBARTExMcP36dYmlRkVFRdSvXx8KCgpy26moqAhLS0u5zweEL5T46aefkJOTg6ZNm0JVVbXUa/f27VuZtW7evImwsDDY2NgAALZt24Y6derg/fv30NTU5GUXUFQx/OLFC4liCQBIS0vjlZD/ueb79+9L7Y+Pj68Rjb2rgspWnpckOTlZEB2g6H2sqJpbRUUFCxYsqFDL2toakZGRMDMzQ/fu3eHp6YnXr18jODgYLVq0EMpkxlcA65PHqHFkZ2dj+fLl3IX78+rQ4nFJ1cnnuUwPHjyAiYkJ/vzzT7lymYRELBaX2idrNeeXQMj318HBAampqZg4caLU/EO+Ey+K+6eVhYuLi8xaYrEYz58/l1g+LjmBhC8/1lpFIQAAIFBJREFU//wz0tLScOTIEa6yOz09HYMGDUL9+vWxb98+3pru7u548+YN9u3bhzp16uDOnTtQUFDAoEGD0K1bN/j6+laoUadOHcTHx6NevXrcuK+y4OMkC4U0J7Ys5HG+K0NV2Xbjxg1kZmaiZ8+eePnyJUaPHo3Lly/DzMwMW7duFSznlFHzYU4eo8ZR0xuqAkV9tWxsbLhcpuImvJcvX8aIESOqbRwUUDQSqjw+bzcijaockSTk+6uhoSFooYSQiMVinDlzRqKlSKdOnbBv3z6JUVWyRlafPn2Kbt264c2bN1xvwKioKDRo0ACnTp2CgYEBbxszMjIwdOhQzinQ19fH8+fPYWdnhxMnTpTq6SeNoKAgDB8+HEpKSoI6yUIhFosrzOWtzA+gyozVq2rbGAzm5DFqHFUxo1NotLS0cOvWLTRt2lTCyXv06BGaN2+Ojx8/VreJlUJaNFAa8lx8hHx/LS0tsXPnTqkNkWWl5PJpRZEVPtGU4gu4tK9YeSOr2dnZ2LlzJ6Kjo6GiooJWrVrh559/LrWszJdLly5JTEfp06dPpfRqEnwKKrp3785Le9OmTfD09MSUKVOwZMkS3Lt3DyYmJggMDERQUBDOnj1bLbb16tULISEhEkUbQNHne9CgQThz5ozMWoyvG5aTx6hx1PSGqkDNz2VKSkqCr68vYmNjARQ5Q5MnT0bTpk1lOv/zJVQhEfL99fX1xZw5c+Dv7w9jY2O57UlLS0P9+vWhra0tNbIij0MmZL5WMWpqavjll18E0crLy4OKigqioqLQuXNnQX9UvXz5UupSPJ9qU6Eodo7y8/OxdOlSuLq6SkRSK8P69euxefNmDBo0CMuXL+f229raYsaMGTLbJjRCtz1ifL0wJ49R41i8eDE8PT0FndEpNE5OTli0aBGXByUSiZCamorZs2fjhx9+qFbbQkND4eTkhDZt2nAX7kuXLsHKygrHjh1D3759q9U+Id9fIQolSi6pVhR54YMsy+J8CQ4Ohr+/Px4+fMgtDa5ZswYmJia88w9r164NQ0NDQZcBb968CRcXF8TGxlZ6zJfQ1KpVC3/99RdGjx4tmKbQY/XS09Nx/fp1qQ6yLHZXVdsjxtcLW65l1Disra2RlJQEIqqRDVWB/89lioyMRFZWlly5TFWFtbU17O3tJSILQFF/urCwMN6v36JFi8o97unpyds+od7fmpgDJo0LFy7A398fSUlJOHDgABo1aoTg4GA0adJE5mH0JZcGvb29cf/+fV5Lg9LYunUrQkJCEBwcLEh0tXXr1mjatClmz56NBg0alIqKVoXjy4eBAwdiyJAhgn0uLC0tsWzZMgwcOFAibWP9+vUICAjg9Vk+duwYnJ2dkZWVVWpetEgkkukHS8kcP2mXdhUVFaxfvx6urq4y28X4umGRPEaNo6Y3VAWKcvJOnTpVI3OZYmNjpVZaurq6ylQt+TmHDh2SuJ2Xl4fk5GTUqlULTZs25e3kCfn+CnGxLhn9qAh5lhsPHjyIUaNGwdnZGbdv30Zubi6Aoh8KS5cuxYkTJ2TSqezSoDQ2bNiAxMRE6Ovrw8jIqNSPE74/CB4+fIiDBw/WqDnTJXFwcMCcOXNw9+5dtG3bttTz5VtENG3aNPz+++/4+PEjiAjXr1/H7t27sWzZMmzZsoWX1vTp0+Hq6oqlS5fKHeGu6rZHjK8P5uQxahyy9IGqTgoLCxEYGIiQkBCkpKRAJBKhSZMmaNiwIZe7VZ3o6uoiKioKZmZmEvujoqLkmgYhbQLJ+/fvMWbMGAwePJiXVn5+PkQiUaXyooQulGjTpk2ZBRIlkXe50dvbG35+fhg9ejT27NnD7e/cuTO8vb1l1hF6aRAoimwJ+Xnt3bs3oqOja6yTN2HCBADSRyPK8/4KOVbv6dOn8PDwqFQKQ3GktCpzahlfF8zJY9RIamqjYSKCk5MTTpw4gdatW6Nly5YgIsTGxmLMmDEICQnB4cOHq80+oGg27C+//IKHDx+iU6dOAIpy8lasWFHhXGBZ0dTUhJeXF77//nuMGjVK5vOEyIsSulCiKgokSvLgwQN069at1H4tLS2kp6fLrNOkSRNERUWVWvI8efKkTKOupLFw4UK5ziuLLVu2wMXFBffu3UOLFi1KLcXzjZQJTVU4P0KN1bO3t8eNGzdKNbuWl8oWXzG+DZiTx6hxCD00XUgCAwNx/vx5hIeHo2fPnhLHzpw5g0GDBmH79u2CJnfzZf78+dDQ0ICPjw/mzp0LANDX18fChQvh4eEh2ONkZGQgIyOD93m9evVCRESE3NWwQhdKVHWeWMOGDZGYmFjq+V68eFGmC/qiRYswY8YMQZcGizExMUFkZCTq1q0rsT89PR02Nja8G49fuXIFly5dwn///VfqWHUXXnzOx48foaysLJieqqoq7yhcyX6U3333HWbOnImYmBipk2r4OMg1vfiK8eVghReMGkdNbjTcr18/9OrVC3PmzJF6fOnSpYiIiEBoaOgXtkw6mZmZACAxfJ4v69atk7hNREhLS0NwcDC6d++OXbt28dLz8/ODl5cXnJ2dBcmLEpqEhAScPXtWaoUj3/xDAFi2bBl27NiBbdu2oW/fvjhx4gQePXqEqVOnYv78+Zg0aVK55ysoKHCRy507d2LhwoVISkoCUOS8e3l5wc3NjbddgPSpHEDRuDgDAwOpbTjKw9jYGAMGDMD8+fPRoEEDuWyqSgoKCrB06VL4+fnhxYsXiI+Ph4mJCebPnw9jY2Per+ObN2/g6elZ5uelomKJqupHKXTxFePrhTl5jBpHTW403LBhQ5w8ebLMCQu3b9+Gg4ODROuCr53PR3CJxWLo6uqiV69emDt3Lm8HsrwLmywXs6oslNi8eTPGjx+PevXqoWHDhqUqHOW5OBIRli5dimXLliEnJwdAUR7djBkzsHjx4grPl+aIVXZpsDiCNGjQIAQFBXFj0oAiRyg8PBynTp3CgwcPeOlqaGggKiqqxi4JLlq0CEFBQVi0aBHGjRvHNS/eu3cvfH19ceXKFV56jo6OSExMhJubm9Rq4uqq7lZWVsbdu3dL5eXGx8ejVatWX32zdgYPiMGoYejq6tKtW7eIiEhdXZ2SkpKIiCgsLIwaN25cnaZR7dq16dmzZ2Uef/r0KSkqKn5Bi0rz/PlzGjlyJOnp6ZGCggKJxWKJ7WtHJBKRWCwmkUhU7ibPczU0NKTly5dXgdVEubm5dP/+fbp27RplZmbKfJ5IJKKXL18KakvJ1+jz101RUZGaNWtGx44d4607evRo2rx5s6C2CknTpk3p9OnTRCT53RIbG0va2tq89dTV1SkqKkoQ24KCgujjx4+l9ufm5lJQUBAvrcaNG9O+fftK7d+7dy8ZGBjIbSPj64Pl5DFqHDW50XBBQQFq1Sr7z0ZBQQH5+flf0KLSjBkzBqmpqZg/f77U2bCyImsvrW3btsmlD8iXF1WVhRLv3r3Djz/+KKhmRkYGCgoKUKdOHVhaWnL73759i1q1aslUAdysWbMK30dZ+qgVU7ys2KRJE0RGRqJevXoyn1sezZo1w9y5c3Hx4kWpeWVC5oTKw9OnT6VW/hYWFiIvL4+3nrm5OT58+CCEaRg7diz69+9fKjqbmZmJsWPHypTnW5y/+SWKrxhfB2y5llHjkDY0PS0tDXZ2dvjvv/+qtdGwWCyGg4MDlJSUpB7Pzc3FyZMnqzXBXENDAxcuXChzSVlWxGIxjIyMYG1tXW57kc/76FWE0HlRQuLm5oZ27drht99+E0zTwcEB33//Pde+oxg/Pz8cPXq0wj55YrEYvr6+Ekuq0uCzNHjlyhW8efMGAwYM4PZt374dCxYsQHZ2NgYNGoT169eX+Tkvi8+X9ksiEol4F3IITdu2bTF16lSMHDlSIhVk0aJFOHXqFO+RX5GRkZgzZw48PT2lVhPznXX84sWLUmMRo6Oj0bNnT5mc+OL8TV1dXfj6+sLHxwfPnj0DUJS/OXPmTHh4eFR7myfGl4NF8hg1juJGwxcvXsSdO3eQlZWFtm3bonfv3tVtmkwX0uqsrAUAAwODCnu+ycL48eOxe/duJCcnY+zYsRg5cqQgUxGWLFmCoKAgrFy5EuPGjeP2t2jRAr6+vrydPCELJUxNTTF//nxcuXIFrVq1EiQSde3aNal92Xr06IF58+bJpDF8+HC58++k4eXlhZ49e3JO3t27d+Hm5oYxY8bAwsICf/31F1eRzYeqbkdTWTw9PeHi4oKnT5+isLAQISEhePDgAbZv345///2Xt562tjbev3+PXr16SewnHi18rK2tIRKJIBKJ0Lt3b4mVgoKCAiQnJ6N///4y2VP8dy8SiTB16lRMnTpVkOIrxtcLi+QxagzSogtBQUFYsGABcnJy5I4u/K8RFhYGHx8f+Pv7y92mpJjc3FyEhIRg27ZtuHz5Mr777ju4ubmhX79+ckcDTE1N4e/vj969e0tEU+Li4mBnZ4d3797JrCV0oURVRKLU1NRw9epVtGzZUmL/3bt30aFDB64YoyxKVtcKhZ6eHo4dOwZbW1sAwLx58xAREYGLFy8CAPbv348FCxYgJiZGsMesKVy4cAGLFi2SmFTj6emJfv368dZq3749atWqhcmTJ0stvOjevXuFGl5eXtz/p0+fDnV1de6YoqIijI2N8cMPP0BRUbFCrbKigYz/XZiTx6gxODg4oEePHpg9ezYAcKOHXFxcuOjCr7/+KngD128BHR0diQtMdnY28vPzoaqqWioaxSd3qySPHj1CYGAgtm/fjvz8fNy/f1/igiQrKioqiIuLg5GRkYSTFxMTg/bt2yMrK0tmLSMjI0yYMIH7zAjF69evAUCQXLWePXuiRYsWWL9+vcT+33//HXfu3KlwibCsNieVQVlZGQkJCTAwMAAAdOnSBQ4ODlxkMSUlBS1btuSiQHx48uQJjh49itTU1FItWKRFNL9mVFVVcfv2bTRv3rzSWkFBQfjpp58q1btPLBZDS0tL0PxNxtcNW65l1BiioqIkWkrs2bMH7du3x+bNmwEULUMuWLCAOXlSkGcmLV+Kh58TUaVyDi0tLXHhwoVSTYgPHDggdWxXeQhZKJGeno558+Zh7969XDRRR0cHw4cPx5IlSyrMiSsLb29v9OnTB9HR0VzKQXh4OCIjIxEWFlbh+VUxpaFBgwZITk7meuHdunWLiygBRcn+n/84kIXw8HA4OTlxkdkWLVogJSUFRAQbGxshn0KluHHjhsQkiLZt28qlY2tri8ePHwvi5BWngnz69Elq6oGhoaFMOl5eXnJ/VhnfINVT1MtglEZJSYlSU1O52507dyZvb2/udnJyMqmrq1eHaV8F+fn5tHz5curUqRPZ2trS7NmzKScnp1KaHz9+pF27dlGfPn1IWVmZhg4dSsePH6eCggK5NQ8fPkxaWlq0fPlyUlVVpb/++ovc3d1JUVGRwsLCeGm5urrSpk2b5LalmDdv3lCzZs1ITU2NfvnlF1qzZg2tWbOGxo0bR2pqamRubk5v376VW//27ds0YsQIsrS0pLZt29LYsWMpPj6+0nbLy2+//UZ2dnZ0/vx5mjZtGtWtW5dyc3O54zt27CBbW1veuu3atSNPT08i+v8WJZmZmeTk5ER///23YPbLy+PHj6lLly4kEolIR0eHdHR0SCQSUefOnenx48e89fbt20eWlpYUEBBAN27coOjoaImND/Hx8dSlS5dSLY/4tAMSiUT04sUL3s+D8e3CnDxGjcHQ0JAiIiKIqKg3lIqKCtfTiojozp07pKOjU13m1XgWLVpEYrGY+vXrRwMHDiRlZWUaO3as3Hrjx48nHR0datWqFfn6+tKrV68Es/X8+fPUp08f0tXVJRUVFercuTOFhoby1lm6dCnVq1ePRo8eTatWraK1a9dKbLIyefJkatGiBT1//rzUsbS0NGrZsiVNmTKFt301lVevXlHXrl1JJBKRhoYGhYSESBzv1asX/fHHH7x11dXVKTExkYiItLW16d69e0REFBUVRUZGRpW2u7LY29tThw4dKC4ujtsXFxdHdnZ2ZG9vz1uvrP6M8vRp7NSpE3Xr1o1OnDhBt2/fpqioKIlNFsRiMXPyGBKwnDxGjWH8+PGIjo7GihUrcPjwYQQFBeHZs2dcwvHOnTvh6+uLyMjIara0ZmJmZoYZM2bg119/BQCcPn0a3333HT58+CDz+KSSiMViGBoactV/ZRESEiK3zZVFqEIJY2Nj+Pv7w97eXurxkydP4rfffpN5pN779++59hnv378v97582mwITUZGBtTV1aGgoCCx/+3bt1BXV5cp2b8kDRs2xNmzZ2FhYQFLS0ssX74cTk5OiI6ORufOnXnlW1YFKioquHz5cqm0gJs3b6Jr164VFsF8zqNHj8o9zmcuspqaGm7evAlzc3NeNpSkKvI3GV83LCePUWNYvHgxhgwZgu7du0NdXR1BQUESF5lt27bJVQH3v0JqaiocHR2523369IFIJMKzZ8/QuHFj3nqjR4+ukn5aJiYmiIyMRN26dSX2p6enw8bGhlcFa3HLjsoWSqSlpcHKyqrM4y1atOA1qk5HR4eriNXW1pb6OhKPNhtVRVm5W/K2yunYsSMuXrwICwsLODo6Yvr06bh79y5CQkLQsWPHypgqCAYGBlKbHhcUFEBfX5+3Xr169QTr22lpacl9juWlKvI3GV83zMlj1Bjq1auH8+fPlxld2L9/v1zVnP8r5Ofnl6rMq127tlyd/AEgMDBQAKtKk5KSItWxyc3NxdOnT2XWEbJQol69ekhJSSnTGU5OTubl+Jw5c4a7/5kzZ/5nms+uXr2ai9Z5eXkhKysLe/fuhZmZWY2orP3rr78wadIkbNy4kWsfc+PGDUyePBmrVq3irdegQQMMGzYMrq6u6NKlS6VsW7FiBWbNmoWlS5dKnRZSnRFfxtcLW65lML4RpE3jOHbsGHr16iURbaiu5dWjR48CAAYNGoSgoCAJJ6ygoADh4eE4deoUHjx4UKHW27dvYWdnh6dPn8LZ2RkWFhYAgJiYGOzatQsGBga4fPkydHR0ZLLN1dUVSUlJOHXqVKklytzcXNjb28PExKRSI9wY1Y+Ojg5ycnKQn5/PNR0u/vfnETlZ2owcPnwYgYGBOHHiBIyNjeHq6orRo0fLFRUsTqn4/AdBTYj4Mr5emJPHYHwjjB07Vqb7BQQEVLEl0il5Efv8a6d27dowNjaGj4+PRDPsspgyZQrCw8Nx+vRpNGjQQOLY8+fP0a9fP/Tu3Rtr1qyRybYnT57A1tYWSkpK+P3332Fubg4iQmxsLP7++2/k5ubixo0bXF85PpiZmcHZ2RnOzs4wMzPjff7XRnp6Og4cOICkpCTMnDkTderUwa1bt9CgQQM0atSoWm0LCgqS+b58xsS9evUKwcHBCAwMRGxsLOzt7eHq6gonJ6dyZ12XJCIiotzjsjRWZjBKUV0VHwwG438TY2PjSlfqGhkZ0cmTJ8s8/t9///Gu5nz48CH179+fq44srpC0t7enhIQEuW1dvXo12draklgsJltbW/L19aW0tDS59Woy0dHRpKurS6amplSrVi1KSkoiIqJ58+bRqFGjqtm6L8O6detISUmJRCIR6erq0vz58yk7O7u6zWL8j8IieQwG46tDSUkJSUlJZebQPXnyBKampvj48SNv7Xfv3iEhIQFA0Qg2Ieb1AkB8fDx27tzJzQPu2bMnRo4cWe2zjoWkT58+sLGxwcqVKyWmmVy+fBkjRoyQuTr5S/Dx48dSEznkzXt78eIFgoKCEBgYiEePHmHw4MFwc3PDkydPsGLFCujr68vU+Do9PR1bt27lGjVbWVnB1dWVNTdmyA1z8hgMxhcnPDwc4eHhUjv7y5L31qhRI+zdu7fMZPcLFy7gp59+wrNnzwSxV2iuXr2K8ePH486dO99UrpWWlhZu3bqFpk2bSjh5jx49QvPmzeVyuoUkOzsbs2fPxr59+/DmzZtSx/m+FyEhIQgICEBoaCgsLS3h7u6OkSNHQltbm7tPUlISLCwsSjmUn3Pjxg3Y29tDRUUF7du3BwBERkbiw4cPCAsLq1ETQxhfD/ybZzEYDEYl8PLyQr9+/RAeHo7Xr1/j3bt3Epss2NvbY968eVIvnLm5uZg/fz769+8vtOmV5vr165gyZQoGDx6M+Ph4wUay1RSUlJSk9gWMj4+Hrq5uNVgkyaxZs3DmzBls2rQJSkpK2LJlC7y8vKCvr4/t27fz1hs7diz09fVx6dIlREVFYeLEiRIOHgDo6+tzM4HLY+rUqXByckJKSgpCQkIQEhKC5ORkDBgwAFOmTOFtG4MBsEgeg8H4wujp6WHlypUYNWqU3BpVWSghNJ8v0/bq1QvOzs4YMmTIN9cSyN3dHW/evPm/9u42pMn2DQP4MfcoKFOiJ1JMZ5t+MChNS3uhMp2Fr2UilGjmSyAqJASSRaEhgmEW1IretUkpQssULMmSQPODpTlJEKqZVkiUJlnhfNnzQRr5X/XXvLfZdvw+ufuWayd+Oryv8z4v1NTUYPHixdBoNBCLxYiLi8OWLVvMcsby70ilUqhUKmzduhUuLi7o6OiAj48PKisrUVVVhYaGhjmt9/XrVzg5OQlSm6OjIzo7O42GIff09GDt2rVzHtRMBHBOHhGZmU6nw8aNG+e1hoeHB9ra2pCdnY3Dhw8b3tYViUTYtm0blErlggh4AODr64ugoCDk5ORgz549Rm8DW5OysjIkJCRg6dKl+PbtG0JCQjA4OIgNGzaguLjY0uVhaGgIcrkcwHT/3fcxKZs2bUJWVtac1/sx4M23x8/FxQX9/f1GIW9gYADOzs5zro0IYMgjIjPbv38/bt68iWPHjs1rHZlMhrt375rsRQmh9Pb22sToFGC6J+/+/ftobW1FV1cXRkdHERgYiPDwcEuXBmD6tBWtVgupVApfX1/U1NQgODgY9fX1RtussyFkj9/u3buRkZGBkydPGv4Jam1tRV5eHhITE+dcGxHA7VoiMrPc3FyoVCr4+fnBz8/PaLL/QjgZQWgLeXacUMbHx+Ho6Ihnz55h5cqVli7np06fPg2xWIwDBw6gqakJsbGx0Ov1GB8fx6lTp5Cbmzun9XJyctDc3IyioiLs3bsX586dw9u3b3Hx4kWUlJQgKSlp1mvpdDrk5eXhwoULmJiYgF6vh4ODA7KyslBSUjJjyDnRbDHkEZFZhYaG/vZ+c3OzmSoxD41GA4VCgUWLFqGvrw+9vb2Qy+U4evQo+vv7/6jhf6GSy+W4ffs2/P39LV3KDFNTUygtLUVdXR10Oh0UCgUKCgrw/v17PH36FD4+PvDz85vzukL3+AHTfX4vX74EAHh7ewvW80e2iSGPiMiEFAoF1qxZ81fMjpuvq1evQq1Wo7KyckFtmxcVFaGwsBDh4eFwdHREY2MjEhMT531MnUQiQU9PD6RSKTw8PKBWqxEcHAytVotVq1YZzvH9nfT09Fl9F4/Uoz/BnjwiMov4+Pj/+zsikQi3bt0yQzXm8+TJE1y6dMno+rJlyzA4OGiBikxHqVTixYsXcHd3h5eXl9F5sB0dHRapS6VS4fz588jMzAQANDU1ITo6GleuXDEct/cnhOjxq6iogJeXFwICAoyO+yOaL4Y8IjILW53av9Bnxwlp586dEIlEli7DSH9/P6Kiogyfw8PDIRKJ8O7du1+emjIbaWlp6OrqQkhICPLz8xEbGwulUmno8ZuNrKwsw3idtLQ0JCcnL6inoPR343YtEZEJLfTZcbZALBZjcHBwRqh2dnaGRqOBTCab83pC9/iNjY1BrVbj2rVrePz4MaKjo5GRkYHt27cvyNBMfw+GPCIiExoZGUFCQgLa29sxOjoKd3d3w+y4hoYGoy3Nv5lcLkd7ezv+/fffGdc/ffqEwMBAvHr1yiJ12dnZITIycsYbqvX19QgLC5vx91er1bNaz1Q9fgDw+vVrVFRUQKVSYWJiAs+fP7e6odlkPtyuJSIyoYU+O05IfX19P50NNzY2hjdv3ligomn79u0zupacnPzH65mqxw+YDqQikQh6vd6qzjUmy2DIIyIykampKVRUVECtVqOvrw8ikQgymQxubm7Q6/VWsxVXV1dn+LmxsXFG/+Xk5CQePHjwR9uiQikvLxd0PaF7/H7crm1paUFMTAyUSiUiIiLmHRrJtnG7lojIBPR6PWJjY9HQ0AB/f/8Z5+t2d3djx44dqK2ttXSZgvgeRL4/gfqRvb09li9fjrKyMsTExFiiPMEJ2eOXnZ2N6upqeHp6Ij09HUlJSViyZInQJZONYsgjIjKB8vJy5Obm4s6dO0YDoB8+fIi4uDgolUqkpKRYqELhyWQytLe3W31IEbLHz87ODlKpFAEBAb99sjvbfkGiH3G7lojIBKqqqnDkyJGfnvARFhaG/Px83LhxwypCXltbGz5+/AitVmu4plKpUFBQgC9fviAuLg5nz561mqO5hOzxS0lJsZpte1p4+CSPiMgE3NzccO/ePaxevfqn9zs7OxEZGWkVA5EjIiIQGhqKQ4cOAQC6u7sRGBiI1NRUrFixAqWlpcjMzERhYaFlCyWyMezoJCIygaGhIbi6uv7yvqurK4aHh81Ykel0dXVBoVAYPldXV2PdunW4fPkyDh48iDNnzqCmpsaCFRLZJoY8IiITmJycxD///LojRiwWY2JiwowVmc7w8PCMQPvo0SNERkYaPgcFBWFgYMASpRHZNPbkERGZgF6vR2pq6i/70MbGxsxckem4urpCq9XC09MTOp0OHR0dOH78uOH+58+fYW9vb8EKiWwTQx4RkQn8rDn/f1nDSxcAEBUVhfz8fJw4cQK1tbVwcnLC5s2bDfc1Gg28vb0tWCGRbeKLF0RENC8fPnxAfHw8WlpaIJFIcP36dezatctwX6FQYP369SguLrZglUS2hyGPiIgEMTIyAolEArFYPOP60NAQJBIJHBwcLFQZkW1iyCMiIiKyQny7loiIiMgKMeQRERERWSGGPCIiIiIrxJBHREREZIUY8oiIiIisEEMeERERkRViyCMiIiKyQv8Bbip8IvXKOLgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGa7OJiYThL4"
      },
      "source": [
        "### Test Train Data Splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiq3QJRlolWV",
        "outputId": "45ca58dc-1321-4f87-f0e1-fa6c8f86e12a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
            "0          0              0        1           0       1             0   \n",
            "1          1              0        0           0      34             1   \n",
            "2          1              0        0           0       2             1   \n",
            "3          1              0        0           0      45             0   \n",
            "4          0              0        0           0       2             1   \n",
            "...      ...            ...      ...         ...     ...           ...   \n",
            "7038       1              0        1           1      24             1   \n",
            "7039       0              0        1           1      72             1   \n",
            "7040       0              0        1           1      11             0   \n",
            "7041       1              1        1           0       4             1   \n",
            "7042       1              0        0           0      66             1   \n",
            "\n",
            "      MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
            "0                 1                0               0             2   \n",
            "1                 0                0               2             0   \n",
            "2                 0                0               2             2   \n",
            "3                 1                0               2             0   \n",
            "4                 0                1               0             0   \n",
            "...             ...              ...             ...           ...   \n",
            "7038              2                0               2             0   \n",
