diff --git "a/2_Churn_Data_Analysis_and_Insights.ipynb" "b/2_Churn_Data_Analysis_and_Insights.ipynb"
new file mode 100644--- /dev/null
+++ "b/2_Churn_Data_Analysis_and_Insights.ipynb"
@@ -0,0 +1,2813 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aTTsTi1Pu-g7"
+      },
+      "source": [
+        "# 📊 Notebook 2: Churn Data Analysis and Insights\n",
+        "## AI for Big Data Management — ESCP Business School\n",
+        "### Final Group Project\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## 📌 Problem Statement\n",
+        "> **\"How can a company predict customer churn based on support interactions and proactively adapt its retention strategy?\"**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## 🗺️ What This Notebook Does\n",
+        "1. Loads the merged dataset from Notebook 1\n",
+        "2. **Exploratory Data Analysis (EDA)** — descriptive statistics and distributions\n",
+        "3. **Qualitative Analysis** — sentiment analysis of support feedback\n",
+        "4. **Quantitative Analysis** — churn vs. support calls, risk scores, complaint types\n",
+        "5. **ML Model** — Random Forest classifier to predict churn\n",
+        "6. **Automatic conclusions** — key business recommendations\n",
+        "7. Exports results and visualizations\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### ⚠️ Before Running\n",
+        "Upload `customer_churn_support_dataset.csv` (output from Notebook 1) using the 📁 Files panel on the left sidebar."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OB3SZ7JXu-g_"
+      },
+      "source": [
+        "---\n",
+        "## 📦 SECTION 1: Install & Import"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3Sda_Jleu-g_",
+        "outputId": "ded0f1a8-2643-4dc2-af38-2e622c52256a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ All libraries imported!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Install ────────────────────────────────────────────────────────────────────\n",
+        "!pip install vaderSentiment --quiet\n",
+        "\n",
+        "# ── Imports ───────────────────────────────────────────────────────────────────\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.gridspec as gridspec\n",
+        "import seaborn as sns\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "# ── Style ─────────────────────────────────────────────────────────────────────\n",
+        "plt.style.use('seaborn-v0_8-whitegrid')\n",
+        "COLORS = {'Yes': '#E74C3C', 'No': '#2ECC71',\n",
+        "          'High': '#E74C3C', 'Medium': '#F39C12', 'Low': '#2ECC71'}\n",
+        "np.random.seed(42)\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "\n",
+        "print('✅ All libraries imported!')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JnkjkJJhu-hA"
+      },
+      "source": [
+        "---\n",
+        "## 📥 SECTION 2: Load Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "id": "9pBEqMxCu-hB",
+        "outputId": "e4d7001c-13e2-46d0-ceea-4aaf4f47e937"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Dataset loaded: 7043 rows × 21 columns\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
+              "0  7590-VHVEG  Female              0     Yes         No       1   \n",
+              "1  5575-GNVDE    Male              0      No         No      34   \n",
+              "2  3668-QPYBK    Male              0      No         No       2   \n",
+              "3  7795-CFOCW    Male              0      No         No      45   \n",
+              "4  9237-HQITU  Female              0      No         No       2   \n",
+              "\n",
+              "         Contract              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
+              "0  Month-to-month           Electronic check           29.85         29.85   \n",
+              "1        One year               Mailed check           56.95       1889.50   \n",
+              "2  Month-to-month               Mailed check           53.85        108.15   \n",
+              "3        One year  Bank transfer (automatic)           42.30       1840.75   \n",
+              "4  Month-to-month           Electronic check           70.70        151.65   \n",
+              "\n",
+              "  InternetService TechSupport Churn  Churn_binary  support_calls  \\\n",
+              "0             DSL          No    No             0              2   \n",
+              "1             DSL          No    No             0              4   \n",
+              "2             DSL          No   Yes             1             15   \n",
+              "3             DSL         Yes    No             0              3   \n",
+              "4     Fiber optic          No   Yes             1              9   \n",
+              "\n",
+              "   avg_call_duration    complaint_type  days_since_last_contact  \\\n",
+              "0                9.7  Contract Dispute                       16   \n",
+              "1                3.8     Billing Issue                       57   \n",
+              "2               21.2     Billing Issue                       12   \n",
+              "3                9.8    Service Outage                       40   \n",
+              "4               12.3  Contract Dispute                       24   \n",
+              "\n",
+              "                              last_contact_sentiment  sentiment_score  \\\n",
+              "0  I love this company, always responsive and car...           0.8720   \n",
+              "1               Great service, no complaints at all!           0.7684   \n",
+              "2  My bill is wrong again. This is the third time...          -0.5255   \n",
+              "3  Everything was resolved in one call. Excellent...           0.6597   \n",
+              "4  My bill is wrong again. This is the third time...          -0.5255   \n",
+              "\n",
+              "  support_churn_risk  \n",
+              "0                Low  \n",
+              "1             Medium  \n",
+              "2               High  \n",
+              "3             Medium  \n",
+              "4               High  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-abe5194f-6c80-41a3-84f7-788da0766083\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customerID</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>Partner</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>Contract</th>\n",
+              "      <th>PaymentMethod</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>InternetService</th>\n",
+              "      <th>TechSupport</th>\n",
+              "      <th>Churn</th>\n",
+              "      <th>Churn_binary</th>\n",
+              "      <th>support_calls</th>\n",
+              "      <th>avg_call_duration</th>\n",
+              "      <th>complaint_type</th>\n",
+              "      <th>days_since_last_contact</th>\n",
+              "      <th>last_contact_sentiment</th>\n",
+              "      <th>sentiment_score</th>\n",
+              "      <th>support_churn_risk</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7590-VHVEG</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9.7</td>\n",
+              "      <td>Contract Dispute</td>\n",
+              "      <td>16</td>\n",
+              "      <td>I love this company, always responsive and car...</td>\n",
+              "      <td>0.8720</td>\n",
+              "      <td>Low</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5575-GNVDE</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>34</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>56.95</td>\n",
+              "      <td>1889.50</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>Billing Issue</td>\n",
+              "      <td>57</td>\n",
+              "      <td>Great service, no complaints at all!</td>\n",
+              "      <td>0.7684</td>\n",
+              "      <td>Medium</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3668-QPYBK</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>53.85</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15</td>\n",
+              "      <td>21.2</td>\n",
+              "      <td>Billing Issue</td>\n",
+              "      <td>12</td>\n",
+              "      <td>My bill is wrong again. This is the third time...</td>\n",
+              "      <td>-0.5255</td>\n",
+              "      <td>High</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7795-CFOCW</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>45</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>Bank transfer (automatic)</td>\n",
+              "      <td>42.30</td>\n",
+              "      <td>1840.75</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>Service Outage</td>\n",
+              "      <td>40</td>\n",
+              "      <td>Everything was resolved in one call. Excellent...</td>\n",
+              "      <td>0.6597</td>\n",
+              "      <td>Medium</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9237-HQITU</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>70.70</td>\n",
+              "      <td>151.65</td>\n",
+              "      <td>Fiber optic</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>12.3</td>\n",
+              "      <td>Contract Dispute</td>\n",
+              "      <td>24</td>\n",
+              "      <td>My bill is wrong again. This is the third time...</td>\n",
+              "      <td>-0.5255</td>\n",
+              "      <td>High</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abe5194f-6c80-41a3-84f7-788da0766083')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-abe5194f-6c80-41a3-84f7-788da0766083 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-abe5194f-6c80-41a3-84f7-788da0766083');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ── Load the final dataset ────────────────────────────────────────────────────\n",
+        "DATASET = 'customer_churn_support_dataset.csv'\n",
+        "\n",
+        "try:\n",
+        "    df = pd.read_csv(DATASET)\n",
+        "    print(f'✅ Dataset loaded: {df.shape[0]} rows × {df.shape[1]} columns')\n",
+        "    display(df.head())\n",
+        "except FileNotFoundError:\n",
+        "    print('❌ File not found. Please upload customer_churn_support_dataset.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 838
+        },
+        "id": "kHvl0cluu-hB",
+        "outputId": "49d4b603-1dbb-4dd5-d276-6f824eddd4dd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📋 Dataset info:\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7043 entries, 0 to 7042\n",
+            "Data columns (total 21 columns):\n",
+            " #   Column                   Non-Null Count  Dtype  \n",
+            "---  ------                   --------------  -----  \n",
+            " 0   customerID               7043 non-null   object \n",
+            " 1   gender                   7043 non-null   object \n",
+            " 2   SeniorCitizen            7043 non-null   int64  \n",
+            " 3   Partner                  7043 non-null   object \n",
+            " 4   Dependents               7043 non-null   object \n",
+            " 5   tenure                   7043 non-null   int64  \n",
+            " 6   Contract                 7043 non-null   object \n",
+            " 7   PaymentMethod            7043 non-null   object \n",
+            " 8   MonthlyCharges           7043 non-null   float64\n",
+            " 9   TotalCharges             7043 non-null   float64\n",
+            " 10  InternetService          7043 non-null   object \n",
+            " 11  TechSupport              7043 non-null   object \n",
+            " 12  Churn                    7043 non-null   object \n",
+            " 13  Churn_binary             7043 non-null   int64  \n",
+            " 14  support_calls            7043 non-null   int64  \n",
+            " 15  avg_call_duration        7043 non-null   float64\n",
+            " 16  complaint_type           7043 non-null   object \n",
+            " 17  days_since_last_contact  7043 non-null   int64  \n",
+            " 18  last_contact_sentiment   7043 non-null   object \n",
+            " 19  sentiment_score          7043 non-null   float64\n",
+            " 20  support_churn_risk       7043 non-null   object \n",
+            "dtypes: float64(4), int64(5), object(12)\n",
+            "memory usage: 1.1+ MB\n",
+            "\n",
+            "📋 Descriptive statistics:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       SeniorCitizen       tenure  MonthlyCharges  TotalCharges  Churn_binary  \\\n",
+              "count    7043.000000  7043.000000     7043.000000   7043.000000   7043.000000   \n",
+              "mean        0.162147    32.371149       64.761692   2281.916928      0.265370   \n",
+              "std         0.368612    24.559481       30.090047   2265.270398      0.441561   \n",
+              "min         0.000000     0.000000       18.250000     18.800000      0.000000   \n",
+              "25%         0.000000     9.000000       35.500000    402.225000      0.000000   \n",
+              "50%         0.000000    29.000000       70.350000   1397.475000      0.000000   \n",
+              "75%         0.000000    55.000000       89.850000   3786.600000      1.000000   \n",
+              "max         1.000000    72.000000      118.750000   8684.800000      1.000000   \n",
+              "\n",
+              "       support_calls  avg_call_duration  days_since_last_contact  \\\n",
+              "count    7043.000000        7043.000000              7043.000000   \n",
+              "mean        5.207440          12.850121                42.436178   \n",
+              "std         3.625463           7.871852                25.250267   \n",
+              "min         1.000000           3.000000                 1.000000   \n",
+              "25%         2.000000           7.000000                21.000000   \n",
+              "50%         5.000000          11.100000                39.000000   \n",
+              "75%         6.000000          14.800000                65.000000   \n",
+              "max        15.000000          35.000000                89.000000   \n",
+              "\n",
+              "       sentiment_score  \n",
+              "count      7043.000000  \n",
+              "mean          0.307120  \n",
+              "std           0.573927  \n",
+              "min          -0.709600  \n",
+              "25%          -0.457400  \n",
+              "50%           0.659700  \n",
+              "75%           0.790200  \n",
+              "max           0.872000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bd111e55-8c8a-4ed0-9ded-302c5fb2e12e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>Churn_binary</th>\n",
+              "      <th>support_calls</th>\n",
+              "      <th>avg_call_duration</th>\n",
+              "      <th>days_since_last_contact</th>\n",
+              "      <th>sentiment_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "      <td>7043.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.162147</td>\n",
+              "      <td>32.371149</td>\n",
+              "      <td>64.761692</td>\n",
+              "      <td>2281.916928</td>\n",
+              "      <td>0.265370</td>\n",
+              "      <td>5.207440</td>\n",
+              "      <td>12.850121</td>\n",
+              "      <td>42.436178</td>\n",
+              "      <td>0.307120</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.368612</td>\n",
+              "      <td>24.559481</td>\n",
+              "      <td>30.090047</td>\n",
+              "      <td>2265.270398</td>\n",
+              "      <td>0.441561</td>\n",
+              "      <td>3.625463</td>\n",
+              "      <td>7.871852</td>\n",
+              "      <td>25.250267</td>\n",
+              "      <td>0.573927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>18.250000</td>\n",
+              "      <td>18.800000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.709600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>35.500000</td>\n",
+              "      <td>402.225000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>-0.457400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>70.350000</td>\n",
+              "      <td>1397.475000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>11.100000</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>0.659700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>55.000000</td>\n",
+              "      <td>89.850000</td>\n",
+              "      <td>3786.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>14.800000</td>\n",
+              "      <td>65.000000</td>\n",
+              "      <td>0.790200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>72.000000</td>\n",
+              "      <td>118.750000</td>\n",
+              "      <td>8684.800000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>89.000000</td>\n",
+              "      <td>0.872000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd111e55-8c8a-4ed0-9ded-302c5fb2e12e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bd111e55-8c8a-4ed0-9ded-302c5fb2e12e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bd111e55-8c8a-4ed0-9ded-302c5fb2e12e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2489.9992387084,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1621468124378816,\n          1.0,\n          0.36861160561002687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2478.9752758409018,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.37114865824223,\n          29.0,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2468.7047672837775,\n        \"min\": 18.25,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64.76169246059918,\n          70.35,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3119.0484860242914,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2281.9169281556156,\n          1397.475,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn_binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2489.939844235915,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2653698707936959,\n          1.0,\n          0.44156130512195013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2488.169319458197,\n        \"min\": 1.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.207440011358796,\n          5.0,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_call_duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2485.467691125417,\n        \"min\": 3.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.850120687207156,\n          11.1,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_since_last_contact\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2475.946750216745,\n        \"min\": 1.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42.43617776515689,\n          39.0,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2489.973766914231,\n        \"min\": -0.7096,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.3071197927019736,\n          0.6597,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# ── Quick overview ────────────────────────────────────────────────────────────\n",
+        "print('📋 Dataset info:')\n",
+        "df.info()\n",
+        "print('\\n📋 Descriptive statistics:')\n",
+        "display(df.describe())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9nUoSfgGu-hB"
+      },
+      "source": [
+        "---\n",
+        "## 📈 SECTION 3: Exploratory Data Analysis (EDA)\n",
+        "\n",
+        "### 3.1 — Churn Distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "DKEYwHmCu-hB",
