{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz8lLSv6mVQo"
      },
      "source": [
        "# **🤖 Data Analysis & Visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpASMyIQMaAq"
      },
      "source": [
        "## **1.** Install required datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = {\n",
        "    \"Year\": [2022, 2023, 2024, 2025, 2026],\n",
        "    \"Subscribers_millions\": [120, 150, 175, 200, 220]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "monthly_price = 8.99\n",
        "\n",
        "df[\"Revenue_Billion_$\"] = df[\"Subscribers_millions\"] * monthly_price * 12 / 1000\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ppxbozwgTnl9",
        "outputId": "c08477ba-d3eb-4906-969b-1af5c9b5eea1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Subscribers_millions  Revenue_Billion_$\n",
              "0  2022                   120            12.9456\n",
              "1  2023                   150            16.1820\n",
              "2  2024                   175            18.8790\n",
              "3  2025                   200            21.5760\n",
              "4  2026                   220            23.7336"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55b712f0-ce05-4341-8ba1-eec86c07835e\" 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>Year</th>\n",
              "      <th>Subscribers_millions</th>\n",
              "      <th>Revenue_Billion_$</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>120</td>\n",
              "      <td>12.9456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023</td>\n",
              "      <td>150</td>\n",
              "      <td>16.1820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024</td>\n",
              "      <td>175</td>\n",
              "      <td>18.8790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2025</td>\n",
              "      <td>200</td>\n",
              "      <td>21.5760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2026</td>\n",
              "      <td>220</td>\n",
              "      <td>23.7336</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-55b712f0-ce05-4341-8ba1-eec86c07835e')\"\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-55b712f0-ce05-4341-8ba1-eec86c07835e 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-55b712f0-ce05-4341-8ba1-eec86c07835e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_c4af6c34-ee85-4ccb-8cb1-df7f5740d3a0\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c4af6c34-ee85-4ccb-8cb1-df7f5740d3a0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2022,\n        \"max\": 2026,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2023,\n          2026,\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers_millions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 120,\n        \"max\": 220,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          150,\n          220,\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Revenue_Billion_$\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.274553568268855,\n        \"min\": 12.945599999999999,\n        \"max\": 23.7336,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          16.182,\n          23.7336,\n          18.879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZd99NpKkKyp"
      },
      "source": [
        "## **2.** Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##   a. Revenue over time"
      ],
      "metadata": {
        "id": "gVVqC2YmUSKP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(df[\"Year\"], df[\"Revenue_Billion_$\"], marker='o')\n",
        "plt.title(\"Prime Video Revenue Growth\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Revenue (Billion $)\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "XUmnolH1T_7I",
        "outputId": "52af07dc-24ac-44c8-a7df-9fde55db79a4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## b. Subscribers growth"
      ],
      "metadata": {
        "id": "k4zWHxTVUI7x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df[\"Year\"], df[\"Subscribers_millions\"], marker='o')\n",
        "plt.title(\"Prime Video Subscribers Growth\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Subscribers (Millions)\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "ReDeHGw7UN2l",
        "outputId": "c8a01f89-1ad2-4be1-dc16-4c4bc834f8f7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RJvr374979+7hjz/+KLGdJ0+eIDc3FwDQo0cPAMAvv/yiMqb46ETPnj2fW9O+fftKXV58rlDxlIiXlxeMjIxUzmMCgMWLF5e57YULFyr/XwiBhQsXwsTEBK+++ioA4NGjRyrjpVKpcsqm+JLwfv364fz586VehfYyRwmKiorw22+/KV8XFhbit99+g5OTE1q2bAmg/PvgecrTY2nat2+Pbt264ffff8e///5b6pjy9t+qVSs4Oztj6dKlKp+5a9cuJCQklPgeCQ4Oxv79+3H69Gll6PH394eNjQ2+//57WFhYKL9GxaysrEp8DxM9D4/0EFUBW1tbLFmyBEOGDEGLFi3wzjvvwMnJCbdv38aOHTvQvn17lb+sy2JtbY0+ffooz+spz9SWiYkJvvnmG7z//vt45ZVX8PbbbyMpKQnLly8vcf7LkCFDsH79enzwwQc4cOAA2rdvD7lcjsuXL2P9+vXYvXs3WrVqhebNm2PYsGH4/fffkZGRgU6dOuH06dNYuXIl+vTpgy5dujy3ptdffx3e3t7o1asX6tWrh9zcXOzduxfbtm1D69atlUev7Ozs8NZbb2HBggWQSCSoV68etm/fXuY5P+bm5oiMjMSwYcMQGBiIXbt2YceOHfjyyy/h5OQE4Oml9unp6XjllVfg7u6OW7duYcGCBfD391cetZo0aRL++ecfvPXWWxg5ciRatmyJ9PR0bN26FUuXLlU5OqYONzc3zJkzBzdv3kTDhg2xbt06nDt3Dr///rvyhpjl3QfPU54ey/L333+je/fu6NOnD8LCwtC1a1fUqFFDeUfmw4cPIyws7IW9mpiYYM6cORgxYgQ6deqEAQMGKC9Zr