            "7039              2                1               0             2   \n",
            "7040              1                0               2             0   \n",
            "7041              2                1               0             0   \n",
            "7042              0                1               2             0   \n",
            "\n",
            "      DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
            "0                    0            0            0                0         0   \n",
            "1                    2            0            0                0         1   \n",
            "2                    0            0            0                0         0   \n",
            "3                    2            2            0                0         1   \n",
            "4                    0            0            0                0         0   \n",
            "...                ...          ...          ...              ...       ...   \n",
            "7038                 2            2            2                2         1   \n",
            "7039                 2            0            2                2         1   \n",
            "7040                 0            0            0                0         0   \n",
            "7041                 0            0            0                0         0   \n",
            "7042                 2            2            2                2         2   \n",
            "\n",
            "      PaperlessBilling  PaymentMethod  MonthlyCharges  \n",
            "0                    1              2           29.85  \n",
            "1                    0              3           56.95  \n",
            "2                    1              3           53.85  \n",
            "3                    0              0           42.30  \n",
            "4                    1              2           70.70  \n",
            "...                ...            ...             ...  \n",
            "7038                 1              3           84.80  \n",
            "7039                 1              1          103.20  \n",
            "7040                 1              2           29.60  \n",
            "7041                 1              3           74.40  \n",
            "7042                 1              0          105.65  \n",
            "\n",
            "[7043 rows x 18 columns] 0       0\n",
            "1       0\n",
            "2       1\n",
            "3       0\n",
            "4       1\n",
            "       ..\n",
            "7038    0\n",
            "7039    0\n",
            "7040    0\n",
            "7041    1\n",
            "7042    0\n",
            "Name: Churn, Length: 7043, dtype: int64\n",
            "(4930, 18)\n",
            "(2113, 18)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = telco.iloc[:, :-2]\n",
        "y = telco.iloc[0:, -1]\n",
        "print(x,y)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Sf4pXZOWThL6",
        "outputId": "22615bf1-bac7-48cd-c195-7a081f7b1f2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                int64\n",
              "SeniorCitizen         int64\n",
              "Partner               int64\n",
              "Dependents            int64\n",
              "tenure                int64\n",
              "PhoneService          int64\n",
              "MultipleLines         int64\n",
              "InternetService       int64\n",
              "OnlineSecurity        int64\n",
              "OnlineBackup          int64\n",
              "DeviceProtection      int64\n",
              "TechSupport           int64\n",
              "StreamingTV           int64\n",
              "StreamingMovies       int64\n",
              "Contract              int64\n",
              "PaperlessBilling      int64\n",
              "PaymentMethod         int64\n",
              "MonthlyCharges      float64\n",
              "TotalCharges        float64\n",
              "Churn                 int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "telco.dropna(inplace=True)\n",
        "telco.dtypes\n",
        "# telco.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoLZ2F7a9D5y",
        "outputId": "1a522962-1427-4cac-8652-461cab2c8b77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "per-feature minimum before scaling:\n",
            " gender               0.00\n",