+        "outputId": "f6648f9b-771b-44ee-e65c-8ba8a85e1a89"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABEoAAAHvCAYAAABDrzoaAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAobtJREFUeJzs3XmYjfX/x/HnObOPMTPGvpNlkG0kJEtE1jZJhL5+SLSIVJQIUVJClLUSCZEsITsRWbIP2dexDGZfz8w55/fHmNMcM5hhZs6MeT2uay5z7vs+9/26z2Lu8z6fxWC1Wq2IiIiIiIiIiAhGRwcQEREREREREckpVCgREREREREREblJhRIRERERERERkZtUKBERERERERERuUmFEhERERERERGRm1QoERERERERERG5SYUSEREREREREZGbVCgREREREREREblJhRIRERERERERkZtUKBEREbkPkydPxt/fH39/fyZPnmxbvmTJEtvyIUOGODCh5GQpXyfvvvuuo+PkOMmPjb+/v0NzDBkyxJZjyZIltuUp3/+Ofp83b97cluXixYsOzSIikts5OzqAiIg8WK5cucLChQvZsWMH58+fJyIiAk9PT4oWLcqjjz5Khw4dqF69uqNj5mgREREsWrSIrVu3cvLkScLCwnBycqJgwYJUq1aN1q1b06ZNG5ycnBySLzw8nIYNG5KYmMixY8cckiEnOnfuHAsXLmTnzp0EBQURGRmJq6srxYsXp3bt2nTo0IG6des6Oma2SqvAYTAYyJ8/P0WLFqVGjRq0bduWRo0aYTAYsjTL3LlzGT16NM8//zxjx47N0mNlFb33RESyhwolIiKSKaxWKzNnzuTrr78mISHBbl14eDjh4eEcP36cefPm8eyzzzJy5Eg8PDwclDbnWrduHR988AGRkZF2yxMSEggKCiIoKIh169bxww8/8O2331K0aNFsz7hp0yYSExOz/bg5ldVqZdKkScyYMQOz2Wy3LjExkVOnTnHq1Cl+/fVXnnvuOUaPHo2Li4uD0jqe1WolIiKCiIgITpw4wZIlS6hduzbjxo2jbNmydttu27Yt0467bt26e77v0KFDGTRoEAD58+fPrEgZdqf33uLFi22vPz8/v+yMJSLywFGhREREMsXEiROZNm0aAC4uLnTp0oW2bdtStGhRIiIi2Lx5M7NmzSIyMpJly5Zx/fp1Zs6c6bBWETnR1q1beeutt7BarQA8+eSTdOnShXLlyhEZGcn+/fuZNWsWQUFBHD58mD59+rBo0SJcXV2zNef9fOB8EN362n/55Zdp27YthQsX5urVq/z555/8+OOPxMTEsHTpUtzd3Rk5cqSDU2e/2bNnU7FiRSwWCyEhIezcuZPZs2dz+fJl9u/fT7du3Zg/fz6lSpWy3adw4cKZcuzQ0FD27Nlzz/fPnz+/Qwskye703lNxREQk82iMEhERuW+HDx+2fVA0GAxMnjyZoUOHEhAQQIkSJahSpQp9+/Zl/vz55MuXD4C//vqLn3/+GYCpU6fa+ta/+uqraR6jffv2tm1+//132/KoqCimTJnCs88+S+3atQkICKBjx44sXrw41T5Sjifw/fffs2XLFlq3bk2NGjXYu3evbbtTp07xwQcf0Lx5c6pXr05AQAAdOnRg7ty5qVoMZJbExESGDRtmK5J0796db7/9lsaNG1O6dGmqVavGyy+/zOLFiylfvjwA//77L6tXrwZg586dtnPr3r17qv2nHGMh5VgqAFevXmX06NG0adOGWrVqUatWLZ566imGDx/OiRMnbNslP37r16+3LUve586dO23LzGYzS5cu5f/+7/9o0KAB1atXp0GDBvTs2ZNly5ZhsVjsjp8ye9++fYmPj+fTTz+lUaNGBAQE8NJLL9n2f+XKFd566y3q1atH7dq16dmzJ2fOnEnzMV2/fj29evWifv36VK9enebNmzN69GhCQ0Pttrt48aLt+E8//TQhISG89tprBAQE8PHHH9/xeTt16hTTp0+33f7qq6/48MMPqV27NiVLlqROnToMGDCAH3/8ETc3NwAWLVpEUFBQmvtLSEhg4sSJttdeq1atmD17dqrt7jR2R8pxT1K+Fm59nM+dO0fXrl2pVasWM2bMAOxfJ2vWrOHChQsMGDCABg0aULNmTV544QW2bNlyx8fkdnx9fSlcuDBFixalatWq9OjRg2XLllGtWjUAgoOD+eSTT9J9nlu3bqVfv340atSI6tWrU79+fV544QW+++47YmNjbds1b96cBg0a2N67v/32G/7+/jRv3vyeHpeUY5TcKiwsjOHDh9OkSRNbt6J58+bZ3tfpOa+0nr/0vPfuNkbJhg0b6Nu3L48//rjt8eratSvz5s3DZDLZbXvrewLg559/pn379tSsWZOGDRsyYsQIoqKibvtYiIjkZmpRIiIi9+2HH36w/d6uXTuaNWuW5naVKlWib9++jB8/3na/7t270759eyZOnAjA33//TUxMDJ6enrb7nT171vaBPV++fLRo0QKA69ev07VrV86ePWt3nEOHDnHo0CH279/P6NGj08xy9uxZJk+eTExMDICtu9CuXbt49dVXiYuLs22bkJBAYGAggYGB7N27lwkTJqT3oUm3zZs3c/nyZSDpA+XtBvb08/Pjk08+4fjx4zRq1ChVV4WMunDhAp06dSIkJMRu+blz5zh37hwrVqzg22+/5bHHHkvX/mJjY3n99dfZvn273fLQ0FD++usv/vrrL1auXMmUKVPSbAkTHR3Nu+++y9q1a23L9u/fT58+fViwYAFvvvmm3YfAv/76i169erF27Vqcnf+7rBk9ejRz586123dQUBBz585lw4YNLFiwIM1uS9HR0QwfPpzNmzcDEB8ff8fzXbBgge1DcOPGjXnqqafS3K5mzZqMGDECV1dXGjZseNtv/99//31WrVplu3327Fk+++wzTCYTffr0uWOWjIiKimLgwIEEBgYCaZ/nuXPnGDVqFNevX7ctO3z4MP369ePHH3/k0Ucfve8cPj4+fPbZZzz33HNYrVY2b97MmTNnbMXA20kebySlsLAwwsLCOHz4MGvWrOG7777LcCuQ9Dwut5OQkEC3bt3siounTp1i1KhRhIaG8uabb2YoS2axWCwMHjyY5cuX2y0PCwtjz5497Nmzh99++43vvvsOHx+fVPePjo7m66+/5ptvvrEti4+PZ/78+Zw/f57vv/8+y89BRCS7qUWJiIjct5QfipO/fbyd9u3b234PCgri9OnTlC5dmlq1agFgMplSjUuQsrn5U089hbu7OwAfffSRrUjSqFEjli5dym+//UajRo2ApG/uN2zYkGaOpUuXUqVKFX755RfWrFlj+1b7k08+sRVJevTowfr165kxY4atJcyqVavYv3//Hc/xXuzatcv2e+PGjW3nmJZHH32Url273neRBGDWrFm2Isk777zDmjVr2LRpE19++SWenp7ExMQwdOhQrFYrPXv2TPXcbNu2jW3bthEQEADA2LFjba+HkiVL8s0337B27VomTpxIwYIFAdiyZQtTpkxJM09gYCBHjhxh8eLFzJ49G19fXwDi4uLo1asXhQoVYsWKFUyaNMnWQiMoKMjueV67dq2tSOLn58eUKVNYs2YN77//PkajkUuXLqVquZDs2rVrbN++nS+//JINGzbwxhtv3PHx2717t+33Vq1a3XHbDh060L59+9sWSf755x8OHz7MvHnzWL16tV3BcebMmZk6LszBgwcJDg5m+vTpbNiwgRdffDHVNtOnT7e14Jo3b56teGE2m5k1a1amZalSpQpVqlSx3b7buCQJCQm2YmWRIkX4/vvv2bRpEytXrqRTp04AHDhwwFbAXbx4MR9++KHt/m3atGHbtm1ptjpLz+NyO2vXrsXLy4uff/6ZZcuW2f1fOG3aNK5evZrufd0qPe+925k1a5atSOLr68vnn3/OmjVrmDFjhu3/kEOHDjFq1Kg07x8SEsIPP/zA2LFjWbNmjV3B56+//uLQoUP3fF4iIjmVWpSIiMh9iYqKsmuNUKlSpTtuX6JECfLnz28brPTcuXM89NBDtG/fngMHDgBJAxam/GY+ZXPzZ599FkhqCbFp0yYAPDw8GD9+vO1D9fjx43niiSeIjY3lp59+4sknn0yVw2w2M3nyZAoVKmRbFhYWRpkyZShdujQGg4G3334bT09PSpcuzVNPPcVvv/0GwL59+6hdu3Z6H6J0SdlK4m7fpmfVcZ999lmKFSsGJD1PXl5eREZGUrRoUcxmM/ny5bMVjJKlHEPi2rVr/Prrr0BSF6yZM2dSoUIFAMqWLYuvry89evQAYN68ebzxxhu2Ykey6OhoJk2aRI0aNYCkLkjJXYVCQkJYsGABZcqUoXLlymzZssXWDeLIkSO2QsWPP/5o21///v1p2bIlAL169eLw4cOsWrWKdevWcfXq1VStSkwmEwMHDrxrwS+tx+9+n7fLly/bCngA48aN4/HHH8dkMhEREcHly5cpXbr0fR0jWXx8PMOGDeOJJ5647TbOzs5MmTLF1rrrww8/tHWNO378eKbkSFalShWOHj0KwKVLl+64bWhoKNHR0QBUqFCBxx9/3LZu2LBhVKlShSJFivDQQw8BScWylC1L3N3dbzv2SXoel9uxWCxMmjTJ9poaN24ce/fuJSgoiISEBDZv3sxLL72U4f0Cd33v3Y7JZOK7776z3R4/frytkFyuXDnKlStHq1atsFqtrFq1ivfee8/2f0Cy2NhYBg0axPPPPw/AW2+9xdatW23/Xx8/ftz2fhUReVCoRYmIiNyX5A8syVJ2mbmdlNsk93FPOd3t5s2bbeNYBAcH2y7IixYtSv369QHsBmasUKGCrUgCSd+alitXDkj6xj+tb+Jr1qxpVyRJvt8333zDt99+yzfffGOXs3jx4rc958yQckyF7JwNKOWH+5deeolx48axceNGQkNDadasGc888wz169e369ZyO7t27bJ1YapUqZKtSJKsQYMGeHl5AUnPe3L3hpRcXFzsPvhWrVrV9nuZMmUoU6ZMmuuSu4ckJibaXi9Aqul4Uxa4duzYkeZ5pFVYu52Uz9udWgGlR6VKlexaVnh7e9s9P8HBwfe1/5SMRuNtu8gla9q0qd17ILnVV2ZngbT/T7gdPz8/2/t9x44ddO/endmzZ3PgwAGMRiNdu3alZcuWqV5/6ZGex+V2atSoYVd4MxqNNGjQwHb733//vaf93o8jR44QFhYGgJeXl917C5IKmMnFbYvFYtdCKqU2bdrY3a5Zs6bt98x+LYiI5ARqUSIiIvfl1jEAIiIiKFCgwB3vk/KDkLe3N5D07Wj9+vXZvn07ISEh7Nu3j0ceeYQNGzbYxoBo3749RmNSjf/atWu2fRw+fDjNQREhqZn+hQsXUn3bX6RIkTS3v3DhAtOnT2f37t0EBwfbxjBJ6daBGTNDcgEBsqYQcztvvvkmf//9NydOnODKlSt89913fPfddxgMBqpVq0bnzp3p0KFDugolKQcoTVlYSmYwGChatKjt+b9y5UqqbQoWLGh7jsH+cbn1G/SU65IH6gwLC7ObnjplV69bnT59Os3lt3ttpMXLy8v2QfR+n7e0HrOUY0ZkZtcbX1/fu86WdGuerMoCSVOIp3WctDg7OzN27FjeeustEhIS2LVrl63rmqenJ02aNKFXr152H+bTKz2Py+2ULFky1bKUr9lbBxHODilb5xQrVgyDwZBqm2LFitlaCCWPk3Sr7HwtiIjkBGpRIiIi98XT05MSJUrYbqfVSiClCxcu2H2gTPmtb8oPtRs3bgTS7naTUSk/hCVLq9XG+fPneeGFF1i0aBFnz55Ns0iSVVIWclIOBnkv0irk3G5QSl9fX3799Vc++eQTGjVqZPtm32q1EhgYyLBhw3j//fczXBxKz/ZpfWi703TRKQsomSG5wHGrjLToSfm8nTx58r7ypKcYlZZbH+v0DECannO81zz3IuX/G8ldZu6kWbNm/PHHH/Tq1YsqVarYXhsxMTH88ccfvPzyy7b/QzIis1tzpXxu0nq937oNZGwA2XvNcju3e49l52tBRCQnUKFERETuW3Kfd4Bly5bdcduUU/tWqlSJUqVK2W4/9dRTtm9zN23aREREhG3qyypVqti1Gkn5rX/dunVtAxum9fPwww+n6zx++OEHW1GldOnS/PDDD2zevJlt27alOeVuZko5q8zWrVttY7ikZdOmTTz99NP88MMP3LhxA7AvMKRVGDp37txt9+fm5kanTp347rvv+Oeff1i2bBmDBg2ydW9YuXIl+/btu+s5pPxGPa1xJiwWi9031ikLbJnF19cXFxcX2+3ffvvttq+L999//76Pl/J5SzlbTVrGjRvH//3f/7FixYr7/jCc8vm+teBzp+c6J9q7d69timcnJye7/0/upFSpUrz//vssW7aM3bt3M2vWLJ555hkgqSXZ559/nmWZ05JWa4yU3VKSBzOG7Hv+Ur7Hrly5kmpqbrB/r2bFe1JEJDdSoURERO5bjx497MYXuXUaymTHjh1j5syZttu3TneaP39+mjZtCiRNqzlnzhxbN4rkD0DJUs70cOrUKfLnz0/hwoVtP+Hh4cTHx+Ph4WH3wflOUn5Aee6552jYsCHFixenUKFCHD582LYuuZtHZnrsscds41PExMQwevToNL8BDgkJsU0PPHbsWBYtWgRgNzbCqVOn7Lq17N+/P82WPnFxcWzdupU5c+bYpuM1Go1UqVKFPn360K9fP9u2t2uSn3Ia5Xr16tm+eT558mSqljFbt261tdLx9fW1zTSUmZydne26XJw7d87udWG1WomMjMTJySlTWg906dLFNiDtnj170pxJBZJmtJkzZw7bt2/nvffes3s93YuUhcKUs05FRUXdtViZk4SHh/Pxxx/bbj/77LN37foUFBTEqlWr+Oabb2wf8r28vGjcuDFffPGF7X10u0FhU75mM9PBgwftZrZJSEiwGwcnZcE2M56/9JzHww8/bCt4RkdH8+eff9qtP3nypK0llIuLC/Xq1UvXsUVEHnRqRyciIvetQoUKvPPOO3zxxRcADB48mH/++Ydnn32W4sWLEx4ezubNm5k5c6at20379u1TFT+SlydPBzx9+nQg6cP7rWNNlC1bliZNmvDnn38SGhrKgAED6NevH35+fmzYsIGxY8ditVqpWbMmv/zyy22bvaeU8hvfP//8k9atWxMXF8fs2bM5f/68bd3u3bu5ePFiqtkh7tfnn39O586diY2NZenSpQQHB/PKK69QsWJFrFYru3btYvLkybYiSJUqVejZsyeAraBz/fp1zGYzffr0oWfPnsTFxTFhwgSKFy+eZrHj3XffJSwsDG9vbxISEqhVqxbOzs5cvHjRruCVcjYjb29vIiIiAPj+++9p3bo13t7eFC5cmBdeeIGFCxcC0LdvX4YPH065cuU4cOAAY8aMse3j1VdfTXcBK6O6devGP//8A2A7ZvXq1QkKCmLYsGGcP38eo9HI0qVLbzu2TXoVKVKEYcOG8dFHHwFJs64cOnSIp59+mpIlSxIZGcmaNWuYNWuWrejXuXNnHnnkkfs6bo0aNWzP58iRI7lx4waFChVi5syZqWYSyinCwsJsYwtFR0eze/dupk2bZps5qEyZMulq5XPgwAEGDhwIwM6dOxk0aBDFihXDZDKxa9cu29gzt75mk+3YsYPdu3fj5eVlNyDw/UpMTOSdd95h8ODBuLq68u2339req56ennaDxN7r83e7996tA1Mnc3FxoVevXowfPx6AIUOGMHz4cB5++GFOnjxp95588cUXb7sfEZG8RoUSERHJFL1798bV1ZUvv/yS+Ph4FixYwIIFC1JtZzQa6d69+20/EDVr1ox8+fIRHR2NyWQCklpb3DqNK8Do0aN5+eWXuXjxIps2bbJNF5ysUKFCjB07Nl1FEkiainblypWYzWYOHDhAu3btgKQPOT/++COvvfYaISEh7NmzhyeffJINGzaka7/pVaVKFebMmcPbb7/NpUuX2L59u923zSk98sgjfP3117auSs7OzvTu3ZuxY8cCSa13Bg8eDECdOnWoX78+U6dOtduHu7s7o0eP5p133iEiIoJ33nknzWN1796dypUr2x07+bGeNGkSkyZN4rPPPqNDhw4MGTKEs2fPsnPnTi5evJiq1RBAhw4dbAWerNC2bVv+/vtvFi5cyLVr1xgwYECqbd577737LpIke/HFFzEYDIwZM4aYmJjbvvYhaWah5KLK/Xj11VfZsGEDZrOZ8PBw2wdePz8/Bg8ebHvuc5LkqaHTUqtWLSZNmnTXgaAhaQaWdevWsWrVKnbu3EmnTp1SbePp6cmHH35ou12jRg1cXV0xmUyEhYXRrVs3IOl9kllat27NgQMHePHFF1Ote/fdd+1m5rrX5+9O773b6dWrF8eOHeP3338nNDTUVmRKqVGjRjnyNSMi4igqlIiISKZ55ZVXaNWqFQsWLGD79u2cO3eOyMhI24Cv9evX56WXXrrjtJ1ubm60bNmSpUuX2pal1fIEkrqbLFmyhO+//57169dz8eJFLBYLJUuWpFmzZvTu3duulcjd1KxZkxkzZjBlyhROnjyJ0Wikbt26DBgwgMqVKzNq1CjGjBnDtWvXqFixYrqmQs6omjVrsmbNGn777Tc2bdrE0aNHCQ0NxWq1UrBgQWrUqMEzzzxDixYtUhWA/u///g+DwcD8+fMJCgqiYMGCPPXUUwwcOPC2H9xbtmzJ/Pnz+fnnn9m5cychISGYTCYKFChA9erV6dixIy1atLC7z/Dhw0lMTLS12ihVqpRtfBJPT09++OEHli5dytKlSzl+/DjR0dH4+PhQo0YNOnXqRPPmzTP9cbvVqFGjaNCgAQsXLuTo0aO2DHXq1KFHjx6ppg2+Xx07duSJJ56wvfbPnj1LREQELi4uFCtWjLp169KpUydq1KiRKcerWbMm06dP5+uvv+bYsWN4eHhQv359Bg0adMfxbXIKFxcXChYsSM2aNWnfvj0tW7ZM92C9BoOBr776iscff5wVK1Zw6tQpwsLCcHJyokSJEjz22GP06NHDbirpokWL8sUXXzBx4kQuXrxIvnz5MqVQlnLGl1KlSvHBBx8wbtw4tm3bRmxsLBUrVuTVV19Nc3rde3n+7vTeux0nJyfGjx9Pq1atWLRoEYcPHyYiIgIvLy+qVavGs88+yzPPPJPpgyWLiORmBmtWzHEoIiIiIiIiIpILqXQsIiIiIiIiInKTCiUiIiIiIiIiIjepUCIiIiIiIiIicpMKJSIiIiIiIiIiN6lQIiIiIiIiIiJykwolIiIiIiIiIiI3qVAiIiIiIiIiInKTCiUiIiIiIiIiIjepUCIiIiIiIiIicpMKJSIiIiIiIiIiN6lQIiIiIiIiIiJykwolIiIiIiIiIiI3qVAiIiIiIiIiInKTCiUiIiIiIiIiIjepUCIiIiIiIiIicpMKJSIiIiIiIiIiN6lQIiIiIiIiIiJykwolIiIiIiIiIiI3qVAiIiIiIiIiInKTCiUiIiIiIiIiIjepUCIiIiIiIiIicpMKJSIiIiIiIiIiN6lQIiIiIiIiIiJykwolIiIiIiIiIiI3OTs6gEhet23bNn766ScOHDhAZGQkhQoVolatWnTv3p26des6Ol6OtGrVKn755ReOHDlCbGwshQsXpn79+vTo0QN/f39Hx7uj5s2bU6tWLSZMmJAl+z99+jQzZ85kx44dXL9+HW9vbypUqEDHjh159tlnbdsNGTKEXbt2sXHjxizJcT8uXbrE7Nmz2bJlC1evXsVsNlOyZEmefPJJ3nrrLdzd3R0dUUREHiDpvRZr3rw59erVY+zYsQ5Mm9qQIUP47bff7Ja5uLhQqlQpnn76aV599VVcXV0dlE4kd1KLEhEHmjhxIn369KF06dJMnz6dP/74gzFjxhATE0P37t1ZuHBhph+zWbNm7Ny5M9P3mx2sVivvv/8+H3zwAY888ghz5sxh1apVDB06lNOnT/Piiy+yadMmh+UbPHgwkydPdtjxN23axHPPPUdISAiffvopa9asYerUqVSpUoUhQ4bw3nvvOSxbeoWEhPDiiy+yd+9ehg4dyurVq/ntt994+eWXmTdvHgMGDLBte+HChXsqjN3r/URE5MHjiGuxrODn58e2bdtsP8uWLaNbt27MnDmTIUOGODpehn399de5Mrc8ONSiRMRBtmzZwtSpUxk+fDhdu3a1LS9VqhQNGzbk7bff5ssvv6R169b4+PhkyjGvXr3KpUuXMmVfjjB//nyWLVvG9OnTeeKJJ2zLS5cuTePGjXnllVf49NNPady4Mc7O2f/f2759+yhVqlS2Hxfg2rVrvPvuuzRr1oyJEydiMBgAKFmyJLVq1aJMmTKMHj2aDh068NhjjzkkY3r88ccfXL9+nfnz51OmTBnb8ooVK+Lq6sqyZcsICQnBz8+Pffv23dMx7vV+IiLyYHHEtVhWMRqNFC5c2Ha7cOHCVKhQgZCQEL755hvef/99ihUr5sCEGbNv3z6KFi3q6BiSh6lFiYiDfP/995QrV46XX3451TqDwcCoUaPYsGGD7Q9z8+bNGThwoN12S5Yswd/fn1OnTgEQFBTEgAEDePzxx6lRowYtWrRg8uTJmM1mdu7cSZMmTQB45ZVXaN68ud1+nn76aWrUqMEjjzxCr169OHz4cKrjHDhwgO7du1OrVi2eeOIJli9fzqVLl+jZsycBAQE8+eSTrFq1yi7jgQMH6NWrFw0bNqR27dp07dqVvXv32tbv3LkTf39/Vq9ezdNPP33HD/Hff/89jz/+uF2RJJmrqyuTJ09m+fLltiKJyWRi/PjxNG/enOrVq9OwYUOGDBnCjRs3bPcbMmQIjz/+uN2+Ll68iL+/P/Pnz7c7/+PHj/Pqq68SEBBAo0aN+PTTT7FYLAD4+/tz7tw5pkyZgr+/PxcvXrzteQDMmzePZs2aUb16dTp06MD+/fsB+PzzzwkICCA6Otpu+/379+Pv78+WLVvS3N8vv/xCTEwMQ4YMsRVJUuratSsbN25M9fgGBgbSsWNHatSoQaNGjViwYIFt3a2vr2QpX4vJj9WiRYvo3Lkz1atXJzIyksmTJ1O3bl2OHTvGyy+/TO3atXniiSeYMWPGHR+X+Ph4IOm5u1Xnzp2ZP38+fn5+TJ482dZCxt/f3/at07Vr1xgyZAiPPfYY1atXp3nz5owdO5a4uDiA297P39+fL7/80u54kydPxt/f35bp33//5dVXX6VBgwbUrFmTtm3bMnfu3Duej4iI5FwZvRZLtmzZMlq2bEn16tVp166d3XVNRq4rtmzZwpNPPskLL7wAQPfu3Xn99df5448/aNu2LTVr1qR9+/a3/dufHlWqVAGw+6Js2bJlvPjii9SpU4d69eoxcOBArl69aluf/Dd8/fr1NGrUiP79+wNgsVj4/vvveeqpp6hZsyatW7dmzpw5dsdL73Xfzp07GTRoEHXr1qV+/foMHjyYmJgYIOk6Y/v27fz222+2bQH+/PNPunTpQu3atQkICOD5559n7dq1dse/evUqffv2pXbt2jRo0IDPP/+cpUuXpro2+/PPP+nWrRv16tWjTp06vPrqq6mudyRvU6FExAESExPZu3cvTZs2TfNDLYCvry/e3t4Z2u97771HSEgIM2fOZM2aNQwaNIgff/yR7777joCAAMaPHw8k/QFcvHgxAIsXL+aDDz6gRYsWLF26lNmzZ5OQkMArr7zClStX7Pb/+eef06dPH5YuXUr58uUZPnw4H374Id26dWPJkiWUKVOGjz76yPYh/8yZM/zvf//DbDYzc+ZMFi5cSLFixejZs2eqP0bTpk3j7bffTtXHNtnly5e5cOECTZs2ve35Fy5cGA8PD9vtjz76iJ9//pn+/fuzatUqPvvsM3bu3Mmrr76K1WrN0GMLMGLECF588UWWL1/OSy+9xI8//sjq1asBbGN99OzZk23btlG8ePHb7mfv3r3s3LmTqVOnMn/+fKxWK/369SMmJoZOnToRGxvLmjVr7O6zcuVKihcvTuPGjdPc565du/D397/tcY1GIyVLlrRbFh0dzYQJE/jggw9YunQptWvXZsSIEZw9eza9D4nNd999R8eOHVm7di358uUDkl7no0eP5o033mD58uU0btyY8ePH24pCaXn88cdxcnKiR48ezJs3L9VrMFnPnj3p1q0bkNS3fOjQoQAMGjSIPXv28O2337Ju3To+/vhjfv31VyZOnHjH+6VH37598fLyYu7cuaxatYoePXrw+eefpyoOiohIznev12IHDhxg27ZtfPPNN/z0008kJiby7rvv2r44yYjp06fz6aefMm3aNNuyEydOsGTJEr788ksWLVqEh4cH77//PrGxsRneP2D7m558fbBs2TLef/99ateuzZIlS/j22285ffo0PXr0sPuSwmw2M3fuXKZOncqIESMAmDFjBl9//TWvv/46v//+O71792bs2LHMmzcPyNh139ixY3nsscf47bffGDRoEEuXLuWnn34Ckq5N/fz8aNOmDdu2bSMgIIDz58/z+uuv89BDD7F06VKWLVtGo0aNGDBgAEeOHLHt9+233+bAgQNMmDCBn376icjISL799lu7Y+/atYvXXnuNIkWK8PPPP/Pjjz9iMpno1q0bISEh9/Q4y4NHhRIRBwgNDcVkMqX64Hq/AgMDadSoEdWqVaNEiRK0adOGBQsW0K5dO1xdXW1/7H18fPDz8wNg5syZNGnShLfffpsKFSpQo0YNvvrqK+Li4liyZInd/p999lkaN25M+fLl6dy5M7GxsdSvX5/mzZvblkVHR3P+/HkAZs+ejdFoZPLkyTz88MP4+/vz6aefki9fPmbPnm2374YNG9KiRYvbNgtN/qYjvY/Z1atXWb58OX379uW5556jTJkyNG3alCFDhhAYGMg///yT7sc1Wdu2bXnqqacoXbo0/fr1w8XFhYMHDwJQqFAhADw9PSlcuDBOTk633U9MTAxffPEFVapUoUaNGnz00UeEhITw119/Ub58eerXr2/32FssFv744w86dOiA0Zj2f9tXr17N8OspLCyMwYMH88gjj1ChQgX69OmD1Wrl0KFDGdoPQOXKlenYsSMlSpSwZYyNjaVnz548/vjjlClThn79+gHYHrPb7WfSpEm2b/KaNm1KixYtGD58uN3YOvny5bMVxQoXLkz+/PmBpAuvuXPnEhAQQPHixWnatCmNGjVi69atd7zf3dy4cYPLly/TsmVLKlWqRKlSpejUqRO//PILjz76aAYfLRERcbR7vRaLjo5mzJgxVK5cmdq1a9OxY0eCgoK4du1ahjO0bduW+vXr23WZuXLlCmPHjqVatWr4+/vTtWtXwsLCOHfuXIb2nZCQwLZt22wtQJILJdOmTePRRx9l6NChlCtXjrp16zJ27FhOnz5t9yVNTEwMPXr0oEaNGvj5+WEymfjhhx948cUXbddVHTt25I033iAqKgrI2HVfgwYN6NixI6VLl6ZTp06UKlXKdn3g5+eH0WjE3d2dwoUL4+rqStGiRVm2bJktd5kyZXjzzTcxm81s374dSCoK7du3jzfeeINmzZpRsWJFRo8ejZeXl92xZ8yYQcmSJfniiy+oWLEiNWrUYPz48URFRfHLL79k6HGWB5fGKBFxgORvLu6lVcOdPPnkk0yZMoXg4GCaNm3Ko48+SsWKFW+7fVRUFGfPnqVDhw52ywsVKkTp0qXtKvQADz/8sO335GaoVatWTbUsMjISSPpAXKtWLbsPo25ubtSpU4fAwEC7fVevXv2O53a7b3tu5/Dhw1it1lQzBwUEBABw5MiRDM8qVKtWLdvvzs7OeHt7ExERkaF9QNK5urm52W4nDyx6+vRpIKmLycCBA7lw4QKlS5dm9+7dXL9+3dY0Ny0GgyHDr6d8+fJRqVIl2+0CBQoApOr2kx63e/5SPmbJxbm7PWYtW7bkiSeeYNeuXezYsYM9e/awePFiFi5cSPv27fnyyy9v+3pISEhgxowZ7Nq1i5CQECwWCyaTCV9f3wyfU0p+fn4EBAQwYsQI/v33Xxo1akRAQADVqlW7r/2KiIhj3Ou1WLVq1exmkEn+25ZZfzvLli1r2yf897f5bn87b9y4YbvGgaSurM7Ozjz77LO2bqZRUVGcPn2aZ555xu6+VatWxdfXlyNHjvD000+nme/ChQuEhYXZ/V0HeOONN2y/Z+S679b9+Pn5ER4eftvzc3Nz4+TJk4waNYpTp07ZPd5hYWEAti/qatSoYXffJ554wu74Bw8e5KmnnrL7UqtQoUJUqlQp1bWv5F0qlIg4QIECBfDw8MjwtwN38/nnn7NgwQJWrFjBvHnzcHV1pV27dnzwwQdpfnOe/A3ArZX25GW3/tFP2a0l+QIjrWXJFx1RUVEcO3bM7g83JI0/kfIiALjrN/vJ34Skt1tI8rndut/kc72XCxpPT0+72/dSnABSNeNN3m9y39wWLVpQsGBBlixZwttvv83KlStp2LDhHb/1KlGiRIZfTymfO7i/At7tnr/kbjgZ3b+LiwuPP/64rZ/31atX+eSTT/j9999p06YNLVq0SHWf6OhounXrhouLC++99x6VKlXCxcWFL7/80q5/9L0wGAx89913zJkzh9WrVzN9+nTy58/Piy++yMCBAzXtoohILnOv12JZ/bczrWuN9Ozf19fXboYeZ2dnW2uMZMnXRt98802qMcNiY2MJDg62W5byeiW5UJPy7/qtMnLdd7vzvJ1169bRv39/WrduzcSJEylUqBAGg4GnnnrKtk1yweTWjLceOyoqiqVLl7Jy5Uq75fHx8fp7LjYqlIg4gJOTE48++igbN25k6NChac7QEh4ezpo1a+jQoYNt/a1/JJM/WCdzcXGhe/fudO/enbCwMNatW8cXX3xBYmIi48aNS3WM5KJB8h/OlKKiou67a5C3tzfFihVj9OjRqdbdrgvJ7RQpUoQKFSqwbt06evfuneY258+f5/Dhw7Rp08b2xz25dUuy5NvJ69Mqdtz6uGa2W4s0ycdL/sPu4uLCCy+8wO+//84bb7zB2rVrbf2Db6dBgwZ8+eWXnDp1igoVKqS5zc8//0zbtm3T3bridhct91JkSi+LxUJMTEyq4l3RokX57LPPWLduHf/++2+ahZKdO3cSHBzMrFmz7MZySe/zebfXQb58+ejXrx/9+vUjODiYFStWMGnSJNzd3Xn77bfTe4oiIpID3Ou12N044roCks6nbNmyd9wmuTDTo0cPXnzxxVTrby1epFSwYEGAO7b6yMzrvlstX76cokWLMmHCBNu+bi3sJBc5bh3PJbmAkjJno0aNeOutt1IdR4USSaYxSkQcpGfPnly5ciXVAFOQ9IFt1KhRfPbZZ7Y+r97e3qkGmEo5KGZYWBjLli3DbDYDSd8svPjiizzzzDMcPXo01f4hqVBSsWJFdu/ebbc+ODiYCxcupGq6mFG1a9fmzJkzFC9enLJly9p+rFYrRYoUyfD+evXqxf79+/n1119TrTOZTAwdOpTPP/+c6OhoqlevjtFoTHVuyWOTJJ9b/vz5iYiIIDEx0bbNgQMHMpwtWXq+UTp48KBtFhbA1hw0ZTeYTp06cfHiRaZNm4bBYODJJ5+84z5feOEFfH19GT16NAkJCanWL1iwgJEjR7Jnz570nortgirl6+7cuXOpLjgy03PPPUffvn3THBQvebT6W6cLTH7Mk8875TdHFy9eZOfOnWk+LymX3e39dfXqVbtBW4sUKUKvXr14/PHHU72/REQkd8jotVh6ZPZ1RWbKly8flStX5syZM3bXZWXLlsVkMtmKIWkpXrw4+fPnT3VdNWnSJD744AMg86/7Uv6dTkhIwMfHx67gkjwBQPJ25cqVA+zHQrNaralmxqlduzanTp1K9RgkJibajRcjeZsKJSIO8thjj/HWW2/xzTffMHjwYPbu3UtQUBA7d+6kT58+ttYgyV1Oatasyd69e1m/fj3nz59n9uzZdv0trVYrI0aM4KOPPuLff//l8uXLbN++nY0bN1KvXj3gvzFE/vrrL44cOYLVauXVV19l69atTJkyhbNnz7J//37efvttfH197zgmRnq88sorREdHM2jQIA4dOsSFCxf45ZdfeO655+yah6bXCy+8wAsvvMCwYcP49NNPOXz4MBcvXmTLli10796d48ePM3HiRLy8vChcuDDPP/88M2bM4Pfff+fChQts2LCBzz77jPr161OzZk3b45qQkMC0adO4cOEC69evTzWIbXq4urri7u7O/v37+ffff+/Yl9jd3Z2hQ4dy/PhxDh48yJgxYyhatCgNGza0bVOqVCkaNWrE1KlTee6553Bxcbnj8f38/GxdTLp3787mzZsJCgri8OHDfPrpp4wcOZI+ffqk2RLjdqpVq4azszPfffcdZ86cYf/+/Xz00UepChWZ6a233mLfvn28/vrrbNu2jQsXLnD69GmWLVvGm2++yUMPPUS7du2A/1oFrV+/ntOnT1O9enWcnZ35/vvvuXDhAjt27OCNN96gTZs2hIWFceTIEUwmU6r7QdLrYOPGjfz999+cOXOG8ePH2xVOIiIiGDRoEOPHj+fkyZNcvnyZ9evXs3fvXtv7S0REcpeMXoulR2ZdV2SV1157jQ0bNjB58mROnTrFyZMn+fzzz3n++efvOD6Hi4sLPXr0YOnSpSxatIigoCCWLl3KzJkzbeN1ZeZ1n7e3N0eOHOHo0aNcv36d2rVrc/LkSVatWsWFCxf47rvvOHDgAMWLF+fIkSMEBwfj7+/PQw89xMyZM9mxYwenTp3i448/TvXlS+/evTl27Jht3LGzZ88yY8YMnn766fuailkeLOp6I+JAb775Jo888gg//vgjr7/+OtHR0RQpUoR69eqxZMkSu4FY+/fvz9WrVxk8eDBOTk60atWKgQMH2poNFihQgB9++IFJkybRvXt34uLiKFasGK1bt7Z1C6hRowZPPvkkP/zwA7/++itbt27lueeew2Kx8MMPPzBt2jTc3d2pV68eY8aMSdWnM6PKli3L3LlzmTBhAq+88goJCQmUK1eOwYMH06VLl3va56effkrjxo1ZuHAhPXv2JC4uzjZt7oQJEyhRooRt2xEjRtgKCNeuXaNAgQK0bNmSQYMG2bZp27Yt+/fv5+eff2bWrFkEBATwySef2D6Mp5fBYOD1119n2rRpdO3a1bavtDRq1IjKlSvz6quvcuPGDapWrcq0adPsBnhNzrZ161Y6duyYrgyNGzdm2bJlzJgxg5EjR3Lt2jV8fX2pWrUq06dPp0mTJhk6pxIlSjBq1Ci++eYbnnnmGdtz9/XXX2doPxnRsmVL5s6dy9y5cxk+fDjXr1/HYDBQunRp2rZtS+/evW1Ng5955hlWrFjBgAEDaNasGVOmTGHMmDF8/fXXtG/fnsqVKzN8+HAKFCjA7t276dq1K4sWLUrzfh999BHDhg2jX79+eHh48MILL/DKK68watQoIKm1z7Rp05g6dSrz5s3DbDZTsmRJevbsSY8ePbLs8RARkayVkWux9Mis64qs0r59e4xGIzNnzmT69Ok4OztTo0YNZs2addeB9d944w1cXV2ZNm0ao0aNokSJErz//vt0794dyNzrvtdee40xY8bQpUsXPvvsM1555RVOnz7Nxx9/jMFgoFmzZowbN45FixYxceJE3n33XebMmcOUKVP4+OOP6dOnDwUKFOCll16ic+fOfPLJJ7brrLp16zJr1iwmT57MSy+9hMViwd/fnwkTJty1Ba/kHQZrZk+7ISIimaJv375YrVamT5/u6CgiIiIiOV5sbCwmk8nWihpgzJgxLF68mH379jkwmeQ2alEiIpKDmEwmrl27xsKFC9m2bVuOaq4rIiIikpP17NmT69evM3r0aEqVKsX+/ftZvHhxulvniiRTixIRkRxkz549dO/enXLlyjFkyBCaNm3q6EgiIiIiucL169cZN24c27dvJyIiguLFi9O2bVv69u2bqouzyJ2oUCIiIiIiIiIicpNmvRERERERERERuUmFEhERERERERGRm1QoERERERERERG5SbPeAImJiYSHh+Pm5obRqNqRiIiI5GwWi4X4+Hh8fHxwdtblnIiISGbSX1YgPDycs2fPOjqGiIiISIaUK1eOggULOjqGiIjIA0WFErBNFVWuXDk8PDwcnEZyKrPZzPHjx6lcuTJOTk6OjiMiDyD9PyPpFRsby9mzZzXdpYiISBZQoQRs3W08PDzw9PR0cBrJqcxmMwCenp76ACMiWUL/z0hGqcuwiIhI5lOhRPIMf3//267bsWMHfn5+JCYmMn36dKZOnUpCQgKfffYZHTp0sG33ySefcPTo0dvu59ixY6mWzZkzhzFjxgCk2p+IiIiIiIjkLCqUSJ7ToUMHvLy87Ja5u7tz8uRJBg8ezOHDh29733r16lG3bl0MBoNt2dWrV1mzZg3e3t6ptg8ODmbSpEmZF15ERERERESylAolkue88cYblCpVKtXyb775hpMnT/L+++8zd+5cLl++nGqbVq1aUbt2bbsm8W+88YbdvymNHTuWqKgo3NzciI+Pz8SzEBERERERkaygjq0iNz3yyCMsX76cXr16pbvP9+bNm1m/fj1ly5ala9eudut27NjBypUrqVOnDjVr1syKyCIiIiIiIpLJVCiRPOf8+fN89913jB8/nnXr1tmWd+vWjbJly6Z7P2azmc8++wyAt956CxcXF9s6k8nEiBEjcHFxYeTIkXZddURERERERCTnUtcbyXP+7//+z+5248aNmT59eoZnmFi6dClnz56ldOnStG3b1m7drFmzOHv2LG+99RaVK1e+78wiIiIiIiKSPdSiRPKMKlWqULVqVT799FP27NnD119/jdFoZOvWrSxYsCBD+7JYLHz77bdAUuElZZHlwoULTJ8+ncqVK/Paa69l6jmIiIiIiIhI1lKLEskzli1bZne7VatWNGjQgO3bt/Pnn3+mGmPkTvbs2cPFixcxGo20bt3abt3o0aNJSEhgzJgxdt1xREREREREJOdToUTyFJPJhKurq+12/vz5bcszYvXq1QAEBARQsGBBu3WbN28G4MUXX0x1vw8++IApU6awcePGDB1PREREREREsocKJZInrFy5kqFDh+Lu7s769evx8vIiKiqKf/75B4AaNWpkaH9bt24F4LHHHku17oknnki1bP/+/YSFhVGlShUefvjhjJ+AiIiIiIiIZAsVSiRPaNKkCYUKFeLChQt07NiRunXrsnv3bq5fv07hwoXp3r0748eP5/jx4wDcuHEDgB9//JE1a9YA2MYkiY+P5+LFiwCUK1cu1bGmT5+ealn37t3ZtWsX//vf/+jQoUNWnKKIiIiIiIhkAhVKJE/Inz8/P/30E9988w3bt29n6dKl+Pj48NxzzzFgwAAKFy7M/v372bVrl939/v33X/7991+7ZREREVitVgC8vb2z7RxEREREREQk66lQInlGsWLF+OSTT267fu7cuXe8v9lsBqBw4cIcO3YsQ8e+275FREREREQkZ9D0wCIZYDTqLSMiIiIiIvIgU4sSkXRycnIiICDA0TFEREREREQkC+nrcRERERERERGRm9SiJJs1OzCcWIvJ0TFE8hwPoyubao1ydAwREREREcnhVCjJZrEWkwolIiIiIiIiIjmUut6IiIiIiIiIiNykQomIiIiIiIiIyE0qlIiIiIiIiIiI3KRCiYiIiIiIiIjITSqUiIiIiIiIiIjc5NBCib+/P9WrV6dGjRq2n08++QSAHTt20LFjR+rUqUO7du1Yvny53X3nzJlDq1atqFOnDl26dOHw4cO2dfHx8QwfPpwmTZpQv359+vfvT2hoaLaem4iIiIiIiIjkPg6fHviPP/6gVKlSdsuCg4N5/fXXGTp0KE8//TT//PMP/fr1o3z58tSoUYONGzcyefJkZs2ahb+/P3PmzKFv376sXbsWT09PJkyYQGBgIAsXLsTDw4Nhw4bxwQcfMG3aNAedpYiIiIiIiIjkBjmy682KFSsoV64cHTt2xM3NjYYNG9K8eXMWLVoEwMKFC+nQoQO1atXC3d2d3r17A7Bp0yYSExNZvHgxr7/+OsWLF8fX15cBAwawefNmrl696sjTEhEREREREZEczuEtSsaPH8++ffuIioqiTZs2DBkyhMDAQKpVq2a3XbVq1Vi9ejUAgYGBtG3b1rbOaDRStWpVDh06RNWqVYmMjOThhx+2ra9QoQLu7u4EBgZStGjR22Yxm82YzeZMPsMkTk5OWbJfEcm4rHqfi9yv5NemXqNyN3qNiIiIZB2HFkpq165Nw4YN+fzzz7lw4QIDBgxg5MiRhIWFpSpo+Pr62sYZCQsLw8fHx269j48PoaGhhIWFAeDt7W233tvb+67jlBw/fvw+zyhtRqORgICALNm3iGTcwYMHsVgsjo4hcluHDh1ydAQRERGRPMuhhZKFCxfafq9QoQLvvvsu/fr145FHHrnrfa1W632tT0vlypXx9PTM8P1EJHepWbOmoyOIpMlsNnPo0CFq1KihlohyRzExMVn2BY+IiEhe5/CuNymVKlUKs9mM0Wi0tQxJFhoaip+fHwAFChRItT4sLIxKlSrZtgkLCyNfvny29eHh4RQsWPCOx3dyctKFqUgeoPe55HT6eyR3o9eHiIhI1nHYYK5Hjhxh7NixdstOnTqFq6srTZs2tZvuF+Dw4cPUqlULgOrVqxMYGGhbZzabOXLkCLVq1aJ06dL4+PjYrT9+/Dgmk4nq1atn4RmJiIiIiIiISG7nsEJJwYIFWbhwITNmzMBkMnHmzBkmTZrESy+9xLPPPktQUBCLFi0iPj6eLVu2sGXLFjp16gRAly5dWLp0Kfv37yc2NpapU6fi6urKE088gZOTE506dWLatGlcvnyZ0NBQvvrqK1q2bEmhQoUcdboiIiIiIiIikgs4rOtN0aJFmTFjBuPHj7cVOp5//nkGDhyIm5sb06dPZ/To0YwcOZKSJUvyxRdfUKVKFQCaNGnCO++8w4ABA7hx4wY1atRgxowZuLu7A9C/f3+io6N59tlnSUxMpFmzZowYMcJRpyoiIiIiIiIiuYTBei+jnj5gYmJiOHr0KFWrVs3ywVwb7BtCrMWUpccQkdQ8jK78HTD27huKOJDZbGb//v3Url1bY1DIHWXntYuIiEhek6MGcxUREREREZHMF2sxEWs2YcGC2WrBghWz1YJPTCIe8WYwGDAYjODshMHdE4OHBwaDwdGxRRxChRIREREREZFcJtocx/n461w2hRKWGE14YkzSjzmasMQYIhJjkpabk36PtyamuZ/vtzhRctPfqVcYjRjc3DF45sPg4YnRwxODpydGL2+MBQvhVLAwxoKFcSpYCKNf0m2Dm1sWn7VI9lChREREREREJAeKs5i4EH+dc3HXORd/jfNx1zgff53zcde4nhiZtQe3WLDGxmCNjQHAnI67GLzy41SsBM6lyt78KYNz6bI4lSiNwVkfPSX30KtVRERERETEwUISojgYfZaD0ecIjL7A2bhgriaEYyX3DClpjYok8eQxEk8es1/h5IRT0eI4ly6HS0V/XCpXxaVyNYxe+R0TVOQuVCgRERERERHJRolWM8diLtkKIwejznHRdMPRsbKO2Yz50kXMly4Sv3ObbbFT8VK2oolL5aq4VKiEwcXVgUFFkqhQIiIiIiIikoXiLQnsijzB7siTHIw+x9Hoi8RZExwdy+HMly9ivnyRuC3rkha4uuFarQauteriVrsuzg9VwmA0Ojak5EkqlIiIiIiIiGSyq6Yw/gw/wp/hR9gVeZI4i8nRkXI+Uzym/Xsw7d9D1I9gyO+Da80A3Go/imvtujgXK+HohJJHqFAiIiIiIiJyn6xWK4dizvNnWFJx5FhskKMj5XrWyHDi/9pM/F+bAXAuUx63hk1xb9gUl/IVHRtOHmgqlIiIiIiIiNyDBKuZbeFH2Rh2iL/C/+VGVs9Ek8clnj9D4vkzRC+YjVPxUrg3bIJbw6a4Vq7m6GjygFGhREREREREJAOOxQSx7MYuVoXsJTQx2tFx8iTz5YtE//oz0b/+jLFQEdwbNcejRVtcypZ3dDR5AKhQIiIiIiIichehiVGsurGXZTd2cSz2kqPjSAqW68HELF1AzNIFuFSqikfLtrg3aYExn5ejo0kupUKJiIiIiIhIGhJvdq1ZemMXW8OPkmg1OzqS3EXCiaMknDhKxKwpuD/WBI+WbXGt+QgGg8HR0SQXUaFEREREREQkhSumUOYHb2P5jd2EJEY5Oo7cC1M8cVvWEbdlHU5Fi+PZ9nk8nmqP0Su/o5NJLqBCiYiIiIiICHAi9jKzr2zij9B9aj3yADFfvUzkD98SNf8H3Js9Rb5nOuFcqoyjY0kOpkKJiIiIiIjkabsjTzL7yka2Rfzr6CiShaxxscSuXkbsH8txe7Qh+Z7vjGv12o6OJTmQCiUiIiIiIpLnWKwWNoYd4ocrmzgcc97RcSQ7Wa3E7/qL+F1/4VK5Kvle+h/u9R53dCrJQVQoERERERGRPCPeksDyG7uZc3UL5+OvOTqOOFjC8aOEfTIEl8pV8Xq5F26P1Hd0JMkBVCgREREREZEHnsVqYcWNPXxz6Q+uJoQ5Oo7kMAnHjxI64l1cqlTH6+WeuAU86uhI4kAqlIiIiIiIyANtR8QxJlxcwbHYS46OIjlcwr+HCR3+Di7VauLVtRduNes4OpI4gAolIiIiIiLyQDoRe4mvLq5ge8QxR0eRXCbhyEFCh76N26MNyd/7TZxLlHZ0JMlGKpSIiIiIiMgDJdgUzjeXVrP8xm4sWB0dR3Kx+N3bid+3C8/2L+DVuQfGfF6OjiTZQIUSERERERF5IMSY4/n+ykbmBm8hzmJydBx5UCQmErN0IXGb1uDVtTcerZ7GYDQ6OpVkIRVKREREREQk11t54x/GX1zOjcRIR0eRB5QlPIyIb78kZtVvePd5G9caAY6OJFlEhRIREREREcm1guJDGHN+MX9F/OvoKJJHJJ49RciH/fFo2Y78Pd/A6JXf0ZEkk6lQIiIiIiIiuY7ZauGnq1v49vIadbMRh4hdt5L4f/7Gu+87uD/WxNFxJBOpUCIiIiIiIrnKidjLDD87nyMxFx0dRfI4S8gNwj4dilvDpnj3HYhTgYKOjiSZQIUSERERERHJFRKtZn64spHpl9eSYDU7Oo6ITfz2LVw/uJf8vd7As0U7R8eR+6RCiYiIiIiI5HgnY68w7OzPakUiOZY1KpKISWOJ3/4nPm9/gNHH19GR5B5pTiMREREREcnR5gX/Seej41UkkVwhfvd2rvfvQfz+PY6OIvdIhRIREREREcmRosxxvHvqR8ZdWKquNpKrWEJuEDr8HSJ/+BZrYqKj40gGqVAiIiIiIiI5zrGYILocncC6sAOOjiJyb6xWopfM58Z7/Ui8dMHRaSQDVCgREREREZEc5ddrO+j+7yTOx19zdBSR+5Z48l9uDOhF7KY1jo4i6aRCiYiIiIiI5AixFhMfnfmZUecXEW9VdwV5cFhjYwn/ajQRMyZhNTvmtT1v3jz8/f2pWbMmV65csVsXExNDo0aN8Pf357PPPnNIvpxEhRIREREREXG407FX6Xp0IitCNACmPLhiViwmdPggLOFh2X7szp07U7lyZeLj45k0aZLduu+//55r167h5+fHm2++me3ZchoVSkRERERExKFWh+zj5X8ncCruyt03FsnlTAf3cuOdPiScPpGtx3VycuKDDz4AYNmyZZw4kXT869ev89133wHw9ttvkz9//mzNlROpUCIiIiIiIg4z/fJahpyZS6zF5OgoItnGHHyZkPdfJ/bP9dl63IYNG/Lkk09iNpsZP348AJMnTyYmJoYqVarQqVMnADZv3kz37t2pX78+AQEB9OrVi5MnT9rta9WqVXTu3Jl69eoREBBAhw4dWLx4cbaeT1ZRoURERERERLJdotXMyHML+fbSH46OIuIQ1vg4wr8YSeTsaVit1mw77uDBg3FxcWHTpk0sWrTIVtz48MMPMRqNbNy4kb59+/LPP/9Qv359mjRpwvbt2+nevTshISEAbNy4kYEDB3L8+HGaN29O+/btuXr1KkOHDuWXX37JtnPJKiqUiIiIiIhItooxx/P2ye9Ycn2no6OIOFz0r/MI/+oTrInZM8hr2bJl+d///gfARx99RGJiIq1ataJ+/foATJw4EavVSu/evfn666+ZNGkSr776KiEhIcybNw+Av/76C4Bnn32WsWPH8sknnzBjxgxee+01/Pz8suU8spKzowOIiIiIiEjecT0hgjdPzuJozEVHRxHJMeI2r8MSForvB2Mwenpm+fH69evH0qVLuX79Om5ubrz33nsAREVFcezYMQCOHz/OmDFjADh79iwABw8eBKBcuXIALFy4kCtXrvDII49Qr149Bg4ciMFgyPL8WU2FEhERERERyRZn4q7y+omZXDKFODqKSI5j2r+HkA/epMCIL3AqUDBLj+Xl5UXjxo357bffePjhhyldujQA0dHRtm02bdqU6n7J0wp369aNGzduMHfuXDZu3MjGjRsBqFChAhMmTMDf3z9L82c1FUpERERERCTL7Y08zdunviPCHOvoKCI5VuLpE4S8148CI7/EuWSZbD9+/vz5MRgMWK1WvvnmG1q0aJHmdgaDgQEDBvDmm28SGBjIvn37WLJkCceOHWPgwIGsWrUqm5NnLo1RIiIiIiIiWWpNyH5eOzFNRRKRdDBfvcyN917HdPxIth/b09OTqlWrArB161bb8i1btjBr1ix27NgBwPz58/noo48IDg6mVq1a9OjRg9GjRwNw8WLu71anFiUiIiIiIpJlfr+xh2Fn52Mh+2b1EMntrJHhhA57hwIjv8S1SvVsPfYbb7zBm2++yYIFCwgKCiJ//vxs3LiRxMREZs2aBcCZM2dYtGgR27dvp3HjxlitVv7++28AWrduna15s4IKJSIiIiIikiVWh+xl+NkFKpKI3ANrTDShwwclFUuq1si247Zo0YJvv/2WadOmsWvXLgCqV69O//79adCgAZA0xbCPjw8rVqxg6dKlGI1GihUrxltvvUWfPn2yLWtWMVizc8LmHComJoajR49StWpVPLN4hOEG+4YQazFl6TFEJDUPoyt/B4x1dAyROzKbzezfv5/atWvj5OTk6DiSg2XntYvIvVoTsp8PzvyEGYujo8gdfL/FiZKb/nZ0DLkDg4cHBT7+EteHazo6Sp6hMUpERERERCRTrQ89yIcqkohkCmtsLKEj38UUeMDRUfIMFUpERERERCTTbA47zOAzc0lUkUQk01hjYwkd8R6mw/sdHSVPUKFEREREREQyxZ/hR3j39I8kWs2OjiLywLHGxRI68n1M/x52dJQHngolIiIiIiJy3/4KP8qgU7NJUJFEJMskF0sSzp12dJQHmgolIiIiIiJyX3ZGnGDgqR8wWRMdHUXkgWeNiiR0+DskXrnk6CgPLBVKRERERETknp2Ovcqg0z8QryKJSLaxhNwgdPggLOGhjo7yQFKhRERERERE7klIQhRvnpxJpDnO0VFE8hzz5YuEjhyMJS7W0VEeOCqUiIiIiIhIhpksiQw89T1BphBHRxHJsxJOHCX88+FYzWrRlZlUKBERERERkQwbfnY++6PPOjqGSJ4Xv+dvImdNdnSMB4oKJSIiIiIikiHfXvqD1aH7HB1DRG6K+X0JMWuWOzrGA0OFEhERERERSbeVN/5h+uW1jo4hIreImDYBU+BBR8d4IKhQIiIiIiIi6bI36jQjzi10dAwRSUtiImFjP8IcfNXRSXI9FUpEREREROSuLsbf4J1TP2DSNMAiOZYlLJTQMR9gjdNMVPcjxxRKPv30U/z9/W23d+zYQceOHalTpw7t2rVj+XL7/lZz5syhVatW1KlThy5dunD48GHbuvj4eIYPH06TJk2oX78+/fv3JzRU80uLiIiIiNyLBEsi757+kdDEaEdHEZG7SDx9gvBJnzk6Rq6WIwolR48eZdmyZbbbwcHBvP7663Tu3JkdO3YwdOhQhg0bxqFDhwDYuHEjkydPZty4cWzfvp1mzZrRt29fYmJiAJgwYQKBgYEsXLiQNWvWYLVa+eCDDxxybiIiIiIiud2EoBUcjbno6Bgikk5x2zYSvVTd5O6VwwslFouFjz/+mB49etiWrVixgnLlytGxY0fc3Nxo2LAhzZs3Z9GiRQAsXLiQDh06UKtWLdzd3enduzcAmzZtIjExkcWLF/P6669TvHhxfH19GTBgAJs3b+bqVfXVEhERERHJiM1hgcwL3uroGCKSQZE/Tifh5DFHx8iVnB0dYMGCBbi5ufH0008zceJEAAIDA6lWrZrddtWqVWP16tW29W3btrWtMxqNVK1alUOHDlG1alUiIyN5+OGHbesrVKiAu7s7gYGBFC1a9LZZzGYzZrM5E8/uP05OTlmyXxHJuKx6n4vcr+TXpl6jcjd6jUh2uWoK4+OzCxwdQ0TuRWICYeNGUHDidxg9PR2dJldxaKHk+vXrTJ48mblz59otDwsLS1XQ8PX1tY0zEhYWho+Pj916Hx8fQkNDCQsLA8Db29tuvbe3913HKTl+/Pi9nMZdGY1GAgICsmTfIpJxBw8exGKxODqGyG0ldzUVEXEks9XCB2d+IsyscUlEcivz5YtETP0S30HDHR0lV3FooeSzzz6jQ4cOVKxYkYsXM9bn0Wq13tf6tFSuXBlPVdpEHng1a9Z0dASRNJnNZg4dOkSNGjXUElHuKCYmJsu+4BFJNuPyWv6JOu3oGCJyn+I2ryOmVl08W7S9+8YCOLBQsmPHDvbt28fvv/+eal2BAgVsLUOShYaG4ufnd9v1YWFhVKpUybZNWFgY+fLls60PDw+nYMGCd8zk5OSkC1ORPEDvc8np9PdI7kavD8lqeyJPMvPyekfHEJFMEjl9Iq5VquNcqoyjo+QKDhvMdfny5dy4cYNmzZpRv359OnToAED9+vWpXLmy3XS/AIcPH6ZWrVoAVK9encDAQNs6s9nMkSNHqFWrFqVLl8bHx8du/fHjxzGZTFSvXj0bzkxEREREJPcKS4zmgzPzMKNuqiIPCmtcLGFfjMBqTnR0lFzBYYWSIUOGsGbNGpYtW8ayZcuYMWMGAMuWLePpp58mKCiIRYsWER8fz5YtW9iyZQudOnUCoEuXLixdupT9+/cTGxvL1KlTcXV15YknnsDJyYlOnToxbdo0Ll++TGhoKF999RUtW7akUKFCjjpdEREREZFcYcTZhQQnhDs6hohkssTTJ4hePM/RMXIFh3W98fHxsRuQNTExqbJVrFgxAKZPn87o0aMZOXIkJUuW5IsvvqBKlSoANGnShHfeeYcBAwZw48YNatSowYwZM3B3dwegf//+REdH8+yzz5KYmEizZs0YMWJE9p6giIiIiEguszZ0P5vCD999QxHJlaIW/Ij7Y01wLlPe0VFyNIP1XkY9fcDExMRw9OhRqlatmuWDuTbYN4RYiylLjyEiqXkYXfk7YKyjY4jckdlsZv/+/dSuXVtjUMgdZee1i+QdkeZYng/8nGsJEY6OItno+y1OlNz0t6NjSDZyqVwVv3FTMeha47Yc1vVGRERERERyjkkXV6pIIpIHJBw/SsyyXxwdI0dToUREREREJI/bH3WGxdd3ODqGiGSTyHnfkXjpgqNj5FgqlIiIiIiI5GEJVjOjzi3CSp7vkS+Sd5jiCf/6czQSR9pUKBERERERycNmX9nIqbgrjo4hItksIfAAsRtWOTpGjqRCiYiIiIhIHnUu7hozL69zdAwRcZCoH2dgiYl2dIwcR4USEREREZE8avT5xcRbEx0dQ0QcxBIWQtT82Y6OkeOoUCIiIiIikgf9fmMPuyJPODqGiDhYzO+LSbx43tExchQVSkRERERE8ph4SwKTgzQ2gYgAiYlEzPza0SlyFBVKRERERETymAXX/uJKQpijY4hIDmHau5O4XX85OkaOoUKJiIiIiEgeEmWO4/vLGxwdQ0RymMhZU7AmaswiUKFERERERCRPmX1lE2FmzXIhIvbMly8Su3aFo2PkCCqUiIiIiIjkETcSIvkpeIujY4hIDhX1y1yspnhHx3A4FUpERERERPKI6ZfXEmsxOTqGiORQlhvXiFm11NExHE6FEhERERGRPOBi/A1+vf63o2OISA4XvfgnLHGxjo7hUM6ODiAiIiIiIlnvm0urSbSaHR1D5L4lWK0sCo1heXgs1xMtFHQyUsvTlV4F81HUxQmA4AQzU69HsTvaRKIVKrg507tQPgI8XW+735dOX+dKoiXNddPLFKCKuwvH4xL4KjiSM/GJFHR2olfBfDzp7W637ejL4fwTk8Cccn7kd8p9bRMs4WHELF+EV6dXHB3FYTL8rMXHxzN37lzb7Q0bNtCvXz/Gjh1LdLQGhRIRERERyWmOx1zij5B9jo4hkikmBUcy/Xo08RZ4Kr87+YxG1kTE0e98KJFmC1FmC/0vhrIxMp5yrs484unC4bgEhgSFccl092JhUy83Ovp62P34ORmxWq2MuhzB8bhEmuZ3J85i5bOrEQQn/LfP3dEm1kXG80Zhr1xZJEkW/dsCLNFRjo7hMBl+5j755BNWrEgaCff06dO88847PPzww1y6dIkxY8ZkekAREREREbk/Uy+vwYLV0TFE7ltYooXfw+MAGFHcm/eLeTO5tC8uBrhhtrA1Kp7FYbFcTrBQ19OFyaV9GVPSlzbe7tT0cCUo4e7T3z7v68FbRfLb/RRxcSIowcyFBDPN87vxYTFv/q9gPhKssDsmadyfeIuVCcGR1PV0ocUtrUxyG2tUJNFLFzo6hsNkuOvNhg0bbIWSZcuW0ahRI958800iIyNp06ZNpgcUEREREZF7dzYumM1hhx0dQyRTOBtgQilfAGp4uACQz8mIm8FAgtVKiNnC5sikQsrTPh4YDAYAhhTzvu9jh5qTuuUUu9m9p5hLUruDkJvddeaERHMt0cy4kj73faycIGblErxe6IrBPXcXfe7FPXW9KVSoEAB//fUXLVq0AMDLy0tdb0REREREcpg5VzerNYk8MLycjAR4uhLg6YrxZhFkY2QcUZak13hJFyfO3+xeU8zFiVXhsUy7FsXi0BgizGmPP3KrG4kWFofGMP1aFH9ExJJoTdq3y83jJdx8O5lu/utqMHAmPpEFITF098tHKVdnrNbc/56zRkYQs2GVo2M4RIZblFSqVIklS5bg7u7OyZMnad68OQDbt2+nePHimR5QRERERETuzY2ESH6/scfRMUSyzPG4BMZfjQSgrKsTtTxcSR4xZMzlCM6nGD9kQWgMs8r44et85/YCn1yJsLv9a2gsE0v5UsbVCRcD7IkxEWW28Hd0Upebiu7OjL8aSQlXJ1p6uzPwQiiH4xLwMBh4xteD3oW8Mu+Es1nMsl/wbPMcBmPuHW/lXmT4bD/88EOmT5/OmDFjGDp0KD4+PoSFhfHmm2/y1ltvZUVGERERERG5B/ODtxJvvfuYDCK5UWBsAgMvhhFlseJlNPBRMW9b6w8ALycDi8sXZG45P0q4GLmWaGFOyO17QZR1daaimzOvFcrH7xUKMaGUL55GA8fjE5kTEoOn0cgLvp6cjE+k3anrLA+Ppbq7CxdNZg7FJTCoSH5mXI/iSFwCc8oVpIufJ3NDYtgVHZ8dD0eWMF8OIv7vrY6Oke0y3KKkRIkSrFmzxm6Zr68vf/zxB0WLFs20YCIiIiIicu/iLCZ+ubbd0TFEskRgbALvBoURY7Hi52RkbEkfKru7EJ2ie80Lvp4UvjmeSHsfD2Zcj+ZoXMJt9znu5tgnyep4utLMy42VEXHsjI6nX2Ev+hX2orqHC8fiEiji7MQjni70OR9Ka293anu6MvxyOGVdnSnu4kSDfG5Mux7NPzEJ1MvnliWPQ3aI/m0B7g2bOjpGtspwi5KnnnoKiyV13y4VSUREREREco5VIXsJN8c4OoZIpruSYGbopaQiSRkXJ6aWKYC/+38Duxa8OS1vylFCPI1J44skj2VyOwm3jC3i5ZR0P1OKxY293OhdyItnfD2YdSMaJ6Bf4aTuNVFmK643xzJxTvqHyHSOjZJTJfx7GNPRQ46Oka0yXCjp3LkzkydP1sCtIiIiIiI52PzgvNdcXvKGL69GEmq2UsrFicllCthmoUnW0MsVgD+j/uvysj8mqSVJRbekThV/RsbT/cwNup+5gdlq5VCsiTYnr9HqxDWCb45rkmi1sic66X5V3FN3xtgVHc/GyHj6FvbC92ZxxtvJwLXEpPtfSUgqkPg45f7xPaJ/y1tTBWe46822bdsIDg5mxowZeHt74+TklGq9iIiIiIg4zu7IkxyPvezoGCKZbn+Mid0xSYOoFnI2MveG/Rf4Vdxd6O6Xjy2R8fwZFc97F8NwMsCOaBPOQKcCngBEWyy2gV6tQFV3F8q5OnEkLpHXL4RSz9OVY/GJnDIl4mEw0N0vn91x4i1WvgqOpJaHC228/5s+t0E+N1ZHxPHRpXDOxSfeXOaaRY9G9onftQ1zyHWc/Ao5Okq2yHChpGfPnlmRQ0REREREMolak8iD6nKKWWz2xyawP9Z+zJHW3lZaerszuXQBpl2P4kBsAmarlZoeLrxV2IvKN7vo3MrZYODzkr7MC4lhW1Q86yLjcDcYaOLlRs+C+SjvZv/RefaNaK4nWhhbwhfDza42kNQFJ8ZiZXeMiXxGAwOKeFHLM/cXSjCbid2wGq8Xuzs6SbbIcKHk+eefz4ocIiIiIiKSCa4nRLA5LNDRMUSyRBsfD9r4eNx1u3Juzowt6Zuh/Xg7GW0Dtt7Na4W9eC2N7XycjIwq4XPX++dGsetWka9jN7vC0IMqw52lzGYzM2bMoG3btjz66KMAREdHM2rUKOLjc++0RyIiIiIiD4JVIXsxk7sHjxSRnMd8+SKmQ/scHSNbZLhQMnbsWFauXMlrr71mK4wkJCRw6tQpPvvss0wPKCIiIiIi6bcqZK+jI4jIAyp27e+OjpAtMlwoWblyJd9++y3PPvusrcmNr68vX375JevXr8/0gCIiIiIikj6nY69yNOaio2OIyAMqbscWLFGRjo6R5TJcKElISKBYsWKplnt4eGjKYBERERERB1oZ8o+jI4jIg8xkInbTGkenyHIZLpQ8/PDDfP/993bLYmNj+fLLL6levXqmBRMRERERkfSzWq3qdiMiWS5u01pHR8hyGZ71ZsiQIfTu3Zsff/wRk8nEM888w4ULF/Dz8+Pbb7/NiowiIiIiInIX+6LOcMkU4ugYIvKASzhxlMQrl3AuVsLRUbJMhgslVapUYf369WzatIkLFy7g7u5OmTJlaNSoEc7OGd6diIiIiIhkgt9D9jg6gojkEXFbN+D1YndHx8gy91TZcHd3p02bNpmdRURERERE7kGCJZF1oQccHUNE8oi4rRtVKElp27ZtjBs3jnPnzmEymVKtP3r0aKYEExERERGR9NkacZQIc6yjY4hIHpF45iSJly7iXKKUo6NkiQwXSoYNG0bjxo3p378/bm5uWZFJREREREQy4I+QfY6OICJ5TNyOLXi90NXRMbJEhgslUVFRDB8+XOORiIiIiIjkAGarhR0RxxwdQ0TymPjtD26hJMPTAz/33HP8/vvvWZFFREREREQy6HD0eXW7EZFsl3DiX8w3rjk6RpbIcLOQl19+mZ49e/Ltt99StGhRDAaD3fo5c+ZkWjgREREREbmz7RH/OjqCiORFVivx+3bh2aKdo5NkugwXSvr374+Pjw/16tXTGCUiIiIiIg72lwolIuIgpn27VSgBuHjxIn/99Reenp5ZkUdERERERNIpPDGawOgLjo4hInlU/L49WC0WDMYMj+qRo2X4bBo1asTp06ezIouIiIiIiGTA3xEnsGB1dAwRyaOskeEknjru6BiZLsMtSqpVq0b//v0JCAigePHiGG+pHL3zzjuZFk5ERERERG5P3W5ExNHi9+3CpVIVR8fIVBkulGzfvp2SJUsSHBxMcHCw3bpbB3YVEREREZGso2mBRcTR4vfuwqvTK46OkakyXCiZO3duVuQQEREREZEMOBF7ieCEcEfHEJE8LuFYIJbYGIweD844phkulACcOHGCNWvWEBQUBEDZsmVp164dpUuXztRwIiIiIiKStu0RD964ACKSCyUmknD0MG516jk6SabJ8GCuq1at4vnnn2fLli2YTCZMJhNr166lXbt27NmzJysyioiIiIjILfZFaYIFEckZEo4FOjpCpspwi5IpU6Ywbtw42rZta7d8yZIljBs3jl9++SXTwomIiIiISNqORF90dAQREQBM/z5YhZIMtyi5dOkSrVq1SrX8mWee0bTBIiIiIiLZ4EZCJFcTwhwdQ0QEgIQTR7FaH5ypyjNcKClRogQHDhxItTwwMJCCBQtmSigREREREbm9wJgLjo4gImJjjYzAHPTg/L+U4a43r7zyCn369OHpp5+mQoUKAJw+fZoVK1bw2muvZXpAERERERGxdyT6wflAIiIPhoRjgTiXKuPoGJkiw4WSzp07U6RIEX799Vf27t2LyWSiTJkyjBw5MtW4JSIiIiIikvnUokREchrTsUA8nmzj6BiZIsOFkl27dtG8eXOaN29utzw+Pp7Vq1fTps2D8cCIiIiIiORUR2M0kKuI5CwJx444OkKmyfAYJa+++mqay8PDwxkyZMh9BxIRERERkdu7agrjWkKEo2OIiNhJvHAOq9ns6BiZIt0tSmbPns2sWbMwmUw0atQo1fqoqChKlSqVqeFERERERMTeEbUmEZGcKMGE+UoQziVz/zgl6S6UvPLKKzzyyCN06dKFQYMGpVrv5ubGY489lqnhRERERETEXqAGchWRHCrx/Nm8VSgxGo3UqFGDn3/+mZo1a9qtCw8Px8fHJ9PDiYiIiIiIveOxlxwdQUQkTYkXzsJjTRwd475leIwSJycnOnToYLv99ttvU79+fR577DH27duXoX39+++//O9//+ORRx6hYcOGDBgwgGvXrgGwY8cOOnbsSJ06dWjXrh3Lly+3u++cOXNo1aoVderUoUuXLhw+fNi2Lj4+nuHDh9OkSRPq169P//79CQ0NzeipioiIiIjkOBfirzs6gohImhLPn3V0hEyR4ULJ6NGjadGiBQDr16/nn3/+YePGjQwdOpQvvvgi3fsxmUz07NmTevXqsWPHDn7//Xdu3LjBiBEjCA4O5vXXX6dz587s2LGDoUOHMmzYMA4dOgTAxo0bmTx5MuPGjWP79u00a9aMvn37EhMTA8CECRMIDAxk4cKFrFmzBqvVygcffJDRUxURERERyXEumfQFoIjkTIkXzjo6QqbIcKHk+PHj9OnTB4ANGzbQtm1bSpQoQbt27Thx4kS69xMbG8vAgQN57bXXcHV1xc/Pj5YtW3LixAlWrFhBuXLl6NixI25ubjRs2JDmzZuzaNEiABYuXEiHDh2oVasW7u7u9O7dG4BNmzaRmJjI4sWLef311ylevDi+vr4MGDCAzZs3c/Xq1YyeroiIiIhIjnEjIZI4i8nRMURE0pR48TxWi8XRMe5buscoSebm5kZCQgIGg4GtW7faWpHExMRgycAD4uPjw4svvmi7ffr0aX777TfatGlDYGAg1apVs9u+WrVqrF69GoDAwEDatm1rW2c0GqlatSqHDh2iatWqREZG8vDDD9vWV6hQAXd3dwIDAylatOhtM5nNZsxZNJ2Rk5NTluxXRDIuq97nIvcr+bWp16jcjV4jeVdQ/A1HRxARuT1TPObgKzgXK+HoJPclw4WSJk2a0L9/f5ydnfHy8qJBgwYkJCQwYcIE6tSpk+EAQUFBtGrVisTERDp16kT//v159dVXUxU0fH19beOMhIWFpRo81sfHh9DQUMLCwgDw9va2W+/t7X3XcUqOHz+e4fzpYTQaCQgIyJJ9i0jGHTx4MEOFXZHsltzVVETkVkGmEEdHEBG5I8v1YMhrhZIRI0Ywe/ZsIiMj+eijjzAYDCQkJHDmzBnGjBmT4QAlS5bk0KFDnDt3juHDh/P++++n635Wq/W+1qelcuXKeHp6Zvh+IpK73Dpzl0hOYTabOXToEDVq1FBLRLmjmJiYLPuCR3K2oHgVSkQkZzNfD3Z0hPuW4UKJu7s7ffv2tVvm6enJd999d88hDAYD5cqVY+DAgXTu3JmmTZvaWoYkCw0Nxc/PD4ACBQqkWh8WFkalSpVs24SFhZEvXz7b+vDwcAoWLHjHHE5OTrowFckD9D6XnE5/j+Ru9PrIu4JM6nojIjmb+fo1R0e4bxkulHTv3h2DwXDb9XPmzEnXfnbs2MGIESNYvXo1RmPSmLLJ/9asWZM1a9bYbX/48GFq1aoFQPXq1QkMDOT5558Hkr6BO3LkCB07dqR06dL4+PgQGBhIyZIlgaQuNSaTierVq2fsZEVEREREchC1KBGRnM7yALQoyfCsN7Vr16ZWrVq2n+rVq+Pj48OZM2d4/PHH072f6tWrExUVxRdffEFsbCwhISFMnjyZunXr0qVLF4KCgli0aBHx8fFs2bKFLVu20KlTJwC6dOnC0qVL2b9/P7GxsUydOhVXV1eeeOIJnJyc6NSpE9OmTePy5cuEhoby1Vdf0bJlSwoVKpTR0xURERERyTFUKBGRnC5Pdr0ZNGhQmsu3bdvG8uXL072f/Pnz8/333zN69GgaNGiAp6cnDRo0YMyYMRQsWJDp06czevRoRo4cScmSJfniiy+oUqUKkDSg7DvvvMOAAQO4ceMGNWrUYMaMGbi7uwPQv39/oqOjefbZZ0lMTKRZs2aMGDEio6cqIiIiIpJjmK0WrpjuPDmBiIijma9ddXSE+2aw3suop2mwWCzUrVuXvXv3ZsbuslVMTAxHjx6latWqWT6Ya4N9Q4i1mLL0GCKSmofRlb8Dxjo6hsgdmc1m9u/fT+3atTUGhdxRdl67SM5xIyGS5gc/dnQMecB8v8WJkpv+dnQMeYAYfXwp8tMKR8e4LxluUXLmzJlUy+Li4li7dm2qKXlFRERERCRzRJpjHR1BROSuLBHhWC0WDMYMj/SRY2S4UNKmTRsMBoNt+t3k3/Pnz6/uLSIiIiIiWSTKHOfoCCIid2e1Yo2JxuCV39FJ7lmGCyUbNmxItczNzQ0/Pz/brDUiIiIiIpK5olUoEZFcwhIdhTGvFEpMJhMWi4XSpUvbLd+3bx8FChTI1GAiIiIiIvKfSBVKRCSXsMZEOzrCfUl3E5Dw8HCef/55pk6dmmrdJ598Qo8ePTCZNEipiIiIiEhWUIsSEcktrFGRjo5wX9JdKJkyZQp+fn589NFHqdbNmzcPq9XKzJkzMzWciIiIiIgk0RglIpJbWPJKi5JNmzYxdOjQNKeg8/DwYOjQofz++++ZGk5ERERERJJEWVQoEZHcwRod5egI9yXdhZIbN27g7+9/2/VVqlThypUrmRJKRERERETsqUWJiOQWeaZFiaenJ6GhobddHxwcjIeHR6aEEhERERERexqjRERyjVw+fmm6CyWPPfYYs2fPvu36cePG0aBBg8zIJCIiIiIit1CLEhHJLaxWi6Mj3Jd0Tw/8xhtv0LFjRy5cuEDXrl0pX748ZrOZkydP8v3333PgwAF++eWXrMwqIiIiIpJnmXP5Bw8RyUMsVkcnuC/pLpSUL1+en376iU8++YRu3bphMBgAsFqt1KtXj59++ony5ctnWVARERERkbzMyZDuxuAiIo6Vywu76S6UAFStWpWff/6ZkJAQLly4gMFgoEyZMvj6+mZRPBERERERARVKJGtYjAZHR5AHkTWPtChJyc/PDz8/v8zOIiIiIiIit+FscHJ0BHkA9W6cSNEGj1E+1pXSMUZKxhgoEm3BLzIB78g4PCOicQuPwhgWBlG5e8pXyUaWPNSiREREREREHMMp/fMwiGTIVZdErrok8rf3rWuMQP6bP8XxSjRSPt6NcjFOlIg1UCzaSsGoRHwj4skXEYN7RDTOYeEQHg4Wc7afh+Qc1rzYokRERERERLKXut6Io0U5WzjkHMuhfGmt9bj5UwgnC5SLd6VcrAulYo0Uj7ZSOMpMgSgTXhGxeETE4BIWgSEsNNdPIyu3kRcKJVevXqVo0aIAXL58meLFi2dpKBERERERsadCieQWZiOc8jBxyiOtIojrzR9foAxFTS48FOdM6VhnSkZD4eRuPxFxeEbG4BYWmdTtJ1rdfnITg6uroyPcl3QVSlq3bs3OnTtxdXWldevWHDhwIKtziYiIiIhICs4qlMgD6KprAlddE9iRZrcfr5s/Sd1+HopzpWysCyVjuNntJwGfSBNeETG4hUcldfuJCM/142M8CAxuHo6OcF/SVSgpX748rVq1omjRophMJjp37nzbbRcsWJBp4UREREREJImTBnOVPCzK2cJBrzgOesWlsTa5209hXCwGysS7UC7WhdIxRorHQKGoRApEmvCKjMMjPAqX8AgMoaGQkJDNZ5F3GDzyQKFk2rRprFq1iqioKA4dOkSjRo2yOpeIiIiIiKSgwVxF7i7BaP2v20+qiVpdgAI3f8pSzOTMQ3GulI5xokQ0FIk2J3X7iYrHMzwa1/BIjKFhEBOd3aeR6xnc3R0d4b6kq1BSpEgRevToAYDZbObNN9/MykwiIiIiInILTQ8skrmuuCZyxTUR7jjbTwm8E42Uj3OlXKxz0vTJURYKRifiG2kin63bTxhERKjbz00GtzxQKEnp7bff5sSJE6xZs4agoCAAypQpQ/v27SldunSmBxQREREREfBx9nR0BJE8KcLZwgGvOA54pbXWvtuPbbafGCPFoi0UijZTINJE/ohYPCKicU6e7ecB7/Zj8Mjd/19luFCyatUq3n//fapWrUqZMmUAWLduHVOnTuX777+nbt26mR5SRERERCSv83NO81OaiOQQCUYrJzziOeERn0a3n+TZfpK6/RQ3OVM+1pWyMU4Ui7FSJHm2n8g4PCNicA2LwBgWnmu7/RjzWouSKVOmMG7cONq2bWu3fMmSJYwbN45ffvkl08KJiIiIiEiSgi75HR1BRDLJZddELrsmst3n1jVO/NftpyTeiU5Js/3EOFMyBoraZvuJt8324xQekeNm+8lzLUouXbpEq1atUi1/5pln+PTTTzMllIiIiIiI2FOLEpG8J8LZzH6vWPan+fb3vPlTBFezgbImV8rHOlMyxkjxGCuFopK6/XhFxOIRHoVzeGS2dfsx+vhm+TGyUoYLJSVKlODAgQPUqVPHbnlgYCAFCxbMtGAiIiIiIvIftSgRkdsxOd2t24/fzZ+ylIh3uTnbT9L0yUWizPhFJeAdEYdHxM3ZfsLCICbm3sI4OWHIn2qE3Fwlw4WSV155hT59+vD0009ToUIFAE6fPs2KFSt47bXXMj2giIiIiIiAr3M+jBiwYHV0FBHJxS65JXDJLQHS7PbjffMnqdtPxThXysQ4UTLGSJFoM4WiEvGJjCdfeAxuEVE4hYUndfux/vf/ktHHF4PBkH0nlAUyXCjp3LkzRYoU4ddff2Xv3r2YTCbKlCnDyJEjU41bIiIiIiIimcPJYMTHOR+hiVGOjiIieUCEs5m9XrHsTUe3n/LxN6dPjjVSwakg7bM3aqbLcKEEoHnz5jRv3jyzs4iIiIiIyB34OXupUCIiOYrJycoxz3iOecYD0NindK4vlBgdHUBERERERNJH45SISE5X1CVVn55cR4USEREREZFcQjPfiEhOV9TV19ER7psKJSIiIiIiuUQhl9w9k4SIPPjyZIuSVatWZUUOERERERG5i9JuBR0dQUTkjvJki5KRI0cSc6/zKYuIiIiIyD0r617Y0RFERO7oQfh/KsOz3gwYMICPPvqI5557jhIlSuDk5GS3vnz58pkWTkRERERE/lPWLfd/ABGRB5eX0Z3irgUcHeO+ZbhQMnLkSMC+C47BYMBqtWIwGDh69GjmpRMREREREZvirgVwN7gQZ01wdBQRkVQqeBRzdIRMkeFCyYYNG7Iih4iIiIiI3IXBYKCMeyGOx152dBQRkVQq5tVCScmSJQG4ceMGly9fpnr16pkeSkRERERE0lbevagKJSKSI1VwfzAKJRkezPXq1av06tWLxx9/nM6dOwMQHBzM008/zYULFzI9oIiIiIiI/KeiR3FHRxARSdOD0qIkw4WSUaNG4efnx6ZNmzAak+7u5+dHo0aNGD16dKYHFBERERGR/1RSoUREcqgHpZCb4a43f//9N3/++Sf58uXDYDAk7cTZmbfffpumTZtmekAREREREfnPg/KNrYg8WAo456OgS35Hx8gUGW5R4uHhgdVqTbU8PDwcs9mcKaFERERERCRtpVwL4mF0dXQMERE7D8r4JHAPhZIGDRrw4YcfcubMGQAiIiLYtWsXb731Fk888URm5xMRERERkRQMBgP+HiUdHUNExM6DMjUw3EOhZNiwYZhMJtq0aUN8fDz169enR48elClThmHDhmVFRhERERERSaG2VzlHRxARsVPZo4SjI2SaDI9R4uPjw7Rp0wgJCeHChQu4ublRqlQpvLy8siKfiIiIiIjcoo7XQ8y+usnRMUREbAK8yjs6QqbJcKEEkqYD3r59O8HBwbi6ulKsWDEaNWqkYomIiIiISDYI8CqPAQNWUo8dKCKS3Qo4ez1QXW8yXChZs2YNgwYNwsvLi5IlS2K1WgkKCsJkMjFx4kTNfCMiIiIiksW8nT2p4F6Uk3FXHB1FRIRHvB5ydIRMleFCyVdffcX7779Pt27dMBqThjixWCzMnz+fzz77TIUSEREREZFsUCf/QyqUiEiO8Ej+Co6OkKkyPJhrcHAwXbp0sRVJAIxGIy+99BJXr17N1HAiIiIiIpK2Og/YN7giknvV9crjhZLmzZvz119/pVq+e/dutSYREREREckmKpSISE7g4+RJJY/ijo6RqdLV9earr76y/e7n58fgwYOpWbMmFStWxGAwcObMGfbu3ctLL72UZUFFREREROQ/RV19KeFagEumUEdHEZE8rI7XQxgMBkfHyFTpKpTs27fP7nblypWJi4vj8OHDdsv279+fqeFEREREROT2Arwe4lLIP46OISJ52IM2Pgmks1Ayd+7crM4hIiIiIiIZVMfrIVaqUCIiDlQ3rxZKUjKbzWzcuJGzZ88SHx9vt85gMPDGG29kWjgREREREbm9Rj5VHB1BRPKw/E7u+HuUcHSMTJfhQsmAAQPYsmULDz30EG5ubnbrVCgREREREck+xVwLUM2zFEdiLjo6iojkQQ3y+2M0ZHiOmBwvw4WSbdu2sXz5csqVK5cFcUREREREJCOa+9ZQoUREHOLJAjUdHSFLZLj0U6ZMGXx9fbMgioiISO4XGhrKgAED8Pf3x9/fn4sX7T+8mM1m5s6dy3PPPUdAQAB16tThf//7Hzt37rTbLj4+ni+++IKmTZtSo0YN2rRpw/z58+22uXr1KoMHD6Z+/fpUr16d9u3bs2TJkiw/RxHJWZr51nB0BBHJg1wNzjTxqeroGFkiwy1KxowZw9ChQ2ndujVFihTBaLSvtTz66KOZFk5ERCQ32bBhA8OHD+f69eu33WbKlCl8++23uLq60q5dO86dO8fff//NP//8w4IFC2zbDRo0iHXr1lG2bFnatWvH6tWrGTFiBN7e3rRr146YmBhefvllLl68SPny5WnSpAlr1qzhgw8+wGg08txzz2XDGYtITlDRoxhl3ApzPv6ao6OISB7SwLsy+ZzcHR0jS2S4ULJhwwY2btzIhg0bUq0zGAwcPXo03fsKCgri008/Zc+ePTg5OdGkSRM+/PBDvL29OXr0KGPGjOHo0aMULFiQzp0707NnT9t9V61axdSpU20XiO+88w6NGjUCwGKxMGnSJH7//XciIiKoWbMmI0aMoHTp0hk9XRERkXQbPnw4zs7ODB8+nFGjRqW5zU8//QRAnz59eOuttzCbzTzxxBMEBwezfv16GjduzO7du1m3bh2FChVi4cKFFChQgJo1a7Jy5UpbEWbRokVcvHgRT09PFixYgK+vL3Xr1mX48OFMnjxZhRKRPKa5b3VmX93k6Bgikoe08H0wu93APXS9+fHHHxk7diy7d+/m4MGDdj8HDhzI0L769u2Lt7c3GzduZMmSJZw4cYLPP/+cuLg4XnvtNRo0aMDWrVuZMGEC06dPZ+3atQAcPXqUwYMH8+677/L333/To0cP3nzzTa5cuQLAvHnzWLFiBTNmzGDTpk2UK1eON954A6vVmtHTFRERSbdOnTqxcuVKmjZtetttEhMTAShUqBAATk5OFClSBICoqCgA1qxZA0CrVq0oUKAAAC+//DLz5s3jf//7HwCnTp0C4KGHHrJ1iW3evDkAFy9e5MyZM5l5aiKSwzVX9xsRyUbOGHnC92FHx8gyGS6U+Pr60rp1a/Lnz4+rq2uqn/SKiIigevXqDBo0iHz58lGsWDGef/559uzZw+bNm0lISKBfv354enry8MMP8+KLL7Jw4UIg6Vu0pk2b0rRpU9zc3HjmmWeoXLkyy5cvB2DhwoX06NGDChUq4OXlxcCBAzl16lSGCzkiIiIZ8fbbb+Pl5XXHbdq3bw/AypUrCQ0N5cCBAxw/fhyDwUDLli0BOH78OADly5fnzz//5KuvvmLq1KmcPn3ath8fHx8g6e9pMovFYvv97NmzmXJOIpI71MxXlsIu3o6OISJ5RN38FfFxzufoGFkmw11vPvroI7788ku6detGsWLFMBgMduvTWyzx9vbms88+s1t2+fJlihQpQmBgIP7+/jg5OdnWVatWjUWLFgEQGBiY6tu6atWqcejQIeLi4jh58iTVqlWzrfPy8qJs2bIcOnSI2rVr3zaT2WzGbDanK39GpTwXEXGsrHqfiyRLWbCwWCx2r7kPP/wQT09P5syZQ4MGDYD//iYGBARw6NAhbty4ASS1kEzZMuSbb75hxowZ1K9fnxo1kr49Pn/+PIsXL6ZFixZMnDjRtm14eLhe6w8wPbdyK4PBQFOfh1l8fYejo4hIHtDiAZ3tJlmGCyXvvfcesbGxtj7Wt8rIGCUpHTp0iJ9++ompU6eyevVqvL3tK+K+vr6EhYVhsVgICwuzfZOWzMfHh5MnTxIeHo7Vak1zfWho6B0zJH+Dl9mMRiMBAQFZsm8RybiDBw/afZAVyWzXrv03oOKRI0fsbu/atYuFCxfi4uLCo48+SnR0NPv27WPs2LEkJCRQpkwZYmJiAAgODmb06NEULlyYGTNm8M8///Dxxx8zduxYChYsSEBAAPv27WPo0KEMHTqUAgUK4OnpSUxMDBcvXmT//v3Zfeoi4kDNfWuoUCIiWc6I4YHv7pfhQsnUqVMzPcQ///xDv379GDRoEA0bNmT16tVpbpey9crdxhu5l/FIKleujKenZ4bvJyK5S82aD3YFXBwvKCjI9nu1atUoWbIkkDQGSe/evYmNjWX8+PG0adMGgFGjRrFgwQJ++eUX3n33Xfz8/Lh69Spt27alQ4cOQFKrky5dunDhwgX8/f3x8PBg7ty5rFy5kuPHj1OkSBGefvppWrduDUDt2rXv2IpScreYmJgs+4JHcq963pXwc/YiJDHK0VFE5AEW4FWegi75HR0jS2W4UFKvXr1MDbBx40bee+89hg0bZhuh38/PL1Xf6rCwMHx9fTEajRQoUICwsLBU6/38/GzbpLW+YMGCd8zi5OSkLjIieYDe55LVjEaj3e/Jr7lz587ZWotUqVLFtrxy5cpAUusTSBqg9ejRoxgMBts2yS0trVYrcXFxeHl54eTkZDe7zbVr14iIiMBgMNjtXx48em4lLS4GJ54p+KhmvxGRLNWqwIPfWyLDhZLu3bunGpckpTlz5qR7X3v37mXw4MFMmjTJNrUvQPXq1Zk/fz6JiYk4OydFPHToELVq1bKtP3z4sN2+Dh06RLt27XBzc6NSpUoEBgbaijoRERGcP39e3yKLiIhDFS9e3Pb7v//+S8WKFQE4ceIEgK2g/8QTT7By5Ur+/PNPYmNj8fDwYOfOnQAULlyYQoUKcfDgQYYPH861a9dYt24dnp6eLF26FIA6derYZtURkbylQ6EGKpSISJbxMLrSruAjjo6R5TJcKLm1Ga/ZbObChQvs37+fbt26pXs/iYmJfPTRR7z77rt2RRKApk2b4uXlxdSpU+nduzfHjx9n8eLFfPHFF0DS9IsdO3Zk8+bNPPbYY6xYsYKzZ8/yzDPPANClSxdmzJhBkyZNKFq0KF9++SVVq1a1DXwnIiKSFV577TUA4uLibMuGDh2Ku7s7lStXZtCgQTz55JNs2LCBYcOGsXXrVqKiotiwYQMAr7zyCgCtW7dm7ty5HDx4kE6dOlG1alVbt9TevXsD4O/vT1hYGNevX6dbt26UKVOGdevW4eLiwnvvvZedpy0iOUhZ98LU9arAnqhTjo6SKcxHbpAw+u/brnfqUAmXjpVJ3HQe8/rzWC9FgdGA8SEfnJ6riNPDty8ax/XfCNdj01znOvpxjA/5YjkbTsJ3h7FejMTg64bzi/44NSxht63pm31YDt/A7cumGPK53NuJiuQSbfwC8HJyd3SMLGew3stgHmnYtm0by5cvZ9y4cenafs+ePXTt2jXNWXL++OMPoqOj+fjjjzl8+DCFChXi1Vdf5eWXX7Zts3btWsaPH09QUBAVK1Zk6NChPProo0BSs+TJkyezYMECoqOjqV+/PqNGjaJYsWJpZomJieHo0aNUrVo1y8coabBvCLEWU5YeQ0RS8zC68nfAWEfHkAecv7//bdfVq1ePuXPnEh8fz7Rp01i1ahVBQUE4OztTsWJFunbtyjPPPMP+/fupXbs2UVFRjB8/nvXr1xMREUGZMmV4/fXXbdMLA5w6dYrPP/+cffv2YTKZqFatGu+8847t76E8uLLz2kVyn1Uh//DBmXmOjpEpLFeiMa89m2q5edcVCInDuXs1iEskcdFx8HDGqX4xLOcjsZ4OBycDrh83xFjRN819JxdKjPWKYfCz/+Dn3O4h8HPH9O4WrMExGBuWwHLoOkQl4DbhCQwFPZJyHLpGwme7cHmzNk4NS2b26YvkOD9XGcjD+Uo7OkaWy7RCicVioW7duuzduzczdpetVCgRefCpUCK5gdlsthVKNAaF3IkKJXInJksiLQ6OINwc4+goWcJyJhzTsL/A2xW3L5sS/9ZGiE3EZVBdnB4pijXRQvzbGyE0Hqf2D+HyctU095NcKHH5qAFO1VKPZWi5Eo3pnc0YG5XE9fXaJG48T+KsQzj3qYnzE6WxmsyYBv+JoYgnrh/Uz+rTFnG4ap6lmF/1HUfHyBYZ7npz5syZVMvi4uJYu3Ztqil9RUREJGNSDgQrInIvXI3OtC/4CPOCtzo6SqazWqwkfH8YLFacO/mDqxOuox4HwFAsqWhocDZi8HbDGhoPiZZ7P1h4fNL+CnnY/UtY0vLE305gDYnDZXDmTnYhklO9WLihoyNkmwwXStq0aYPBYLBNv5v8e/78+RkxYkRm5xMREckznJycCAh48EeSF5Gs16HQYw9kocTyVxDWU2EYiufDqUkpDEYDhpJetvXWRAuWPVewXogEFyNOTUrdfadhcST+cQZraDzGkl4YHy+BwckILjcL1wkW+39djFguRmL+/TTOHSphLJYPq9V6xwkvRHK7/E7utM4Ds90ky3ChJHnAuZTc3Nzw8/PTt2AiIiIiIjlARY9i1M5Xjv3RZx0dJdNYzRYSlyTNEub0dAUMRvvCROLqMyTOTZpm3fhwQZy7V8NY5u4t3hOm7Lf9bgYMf5zB9aMGGIp7gbMRy+HrWGMSsOwPBsBQ1puEWYcwFPXE2KgkpjF/YzkeCm5OOLUoi0un249XJZJbtfV7BE8nN0fHyDYZLpSULKlBikREcrLgbk9jTTHriohkD4O7O0V+WuHoGCI2HQo1eKAKJeZtQVivxoCvG06NUn8mMZbzxqllWSzHQrAE3iBh1iFcBz6CoUDaM3QYS3phzeeCU8MSODUrg+VcOAlf/YP1bASJS0/i8nJVnFqVw7zyNPG91wJgqFwA65VorMdDcR3WgMQFx7CcDMP1y6ZY/r5M4vx/Mfr74VSrcJY+FiLZ7cXCjzk6QrZKd6GkefPmd21OZjAYWL9+/X2HEhGRe2eNi8Mar0KJiEhe18qvNhODfickMcrRUTKFZetFAJzqFcPgnLolu7FqQYxVC2I1WzCN2oH1RBgJ8//F9fXaae7P9ZaxRZweLoSlQXHMmy4ktR55uSouXatirFwAy5lwDAU9MFYviGnoNpyalMJYtSCWiXsxlPTCWNgTAorA/H+xHL6uQok8UGrnK0cljxJ33/ABku5Cydixt58t4sKFC0ycOBGz2ZwpoURERERE5P64G13pVrQpXwetdHSU+2YNj8dyNAQAY91i/y0PicN8IDhphpvnKiaNWeJkxFjZD/OJMKynw+6830SLfdHF8+bHo4T/BoF1erQYTo8mHdM0eR8YDTgnz6QTnQDF893c0PDfMpEHSOcijR0dIdulu1BSr17q0ZxNJhPTpk3jhx9+oEOHDrz99tuZGk5ERERERO7dS4Uf54crG4k0xzo6yn2xHPz/9u48PKr6YPv4fWbNMtl3QkIgIYFggmELIosEEYsI4oMRrEuoFCqCghWxKqhoRSsRquJS3lbbVx5LXR6lVaEsVetS9FGwkRdBARVB9gTMRpKZef9gTI2EPcmZzHw/1+Xl8DvbPUoOzD2/c84+ySvJasjSLbZx3Ftdr4YlZZIkIyta1vyjMzm82w8dHfNdduP+cLca/vyZJMnx8BB5Py9X3W8+lOrcci4aKiMuVF63R56y/Ue3y4w+JoP7k33yvL9Ltsn5MiIdRwdddnkPHp3F6d3v+28c4WjR9w6YKcOZqBExPc2O0eZO+x4l31u9erUeeOABpaSk6Pnnn1e3bt1aMhcAAACAs+SyhmhC4kD97ttVZkc5K56dRy8fMhLCmswAsXSMkGVAB3ne26X6334sT99keffXyPP/DkiGZL24s6SjhYr326qjG3m9MrKiZaS65P2iQkfufk/WngnybDsk79ffSSFW2S7LanJ8b51bDX8ok9EtVtYh/3mSjrUgUe63vlHdwv+V95vKxjEgUExKuVAWI/ge2nLaRclXX32l++67T5s3b9att96qMWPGtEYuAAAAAC3gp4mD9X/3vKUaT53ZUc5cpe9ylrBjP77Yp+TLnRIu97u75H53p2SzyMiOke3STFl7JzW7O8NqkeO2vmp4das8H+2R+52dktMqS99k2a7IlqVjRJP1G176XN7yI3Lc1rfJfRttV3WXt6bh6EyUUJtsJT2azHgB2rN0Z7xGxvYyO4YpTrkoqa2t1eLFi7V06VJdeeWVWrRokVwu18k3BAAAAGCaaFu4rkgYoD/tedPsKGfMPilP9kl5zS4z7FbZ/itbtv/KPu72tiFpsg1Ja7qdyyH7T7tLP+1+8uNP6Cb7hGNn0BsRDjlm9D7p9kB7dH3yhbIG4WwS6TSKkhEjRqi+vl6zZs1SVlaWNm3a1Ox6ffv2bbFwAAAAAM7edUkXaNned3TE22B2FADtQAdHrEbFBW8JeMpFidVqldVq1ZIlS467jmEYWrNmTYsEAwAAANAy4u2Ruiy+UMv2vWt2FADtwPXJw2QzrGbHMM0pFyVr165tzRwAAAAAWlFJ0lC9tO99Nchz8pUBBK0UR4zGxAX3lSLBecERAAAAEGQ6OGN1SVwfs2MA8HMTk4pkt5zxA3IDAkUJAAAAECRu6DBCIYbd7BgA/FSCPVJj4wvNjmE6ihIAAAAgSKQ4YnRt8gVmxwDgp6Z2uFiOIJ9NIlGUAAAAAEFlYlKREu1RZscA4Gd6hKVpbByzSSSKEgAAACCohFmduin1ErNjAPAjhgzdnjZWhmGYHcUvUJQAAAAAQWZUbG/lhaebHQOAn7gktrfyXRlmx/AbFCUAAABAkDEMQ7d1vEyG+PYYCHZhFqdmdBxldgy/QlECAAAABKF8V4Z+EltgdgwAJvt5yoVKsEeaHcOvUJQAAAAAQWpG6iiFWBxmxwBgknRngq5JHGJ2DL9DUQIAAAAEqSRHtCYmDTU7BgCTzEobIzuPAz4GRQkAAAAQxEqShyrdGW92DABtbFBkdw2OyjU7hl+iKAEAAACCWIjFoXkZE2Thxq5A0Agx7JqVdpnZMfwWRQkAAAAQ5ApcnfXTxMFmxwDQRm7uOEqdQhLMjuG3KEoAAAAAaHrqSGU4E82OAaCVFUZ01YSEgWbH8GsUJQAAAADktNh1X+cJsvIRAQhYEdYQzcsYL8PgUrsT4SwIAAAAQJKUH95J1yZdYHYMAK1kdtpYJTtizI7h9yhKAAAAADSa2uFiZYYkmx0DQAsris7TpXF9zY7RLlCUAAAAAGjksNh0X8YE2fioAASMWJtLc9KvMDtGu8HZDwAAAEATPcLTNDF5mNkxALSQuZ2KFWt3mR2j3aAoAQAAAHCMKR0uUl54utkxAJyl0XF9NTT6HLNjtCsUJQAAAACOYTesKu1Sohgb30ID7VW6M16z08aaHaPdoSgBAAAA0KwkR7R+0+UaHhkMtEMhFodKu5TIZQ0xO0q7wxkPAAAAwHH1i+iq6akjzY4B4DTNSR+n7LAOZsdolyhKAAAAAJzQxOQiXRidb3YMAKfoyoTzNSquj9kx2i2KEgAAAAAnNS9jvDqHJJodA8BJ9AzP0KyOY8yO0a5RlAAAAAA4qXBriB7pMlFhFqfZUQAcR6I9So9klshusZkdpV2jKAEAAABwSrqEJmlexnizYwBohtOwaVHmzxRvjzQ7SrtHUQIAAADglA2P6amSpKFmxwDwI/dkXKke4WlmxwgIFCUAAAAATsvNqZdoWHSe2TEA+JQkDdXI2N5mxwgYFCUAAAAATovFsGh+56tV4OpsdhQg6F0S21szUkeZHSOgUJQAAAAAOG1Oi12/zfwZT8IBTDQ4KlfzMsbLMAyzowQUihIAAAAAZyTKFq4nsiYrgZtHAm2ul6uLHu5yrWyG1ewoAYeiBAAAAMAZ6+CM1ZNdJyvSGmp2FCBo5ISm6tGs6xVicZgdJSBRlAAAAAA4K11DO+jxrJ8rlA9tQKtLd8brya6TFUE52WooSgAAAIB2rqioSDk5OcrNzdXWrVubLFu3bp1ycnJUVFTUqhl6ujL0SOZE2bkMAGg1ifYoPdX1F4qzR5gdJaBRlAAAAAABwu12a9GiRaYdf0BkjuZ3vlpWPmYALS7KGqYnu05RqjPW7CgBjzMYAAAAECAsFov+/ve/65NPPjEtw/CYnnqwy9XcYBJoQWEWpx7PmqSs0GSzowQFihIAAAAgQAwdOlSStGDBghOut2rVKl111VUqKChQfn6+Ro8erWeffVYej6dFclwUc64WZk6U07C1yP6AYBZlDdPvsn+hfFeG2VGCBkUJAAAAECCKi4sVHx+vDz74QG+99Vaz6zz33HOaNm2aPv74YxUWFmr48OH68ssvNX/+fN15550tlmVwVK4ey/q5wizOFtsnEGwS7JH6fc6NygvvZHaUoEJRAgAAAASI0NBQTZ06VZJUWlp6zAyRyspKlZaWSpJuueUWPfXUUyotLdVDDz0kSXr55ZePuRns2SiM7Kqnuk5RhDWkxfYJBItUR6yeyZmmrqEpZkcJOhQlAAAAQAApLi5Wenq6Nm/erOXLlzdZtn79elVXV0uSRo0a1Tg+YsQI2WxHL5NZt25di+bp6crQkuypirGFt+h+gUDWJSRJz+ZMV5oz3uwoQYmiBAAAAAggdrtdM2bMkCQ9+uijqq+vb1xWXl7e+DomJqbxtcViUWRkpCTp0KFDLZ6pe1hH/T77RiXYI1t830Cg6RGWpmdypinREWV2lKBFUQIAAAAEmJEjRyo3N1c7d+7UK6+80jj+w3LkwIEDja8bGhoaC5K4uLhWyZQZmqxncqapgyPm5CsDQaqPK1NLsm9QNDOwTEVRAgAAAAQYwzD0y1/+UpK0YsWKxvGCggKFhYVJkl577bXG8ddff11ut1sWi0XnnXdeq+VKc8br2Zzpygnt0GrHANqrwVG5eqLrZIVzTx/TUZQAAAAAAWjgwIHq379/k0tvXC5X42U5ixYt0k033aRbbrlFd9xxhySppKREaWlprZoryRGtZ3Oma1h0XqseB2hPihMGHH2ktsVudhTID4qSf/7znxowYIBmzpx5zLLXX39dl156qQoKCnT55ZfrnXfeaVzm8Xi0cOFCDRs2TH379tX111+vHTt2NC6vqKjQjBkzNGDAAA0cOFB33nmnamtr2+Q9AQAAAP7g+1klP3Tddddp4cKFysvL09tvv601a9YoOztb999/v2bPnt0mucKsTpV2KdGUlItkyGiTYwL+yG5YNTf9Ct2ZPk42w2p2HPjYzDz4kiVL9OKLL6pTp2OfCb1p0ybNnj1bjz/+uPr376+VK1dq2rRpWrFihZKTk7V06VL99a9/1ZIlS5SUlKSFCxfqxhtv1KuvvirDMDRnzhzV1dXpb3/7m+rr63XzzTdrwYIFuuuuu0x4pwAAAEDrWbt2bbPj+fn52rx58zHjI0eO1MiRI1s71gkZhqGpHS5WVmiy5nz5Z9V66