1OnDj7++GOV8cHBwVi9ejUkEolyusvIyAjt2rXD7t270blz5xK3A2jZsiX27t2Ln3/+GW5ubvD29lZeXEBUKq1eO0ZUCYovWT9z5sxzxw0bNkxYWVmVua60S9Z/+OEHlXHFl+tu2LChXDUcOHBAhIaGCjs7O2Fubi7q1asnhg8fLs6ePVvu/nbs2CEAiFq1apW4xPvZmv57mffixYuFt7e3MDMzE61atRKHDx8WnTp1UrlkXYinlxrPmTNHNGnSRJiZmYkaNWqIli1bihkzZojMzEzlOJlMJmbMmCG8vb2FiYmJ8PDwEJMnT1Zepv88a9asEe+8846oV6+esLCwEObm5sLX11d89dVXIisrS2Xsw4cPRb9+/YSlpaWoUaOGeP/990VcXFypl6xbWVmJ69evi5CQEGFpaSlcXFzEtGnTVL5O//zzjwgJCRHOzs7C1NRUeHp6ivfff18kJyerfO6jR4/EuHHjRO3atYWpqalwd3cXw4YNE2lpaSpf5//u+7L2QadOnUSTJk3E2bNnRVBQkDA3NxdeXl5i4cKFJd5f3n0AQISHh5d4f3l7LMuTJ0/EL7/8IoKCgoStra0wNjYWrq6u4rXXXhOrV69WuRT+eV8HIYRYt26dCAgIEGZmZsLBwUEMGjRIeduIZ8XHxwsAwsfHR2X5N998IwCIKVOmlHjP5cuXRceOHYWFhYUAoLx8vfiS9eLbFBQr/rlMSkoq19eB9I9ECJ7RRURERPqP5/QQERGRQWDoISIiIoPA0ENEREQGgaGHiIiIDAJDDxERERkEhh4iIiIyCLw5IZ7euv7+/fuwsbHhbc2JiIh0hBAC2dnZcHNzK9dzFBl68PR5OP99iB8RERHphjt37sDd3f2F4xh6ANjY2AB4+kWztbXV2HZlMhn27NmDkJAQ5S3m9Y2+98j+dJ++96jv/QH63yP7q7isrCx4eHgo/x5/EYYe/N+Tem1tbTUeeiwtLWFra6uX38iA/vfI/nSfvveo7/0B+t8j+3t55T01hScyExERkUFg6CEiIiKDwNBDREREBoGhh4iIiAwCQw8REREZBIYeIiIiMggMPURERGQQGHqIiIjIIDD0EBERkUFg6CEiIqJKIVcInEpKR3SaBKeS0iFXCK3Wo9XQM3v2bLRu3Ro2NjZwdnZGnz59kJiYqFyfnp6O8ePHo1GjRrCwsICnpycmTJiAzMxMle3cvn0bPXv2hKWlJZydnTFp0iQUFRVVdTtERET0/0XGJaPDnP0YvOwsVl01wuBlZ9Fhzn5ExiVrrSathp5Dhw4hPDwcJ0+eRFRUFGQyGUJCQpCbmwvg6dPP79+/jx9//BFxcXFYsWIFIiMj8e677yq3IZfL0bNnTxQWFuL48eNYuXIlVqxYgalTp2qrLSIiIoMWGZeMMX/HIDkzX2V5SmY+xvwdo7Xgo9UHjkZGRqq8XrFiBZydnREdHY2OHTvCz88PGzduVK6vV68evv32WwwePBhFRUUwNjbGnj17cOnSJezduxcuLi7w9/fHrFmz8Pnnn2P69OkwNTWt6raIiIgMllwhMGPbJZQ2kSUASADM2HYJ3XxdYSQt34NCNaVaPWW9eNrKwcHhuWNsbW1hbPy09BMnTqBp06ZwcXFRjgkNDcWYMWMQHx+PgICAEtsoKChAQUGB8nVWVhaAp0+ClclkGumleHvP/lcf6XuP7E/36XuP+t4foP896lt/p5LSSxzheZYAkJyZjxPXUhHoXfbf9+Wh7tdMIoTQ7llF/59CoUDv3r2RkZGBo0ePljomLS0NLVu2xODBg/Htt98CAEaPHo1bt25h9+7dynF5eXmwsrLCzp07ERYWVmI706dPx4wZM0osj4iIgKWlpYY6IiIiMjzRaRKsumr0wnFDG8jRsubLRZC8vDwMHDhQeUDkRarNkZ7w8HDExcWVGXiysrLQs2dP+Pr6Yvr06S/1WZMnT8bEiRNVtu3h4YGQkJByfdHKSyaTISoqCt26dYOJiYnGtlud6HuP7E/36XuP+t4foP896lN/BUUKHN12CcD9F44NCQ586SM9xTM15VUtQs+4ceOwfft2HD58GO7u7iXWZ2dno3v37rCxscHmzZtVvilcXV1x+vRplfEPHjxQriuNmZkZzMzMSiw3MTGplG+4ytpudaLvPbI/3afvPep7f4D+96jr/R1MTMWMbZeQlJb73HESAK525giq7/zS5/So+/XS6tVbQgiMGzcOmzdvxv79++Ht7V1iTFZWFkJCQmBqaoqtW7fC3NxcZX1QUBAuXryI1NRU5bKoqCjY2trC19e30nsgIiIyZHfS8/DeqrMYvvwMktJy4WRjhhHtvSDB04DzrOLX03r5VvlJzICWj/SEh4cjIiIC//77L2xsbJCSkgIAsLOzg4WFhTLw5OXl4e+//0ZWVpbyUJaTkxOMjIwQEhICX19fDBkyBHPnzkVKSgq+/vprhIeHl3o0h4iIiF5evkyOpYeuY8nB6ygoUsBIKsGIdnXwYdcGsDE3QaC3I2Zsu6RyUrOrnTmm9fJFd79aWqlZq6FnyZIlAIDOnTurLF++fDmGDx+OmJgYnDp1CgBQv359lTFJSUmoU6cOjIyMsH37dowZMwZBQUGwsrLCsGHDMHPmzCrpgYiIyJAIIbA3IRUzt8fjTvoTAEBQXUfMeL0JGrrYKMd196uFbr6uOHEtFXuOnEJIcKBGprRehlZDz4suHOvcufMLxwCAl5cXdu7cqamyiIiIqBQ303IxY1s8DiQ+BAC42prjq54+eK1ZLUgkJcOMkVSCQG8HPEoQCPR20GrgAarJicxERERUfT0plGPRgWv4/fANFMoVMDGS4N0OdTH+lfqwMtOdKKE7lRIREVGVEkJgd3wKZm1PwL2Mp1NZwQ1qYnrvJqjnZK3l6tTH0ENEREQlXH+Yg+lb43HkahoAoLa9Baa85oPQJq6lTmXpAoYeIiIiUsotKMKC/dfw59EbkMkFTI2keL9TXYztXB8Wpi++03J1xtBDREREEEJg+4VkfLsjASlZTy8z79LICdN6NUGdmlZark4zGHqIiIgM3JUH2Zj2bzxO3HgEAPBwsMC015rgVR9nnZ3KKg1DDxERkYHKzpfhl71XseL4TcgVAmbGUoztXB/vd6oLcxPdnsoqDUMPERGRgRFCYMu5e/hu52U8zC4AAIT4umDKa77wcLDUcnWVh6GHiIjIgFy6n4VpW+Nw5uZjAEAdR0tM790EnRs5a7myysfQQ0REZAAyn8gwL+oKVp24CYUALEyMMO6V+hgV7A0zY/2byioNQw8REZEeUygE/om5izm7LuNRbiEAoEdTV3zV0xe17S20XF3VYughIiLSUxfvZmLq1jjE3s4AANRzssKM3n7o0KCmdgvTEoYeIiIiPfM4txA/7klExOnbEAKwMjXCh10bYHg7b5gaS7VdntYw9BAREekJuUJg3Zk7mLv7MjLyZACA3s3d8GUPH7jamWu5Ou1j6CEiItIDsbcfY9rWeFy4mwkAaORigxmvN0Hbuo5arqz6YOghIiLSYY9yCjA3MhHrzt4BANiYGePjbg0xJMgLJkaGO5VVGoYeIiIiHSRXCKw+dQs/7k5EVn4RAKBfC3d8HtYIzjacyioNQw8REZGOOXszHVP/jcel5CwAgG8tW8x8vQla1XHQcmXVG0MPERGRjkjNzsf3uy5jU8w9AICtuTEmhTbCwEAvGEn158GglYWhh4iIqJqTyRVYdeIWfom6guyCp1NZb7fywGfdG8HR2kzL1ekOhh4iIqJq7OSNR5j2bzwSH2QDAJq522Hm637w97DXbmE6iKGHiIioGnqQlY9vdyRg6/n7AIAalib4rHtj9G/lwamsCmLoISIiqkYKixRYfiwJ8/ddRW6hHBIJMLCNJz4NaYQaVqbaLk+nMfQQERFVE0evpmHa1jhcf5gLAAjwtMes1/3gV9tOy5XpB4YeIiIiLUsvAMavPY/I+AcAAEcrU3we1hhvtnCHlFNZGsPQQ0REpCUFRXL8dugGFp4zQqHiAaQSYGhQHXzcrSHsLEy0XZ7eYeghIiLSggOJqZixNR43H+UBkKCVlz1m9WkKn1q22i5NbzH0EBERVaE76XmYuf0Soi49ncpysjZFd9cnmDK0NUxNeaJyZWLoISIiqgL5MjmWHrqOJQevo6BIASOpBCPa1cHYTt44sn8PJBKeu1PZGHqIiIgqkRACexNSMXN7PO6kPwEABNV1xIzXm6Chiw1kMpmWKzQcDD1ERESV5GZaLmZsi8eBxIcAAFdbc3z9mg96Nq3FIztawNBDRESkYU8K5Vh04Bp+P3wDhXIFTIwkGBVcF+O61IeVGf/q1RZ+5YmIiDRECIHIuBR8syMB9zKeTmUFN6iJ6b2boJ6TtZarI4YeIiIiDbj+MAfTt8bjyNU0AEBtewtMec0HoU1cOZVVTTD0EBERvYTcgiLM338Vy44mQSYXMDWS4v1OdTG2c31YmBppuzx6BkMPERFRBQghsP1CMr7dkYCUrHwAwCuNnTH1NV/UqWml5eqoNAw9REREarryIBvT/o3HiRuPAAAeDhaY9loTdPV10XJl9DwMPUREROWUnS/DL3uvYsXxm5ArBMyMpRjbuT7e71QX5iacyqruGHqIiIheQAiBLefu4budl/EwuwAAEOLrgimv+cLDwVLL1VF5MfQQERE9x6X7WZi2NQ5nbj4GAHjXtMK0Xr7o3MhZy5WRuhh6iIiISpH5RIaf9yTir5O3oBCAhYkRxr1SH6OCvWFmzKksXcTQQ0RE9AyFQuCf6LuYE3kZj3ILAQA9m9bCVz194GZvoeXq6GUw9BAREf1/F+9mYurWOMTezgAA1HOywozefujQoKZ2CyONYOghIiKD9zi3ED/uSUTE6dsQArAyNcKHXRtgeDtvmBpLtV0eaQhDDxERGSy5QmDdmTuYu/syMvJkAIDX/d3wZQ8fuNiaa7k60jSGHiIiMkixtx9j2tZ4XLibCQBo5GKDGa83Qdu6jlqujCoLQw8RERmURzkFmBN5GevP3gUA2JgZ4+NuDTE0yAvGRpzK0mcMPUREZBCK5ApEnL6NH3cnIiu/CADQr4U7vghrDCcbMy1XR1WBoYeIiPTe2ZvpmPJvPBKSswAAvrVsMatPE7T0ctByZVSVGHqIiEhvpWbn4/tdl7Ep5h4AwNbcGJNCG2FgoBeMpBItV0dVjaGHiIj0jkyuwKoTt/BL1BVkFxRBIgHebuWBSaGN4GjNqSxDxdBDRER65eSNR5j2bzwSH2QDAJq522