            "SeniorCitizen        0.00\n",
            "Partner              0.00\n",
            "Dependents           0.00\n",
            "tenure               0.00\n",
            "PhoneService         0.00\n",
            "MultipleLines        0.00\n",
            "InternetService      0.00\n",
            "OnlineSecurity       0.00\n",
            "OnlineBackup         0.00\n",
            "DeviceProtection     0.00\n",
            "TechSupport          0.00\n",
            "StreamingTV          0.00\n",
            "StreamingMovies      0.00\n",
            "Contract             0.00\n",
            "PaperlessBilling     0.00\n",
            "PaymentMethod        0.00\n",
            "MonthlyCharges      18.25\n",
            "dtype: float64\n",
            "per-feature maximum before scaling:\n",
            " gender                1.0\n",
            "SeniorCitizen         1.0\n",
            "Partner               1.0\n",
            "Dependents            1.0\n",
            "tenure               72.0\n",
            "PhoneService          1.0\n",
            "MultipleLines         2.0\n",
            "InternetService       2.0\n",
            "OnlineSecurity        2.0\n",
            "OnlineBackup          2.0\n",
            "DeviceProtection      2.0\n",
            "TechSupport           2.0\n",
            "StreamingTV           2.0\n",
            "StreamingMovies       2.0\n",
            "Contract              2.0\n",
            "PaperlessBilling      1.0\n",
            "PaymentMethod         3.0\n",
            "MonthlyCharges      118.6\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"per-feature minimum before scaling:\\n {}\".format(x_train.min(axis=0)))\n",
        "print(\"per-feature maximum before scaling:\\n {}\".format(x_train.max(axis=0)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykX8fIuLThL-"
      },
      "source": [
        "## Scalling the retrieved features\n",
        "- We will be using MinMaxScaler algorithm here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-z0q9bE9D8R",
        "outputId": "e83bdae7-2a3b-40ef-c293-3186764f7226"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "per-feature minimum after scaling:\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            "per-feature maximum after scaling:\n",
            " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler.fit(x_train)\n",
        "x_test_scaled = scaler.transform(x_test)\n",
        "x_train_scaled = scaler.transform(x_train)\n",
        "print(\"per-feature minimum after scaling:\\n {}\".format(\n",
        "    x_train_scaled.min(axis=0)))\n",
        "print(\"per-feature maximum after scaling:\\n {}\".format(\n",
        "    x_train_scaled.max(axis=0)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPDywHDWThMA"
      },
      "source": [
        "### KNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbfG3HkZ9D-x",
        "outputId": "d7d5e3c2-97eb-4995-f5f0-52b112838df7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set accuracy: 0.84\n",
            "Test set accuracy: 0.77\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "knn=KNeighborsClassifier()\n",
        "\n",
        "knn.fit(x_train, y_train)\n",
        "\n",
        "print(\"Train set accuracy: {:.2f}\".format(knn.score(x_train, y_train)))\n",
        "print(\"Test set accuracy: {:.2f}\".format(knn.score(x_test, y_test)))\n",
        "\n",
        "knnTrain = knn.score(x_train, y_train)\n",
        "knnTest  = knn.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ymeee4DnThMC"
      },
      "source": [
        "### Confusion Matrix for KNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "oCptkSgyThMC",
        "outputId": "c9311280-b8ce-43eb-dee1-221f965c0022",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1346  193]\n",
            " [ 283  291]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the dataset and fitting the model\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "knn.fit(x_train, y_train)\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred = knn.predict(x_test)\n",
        "\n",
        "# Creating confusion matrix for logistic regression\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "print(confusion_mat)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKGY2aOwThMD"
      },
      "source": [
        "### Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "MkEmGuI99EDh",