kzNA7S1OFuESjNLVODqbHYU/IipM0qcTudxi5IXXnhBQ4YM0ZAhQ+R0OjV69GhlZ2c3PuJs2bJlKikpUWZmplwul2bOnKmtW7fqk08+0f79+7V69WrNnDlTsbGxSkpK0tSpU/XSSy81mXoIAAAAwFwXxZyrZ3OmKdkebXYUoM30CEvT891nUpL4KVOLkmuvvVYRERHNLtu4caNyc3ObjOXm5qqsrEy1tbX64osvmix3uVzq1KmTysrKtGnTJlmtVuXk5DQu79Gjh6qrq7Vt27bWeTMAAAAAzkj3sI767+4z1TM8w+woQKsbFdtHz+RMU5Ij2uwoOA5TL705kYqKCkVFNX1udFRUlL744gsdOnRIXq+32eXl5eWKjo6Wy+WSYRhNlklNnx3/Y263W263uwXfxX9YrVxvBviL1vo5NxvnGcB/tPZ5JlDPYwhucfYI/Z/sqbrv6xe0/MCHZscBWpxVFs3oOErXJl1gdhSchN8WJZLk9XrPePnJtm3Oli1bTnubU2GxWFRQUNAq+wZw+v7973/L4/GYHaNFcZ4B/EsgnmeAtuCw2HRfxgTlhaerdMdy1Xq5bB6BIdIaqt90uVbnReacfGWYzm+LkpiYGFVUVDQZq6ioUGxsrKKjo2WxWJpdHhcXp9jYWFVWVsrtdjd+w/r9unFxccc9ZnZ2duNz5QEErvz8fLMjAAhwrX2eqa6ubrUveAB/UJxwvvq4snT79ue0uWan2XGAs9I3Ikv3Z0xQsiPG7Cg4RX5blJxzzjn69NNPm4yVlZXpkksukdPpVNeuXbVx40b169dPknT48GF9/fXXys/PV2pqqrxerz777DP16NGjcdvIyEh17nz8m+VYrVamrgNBgJ9zAK2ttc8znMcQDLqEJmlpt5v12K7X9ac9b8mr058xDpjJYdg0PXWkrkkc0uS2EPB/pt7M9USKi4v13nvv6c0339SRI0f04osv6ssvv9To0aMlSRMmTNCf/vQnbd26VZWVlVqwYIG6d++uvLw8xcbGasSIEVq0aJEOHjyo3bt3a/HixRo3bpxsNr/thgAAAAD8gN1i0y0dR+vprlOUaI86+QaAn8gJ7aD/7j5T1yZdQEnSDpnaGuTl5UmSGhoaJEmrV6+WdHT2R3Z2thYsWKD58+dr586dysrK0tNPP62EhARJ0vjx47Vv3z5dc801qqqqUmFhoR5//PHGfc+bN0933323hg0bJrvdrlGjRmnmzJlt/A4BAAAAnK3CyGy9mHur7vvqRa2q+MTsOMBxWWTo2qQLNK3DT2S38CV9e2V4z+SupwGmurpamzZtUvfu3Vv9HiX919+uGk9dqx4DwLFCLQ79q+BBs2O0iT3jhst7pNbsGEDQMZwhSnpxVZscqy3/7gL4m1f2f6CHdvyPqj1HzI4CNNHBEaP7M65S74hMs6PgLPntpTcAAAAA8GOXxffTX3J/qT4uPozCf1wa20cv5M6iJAkQzAUCAAAA0K6kOeP1+5wb9frBj1S6Y7n2N3xndiQEqTRnnG5LG6vBUblmR0ELoigBAAAA0C6NjO2twVE9tHjXG1q291255TE7EoJEiGHX9SkXqiRpqBzciyTg8H8UAAAAQLvlsoZodtpYjYnrpwe+fkmfVH1pdiQEuGHRebq14xh1cMaaHQWthKIEAAAAQLvXLSxVf8yZrlcOfKDf7vybyhuqzI6EAJPhTNTs9LEaEJljdhS0MooSAAAAAAHBMAyNjS9UUfQ5+u3O1/Q/+9fJo6B/yCfOUpjFqckpw3V10hDZDavZcdAGKEoAAAAABJQoW7jmdirWlQnn68ldK/WPQ5+aHQntkEWGLo4t0IzUUUpyRJsdB22IogQAAABAQMoJS9WirJ9pY9UOPbHrDb1z+DOzI6EdsMjQ8JiempJykTJDk82OAxNQlAAAAAAIaD3C07S462RtqNyuxbtW6IPvPjc7EvwQBQm+R1ECAAAAICic6+qsJdk36MPvvtDiXW9ofeV2syPBDxg/KEiyKEggihIAAAAAQaZvRJaezZmu9w5v1uKdb+jT6q/NjgQTHC1I8jUlZQQFCZqgKAEAAAAQlAZE5mhAZI4++m6r/rzvHa0tL1ODPGbHQitzGDYNj+mpiclF6hqaYnYc+CGKEgAAAABBrXdEpnpHZGpPXYVe2PeeXtr/Lx1sqDQ7FlpYB0esrkg4T2PjCxVjc5kdB36MogQAAAAAJCU5ojUtdaQmp1ykleUb9Pzef2pj9Q6zY+EsWGTo/MhuKk48XwMju8liWMyOhHaAogQAAAAAfsBhsenSuD66NK6Pyqq+0vN739Hfyzeo3us2OxpOUYwtXGPi+umKhAHq6IwzOw7aGYoSAAAAADiOvPBOyuvcSb/sOForyzdo5cH1+qTqK3nlNTsafsSQoV6uzhob318jYs6Vw8LHXZwZfucAAAAAwEnE2SN0VeIgXZU4SLvryrXy4AatLN/ApTl+oEdYmi6OLdCImHOV5Ig2Ow4CAEUJAAAAAJyGZEeMrksequuSh2rHkf2+0mS9ttR8a3a0oJEb1lHDovN1UUxPpYckmB0HAYaiBAAAAADOUJozXpNSLtSklAu1rWaPVpav1z8qPtXmml1mRwsoVllU4Oqsoug8FcXkKcURY3YkBDCKEgAAAABoAV1Ck3RD6MW6ocPF2l9/WO8f3qL3Dn+mfx3ewuOGz0BmSLL6RGSqX0SW+kRkKdoWbnYkBAmKEgAAAABoYfH2yMYn53i9Xm2p2aX//W6rPqz8Qh9/t02H3NVmR/Q7nUMS1TciS31cWeoTkak4e4TZkRCkKEoAAAAAoBUZhqGcsFTlhKXqp0mDG4uTjyq3aVP1N9pcvUvbancH1eOH7YZVnUMSlRfe6Wg5EpGlBHuk2bEASRQlAAAAANCmflicfK/e69a2mt3aXLNLm6t3aXPNTm2p3hUQM0+S7dHqGtZBXUOT1TU0RV1DOygjJFF2w2p2NKBZFCUAAAAAYDK7Yf1PeRL3n/HddeX6rHqXttbu1p66Cu32/bO3vkLlDVXmBf6RGFu4EuyRSrRHKcUR6ytEUpQVmqJIW6jZ8YDTQlECAAAAAH4q2RGjZEeMLlCPY5bVeuq0p+6QdtdVaE/90QJlT12FKhqqVOOpU62nTrWe+sbXNZ76xjGvvMfsz2HY5LTY5DDsP/j30dchFofibC4lOqKUaI9Sgj3qB68j5bDw0RKBg9/NAAAAANAOhVgc6hSSoE4hCae97feFiUWGHBabnIZdhmG0Qkqg/aEoAQAAAIAgE2JxKMTiMDsG4JcsZgcAAAAAAADwFxQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPgFblOzcuVOTJ09WYWGhhg4dqocfflgej8fsWAAAAAAAwI/ZzA7QWqZPn64ePXpo9erVOnDggKZMmaL4+HhNnDjR7GgAAAAAAMBPBeSMkrKyMn322We69dZbFRERoYyMDJWUlGjZsmVmRwMAAAAAAH4sIGeUbNy4UampqYqKimoc69Gjh7Zv367Kykq5XC7TsoVaHKYdGwhmwfSzZ4SEmB0BCEr87AEAEBgCsiipqKhQZGRkk7HvS5Py8vJjipLv711SVVUlt9vdKpmsVqsk6bWut7fK/gGcXHV1tSS12s+52b4/z7h+x+w5wCxtdZ6pra2VJO6/BgBAKwjIokSSvF7vKa975MgRSdLXX3/dWnEAAABa3JEjR0ydKQsAQCAKyKIkNjZWFRUVTcYqKipkGIZiY2OPWT8qKkoZGRlyOp2yWALyti0AACCAeDweHTlypMllxgAAoGUEZFFyzjnn6Ntvv9XBgwcbi5GysjJlZWUpPDz8mPVtNpvi4uLaOiYAAMAZYyYJAACtIyCnT+Tm5iovL0+lpaWqrKzU1q1b9cwzz2jChAlmRwMAAAAAAH7M8J7OzTzakd27d2vOnDn64IMP5HK5NH78eE2bNk2GYZgdDQAAAAAA+KmALUqAM1FUVKSGhgatWLFCYWFhjePr1q3Tr371K61du9bEdADaq1dffVX33nuv3njjDSUlJTVZdvXVVysxMVGPPPKISekAAADwQwF56Q1wNurq6vTEE0+YHQNAABkzZox69uypBx54oMn4K6+8oi1btuiOO+4wKRkAAAB+jKIE+JHp06dr6dKl2r59e7PLd+/erRtuuEGFhYXq3bu3Zs6cecxTlgDgx+655x794x//0LvvvitJ+u677/Twww9r1qxZOnLkiH7xi1+osLBQffv21W233abKykpJUk1NjWbPnq3zzjtPBQUFGj9+vD799FMz3woAAEBAoygBfiQrK0vFxcW6//77m10+depURUREaM2aNVq5cqX27t2ru+++u41TAmhvOnXqpBtuuEHz5s1TXV2dFi5cqIyMDI0bN05Tp05VSkqK3nzzTa1YsUJ79uzRQw89JEn64x//qP3792vVqlVat26dBg0apDlz5pj8bgAAAAIXRQnQjOnTp2vz5s1atWpVk/FNmzZp48aNmjVrllwul+Lj4zV58mStWbNGdXV1JqUF0F5MmjRJdrtdt99+u1566SXNmzdPZWVl+vzzzzVr1iyFhoYqLi5O06dP1/Lly+X1enX48GHZ7XaFhITI4XBo6tSpevnll81+KwAAAAGLogRohsvl0q233qr58+ertra2cfybb75RVFSUEhISGsfS09NVX1+vPXv2mBEVQDtit9t177336rXXXtPEiROVmZmpHTt2yO12q7CwUHl5ecrLy9PEiRNVX1+v8vJyXXXVVdq+fbuGDBmi22+/XWvWrDH7bQAAAAQ0m9kBAH912WWXadmyZXr66afVv39/STrhrBEePQ3gVPTu3VuS1KtXL0mS0+lUWFiY1q9f3+z6sbGxev3117Vu3TqtXbtWc+fO1fLly/Xoo4+2WWYAAIBgwowS4ATmzp2rZ599Vjt27JAkpaWl6dChQ9q/f3/jOtu2bZPT6TzmkZ8AcCrS09NVXV3deJ6RpMrKSpWXl0uSqqqq5Ha7NWDAAN1111164YUXtHLlysblAAAAaFkUJcAJdO/eXZdddpkWLVokScrLy1NmZqZKS0tVXV2tPXv26Mknn9Qll1wiu91ublgA7VJ2drYKCgr061//WgcPHtThw4d1991367bbbpMk3XTTTXrooYdUWVkpj8ej9evXKzo6WlFRUSYnBwAACEwUJcBJzJgxQw0NDZKOXl7zxBNPaO/evbrgggtUXFysnj17au7cuSanBNCelZaWyuv1atiwYRo+fLjcbrcefPBBSdJ9992nr776SoMHD1bfvn313HPPafHixbJY+CMcAACgNRher9drdggAAAAAAAB/wNdRAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAAAAAAAAPhQlAHAWcnJy9Pbbb5sdAwAAAEALsZkdAAD82bZt27R48WK9//77qqqqUlxcnIqKijRt2jRFR0ebHQ8AAABAC2NGCQAcx6ZNmzRu3DglJydr+fLl+vjjj7V48WJt3rxZEyZMUG1trdkRAQAAALQwihIAOI558+Zp4MCBmjVrluLj42W1WtW9e3c9+eSTOvfcc7V3715J0r59+3TdddcpPz9fI0eO1JYtWyRJL7/8ss4///wm+ywuLtZjjz0mSXrsscc0ZcoUzZgxQ7169ZIkXXPNNXrqqac0a9Ys9erVS4MGDdKrr77ahu8aAAAACG4UJQDQjAMHDujjjz/W1Vdffcwyl8ul+fPnKz09XZK0bNky3XPPPXrvvfcUHx+vRx555JSPs2HDBvXr108ffvhh49jSpUs1evRorVu3TsXFxZo3b57q6+vP/k0BAAAAOCmKEgBoxo4dOyRJnTt3Pum6Y8aMUefOneVyuVRUVKTt27ef8nGsVqsmTJggq9XaOFZQUKBBgwbJbrfrJz/5iSorKxtnrwAAAABoXRQlANAMwzAkSR6P56TrduzYsfG10+k8rdkfycnJjcdqbn8hISGSxP1QAAAAgDZCUQIAzfj+sprPP//8pOv+uOg4Ebfb3eTXNtuxDx+zWDg1AwAAAGbhb+MA0IyYmBj169dPzzzzzDHLampqdPnll+ujjz464T6cTqdqamoaf+12u7Vz584WzwoAAACg5VCUAMBx3HnnndqwYYNuueUW7d69Wx6PR5s2bdKkSZMUEhKi/Pz8E27fqVMnVVVV6Z133lFdXZ2efvppeb3eNkoPAAAA4ExQlADAcXTr1k1/+ctf5PF4NHbsWBUUFGjGjBnq37+//vCHP8hut59w+3POOUclJSWaOXOmBg8eLJvNpoKCgjZKDwAAAOBMGF6+3gQAAAAAAJDEjBIAAAAAAIBGFCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+FCUAAAAAAAA+/x9K1eHxa21KKAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Plot 1 saved: plot1_churn_distribution.png\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 1: Overall Churn Distribution ───────────────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+        "fig.suptitle('Overall Customer Churn Distribution', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Count plot\n",
+        "churn_counts = df['Churn'].value_counts()\n",
+        "bars = axes[0].bar(churn_counts.index, churn_counts.values,\n",
+        "                   color=[COLORS.get(k, '#3498DB') for k in churn_counts.index],\n",
+        "                   edgecolor='white', linewidth=1.5)\n",
+        "for bar, val in zip(bars, churn_counts.values):\n",
+        "    axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 50,\n",
+        "                 str(val), ha='center', fontweight='bold', fontsize=12)\n",
+        "axes[0].set_title('Customer Count by Churn Status')\n",
+        "axes[0].set_xlabel('Churn')\n",
+        "axes[0].set_ylabel('Number of Customers')\n",
+        "\n",
+        "# Pie chart\n",
+        "axes[1].pie(churn_counts.values, labels=churn_counts.index,\n",
+        "            colors=[COLORS.get(k, '#3498DB') for k in churn_counts.index],\n",
+        "            autopct='%1.1f%%', startangle=90,\n",
+        "            textprops={'fontsize': 12, 'fontweight': 'bold'})\n",
+        "axes[1].set_title('Churn Percentage')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot1_churn_distribution.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Plot 1 saved: plot1_churn_distribution.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dHAE0NJ5u-hC"
+      },
+      "source": [
+        "### 3.2 — Support Calls vs Churn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 662
+        },
+        "id": "NnE68G-ru-hC",
+        "outputId": "24c07b6e-3cd0-42a1-e26d-f344607c467c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x600 with 2 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABW4AAAJRCAYAAADVvy7oAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAsl5JREFUeJzs3Xd8jff///FnEpKIWFFi74qRhNgiRkNrU6q1VWmtqlmrVig1GqWovZpQNVuzqPlBlVoVe2tojcYogsj4/ZFfrm+OJJwTJ3LwuN9ubs655uu8z5XkOs/zvt6XXUxMTIwAAAAAAAAAADbDPrULAAAAAAAAAACYIrgFAAAAAAAAABtDcAsAAAAAAAAANobgFgAAAAAAAABsDMEtAAAAAAAAANgYglsAAAAAAAAAsDEEtwAAAAAAAABgYwhuAQAAAAAAAMDGENwCAAAAAAAAgI0huAUAAIAGDhwoDw8PeXh4aOXKlcb0KVOmGNOnTJmSihWmLn9/f6MdLl++/ML2e/nyZWO//v7+L2y/L4v478ulS5dSuxwAAACrSpPaBQAAgJfLwYMHtXz5ch0/flw3btzQ7du3lTZtWrm7u8vT01PNmjVT5cqVU7tMm7J161Z17dpVFSpUUHBwsEXrPnz4UD/99JO2b9+uEydO6Pbt24qJiVGWLFnk4eGhWrVqqUmTJnJ0dEyh6l+shQsX6ssvvzSe+/r6av78+alYEV6EyMhIrVu3Tlu2bNHRo0d169YtPX78WBkzZlSRIkX01ltv6f3335erq2tqlwoAAPDCENwCAACzTZw4UTNmzEgwPTIyUhcvXtTFixe1du1ade3aVb169XrxBdqoX3/9NVnr7d+/X71799b169cTzLt27ZquXbum//3vf5o7d66mTZumIkWKPG+pqe6nn34yef7777/r2rVrcnd3T6WKUlfOnDm1a9cuSZKDg0MqV5Myzp49q+7du+vChQsJ5oWFhSksLEx79+7V7NmzNXnyZJUrVy4VqgQAAHjxGCoBAACYZc+ePUZo6+DgoB49emjdunXavn27Fi1apLfffttYdvr06Tp//nxqlWpToqKitG3bNovXO3nypDp06GCEtuXKldN3332nzZs3a82aNRo3bpyKFi0qSbp06ZI6dOigO3fuWLX2F+3s2bM6evSoJBnHU3R0tFatWpWaZaUqBwcHZcuWTdmyZZObm1tql2N1165dU9u2bY3QtmjRogoMDNTGjRv1yy+/6Ntvv1WZMmUkxYa4n3zyif7666/ULBkAAOCFIbgFAABm2bFjh/G4bt26+vTTT1WkSBHlzJlT5cqV0+TJk1WyZEllz55dZcqU0blz54zl27Zta4xDuXfvXpPtxh/D08PDw5geGRlpTPPx8VFMTIwWLVqkxo0bq1SpUqpYsaI+//xzXbt2zWR7y5YtM9YbOXKkwsLCNHToUFWrVk1eXl6qXbu25s2bp+jo6ERf55YtW9SlSxdVqVJFnp6eqlixolq3bq1FixYpIiIiydobNmyomzdvqnPnzvLx8dHw4cPVtm1blShRQrdu3ZIk7du3L8HrTMrw4cP16NEjSVKtWrUUFBSkWrVqKW/evCpatKjeffddLVu2TGXLlpUUG4AtWrTIZBt//vmnevXqpapVq8rT01NlypRRq1atrBaE3rlzRxMnTlSjRo3k4+MjLy8v+fv7q2/fvjp8+LDF24vf27Zbt27KnTt3gulPin9sHT16VMePH9cnn3yi8uXLq3Tp0mrbtq3+/PPPRNddt26d2rdvrwoVKqhkyZKqXLmyunXrpkOHDj2z1sjISPn5+Rn7jv/zEefnn3825jdt2tSYbkm7PWuM23Xr1qlDhw6qXLmySpYsKV9fX7Vq1Uo//vijoqKinvk63nnnHWP769evTzA/JCTEmF+uXDnjmHz48KFmz56tZs2aqWzZsvL09FSNGjXUtWtX/e9//3vmfuOMGzdON2/elCSVKlVKy5YtU8OGDVWgQAEVKlRIderU0aJFi1SnTh1JUnh4uGbOnJnk9v7880999NFHKlu2rMqWLatu3bolGPv2WeM2xx83N/7vqyePtVmzZsnPz0+lSpWSlPD3gST98MMPatCggby9veXr66uAgADdu3fP7PYBAACvN4ZKAAAAZomMjDQe37hxQ9HR0bK3/7/vgO3t7bVixQrZ2dlZZX9p0vzfacqjR4/0zTffaNasWca0hw8fas2aNTp48KBWrFihLFmySDK9nPz27dtq0aKFSQ+9ixcvaty4cbpy5YqGDh1qTI+OjtaAAQO0evVqkzpu376t/fv3a//+/frpp580d+5cZcqUKUG99+/f17Bhw7R9+3aj5uQ6ceKEEeA5ODho2LBhiV4m7+zsrICAAO3cuVNVq1Y1euBKsYHe559/bhJQP378WAcOHNCBAwd05swZff7558mu8c6dO3r//fcThGJXrlzRlStXtGHDBn311Vdq3LixWduLiooy2r5AgQIqUaKEEbKfP39eR44ckbe391O3cfjwYU2YMEHh4eHGtH379unDDz/UTz/9pIIFCxrTAwICtHjxYpP1b968qS1btmj79u2aNm2aatSokeS+0qRJo8aNG2vOnDmSYofDqF69uskyW7duNR43atRIknXbbezYsQnG/40bWuDAgQPavn27pk6davKz9KT69etr2rRpRr316tUzmb9582bjce3ateXk5KSIiAh9+OGHCULmf/75R//884+2bt2qvn37qlOnTk+t/+bNm9qwYYPxfPDgwXJ2dk6wnL29vQYPHiwPDw/5+fnJ09Mz0e0dO3ZMX3zxhR48eGBM27Jli44cOaJ169Yl+nObXGvWrNGCBQuSnH///n1NnjxZ3333nTHt0aNHWrx4sf766y/NmzfParUAAIBXFz1uAQCAWYoVK2Y83rt3r1q3bq2ff/5ZV65cMaZbK7R9UlRUlIKDgzVy5Eht2LBBgYGBSp8+vaTYwCt+oBvfhg0blD59egUHB2vNmjVq2bKlMW/RokU6deqU8XzOnDlGcJg5c2aNGzdOGzdu1KxZs5Q/f35Jsb0PR44cmei+bty4od9++02BgYHasmWLPv30U02ZMsWkR5+Pj4927dpljFmalH379hmPS5Uq9dTxXYsWLaqOHTuahLaPHz/WyJEjjdB2wIAB2rJliyZMmGCE7fPmzdPVq1efWsfTLFmyxAgf27Ztq/Xr12v79u2aOXOm3njjDUVGRlrUu3D37t3GsBB169aVJJMQ8Wm9buNMnDhRNWvW1IYNGzR79my98cYbkqQHDx6Y3BTu6NGjRmjr4uKiWbNmafPmzerQoYOk2ONt7Nixz9zfe++9ZzzeunVrgpB89+7dkmLD9wYNGkiyXrtdv37dCA6LFi2qH374Qdu3b9fPP/+smjVrSpK2bdv2zN7VcXVJ0s6dO02+oJFMx2eOC583b95shLb16tXTqlWrtH37dgUFBRnh+MSJE585pMHBgweNXsHu7u5Gz9XEZM+eXd26dZO3t7fJF0bxff3112rXrp02b96siRMnKl26dJJifzZXrFjx1FostXjxYjVv3lzr1q1LtI1v3ryp+fPna+zYsdq4caO6d+9uzNu9e7dCQkKsWg8AAHg10eMWAACYpVGjRlq4cKFOnDghKTZ0OXjwoCQpW7ZsKlOmjKpUqaJ33nnH6P1qTe3bt1fz5s0lSQULFtT9+/c1fPhwSdIvv/yiAQMGJFgnOjpakyZNUoECBSTF9rKM680aExOjDRs2yMPDQxEREZo7d66x3oQJE+Tn5ycptvdngQIFVLt2bcXExGj9+vXq16+fcuTIYbKviIgI9e7d27hEOk78Xn5p06ZVtmzZnvlaL1++bDyO30vUXH/99ZcxhEK6dOmMQDJPnjxasmSJ9u3bp6ioKB05ciTB6zBX/Brr1KmjwoULS4q9mdbEiRN15coVubu7m31DrZ9//tl4XL9+fUmSl5eX8ufPr0uXLmn9+vUaNGiQHB0dk9xG9uzZ9fXXX8vOzk4FCxZU9+7dFRAQIEk6ffq0Se1x4WapUqWMnrK9e/dWcHCwHj9+rAsXLuj27dvKnDlzkvsrVKiQfHx8dOjQIYWFhWn//v2qUKGCpNgby8WFr76+vkaIbK12u3LlimJiYiRJ3t7exvudM2dOjRo1Sn5+fnJ3dzcJ9BNTuHBhlShRQsePH9ft27d18OBB4zWcP3/eGPIkV65cxvTQ0FBj/Ro1ahhf6uTMmVMTJkzQiRMn5O7u/swers97nD/J09NTffr0kSTlzZtXx44dM3pEx/+Sxhry58+f5Jc4UuyXBX379lWTJk0kSZ999pl27txpDNtx+vRpeXl5WbUmAADw6iG4BQAAZnF0dNTChQs1efJkrVixwqRH4I0bN7Rx40Zt3LhRX331lbp27arOnTtbtQdu3BiXcapWrWo8/ueff3Tr1q0EgXHx4sWN0Db+enG9BeNC6LjQSpJcXV1VpUoVk3Xy58+vN998U6dPn1Z0dLT++OOPBAGtJCMMfF7xL/WO6zVoicKFCxuXvz8pZ86cxuP79+9bXtz/V6hQIePxp59+qoYNG6pcuXIqU6aMEfCZ6+7du8Yl+UWLFtWbb75pzKtbt65mzJih27dva9u2bapdu3aS26lTp47JMRe/B2dcb9645Z48nqTYY/yNN97QP//8Iym2fZ4W3EqxvW7jxsTdtGmT8drj35Au/rAH1mq3fPnyKU2aNIqMjNTy5ct18+ZNVa1aVT4+PvLw8FCrVq3M3lb9+vV1/PhxSbE9h+PqiD9MQsOGDY22jf8ahg8frj/++EMVKlSQj4+PSpYsqZIlS5q13+c9zp/05M9k6dKljcfx339rSGy84SfF9RyP4+3tbQS31q4HAAC8mhgqAQAAmM3V1VVffPGF9uzZowULFujTTz+Vn5+fXF1djWUePnyoiRMnJhg/9HnlyZPH5Hn27NlNnsfd4Ci+uJtbJbVeWFiYJOnvv/82puXIkSPRwDl+z9S4YO9p234e8dszueHqyZMn1adPH9WqVUve3t7GTZPiX9Yd12MzOVq1aqWKFStKih0HODg4WD179lTVqlVVr149zZs3zySYe5pffvnFGBM4a9asWrx4sfEv/ljBzxouIVeuXCbPM2bMaDx+cgiAuKERqlevrpIlSxrtE/+9Nad96tWrJxcXF2ObceJuVubi4qJatWoZ063VblmzZtXQoUONY3Xr1q0aMWKE3n33XVWqVElffPGFyQ0Cn6ZBgwbGduIHzvFfT9wwCVLszfLiQtIHDx5o2bJl6tevn2rVqiV/f39NnjzZuCHf01jjOI/vyd7jT3v/n5c5P+vxvySRTHvfW7seAADwaqLHLQAAsJijo6MqV66sypUrS4odJmDr1q0aNWqUbty4ISn2buqJ9fp7MgxL7k28zAnVEgtg46/3rPlJSWqMTWv0GpRMLxs/c+aMxesfPHhQ7du3f64bpD2Lo6Oj5s+fr40bN2rt2rU6cOCA0Wv53LlzGjdunLZt26Y5c+bIycnpqduKH8ju2bNHe/bsSXS5nTt3KiwsTFmzZk10vrnDMixYsEBjxowxa9lnSZ8+verUqaOVK1fqn3/+0ZEjR+Tq6qqLFy9Kkt555x2T48Ka7daiRQuVKVNGP/74o3777TdduHBBUuwN0FasWKENGzZowYIFz7ypW44cOVSuXDn98ccfunjxos6dOydXV1cdOXJEklSyZEkVKVLEWN7Ozk6BgYFq2LChVq9erb179xo/91euXNF3332nTZs2KTg4+KnDpsQ/zs+ePauYmJjn6qWfNm1ai9dJ7OfdnJ8bc37Wn3ZTOAAAAHPQ4xYAAJglMjJS586d0x9//JFgnqOjo+rUqaPRo0cb0+KPgxk/ULtz547JunE3anqWJ2+kde3aNZPncWOIxpdYz9j424kLAOP31Lx69arJTabixO+V+2TPTmurXLmyEWAdO3bsqW106tQp1apVS1OnTjVuFDdjxgwjfPL09NTixYv1v//9T7t27dLbb79ttTodHBxUr149TZs2Tb///rt++eUXDRkyxOhpuG/fPm3atOmp27h06ZIxVvKzREZGas2aNc9Vc2RkpKZOnWo8b9KkiVatWqWdO3dq165diR5HzxL/JmW//vqrtm/fbjyPP0xCHGu0W5yiRYtq2LBh2rBhg37//Xd99913qlGjhqTYXqyTJk0yaztx4wpLsb13N2/ebISa8Xvbxle9enVNmDBBu3bt0pYtWzR69Ggj4D1z5oyWLFny1H2WLl3aCEBv3ryp33//Pcllw8LCVKtWLY0fP97snsRJedrvo3v37unff/99ru0DAABYC8EtAAB4plOnTql06dKqV6+e2rdvr/Pnzye6XPzLneP3iox/WfHu3btN1vnhhx/MqiH+ZduStGvXLuNxnjx5Er0R0vHjxxOEt/H3HzcWZ8mSJY2xTO/fv6///e9/JuucPXtWZ8+elRTbq8/SMVzjPHz40Kzl8uXLZ4yhGRMTo+HDhysiIiLR7Q0ePFihoaGaMmWKZsyYIck0DG/btq3KlCkjd3d3ZcyY0RjLVJKioqKS9Tqio6O1Z88e/fDDD1qxYoWk2F6YhQoVUtu2bTVkyBBj2aSGlYgTv7dt69atderUqQT/goKCjGXi38QsOcLCwnT37l3jeY8ePVSsWDFlz55d//33n0lol1iAn5hy5coZYylv2rTJGG7A3d1dlSpVMtmetdrt3LlzWrNmjSZPnmz83GXJkkW1atXSjBkzjGEC4n/h8DR16tQxeqzGBbdSbMjZoEEDk2UPHDigZcuWacGCBca0PHnyqFmzZgoMDDSmPWvfrq6uev/9943nX331lcl7Eyc6OlpDhgxRaGio5s6da/IFUXLE/320Z88ek/fZ3N9HAAAALwLX7wAAgGfy8PCQt7e3Dhw4oMjISH344Yfq0qWLKlWqpPTp0+vOnTs6cOCAJk+ebKwTv6ehl5eXMbbq0qVLlSFDBpUqVUrr1q0zAtFnmTlzpvLnz69y5crp+PHjJvtK7EZhUmww2bt3bwUEBChDhgxatmyZcXMgBwcHo5dh2rRp1bFjR02YMEGSNHDgQA0bNkwlS5bU2bNnTYKi999/36JemfED5RMnTmjbtm3KnTu38ufP/9RL4YcPH67Dhw8rLCxMe/bsUcuWLfXxxx+rRIkScnBwUEhIiKZMmWL0PsyRI4f69OkjKTY0j7tUf+PGjfLx8VFYWJi+++47k/B4z549qlKlisU9iO3t7fXll1/q3LlzSps2rSIiIlSlShU5OTnp2rVrJuMbx7/R2JNiYmK0evVq43m9evUSXa58+fLKli2bbty4oRMnTujkyZMqVqyYRTXHyZQpk9KmTavHjx9LklauXKnGjRvrzJkzGjt2rLJmzWqMfbx161Y1bNgwyaEZ4nvvvfc0YcIEXbx40QjO69evbzKshrXaLa62uJD0woUL+uSTT5Q1a1Y9ePBAW7du1X///ScptkeuObJkyaIqVapo+/btOnz4sNEr1dfXN8HxPmPGDOPLjYcPH+qdd95R+vTpdevWLc2fP99Yzpx99+rVS7t27dL58+d1+vRpNWvWTF26dFGpUqXk7OysM2fOaNq0acYNBV1dXTVs2DCzXlNSvLy8jMfnzp1Tjx49VL9+fZ06dUpz5sxR9uzZuXkYAACwCQS3AADALJMmTVLnzp11/PhxXb9+XSNHjkxy2Vq1aqlr167G8/fee08zZ87UjRs3FBMTozlz5kiKDU+nTZumzp07P3P/9erVU69evRJML1y4sD7++ONE1/Hz89O5c+cSvVz9k08+MRljs2PHjjp16pTWrl2rW7duqXfv3olub8CAAc+sNb4CBQrIzc1NN2/e1OPHj9WlSxdJ0pYtWxLccC0+d3d3LV68WN27d9fp06d19OjRRF+/JBUpUkRTp041xhNt3769Dhw4ICk24Nu6dauk2OEk5syZow8++ECPHz/WunXrtG7dOp06dcqi1yRJo0aNUqdOnXT37l0FBAQkukzNmjWNy/YTs3fvXmN4B3d3d5UtWzbR5ezt7VWnTh0FBwdLiu11O3DgQItrliRnZ2e1aNHC2NaUKVM0ZcoUSVLZsmXVr18/de/eXZI0ZswYbdmyxVj2ad59911NmjRJUVFRxhADiR131mg3SWrTpo3+97//ad++fVq/fr3Wr1+fYBk3N7ckj5nE1K9fX9u3b1d0dLTRCzWx1zBo0CCdPn1aV69e1cSJEzVx4sQEy3h7e5v0pk1K+vTptXDhQvXq1Uv79u3TxYsXk3xvc+TIoW+//dbo3ZxcHh4eeuutt4ye0b/++qt+/fVXSVKnTp10+PBhglsAAGATGCoBAACYJXv27Fq2bJm+/vprvfPOO8qbN6+cnZ1lb28vFxcXFSpUSI0aNdLs2bP13XffydHR0VjXxcVFCxculL+/v1xdXeXi4qIyZcpo9uzZqlGjhlxcXJ65/yFDhmjAgAEqVKiQ0qZNq2zZsqlFixZatGiRyd3p48uYMaOWLFmixo0by83NTY6OjvLw8NCoUaMSBLMODg6aMGGCpkyZomrVqsnNzU1p0qRR5syZ5evrq3Hjxmn27Nlydna2qN2cnZ01adIkFS9eXI6OjsqYMaN8fHzMes358+fXzz//rAkTJqh27drKnTu30qVLZ7z+6tWra+zYsVq1apVJCP3OO+8oMDBQJUqUkIuLi9zc3NSoUSMtW7ZMJUqU0KBBg5Q1a1alTZtWpUuXtuj1xClTpoyWL1+utm3bqkCBAnJ1dZWDg4OyZMkiX19fjR07VlOnTn3qzabiD3tQp06dpy5bt25d4/GaNWsUGRmZrLolqV+/fvrss89UoEABOTk5KXfu3OrSpYvmz5+vWrVqqWnTpnJxcZGLi4tKlChh1jazZ8+uatWqGc89PDwS7RVsjXaTYm+ONXfuXA0aNEg+Pj7KmjWr0qRJIxcXF3l4eOiTTz7R6tWrVahQIbPbpVatWiY33XJxcVGtWrUSLFeoUCEtW7ZMnTp10ptvvqlMmTLJwcFBmTJlUtmyZTV48GAtWrTomTdXi5M1a1YFBwdr5syZaty4sfLlyycXFxelSZNGbm5uqlSpkoYOHaoNGzYk+3h9UmBgoFq3bq1s2bLJ0dFRhQsX1tChQ9W3b1+lT5/eKvsAAAB4XnYx5tw6GQAAIBV4eHgYj48cOWJWELRy5UoNGjRIUmwv3cR6AwIpYfjw4frxxx8lSf3791fHjh1TuSIAAAC8zOhxCwAAADynf//91xiv18nJSU2aNEnligAAAPCyY4xbAAAAIBkePHiggwcPKjw8XNOnT1d4eLgkqUWLFnJzc0vl6gAAAPCyI7gFAAAAkiEsLEwdOnQwmZY/f3716NEjlSoCAADAq4ShEgAAAIBkSJs2rXFjrhw5cuj999/Xjz/+mOTN8gAAAABLcHMyAAAAAAAAALAx9LgFAAAAAAAAABtDcAsAAAAAAAAANobgFgAAAAAAAABsDMEtAAAAAAAAANgYglsAAAAAAAAAsDEEtwAAAAAAAABgYwhuAQAAAAAAAMDGENwCAAAAAAAAgI0huAUAAAAAAAAAG0NwCwAAAAAAAAA2huAWAAAAAAAAAGwMwS0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBjCG4BAAAAAAAAwMYQ3AIAAAAAAACAjSG4BQAAAAAAAAAbQ3ALvIQOHz6s3r1766233pKnp6d8fHzUrFkzLVmyJLVLeyF27dqlLl26qHLlyvL09FSNGjXUs2dP7d+/3+JtTZkyRR4eHnr06JEkqW3btvrggw+eqz5/f395eHgY/0qXLq26detqxIgRunDhgsmyly9floeHhxYvXvxc+0yqjt69e6f4fiTJw8NDgYGBKbLtF72vI0eOqFevXvLz85Onp6eqVq2qTp06adu2bSbLtW3bVm3btk2xOgAAeNkNHDjQ5Jwo7rytV69e2rNnT4Ll45+7WLuOKlWqpPh+JOucS1rbokWL5OvrKy8vL/3zzz9JLrdu3Tp9+OGHqlKlikqWLKmKFSuqY8eO2rdv3wusNnVERkbqxx9/VIsWLVS2bFmVKlVK77zzjkaNGqXLly9bvL0nj4Nnnb+uXLnS5GflyX/t27dPzssC8ApIk9oFALDM3r179dFHH6lu3br69ttvlS1bNoWFhemnn37SsGHDFB4ero8++ii1y7TYgAEDlCdPHn322WdPXW7SpEmaNWuWWrdurW7dusnNzU2XLl3SggUL1LZtWwUEBKh58+YvqOqk1axZUyNGjJAkhYeH6+TJk1q0aJEaN26ssWPHql69epKknDlzateuXcqQIYPZ2za3rZYvX660adMm/0UkITQ0VLVq1dKpU6eMabt27ZKLi4vV9/WiLVmyRAEBAWrUqJEmTZqknDlz6u+//9bSpUvVpUsXde3aVb169UrtMgEAeGm4ublp9erVkqSIiAj99ddfWrt2rTp06KAPP/xQAwcONJa19NxlxYoV+vnnnxUcHPzU5QYPHqzHjx8n7wU8w1tvvaWxY8eqYsWKkmI7Bdiar7/+WmXLltXIkSOVLVu2RJeZNm2apk6dqu7du2vo0KFycXHRX3/9pZkzZ6pjx44KCgqSj4/PC678+T35/iQmIiJCnTt31rFjx9S1a1eNGjVKadKk0bFjx/Tdd99pzZo1mj17try9vVO83kWLFil//vwJpjs6Oqb4vgHYJoJb4CWzePFiubu7KzAwUHZ2dpJiwz9PT089fPhQx44dS+UKk+fQoUPKkyfPU5fZsWOHpk+frmHDhql169bG9Dx58sjX11c9e/ZUYGCg6tSpo0yZMqV0yU/l5ORkcmKcP39+vfPOOxo+fLj69+8vDw8PFS5cWA4ODkmeQCfFnLaSYj8opYRDhw4lmGbpa7BFJ0+e1MiRI9W+fXsNGDDAmJ47d26VL19eWbNm1ezZs9W4cWMVLFgwFSsFAODlYW9vb3KekDt3blWuXFmVK1dW3759VaRIETVr1kyS5ecuiZ2TJMaSL8gtce3aNf39998m0zJnzpwi+0quiIgIPXjwQKVLl1bu3LmTXG7hwoWqX7++unXrZkzLlSuXypQpozZt2ujw4cMvXXCb2PuTmG+//VZ//PGHli5dqhIlShjTCxQooKpVq+q9997T+PHjtXDhwpQsV5KUJUuWV+K8GoD1MFQC8JJ5+PChoqKiEu01MHr0aJNLcBK7DCzuMpxz585Jir10rHbt2tqzZ48aNWokLy8v1axZUz/99FOCdQ4fPqyPPvpIpUuXVqVKlTRu3DhFRUUZy929e1fDhw83LjGvXr26Ro0apfDwcGOZtm3bqlu3bpo0aZJ8fHy0cOFCeXh46NKlS5o6dao8PDySvBxp3rx5KlCggFq1apVgnp2dnUaOHKktW7YYoe39+/c1atQoVa1aVSVLllS1atX0xRdf6NatW+Y0tSTp119/1XvvvacyZcqoTJkyatGihX777Tez13+yxgEDBihdunSaP3++pIRDGERERGjs2LHy9/eXl5eXqlSpogEDBhg1J9ZWU6ZMUbly5bR582b5+fmpR48ekhJ//yMiIhQQEKCKFSuqVKlS6tKli27cuGHMf9YxM2XKFPXr18+oJa6XzJOXf127dk19+/ZVpUqV5OnpqVq1amny5MmKjIw02dfo0aO1aNEi1axZU6VLl1azZs105MiRZ7ZlTEyMvv32W1WpUkVeXl5q06aNMQxFjx49VLNmTcXExJiss27dOnl4eOjs2bOJbjMoKEjp0qUz2u9JPXv21I4dOxKEtrt27VKDBg3k6ekpf39/bd682Zj35FAcceK31969e+Xh4aFffvlFDRs2VOXKlSXF/mw2btxYe/fuVdOmTVWqVCm9/fbbJj+bAAC8rBo0aKDKlStr1qxZxrQnz0OWLFmihg0bqnTp0ipfvrw6dOhgdFJo27atli1bpn379snDw0MrV6586t/U+EMlxFm0aJEx9FjTpk11+PBhY15i68Q/b9u7d6+qVasmSWrXrp38/f2NuuJfIh8REaEJEybI399fnp6e8vX11cCBAxUWFmayr+T+zV+5cqUaNmwoLy8vlS1bVh07dtTRo0clxZ5jeHl5SdIzz7MfPnyoiIiIBNMdHR21dOlS44q+pIbfSmw4iqFDhyooKEg1atSQl5eX3nvvPZPzPHM+h0jSuXPn1KVLF5UrV06enp6qV69egl7WHh4emjVrljp37iwvLy8FBwcn+v4k9rp/+OEHNWnSxCS0jZMxY0YFBQUZ5+6SdOHCBX322WeqUKGCPD099fbbb2v69OmKjo5OdB/WlNR5f0xMjBYsWKDGjRurdOnS8vX11bBhw/Tff/+ZrD916lT5+fnJ29tbLVq00MmTJ1WiRAmjp3jcz9D//vc/k/USO66//fZb1a9fX97e3qpevboCAwNNjqG4z30bNmxQvXr15O3trQYNGmjHjh0m2/7zzz/Vtm1blS5dWn5+furfv79u3LihmzdvysvLK9Fe7B07dtT777//fI0JvCQIboGXTLVq1XTt2jW1bt1aGzdu1N27d597mzdu3NC0adMUEBCgn376SeXKldOgQYNMTl4laejQoWrdurVWrVqlzp07a/78+Zo7d64xv0uXLtq6dasCAgL0yy+/aMCAAVq9erX69+9vsp3Tp0/r0qVLWrFihRo3bqytW7dKkjp06KBdu3YpZ86cCWqMjIzUwYMHVb16daOn8ZMyZ86sjBkzGs9HjRqlNWvWaOzYsdq8ebMmTJigvXv3atiwYWa1y4ULF9SrVy/Vrl1bq1at0rJly+Tp6alOnTo9dXywp0mfPr0qVaqU6LhuUuxlauvWrdPo0aO1adMmffvttzp+/LgRlibVVlFRUQoODtb06dMVEBCQ5P7nz5+v7Nmz68cff9TEiRN16NAhffHFF2bX36FDB7Vp00ZSbGA5ePDgBMs8evRI7dq104kTJ/TNN99o/fr1+vjjjzV79mx9/fXXJsvu3LlTf/75p2bMmKGgoCDduXMnwfGSmLVr1+rOnTv6/vvvNXfuXP3999/q3r27YmJi1Lx5c12+fDnBeGzr1q2Tj4+PihQpkug29+3bp4oVKypdunSJzk+XLp3eeOMNk2lXrlzRokWLNG7cOC1fvlzZs2dXv379kvVzOWPGDPXs2dPkw8rNmzc1depUDRkyRD///LMKFy6soUOHJvv4AwDAltSsWVOXLl1KtFfknj17FBAQoI8++kjr1q1TcHCwMmXKpA4dOujBgweaMmWKSpYsKR8fH+3atcsYhkpK/G/qkw4ePKi9e/dq+vTpWrx4sWJiYtS1a1eTDgdP4+PjowkTJkiKDdOWL1+e6HJDhgzRDz/8oB49emj9+vUaM2aM9u7dq08++cTkS+bk/M1fvny5Bg0apFq1aunnn3/WggUL9PjxY7Vr105Xr16Vj4+PWefZUuxnjA0bNqhPnz76448/Eg1xLfW///1PR44c0ezZs7Vo0SJFR0erc+fOun//vrHMsz6HhIWFqXXr1rp9+7ZmzZqltWvXqnHjxho9erSCgoJM9rds2TKVLVtWv/zyi5o3b27W+3P06FGFh4erevXqSb6OnDlzGkN4xMTEGJ8FFixYoI0bN6pnz5767rvvtGjRoudpLrMldt4/ffp0jR07VvXr19fq1as1duxY7dq1S927dzfWW7ZsmaZMmaIWLVoYn+eGDBli0hHHXCNGjNDcuXP14Ycfau3atRowYICWLVum4cOHmyx35swZrVy5UoGBgVq2bJnSpUun/v3768GDB5Kkixcvqn379sqbN6+WLl2qqVOn6vjx4+ratavc3Nz0zjvv6Oeff07ws/L7778T3OK1wVAJwEumZcuWunnzpmbPnq0ePXrI3t5exYsXl5+fn5o0aZKsS7jv37+vzz//XKVKlZIkDR8+XL/88ovWrl2r0qVLG8s1bNhQtWrVkiR99NFH2rx5s9asWaNOnTrp0KFD2r9/vyZOnGgskzdvXl29elXjxo3TP//8Y5woXr16VStWrDB6xsaN2eTi4pLkpUG3bt1SRETEUy/xelLv3r3VrVs35c2bV1LsSVfdunW1aNEixcTEJBkAxzlx4oQiIyPVtGlTI7AbNGiQ6tevbxIQWypnzpwJvmmOc+zYMXl4eBg9RHLmzKnZs2frzp07kmTU8WRbhYeHq3379kaviqQUKVLEuASuYMGCat++vSZPnqxbt24pS5Ysz6w9ffr0RrCZ1Hv166+/6uLFi1q2bJkxFli+fPl05swZLVmyRH379jXe83v37mnUqFHG88aNG2vKlCm6d++eXF1dn1pH/AC+V69e6tevn06cOCFfX1/lz59fK1euNMYzu3v3rnbu3PnUUPvatWtJ9sZIyr///qvly5cbl3a2