Hm637w97DXbmGkdQw9RESkF1Iy8/HdzgRsPX8fAFDD0gSfdW+M/q08OJVFABh6iIhIxxUWKbD8WBLm77uK3EI5JBJgUKAnPg1pBHtLU22XR9UIQw8REemso1fTMG1rHK4/zAUABHjaY9brfvCrbaflyqg6YughIqJqS64QOJWUjug0CRyT0hFU3xlGUgnuZTzBtzsuYefFFACAo5UpvghrjH4t3CHlVBaVQau3npw9ezZat24NGxsbODs7o0+fPkhMTFQZk5+fj/DwcDg6OsLa2hr9+vXDgwcPVMbcvn0bPXv2hKWlJZydnTFp0iQUFRVVZStERKRhkXHJ6DBnPwYvO4tVV40weNlZtP9+Hz5cG4uuPx3CzospkEqA4e3qYP+nnfFWKw8GHnourYaeQ4cOITw8HCdPnkRUVBRkMhlCQkKQm5urHPPxxx9j27Zt2LBhAw4dOoT79++jb9++yvVyuRw9e/ZEYWEhjh8/jpUrV2LFihWYOnWqNloiIiINiIxLxpi/Y5Ccma+yPCWrAP+eu48nMjna1HHAjgnBmN67CewsTLRUKekSrU5vRUZGqrxesWIFnJ2dER0djY4dOyIzMxN//vknIiIi8MorrwAAli9fDh8fH5w8eRJt27bFnj17cOnSJezduxcuLi7w9/fHrFmz8Pnnn2P69OkwNeVJbEREukSuEJix7RLEc8bYW5gg4r1APiuL1FKtzunJzHz6pFsHh6e3BY+OjoZMJkPXrl2VYxo3bgxPT0+cOHECbdu2xYkTJ9C0aVO4uLgox4SGhmLMmDGIj49HQEBAic8pKChAQUGB8nVW1tPbkstkMshkMo31U7wtTW6zutH3Htmf7tP3HvWxv1NJ6SWO8PxXxhMZTl5/iEBv3X+MhD7uw2dVZn/qbrPahB6FQoGPPvoI7du3h5+fHwAgJSUFpqamsLe3Vxnr4uKClJQU5ZhnA0/x+uJ1pZk9ezZmzJhRYvmePXtgaWn5sq2UEBUVpfFtVjf63iP703363qM+9RedJgFg9MJxe46cwqOE5x0P0i36tA9LUxn95eXlqTW+2oSe8PBwxMXF4ejRo5X+WZMnT8bEiROVr7OysuDh4YGQkBDY2tpq7HNkMhmioqLQrVs3mJjo53yzvvfI/nSfvveoj/2ln7oNXL38wnEhwYF6c6RH3/bhsyqzv+KZmvKqFqFn3Lhx2L59Ow4fPgx3d3flcldXVxQWFiIjI0PlaM+DBw/g6uqqHHP69GmV7RVf3VU85r/MzMxgZlby2SsmJiaV8g1XWdutTvS9R/an+/S9R33oT6EQ+N/RG5iz6/mBRwLA1c5cefm6vtCHffg8ldGfutvT6hlgQgiMGzcOmzdvxv79++Ht7a2yvmXLljAxMcG+ffuUyxITE3H79m0EBQUBAIKCgnDx4kWkpqYqx0RFRcHW1ha+vr5V0wgREb2U9NxCjFp1Ft/tvAy5AFp42UOCpwHnWcWvp/Xy1avAQ1VDq0d6wsPDERERgX///Rc2NjbKc3Ds7OxgYWEBOzs7vPvuu5g4cSIcHBxga2uL8ePHIygoCG3btgUAhISEwNfXF0OGDMHcuXORkpKCr7/+GuHh4aUezSEiourlzM10TFgTi+TMfJgaSzGtly8GtvHE7vgUzNh2SeWkZlc7c0zr5YvufrW0WDHpKq2GniVLlgAAOnfurLJ8+fLlGD58OABg3rx5kEql6NevHwoKChAaGorFixcrxxoZGWH79u0YM2YMgoKCYGVlhWHDhmHmzJlV1QYREVWAQiGw5NB1/Bx1BXKFQN2aVlg4sAV83Z6eW9ndrxa6+brixLVU7DlyCiHBgXo3pUVVS6uhR4gXn3Vvbm6ORYsWYdGiRWWO8fLyws6dOzVZGhERVaK0nAJ8vO4cjlxNAwD08XfDN280hbWZ6l9LRlIJAr0d8ChBINDbgYGHXkq1OJGZiIgMx4nrj/Dh2likZhfA3ESKmb398FYrd0gkDDRUuRh6iIioSsgVAgv3X8Ov+65AIYD6ztZYNLAFGrnaaLs0MhAMPUREVOlSs/Px0dpzOH79EQDgrZbumPF6E1ia8q8hqjr8biMiokp19GoaPloXi7ScQliYGOHbN/zQt4X7i99IpGEMPUREVCmK5Ar8uu8qFh64BiGAxq42WDiwBeo7W2u7NDJQDD1ERKRxKZn5mLA2FqeT0gEAA9p4YFqvJjA3efEztYgqC0MPERFp1MHEVExcfx7puYWwMjXCd32b4nX/2toui4ihh4iINEMmV+CnPVew9NB1AIBvLVssGtQC3jWttFwZ0VMMPURE9NLuZzzB+DWxiL71GAAwpK0Xvurpw+ksqlYYeoiI6KXsvfQAn/5zHhl5MtiYGWPOm83QoymfjUXVD0MPERFVSGGRAnMjL+N/R5MAAM3c7bBwQAt4OlpquTKi0jH0EBGR2u6k52Hcmlicv5MBABjZ3hufhzWCmTGns6j6YughIiK1RMalYNI/55GdXwRbc2P8+FZzhDRx1XZZRC/E0ENEROVSUCTH7J2XseL4TQBAgKc9FgwIgHsNTmeRbmDoISKiF7r1KBfjImJx8V4mAGB0x7qYFNoIJkZSLVdGVH4MPURE9FzbL9zHFxsvIqegCDUsTfBT/+Z4pbGLtssiUhtDDxERlSpfJses7Zew+tRtAEArrxpYMDAAtewstFwZUcUw9BARUQk3HuYgPCIWCclZAICxnethYreGMOZ0Fukwhh4iIlKxJfYevtx8EXmFcjhameLnt/3RqaGTtssiemkMPUREBAB4UijH9K3xWHf2DgCgbV0H/PpOAFxszbVcGZFmMPQQERGupWYjfHUsEh9kQyIBxr/SAB++2gBGUom2SyPSGIYeIiID90/0XUzZEocnMjlqWpth/jv+aFe/prbLItI4tUJPQkIC1q5diyNHjuDWrVvIy8uDk5MTAgICEBoain79+sHMzKyyaiUiIg3KLSjClH/jsCnmHgCgQ/2amPe2P5xs+Huc9FO5TsOPiYlB165dERAQgKNHjyIwMBAfffQRZs2ahcGDB0MIga+++gpubm6YM2cOCgoKKrtuIiJ6CZdTstB74VFsirkHqQT4pFtDrBzZhoGH9Fq5jvT069cPkyZNwj///AN7e/syx504cQK//vorfvrpJ3z55ZeaqpGIiDRECIF1Z+5g2tZ4FBQp4GJrhl/fCUDbuo7aLo2o0pUr9Fy5cgUmJiYvHBcUFISgoCDIZLKXLoyIiDQrp6AIX266iK3n7wMAOjV0ws/9m8PRmkd3yDCUK/S8KPBkZGSoHAEqT0AiIqKqE38/E+MiYpGUlgsjqQSfhjTC+x3rQsqrs8iAqH1rzTlz5mDdunXK1/3794ejoyNq166N8+fPa7Q4IiJ6OUII/HXyFt5YfBxJabmoZWeOdaPbYkznegw8ZHDUDj1Lly6Fh4cHACAqKgpRUVHYtWsXwsLCMGnSJI0XSEREFZOVL8O4iFhM2RKHwiIFXm3sjJ0TgtGqjoO2SyPSCrXv05OSkqIMPdu3b0f//v0REhKCOnXqIDAwUOMFEhGR+i7czcC4iFjcTs+DsVSCL8Ia490O3pBIeHSHDJfaR3pq1KiBO3ee3qI8MjISXbt2BfD0EKpcLtdsdUREpBYhBJYfS0K/JcdxOz0Pte0tsOGDIIwKrsvAQwZP7SM9ffv2xcCBA9GgQQM8evQIYWFhAIDY2FjUr19f4wUSEVH5ZObJMOmf89hz6QEAILSJC+b2aw47S15cQgRUIPTMmzcPderUwZ07dzB37lxYW1sDAJKTkzF27FiNF0hERC927k4GPlp/EfcynsDUSIovezTGsHZ1eHSH6Blqhx4TExN8+umnJZZ//PHHGimIiIjKT6EQ2H9fgh2nzqBIIeDpYIlFA1ugqbudtksjqnYq9MDRq1ev4sCBA0hNTYVCoVBZN3XqVI0URkREz/c4txAfr4vFwVtGAAR6NquF2X2bwtac01lEpVE79Pzxxx8YM2YMatasCVdXV5VDpxKJhKGHiKgKnL2ZjvFrYpGcmQ9jicCU13wxtB2vziJ6HrVDzzfffINvv/0Wn3/+eWXUQ0REz6FQCCw9fB0/7bkCuUKgjqMl3qqdhYFtPBh4iF5A7UvWHz9+jLfeeqsyaiEioudIyynA8BVnMDcyEXKFwOv+btg8pi3crbRdGZFuUDv0vPXWW9izZ09l1EJERGU4eeMRevx6BIevPIS5iRRz+jXFL2/7w9qsQqdmEhkktX9a6tevjylTpuDkyZNo2rRpiYeLTpgwQWPFEREZOrlCYNGBa/hl7xUoBFDf2RqLBrZAI1cbbZdGpHPUDj2///47rK2tcejQIRw6dEhlnUQiYeghItKQ1Ox8fLzuHI5dewQAeLOlO2a+3gSWpjy6Q1QRav/kJCUlVUYdRET0jGPX0vDh2nNIyymAhYkRvunjh34t3bVdFpFOe6l/LgghAIBXDBARaYhcIfDr3itYcOAahAAaudhg0aAA1HfmdBbRy1L7RGYAWLVqFZo2bQoLCwtYWFigWbNm+OuvvzRdGxGRQXmQlY+Bf5zE/P1PA8+ANh74d1x7Bh4iDVH7SM/PP/+MKVOmYNy4cWjfvj0A4OjRo/jggw+QlpbGx1EQEVXAwcRUTFx/Hum5hbAyNcJ3fZvidf/a2i6LSK+oHXoWLFiAJUuWYOjQocplvXv3RpMmTTB9+nSGHiIiNRTJFfgp6gqWHLwOAPCpZYtFAwNQ18lay5UR6R+1Q09ycjLatWtXYnm7du2QnJyskaKIiAzB/YwnmLAmFmdvPQYADGnrha96+sDcxEjLlRHpJ7XP6alfvz7Wr19fYvm6devQoEEDjRRFRKTv9iU8QI/5R3D21mPYmBlj0cAWmNXHj4GHqBKpfaRnxowZePvtt3H48GHlOT3Hjh3Dvn37Sg1DRET0fwqLFPhh92X8ceTp7T+a1rbDwoEB8HLksySIKpvaoadfv344deoU5s2bhy1btgAAfHx8cPr0aQQEBGi6PiIivXEnPQ/j18Ti3J0MAMCI9nXwRVhjmBnz6A5RVajQfXpatmyJv//+W9O1EBHprd3xKZi04Tyy8otga26MH95qjtAmrtoui8iglCv0ZGVlwdbWVvn/z1M8joiIgIIiOWbvvIwVx28CAPw97LFgQAA8HCy1WxiRASpX6KlRowaSk5Ph7OwMe3v7Uu/ALISARCKBXC7XeJFERLro1qNcjIuIxcV7mQCA94K9MSm0MUyNK3RfWCJ6SeUKPfv374eDgwMA4MCBA5VaEBGRPthxIRlfbLyA7IIi2Fua4Ke3muNVHxdtl0Vk0MoVejp16lTq/xMRkap8mRzf7LiEv0/eBgC08qqB+QMC4GZvoeXKiKhcoefChQvl3mCzZs0qXAwRkS678TAH4RGxSEh+eu7j2M718HG3hjAx4nQWUXVQrtDj7+8PiUSifKp6WdQ9p+fw4cP44YcfEB0djeTkZGzevBl9+vRRrs/JycEXX3yBLVu24NGjR/D29saECRPwwQcfKMfk5+fjk08+wdq1a1FQUIDQ0FAsXrwYLi48jExEVeffc/fw5aaLyC2Uw9HKFD+/7Y9ODZ20XRYRPaNcoScpKalSPjw3NxfNmzfHyJEj0bdv3xLrJ06ciP379+Pvv/9GnTp1sGfPHowdOxZubm7o3bs3AODjjz/Gjh07sGHDBtjZ2WHcuHHo27cvjh07Vik1ExE960mhHDO2xWPtmTsAgEBvB8wfEAAXW3MtV0ZE/1Wu0OPl5VUpHx4WFoawsLAy1x8/fhzDhg1D586dAQCjR4/Gb7/9htOnT6N3797IzMzEn3/+iYiICLzyyisAgOXLl8PHxwcnT55E27ZtK6VuIiIAuJaajfDVsUh8kA2JBBj/SgNMeKU+jDmdRVQtlSv0bN26tdwbLD4Cownt2rXD1q1bMXLkSLi5ueHgwYO4cuUK5s2bBwCIjo6GTCZD165dle9p3LgxPD09ceLEiTJDT0FBAQoKCpSvi+89JJPJIJPJNFZ/8bY0uc3qRt97ZH+6r7J63BR7D9O3JeCJTIGa1qb46c2maFfPEUIhh0xRdbfu4D7Ufezv5bddXhLxohN1AEil5ftXy8vcp0cikZQ4p6egoACjR4/GqlWrYGxsDKlUij/++ANDhw4FAERERGDEiBEqAQYA2rRpgy5dumDOnDmlftb06dMxY8aMEssjIiJgackbhhFR2QrkwD9JUpx++PT3YkM7BYbUV8DWVMuFERmgvLw8DBw4EJmZmeW6OXK5jvQoFIqXLqwiFixYgJMnT2Lr1q3w8vLC4cOHER4eDjc3N5WjO+qaPHkyJk6cqHydlZUFDw8PhISEaPSO0jKZDFFRUejWrRtMTEw0tt3qRN97ZH+6T5M9XnmQjQnrLuD6w1xIJcD4LvUwplNdGElL3rC1qnAf6j72V3EvekrEf1Xo2VtV4cmTJ/jyyy+xefNm9OzZE8DTy+HPnTuHH3/8EV27doWrqysKCwuRkZEBe3t75XsfPHgAV9eyn2ljZmYGMzOzEstNTEwq5RuusrZbneh7j+xP971Mj0IIrD97B1P/jUdBkQIutmb49Z0AtK3rqOEqK477UPexv4ptUx3lCj3z58/H6NGjYW5ujvnz5z937IQJE9QqoCzF59f8d2rNyMhIeeSpZcuWMDExwb59+9CvXz8AQGJiIm7fvo2goCCN1EFEhi2noAhfb76ILefuAwA6NnTCvP7N4Whd8h9ORFS9lSv0zJs3D4MGDYK5ubnyJOLSSCQStUJPTk4Orl27pnydlJSEc+fOwcHBAZ6enujUqRMmTZoECwsLeHl54dChQ1i1ahV+/vlnAICdnR3effddTJw4EQ4ODrC1tcX48eMRFBTEK7eI6KXF38/E+IhY3EjLhZFUgk9CGuKDjvUg1eJ0FhFVnNr36dHkPXvOnj2LLl26KF8Xn2czbNgwrFixAmvXrsXkyZMxaNAgpKenw8vLC99++63KzQnnzZsHqVSKfv36qdyckIioooQQ+PvUbczafgmFRQrUsjPHggEBaFXHQdulEdFL0Oo5PZ07d37uXZ5dXV2xfPny527D3NwcixYtwqJFizRdHhEZoKx8GSZvuogdF5IBAK82dsaPbzVHDStenkWk68odembOnFmucVOnTq1wMURE2nTxbibCI2JwOz0PxlIJvghrjHc7eEMi4XQWkT4od+iZPn063Nzc4OzsXObRGYlEwtBDRDpHCIGVx2/iu52XUShXoLa9BRYODECAZw1tl0ZEGlTu0BMWFob9+/ejVatWGDlyJF577bVy37SQiKi6ysyT4bON57E7/gEAIMTXBT+82Rx2lvp76TCRoSp3atmxYweuX7+OwMBATJo0CbVr18bnn3+OxMTEyqyPiKjSxN5+jJ4LjmB3/AOYGEkwrZcvfhvSkoGHSE+pdajGzc0NkydPRmJiItatW4fU1FS0bt0a7du3x5MnTyqrRiIijRJC4H9HbuCtpSdw9/ETeDpYYuOYdhjRnufvEOmzCl+91bp1a9y8eROXLl1CbGwsZDIZLCwsNFkbEdFLkSsETiWlIzpNAsekdATVd0bWExk+3XAe+y6nAgB6Nq2F2f2awtacR3eI9J3aoefEiRNYtmwZ1q9fj4YNG2LEiBEYOHCgRp9ZRUT0siLjkjFj2yUkZ+YDMMKqq2fhaGUKhRB4nCeDqbEUU17zxeBATx7dITIQ5Q49c+fOxYoVK5CWloZBgwbhyJEjaNasWWXWRkRUIZFxyRjzdwz+e53po9xCAICzjRmWj2iNJm52VV8cEWlNuUPPF198AU9PT/Tv3x8SiQQrVqwodVzxIyKIiLRBrhCYse1SicDzLKlEgsauPDpNZGjKHXo6duwIiUSC+Pj4MsfwEDERadvppPT/P6VVtpSsfJxOSkdQverzlHQiqnzlDj0HDx6sxDKIiDQjNfv5gUfdcUSkP3h3QSLSK2bG5fu15mxjXsmVEFF1U67fDt9//z3y8vLKtcFTp05hx44dL1UUEVFFHLuWhq82xz13jARALTtztPHmE9OJDE25Qs+lS5fg5eWFsWPHYteuXXj48KFyXVFRES5cuIDFixejXbt2ePvtt2FjY1NpBRMR/ZdcIfBz1BUM/vMUHuUWws3u6VGc/55lWPx6Wi9fGEl5DiKRoSnXOT2rVq3C+fPnsXDhQgwcOBBZWVkwMjKCmZmZ8ghQQEAARo0aheHDh8PcnIeNiahqPMjKx4drY3HyRjoA4J3WHpjWqwkOXUl95j49T7namWNaL19096ulrXKJSIvKfSJz8+bN8ccff+C3337DhQsXcOvWLTx58gQ1a9aEv78/atasWZl1EhGVcOjKQ0xcdw6PcgthZWqE7/o2xev+tQEA3f1qoZuvK05cS8WeI6cQEhyIoPrOPMJDZMDUviOzVCqFv78//P39K6EcIqIXK5Ir8HPUFSw+eB0A4FPLFosGBqCuk7XKOCOpBIHeDniUIBDo7cDAQ2TgKvzsLSIibbif8QQT1sTi7K3HAIDBbT3xdU9fmJsYabkyIqruGHqISGfsv/wAE9efR0aeDDZmxpjdrylea+am7bKISEcw9BBRtSeTK/DD7kT8fvgGAKBpbTssHBgAL0crLVdGRLqEoYeIqrW7j/MwLiIW5+5kAACGt6uDyT0aw8yY01lEpJ6XDj1ZWVnYv38/GjVqBB8fH03UREQEANgdn4JJG84jK78ItubGmPtmc3T3c9V2WUSko9QOPf3790fHjh0xbtw4PHnyBK1atcLNmzchhMDatWvRr1+/yqiTiAxIYZECs3clYPmxmwCA5h72WDggAB4OltotjIh0mtrP3jp8+DCCg4MBAJs3b4YQAhkZGZg/fz6++eYbjRdIRIbl9qM8vLn0uDLwvBfsjQ3vBzHwENFLUzv0ZGZmwsHh6TNrIiMj0a9fP1haWqJnz564evWqxgskIsOx82Iyes4/ggt3M2FvaYL/DW2Fr3r6wrScDxElInoetae3PDw8cOLECTg4OCAyMhJr164FADx+/JiPnyCiCsmXyfHtjgT8dfIWAKClVw0sGBAAN3sLLVdGRPpE7dDz0UcfYdCgQbC2toaXlxc6d+4M4Om0V9OmTTVdHxHpuaS0XISvjsGl5CwAwJjO9TCxW0OYGPHoDhFpltqhZ+zYsQgMDMTt27fRrVs3SKVPfzHVrVuX5/QQkVr+PXcPX266iNxCORysTPFz/+bo3MhZ22URkZ5SK/TIZDI0btwY27dvxxtvvKGyrmfPnhotjIj0V75Mjhnb4rHm9B0AQBtvB8x/JwCudpwiJ6LKo1boMTExQX5+fmXVQkQG4FpqDsJXxyDxQTYkEmBcl/r48NUGMOZ0FhFVMrV/y4SHh2POnDkoKiqqjHqISI9tjL6LXguOIvFBNmpam+GvkYH4JKQRAw8RVQm1z+k5c+YM9u3bhz179qBp06awslJ99s2mTZs0VhwR6Ye8wiJM/Tce/0TfBQC0q+eIX97xh7MNp7OIqOqoHXrs7e1512UiKrcrD7IRvjoGV1NzIJUAH3VtiPAu9WEklWi7NCIyMGqHnuXLl1dGHUSkZ4QQWH/2DqZtjUe+TAFnGzP8+k4Aguo5ars0IjJQFXrgaFFREQ4ePIjr169j4MCBsLGxwf3792Frawtra2tN10hEOianoAhfb76ILefuAwCCG9TEvLf9UdPaTMuVEZEhUzv03Lp1C927d8ft27dRUFCAbt26wcbGBnPmzEFBQQGWLl1aGXUSkY64dD8L4yJicCMtF0ZSCT4JaYgPOtaDlNNZRKRlal8y8eGHH6JVq1Z4/PgxLCz+7xbxb7zxBvbt26fR4ohIdwghsPrULfRZfAw30nJRy84ca0e3xdjO9Rl4iKhaUPtIz5EjR3D8+HGYmpqqLK9Tpw7u3bunscKISHdk58vwxaaL2HEhGQDwSmNn/PhWczhYmb7gnUREVUft0KNQKCCXy0ssv3v3LmxsbDRSFBHpjrh7mQiPiMGtR3kwlkrwWfdGGNWhLo/uEFG1o/b0VkhICH755Rfla4lEgpycHEybNg09evTQZG1EVI0JIbDiWBL6Lj6OW4/yUNveAus/CMJonr9DRNWU2kd6fvrpJ4SGhsLX1xf5+fkYOHAgrl69ipo1a2LNmjWVUSMRVTOZT2T4/J8LiIxPAQB083XBj282h52liZYrIyIqm9qhx93dHefPn8fatWtx4cIF5OTk4N1338WgQYNUTmwmIv107k4GxkXE4O7jJzAxkmBymA9GtK8DiYRHd4ioeqvQfXqMjY0xePBgTddCRNWYEAJ/Hk3C97suo0gh4OlgiYUDA9DM3V7bpRERlUuFQk9iYiIWLFiAhIQEAICPjw/GjRuHxo0ba7Q4IqoeMvIK8emG89ibkAoA6NHUFd/3awZbc05nEZHuUPtE5o0bN8LPzw/R0dFo3rw5mjdvjpiYGDRt2hQbN26sjBqJSIuib6Wjx69HsDchFabGUszq44dFA1sw8BCRzlH7SM9nn32GyZMnY+bMmSrLp02bhs8++4wPIyXSEwqFwO9HbuCH3YmQKwS8a1ph4cAANHGz03ZpREQVovaRnuTkZAwdOrTE8sGDByM5OVkjRRGRdj3KKcDIlWfw/a7LkCsEejd3w7bxHRh4iEinqX2kp3Pnzjhy5Ajq16+vsvzo0aMIDg7WWGFEpB2nbjzChLWxeJBVADNjKab3boJ3Wnvw6iwi0nnlCj1bt25V/n/v3r3x+eefIzo6Gm3btgUAnDx5Ehs2bMCMGTMqp0oiqnQKhcDig9fwc9QVKARQz8kKiwa1QGNXW22XRkSkEeUKPX369CmxbPHixVi8eLHKsvDwcHzwwQcaKYyIqk5aTgE+2xSDI1fTAAB9W9TGrNf9YGVWoQs8iYiqpXL9RlMoFJVdBxFpyZVMCb5ZdAIPcwphYWKEma83wVutPLRdFhGRxvGfcUQGSq4Q+HXfNSy+JIVAIRq6WGPRwBZo4MIHBxORfipX6Jk/fz5Gjx4Nc3NzzJ8//7ljJ0yYoJHCiKjyPMjKx4drY3HyRjoACd5qWRszX28KC1MjbZdGRFRpyhV65s2bh0GDBsHc3Bzz5s0rc5xEImHoIarmDl95iI/XncOj3EJYmhqhn2chpvZpAhMTBh4i0m/lCj1JSUml/j8R6Y4iuQLz9l7B4oPXIQTQ2NUGv/ZvhstnDmm7NCKiKqHWzQllMhnq1aunfOYWEemG5MwnGPDHSSw68DTwDAr0xJbw9qjrZKXt0oiIqoxaocfExAT5+fka+/DDhw+jV69ecHNzg0QiwZYtW0qMSUhIQO/evWFnZwcrKyu0bt0at2/fVq7Pz89HeHg4HB0dYW1tjX79+uHBgwcaq5FI1x24nIoevx7BmZuPYW1mjIUDA/DtG01hzuksIjIwaj+GIjw8HHPmzEFRUdFLf3hubi6aN2+ORYsWlbr++vXr6NChAxo3boyDBw/iwoULmDJlCszNzZVjPv74Y2zbtg0bNmzAoUOHcP/+ffTt2/elayPSdTK5ArN3JmDEijN4nCeDX21bbB/fAa81c9N2aUREWqH2JetnzpzBvn37sGfPHjRt2hRWVqqHxzdt2lTubYWFhSEsLKzM9V999RV69OiBuXPnKpfVq1dP+f+ZmZn4888/ERERgVdeeQUAsHz5cvj4+ODkyZPKO0YTGZq7j/Mwfk0sYm9nAACGt6uDyT0aw8yYR3eIyHCpfaTH3t4e/fr1Q2hoKNzc3GBnZ6fyR1MUCgV27NiBhg0bIjQ0FM7OzggMDFSZAouOjoZMJkPXrl2Vyxo3bgxPT0+cOHFCY7UQ6ZI98SnoOf8oYm9nwMbcGEsHt8D03k0YeIjI4Kl9pGf58uWVUUcJqampyMnJwffff49vvvkGc+bMQWRkJPr27YsDBw6gU6dOSElJgampKezt7VXe6+LigpSUlDK3XVBQgIKCAuXrrKwsAE9P1JbJZBrroXhbmtxmdaPvPepSf4VFCvyw5wpWnHh6zluz2rb45e1m8KhhWWb9utRfRel7j/reH6D/PbK/l992eUmEEEKdNyQlJaGoqAgNGjRQWX716lWYmJigTp06ahWgLEQiwebNm5XP+bp//z5q166NAQMGICIiQjmud+/esLKywpo1axAREYERI0aoBBgAaNOmDbp06YI5c+aU+lnTp08v9eGoERERsLS0rFD9RNqUlg+suGKEO7lPn4TeuZYCvTwVMFb7WC4Rke7Iy8vDwIEDkZmZCVvbFz8cWe0jPcOHD8fIkSNLhJ5Tp07hf//7Hw4ePKjuJktVs2ZNGBsbw9fXV2W5j48Pjh49CgBwdXVFYWEhMjIyVI72PHjwAK6urmVue/LkyZg4caLydVZWFjw8PBASElKuL1p5yWQyREVFoVu3bjAxMdHYdqsTfe9RF/qLjH+AeZvjkVNQBDsLY8zp64dXGzuX67260N/L0vce9b0/QP97ZH8VVzxTU15qh57Y2Fi0b9++xPK2bdti3Lhx6m6uTKampmjdujUSExNVll+5cgVeXl4AgJYtW8LExAT79u1Dv379AACJiYm4ffs2goKCyty2mZkZzMzMSiw3MTGplG+4ytpudaLvPVbH/vJlcny3MwGrTtwCALTwtMeCgS1Q295C7W1Vx/40Td971Pf+AP3vkf1VbJvqUDv0SCQSZGdnl1iemZkJuVyu1rZycnJw7do15eukpCScO3cODg4O8PT0xKRJk/D222+jY8eO6NKlCyIjI7Ft2zbl0SQ7Ozu8++67mDhxIhwcHGBra4vx48cjKCiIV26RXktKy8W4iBjE33/6r5z3O9XFpyGNYGLE+SwiorKoHXo6duyI2bNnY82aNTAyeno1iFwux+zZs9GhQwe1tnX27Fl06dJF+bp4ymnYsGFYsWIF3njjDSxduhSzZ8/GhAkT0KhRI2zcuFHlc+bNmwepVIp+/fqhoKAAoaGhWLx4sbptEemMrefv48tNF5FTUAQHK1P81L85ujQq33QWEZEhUzv0zJkzBx07dkSjRo0QHBwMADhy5AiysrKwf/9+tbbVuXNnvOg86pEjR2LkyJFlrjc3N8eiRYvKvMEhkb7Il8kxY9slrDn99OqsNnUcMH9AAFztzF/wTiIiAipwnx5fX19cuHAB/fv3R2pqKrKzszF06FBcvnwZfn5+lVEjkcG7lpqDPouOYc3p25BIgPGv1EfEe4EMPEREalD7SA8AuLm54bvvvtN0LURUik0xd/H1ljjkFcpR09oU8972R3ADJ22XRUSkc9Q+0hMZGam8ZBwAFi1aBH9/fwwcOBCPHz/WaHFEhiyvsAiTNpzHxPXnkVcoR1BdR+ycEMzAQ0RUQWqHnkmTJimvi7948SImTpyIHj16ICkpSeXeN0RUcVceZOP1hcewIfoupBLg464N8feoQDjbcjqLiKii1J7eSkpKUt4wcOPGjejVqxe+++47xMTEoEePHhovkMiQCCGw4exdTN0ah3yZAk42Zpj/TgCC6jlquzQiIp2ndugxNTVFXl4eAGDv3r0YOnQoAMDBwUHtOyMS0f/JLSjC11visDn2HgAguEFNzHvbHzWtS95Ik4iI1Kd26OnQoQMmTpyI9u3b4/Tp01i3bh2Ap3dKdnd313iBRIYgITkL4atjcCMtF1IJ8ElII4zpVA9SqUTbpRER6Q21z+lZuHAhjI2N8c8//2DJkiWoXbs2AGDXrl3o3r27xgsk0mdCCKw+dQuvLzqGG2m5cLU1x9rRQQjvUp+Bh4hIw9Q+0uPp6Ynt27eXWD5v3jyNFERkKLLzZZi86SK2X0gGAHRp5ISf+vvDwcpUy5UREemnCt2nRy6XY/PmzUhISADw9Mnnffr0gbFxhTZHZHDi7mViXEQMbj7Kg7FUgkmhjfBecF0e3SEiqkRqp5T4+Hj06tULDx48QKNGjQA8fTSFk5MTtm3bxrsyEz2HEAKrTtzCtzsSUChXoLa9BeYPCEBLrxraLo2ISO+pHXpGjRoFPz8/REdHo0aNp7+oHz9+jOHDh2P06NE4fvy4xosk0geZT2T4YuMF7IpLAQB09XHBj281g70lp7OIiKqC2qHn3LlzOHv2rDLwAECNGjXw7bffonXr1hotjkhfnL+TgXFrYnAn/QlMjCSYHOaDEe3rQCLhdBYRUVVRO/Q0bNgQDx48QJMmTVSWp6amon79+horjEgfCCGw7NhNfL8rATK5gIeDBRYOaIHmHvbaLo2IyOCUK/Q8e9PB2bNnY8KECZg+fTratm0LADh58iRmzpyJOXPmVE6VRDooI68Qn264gL0JDwAAYX6u+L5fM9hZmGi5MiIiw1Su0GNvb69yGF4Igf79+yuXCSEAAL169YJcLq+EMol0S/StxxgfEYP7mfkwNZLi69d8MKStF6eziIi0qFyh58CBA5VdB5FeUCgEfj9yAz/sToRcIVDH0RILB7aAX207bZdGRGTwyhV6OnXqVNl1EOm89NxCTFx/DgcTHwIAejV3w3dv+MHGnNNZRETVgdonMh8+fPi56zt27FjhYoh01emkdExYE4uUrHyYGUsxrVcTDGjjweksIqJqRO3Q07lz5xLLnv3FznN6yJAoFAKLD17Dz1FXoBBAXScrLBrYAj61bLVdGhER/Yfaoefx48cqr2UyGWJjYzFlyhR8++23GiuMqLp7mF2AievP4cjVNABA34DamNXHD1ZmfBwLEVF1pPZvZzu7kidkduvWDaamppg4cSKio6M1UhhRdXb8eho+XHsOD7MLYG4ixczX/fBWS3dOZxERVWMa+yepi4sLEhMTNbU5Iq2TKwROJaUjOk0Cx6R0BNV3BgAs2H8V8/ddhUIADZytsWhQCzR0sdFytURE9CJqh54LFy6ovBZCIDk5Gd9//z38/f01VReRVkXGJWPGtktIzswHYIRVV8/C2cYM9pYmuPIgBwDQv5U7ZvT2g4WpkXaLJSKiclE79Pj7+0MikShvSFisbdu2WLZsmcYKI9KWyLhkjPk7BuI/y1OzC5CaXQBTYym+79sUfVu4a6U+IiKqGLVDT1JSksprqVQKJycnmJuba6woIm2RKwRmbLtUIvA8y87CBK/7166ymoiISDPUDj1eXl6VUQdRtXA6Kf3/T2mV7WF2AU4npSOonmMVVUVERJogLe/AEydOYPv27SrLVq1aBW9vbzg7O2P06NEoKCjQeIFEVSk1+/mBR91xRERUfZQ79MycORPx8fHK1xcvXsS7776Lrl274osvvsC2bdswe/bsSimSqKo425Rvmra844iIqPood+g5d+4cXn31VeXrtWvXIjAwEH/88QcmTpyI+fPnY/369ZVSJFGVEQLPu9OOBEAtO3O08XaoqoqIiEhDyh16Hj9+DBcXF+XrQ4cOISwsTPm6devWuHPnjmarI6pCuy4mY9iKM8qTmP8bfopfT+vlCyMpb0JIRKRryh16XFxclFduFRYWIiYmBm3btlWuz87OhokJnyZNumnl8ZsYGxGDwiIFQnxdMP8df7jaqU5hudqZY8ngFujuV0tLVRIR0cso99VbPXr0wBdffIE5c+Zgy5YtsLS0RHBwsHL9hQsXUK9evUopkqiyCCEwd3cilhy8DgAY3NYTM3r7wUgqQc9mbjhxLRV7jpxCSHAgguo78wgPEZEOK3fomTVrFvr27YtOnTrB2toaK1euhKmpqXL9smXLEBISUilFElUGmVyBzzdewKaYewCAT0MaIrxLfeXzs4ykEgR6O+BRgkCgtwMDDxGRjit36KlZsyYOHz6MzMxMWFtbw8hI9db7GzZsgLW1tcYLJKoMOQVFGLs6BoevPISRVILZfZuifysPbZdFRESVSCNPWQcABwdezUK64WF2AUauOIOL9zJhYWKExYNaoEtjZ22XRURElUxjT1kn0gU303IxdNlp3E7Pg4OVKZYNbw1/D3ttl0VERFWAoYcMxrk7GXh3xRk8yi2Eh4MFVo0MhHdNK22XRUREVYShhwzCgcRUjP07Bk9kcvjVtsXy4W3gZGOm7bKIiKgKMfSQ3ttw9g6+2HQRcoVAcIOaWDK4JazN+K1PRGRo+Juf9JYQAosOXMOPe64AAPoG1Mb3/ZrB1Ljc9+QkIiI9wtBDekmuEJi+NR5/nbwFABjTuR4+C22kvAcPEREZHoYe0jv5Mjk+XBuL3fEPIJEA03s1wbB2dbRdFhERaRlDD+mVjLxCjFp5FmdvPYapkRS/vOOPHk35rCwiImLoIT1yL+MJhi07jWupObAxN8YfQ1uhbV1HbZdFRETVBEMP6YXLKVkYtuw0HmQVwNXWHCtHtkEjVxttl0VERNUIQw/pvBPXH2H0qrPILihCA2drrBzZBm72Ftoui4iIqhmGHtJp2y/cx8R151EoV6BNHQf8MbQV7CxNtF0WERFVQww9pLOWHU3CrB2XIATQvYkrfnnHH+YmRtoui4iIqimGHtI5CoXAnMjL+O3wDQDA0CAvTOvVBEZS3oOHiIjKxtBDOqWwSIHP/jmPLefuAwAmhTbC2M71eNNBIiJ6IYYe0hk5BUUY83c0jlxNg7FUgu/7NcObLd21XRYREekIhh7SCanZ+Rix/Azi72fB0tQIiwe1QOdGztoui4iIdAhDD1V7Nx7mYNjy07iT/gSOVqZYPqI1mrnba7ssIiLSMQw9VK3F3n6Md1eeRXpuIbwcLbFyRBvUqWml7bKIiEgHMfRQtbUv4QHCI2KQL1Ogmbsdlg1vjZrWZtoui4iIdJRUmx9++PBh9OrVC25ubpBIJNiyZUuZYz/44ANIJBL88ssvKsvT09MxaNAg2Nrawt7eHu+++y5ycnIqt3CqdOvO3Mbov6KRL1OgU0MnrHmvLQMPERG9FK2GntzcXDRv3hyLFi167rjNmzfj5MmTcHNzK7Fu0KBBiI+PR1RUFLZv347Dhw9j9OjRlVUyVTIhBObvu4rPN16EXCHQr4U7/jesFazMeFCSiIhejlb/JgkLC0NYWNhzx9y7dw/jx4/H7t270bNnT5V1CQkJiIyMxJkzZ9CqVSsAwIIFC9CjRw/8+OOPpYYkqr6K5ApM3RqPiFO3AQDhXerh05BGvAcPERFphFaP9LyIQqHAkCFDMGnSJDRp0qTE+hMnTsDe3l4ZeACga9eukEqlOHXqVFWWSi/pSaEcH/wdg4hTtyGRALNeb4JJoY0ZeIiISGOq9ZzBnDlzYGxsjAkTJpS6PiUlBc7OqvdqMTY2hoODA1JSUsrcbkFBAQoKCpSvs7KyAAAymQwymUwDlUO5vWf/q4800ePjvEK8/3csYu9kwtRYip/fbIrQJi7V4uum7/tQ3/sD9L9Hfe8P0P8e2d/Lb7u8qm3oiY6Oxq+//oqYmBiN/2t/9uzZmDFjRonle/bsgaWlpUY/CwCioqI0vs3qpqI9phcASxOM8OCJBBZGAu81KoT8VjR23tJwgS9J3/ehvvcH6H+P+t4foP89sj/15eXlqTW+2oaeI0eOIDU1FZ6ensplcrkcn3zyCX755RfcvHkTrq6uSE1NVXlfUVER0tPT4erqWua2J0+ejIkTJypfZ2VlwcPDAyEhIbC1tdVYDzKZDFFRUejWrRtMTEw0tt3q5GV6TEjOxrd/xSD1SQFcbc2wbGhLNHCxrqRKK0bf96G+9wfof4/63h+g/z2yv4ornqkpr2obeoYMGYKuXbuqLAsNDcWQIUMwYsQIAEBQUBAyMjIQHR2Nli1bAgD2798PhUKBwMDAMrdtZmYGM7OSlz+bmJhUyjdcZW23OlG3x+PX0vD+X9HILihCIxcbrBjZGrXsLCqxwpej7/tQ3/sD9L9Hfe8P0P8e2V/FtqkOrYaenJwcXLt2Tfk6KSkJ586dg4ODAzw9PeHo6Kgy3sTEBK6urmjUqBEAwMfHB927d8d7772HpUuXQiaTYdy4cXjnnXd45VY1tvX8fXyy/hxkcoE23g74Y2gr2Fno7w86ERFVD1q9euvs2bMICAhAQEAAAGDixIkICAjA1KlTy72N1atXo3Hjxnj11VfRo0cPdOjQAb///ntllUwv6X9HbmDCmljI5AI9m9bCqpFtGHiIiKhKaPVIT+fOnSGEKPf4mzdvlljm4OCAiIgIDVZFlUGhEPhuZwL+dzQJADC8XR1Mfc0XUikvSScioqpRbc/pIf1RWKTApxvOY+v5+wCAL8Ia4/2OdXkPHiIiqlIMPVSpsvNl+ODvaBy79gjGUgnmvtkMfVu4a7ssIiIyQAw9VGlSs/IxbPkZJCRnwcrUCEsGt0THhk7aLouIiAwUQw9ViusPczD0z9O4l/EENa1NsWJEG/jVttN2WUREZMAYekjjom89xrsrzyAjT4Y6jpZYNTIQno6av9M1ERGROhh6SKOiLj3A+DUxyJcp0NzDHsuGtYKjdckbQRIREVU1hh7SmDWnb+OrzRehEECXRk5YNKgFLE35LUZERNUD/0ailyYEMH//NSw4cAMA0L+VO757oymMjbR670siIiIVDD30UorkCqy7IcWJ1KeBZ/wr9TGxW0Peg4eIiKodhh6qsCeFcoxdcw4nUqWQSoCZr/thcFsvbZdFRERUKoYeqpD03EK8u/IMYm9nwEQi8Os7AejRvLa2yyIiIioTQw+p7U56HoYtO40babmwszDG8Lr56ObrrO2yiIiInotnmpJa4u5lou+S47iRlova9hZYO6oN6tpquyoiIqIX45EeKrejV9Pwwd/RyCkoQmNXG6wc2QYOFka4ou3CiIiIyoGhh8plS+w9TPrnPGRygbZ1HfD70FawNTeBTCbTdmlERETlwtBDzyWEwB9HbuC7nZcBAD2b1cLP/ZvDzNhIy5URERGph6GHyqRQCHyzIwHLjiUBAEa298bXPX0glfIePEREpHsYeqhUBUVyTFx/HjsuJAMAvurhg/c61tVyVURERBXH0EMlZOXLMHrVWZy8kQ4TIwl+eLM5+gTwHjxERKTbGHpIRUpmPoYvP43LKdmwNjPG0sEt0aFBTW2XRURE9NIYekjpWmo2hi07g3sZT+BkY4YVI1qjiZudtssiIiLSCIYeAgCcvZmOd1eeReYTGerWtMLKkW3g4WCp7bKIiIg0hqGHsDs+BRPWxKKgSAF/D3ssG94aDlam2i6LiIhIoxh6DNzfJ29h6r9xUAjg1cbOWDiwBSxMeQ8eIiLSPww9BkoIgZ+jrmDB/msAgHdae+CbPn4wNuLj2IiISD8x9BggmVyBrzZfxPqzdwEAH77aAB91bQCJhDcdJCIi/cXQY2DyCosQvjoGBxIfQioBvn2jKQa08dR2WURERJWOoceAPMopwMgVZ3D+bibMTaRYOKAFuvq6aLssIiKiKsHQYyBuP8rD0GWncPNRHuwtTfDnsNZo6VVD22URERFVGYYeA3DxbiZGrDiNtJxC1La3wMqRbVDf2VrbZREREVUphh49d/jKQ4z5Oxq5hXL41LLFihGt4WJrru2yiIiIqhxDjx7bFHMXn/1zAUUKgfb1HbF0cEvYmJtouywiIiKtYOjRQ0IILD10A3MiLwMAejd3w49vNYepMe/BQ0REhouhR8/IFQKztl/CiuM3AQDvBXtjcpgPpFLeg4eIiAwbQ48eyZfJMXH9Oey8mAIA+LqnD0YF19VyVURERNUDQ4+eyHwiw3urzuJ0UjpMjCT4qb8/ejd303ZZRERE1QZDjx5IznyC4cvOIPFBNqzNjPH7kJZoV7+mtssiIiKqVhh6dNyVB9kYtuw0kjPz4WxjhhUj2sDXzVbbZREREVU7DD067HRSOkatPIOs/CLUdbLCqpFt4F7DUttlERERVUsMPToqMi4ZE9aeQ2GRAi087fHnsNaoYWWq7bKIiIiqLYYeHbTqxE1M2xoPIYCuPi5YMCAAFqZG2i6LiIioWmPo0SFCCPywOxGLD14HAAwM9MTM3k1gbMSbDhIREb0IQ4+OkMkV+GLjRWyMuQsAmNitIca/Uh8SCW86SEREVB4MPTogt6AIY1fH4NCVhzCSSvBtHz+808ZT22URERHpFIaeai4tpwAjV5zBhbuZMDeRYtHAFnjVx0XbZREREekchp5q7GZaLoYtP41bj/JQw9IEy4a3RoBnDW2XRUREpJMYeqqpC3czMGL5GTzKLYR7DQusGtkGdZ2stV0WERGRzmLoqYYOJqZi7OoY5BXK0cTNFstHtIazjbm2yyIiItJpDD3VzD/Rd/HFxgsoUggEN6iJJYNbwtqMu4mIiOhl8W/TakIIgcUHr+OH3YkAgD7+bpj7ZnOYGvMePERERJrA0FMNyBUC07fG46+TtwAA73esi8+7N4ZUynvwEBERaQpDj5bly+T4aO05RManQCIBpvT0xcgO3toui4iISO8w9GhRZp4Mo1adwZmbj2FqJMXPbzfHa83ctF0WERGRXmLo0ZL7GU8wbNlpXE3NgY2ZMX4f2gpB9Ry1XRYREZHeYujRgsspWRi+7AxSsvLhYmuGlSPboLGrrbbLIiIi0msMPVXs5I1HeG/VWWTnF6G+szVWjmyD2vYW2i6LiIhI7/F66EoiVwicSkpHdJoEp5LSIVcI7LiQjKF/nkZ2fhFaedXAPx8EMfAQERFVEa2GnsOHD6NXr15wc3ODRCLBli1blOtkMhk+//xzNG3aFFZWVnBzc8PQoUNx//59lW2kp6dj0KBBsLW1hb29Pd59913k5ORUcSeqIuOS0WHOfgxedharrhph8LKzCJi5B+ERMSiUKxDi64K/RwXC3tJUq3USEREZEq2GntzcXDRv3hyLFi0qsS4vLw8xMTGYMmUKYmJisGnTJiQmJqJ3794q4wYNGoT4+HhERUVh+/btOHz4MEaPHl1VLZQQGZeMMX/HIDkzX2V5Vn4RAKBjw6d3WTY3MdJGeURERAZLq+f0hIWFISwsrNR1dnZ2iIqKUlm2cOFCtGnTBrdv34anpycSEhIQGRmJM2fOoFWrVgCABQsWoEePHvjxxx/h5la1l3/LFQIztl2CeM6Yqw+0exSKiIjIUOnUicyZmZmQSCSwt7cHAJw4cQL29vbKwAMAXbt2hVQqxalTp/DGG2+Uup2CggIUFBQoX2dlZQF4OqUmk8kqXN+ppPQSR3j+KzkzHyeupSLQ26HCn1OdFH+9XubrVp2xP92n7z3qe3+A/vfI/l5+2+WlM6EnPz8fn3/+OQYMGABb26eXd6ekpMDZ2VllnLGxMRwcHJCSklLmtmbPno0ZM2aUWL5nzx5YWlpWuMboNAmAF09b7TlyCo8Snnc8SPf896icvmF/uk/fe9T3/gD975H9qS8vL0+t8ToRemQyGfr37w8hBJYsWfLS25s8eTImTpyofJ2VlQUPDw+EhIQoA1VFOCalY9XVsy8cFxIcqFdHeqKiotCtWzeYmJhouxyNY3+6T9971Pf+AP3vkf1VXPFMTXlV+9BTHHhu3bqF/fv3q4QSV1dXpKamqowvKipCeno6XF1dy9ymmZkZzMzMSiw3MTF5qR0SVN8ZtezMkZKZX+p5PRIArnbmCKrvDCM9e5joy37tqjv2p/v0vUd97w/Q/x7ZX8W2qY5qfZ+e4sBz9epV7N27F46Oqo9pCAoKQkZGBqKjo5XL9u/fD4VCgcDAwKouF0ZSCab18gXwNOA8q/j1tF6+ehd4iIiIdIFWQ09OTg7OnTuHc+fOAQCSkpJw7tw53L59GzKZDG+++SbOnj2L1atXQy6XIyUlBSkpKSgsLAQA+Pj4oHv37njvvfdw+vRpHDt2DOPGjcM777xT5VduFevuVwtLBreAq525ynJXO3MsGdwC3f1qaaUuIiIiQ6fV6a2zZ8+iS5cuytfF59kMGzYM06dPx9atWwEA/v7+Ku87cOAAOnfuDABYvXo1xo0bh1dffRVSqRT9+vXD/Pnzq6T+snT3q4Vuvq44cS0Ve46cQkhwoF5OaREREekSrYaezp07Q4iyr2J63rpiDg4OiIiI0GRZGmEklSDQ2wGPEgQCvR0YeIiIiLSsWp/TQ0RERKQpDD1ERERkEBh6iIiIyCAw9BAREZFBYOghIiIig8DQQ0RERAaBoYeIiIgMAkMPERERGQSGHiIiIjII1f4p61Wh+M7P6j6i/kVkMhny8vKQlZWlt0/O1fce2Z/u0/ce9b0/QP97ZH8VV/z3dnme4AAw9AAAsrOzAQAeHh5aroSIiIjUlZ2dDTs7uxeOk4jyxiM9plAocP/+fdjY2EAi0dwzsrKysuDh4YE7d+7A1tZWY9utTvS9R/an+/S9R33vD9D/HtlfxQkhkJ2dDTc3N0ilLz5jh0d6AEilUri7u1fa9m1tbfXyG/lZ+t4j+9N9+t6jvvcH6H+P7K9iynOEpxhPZCYiIiKDwNBDREREBoGhpxKZmZlh2rRpMDMz03YplUbfe2R/uk/fe9T3/gD975H9VR2eyExEREQGgUd6iIiIyCAw9BAREZFBYOghIiIig8DQQ0RERAaBoQfA7Nmz0bp1a9jY2MDZ2Rl9+vRBYmKiypj8/HyEh4fD0dER1tbW6NevHx48eKBcf/78eQwYMAAeHh6wsLCAj48Pfv31V5VtbNq0Cd26dYOTkxNsbW0RFBSE3bt3v7C+CxcuIDg4GObm5vDw8MDcuXP1qsebN29CIpGU+HPy5Mlq19/Ro0fRvn17ODo6wsLCAo0bN8a8efNeWN/L7sPq3J8m9l9V9visY8eOwdjYGP7+/i+sT1f2YUX606WfwYMHD5Zaa0pKynPr06XfoxXpUZf2IQAUFBTgq6++gpeXF8zMzFCnTh0sW7bsufXdvn0bPXv2hKWlJZydnTFp0iQUFRWVuz8AgCARGhoqli9fLuLi4sS5c+dEjx49hKenp8jJyVGO+eCDD4SHh4fYt2+fOHv2rGjbtq1o166dcv2ff/4pJkyYIA4ePCiuX78u/vrrL2FhYSEWLFigHPPhhx+KOXPmiNOnT4srV66IyZMnCxMTExETE1NmbZmZmcLFxUUMGjRIxMXFiTVr1ggLCwvx22+/6U2PSUlJAoDYu3evSE5OVv4pLCysdv3FxMSIiIgIERcXJ5KSksRff/0lLC0tn7s/NLEPq3N/mth/VdljscePH4u6deuKkJAQ0bx58+fWpkv7sCL96dLP4IEDBwQAkZiYqFKrXC4vszZd+z1akR51aR8KIUTv3r1FYGCgiIqKEklJSeL48ePi6NGjZdZWVFQk/Pz8RNeuXUVsbKzYuXOnqFmzppg8eXK5+xNCCIaeUqSmpgoA4tChQ0IIITIyMoSJiYnYsGGDckxCQoIAIE6cOFHmdsaOHSu6dOny3M/y9fUVM2bMKHP94sWLRY0aNURBQYFy2eeffy4aNWpU3nZKVZ16LP5hjY2NVa+J56jK/t544w0xePDgMtdXxj6sTv1Vxv4TovJ7fPvtt8XXX38tpk2b9sJQoIv7UJ3+dOlnsDgQPH78uNy16Nrv0Yr0qEv7cNeuXcLOzk48evSo3LXs3LlTSKVSkZKSoly2ZMkSYWtrq7JfX4TTW6XIzMwEADg4OAAAoqOjIZPJ0LVrV+WYxo0bw9PTEydOnHjudoq3URqFQoHs7Oznjjlx4gQ6duwIU1NT5bLQ0FAkJibi8ePH5e6ptNqA6tFjsd69e8PZ2RkdOnTA1q1by9tKmXUBld9fbGwsjh8/jk6dOpU5pjL2YXXqr5gm919xbUDl9Lh8+XLcuHED06ZNK1cturYP1e2vmC79DPr7+6NWrVro1q0bjh079txadPX3qDo9FtOFfbh161a0atUKc+fORe3atdGwYUN8+umnePLkSZnbOHHiBJo2bQoXFxflstDQUGRlZSE+Pr7cPfGBo/+hUCjw0UcfoX379vDz8wMApKSkwNTUFPb29ipjXVxcypxjPX78ONatW4cdO3aU+Vk//vgjcnJy0L9//zLHpKSkwNvbu8TnFq+rUaNGedpSUd16tLa2xk8//YT27dtDKpVi48aN6NOnD7Zs2YLevXtXy/7c3d3x8OFDFBUVYfr06Rg1alSZ9Wh6H1a3/jS9/yq7x6tXr+KLL77AkSNHYGxcvl+BurQPK9KfLv0M1qpVC0uXLkWrVq1QUFCA//3vf+jcuTNOnTqFFi1alLodXfs9WpEedWkf3rhxA0ePHoW5uTk2b96MtLQ0jB07Fo8ePcLy5ctL3U5KSopK4Cn+3OJ15cXQ8x/h4eGIi4vD0aNHK7yNuLg4vP7665g2bRpCQkJKHRMREYEZM2bg33//hbOzc4U/qyKqW481a9bExIkTla9bt26N+/fv44cffqjQD2tV9HfkyBHk5OTg5MmT+OKLL1C/fn0MGDCgwp+njurWn6b3H1B5PcrlcgwcOBAzZsxAw4YNK7ztl1Xd+tOln8FGjRqhUaNGytft2rXD9evXMW/ePPz1118V/jx1VbcedWkfKhQKSCQSrF69WvmE9J9//hlvvvkmFi9eDAsLiwp/5guVeyLMAISHhwt3d3dx48YNleX79u0rdX7V09NT/PzzzyrL4uPjhbOzs/jyyy/L/Jzik+i2b9/+wpqGDBkiXn/9dZVl+/fvFwBEenr6C9//X9Wxx9IsXLhQuLq6qv2+qurvWbNmzRINGzYsc70m92F17K80Fd1/QlRuj48fPxYAhJGRkfKPRCJRLtu3b1+pNenKPqxof6XRpZ/BTz/9VLRt27bM9br6e/RZL+qxNNV1Hw4dOlTUq1dPZdmlS5cEAHHlypVSa5oyZUqJc9Nu3LghADz3Qpn/YugRQigUChEeHi7c3NxK/YIXn7z1zz//KJddvny5xMlbcXFxwtnZWUyaNKnMz4qIiBDm5uZiy5Yt5aqt+AS8Z8/Anzx5ston4FXnHkszatQoERAQUO7xVdnff82YMUN4eXmVuV4T+7A691cadfefEFXTo1wuFxcvXlT5M2bMGNGoUSNx8eJFlatUnqUr+7Ci/ZVGl34Gu3btKt54440y1+vi79H/elGPpamu+/C3334TFhYWIjs7W7lsy5YtQiqViry8vFLfU3wi84MHD1S2Y2trK/Lz88vdI0OPEGLMmDHCzs5OHDx4UOVSv2e/+B988IHw9PQU+/fvF2fPnhVBQUEiKChIuf7ixYvCyclJDB48WGUbqampyjGrV68WxsbGYtGiRSpjMjIylGMWLFggXnnlFeXrjIwM4eLiIoYMGSLi4uLE2rVrX3gJsa71uGLFChERESESEhJEQkKC+Pbbb4VUKhXLli2rdv0tXLhQbN26VVy5ckVcuXJF/O9//xM2Njbiq6++KrM/TezD6tyfJvZfVfb4X6Vd3aTL+7Ai/enSz+C8efPEli1bxNWrV8XFixfFhx9+KKRSqdi7d2+Z/ena79GK9KhL+zA7O1u4u7uLN998U8THx4tDhw6JBg0aiFGjRinHbNq0SSWUFl+yHhISIs6dOyciIyOFk5MTL1mvCACl/lm+fLlyzJMnT8TYsWNFjRo1hKWlpXjjjTdEcnKycv20adNK3caz/0Lu1KlTqWOGDRumsp3//qv6/PnzokOHDsLMzEzUrl1bfP/993rV44oVK4SPj4+wtLQUtra2ok2bNiqXRFan/ubPny+aNGmirDUgIEAsXrxY5f4ZlbEPq3N/mth/Vdnjf5UWCnR5H1akP136GZwzZ46oV6+eMDc3Fw4ODqJz585i//79z+1PCN36PVqRHnVpHwrx9FL3rl27CgsLC+Hu7i4mTpyoEq6WL18uANWIcvPmTREWFiYsLCxEzZo1xSeffCJkMplaPUr+f6NEREREeo336SEiIiKDwNBDREREBoGhh4iIiAwCQw8REREZBIYeIiIiMggMPURERGQQGHqIiIjIIDD0EBERkUFg6CEinSCEQNeuXREaGlpi3eLFi2Fvb4+7d+9qoTIi0hUMPUSkEyQSCZYvX45Tp07ht99+Uy5PSkrCZ599hgULFsDd3V2jnymTyTS6PSLSLoYeItIZHh4e+PXXX/Hpp58iKSkJQgi8++67CAkJQUBAAMLCwmBtbQ0XFxcMGTIEaWlpyvdGRkaiQ4cOsLe3h6OjI1577TVcv35duf7mzZuQSCRYt24dOnXqBHNzc6xevVobbRJRJeGzt4hI5/Tp0weZmZno27cvZs2ahfj4eDRp0gSjRo3C0KFD8eTJE3z++ecoKirC/v37AQAbN26ERCJBs2bNkJOTg6lTp+LmzZs4d+4cpFIpbt68CW9vb9SpUwc//fQTAgICYG5ujlq1amm5WyLSFIYeItI5qampaNKkCdLT07Fx40bExcXhyJEj2L17t3LM3bt34eHhgcTERDRs2LDENtLS0uDk5ISLFy/Cz89PGXp++eUXfPjhh1XZDhFVEU5vEZHOcXZ2xvvvvw8fHx/06dMH58+fx4EDB2Btba3807hxYwBQTmFdvXoVAwYMQN26dWFra4s6deoAAG7fvq2y7VatWlVpL0RUdYy1XQARUUUYGxvD2Pjpr7CcnBz06tULc+bMKTGueHqqV69e8PLywh9//AE3NzcoFAr4+fmhsLBQZbyVlVXlF09EWsHQQ0Q6r0WLFti4cSPq1KmjDELPevToERITE/HHH38gODgYAHD06NGqLpOItIzTW0Sk88LDw5Geno4BAwbgzJkzuH79Onbv3o0RI0ZALpejRo0acHR0xO+//45r165h//79mDhxorbLJqIqxtBDRDrPzc0Nx44dg1wuR0hICJo2bYqPPvoI9vb2kEqlkEqlWLt2LaKjo+Hn54ePP/4YP/zwg7bLJqIqxqu3iIiIyCDwSA8REREZBIYeIiIiMggMPURERGQQGHqIiIjIIDD0EBERkUFg6CEiIiKDwNBDREREBoGhh4iIiAwCQw8REREZBIYeIiIiMggMPURERGQQGHqIiIjIIPw/r1a0I/0Vl9AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmgylC1ENCHy"