        "outputId": "4dc3f37b-f3e5-42ee-fb70-3d8c44210974",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training set: 0.998\n",
            "Accuracy on test set: 0.724\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree = DecisionTreeClassifier()\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "tree.fit(x_train, y_train)\n",
        "\n",
        "print(\"Accuracy on training set: {:.3f}\".format(tree.score(x_train, y_train)))\n",
        "print(\"Accuracy on test set: {:.3f}\".format(tree.score(x_test, y_test)))\n",
        "\n",
        "DTTrain = tree.score(x_train, y_train)\n",
        "DTTest  = tree.score(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lmw_2AslThMD"
      },
      "source": [
        "### Confusion Matrix for Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "DN6B8IWFThME",
        "outputId": "1d044b53-34f0-45ef-c44a-08c7c3e31b0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1231  308]\n",
            " [ 286  288]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the dataset and fitting the model\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "tree.fit(x_train, y_train)\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred = tree.predict(x_test)\n",
        "\n",
        "# Creating confusion matrix for Decision Tree\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "print(confusion_mat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReFUEsikThME"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "ICkdtkM4ThMF",
        "outputId": "1747c9dd-f8ec-4b50-94d0-e62a5809f30d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 0.8097491717936584\n",
            "Accuracy on train set: 0.8020283975659229\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "clf = LogisticRegression(solver='liblinear')\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Print the accuracy from the testing data.\n",
        "print(\"Accuracy on test set: {}\".format(clf.score(x_test, y_test)))\n",
        "print(\"Accuracy on train set: {}\".format(clf.score(x_train, y_train)))\n",
        "\n",
        "LRTrain = clf.score(x_train, y_train)\n",
        "LRTest  = clf.score(x_test, y_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oepMedpoThMG"
      },
      "source": [
        "### Confusion Matrix for Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "7rPoahijThMH",
        "outputId": "6a86bee1-c3d9-4b66-a8d6-bfbd1688ba63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1386  153]\n",
            " [ 249  325]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the dataset and fitting the model\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "clf.fit(x_train, y_train)\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred = clf.predict(x_test)\n",
        "\n",
        "# Creating confusion matrix for logistic regression\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "print(confusion_mat)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "PJQqnI4rThMH",
        "outputId": "6af58e2e-5d9a-41cb-a4d2-f986cae8eafa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN → 0.7747278750591576%  DT → 0.7240889730241363%  LR → 0.8097491717936584%\n"
          ]
        }
      ],
      "source": [
        "#import matplotlib.pyplot as plt\n",
        "\n",
        "names = ['KNN', 'DT', 'LR']\n",
        "values = [knnTest, DTTest, LRTest]\n",
        "\n",
        "plt.figure(figsize=(18, 4))\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.bar(names, values)\n",
        "plt.suptitle('Categorical Plotting')\n",
        "plt.show()\n",
        "print(\"KNN → {}%  DT → {}%  LR → {}%\".format(knnTest, DTTest, LRTest))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PpWUzOnjThMI"
      },
      "source": [
        "### Applying Bagging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "wS0CeenRThMJ",
        "outputId": "b61f0941-c551-4730-dc67-e172a34cc29d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Bagging Logistic Regression Accuracy :0.8097491717936584 → 0.8106956933270232\n",
            "After Bagging Decision Tree Accuracy :0.7240889730241363 → 0.779460482725982\n",
            "After Bagging KNNeighbour Accuracy :0.7747278750591576 → 0.7728348319924279\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "logreg  = LogisticRegression(solver='liblinear')\n",
        "dectree = DecisionTreeClassifier()\n",
        "knn     = KNeighborsClassifier()\n",
        "\n",
        "\n",
        "bagging_logreg = BaggingClassifier(base_estimator=logreg, n_estimators=10, random_state=42)\n",