a9dOvXv31pkzZ1SmTBmLtuXr62v87MS5fv265s6dq6JFi0qK7V2wbds2HT9+PMkPXgAAvCzi/pbduHFDuXLlMpl39OhRpUuXTo0aNVKaNLEfW0ePHq0zZ87IwcFBmTNnVpo0aZQ2bdoE5ySJ/U19Unh4uL7++ms5OTlJig1YW7Vqpd27d+vtt99+Zu2Ojo7G+WCmTJkSHebh2rVrWr16tfr27at3331XUuw50cCBA9WjRw8dOHBA5cqVk5S8v/mzZ89WtWrV1LNnT2PaN998o2rVqmnlypXq1q1bkueOT/ryyy8lSevXr9e6devk7OwsHx8f1ahRQ++++26yhoAIDw/X6NGjjTbu37+/2rdvr927d+udd96R9OzPIcuXL9edO3c0efJkZc+eXZLUuXNnHTp0SMHBwWrXrp2xvwwZMqhTp07G82e9P1LseyTJrGHI4sybN8/kC/3cuXMrKChIO3fufCE3rn3yvP/x48eaO3euGjdubLz+fPny6YsvvtCnn36qgwcPqkyZMlqxYoWKFStmhLkFCxbUw4cPLb5/w7Vr17Ry5Up17drV6IWbL18+Xb9+XWPHjlWvXr3k7u4uKfZz35IlS4z2b926tQYMGKBLly6pWLFiCg4OlpOTk0aOHGn8nAcEBGjp0qUKCwtTy5YttXbtWu3du1eVKlWSJG3cuFGOjo4mX9YArzJ63AIvGTs7O3Xv3l27du3SN998ow8++EAPHjzQzJkzVa9evWR90+vo6GgS+Lm4uKhgwYIJLqWKO7GMU6JECV25ckWSFBISkugycWNhHT9+3JiWJ08ei8egjQtZn7z8/Wns7e0VHBysOnXqqFy5cvLx8dGCBQsUHh5uVi+CMmXKyM3NTW3atNH8+fN18uRJOTg4yMfHR+nTp7eo/vgiIyPl4OCQ6LyaNWtq586dRq+MsLAw5ciRQx4eHs/crqen5zOXKVu2rMlzDw8PRUdH6+LFi2bVbo6QkBA5OTklCJF9fHz04MEDnT9/3phWsmRJk5stxJ3UxQXVSXkyFI1rn/Pnz8vOzk4ffPCBNm3aZPTo+PXXX5U2bVrVrVs3yW3a2dlZdHxJsWMXx/8gEPc4fk8ScyX2/rm4uBgf4OJv/8nL3gAAeBnFDaGU2HlRlSpVFB0drebNm2vx4sW6cOGCXFxcVKpUqWfeqMmccyJPT08jUJRMzyWs5ejRo4qJiTHr/NjSv/n37t3TxYsXE2z7jTfeUN68eU22bY4MGTJo0qRJxtVzNWvW1NmzZzVmzBi98847Zo8nHJ+Xl5dJG5csWVKSjM8P0rM/h4SEhChfvnxGaBvHx8dHf/31l+7du2dMM+d9f5KlnzHs7Oz033//afTo0fL391eZMmXk4+OjkJAQ3b592+L9P+m9996Tj49Pgn8TJ040WS7+az137pzu3buXYGiPuKAz7lg4e/ZsghusPfnZwBxHjx5VdHR0gv1VrlxZMTExJsfek+fKcR1F4o7rI0eOqGTJkkZoK8V+nhw/fryyZs2qcuXK6c033zTpPb9+/XrVrVv3qZ08gFcJPW6Bl1SGDBlUv3591a9fX1JsT81+/fppzJgxqlOnjrJmzWr2tlxdXWVvb/o9jouLS4LLvZ/sZeri4qL79+8rOjraOGl68uYPcX9Q4wdZyemtmiVLFqVLl06XLl0ya/mYmBh17NhR//zzjwYOHGicnAcHBz/zzsNxcuTIoWXLlmnu3LlasGCBxo4dq9y5c6tr167PdWnOpUuXkvxWv0WLFnJ3d9cPP/ygQYMGKSIiQpUqVdLgwYOTvMQ/jjnt+uQycb1n4y5XsoZ79+4pffr0CXo0J3YsuLi4mCxj7slzYsei9H+vo2nTppo0aZJ++eUXNWvWTOvWrVODBg0S7C++nDlzmn18xXlyWIXkfMEQJ7EbpyRVb3K2DwCArbl06ZLs7OwS9LaVYjsILFmyRPPmzdPkyZMVEBCgIkWKqE+fPqpZs+ZTt2vOzciSOpcwd6gEc1hyfmzp3/y4bScWXrm6uibrS2Qp9oZkLVu2VMuWLRUdHa1NmzZp8ODBCggI0KpVqyza1pOvO+41xg+jn/U55N69e4m+n/HbMO5xcj5jxPVmvnjxoooXL/7M5f/55x+1adNG+fPn17Bhw5Q3b16lSZNGn3/+ucX7TszUqVONqwXje/K1xX8edywMGTIkwVAFkoz7WcRvqzjJCT/j9tehQweT9y7uWI1//4xnnev/999/z7yKLG7Yi6FDh+r+/fvav3+/+vTpY3HdwMuK4BZ4ycTd5Cj+t9dS7DfYffr00aeffqrz588bwe2TJ3uJnYyGh4cnGDrg/v37CYYlePIEMO6Pv729vXHycPfuXZMwK+6k63mGFpBie2KUL19eW7du1eDBg02+lY1z584dbdy4UU2bNtW5c+d08uRJjRgxQk2bNjWWsXS8rjx58mj48OEaPny4zpw5o+DgYA0ZMkR58uQxhjOwxM2bN/XHH3+odevWSS7z1ltv6a233lJERIR+++03TZgwQZ06ddKWLVss3t+TnnwP446H+D2IzTlmniZjxoy6f/9+gmPKWseClPixKP3f63Bzc1Pt2rW1du1a+fv76/fff9ePP/741G1WrlxZP/30k+7cuZNoj/DHjx9ryZIleu+995IcB/dJiQW5yf0gBQDAq2bjxo0qWbJkkpexe3h4aNy4cYqJiVFISIhmz56tzz77TOvXr1eBAgWea9/POidK7Eqc5JwTSUrQGcIa50RxgVv8Hqdx7t27Z9HwYlJsgPZkPfb29qpTp44OHjyohQsXmpzbmdM2SZ2vxT/PetbnkIwZMyY6zm9cGz5vr8uSJUsqY8aM+vXXX5O8MuvIkSP677//5Ofnp82bNys8PFzffPONChUqZCzz33//WXxFYWJy5syp/PnzW7RO3H779etn3JAtvrjgO126dAmOlyd7dCf1/t6/f9/4/BW3v8DAQJNe4nGS+nlOTNasWZ95pV3jxo01YcIEbdmyRbdv31bhwoWNXuvA64ChEoCXyPXr11WuXDlNnz490flxlxTFjSmUMWNG3bx502SZJ284JsXeTTX+HV7Dw8N14cIFk5MRKfYuo/EdO3bMWCbuspv9+/ebLHPgwAHZ29snepfWJz2rF2GHDh109epVTZs2LdF1R44cqTFjxujGjRt6/PixJNMTh3v37mnTpk1m7UuKHeM2/k3E3nzzTY0cOVKurq46efLkM9d/UnR0tEaMGCEHBweT8bjiz9+0aZNxcuro6KgaNWqoR48eunLlislJTXJ7XD55w67jx4/LwcHBGBvZ3GPmaTV4e3vr0aNHJseUFHssuLq6PvcHLUn6448/TJ7HXZIVv1dyixYttG/fPgUHB6tIkSLPHP+3bdu2ioqK0pgxYxKdP3nyZH311Vc6e/as2XXGnSjHb9M///zT7PUBAHhVBQcH69ixY+rSpUui8w8cOGD8zbSzs5O3t7dGjRqlqKgonT592lguuedER44c0cOHD43nx44dkxR7vifF/g3/77//jOEcpKT/hidVg6enp+zt7ROctxw4cECSnnlu8jSurq4qUqRIgm1fv35doaGhFm1706ZNKl++fJI3z718+bKyZcsmOzs7I9yNf24TGRmpo0ePJlgvqTaOf0+OZ30O8fb2VmhoqDEWbZwDBw6ocOHCZg1f9rRjxNHRUW3bttUvv/yS6Ou/c+eOBg4cqG+++UZRUVGJfsY4ePCgLl68mGpXRBUsWFAZM2ZUaGio8ufPb/zLkyePIiMjjVoLFSpkvAdxnvxsEPf+3rp1y5j233//6cKFC8ZzT09POTg46O+//zbZX7Zs2WRvb29Wj/c4RYsWVUhIiMlxcvjwYbVs2VJ//fWXUVPdunW1bt06rV27Vs2aNTN7+8CrgOAWeIlkz55drVu31owZMzRmzBgdPnxYV65c0cmTJzV79mxNnDhRjRs3Vr58+STFnugcPHhQmzdv1l9//aUFCxYk+GMtxV7CMn78eB04cEBnz55VQECAIiMj1bhxY5PlVq9erQ0bNujSpUuaN2+eDh06pCZNmhj7qlSpksaOHavt27crNDRUq1at0owZM/Tuu+8mGJcqPkdHRzk7O+vw4cM6efJkkmN5Va5cWZ999pm+++47DRgwQAcPHtSVK1e0d+9ederUSb/++qu+/vpr5cyZU4UKFVKmTJm0aNEiXbhwQYcPH9bHH39s3Khi7969zxwe4PDhw+rWrZtWrFih0NBQhYaGat68eQoPD3/meFCPHj3SjRs3dOPGDf3999/asWOHPvzwQ23btk2BgYGJXhJkb2+vOXPmqFevXtq/f7/++ecfHTt2TD/++KOKFi2qzJkzm91WSTlz5oxmzZqlixcvavPmzQoKClKtWrWMkzRzjpm4ZTdv3pzoOHA1a9ZU4cKF9cUXX2jfvn3666+/FBwcrOXLl+ujjz4y7sr7PO7du6cxY8bo3Llz2rNnjyZPnqySJUuafOtfvnx5FSxYULNmzTJraIvChQtrxIgRWr16tTp37qw9e/boypUrOnTokPr37685c+ZoyJAhFn0QivtCY8aMGfrrr7+0Z88eTZkyhTG5AACvjejoaOOc6Nq1azp06JCGDh2q0aNHq3PnzkneCGzbtm3q1q2bNm3apCtXruj8+fOaMWOGnJ2djb/FGTNm1MWLFxUSEpJor8yncXZ21uDBg3X69GkdOXJEo0ePlru7u3x9fSXF/g1//PixZsyYodDQUG3evFkrV6402UZcz8Pdu3fr+PHjCYK7bNmyqUmTJpo1a5bWrl2r0NBQbdmyRWPGjFHFihUTjDdqqU8++UQ7d+7U1KlTdfHiRR0+fFg9e/ZU5syZ9d5775m9nRo1asjHx0c9e/bUggULdOLECf399986dOiQhg0bpi1bthg3tMqQIYMKFCigVatW6ciRIzp79qyGDh2a6Pmdo6OjSRuPHz9e2bNnNxkb9VmfQ5o2barMmTOrd+/eOnLkiC5cuKDJkyfrf//7n8mNyBLzrPcnTpcuXVSlShV16dJFU6dO1alTpxQaGqoNGzaoZcuWevjwoSZMmCAHBwfjxs0zZ87U5cuXtXnzZo0cOVJvvfWWQkNDdeHCBUVHR5vd9taQJk0affzxx1q8eLGCgoJ08eJFnThxQoMGDdL7779vhN6NGjXSsWPHNGPGDOOzwJNDyOXLl8/4DHX69GmdOHFC/fr1M27EJsWOo9ysWTNNnTpVP//8s0JDQ/Xnn3+qR48eatOmjUVDsMV1nOjfv78uXLigI0eOaOTIkYqIiDAZMqJly5bavXu3Tp48meAzKvCqY6gE4CUzcOBAlSxZUsuXL9e6det069YtOTs7680339SAAQPUvHlzY9kePXro2rVrGjBggBwcHFS7dm317t1bn332mck2XVxc9Omnn2rEiBE6f/683N3dNXbs2ASXvvTv31/ff/+99u/fL2dnZ3Xo0EEtWrQw5n/33XcaP368Bg8erNu3b8vd3V1t2rQxTvSSYmdnp27dumnGjBlq3bq15syZk+TlL927d1fZsmX1/fffq1u3brp//76yZ8+uChUqaOXKlUaPSxcXFwUGBmrMmDFq3Lix8ufPr169esnHx0eHDh1Sjx49Eu25G1/Lli314MEDzZkzRyNHjlTatGlVpEgRffvtt8880d6yZYsxtIGDg4OyZcsmX19fBQQEqHDhwkmu991332ncuHHq2bOn7ty5oyxZsqhChQoaMWJEkm1liW7duuno0aN6//339fjxY1WtWlUjR4405ptzzDRq1Ehr1qxRr1699NZbb2nq1Kkm+3B0dNT8+fM1btw4ffbZZ8blbp9//rk+/PBDi+pNygcffKBHjx6pbdu2unv3rsqWLatRo0YlWK5u3bqaM2eOGjVqZNZ233vvPXl4eGju3Lnq16+fbt++rTfeeEPe3t5avHixcbJuLh8fH/Xu3VuLFi3Szz//rOLFi2vo0KHq3LmzRdsBAOBldfPmTfn5+UmKPY/JlCmTSpUqpTlz5hjTE9OzZ085ODho3Lhxun79ulxcXFS8eHHNnj3b+AL8o48+Uv/+/dWqVSv16dPHrCu84vj5+alo0aL65JNPFBYWpuLFi2vGjBnGcGT16tXT4cOH9cMPPxjnpl9++aVxfwkptsdszZo1NX/+fK1YsUI7d+5MsJ+AgAC5ubkpMDBQN27cUJYsWfT222+rb9++ZtealHfffVfR0dGaP3++EWpXqFBBo0ePtuhy9bhzt+DgYK1evVrTp0/XvXv3lDFjRnl7e2vOnDmqWrWqsfz48eMVEBCgNm3aKEuWLGrfvr2yZs1qcgMpKfZLdC8vL3Xu3Fk3btyQh4eHpk+fbjLk2bM+h7i5uSk4OFjjx4/XRx99pEePHqlQoUIaN26c3n333ae+rsTen8RuhOfo6KiZM2dqxYoVWrlypebPn6+oqCjlzp1bb7/9tj788EOjPcuUKaO+ffsqODhYP/74o7y8vDRhwgTdunVL3bt3V4sWLbR582az295aOnfurPTp02vRokUaP368HB0dVb58eS1atMi4GrNNmza6du2aFixYoGnTpqlkyZIaPny4Scjv4uKir7/+WmPHjtV7772nnDlzqnv37tq2bZvJTeWGDRum7Nmza8qUKbp69arSp08vPz8/LVy40OwhxaTYjhPz589XYGCg3n33Xbm6usrX11cDBgwwGT7D29tb7u7u8vHxMW5wBrwu7GK4wwnwWhs4cKB27typ3bt3J7nMypUrNWjQIK1fv/6poSNgS6Kjo9WkSROVLVtWw4YNS+1yAAAAXhv+/v4qVaqUJk6cmOQy5nwOQcp69OiRvL291b179wSde2zJ0aNH1axZMy1btuy5hhgBXkb0uAUAvFLCw8P177//avr06bp69aq6du2a2iUBAAAAsNDNmzd17tw5ffHFF6pfvz6hLV5LjHELAHilbN68WXXq1NGpU6c0e/ZsZcuWLbVLAgAAAGChvn37qkuXLipTpowxdBzwumGoBAAAAAAAAACwMfS4BQAAAAAAAAAbQ3ALAAAAAAAAADaG4BYAAAAAAAAAbEya1C7AGiIjI3Xnzh05OTnJ3p4sGgAA4FURHR2tR48eKVOmTEqT5pU4dTUL57cAAACvJkvOb1+Js987d+7o4sWLqV0GAAAAUkiBAgWUNWvW1C7jheH8FgAA4NVmzvntKxHcOjk5SYp9wenSpUvlavC6iIqK0unTp1W0aFE5ODikdjkAkKL4nYfU8uDBA128eNE433tdcH6bOH4XWQ9taR20o3XQjtZBO1oPbWkdtGPiLDm/fSWC27jLx9KlSycXF5dUrgavi6ioKEmSi4sLv4AAvPL4nYfU9roNF8D5beL4XWQ9tKV10I7WQTtaB+1oPbSlddCOT2fO+e3rdQYMAAAAAAAAAC8BglsAAAAAAAAAsDEEtwAAAAAAAABgYwhuAQAAAAAAAMDGENwCAAAAAAAAgI0huAUAAAAAAAAAG0NwCwAAAAAAAAA2huAWAAAAAAAAAGwMwS0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBj0qR2AQAAAAAAAACkz87OSbV9Tynycart+3mtXLlSgwYN0qlTp1K7FKuixy0AAAAAAAAAi7Vt21bFihXT3r17E8ybOnWqvvzyy+fex/79+/Xpp5/K19dX3t7e8vPzU48ePXT48OHn3ratI7gFAAAAAAAAkCxZsmTR0KFD9fDhQ6tve9GiRWrfvr1KlSqlNWvW6M8//9TixYuVPXt2tW7dWps2bbL6Pm0JwS0AAAAAAACAZPnggw/k7Oysb7/99qnL3bp1S4MGDVKNGjXk7e2thg0bau3atUku//fff2vMmDH67LPP1KlTJ2XNmlV2dnbKmzevhgwZoq5du+rWrVsm64SEhOi9996Tt7e3atSooc2bNxvz/P39FRgYmKD2gQMHSoodbqFcuXJaunSpKlSooHnz5mnv3r3y8PDQkSNH1KpVK/n4+Mjf318///yzha2UPAS3AAAAAAAAAJIlTZo0Gj16tIKDg3XkyJEkl+vRo4cuXryoH374Qfv371fr1q3Vt29f7du3L9HlN27cKEn68MMPE53fvXt3NW/e3GTa999/r2nTpmnfvn0qV66cvvjiC0VERJj9WiIiInTgwAFt3bpVH330kTF90qRJ+uqrr/THH3/o7bff1tChQ3X79m2zt5tcBLcAAAAAAAAAks3Ly0tt27bV4MGD9fjx4wTzT58+rX379unzzz9Xrly55OjoqBYtWqh48eL66aefEt3mxYsXlSdPHjk7O5tdxyeffCJ3d3c5OzurYcOGunPnjq5fv272+o8ePdKHH34oV1dX2dnZGdNbt26tAgUKKE2aNGrQoIEiIiJ06dIls7ebXAS3AAAAAAAAAJ5Lz5499ejRI82cOTPBvL/++kuS5OHhYTK9cOHCCg0NTXR7dnZ2Sps2rUU15MuXz3gcF/g+evQo2duIkz9/fuOxi4uLJKXImL5PSpPiewAAADbp/PnzZl/eExUVpQMHDig6OloODg5mrZM5c2YVKlToOSoEAAAA8LJwdnbWqFGj1LFjR9WuXdtkXlLhaUxMjEnP1vgKFSqkFStW6N69e3J1dTWrBnt7y/qoRkVFJZiWWFicVI0pjeAWAIDX0L///qs333xT0dHRKbYPBwcHXb16VW+88UaK7QMAAACA7ahQoYKaNGmiwYMHy9fX15heoEABSdLJkydVrlw5Y/qZM2fk4+OT6Lbq1KmjwMBAfffddxowYECC+ePHj9e1a9c0YcIEs2pzcnIy6SUbGRmpy5cv68033zRr/dRAcAsAwGvojTfe0JkzZ8zucXvs2DG1a9dOQUFBKlmypFnrZM6cmdAWAPBcPjs7x+J1phT5OAUqAQCYq3///qpfv75++uknZcmSRZJUsmRJeXt7KzAwUN9++62yZMmixYsX69y5cxo9enSi28mePbsCAgI0ZMgQPXz4UJ07d5a7u7uuXLmiBQsWaMWKFZo2bZrZdRUuXFg7d+7Uv//+q/Tp02vq1Kmp1pPWXAS3AAC8piwZxiDuEqJixYqpTJkyKVUSAAAAgJecq6urRowYoc6dOxvBrSRNmzZNo0ePVrNmzfTgwQMVKVJE8+bNk7e3d5Lbatq0qQoUKKC5c+eqSZMmun//vrJmzapKlSpp+fLlKly4sNl19enTR1988YVq1qypLFmyqFOnTipduvTzvNQUR3ALAAAAAAAA2ICX7aqB4ODgRKfXqFFDx48f1+HDh41p2bJl06RJkyzeR5kyZZ7ZeaRp06Zq2rSpybSKFSvq1KlTxvNChQrpxx9/NFmmVatWFm1Diu25++S0lGLZiL0AAAAAAAAAgBRHcAsAAAAAAAAANobgFgAAAAAAAABsDGPcAgAAAHhpfKvtynj+oMV3gX7ZxgwEAACgxy0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBjCG4BAAAAAAAAwMYQ3AIAAAAAAACAjSG4BQAAAAAAAAAbkya1CwAAAABgGz47OydZ600p8rGVKwEA4PV0a+SAVNt3lmHjUnT7Hh4eGjNmjJo2bZqi+7GWDz74QIUKFdLYsWNTrQaCWwAAAAAAAADJFhYWpjlz5mjr1q26evWqHB0dlT9/fpUpU0be3t5ycHBI7RJfSgS3AAAAAAAAAJIlNDRULVu2VPHixfXtt9+qaNGiCg8P17Zt2zRixAj9/fffmjp1amqX+VJijFsAAAAAAAAAyRIQEKBMmTJp+vTpKlasmOzt7eXq6qp69eqpd+/eypo1q+7cuSNJevDggQYNGqRy5crJx8dHw4YNU2RkpCRpypQpqlKlism2Fy9eLA8PD+O5h4eHFixYoHr16undd9+VJPn7+2vevHkaNWqUKlWqpPLly6tfv3569OiRsd4ff/yhtm3bqkKFCipbtqy6du2q0NBQY/61a9fUqVMnlS1bVtWrV9eCBQtSqLUsQ3ALAAAAAAAAwGI3b97U7t271b59e6VJk/DC/mLFimnYsGHKlCmTJCkoKEgNGzbU77//rokTJ2rJkiXavHmzRftcsmSJvvnmG/3000/GtPnz56ts2bLauXOn5s6dq3Xr1mn58uWSpHPnzqljx46qWbOmdu3apc2bN8vFxUUfffSRIiIiJEmDBg1SWFiYfvnlF61fv16XL1/WmTNnktssVkNwCwAAAAAAAMBioaGhiomJ0ZtvvmnW8m+99ZZ8fX2VJk0a1ahRQ1mzZrU4IK1SpYqKFSsmOzs7Y1qpUqVUt25dpU2bVt7e3ipUqJBOnz4tKTboLVKkiNq3by9HR0dlyZJFgwcPVmhoqA4cOGCEz5988omyZ8+u9OnTq1+/fjYxLi9j3AIAAAAAAACwWFx4mjZtWrOWz5s3r8lzZ2dnkyENzJEvX75nTnNxcTG2e/78eZ04cUJeXl4my6RJk0aXL19WxowZE9Tm5OSU6H5eNIJbAAAAAAAAABYrUKCA7O3tFRISopIlSz5zeXt7yy7+j46OTjAtsZD4adt1dnZW1apVNWvWrETnHzx40Ox9v2gMlQAAAAAAAADAYhkzZlSNGjU0c+ZMPXjwIMH8ixcvqnbt2iY3AkuKk5NTgm1cuHDhuWssWLCgTp48qaioKGNaVFSULl++LEnKmTOnJBnPJSk8PFyXLl167n0/L4JbAAAAAAAAAMkyfPhwSVLz5s21f/9+RUVF6d69e1q7dq3GjBkjX1/fBEMkJKZw4cK6f/++Nm/erOjoaO3bt0/bt29/7vpatmyp27dva/z48frvv/907949BQYGqlmzZrp3755y5sypkiVLat68efr333917949jR8//rn3aw0EtwAAAAAAAACSJUeOHFqxYoWqVKmiwYMHq0yZMqpZs6aWLl2qDz/80Ah2n8Xf318tW7bU4MGDVa5cOS1evFhdu3Z97vpy5cqlWbNm6c8//1TVqlVVtWpVnT59Wt9//71cXV0lSVOmTFH69On19ttvq27dusqbN698fHyee9/PizFuAQAAAMCKPjs7J1nrTSnysZUrAQC8bLIMG5faJSSLm5ubBgwYoAEDBhjToqKidPjwYeP5qVOnEqy3detW47GdnZ0CAgIUEBBgssx7771n9jbiLF261OR5pUqV9OOPPyZZf+7cuTVv3jyTaR07dkxy+Rcl1Xvc7ty5U76+vurdu3eSy9y/f181atTQwIEDX2BlAAAAAAAAAJA6UrXH7ezZs7V8+XLlz5//qctNmTJF9+7de0FVAQAAAAAAAEDqStUet05OTs8Mbk+ePKm1a9eqSZMmL7AyAAAAAAAAAEg9qRrctmvXThkyZEhyfkxMjAICAtS7d29lzJjxBVYGAAAAAAAAAKnHpm9OtmTJEtnZ2alp06aaOnXqM5ePiopSVFTUC6gMkHGsccwBeB1ER0cb//N7Dy8SxxsAAABeVzYb3IaFhenbb7/VggULZGdnZ9Y6p0+fTuGqgIRCQkJSuwQASHFnz541/k+TxmZPHwAAAADglWGzn7zGjh2rd999Vx4eHmavU7RoUbm4uKRgVcD/iYqKUkhIiLy8vOTg4JDa5QBAioqMjJQkFSlSRKVLl07dYvBaCQ8P58t5IIV8q+3KeP6g2R1lJGlKkY9TsCIAABCfzQa3q1evVsaMGbVy5UpJ0sOHDxUdHa1t27Zp7969ia7j4OBAgIYXjuMOwOvA3t7e+J/feXiRON4AAADwurLZ4HbHjh0mz+fPn6+rV69q0KBBqVQRAAAAAAAAALwYqRrcenl5Sfq/yy83b94sKXbM0Bw5cpgs6+rqqnTp0iWYDgAAAAAAAACvmlQNbi25qdNnn32WgpUAAAAAAAAAgO2wT+0CAAAAAAAAAACmCG4BAAAAAAAAwMYQ3AIAAAAAAACAjSG4BQAAAAAAAAAbQ3ALAAAAAAAAADaG4BYAAAAAAAAAbAzBLQAAAAAAAADYGIJbAAAAAAAAALAxBLcAAAAAAAAAYGMIbgEAAAAAAADAxhDcAgAAAAAAAICNIbgFAAAALHD8+HG1a9dO5cqVU5UqVfT555/r5s2bkqQ9e/aoWbNmKlOmjOrXr6/Vq1ebrBsUFKTatWurTJkyatmypY4ePZoaLwEAAAAvgTSpXQAAAADwsoiMjFSnTp3UtGlTzZkzR/fv31ffvn0VEBCgIUOGqFu3bho8eLAaNmyoAwcOqGvXripYsKC8vLy0detWTZkyRXPmzJGHh4eCgoLUpUsXbdq0SS4uLqn90oDX3mdn51i8TkxMjD5SmRSoBgAAetwCAAAAZrtx44Zu3Lihxo0by9HRUVmyZNHbb7+tEydOaM2aNSpQoICaNWsmJycn+fr6yt/fX8uWLZMkLVmyRE2bNlWpUqXk7Oysjz/+WJK0bdu21HxJAAAAsFEEtwAAAICZ3N3dVbx4cS1ZskT3799XWFiYNm3apBo1aujYsWMqUaKEyfIlSpQwhkN4cr69vb2KFy+ukJCQF/oaAAAA8HJgqAQAAADATPb29poyZYrat2+v77//XpJUoUIF9e3bV926dZO7u7vJ8pkzZ9atW7ckSbdv31amTJlM5mfKlMmYn5ioqChFRUVZ+VUkLSYmJlnrvaga4/aTnDppx8T3ZWmtL7JG6cW+18nZV9w6L7pdXjVx7Uc7Ph/a0XpoS+ugHRNnSXsQ3AIAAABmioiIUJcuXVSnTh116dJF4eHhGjFihD7//HOz1rc0GDp9+nRyyky2//RfstY7fPiwdQt5hrt371q8zous8WVpR8nytnzRNSanLZNbY3LfN0n0nLcS2tE6aEfroS2tg3ZMPoJbAAAAwEx79uzR5cuX1adPHzk4OChDhgzq0aOHGjdurKpVq+r27dsmy9+6dUtubm6SpCxZsiSYf/v2bb355ptJ7q9o0aIv9MZlGc8fTNZ6pQuVtm4hSYiKipJCtitDhgyys7OzaN0XVaNk++0oJb8tX2SNUvLaMrk1JmdfMTEx0l3Jy8tLDg4OydovYo/HkJAQ2vE50Y7WQ1taB+2YuPDwcLO/nCe4BQAAAMwUFRWl6Ohok56zERERkiRfX1/99NNPJssfPXpUpUqVkiR5enrq2LFjatKkibGt48ePq1mzZknuz8HB4YV+0LE0DI3zoj+M2dnZWVwr7Zg4S9syNd5rSyW3xuS+b3H7JJR4frSjddCO1kNbWgftaMqStuDmZAAAAICZfHx85OLioilTpujBgwe6deuWpk+frvLly6tx48a6cuWKli1bpkePHmnHjh3asWOHPvjgA0lSy5Yt9fPPP+vw4cN68OCBpk+fLkdHR9WoUSN1XxQAAABsEsEtAAAAYKYsWbJo7ty5OnjwoKpVq6YGDRrI2dlZEyZMUNasWTVz5kwtXLhQZcuW1VdffaWvv/5axYoVkyRVq1ZNffr0Ua9evVShQgX99ttvmjVrlpydnVP5VQEAAMAWMVQCAAAAYAFPT08FBwcnOq98+fJatWpVkuu2atVKrVq1SqnSAAAA8Aqhxy0AAAAAAAAA2BiCWwAAAAAAAACwMQyVAADAK+Kvv/7Sv//+myLbPnnypPF/St0R9o033lC+fPlSZNsAAAAA8LIhuAUA4BXw119/qbiHh8IfPkzR/bRr1y7Ftu3i7KwTp04R3gIAAACACG4BAHgl/Pvvvwp/+FBTSxfVmxlcUmQfdx5HKlPalDl1OHM3XN0Pn9a///5LcAsAAAAAIrgFAOCV8mYGF3lnck3tMgAAAAAAz4mbkwEAAAAAAACAjSG4BQAAAAAAAAAbQ3ALAAAAAAAAADaG4BYAAAAAAAAAbAzBLQAAAAAAAADYGIJbAAAAAAAAALAxBLcAAAAAAAAAYGMIbgEAAAAAAADAxhDcAgAAAAAAAICNIbgFAAAAAAAAABtDcAsAAAAAAAAANobgFgAAAAAAAABsDMEtAAAAAAAAANgYglsAAAAAAAAAsDEEtwAAAAAAAABgYwhuAQAAAAAAAMDGENwCAAAAAAAAgI0huAUAAAAAAAAAG0NwCwAAAAAAAAA2huAWAAAAAAAAAGwMwS0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBjUj243blzp3x9fdW7d+8E8zZt2qRGjRrJx8dHtWvX1tKlS1OhQgAAAAAAAAB4sdKk5s5nz56t5cuXK3/+/AnmHTlyRJ9//rm++eYb1ahRQ7t379ann36qQoUKqVy5cqlQLQAAAAAAAAC8GKna49bJySnJ4Pb27dvq3LmzatWqpTRp0qh69eoqWrSo9u/fnwqVAgAAAAAAAMCLk6o9btu1a5fkvGrVqqlatWrG88jISN24cUPu7u4vojQAAAAAAAAASDWpGtxaIjAwUC4uLqpXr16Sy0RFRSkqKuoFVoXXWdyxxjEHwBa8Kr+L+FuOJ3E8AAAA4HVl88FtTEyMAgMDtXbtWgUFBcnJySnJZU+fPv0CKwNihYSEpHYJAPDK/A08ffq07O1T/d6pAAAAAJDqbDq4jY6O1qBBg3TkyBEtXrxYefPmferyRYsWlYuLywuqDq+7qKgohYSEyMvLSw4ODqldDoDXXHR0dGqXYBVFixZV6dKlU7sM2JDw8PBX5osJAAAAwBI2Hdx+9dVXOnPmjBYvXqzMmTM/c3kHBwcCNLxwHHcAbMGr8nuI36l4EscDAAAAXlc2G9weOHBAq1ev1vr1680KbQEAAAAAAADgVZGqwa2Xl5ckKTIyUpK0efNmSbFjhq5YsUJ3797VW2+9ZbJO+fLlNW/evBdbKAAAAAAAAAC8QKka3D7tpk5fffWVvvrqqxdYDQAAAAAAAADYBm7bDAAAAAAAAAA2huAWAAAAAAAAAGwMwS0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBjCG4BAAAAAAAAwMYQ3AIAAAAAAACAjSG4BQAAAAAAAAAbQ3ALAAAAAAAAADaG4BYAAAAAAAAAbAzBLQAAAAAAAADYGIJbAAAAAAAAALAxBLcAAAAAAAAAYGMIbgEAAAAAAADAxhDcAgAAAAAAAICNIbgFAAAAAAAAABtDcAsAAAAAAAAANobgFgAAAAAAAABsDMEtAAAAAAAAANgYglsAAAAAAAAAsDEEtwAAAAAAAABgYwhuAQAAAAAAAMDGENwCAAAAAAAAgI0huAUAAAAAAAAAG0NwCwAAAAAAAAA2huAWAAAAAAAAAGwMwS0AAAAAAAAA2BiCWwAAAAAAAACwMQS3AAAAAAAAAGBjCG4BAAAAAAAAwMYQ3AIAAAAAAACAjSG4BQAAAAAAAAAbQ3ALAAAAAAAAADaG4BYAAAAAAAAAbAzBLQAAAAAAAADYGIJbAAAAAAAAALAxBLcAAAAAAAAAYGMIbgEAAAAAAADAxhDcAgAAAAAAAICNIbgFAAAAAAAAABtDcAsAAAAAAAAANsYqwe2dO3essRkAAAAAAAAAgJIR3B47dkxNmzY1nvfs2VMVK1ZU5cqVdejQIasWBwAAAAAAAACvI4uD21GjRqlWrVqSpM2bN+vAgQPaunWrBg8erK+//trqBQIAAAAAAADA68bi4Pb06dPq1KmTJGnLli2qV6+ecuXKpfr16+vMmTNWLxAAAAAAAAAAXjcWB7dOTk56/PixoqKitHPnTr311luSpPDwcEVHR1u9QAAAAAAAAAB43aSxdIVq1aqpR48eSpMmjVxdXVWpUiU9fvxYEydOVJkyZVKiRgAAAAAAAAB4rVjc4zYgIEBly5ZVoUKFNHfuXNnZ2enx48e6cOGCvvzyy5SoEQAAAAAAAABeKxb3uHV2dlaXLl1Mprm4uGju3LlWKwoAAAAAAAAAXmdmBbfNmzeXnZ2dWRv88ccfn6sgAAAAwNZNnz5dixYt0r1791S6dGmNGjVKefLk0Z49ezRhwgSdP39eOXPmVOfOndWoUSNjvaCgIC1atEg3btyQh4eHBg8eLE9Pz1R8JQAAALBVZgW3fn5+Zge3AAAAgK159OiRli5dqrZt20qStmzZouXLlyt//vz67LPPlD59erO3tWjRIq1evVpBQUHKnj27Jk2apAULFqhTp07q1q2bBg8erIYNG+rAgQPq2rWrChYsKC8vL23dulVTpkzRnDlz5OHhoaCgIHXp0kWbNm2Si4tLSr10AAAAvKTMCm4/++yzFCtg586dGjBggCpWrKiJEyeazFu/fr2mT5+uy5cvq2DBgurTp4/8/PxSrBYAAAC8mr788kudPn1abdu21fnz59WnTx998sknOn36tEaPHq2vvvrK7G3NmzdPAwYMUKFChSRJQ4YMkSTNnTtXBQoUULNmzSRJvr6+8vf317Jly+Tl5aUlS5aoadOmKlWqlCTp448/VlBQkLZt26b69etb+RUDAADgZWdWcNu3b1+zNzhhwgSzl509e7bR0+FJJ06c0IABAzR16lRVqlRJGzduVPfu3bVhwwblyJHD7H0AAAAAW7Zs0Zo1ayRJq1atkp+fn7p37667d++qbt26Zm/n2rVrunz5su7cuaN69eopLCxMFStWVEBAgI4dO6YSJUqYLF+iRAn98ssvkqRjx46pXr16xjx7e3sVL15cISEhBLcAAABIwKzg1tHRMUV27uTkpOXLl2v06NF69OiRybxly5apevXqql69uiSpUaNGWrhwoVavXq1OnTqlSD0AAAB4NT169EhvvPGGJGn37t1q3bq1JMnV1VX37983eztXr16VJG3YsEHz589XTEyMevTooSFDhujhw4dyd3c3WT5z5sy6deuWJOn27dvKlCmTyfxMmTIZ8xMTFRWlqKgos+t7XjExMcla70XVGLef5NRJOya+L0trfZE1Si/2vU7OvuLWedHt8qqJaz/a8fnQjtZDW1oH7Zg4S9rDrOB2zJgxZm3shx9+MHvHktSuXbsk5x07dswIbeOUKFFCISEhSa7zok9s8XrjFxAAWxIVFaUcrnbK4PpIadLZp3Y5FssQ9Ug5XO34W44ErHU8vPnmm1q5cqWcnZ119uxZ+fv7S5J+++035cyZ0+ztxIU0H3/8sRHSfvbZZ/rkk0/k6+tr9vrmOn36tEXLP6//9F+y1jt8+LB1C3mGu3fvWrzOi6zxZWlHyfK2fNE1Jqctk1tjct83SU/9nArz0Y7WQTtaD21pHbRj8pkV3D7p9OnTOnbsmCIiIoxp165d0/z589WqVSurFJZUj4SzZ88+tS7gReMXEABbcPr0aXUu66jKFa6kdinJ8oakzuGOOn36tOztX77gGbbviy++UP/+/XXv3j0NHjxYmTJl0u3bt9W9e3eLxreN67WbMWNGY1ru3LkVExOjx48f6/bt2ybL37p1S25ubpKkLFmyJJh/+/Ztvfnmm0nur2jRoi/0xmUZzx9M1nqlC5W2biFJiIqKkkK2K0OGDBbfPPlF1SjZfjtKyW/LF1mjlLy2TG6NydlXTEyMdFfy8vKSg4NDsvaL2OMxJCSEdnxOtKP10JbWQTsmLjw83OwM0+LgdvHixfryyy+VNWtW/fvvv3J3d9f169eVO3du9ezZ0+Jin8bSHgkv+sQWrzd+AQGwJdHR0fr8QIRquxTSm67pUrsci52590AzD5zU6q+LqnTp0qldDmyIJSe2T5MrVy5t3LjRZFrmzJm1YcOGBMMbPE2OHDnk6uqqEydOqGTJkpKkK1euKG3atKpevbpWrVplsvzRo0eNm5F5enrq2LFjatKkiaTYc4njx48bNzNLjIODwws9z7A0DI3zos+F7OzsLK6VdkycpW2ZGu+1pZJbY3Lft7h98png+dGO1kE7Wg9taR20oylL2sLi4Hbu3LmaN2+eKlWqJG9vb23fvl03btzQ6NGj5enpaenmkpRUj4S4HguJ4UBAauC4A2ALHBwcdPVejO7ec1Kkw8v3Jebde9G6ei+G36lIwFrHwzvvvKMDBw4k6NFtSWgrSWnSpFGzZs00Y8YMlS9fXq6urvruu+/UsGFDNWnSRNOmTdOyZcvUqFEj/f7779qxY4eWLFkiSWrZsqX69OmjBg0ayMPDQ3PnzpWjo6Nq1KhhldcIAACAV4vF1yKGhYWpUqVKsSvb2ysmJkbZsmVTv379NGLECKsV5unpqaNHj5pMCwkJMXosAAAAAOZq0aKFpkyZYtGNyJLSt29fVa1aVe+//75q1aqlAgUKaMiQIcqaNatmzpyphQsXqmzZsvrqq6/09ddfq1ixYpKkatWqqU+fPurVq5cqVKig3377TbNmzZKzs/Nz1wQAAIBXj8U9bnPlyqXff/9dlSpVUrZs2bR//36VL19eGTJk0OXLl61W2AcffKBmzZpp+/btqly5stasWaOLFy+qUaNGVtsHAAAAXg+7du3S9evXNWvWLGXMmDFBT95du3aZvS1HR0cNHz5cw4cPTzCvfPnyCYZLiK9Vq1ZWuycEAAAAXm0WB7edO3dWx44d9fvvv+u9995T165dVa5cOZ0/f15ly5a1aFteXl6SpMjISEnS5s2bJcX2rC1atKgCAwM1ZswYXblyRUWKFNHMmTOVLVs2S0sGAADAa65Dhw6pXQIAAABgEYuD20aNGqlMmTLKkCGDunTpoqxZsyokJERlypRRy5YtLdpWSEjIU+e/8847eueddywtEQAAADARd0MwAAAA4GVhcXD7+PFj5cmTx3j+/vvvq27dunJ1dbVqYQAAAIC1REVFae7cufr5559148YN/fHHH7p//74mTJigAQMGyMnJKbVLBAAAAEyYfXOymJgYDRo0SOPGjUswr3nz5vrmm2+sWhgAAABgLWPHjtW6devUuXNnPXr0SFJsh4Rz585pzJgxqVwdAAAAkJDZwe3333+v33//XY0bN04wb+LEiVq9erXWrFlj1eIAAAAAa1i3bp2mTZumxo0by87OTpKUOXNmBQYGGvdZAAAAAGyJ2cHtTz/9pFGjRhk3FIuvaNGiGjFihIKCgqxaHAAAAGANjx8/Vo4cORJMT5cune7fv58KFQEAAABPZ3Zwe/nyZZUvXz7J+ZUrV9bFixetURMAAABgVSVLltS8efNMpj148ECBgYHy9PRMpaoAAACApJl9czI7OzvFxMQkOT8iIkLR0dFWKQoAAACwpoEDB+rjjz/W999/r4iICDVq1EihoaFyc3PTtGnTUrs8AAAAIAGzg1svLy/98ssvevfddxOd/8MPP6hEiRLWqgsAAACwmmLFimnz5s3atm2bQkND5ezsrHz58snPz09p0ph9SgwAAAC8MGafpXbs2FE9evTQgwcP9P777xsnuA8fPlRQUJAmT56sGTNmpFihAAAAwPNwdnZW3bp1U7sMAAAAwCxmB7d+fn4aNGiQxowZo7Fjxyp37tyKiorSlStXlC5dOo0cOVJ+fn4pWSsAAACQLLt27dL48eN16dIlRUREJJh/4sSJVKgKAAAASJpF14W9//77qlOnjnbu3KnLly/Lzs5OBQoUkK+vr9KnT59SNQIAAADPZejQoapatap69OghJyen1C4HAAAAeCaLB/TKkCGD6tWrlxK1AAAAACni3r17GjZsGOPZAgAA4KVhn9oFAAAAACnt3Xff1dq1a1O7DAAAAMBsdDkAAADAK69Vq1bq0KGDpk2bJnd3d9nZ2ZnMDwoKSqXKAAAAgMQR3AIAAOCV16NHD2XKlEkVKlRgjFsAAAC8FCwObgcPHqzRo0cnmH7v3j0NGDBA3333nVUKAwAAAKzl8uXL2r17t1xcXFK7FAAAAMAsZge3oaGhunjxolavXq169eopJibGZP6lS5e0a9cuqxcIAAAAPC8/Pz+dP39enp6eqV0KAAAAYBazg9uTJ09q8uTJevz4sTp27JhgvpOTk1q0aGHV4gAAAABrKFGihHr06CEfHx/lzJlT9vam9+jt06dPKlUGAAAAJM7s4Pbtt9/W22+/rcaNG2vVqlUpWRMAAABgVb/99pty586t69ev6/r16ybznrxRGQAAAGALLB7jNmfOnClRBwAAAJBigoODU7sEAAAAwCIWB7d///23jh49yvhgAAAAeKmcOXNGGzdu1JUrVyRJ+fPnV/369ZU3b95UrgwAAABIyOLgtlq1aurRo4e8vb2VK1cupUljugnGBwMAAICtWb9+vfr376/ixYsrX758kqRNmzZp2rRpmjdvnsqVK5fKFQIAAACmLA5u//zzT+XOnVthYWEKCwszmcf4YAAAALBFU6dO1fjx41WvXj2T6StXrtT48eO1dOnSVKoMAAAASJzFwS3jgwEAAOBl8/fff6t27doJpjdq1EhfffVVKlQEAAAAPJ3Fwa3E+GAAAAB4ueTKlUt//vmnypQpYzL92LFjypo1aypVBQAAACTN4uCW8cEAAADwsmnXrp06deqkhg0bqnDhwpKk8+fPa82aNercuXMqVwcAAAAkZHFwy/hgAAAAeNm0aNFC2bNn14oVK3Tw4EFFREQoX758GjFiRILzWgAAAMAWWBzcMj4YAAAAXjb79u2Tv7+//P39TaY/evRIv/zyi+rWrZtKlQEAAACJs7d0hbjxwZ7E+GAAAACwVZ988kmi0+/cuaOBAwe+4GoAAACAZ7O4xy3jgwEAAOBlsWDBAs2ZM0cRERHy8/NLMP/evXvKkydPKlQGAAAAPJ3FwS3jgwEAAOBl0a5dO5UtW1YtW7ZU3759E8x3cnJS5cqVU6EyAAAA4OksDm4lJTo+GAAAAGBr7O3t5eXlpR9++EHe3t4m8+7cuaNMmTKlUmUAAADA01kc3EZHR2vRokXavn27rl+/LknKkSOH/P391aJFC9nZ2Vm9SAAAAOB5ODg4qGnTplq5cqUkqWfPntq4caOyZMmiadOmycfHJ5UrBAAAAExZHNwGBARo69atatCggapXr66YmBj9/fff+u6773T69GkNHz48JeoEAAAAkm3UqFGqVauWJGnz5s06cOCAtm7dqoMHD+rrr7/WDz/8kMoVAgAAAKYsDm7XrFmjFStWqFChQibT33//fb3//vsEtwAAALA5p0+fVnBwsCRpy5YtqlevnnLlyqWcOXNqxIgRqVwdAAAAkJC9pStkyJAh0Tvv5s2bVxkyZLBKUQAAAIA1OTk56fHjx4qKitLOnTv11ltvSZLCw8MVHR2dytUBAAAACVkc3Pbo0UOjR4/W7du3jWm3b9/W119/rc8++8yatQEAAABWUa1aNfXo0UPdu3eXq6urKlWqpMePH2vixIkqU6ZMapcHAAAAJGDxUAmzZs3StWvXtHTpUmXMmFHR0dG6d++e0qZNq4wZM+rbb781lt21a5dViwUAAACSIyAgQAsWLNDdu3c1ZMgQ2dnZ6fHjx7pw4YJGjx6d2uUBAAAACVgc3Hbt2jUl6gAAAABSjLOzs7p06WIyzcXFRXPnzk2ligAAAICnszi4bdKkifH41q1bsrOzU+bMma1ZEwAAAGBVbdu2lZ2dXZLzg4KCXmA1AAAAwLNZHNyGhYUpICBA//vf/xQRESEptgdDjRo1NHToULm5uVm9SAAAAOB5lC5d2uR5VFSUQkNDdfjwYbVp0yZ1igIAAACewuLgtlevXrKzs9PEiROVK1cuSdLly5f1/fffq3fv3vr++++tXiQAAADwPPr27Zvo9F27dmn16tUvuBoAAADg2SwObo8cOaJdu3YpQ4YMxrRixYqpXLlyql69ulWLAwAAAFKSr6+vevTokdplAAAAAAlYHNzmzZtXDx8+NAlupdjLzfLmzWu1wgAAAABruXDhQoJpDx8+1KZNm5QxY8ZUqAgAAAB4OouD2x49eqhv375q06aNChQooOjoaP31119avHixOnbsaHJSXLBgQasWCwAAACRH3bp1ZWdnp5iYGEkyHmfIkEEBAQGpWxwAAACQiGQFt5K0b9++BPP27NljnATb2dnpxIkTz18hAAAA8Jy2bNmSYJqTk5Pc3Nxkb2+fChUBAAAAT2dxcJvYSS8AAABgqyIiIhQdHZ1gWK9Dhw4pS5YsqVQVAAAA8HQWB7e5c+dOiToAAAAAq7tz545atWqlUqVK6auvvjKZ9+WXXyp9+vSaO3euHB0dU6lCAAAAIHEWB7fFihWTnZ1dkvMZHgEAAAC2YurUqXJzc9OQIUMSzFu0aJE++eQTzZ49W59++mkqVAcAAAAkzeLgdvbs2SbPo6OjdenSJa1du1Yff/yx1QoDAAAAnte2bds0depUubi4JJiXLl06DR48WH369CG4BQAAgM2xOLitWrVqotOrV6+ugQMH6p133nnuogAAAABrCAsLk4eHR5LzixUrpqtXr77AigAAAADzWO0Wujly5NDJkyettTlJ0vHjx9WuXTuVK1dOVapU0eeff66bN29adR8AAAB4dbm4uOjWrVtJzr9+/brSpUv3AisCAAAAzGNxj9slS5YkmPbgwQPt2LFD+fLls0pRkhQZGalOnTqpadOmmjNnju7fv6++ffsqICBAkydPttp+AAAA8OqqXLmyFixYoD59+iQ6f/z48apUqdILrgoAAAB4NouD25kzZyaY5uTkpPz582vcuHFWKUqSbty4oRs3bqhx48ZydHSUo6Oj3n77bc2bN89q+wAAAMCr7dNPP1WzZs0UGhqq1q1bq2DBgoqKitLZs2c1b948/fnnn1q6dGlqlwkAAAAkYHFwu3Xr1pSoIwF3d3cVL15cS5YsUc+ePfXw4UNt2rRJNWrUeCH7BwAAwMuvYMGCWrhwob788ku1adNGdnZ2kqSYmBhVqFBBCxcuVMGCBVO5SgAAACAhi4LbK1euyNHRUdmyZZMUOyZYUFCQHjx4oJo1a8rX19dqhdnb22vKlClq3769vv/+e0lShQoV1Ldv3yTXiYqKUlRUlNVqAJ4m7ljjmANgC16V30X8LceTrHE8FC9eXD/88INu3ryp0NBQ2dnZKV++fMqcOfPzFwgAAACkELOD2/379+vjjz/WqFGj1KBBA0VERKhNmzZ6/PixPDw89Omnn+qbb77RW2+9ZZXCIiIi1KVLF9WpU0ddunRReHi4RowYoc8//1xTp05NdJ3Tp09bZd+AJUJCQlK7BAB4Zf4Gnj59Wvb2Vrt3KmDCzc1Nbm5uqV0GAAAAYBazg9spU6aoS5cuatCggSTp119/1Y0bN7R582ZlzZpVa9eu1dy5c60W3O7Zs0eXL19Wnz595ODgoAwZMqhHjx5q3Lixbt++nWgPiaJFi8rFxcUq+weeJSoqSiEhIfLy8pKDg0NqlwPgNRcdHZ3aJVhF0aJFVbp06dQuAzYkPDz8lfliAgAAALCE2cFtSEiIpk+fbjzfsWOHqlatqqxZs0qSatWqpeHDh1utsKioKEVHRysmJsaYFhER8dR1HBwcCNDwwnHcAbAFr8rvIX6n4kkcDwAAAHhdmX0tYkxMjNKlS2c8379/vypUqGA8d3JysmpvHx8fH7m4uGjKlCl68OCBbt26penTp6t8+fKMRwYAAIBnunbtmvH4n3/+ScVKAAAAAMuZHdy6u7vr3LlzkqSTJ0/qn3/+UeXKlY35Fy9eVJYsWaxWWJYsWTR37lwdPHhQ1apVU4MGDeTs7KwJEyZYbR8AAAB4ddWpU8e4YqtOnTqpXA0AAABgGbOHSqhXr5769++v+vXr66efflLp0qVVuHBhSdL9+/cVGBgoPz8/qxbn6emp4OBgq24TAAAAr4eCBQuqdu3acnd3V0REhFq0aJHksj/++OMLrAwAAAB4NrOD227duunOnTtavny5ChYsqKFDhxrzAgMDdfbsWauOcQsAAAA8jxkzZmj9+vW6d++eQkJCrN7JAAAAAEhJZge3adKkMQlr4+vSpYu++OILpU2b1mqFAQAAAM8je/bsat++vaTYG9927949dQsCAAAALGB2cPs07u7u1tgMAAAAkCJ69uypM2fOaOPGjbpy5YokKV++fGrQoIHy5s2bytUBAAAACZl9czIAAADgZbV+/Xo1adJEO3bsUEREhCIiIvTrr7+qfv362r9/f2qXBwAAACRglR63AAAAgC2bOnWqxo8fr3r16plMX7lypcaPH6+lS5emUmUAAABA4szqcXvt2jXj8T///JNixQAAAAAp4e+//1bt2rUTTG/UqJHOnz+fChUBAAAAT2dWcFunTh1FREQYjwEAAICXSa5cufTnn38mmH7s2DFlzZo1FSoCAAAAns6soRIKFiyo2rVry93dXREREWrRokWSy/74449WKw4AAACwhnbt2qlTp05q2LChChcuLEk6f/681qxZo86dO6dydQAAAEBCZgW3M2bM0Pr163Xv3j2FhITIz88vpesCAAAArKZFixbKnj27VqxYoYMHDyoiIkL58uXTiBEjEox7CwAAANgCs4Lb7Nmzq3379pKkqKgode/ePSVrAgAAAKzO399f/v7+qV0GAAAAYBazgtv4evbsqTNnzmjjxo26cuWKJClfvnxq0KCB8ubNa/UCAQAAAAAAAOB1Y9bNyeJbv369mjRpoh07digiIkIRERH69ddfVb9+fe3fvz8lagQAAAAAAACA14rFPW6nTp2q8ePHJxgLbOXKlRo/fryWLl1qteIAAAAAAAAA4HVkcY/bv//+W7Vr104wvVGjRjp//rxVigIAAACsaf369aldAgAAAGARi4PbXLly6c8//0ww/dixY8qaNatVigIAAACsacSIEQoPD0/tMgAAAACzWTxUQrt27dSpUyc1bNhQhQsXliSdP39ea9asUefOna1eIAAAAPC8evXqpSFDhujdd99Vrly55ODgYDK/YMGCqVQZAAAAkDiLg9sWLVooe/bsWrFihQ4ePKiIiAjly5dPI0aMSDDuLQAAAGALRowYIcl0yAQ7OzvFxMTIzs5OJ06cSK3SAAAAgERZHNxKkr+/v/z9/a1dCwAAAJAitmzZktolAAAAABZJVnALAAAAvExy584tSQoLC9M///wjT0/PVK4IAAAAeDqLb04GAAAAvGyuXbumjh07qkqVKmrRooUk6fr162rYsKFCQ0NTuToAAAAgIYJbAAAAvPJGjhwpNzc3bdu2Tfb2safAbm5u8vPz06hRo1K5OgAAACAhi4Pb+Dd0AAAAAF4Gv//+uwICApQzZ07Z2dlJktKkSaOePXvq8OHDqVscAAAAkAiLg9sRI0YoPDw8JWoBAAAAUkS6dOkUExOTYPqdO3cUFRWVChUBAAAAT2fxzcl69eqlIUOG6N1331WuXLnk4OBgMr9gwYJWKw4AAACwhkqVKumLL75Q7969JUn//fefTp48qcDAQNWoUSN1iwMAAAASYXFwO2LECEmmQybY2dkpJiZGdnZ2OnHihPWqAwAAFjlzN+WuirnzOFKZ0lp86mCWlKwbkKShQ4dqwIABqlu3riSpYsWKsrOzU7169TR06NBUrg4AAABIyOJPX1u2bEmJOgAAwHN444035OLsrO6HT6d2Kcnm4uysN954I7XLwCsqU6ZMmjFjhm7evKnQ0FA5OTkpT548cnV1Te3SAAAAgERZHNzmzp1bkhQWFqZ//vlHnp6eVi8KAABYJl++fDpx6pT+/fffFNn+sWPH1K5dOwUFBalkyZIpso833nhD+fLlS5FtA5J0/fp1/fbbb7p+/bocHR2VI0cO+fn5Ed4CAADAJlkc3F67dk1ffPGFdu/erTRp0ujo0aO6fv26OnbsqGnTpilv3rwpUScAAHiGfPnypVjwGXfzpmLFiqlMmTIpsg8gJW3cuFF9+/aVq6urcufOrZiYGF25ckURERGaNGmSqlevnqztfvXVV/r+++916tQpSdKePXs0YcIEnT9/Xjlz5lTnzp3VqFEjY/mgoCAtWrRIN27ckIeHhwYPHkxHCAAAACTK3tIVRo4cKTc3N23btk329rGru7m5yc/PT6NGjbJ6gQAAAMDz+uabb9S/f3/99ttvWrFihVauXKk9e/bo888/15gxY5K1zRMnTmjVqlXG8+vXr6tbt25q0aKF9uzZo8GDB2vo0KEKCQmRJG3dulVTpkzR+PHj9dtvv+mtt95Sly5dFB7OGM8AAABIyOLg9vfff1dAQIBy5swpOzs7SVKaNGnUs2dPHT582Nr1AQAAAM/t+vXratmypdHxQJLs7e3VvHlzXbt2zeLtRUdHa/jw4Wrfvr0xbc2aNSpQoICaNWsmJycn+fr6yt/fX8uWLZMkLVmyRE2bNlWpUqXk7Oysjz/+WJK0bdu253txAAAAeCVZPFRCunTpFBMTk2D6nTt3jMsoAQAAAFvi7++v3bt3q0aNGibT//jjj2QNk/Djjz/KyclJDRs21KRJkyTFjgVdokQJk+VKlCihX375xZhfr149Y569vb2KFy+ukJAQ1a9fP9H9REVFvdBz7MTO883xomqM209y6qQdE9+XpbW+6M98L/K9Ts6+4tbhs/DziWs/2vH50I7WQ1taB+2YOEvaw+LgtlKlSvriiy/Uu3dvSdJ///2nkydPKjAwMMGJMAAAAJBavvnmG+Oxm5ubBgwYIG9vbxUpUkR2dna6cOGCDh48qObNm1u03X///VdTpkxRcHCwyfTbt2/L3d3dZFrmzJl169YtY36mTJlM5mfKlMmYn5jTp09bVNvz+k//JWu9F33l3d27dy1e50XW+LK0o2R5W77oGpPTlsmtMbnvmyRjSBQ8H9rROmhH66EtrYN2TD6Lg9uhQ4dqwIABqlu3riSpYsWKsrOzU7169TR06FCrFwgAAAAkx6FDh0yeFy1aVA8fPtTRo0dNplka8owZM0ZNmzZVkSJFdPnyZYvWtbRHX9GiReXi4mLROs8j4/mDyVqvdKHS1i0kCVFRUVLIdmXIkMEYts1cL6pGyfbbUUp+W77IGqXktWVya0zOvmJiYqS7kpeXlxwcHJK1X8QejyEhIbTjc6IdrYe2tA7aMXHh4eFmfzlvcXCbKVMmzZgxQzdv3lRoaKicnJyUJ08eubq6WlwoAAAAkFKe7BFrDXv27NGhQ4e0du3aBPOyZMmi27dvm0y7deuW3Nzckpx/+/Ztvfnmm0nuz8HB4YV+0LE0DI3zoj+M2dnZWVwr7Zg4S9syNd5rSyW3xuS+b3H7JJR4frSjddCO1kNbWgftaMqStrA4uJVib+7w22+/6fr163J0dFSOHDnk5+dHeAsAAACbFBUVpa1bt+rixYt69OiRyTw7Ozt9+umnZm1n9erVCgsL01tvvSXp/3rQVqxYUR06dEgQ6B49elSlSpWSJHl6eurYsWNq0qSJUdPx48fVrFmz53ptAAAAeDVZHNxu3LhRffv2laurq3Lnzq2YmBhduXJFERERmjRpUrJu7gAAAACkpF69emnHjh0qVKiQnJycTOZZEtwOHDhQPXv2NJ5fvXpVzZs316pVqxQdHa2ZM2dq2bJlatSokX7//Xft2LFDS5YskSS1bNlSffr0UYMGDeTh4aG5c+fK0dGR+0QAAAAgURYHt99884369++vNm3ayN7eXpIUHR2txYsXa8yYMQS3AAAAsDm7du3S6tWrVaBAgefaTqZMmUxuMBYZGSlJypEjhyRp5syZGjVqlEaMGKHcuXPr66+/VrFixSRJ1apVU58+fdSrVy+FhYXJy8tLs2bNkrOz83PVBAAAgFeTxcHt9evX1bJlSyO0lSR7e3s1b95cgYGBVi0OAAAAsIZ8+fIpc+bMVt9unjx5dOrUKeN5+fLltWrVqiSXb9WqlVq1amX1OgAAAPDqsTi49ff31+7duxNc0vXHH3/Q2xYAAAA2afTo0Ro8eLDq1Kmj7Nmzm3RCkGIDVwAAAMCWmBXcfvPNN8ZjNzc3DRgwQN7e3ipSpIjs7Ox04cIFHTx4UM2bN0+xQgEAAIDk2rJli7Zu3aotW7YkmGdnZ6cTJ06kQlUAAABA0swKbg8dOmTyvGjRonr48KGOHj1qMu3w4cNWLQ4AAACwhu+//15jx46Vv79/gpuTAQAAALbIrOA2ODg4pesAAAAAUkzmzJlVp04dQlsAAAC8NCwe4zYqKkpbt27VxYsX9ejRI5N5dnZ2+vTTT61WHAAAAGANQ4YMUWBgoNq0aaMcOXLIzs7OZL6jo2MqVQYAAAAkzuLgtlevXtqxY4cKFSqUoMcCwS0AAABsUb9+/fTgwQMtXLgw0fmMcQsAAABbY3Fwu2vXLq1evVoFChRIgXIAAAAA65s+fXpqlwAAAABYxOLgNl++fMqcOXMKlAIAAACkjAoVKqR2CQAAAIBFLA5uR48ercGDB6tOnTrKnj277O3tTeaXL1/easUBAAAA1tC2bdsE49rGFxQU9AKrAQAAAJ7N4uB2y5Yt2rp1q7Zs2ZJgnp2dHeODAQAAwOaULl3a5HlUVJRCQ0N1+PBhtWnTJnWKAgAAAJ7C4uD2+++/19ixY+Xv75/g5mQAAACALerbt2+i0+Pu3wAAAADYGvtnL2Iqc+bMqlOnjjJkyCBHR8cE/wAAAICXha+vrzZv3pzaZQAAAAAJWNzjdsiQIQoMDFSbNm2UI0eOBGOFEd4CAADA1ly4cCHBtIcPH2rTpk3KmDFjKlQEAAAAPJ3FwW2/fv304MEDLVy4MNH51h7jdvr06Vq0aJHu3bun0qVLa9SoUcqTJ49V9wEAAIBXW926dWVnZ6eYmBhJMh5nyJBBAQEBqVscAAAAkAiLg9vp06enRB2JWrRokVavXq2goCBlz55dkyZN0oIFCzRkyJAXVgMAAABefondWNfJyUlubm6yt7d49DAAAAAgxVkc3FaoUCEl6kjUvHnzNGDAABUqVEiSCGwBAACQLLlz507tEgAAAACLWBzctm3bNsG4tvEFBQU9V0Fxrl27psuXL+vOnTuqV6+ewsLCVLFiRQUEBMjNzc0q+wAAAMCrzd/f/6nnrlLssAncoAwAAAC2xuLgtnTp0ibPo6KiFBoaqsOHD6tNmzbWqktXr16VJG3YsEHz589XTEyMevTooSFDhmjatGmJrhMVFaWoqCir1YDXz/nz53X79m2zlo2OjtahQ4cUGRlp9iWWmTNnNnqQA8DLJDo62vifv7V4kZ73eBs7dmyS80JDQzVp0iSOaeD/tXfncVWV+x7Hv7AVUBGUSCmcMxwShBwocigaLE1EjpKWGadJrRxIUCunqySZ1rG4TnlOWidT07xHUitTMscsMYsM0dRMSVRQREQENtw/vOwrgspGYC/g8369einPmn77cbdZ+7ue9SwAAGBIVge3Y8eOLbF927Ztio2NvemCChU+OOL5559X48aNJUkjR47UCy+8oEuXLsnR0bHYNgcOHCi346PmSU9P1yOPPGIJJyqCyWTS119/rQYNGlTYMQCgIvz++++WP2vVsvr0AbCZkqb5ysnJ0YIFC7R48WKFhIRo9OjRNqgMAAAAuL5y++YVEBCgUaNGldfu5O7uLklycXGxtHl6eqqgoEBpaWm6/fbbi23j5eWlunXrllsNqHn2799f6hG3v/32m8LCwrRkyRK1b9++VNsw4hZAVZWXlydJat26dbG7b4CKlJWVVa4X5zdu3KgZM2botttu07Jly9S2bdty2zcAAABQnqwObo8cOVKsLTs7Wxs2bCgSst4sDw8POTs7KzExUXfddZckKTk5WbVr11ajRo1K3MZkMslkMpVbDah57rzzTqu3ad++vbp06VIB1QCAcRROCWNvb8/vWlSq8nq/HT16VNOnT1dSUpIiIiLUr1+/ctkvAAAAUFGsDm4fe+wx2dnZWaYyKPx7/fr1NXXq1PIrrFYtDRgwQAsWLFCXLl3k7OysuXPnqm/fvtyiCQAAgFLJzs7W3LlztXTpUj3xxBOaM2eOnJ2dbV0WAAAAcENWJ6CbNm0q1ubo6Cg3N7dSP6CptMaOHaucnBwNHDhQubm56tWrlyZOnFiuxwAAAED11atXL+Xm5ioyMlKtW7dWYmJiietx9wwAAACMxurg1tPTsyLqKJGDg4OmTJmiKVOmVNoxAQAAUH0UTqW1aNGia65jZ2dX4uAEAAAAwJZKHdwGBgbKzs7uuuvY2dlp48aNN10UAAAAUB7i4uJsXQIAAABQJqUObt96661rLjt27JjmzJkjs9lcLkUBAAAAAAAAQE1W6uC2a9euxdpycnK0YMECLV68WCEhIRo9enS5FgcAAAAAAAAANZHVc9wW2rhxo2bMmKHbbrtNy5YtU9u2bcuzLgAAAAAAAACosawObo8eParp06crKSlJERER6tevX0XUBQAAAAAAAAA1ln1pV8zOztY777yj/v37684779SXX35JaAsAAAAAAAAAFaDUI2579eql3NxcRUZGqnXr1kpMTCxxvS5dupRbcQAAAAAAAABQE5U6uDWZTDKZTFq0aNE117Gzs9OmTZvKpTAAAAAAAAAAqKlKHdzGxcVVZB0AAAAAAAAAgP9T6jluAQAAAAAAAACVg+AWAAAAAAAAAAyG4BYAAAAAAAAADIbgFgAAAAAAAAAMhuAWAAAAAAAAAAyG4BYAAAAAAAAADIbgFgAAAAAAAAAMhuAWAAAAAAAAAAyG4BYAAAAAAAAADIbgFgAAAAAAAAAMhuAWAAAAAAAAAAyG4BYAAAAAAAAADIbgFgAAAAAAAAAMhuAWAAAAAAAAAAyG4BYAAAAAAAAADKaWrQsAKtqff/6p1NTUct/v/v37LX+aTKZy378kubu7q1mzZhWybwA4fPiw0tPTS7VuWT7zGjRooFatWpW1PAAAAACo0QhuUa39+eefatOurbKzLlbYMYYOHVph+3aqW0dJifsJbwGUu9TUVN15553Kz8+3ajtrPvNMJpNSUlLk7u5ubXkAAAAAUOMR3KJaS01NVXbWRTWd/qgcW7qV+/7N57Nlqu9U7vuVpEtHzujYpK+UmppKcAug3Lm7u+vgwYOlHnFrNpsVHx+vTp06WTXiltAWAAAAAMqG4BY1gmNLN9Vt29jWZQCAoVgzjYHZbJa9vb18fX0rbHoYAAAAAMD/4+FkAAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDBVJridMWOG2rRpY+syAAAAAAAAAKDCVYngNjExUWvWrLF1GQAAAAAAAABQKQwf3Obn52vKlCkKCwuzdSkAAAAAAAAAUClq2bqAG1m+fLkcHR3Vt29fzZkz57rrms1mmc3myikMVUJ1eD/wvgZgBIWfQ3weobLxngMAAEBNZejgNjU1VTExMfr3v/9dqvUPHDhQwRWhqqkO74kDBw7I3t7wg+MB1BAJCQm2LgEAAAAAagRDB7fR0dEKCQlR69atdfz48Ruu7+Xlpbp161ZCZagq8vPzbV3CTfPy8pKvr6+tywBQw5nNZiUkJMjb21smk8nW5aAGycrKqhYXYgEAAABrGTa43blzp3766SetXbu21NuYTCa+TKKI6vB+4H0NwEj4TEJl4/0GAACAmsqwwW1sbKzS0tL0wAMPSJIKCgokSf7+/po8ebL69Oljy/IAAAAAAAAAoMIYNridMGGCRo8ebfk5JSVFTzzxhNasWSNXV1cbVgYAAAAAAAAAFcuwwa2rq2uRgDYvL0+S5OHhYauSAAAAAAAAAKBSVJlH1Tdp0kRJSUm2LgMAAAAAAAAAKlyVCW4BAAAAAAAAoKYguAUAAAAAAAAAgyG4BQAAAAAAAACDIbgFAAAArJCcnKyXX35Z/v7+CggI0IQJE5SRkSFJSkxM1JAhQ9SpUyc98sgj+vDDD4tsu379evXt21d+fn4KCQnRtm3bbPESAAAAUAUQ3AIAAABWGD58uFxcXBQXF6fVq1fr4MGDmjlzprKzszVs2DDdc8892rp1q/7xj39o4cKF2rBhg6TLoe748eMVERGh77//XmFhYXrllVeUkpJi41cEAAAAIyK4BQAAAEopIyNDHTp00NixY1WvXj15eHiof//+2r17tzZv3qzc3FyNGDFCdevW1V133aWBAwdqxYoVkqSVK1eqZ8+e6tmzpxwdHRUUFCQvLy/Fxsba+FUBAADAiGrZugAAAACgqnBxcVF0dHSRthMnTqhRo0bat2+f2rRpI5PJZFnWvn17rVy5UpK0b98+9ezZs8i27du3V0JCwjWPZzabZTaby/EVXF9BQUGZtqusGguPU5Y66ceSj2VtrZVZo1S5/9ZlOVbhNpXdL9VNYf/RjzeHfiw/9GX5oB9LZk1/ENwCAAAAZZSQkKBPPvlE8+fP15dffikXF5ciyxs0aKD09HTl5+crPT1drq6uRZa7urrq999/v+b+Dxw4UCF1X0uGMsq03d69e8u3kBs4f/681dtUZo1VpR8l6/uysmssS1+Wtcay/rtJuu4FGJQe/Vg+6MfyQ1+WD/qx7AhuAQAAgDKIj4/XiBEjNHbsWAUEBOjLL78scT07OzvL360d0efl5aW6deveVJ3WcDm8p0zb+bbyLd9CrsFsNksJm1W/fv0i/VoalVWjZPx+lMrel5VZo1S2vixrjWU5VkFBgXRe8vb2LjLaHtYxm81KSEigH28S/Vh+6MvyQT+WLCsrq9QX5wluAQAAACvFxcUpMjJSkyZNUnBwsCTJzc1Nf/zxR5H10tPT1aBBA9nb26thw4ZKT08vttzNze2axzGZTJX6RcfaMLRQZX8Zs7Ozs7pW+rFk1valLf6trVXWGsv671Z4TEKJm0c/lg/6sfzQl+WDfizKmr7g4WQAAACAFfbs2aPx48frvffes4S2ktShQwclJSUpLy/P0paQkKCOHTtalv/6669F9nXlcgAAAOBKBLcAAABAKeXl5WnixImKiIhQt27diizr2bOnnJ2dNX/+fF28eFE///yzVq1apcGDB0uSQkNDtWPHDm3evFmXLl3SqlWr9McffygoKMgWLwUAAAAGx1QJAAAAQCnt3btXhw4dUlRUlKKiooos++qrr7RgwQJNmTJFH3zwgdzd3RUeHq77779f0uX5amfPnq3o6GglJyerdevWWrhwoW699VYbvBIAAAAYHcEtAAAAUEqdO3dWUlLSdddZtmzZNZc98sgjeuSRR8q7LAAAAFRDTJUAAAAAAAAAAAZDcAsAAAAAAAAABsNUCQAAAAAAoFycnTbe6m0KCgrklpGhc2tdZGdnZ9W2DSfPtPp4AFBVENwCAAAAAAAAuCYuytgGwS0AAAAAANVYWQIXoLqozMCRsBHljeAWAAAAAAAAqGK4KFP98XAyAAAAAAAAADAYglsAAAAAAAAAMBiCWwAAAAAAAAAwGOa4RbXn4Wyn9tnn5JRusnUpVsnOPqdMZ+ueuggAAAAAAIDqgeAW1d6wTg6aemyrdMzWlVhvaicHW5cAAAAAAAAAGyC4RbW3MD5HPz39kJxauNm6FKtk/3FGP8SvV5CtCwEAAABqgNBFm6zepkCSW168zq11kZ2ddXfLNZw80+rjwbbOThtfqcfjPQKA4BbVXkpmgX5zclXdBu62LsUqWU5mpWQW2LoMAAAAwGbKEqaerXewAioBAFSmyrxQYuSLJAS3AAAAAAAAqDSVPXoZqKoIbgEAAAAAAAzG2nCzoKBAbhkZTN0BVCP2ti4AAAAAAAAAAFAUwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABgMwS0AAAAAAAAAGAzBLQAAAAAAAAAYDMEtAAAAAAAAABiMoYPb5ORkvfzyy/L391dAQIAmTJigjIwMW5cFAAAAAAAAABXK0MHt8OHD5eLiori4OK1evVoHDx7UzJkzbV0WAAAAAAAAAFQowwa3GRkZ6tChg8aOHat69erJw8ND/fv31+7du21dGgAAAAAAAABUqFq2LuBaXFxcFB0dXaTtxIkTatSo0TW3MZvNMpvNFV0aqpDC98OlI2cqZv/ns2Wq71Qh+y6smfc1ACMo/Bzi8wiVjfccAAAAairDBrdXS0hI0CeffKL58+dfc50DBw5UYkWoCk6dOiVHJ0cdm/SVrUspE0cnR506dUp79+61dSkAIOny72MAAAAAQMWrEsFtfHy8RowYobFjxyogIOCa63l5ealu3bqVWBmqgsTfEpWamlru+/3tt98UFhamJUuWqH379uW+f0lyd3dXs2bNKmTfAGANs9mshIQEeXt7y2Qy2boc1CBZWVlcnAcAAECNZPjgNi4uTpGRkZo0aZKCg4Ovu67JZOLLJIpp2bKlWrZsWWH7b9++vbp06VJh+wcAI+F3LSob7zcAAADUVIYObvfs2aPx48frvffeU7du3WxdDgAAAAAAAABUCntbF3AteXl5mjhxoiIiIghtAQAAAAAAANQohg1u9+7dq0OHDikqKkre3t5F/ktOTrZ1eQAAAAAAAABQYQw7VULnzp2VlJRk6zIAAAAAAAAAoNIZdsQtAAAAAAAAANRUBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDC1bF0AYCSHDx9Wenp6qdbdv3+/5U+TyVSqbRo0aKBWrVqVtTwAAAAAAADUEAS3wP9JTU3VnXfeqfz8fKu2Gzp0aKnXNZlMSklJkbu7u7XlAQAAAAAAoAYhuAX+j7u7uw4ePFjqEbdms1nx8fHq1KmTVSNuCW0BAAAAAABwIwS3wBWsmcbAbDbL3t5evr6+pQ5uAQAAAAAAgNLg4WQAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAQCVJTk7Wiy++KH9/fz3wwAOaNWuW8vPzbV0WAAAADKiWrQsAAAAAaoqRI0fqrrvu0saNG5WWlqZhw4bJ3d1df//7321dGgAAAAyGEbcAAABAJUhISND+/fsVERGh+vXrq0WLFgoLC9OKFStsXRoAAAAMiOAWAAAAqAT79u2Tp6enXF1dLW133XWXjhw5oszMTBtWBgAAACOqFlMlFM4LdvHiRRtXgprEbDZLkrKysmQymWxcDQBULD7zYCuF53fVYR7Y9PR0ubi4FGkrDHHPnj0rZ2dnS3vh671w4YLl/7/K4JbnVKbtzp8/X86VlCw/P1+3yln18pxkZ2dn1baVVaNk/H6Uyt6XlVmjJNk1aGT1NtlODcp2rNrZ1m+jAuWZ6yu7Xj3Zybjvyez6DSrtWGVRoALlmWrTjzepqvSjVH37kn4s6mbek5Wp0v/dsi//vinN+a1dQUFBQUUXVNHS0tL0xx9/2LoMAAAAVJAWLVrolltusXUZN2XBggXasGGDVq9ebWk7evSoHnnkEW3cuFFNmza1tHN+CwAAUL2V5vy2Woy4dXV1VYsWLeTo6Ch7e2Z/AAAAqC7y8/N16dKlItMLVFVubm5KT08v0paeni47Ozu5ubkVaef8FgAAoHqy5vy2WgS3tWrVqvIjMAAAAFCyK6cQqMo6dOigEydO6MyZM5agNiEhQa1bt1a9evWKrMv5LQAAQPVV2vNbLt8DAAAAlaB9+/by9vbWO++8o8zMTB06dEiLFy/W4MGDbV0aAAAADIjgFignq1ev1n333WfrMgDgmtq0aaMtW7bYuoxrCg8P14QJE2xdBlCh3n//fZ06dUr33Xefhg4dquDgYD355JO2LgsAAAAGRHCLaiUwMFA9evRQVlZWkfZdu3YpMDDwpva9du1aDRo0SH5+furUqZMGDBigzz///Kb2CQDl6fDhwxo7dqwCAgLUsWNHBQYGKioqqticmgBsx8PDQ4sWLdLPP/+s7du3a+TIkbKzM+5Tlo0kOTlZL7/8svz9/RUQEKAJEyYoIyPD1mVVaTNmzFCbNm1sXUaVNn/+fHXr1k2+vr4KCwvT8ePHbV1SlfPbb79p6NCh6ty5s+677z5FRETozJkzti6rSti6dasCAgIUHh5ebNn69evVt29f+fn5KSQkRNu2bbNBhVXD9fpxw4YNCgoKkp+fn3r16qXPPvvMBhVWDdfrx0IXLlzQ/fffz2ANKxDcotrJycnRvHnzynWfc+bM0YwZM/T8889r165d2rp1q5599llFR0fr/fffL9djAUBZJCYmasCAAfLw8FBsbKz27NmjuXPnKikpSYMHD1Z2dratSwSAmzJ8+HC5uLgoLi5Oq1ev1sGDBzVz5kxbl1VlJSYmas2aNbYuo0pbunSpYmNj9fHHH2vbtm1q3bq1lixZYuuyqpS8vDy9+OKL8vX11Y4dO7R27VqdOXNGU6dOtXVphrdo0SJFRUWpefPmxZYlJiZq/PjxioiI0Pfff6+wsDC98sorSklJsUGlxna9fvzll18UERGhUaNG6ccff9Trr7+uadOmaffu3Tao1Niu149XiomJUWZmZiVVVT0Q3KLaGTlypJYuXaojR45cc52UlBSNGDFC/v7+6tSpk8LDw685Iu3IkSNasGCBZsyYoYceekgODg6qW7euevfurbffflsFBQVF1v/mm2/04IMPytvbW+PGjVNubq4k6emnn9bs2bMt6x06dEht2rSxXJUPDAzU/Pnz9eCDD2rKlCk6fvy42rRpo+3btys4OFi+vr4aNGgQV/EBlGjatGnq1q2bIiMj5e7uLpPJpHbt2mn+/Pny9fXVqVOnJEmnT5/WM888Ix8fH/Xu3VsHDhyQVPJ0L6GhoYqJiZF0+SRr2LBhGjNmjO6++25Jlz/XFixYoMjISN19993q3r17kRAgOTlZw4cPl7+/v7p06aJx48YVOVH77LPPFBgYqE6dOum//uu/lJ+fX6F9BKDqysjIUIcOHTR27FjVq1dPHh4e6t+/P1+eyyg/P19TpkxRWFiYrUup0j788EOFh4erVatWcnZ21sSJEzVx4kRbl1WlnD59WqdPn1a/fv3k4OCghg0b6uGHH1ZiYqKtSzM8R0dHrVq1qsSgbOXKlerZs6d69uwpR0dHBQUFycvLS7GxsTao1Niu14/p6ekaNmyYHnroIdWqVUs9e/aUl5cXv3tKcL1+LLR//36tXbtW/fv3r8TKqj6CW1Q7rVu3VmhoqKKioq65zksvvaT69etr06ZN+vrrr3Xq1ClNmTKlxHU3btwoT09P3X///cWWBQYGavTo0ZafL1y4oPj4eH3xxRdasWKF1q9fr2+//bbUta9bt04ffvhhkSvMH3/8sRYuXKjNmzcrKytL//znP0u9PwA1Q1pamvbs2aMhQ4YUW+bs7Kzo6Gg1a9ZMkrRixQpNnTpVO3bskLu7u959991SH2fv3r3q2rWrfvzxR0vb0qVLFRQUpF27dik0NFTTpk1Tbm6uCgoK9NJLL+m2227T5s2b9dVXX+nkyZOW0XGHDx/W5MmT9frrr2vnzp2666679N13391kTwCorlxcXBQdHS13d3dL24kTJ9SoUSMbVlV1LV++XI6Ojurbt6+tS6myTp48qePHj+vcuXPq3bu3/P39NWrUKG7xt1Ljxo3Vrl07rVixQhcuXFBaWpo2bNhQ4ncvFDV06FDVr1+/xGX79u1T+/bti7S1b99eCQkJlVFalXK9fuzRo4defvlly895eXk6ffq0GjduXFnlVRnX60dJKigo0NSpUxUeHi4XF5dKrKzqI7hFtTRy5EglJSXpm2++KbYsMTFR+/btU2RkpJydneXu7q4XX3xRmzZtUk5OTrH1jx07phYtWpTquJcuXdLIkSNVt25dtW/fXq1atbruyN+rde/eXc2bNy8y193gwYPVuHFjNWjQQN26ddOhQ4dKvT8ANcOxY8ckSS1btrzhuv369VPLli3l7OyswMBAqz6jTCaTBg8eLJPJZGnz8/NT9+7dVbt2bT322GPKzMzUqVOnlJCQoIMHDyoyMlJ16tTRLbfcopEjRyo2NlYFBQXauHGj2rdvb7mTYcCAAWratKn1Lx5AjZSQkKBPPvlEI0aMsHUpVU5qaqpiYmKuOWgBpVN4y/lXX32lxYsXa82aNUpJSWHErZXs7e0VExOjTZs26e6771ZAQIDy8vI0duxYW5dWpaWnp8vV1bVIm6urq86ePWujiqqH2bNnW+6+hXVWrFghOzs7hYSE2LqUKofgFtWSs7OzIiIiFB0dXWxex+PHj8vV1VW33nqrpa1Zs2bKzc3VyZMni+3Lzs6u1LfvNmzYUPXq1bP87OTkVGIYfC2enp7F2po0aWL5e506dXTp0qVS7w9AzVB4sac0n1VXfqY4OjpapnMpDQ8Pj2IPUbpyf05OTpKk7OxsHTt2TGazWf7+/vL29pa3t7f+/ve/Kzc3V2fPntXJkyeLbCup1BfJANRs8fHxeu655ywPY4R1oqOjFRISotatW9u6lCqtcLq0559/Xo0bN5aHh4dGjhypuLg4ztetkJOTo+HDh+vRRx/V7t27tWXLFtWvX18RERG2Lq3Ku3pKP5RdQUGBZs2apbVr12r+/PlydHS0dUlVSlpamt577z1NnTqVB7KWQS1bFwBUlODgYK1YsUILFy7UPffcY2m/XpBa0odIixYt9O2336qgoOCGHzLWfAiVFLBcOYqtLPsEUDMVToNw8ODBG966Zc1nitlsLvJzrVrFTxvs7Uu+Buzo6Ki6devqp59+KnF5Tk6O8vLyirQxxy2AG4mLi1NkZKQmTZqk4OBgW5dT5ezcuVM//fST1q5da+tSqrzCaTuuvOXX09NTBQUFSktL0+23326r0qqUnTt36vjx43r11VdlMplUv359jRo1Sv369VN6eroaNGhg6xKrpIYNGxZ7hkt6errc3NxsU1AVlp+fr9dee02//PKLli1bxh1iZfDWW28pODhYbdq0sXUpVRIjblGtTZ48WUuWLLHcRixJTZs21blz55SammppO3z4sBwdHUsMPB566CGlpqaWeIK7bds2hYaGFgsfSuLg4FBk9O+ff/5p7csBgBI1bNhQXbt21eLFi4stu3jxokJCQhQfH3/dfTg6OurixYuWn81ms5KTk8tcU7NmzZSVlVXk8zczM9Nyi16jRo2KPdmYqWAAXM+ePXs0fvx4vffee4S2ZRQbG6u0tDQ98MAD8vf3t9yy6u/vr3Xr1tm4uqrFw8NDzs7ORR6ilZycrNq1azP3shXMZrPy8/OLjA615o5FlKxDhw769ddfi7QlJCSoY8eONqqo6poxY4YOHjxIaHsTYmNjtWrVKvn7+8vf31///Oc/tW7dOvn7+9u6tCqB4BbVWrt27RQcHKw5c+ZY2ry9vXXHHXfonXfeUVZWlk6ePKn58+erT58+ql27drF9NG3aVC+99JImTZqklStXKjs7WxcvXtTatWsVHh6ufv36lTgK7WotWrTQzp07de7cOZ0+fVrLly8vz5cKoIZ74403tHfvXr366qtKSUlRfn6+EhMT9fzzz8vJyUk+Pj7X3b558+a6cOGCtm3bppycHC1cuPCmbrHz8vKSn5+f3nzzTZ05c0YZGRmaMmWKxo0bJ+nywx5+++03bd68WTk5OVq6dGmJ09UAgHT5gTATJ05URESEunXrZutyqqwJEybo66+/1po1a7RmzRp98MEHkqQ1a9YoMDDQxtVVLbVq1dKAAQO0YMECHT16VGlpaZo7d6769u1bqu8GuMzPz09169ZVTEyMLl68qLNnz2r+/Pnq0qULo21vQmhoqHbs2KHNmzfr0qVLWrVqlf744w8FBQXZurQqJT4+XrGxsfrggw94P96E7777Tl988YXld8+gQYMUGBioNWvW2Lq0KoHfKKj2xowZoy+//FIODg6SLt8mPG/ePE2fPl3333+/6tSpo4ceeui68yi98soratq0qT755BPNmDFDtWvXlpeXl2bNmlXqJ54+99xz2rdvn3r06KFmzZpp/Pjx2rJlS3m8RABQ27Zt9dlnnykmJkb9+/dXVlaWPDw89Pjjj+uFF14o8cLUlTp06KCwsDCFh4fLZDLp2WeflZ+f303V9M4772jatGl68MEH5eDgoHvvvVdvvfWWJKljx46aOHGipk6dqoyMDPXt21ePPvoo87EBKNHevXt16NAhRUVFKSoqqsiyr776qsTnBKA4V1fXIg8sKrxrzMPDw1YlVWljx45VTk6OBg4cqNzcXPXq1YuHk1mpYcOG+te//qWZM2eqR48ecnBwUNeuXTV16lRbl2Z43t7ekv7//+ONGzdKujyy1svLS7Nnz1Z0dLSSk5PVunVrLVy4sMhzXnDZ9frx888/1/nz5/XAAw8U2aZLly768MMPK7dQg7teP179O8bZ2Vl16tThd08p2RXwDQkAAAAAAAAADIWpEgAAAAAAAADAYAhuAQAAAAAAAMBgCG4BAAAAAAAAwGAIbgEAAAAAAADAYAhuAQAAAAAAAMBgCG4BAAAAAAAAwGAIbgEAAAAAAADAYAhuAQAAAAAAAMBgCG4B4Ca0adNGW7ZssXUZAAAAgCTp6aef1uzZs212/JMnTyokJEQdO3bUiRMnbFZHVTNhwgSFh4dLkmJiYhQaGmrjigAYAcEtAFzD4cOHNXbsWAUEBKhjx44KDAxUVFSU0tPTbV2aRWZmpt58800FBgbKx8dHnTp10vDhw5WUlGRZZ+fOnUpISCj1PhcvXqy8vLyKKBcAAKBGCQwMVI8ePZSVlVWkfdeuXQoMDLRRVRXryy+/VFpamnbt2qXbbrut2PLc3Fy9//776tWrl3x9feXn56enn35au3fvtkG1pbNhwwYdPXr0uuts27ZNYWFh6tSpk/z8/NS3b18tXrxY+fn5lVQlgOqI4BYASpCYmKgBAwbIw8NDsbGx2rNnj+bOnaukpCQNHjxY2dnZti5RkhQREaHff/9dS5Ys0c8//6xvvvlGt912m5555hllZmZKkpYsWaJff/21VPs7c+aMZs6cKbPZXJFlAwAA1Bg5OTmaN2+ercuoNJmZmWrcuLGcnJxKXP7WW28pLi5O77//vuLj47V161YFBATo2Wef1bFjxyq52tJ5//33rxvcrly5UiNHjlRQUJC2bdum77//XuPGjdNHH32k1157rRIrBVDdENwCQAmmTZumbt26KTIyUu7u7jKZTGrXrp3mz58vX19fnTp1yrLu6dOn9cwzz8jHx0e9e/fWgQMHJEmrV6/WfffdV2S/oaGhiomJkXT5Fqhhw4ZpzJgxuvvuuyVdvrVtwYIFioyM1N13363u3btrzZo116xz+/btGjhwoJo1ayY7Ozu5ubnptdde04QJE2Q2mzV8+HBt3rxZUVFReuaZZyRJCQkJevLJJ9W5c2cFBARoypQpys3NVWpqqnr06KGCggJ17txZq1evLvE2rfvuu0+rV6+WJP38888KDQ2Vn5+f/P399cYbbxgm1AYAADCCkSNHaunSpTpy5EiJy48fP642bdro0KFDlrbZs2fr6aeflnR5dO7dd9+tTZs2KTAwUH5+fpozZ44SEhIUFBQkPz8/vfLKK8rNzbVsn52drbFjx8rPz08PP/ywvvrqK8uy9PR0RUREqFu3bvLz89OIESN08uTJIrV8+umn6tq1q9auXVtizcuXL9djjz2mjh076tFHH9X69eslSXPmzNG8efP0yy+/yNvbW8nJycW23b59u/r06aM2bdrIZDLJ2dlZI0aMUFRUlBwcHCRdHqm8bNkyyzZbtmxRmzZtitT49ddfq0+fPvLx8dGQIUN0+vRpSZfPwR9++GGtXLlS3bt3l6+vryZPnlzkjrJr1S9dPh+fNWuW+vbtqxdffFFBQUE6ePCgXnrppRJD2IyMDM2YMUMREREKCQlRnTp15OjoqO7du+v999+Xs7OzcnJyJEmxsbHq3bu3/Pz8FBgYqE8//bTE/r3SxYsXNX78eN17773y8/PToEGDSj0oA0DVR3ALAFdJS0vTnj17NGTIkGLLnJ2dFR0drWbNmlnaVqxYoalTp2rHjh1yd3fXu+++W+pj7d27V127dtWPP/5oaVu6dKmCgoK0a9cuhYaGatq0aUVOxK/UsmVLffLJJ/rzzz8tbQ4ODgoODparq6sWLFggT09PTZw4UR999JEkKTw8XPfcc4927dqlVatW6dtvv9Xy5cvl7u6uf/3rX5Kk3bt3KyQk5Ib1jxs3TgMHDlR8fLy++OILJSUlacWKFaV+/QAAANVd69atFRoaqqioqDLv4+LFi9q5c6fWrVunKVOmaMGCBZo3b56WLFmi1atX67vvvlNcXJxl/TVr1qh3797atWuXhgwZooiICEs4O2HCBGVnZ2vdunXaunWr6tatWyyQ/OGHHxQXF6c+ffoUqyUuLk6zZs3S9OnTtXv3bo0aNUqRkZFKSkrSmDFjNGLECPn4+CghIUGenp7Ftm/ZsqX+53/+R4mJiUXag4KC1Lhx41L3ySeffKIPP/xQW7dulZ2dnaZOnWpZdvLkSSUkJGjDhg36/PPPFRcXp6VLl96w/kLr1q3Tm2++qYULFyo2NlaSNG/ePEVHRxerY9u2bcrLy9PAgQOLLfPx8dGkSZPk4OCgY8eOafz48Zo4caL27NmjN998U9OnT9f+/fuv+zo/+ugjpaam6ptvvtGuXbvUvXt3TZo0qdT9BKBqI7gFgKsU3qLVsmXLUq3fr18/tWzZUs7OzgoMDLzmaIqSmEwmDR48WCaTydLm5+en7t27q3bt2nrssceUmZlZZITvld5++22dO3dODz/8sHr16qVJkyZp06ZN153q4D//+Y+GDx8uk8mk22+/XV26dCnzVfuMjAzVrVtX9vb2atSokT777DPLyF4AAABcNnLkSCUlJembb74p0/b5+fl68sknVadOHQUGBqqgoEC9evWSm5ubWrZsqVatWhW5ld/Hx0cPPvigHBwcNGTIENWrV087duxQWlqavv32W4WHh8vV1VXOzs6KiIjQ9u3bLSNWJSk4OFjOzs6ys7MrVsuqVav0+OOPq3Pnzqpdu7Z69+6tdu3a6euvvy7Va5k0aZJcXFwUHByswMBARUZGau3atZZRqaX15JNPqnHjxnJ1dVVYWJi2bNlimU/20qVLGjNmjOrUqaM77rhDffr00ebNm0tdv4+Pj3x8fEp8/Vc7fvy4PD09LaOFr6VJkyb6/vvvFRAQIDs7O91777265ZZbtG/fvutul5GRodq1a8vJyUkODg566aWXLHe/Aaj+CG4B4CqFJ2ilfZBAkyZNLH93dHS85ujYknh4eBQ7Ibxyf4Vzg11r+oG2bdtq3bp1+vzzzzVgwAClpKRo9OjRCg0N1YULF0rc5vvvv9cTTzwhPz8/eXt7a/369VafKBd69dVX9frrryskJETvvvuuVaE1AABATVEYkEZHR5d5WqnCB305OjpKUpHRqY6Ojrp06ZLl59atW1v+bjKZ5OnpqZMnT1oGKAQHB8vb21ve3t565JFHZDKZdOLECcs2t99++zXrOH78uO64444ibc2bNy9xWoSS3H777Vq+fLnWrVunsLAwZWdna+LEierTp49lVHBpXDnIwtPTUzk5OZaHCLu6usrNza3IMQsHQpSm/pJGCl9Pab432NnZadmyZXrooYfk4+Mjb29vnT59+obn4U8++aSOHDminj17asKECdq0aZNVtQGo2ghuAeAqhdMgHDx4sFTrl+ZKfKGrR8LWqlWr2Dr29tZ/NHfo0EEvvPCCFi1apNjYWB0+fFj/+c9/iq136NAhjR49Wv3799fOnTuVkJCgxx9/3KpjXfkaBg4cqM2bN+upp57S77//ruDgYG3cuNHq+gEAAKq74OBgNW7cWAsXLrzhuiXdPXX1OeL1zhlLWubo6GgZFLBlyxYlJCRY/vvtt9/k4+NjWffKu8Gudq2g0ZpzYulyuDx06FDFxMTom2++UW5urmVqr6uVFIxe2VZQUFBk2dX9V1BQYKmvNPVf7/VfrUWLFkpOTlZWVtZ111u5cqU++OADRUVF6aefflJCQoI8PDxuuP8mTZpo/fr1mjVrlpydnTV58mSNHj261PUBqNoIbgHgKg0bNlTXrl21ePHiYssuXryokJAQxcfH33A/jo6OunjxouVns9lc6pEIpXHgwAFFRUUVO5Ft1aqVmjRpUuTYhRITE+Xg4KChQ4fKyclJBQUFxeYXu95rOH/+vGUkgySdPXtWDRs21N/+9jfNmzdPw4YN06pVq27+xQEAAFRDkydP1pIlSywjX6X/H0F75UjcK5eXxZV3QRWegzZu3Fienp6yt7cvMp9rbm6uVSNdmzVrpsOHDxdpO3z4sJo2bXrDbVNSUjR16lRlZmYWab/11lvVtm1by3mng4NDkf648nkOJbUlJyfLyclJDRs2lCRlZmbqzJkzluV//fWXZYTyzdRfkoCAADk5Oenjjz8utuzAgQPq3bu3MjIylJCQoM6dO+uee+6RyWTS6dOnrzkd2pUuXLggs9msgIAATZw4UStXrtTXX3+ts2fPlqleAFULwS0AlOCNN97Q3r179eqrryolJUX5+flKTEzU888/LycnpyIjEq6lefPmunDhgrZt26acnBwtXLiw2GiAm+Hu7q4vvvhCkydP1l9//aWCggJlZmbq448/1h9//KEePXpIuvxl4M8//9T58+fl6emp7OxsJSYm6ty5c5o1a5YcHBx06tQpFRQUWEZhHDlyRFlZWWrevLmOHDmiAwcOKDs7W3PmzFG9evUkXT7xDgwM1LZt25Sfn6/z58/rwIEDRR7cBgAAgP/Xrl07BQcHa86cOZY2Nzc31a9fXxs2bJDZbNa2bdu0d+/emzrOnj17tH37duXm5mr58uXKzs5Wt27dVL9+ffXu3VuzZ89WSkqKsrOz9e677+rZZ58t9Xlqv3799MUXX2jv3r3Kzc3V6tWrdfDgwRIfZHY1Nzc37dixQ5GRkTp8+LDy8/N18eJFrV27Vjt37lRgYKCky6NYN2/erOzsbB09elRffPFFsX0tW7ZMqampSk9P10cffaSePXtaRs06ODho7ty5ys7O1u+//65169ZZ9l2W+h0dHXX06NFigbN0eRqM119/XTExMVq4cKEyMzOVk5Oj7777Ti+88IIeeOABubi4yNPTU4cPH9a5c+eUnJysqKgo3X777TcMzUeNGqWZM2cqMzNT+fn5+umnn9SgQQO5urresL8BVH3F79EFAKht27b67LPPFBMTo/79+ysrK0seHh56/PHH9cILL6h27do33EeHDh0UFham8PBwmUwmPfvss/Lz8yu3Gt3c3PTpp5/qv//7v/XEE08oPT1djo6O8vHx0eLFi+Xl5SVJCg0N1Zw5c7Rjxw6tWbNGTz31lIYMGaI6depoxIgRev311zVixAiFh4fr7bfflp+fnwYMGKDw8HA988wz6tWrlwYNGiRnZ2eFh4frhx9+kHR5ft4333xTb775pv766y85OzurR48eGjVqVLm9RgAAgOpmzJgx+vLLLy0PszKZTJoyZYpmzZqljz/+WA8++KCeeuop7dixo8zHCA0N1WeffaZXXnlFjRo10j/+8Q+5uLhIuvxwsOnTp6tPnz6yt7eXr6+v5s2bV+qpDvr06aPk5GSNGzdOqampatWqlT788EO1aNHihts6ODjo3//+t2JiYvTcc8/pzJkzsre3V7t27fTOO++oe/fuki73UWRkpPz9/dWuXTs999xzxaYHCAoK0jPPPKM///xTvr6+mjJlimWZi4uLvLy89PDDD+v8+fMKCgrSoEGDylz/oEGD9Pbbb2vHjh2aP39+seV/+9vfdOutt2rRokVauHCh7Ozs1KJFC4WHhys4OFiSNHjwYP3www/q2bOnPD09NXXqVP3666+aM2eObr311msee/r06Zo8ebJ69OghOzs73XnnnZo7d26ZplcDUPXYFZTn8C8AAAAAAIAKcvz4cT344INav359sYeMSdLq1av1zjvvaPv27TaoDgDKF5doAAAAAAAAAMBgCG4BAAAAAAAAwGCYKgEAAAAAAAAADIYRtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDAEtwAAAAAAAABgMAS3AAAAAAAAAGAwBLcAAAAAAAAAYDD/C+AyE9t11k7YAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📊 Mean support calls — Churned   : 10.02\n",
+            "📊 Mean support calls — Not Churned: 3.47\n",
+            "✅ Plot 2 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 2: Support Calls vs Churn ───────────────────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+        "fig.suptitle('Support Calls Analysis vs Churn', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Box plot\n",
+        "churned = df[df['Churn'] == 'Yes']['support_calls']\n",
+        "not_churned = df[df['Churn'] == 'No']['support_calls']\n",
+        "bp = axes[0].boxplot([not_churned, churned], labels=['No Churn', 'Churned'],\n",
+        "                     patch_artist=True, notch=False)\n",
+        "bp['boxes'][0].set_facecolor('#2ECC71')\n",
+        "bp['boxes'][1].set_facecolor('#E74C3C')\n",
+        "axes[0].set_title('Support Calls Distribution by Churn')\n",
+        "axes[0].set_ylabel('Number of Support Calls')\n",
+        "axes[0].set_xlabel('Churn Status')\n",
+        "\n",
+        "# Histogram\n",
+        "axes[1].hist(not_churned, bins=15, alpha=0.7, color='#2ECC71', label='No Churn')\n",
+        "axes[1].hist(churned, bins=15, alpha=0.7, color='#E74C3C', label='Churned')\n",
+        "axes[1].set_title('Distribution of Support Call Frequency')\n",
+        "axes[1].set_xlabel('Number of Support Calls')\n",
+        "axes[1].set_ylabel('Number of Customers')\n",
+        "axes[1].legend(fontsize=11)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot2_support_calls_vs_churn.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "\n",
+        "# Print summary stats\n",
+        "print(f'📊 Mean support calls — Churned   : {churned.mean():.2f}')\n",
+        "print(f'📊 Mean support calls — Not Churned: {not_churned.mean():.2f}')\n",
+        "print('✅ Plot 2 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FXt_i9vgu-hC"
+      },
+      "source": [
+        "### 3.3 — Complaint Type vs Churn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 626
+        },
+        "id": "n8zvEY92u-hC",
+        "outputId": "74a09ede-5fc7-4be4-9527-235df60833cc"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x600 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Plot 3 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 3: Complaint Type vs Churn ──────────────────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+        "fig.suptitle('Complaint Type Analysis vs Churn', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Grouped bar chart\n",
+        "ct_churn = pd.crosstab(df['complaint_type'], df['Churn'])\n",
+        "ct_churn.plot(kind='bar', ax=axes[0], color=['#2ECC71', '#E74C3C'],\n",
+        "              edgecolor='white', linewidth=0.8)\n",
+        "axes[0].set_title('Complaint Types by Churn Status')\n",
+        "axes[0].set_xlabel('Complaint Type')\n",
+        "axes[0].set_ylabel('Number of Customers')\n",
+        "axes[0].tick_params(axis='x', rotation=30)\n",
+        "axes[0].legend(['No Churn', 'Churned'])\n",
+        "\n",
+        "# Normalized stacked bar\n",
+        "ct_pct = ct_churn.div(ct_churn.sum(axis=1), axis=0)\n",
+        "ct_pct.plot(kind='bar', stacked=True, ax=axes[1],\n",
+        "            color=['#2ECC71', '#E74C3C'], edgecolor='white', linewidth=0.8)\n",
+        "axes[1].set_title('Churn Rate by Complaint Type (%)')\n",
+        "axes[1].set_xlabel('Complaint Type')\n",
+        "axes[1].set_ylabel('Proportion')\n",
+        "axes[1].tick_params(axis='x', rotation=30)\n",
+        "axes[1].legend(['No Churn', 'Churned'])\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot3_complaint_type_vs_churn.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Plot 3 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-hFLpGjpu-hC"
+      },
+      "source": [
+        "### 3.4 — Sentiment Score vs Churn (Qualitative Analysis)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "ynIyBMc2u-hD",
+        "outputId": "275b4e44-05aa-4274-a99a-f41099e8d39b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x500 with 3 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📊 Mean sentiment (No Churn): 0.519\n",
+            "📊 Mean sentiment (Churned) : -0.279\n",
+            "✅ Plot 4 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 4: Sentiment Score vs Churn ─────────────────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+        "fig.suptitle('Customer Sentiment Analysis vs Churn', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Violin plot\n",
+        "df_plot = df.copy()\n",
+        "groups = [df_plot[df_plot['Churn'] == g]['sentiment_score'] for g in ['No', 'Yes']]\n",
+        "vp = axes[0].violinplot(groups, showmedians=True)\n",
+        "for i, pc in enumerate(vp['bodies']):\n",
+        "    pc.set_facecolor(['#2ECC71', '#E74C3C'][i])\n",
+        "    pc.set_alpha(0.7)\n",
+        "axes[0].set_xticks([1, 2])\n",
+        "axes[0].set_xticklabels(['No Churn', 'Churned'])\n",
+        "axes[0].set_title('Sentiment Score Distribution')\n",
+        "axes[0].set_ylabel('VADER Compound Score')\n",
+        "axes[0].axhline(0, color='gray', linestyle='--', alpha=0.5, label='Neutral')\n",
+        "axes[0].legend()\n",
+        "\n",
+        "# KDE plot\n",
+        "for churn_val, color, label in [('No', '#2ECC71', 'No Churn'), ('Yes', '#E74C3C', 'Churned')]:\n",
+        "    subset = df_plot[df_plot['Churn'] == churn_val]['sentiment_score']\n",
+        "    axes[1].hist(subset, bins=20, alpha=0.6, color=color, label=label, density=True)\n",
+        "axes[1].set_title('Sentiment Score Density')\n",
+        "axes[1].set_xlabel('VADER Score')\n",
+        "axes[1].set_ylabel('Density')\n",
+        "axes[1].legend()\n",
+        "\n",
+        "# Mean sentiment bar\n",
+        "mean_sent = df_plot.groupby('Churn')['sentiment_score'].mean()\n",
+        "bars = axes[2].bar(mean_sent.index, mean_sent.values,\n",
+        "                   color=[COLORS.get(k) for k in mean_sent.index],\n",
+        "                   edgecolor='white')\n",
+        "for bar, val in zip(bars, mean_sent.values):\n",
+        "    axes[2].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
+        "                 f'{val:.3f}', ha='center', fontweight='bold')\n",
+        "axes[2].set_title('Average Sentiment Score by Churn')\n",
+        "axes[2].set_ylabel('Mean Compound Score')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot4_sentiment_vs_churn.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "\n",
+        "print(f'📊 Mean sentiment (No Churn): {mean_sent[\"No\"]:.3f}')\n",
+        "print(f'📊 Mean sentiment (Churned) : {mean_sent[\"Yes\"]:.3f}')\n",
+        "print('✅ Plot 4 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-m8Tmhilu-hD"
+      },
+      "source": [
+        "### 3.5 — Support Churn Risk Analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 627
+        },
+        "id": "CCyn_YjPu-hD",
+        "outputId": "c7a63a6b-f025-4572-ea42-6f9e9f339556"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x600 with 3 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Plot 5 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 5: Churn Risk vs Actual Churn ───────────────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+        "fig.suptitle('Support Churn Risk Score vs Actual Churn', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Grouped bar\n",
+        "risk_churn = pd.crosstab(df['support_churn_risk'], df['Churn'])\n",
+        "risk_order = ['Low', 'Medium', 'High']\n",
+        "risk_churn = risk_churn.reindex(risk_order)\n",
+        "risk_churn.plot(kind='bar', ax=axes[0], color=['#2ECC71', '#E74C3C'],\n",
+        "                edgecolor='white', linewidth=0.8)\n",
+        "axes[0].set_title('Risk Category vs Churn Count')\n",
+        "axes[0].set_xlabel('Support Churn Risk')\n",
+        "axes[0].set_ylabel('Number of Customers')\n",
+        "axes[0].tick_params(axis='x', rotation=0)\n",
+        "axes[0].legend(['No Churn', 'Churned'])\n",
+        "\n",
+        "# Heatmap crosstab\n",
+        "risk_pct = pd.crosstab(df['support_churn_risk'], df['Churn'], normalize='index').reindex(risk_order)\n",
+        "sns.heatmap(risk_pct, annot=True, fmt='.2%', cmap='RdYlGn_r',\n",
+        "            ax=axes[1], linewidths=0.5, cbar_kws={'label': 'Proportion'})\n",
+        "axes[1].set_title('Churn Rate Heatmap by Risk Level')\n",
+        "axes[1].set_ylabel('Support Churn Risk')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot5_risk_vs_churn.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Plot 5 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XL29MYVsu-hD"
+      },
+      "source": [
+        "### 3.6 — Correlation Heatmap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 924
+        },
+        "id": "znjR0gowu-hD",
+        "outputId": "f518b8b0-85a5-4ad1-d509-b1b2d94e20ae"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x900 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Plot 6 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 6: Correlation Heatmap ───────────────────────────────────────────────\n",
+        "numeric_cols = ['tenure', 'MonthlyCharges', 'TotalCharges', 'SeniorCitizen',\n",
+        "                'support_calls', 'avg_call_duration', 'days_since_last_contact',\n",
+        "                'sentiment_score', 'Churn_binary']\n",
+        "\n",
+        "corr_matrix = df[numeric_cols].corr()\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(12, 9))\n",
+        "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
+        "sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm',\n",
+        "            mask=mask, ax=ax, linewidths=0.5,\n",
+        "            cbar_kws={'label': 'Pearson Correlation'},\n",
+        "            annot_kws={'size': 9})\n",
+        "ax.set_title('Correlation Matrix — Numeric Features vs Churn', fontsize=14, fontweight='bold')\n",
+        "plt.xticks(rotation=45, ha='right')\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot6_correlation_heatmap.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Plot 6 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xZoXaJUVu-hD"
+      },
+      "source": [
+        "---\n",
+        "## 🌲 SECTION 4: Random Forest Classifier\n",
+        "\n",
+        "### [ML MODEL — Following Hands-On Activity II methodology]\n",
+        "\n",
+        "Pipeline: **CLEAN → ENCODE → SPLIT 80-20 → TRAIN → PREDICT → EVALUATE → FEATURE IMPORTANCE**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "030S0fsAu-hE",
+        "outputId": "f4fa3eb2-6cb0-4634-ec1f-2e973aa31ca8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Features shape: (7043, 17)\n",
+            "   Target distribution: {0: 5174, 1: 1869}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Step 1: Prepare features ──────────────────────────────────────────────────\n",
+        "df_model = df.copy()\n",
+        "\n",
+        "# Encode categorical columns as numbers\n",
+        "le = LabelEncoder()\n",
+        "cat_cols = ['gender', 'Partner', 'Dependents', 'Contract', 'PaymentMethod',\n",
+        "            'InternetService', 'TechSupport', 'complaint_type', 'support_churn_risk']\n",
+        "\n",
+        "for col in cat_cols:\n",
+        "    df_model[col + '_enc'] = le.fit_transform(df_model[col].astype(str))\n",
+        "\n",
+        "# Define features (X) and target (y)\n",
+        "feature_cols = [\n",
+        "    'SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',\n",
+        "    'gender_enc', 'Partner_enc', 'Dependents_enc', 'Contract_enc',\n",
+        "    'PaymentMethod_enc', 'InternetService_enc', 'TechSupport_enc',\n",
+        "    'support_calls', 'avg_call_duration', 'days_since_last_contact',\n",
+        "    'sentiment_score', 'complaint_type_enc', 'support_churn_risk_enc'\n",
+        "]\n",
+        "\n",
+        "X = df_model[feature_cols]\n",
+        "y = df_model['Churn_binary']\n",
+        "\n",
+        "print(f'✅ Features shape: {X.shape}')\n",
+        "print(f'   Target distribution: {y.value_counts().to_dict()}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zZfHTyi3u-hE",
+        "outputId": "65f95ec2-924a-4720-ed9f-90a039d917ad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Train size: 5634 | Test size: 1409\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Step 2: Split 80-20 ───────────────────────────────────────────────────────\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.20, random_state=42, stratify=y\n",
+        ")\n",
+        "print(f'✅ Train size: {X_train.shape[0]} | Test size: {X_test.shape[0]}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u-rYjJ-Mu-hE",
+        "outputId": "5b9850f4-a455-40f2-cfbc-1a77a69038bc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Random Forest trained!\n",
+            "   Trees: 200 | Max depth: 10\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Step 3: Train Random Forest ───────────────────────────────────────────────\n",
+        "rf_model = RandomForestClassifier(\n",
+        "    n_estimators=200,\n",
+        "    max_depth=10,\n",
+        "    min_samples_split=5,\n",
+        "    random_state=42,\n",
+        "    n_jobs=-1\n",
+        ")\n",
+        "rf_model.fit(X_train, y_train)\n",
+        "print('✅ Random Forest trained!')\n",
+        "print(f'   Trees: {rf_model.n_estimators} | Max depth: {rf_model.max_depth}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3IOhdM9Eu-hE",
+        "outputId": "30b358e5-31da-48a5-ad54-5c103455b4d2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=======================================================\n",
+            "  Classification Accuracy: 0.9965 (99.65%)\n",
+            "=======================================================\n",
+            "\n",
+            "  Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    No Churn       1.00      1.00      1.00      1035\n",
+            "     Churned       1.00      0.99      0.99       374\n",
+            "\n",
+            "    accuracy                           1.00      1409\n",
+            "   macro avg       1.00      0.99      1.00      1409\n",
+            "weighted avg       1.00      1.00      1.00      1409\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Step 4: Predict & Evaluate ────────────────────────────────────────────────\n",
+        "y_pred = rf_model.predict(X_test)\n",
+        "acc = accuracy_score(y_test, y_pred)\n",
+        "\n",
+        "print('=' * 55)\n",
+        "print(f'  Classification Accuracy: {acc:.4f} ({acc*100:.2f}%)')\n",
+        "print('=' * 55)\n",
+        "print('\\n  Classification Report:')\n",
+        "print(classification_report(y_test, y_pred, target_names=['No Churn', 'Churned']))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 725
+        },
+        "id": "xV7DT6kLu-hE",
+        "outputId": "10d3f1ce-6b81-4a0e-f786-38562827a133"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x700 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Plot 7 saved.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 7: Confusion Matrix + Feature Importance ─────────────────────────────\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n",
+        "fig.suptitle('Random Forest Model Results', fontsize=16, fontweight='bold')\n",
+        "\n",
+        "# Confusion Matrix\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[0],\n",
+        "            xticklabels=['No Churn', 'Churned'],\n",
+        "            yticklabels=['No Churn', 'Churned'],\n",
+        "            linewidths=0.5, cbar=False)\n",
+        "axes[0].set_title(f'Confusion Matrix\\n(Accuracy: {acc*100:.2f}%)')\n",
+        "axes[0].set_ylabel('Actual')\n",
+        "axes[0].set_xlabel('Predicted')\n",
+        "\n",
+        "# Feature Importance\n",
+        "importances = pd.Series(rf_model.feature_importances_, index=feature_cols).sort_values(ascending=True)\n",
+        "colors_fi = ['#E74C3C' if v > importances.median() else '#3498DB' for v in importances.values]\n",
+        "importances.plot(kind='barh', ax=axes[1], color=colors_fi, edgecolor='white')\n",
+        "axes[1].set_title('Random Forest — Feature Importances')\n",
+        "axes[1].set_xlabel('Feature Importance Score')\n",
+        "axes[1].axvline(importances.median(), color='gray', linestyle='--', alpha=0.5, label='Median')\n",
+        "axes[1].legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.savefig('plot7_model_results.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Plot 7 saved.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mR8iDcHmu-hE"
+      },
+      "source": [
+        "---\n",
+        "## 📝 SECTION 5: Automatic Business Conclusions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "P8FWqxc2u-hE",
+        "outputId": "3519e297-a889-4e07-fd8c-79a155880f47"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "======================================================================\n",
+            "               KEY FINDINGS — EXECUTIVE SUMMARY\n",
+            "======================================================================\n",
+            "\n",
+            "1. CHURN RATE\n",
+            "   Overall churn rate: 26.5% of customers left.\n",
+            "   Industry benchmark is ~15-25%, so this is within expected range.\n",
+            "\n",
+            "2. SUPPORT CALLS ARE A STRONG PREDICTOR\n",
+            "   Churned customers averaged 10.0 support calls\n",
+            "   vs. only 3.5 for non-churned customers (2.9x more).\n",
+            "   → ACTION: Flag customers with 5+ support calls for proactive outreach.\n",
+            "\n",
+            "3. SENTIMENT IS TELLING\n",
+            "   Churned customers averaged a sentiment score of -0.279\n",
+            "   vs. 0.519 for loyal customers.\n",
+            "   → ACTION: Monitor post-call sentiment. Score < -0.3 = immediate escalation.\n",
+            "\n",
+            "4. MOST COMMON COMPLAINT AMONG CHURNERS\n",
+            "   \"Billing Issue\" is the #1 complaint type for churned customers.\n",
+            "   → ACTION: Prioritize resolution of Billing Issue issues with a dedicated team.\n",
+            "\n",
+            "5. RISK SCORE ACCURACY\n",
+            "   92.0% of \"High Risk\" customers actually churned.\n",
+            "   → ACTION: Automate retention offers for High Risk customers immediately.\n",
+            "\n",
+            "6. TOP ML PREDICTOR\n",
+            "   Most important feature in Random Forest: \"avg_call_duration\"\n",
+            "   Model accuracy: 99.65%\n",
+            "   → ACTION: Integrate this variable into the company's CRM for real-time scoring.\n",
+            "\n",
+            "======================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Auto-generate key insights ────────────────────────────────────────────────\n",
+        "churn_rate       = df['Churn_binary'].mean() * 100\n",
+        "avg_calls_churn  = df[df['Churn']=='Yes']['support_calls'].mean()\n",
+        "avg_calls_no     = df[df['Churn']=='No']['support_calls'].mean()\n",
+        "avg_sent_churn   = df[df['Churn']=='Yes']['sentiment_score'].mean()\n",
+        "avg_sent_no      = df[df['Churn']=='No']['sentiment_score'].mean()\n",
+        "top_complaint    = df[df['Churn']=='Yes']['complaint_type'].value_counts().index[0]\n",
+        "top_feature      = importances.index[-1]  # highest importance\n",
+        "high_risk_churn  = pd.crosstab(df['support_churn_risk'], df['Churn'], normalize='index')['Yes']['High']\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('               KEY FINDINGS — EXECUTIVE SUMMARY')\n",
+        "print('=' * 70)\n",
+        "print(f'''\n",
+        "1. CHURN RATE\n",
+        "   Overall churn rate: {churn_rate:.1f}% of customers left.\n",
+        "   Industry benchmark is ~15-25%, so this is within expected range.\n",
+        "\n",
+        "2. SUPPORT CALLS ARE A STRONG PREDICTOR\n",
+        "   Churned customers averaged {avg_calls_churn:.1f} support calls\n",
+        "   vs. only {avg_calls_no:.1f} for non-churned customers ({avg_calls_churn/avg_calls_no:.1f}x more).\n",
+        "   → ACTION: Flag customers with 5+ support calls for proactive outreach.\n",
+        "\n",
+        "3. SENTIMENT IS TELLING\n",
+        "   Churned customers averaged a sentiment score of {avg_sent_churn:.3f}\n",
+        "   vs. {avg_sent_no:.3f} for loyal customers.\n",
+        "   → ACTION: Monitor post-call sentiment. Score < -0.3 = immediate escalation.\n",
+        "\n",
+        "4. MOST COMMON COMPLAINT AMONG CHURNERS\n",
+        "   \"{top_complaint}\" is the #1 complaint type for churned customers.\n",
+        "   → ACTION: Prioritize resolution of {top_complaint} issues with a dedicated team.\n",
+        "\n",
+        "5. RISK SCORE ACCURACY\n",
+        "   {high_risk_churn*100:.1f}% of \"High Risk\" customers actually churned.\n",
+        "   → ACTION: Automate retention offers for High Risk customers immediately.\n",
+        "\n",
+        "6. TOP ML PREDICTOR\n",
+        "   Most important feature in Random Forest: \"{top_feature}\"\n",
+        "   Model accuracy: {acc*100:.2f}%\n",
+        "   → ACTION: Integrate this variable into the company's CRM for real-time scoring.\n",
+        "''')\n",
+        "print('=' * 70)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 992
+        },
+        "id": "Da4GgPk1u-hF",
+        "outputId": "d74ba65e-e6a5-4b5c-960a-988976f77aee"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x1000 with 6 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Executive Dashboard saved: plot8_executive_dashboard.png\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Plot 8: Executive Dashboard ────────────────────────────────────────────────\n",
+        "fig = plt.figure(figsize=(18, 10))\n",
+        "fig.suptitle('Customer Churn — Executive Dashboard\\n(AI for Big Data Management)',\n",
+        "             fontsize=18, fontweight='bold', y=1.01)\n",
+        "\n",
+        "gs = gridspec.GridSpec(2, 3, figure=fig, hspace=0.4, wspace=0.35)\n",
+        "\n",
+        "# 1 — Churn rate gauge (bar)\n",
+        "ax1 = fig.add_subplot(gs[0, 0])\n",
+        "ax1.barh(['Retained', 'Churned'], [100-churn_rate, churn_rate],\n",
+        "          color=['#2ECC71', '#E74C3C'], height=0.5)\n",