        "bagging_logreg.fit(x_train, y_train)\n",
        "\n",
        "bagging_decisiontree = BaggingClassifier(base_estimator=dectree, n_estimators=10, random_state=42)\n",
        "bagging_decisiontree.fit(x_train, y_train)\n",
        "\n",
        "knn_bagging = BaggingClassifier(base_estimator=knn, n_estimators=10, random_state=42)\n",
        "knn_bagging.fit(x_train, y_train)\n",
        "\n",
        "bagging = [bagging_logreg.score(x_test, y_test), bagging_decisiontree.score(x_test, y_test), knn_bagging.score(x_test, y_test)]\n",
        "\n",
        "print(\"After Bagging Logistic Regression Accuracy :{} → {}\" .format(LRTest, bagging_logreg.score(x_test, y_test)))\n",
        "print(\"After Bagging Decision Tree Accuracy :{} → {}\" .format(DTTest, bagging_decisiontree.score(x_test, y_test)))\n",
        "print(\"After Bagging KNNeighbour Accuracy :{} → {}\" .format(knnTest, knn_bagging.score(x_test, y_test)))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvTQa_LHThMK"
      },
      "source": [
        "### Applying Adaboost\n",
        "- KNN is not supported for AdaBoost because it does not support sample weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "qJIpPw_jThMK",
        "outputId": "0db17b90-251d-43ee-ba36-9070d7da78bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Adaboosting Logistic Regression Accuracy :0.8097491717936584 → 0.7988641741599621\n",
            "After Adaboosting Decision Tree Accuracy :0.7240889730241363 → 0.7373402744912447\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "\n",
        "adaboost_logreg = AdaBoostClassifier(base_estimator=logreg, n_estimators=10, random_state=42)\n",
        "adaboost_logreg.fit(x_train, y_train)\n",
        "\n",
        "adaboost_decTree = AdaBoostClassifier(base_estimator=dectree, n_estimators=10, random_state=42)\n",
        "adaboost_decTree.fit(x_train, y_train)\n",
        "\n",
        "### KNN is not supported for AdaBoost because it does not support sample weights ###\n",
        "# adaboost_knn = AdaBoostClassifier(base_estimator=knn, n_estimators=10, random_state=42)\n",
        "# adaboost_knn.fit(x_train, y_train)\n",
        "\n",
        "adaboost = [adaboost_logreg.score(x_test, y_test), adaboost_decTree.score(x_test, y_test), 0.0000]\n",
        "\n",
        "print(\"After Adaboosting Logistic Regression Accuracy :{} → {}\" .format(LRTest, adaboost_logreg.score(x_test, y_test)))\n",
        "print(\"After Adaboosting Decision Tree Accuracy :{} → {}\" .format(DTTest, adaboost_decTree.score(x_test, y_test)))\n",
        "#print(\"After Adaboosting KNNeighbour Accuracy :{} → {}\" .format(knnTest, adaboost_knn.score(x_test, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQLAfaXZThMK"
      },
      "source": [
        "### Applying Stacking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "RsA45A8qThML",
        "outputId": "78c1f2ae-3e49-4a27-988e-a0072d4af7aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Bagging Logistic Regression Accuracy : 0.8097491717936584 → 0.8154283009938476\n",
            "After Bagging Decision Tree Accuracy : 0.7240889730241363 → 0.7288215806909607\n",
            "After Bagging KNNeighbour Accuracy : 0.7747278750591576 → 0.7643161381921438\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import StackingClassifier\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
        "\n",
        "\n",
        "stacking_logreg = StackingClassifier(estimators=[('lr', logreg), ('dt', dectree), ('knn', knn)], final_estimator=LogisticRegression())\n",
        "stacking_logreg.fit(x_train, y_train)\n",
        "\n",
        "stacking_decTree = StackingClassifier(estimators=[('lr', logreg), ('dt', dectree), ('knn', knn)], final_estimator=DecisionTreeClassifier())\n",
        "stacking_decTree.fit(x_train, y_train)\n",
        "\n",
        "stacking_knn = StackingClassifier(estimators=[('lr', logreg), ('dt', dectree), ('knn', knn)], final_estimator=KNeighborsClassifier())\n",
        "stacking_knn.fit(x_train, y_train)\n",
        "\n",
        "stacking = [stacking_logreg.score(x_test, y_test), stacking_decTree.score(x_test, y_test), stacking_knn.score(x_test, y_test)]\n",
        "\n",
        "print(\"After Bagging Logistic Regression Accuracy : {} → {}\" .format(LRTest, stacking_logreg.score(x_test, y_test)))\n",
        "print(\"After Bagging Decision Tree Accuracy : {} → {}\" .format(DTTest, stacking_decTree.score(x_test, y_test)))\n",
        "print(\"After Bagging KNNeighbour Accuracy : {} → {}\" .format(knnTest, stacking_knn.score(x_test, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSgq3zq9ThML"
      },
      "source": [
        "### Logistic Regression after Adaboosting, Bagging and Stacking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "5lmj78pgThML",
        "outputId": "c4ec5a27-4c9d-474e-a3e6-c3bb0652299f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Logistic Regression Accuracy :80.97491717936583%\n",
            "After Adaboosting Logistic Regression Accuracy :80.97491717936583% → 79.88641741599622%\n",
            "After Bagging Logistic Regression Accuracy :80.97491717936583% → 81.06956933270232%\n",
            "After Stacking Logistic Regression Accuracy:80.97491717936583% → 81.54283009938476%\n"
          ]
        }
      ],
      "source": [
        "\n",
        "names = ['LR', 'Adaboosted', 'Bagging', 'Stacking']\n",
        "values = [LRTest, adaboost[0], bagging[0], stacking[0]]\n",
        "\n",
        "plt.figure(figsize=(25, 5))\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.bar(names, values)\n",
        "plt.suptitle('Ensembling on Logistic Regression')\n",
        "plt.show()\n",
        "\n",
        "print(\"After Logistic Regression Accuracy :{}%\" .format(LRTest*100))\n",
        "print(\"After Adaboosting Logistic Regression Accuracy :{}% → {}%\" .format(LRTest*100, adaboost[0]*100))\n",
        "print(\"After Bagging Logistic Regression Accuracy :{}% → {}%\" .format(LRTest*100, bagging[0]*100))\n",
        "print(\"After Stacking Logistic Regression Accuracy:{}% → {}%\" .format(LRTest*100, stacking[0]*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m07NJoguThMN"
      },
      "source": [
        "### KNN After Adaboosting, Bagging and Stacking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "27nKgeICThMO",
        "outputId": "0d34b20f-2cad-4929-c96d-e7d95b67f182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After KNNeighbours Accuracy :77.47278750591576%\n",
            "After Bagging KNNeighbours Accuracy :77.47278750591576% → 77.28348319924278%\n",
            "After Stacking KNNeighbours Accuracy:77.47278750591576% → 76.43161381921438%\n"
          ]
        }
      ],
      "source": [
        "names = ['KNN', 'Bagging', 'Stacking']\n",
        "values = [knnTest, bagging[0], stacking[0]]\n",
        "\n",
        "plt.figure(figsize=(18, 5))\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.bar(names, values)\n",
        "plt.suptitle('Ensembling on KNNeighbours')\n",
        "plt.show()\n",
        "\n",
        "print(\"After KNNeighbours Accuracy :{}%\" .format(knnTest*100))\n",
        "print(\"After Bagging KNNeighbours Accuracy :{}% → {}%\" .format(knnTest*100, bagging[2]*100))\n",
        "print(\"After Stacking KNNeighbours Accuracy:{}% → {}%\" .format(knnTest*100, stacking[2]*100))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8Ym0KkTThMS"
      },
      "source": [
        "### Decision Tree After Adaboosting, Bagging, Stacking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "UwrSKC1AThMT",
        "outputId": "62a6b2f6-fefc-44ef-ad04-08fbcd92a0ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After Decision Tree Accuracy :72.40889730241364%\n",
            "After Adaboosting Decision Tree Accuracy :72.40889730241364% → 73.73402744912447%\n",
            "After Bagging Decision Tree Accuracy :72.40889730241364% → 77.9460482725982%\n",
            "After Stacking Decision Tree Accuracy:72.40889730241364% → 72.88215806909606%\n"
          ]
        }
      ],
      "source": [
        "names = ['DT', 'Adaboost', 'Bagging', 'Stacking']\n",
        "values = [DTTest, adaboost[1], bagging[1], stacking[1]]\n",
        "\n",
        "plt.figure(figsize=(25, 8))\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.bar(names, values)\n",
        "plt.suptitle('Ensembling on Decision Tree')\n",
        "plt.show()\n",
        "\n",
        "print(\"After Decision Tree Accuracy :{}%\" .format(DTTest*100))\n",
        "print(\"After Adaboosting Decision Tree Accuracy :{}% → {}%\" .format(DTTest*100, adaboost[1]*100))\n",
        "print(\"After Bagging Decision Tree Accuracy :{}% → {}%\" .format(DTTest*100, bagging[1]*100))\n",
        "print(\"After Stacking Decision Tree Accuracy:{}% → {}%\" .format(DTTest*100, stacking[1]*100))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}