+        "ax1.set_xlim(0, 100)\n",
+        "ax1.set_title(f'Churn Rate: {churn_rate:.1f}%', fontweight='bold')\n",
+        "ax1.set_xlabel('Percentage')\n",
+        "\n",
+        "# 2 — Avg calls by churn\n",
+        "ax2 = fig.add_subplot(gs[0, 1])\n",
+        "ax2.bar(['No Churn', 'Churned'], [avg_calls_no, avg_calls_churn],\n",
+        "        color=['#2ECC71', '#E74C3C'], edgecolor='white')\n",
+        "ax2.set_title('Avg Support Calls by Churn', fontweight='bold')\n",
+        "ax2.set_ylabel('Avg Calls')\n",
+        "for i, v in enumerate([avg_calls_no, avg_calls_churn]):\n",
+        "    ax2.text(i, v + 0.1, f'{v:.1f}', ha='center', fontweight='bold')\n",
+        "\n",
+        "# 3 — Avg sentiment\n",
+        "ax3 = fig.add_subplot(gs[0, 2])\n",
+        "ax3.bar(['No Churn', 'Churned'], [avg_sent_no, avg_sent_churn],\n",
+        "        color=['#2ECC71', '#E74C3C'], edgecolor='white')\n",
+        "ax3.set_title('Avg Sentiment Score by Churn', fontweight='bold')\n",
+        "ax3.set_ylabel('VADER Score')\n",
+        "ax3.axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
+        "\n",
+        "# 4 — Complaint type (churned only)\n",
+        "ax4 = fig.add_subplot(gs[1, 0])\n",
+        "ct_churned = df[df['Churn']=='Yes']['complaint_type'].value_counts()\n",
+        "ax4.pie(ct_churned.values, labels=ct_churned.index, autopct='%1.0f%%',\n",
+        "        colors=sns.color_palette('Set2', len(ct_churned)),\n",
+        "        textprops={'fontsize': 9})\n",
+        "ax4.set_title('Complaint Types (Churned Only)', fontweight='bold')\n",
+        "\n",
+        "# 5 — Risk distribution\n",
+        "ax5 = fig.add_subplot(gs[1, 1])\n",
+        "risk_counts = df['support_churn_risk'].value_counts()[['Low', 'Medium', 'High']]\n",
+        "ax5.bar(risk_counts.index, risk_counts.values,\n",
+        "        color=['#2ECC71', '#F39C12', '#E74C3C'], edgecolor='white')\n",
+        "ax5.set_title('Customers by Risk Category', fontweight='bold')\n",
+        "ax5.set_ylabel('Count')\n",
+        "\n",
+        "# 6 — Top 5 features\n",
+        "ax6 = fig.add_subplot(gs[1, 2])\n",
+        "top5 = importances.tail(5)\n",
+        "ax6.barh(top5.index, top5.values, color='#3498DB', edgecolor='white')\n",
+        "ax6.set_title('Top 5 ML Predictors', fontweight='bold')\n",
+        "ax6.set_xlabel('Importance')\n",
+        "\n",
+        "plt.savefig('plot8_executive_dashboard.png', dpi=150, bbox_inches='tight')\n",
+        "plt.show()\n",
+        "print('✅ Executive Dashboard saved: plot8_executive_dashboard.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Qualitative Insights from Customer Support Data\n",
+        "\n",
+        "While the dataset primarily consists of structured variables, the synthetic support interaction features allow us to approximate customer experience and sentiment.\n",
+        "\n",
+        "### Key Observations\n",
+        "\n",
+        "- Customers with **negative sentiment scores** are more likely to churn, indicating dissatisfaction with service interactions.\n",
+        "- Higher **support call frequency** suggests unresolved issues or repeated frustration.\n",
+        "- Certain **complaint types** (e.g., technical failures or contract disputes) appear more frequently among churned customers.\n",
+        "\n",
+        "### Interpretation\n",
+        "\n",
+        "These patterns suggest that churn is not only driven by pricing or contract structure, but also by **customer experience and emotional dissatisfaction**.\n",
+        "\n",
+        "Customers who repeatedly interact with support and express negative sentiment may feel that their issues are not being resolved effectively, increasing their likelihood of leaving.\n",
+        "\n",
+        "### Business Implication\n",
+        "\n",
+        "Telecom companies should:\n",
+        "- proactively identify customers with repeated complaints\n",
+        "- prioritize faster resolution for negative sentiment cases\n",
+        "- use support interaction data as an early warning system for churn"
+      ],
+      "metadata": {
+        "id": "FIszRO3sj1oU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7f0aOFSpu-hF"
+      },
+      "source": [
+        "---\n",
+        "## 💾 SECTION 6: Export Results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ── Export predictions ONLY for the test set (avoids train/test contamination) ──\n",
+        "\n",
+        "# Predict on the holdout test set\n",
+        "y_pred = rf_model.predict(X_test)\n",
+        "y_pred_proba = rf_model.predict_proba(X_test)[:, 1].round(4)\n",
+        "\n",
+        "# Recover original row indices for the test set\n",
+        "test_indices = X_test.index\n",
+        "\n",
+        "# Build output table from only test rows\n",
+        "output_df = df.loc[test_indices, [\n",
+        "    'customerID', 'tenure', 'MonthlyCharges', 'Contract',\n",
+        "    'support_calls', 'avg_call_duration', 'complaint_type',\n",
+        "    'days_since_last_contact', 'sentiment_score', 'support_churn_risk',\n",
+        "    'Churn', 'Churn_binary'\n",
+        "]].copy()\n",
+        "\n",
+        "# Add predictions\n",
+        "output_df['churn_predicted'] = y_pred\n",
+        "output_df['churn_probability'] = y_pred_proba\n",
+        "\n",
+        "# Optional: include whether prediction was correct\n",
+        "output_df['prediction_correct'] = (\n",
+        "    output_df['Churn_binary'].values == output_df['churn_predicted']\n",
+        ")\n",
+        "\n",
+        "# Export\n",
+        "output_df.to_csv('churn_analysis_results.csv', index=False)\n",
+        "\n",
+        "print(f'✅ Results exported: churn_analysis_results.csv')\n",
+        "print(f'   Shape: {output_df.shape}  ← test set only')\n",
+        "print(f'   Accuracy on test set: {acc*100:.2f}%')\n",
+        "display(output_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "id": "FWVEyzgneuyE",
+        "outputId": "d0b15fef-6e5b-4371-852e-9a74012a53ab"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Results exported: churn_analysis_results.csv\n",
+            "   Shape: (1409, 15)  ← test set only\n",
+            "   Accuracy on test set: 99.65%\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      customerID  tenure  MonthlyCharges        Contract  support_calls  \\\n",
+              "437   4376-KFVRS      72          114.05        Two year              6   \n",
+              "2280  2754-SDJRD       8          100.15  Month-to-month              2   \n",
+              "2235  9917-KWRBE      41           78.35        One year              5   \n",
+              "4460  0365-GXEZS      18           78.20  Month-to-month              6   \n",
+              "3761  9385-NXKDA      72           82.65        Two year              2   \n",
+              "\n",
+              "      avg_call_duration     complaint_type  days_since_last_contact  \\\n",
+              "437                 3.6  Technical Failure                       82   \n",
+              "2280               10.3     Service Outage                       77   \n",
+              "2235                9.0     Service Outage                       79   \n",
+              "4460               11.5   Contract Dispute                       56   \n",
+              "3761               11.6     Service Outage                       81   \n",
+              "\n",
+              "      sentiment_score support_churn_risk Churn  Churn_binary  churn_predicted  \\\n",
+              "437            0.5719             Medium    No             0                0   \n",
+              "2280           0.4404                Low    No             0                0   \n",
+              "2235          -0.7096             Medium    No             0                0   \n",
+              "4460          -0.3612               High    No             0                0   \n",
+              "3761           0.7425                Low    No             0                0   \n",
+              "\n",
+              "      churn_probability  prediction_correct  \n",
+              "437              0.0000                True  \n",
+              "2280             0.0070                True  \n",
+              "2235             0.0036                True  \n",
+              "4460             0.0598                True  \n",
+              "3761             0.0000                True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a7133086-8f47-4530-ae38-09b6a744c82d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customerID</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>Contract</th>\n",
+              "      <th>support_calls</th>\n",
+              "      <th>avg_call_duration</th>\n",
+              "      <th>complaint_type</th>\n",
+              "      <th>days_since_last_contact</th>\n",
+              "      <th>sentiment_score</th>\n",
+              "      <th>support_churn_risk</th>\n",
+              "      <th>Churn</th>\n",
+              "      <th>Churn_binary</th>\n",
+              "      <th>churn_predicted</th>\n",
+              "      <th>churn_probability</th>\n",
+              "      <th>prediction_correct</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>437</th>\n",
+              "      <td>4376-KFVRS</td>\n",
+              "      <td>72</td>\n",
+              "      <td>114.05</td>\n",
+              "      <td>Two year</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>Technical Failure</td>\n",
+              "      <td>82</td>\n",
+              "      <td>0.5719</td>\n",
+              "      <td>Medium</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2280</th>\n",
+              "      <td>2754-SDJRD</td>\n",
+              "      <td>8</td>\n",
+              "      <td>100.15</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>2</td>\n",
+              "      <td>10.3</td>\n",
+              "      <td>Service Outage</td>\n",
+              "      <td>77</td>\n",
+              "      <td>0.4404</td>\n",
+              "      <td>Low</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0070</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2235</th>\n",
+              "      <td>9917-KWRBE</td>\n",
+              "      <td>41</td>\n",
+              "      <td>78.35</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>Service Outage</td>\n",
+              "      <td>79</td>\n",
+              "      <td>-0.7096</td>\n",
+              "      <td>Medium</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0036</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4460</th>\n",
+              "      <td>0365-GXEZS</td>\n",
+              "      <td>18</td>\n",
+              "      <td>78.20</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>6</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>Contract Dispute</td>\n",
+              "      <td>56</td>\n",
+              "      <td>-0.3612</td>\n",
+              "      <td>High</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0598</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3761</th>\n",
+              "      <td>9385-NXKDA</td>\n",
+              "      <td>72</td>\n",
+              "      <td>82.65</td>\n",
+              "      <td>Two year</td>\n",
+              "      <td>2</td>\n",
+              "      <td>11.6</td>\n",
+              "      <td>Service Outage</td>\n",
+              "      <td>81</td>\n",
+              "      <td>0.7425</td>\n",
+              "      <td>Low</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7133086-8f47-4530-ae38-09b6a744c82d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a7133086-8f47-4530-ae38-09b6a744c82d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a7133086-8f47-4530-ae38-09b6a744c82d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(output_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"customerID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2754-SDJRD\",\n          \"9385-NXKDA\",\n          \"9917-KWRBE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 8,\n        \"max\": 72,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8,\n          18,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.875279525098133,\n        \"min\": 78.2,\n        \"max\": 114.05,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          100.15,\n          82.65,\n          78.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Two year\",\n          \"Month-to-month\",\n          \"One year\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_call_duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.3037857073363583,\n        \"min\": 3.6,\n        \"max\": 11.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.3,\n          11.6,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"complaint_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Technical Failure\",\n          \"Service Outage\",\n          \"Contract Dispute\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_since_last_contact\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 56,\n        \"max\": 82,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          77,\n          81,\n          79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6349713418730014,\n        \"min\": -0.7096,\n        \"max\": 0.7425,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.4404,\n          0.7425,\n          -0.7096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_churn_risk\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Medium\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn_binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"churn_predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"churn_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025723374584218143,\n        \"min\": 0.0,\n        \"max\": 0.0598,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_correct\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 208
+        },
+        "id": "GKSHRO8hu-hF",
+        "outputId": "53ab9bbe-0fe8-4c67-d8ec-2ea992f5b9d8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_c41e4203-88ab-4981-9b2a-20287389b7a8\", \"churn_analysis_results.csv\", 124272)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: churn_analysis_results.csv\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_a36b65b6-fab0-4c19-b715-64079c156f4f\", \"plot1_churn_distribution.png\", 67338)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot1_churn_distribution.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_8d0d12ed-49cb-4e4a-bebf-e5cbe5645b06\", \"plot2_support_calls_vs_churn.png\", 72326)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot2_support_calls_vs_churn.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_319623bf-198b-498f-b19e-e15b2b4d1b73\", \"plot3_complaint_type_vs_churn.png\", 106832)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot3_complaint_type_vs_churn.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_30afd415-9ea6-47e8-8e05-571e5a93baf6\", \"plot4_sentiment_vs_churn.png\", 109932)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot4_sentiment_vs_churn.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_d72a71ec-4264-4288-b46e-c3e906590a20\", \"plot5_risk_vs_churn.png\", 93088)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot5_risk_vs_churn.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_d2e1dc54-bd2a-49eb-b4df-dcd4e4182db9\", \"plot6_correlation_heatmap.png\", 152454)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot6_correlation_heatmap.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_419380aa-23ce-4a50-9f1a-f6195dbd8752\", \"plot7_model_results.png\", 123700)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot7_model_results.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_30cd5aa6-5dd4-4f4f-ba57-552f25cf3fa9\", \"plot8_executive_dashboard.png\", 193578)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ Downloading: plot8_executive_dashboard.png\n",
+            "\n",
+            "🎉 All downloads triggered!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Download files ─────────────────────────────────────────────────────────────\n",
+        "from google.colab import files\n",
+        "\n",
+        "files_to_download = [\n",
+        "    'churn_analysis_results.csv',\n",
+        "    'plot1_churn_distribution.png',\n",
+        "    'plot2_support_calls_vs_churn.png',\n",
+        "    'plot3_complaint_type_vs_churn.png',\n",
+        "    'plot4_sentiment_vs_churn.png',\n",
+        "    'plot5_risk_vs_churn.png',\n",
+        "    'plot6_correlation_heatmap.png',\n",
+        "    'plot7_model_results.png',\n",
+        "    'plot8_executive_dashboard.png',\n",
+        "]\n",
+        "\n",
+        "for f in files_to_download:\n",
+        "    try:\n",
+        "        files.download(f)\n",
+        "        print(f'✅ Downloading: {f}')\n",
+        "    except Exception as e:\n",
+        "        print(f'⚠️ Could not download {f}: {e}')\n",
+        "\n",
+        "print('\\n🎉 All downloads triggered!')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## SECTION 7: Ethical Considerations & Limitations\n",
+        "\n",
+        "### Why This Section Matters\n",
+        "Any AI system used to make decisions about customers carries ethical responsibilities. This section outlines key limitations and risks of our approach.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Limitation 1 — Synthetic Data Bias\n",
+        "The support interaction variables were generated synthetically using churn labels. This means the model partially learns from patterns we designed, rather than independent real-world behavior.\n",
+        "\n",
+        "Impact: Model performance may be overstated compared to real-world deployment.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Limitation 2 — Model Bias Risks\n",
+        "The dataset includes demographic variables (e.g., SeniorCitizen). If used improperly, the model may produce biased or unfair predictions.\n",
+        "\n",
+        "Mitigation: Monitor feature importance and consider excluding sensitive variables.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Limitation 3 — Ethical Use of Predictions\n",
+        "Churn prediction enables targeted retention, but may also lead to unequal treatment of customers.\n",
+        "\n",
+        "Principle: Models should support better service for all customers, not justify neglecting low-risk ones.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Limitation 4 — Dataset Scope\n",
+        "The dataset is limited to one telecom company (~7,000 customers). Results may not generalize across industries or regions.\n",
+        "\n",
+        "### Summary\n",
+        "These limitations highlight that while the model provides useful insights, it should be used as a decision-support tool rather than a fully automated decision system.\n"
+      ],
+      "metadata": {
+        "id": "XGlLZ6gQilO5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## SECTION 8: Final Verification"
+      ],
+      "metadata": {
+        "id": "8bQ1jIDHi8Tq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5R5ZJ0JRu-hF",
+        "outputId": "6b791fdb-4198-4d5c-8c8f-c70c33ca1700"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "============================================================\n",
+            "        NOTEBOOK 2 — FINAL VERIFICATION REPORT\n",
+            "============================================================\n",
+            "  Dataset rows          : 7043\n",
+            "  Overall churn rate    : 26.5%\n",
+            "  Model accuracy        : 99.65%\n",
+            "  Plots generated       : 8\n",
+            "  Key predictor (ML)    : avg_call_duration\n",
+            "  Top churner complaint : Billing Issue\n",
+            "============================================================\n",
+            "============================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ── Final verification ────────────────────────────────────────────────────────\n",
+        "print('=' * 60)\n",
+        "print('        NOTEBOOK 2 — FINAL VERIFICATION REPORT')\n",
+        "print('=' * 60)\n",
+        "print(f'  Dataset rows          : {df.shape[0]}')\n",
+        "print(f'  Overall churn rate    : {churn_rate:.1f}%')\n",
+        "print(f'  Model accuracy        : {acc*100:.2f}%')\n",
+        "print(f'  Plots generated       : 8')\n",
+        "print(f'  Key predictor (ML)    : {top_feature}')\n",
+        "print(f'  Top churner complaint : {top_complaint}')\n",
+        "print('=' * 60)\n",
+        "print('=' * 60)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sksP7U51u-hF"
+      },
+      "source": [
+        "---\n",
+        "## 📌 Summary — What Was Done in This Notebook\n",
+        "\n",
+        "| Section | Method | Output |\n",
+        "|---|---|---|\n",
+        "| EDA | Distribution plots, crosstabs | 6 charts |\n",
+        "| Qualitative | VADER sentiment analysis | Plot 4 |\n",
+        "| Quantitative | Support calls, tenure, billing | Plots 2, 3, 5, 6 |\n",
+        "| ML Model | Random Forest (80-20 split) | Accuracy + feature importances |\n",
+        "| Conclusions | Auto-generated insights | Executive summary printed |\n",
+        "| Export | CSV + 8 PNG charts | All saved and downloaded |\n",
+        "\n",
+        "**➡️ Next Step:** Use `churn_analysis_results.csv` as knowledge file for your Custom GPT."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Key Business Recommendations\n",
+        "\n",
+        "- Prioritize customers with frequent support interactions and negative sentiment for proactive retention strategies\n",
+        "- Focus retention efforts on customers with month-to-month contracts, as they show higher churn risk\n",
+        "- Improve customer service response quality to reduce repeated complaints\n",
+        "- Use churn predictions as a decision-support tool rather than fully automated decision-making"
+      ],
+      "metadata": {
+        "id": "QOxBWrLKk9CB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import joblib\n",
+        "\n",
+        "joblib.dump(rf_model, \"rf_model.pkl\")\n",
+        "joblib.dump(feature_cols, \"feature_cols.pkl\")\n",
+        "\n",
+        "print(\"Saved rf_model.pkl and feature_cols.pkl\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kh-rZ1TEk-Ja",
+        "outputId": "9ef0643f-c825-43b6-8c3c-4ceed82247af"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved rf_model.pkl and feature_cols.pkl\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file