      },
      "source": [
        "## **3.**  Forecast revenue with the following ARIMA code  "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## a. generate forecast"
      ],
      "metadata": {
        "id": "MExel6_cWWUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "\n",
        "model = ARIMA(df[\"Revenue_Billion_$\"], order=(1,1,1))\n",
        "model_fit = model.fit()\n",
        "\n",
        "forecast = model_fit.forecast(steps=3)\n",
        "\n",
        "print(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Bz1h9EuUuKR",
        "outputId": "00c6c577-9c37-4c2f-fb9e-8877a3f9a44e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5    25.865229\n",
            "6    27.969662\n",
            "7    30.047247\n",
            "Name: predicted_mean, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## b. create future years"
      ],
      "metadata": {
        "id": "lKeWlLwyWeCR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "future_years = [2027, 2028, 2029]"
      ],
      "metadata": {
        "id": "1MQYWg7AWVrT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## c. combine historical + forecast"
      ],
      "metadata": {
        "id": "t3qpwrTuWn92"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "forecast_df = pd.DataFrame({\n",
        "    \"Year\": future_years,\n",
        "    \"Forecast_Revenue\": forecast.values\n",
        "})"
      ],
      "metadata": {
        "id": "c5AqIZX8Wwr0"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## d. create graph"
      ],
      "metadata": {
        "id": "i2dwbQ6dW10Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot historical data\n",
        "plt.plot(df[\"Year\"], df[\"Revenue_Billion_$\"], marker='o', label=\"Actual Revenue\")\n",
        "\n",
        "# Plot forecast data\n",
        "plt.plot(forecast_df[\"Year\"], forecast_df[\"Forecast_Revenue\"],\n",
        "         marker='o', linestyle='dashed', label=\"Forecast Revenue\")\n",
        "\n",
        "# Titles and labels\n",
        "plt.title(\"Prime Video Revenue Forecast\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Revenue (Billion $)\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "WuGtxGqZW9ST",
        "outputId": "afcd5064-dc71-4c62-978c-c8f240f72ea7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## e. Interpretation\n",
        "\n",
        "## Forecast Interpretation\n",
        "\n",
        "The forecast shows a continued increase in revenue over the next years.\n",
        "\n",
        "This trend is driven by the growth in subscribers and assumes stable pricing.\n",
        "\n",
        "However, predictions may vary depending on:\n",
        "- Market saturation\n",
        "- Competition (Netflix, Disney+)\n",
        "- Pricing strategy changes"
      ],
      "metadata": {
        "id": "UNK2EOXKXGmM"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKBcx3fyCFly"
      },
      "source": [
        "## **4.** Decision-making about pricing strategy for Prime Video"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def pricing_decision(row):\n",
        "    if row[\"Subscribers_millions\"] > 200:\n",
        "        return \"Increase price\"\n",
        "    elif row[\"Subscribers_millions\"] < 150:\n",
        "        return \"Decrease price\"\n",
        "    else:\n",
        "        return \"Keep price stable\"\n",
        "\n",
        "df[\"Pricing_Strategy\"] = df.apply(pricing_decision, axis=1)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "7VSlbFj2VXdw",
        "outputId": "d8c1533c-3cea-4933-855a-159d3839e7f0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Subscribers_millions  Revenue_Billion_$   Pricing_Strategy\n",
              "0  2022                   120            12.9456     Decrease price\n",
              "1  2023                   150            16.1820  Keep price stable\n",
              "2  2024                   175            18.8790  Keep price stable\n",
              "3  2025                   200            21.5760  Keep price stable\n",
              "4  2026                   220            23.7336     Increase price"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d6a00c8-c22b-41a1-8c1f-d8a5677b8e0f\" 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>Year</th>\n",
              "      <th>Subscribers_millions</th>\n",
              "      <th>Revenue_Billion_$</th>\n",
              "      <th>Pricing_Strategy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>120</td>\n",
              "      <td>12.9456</td>\n",
              "      <td>Decrease price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023</td>\n",
              "      <td>150</td>\n",
              "      <td>16.1820</td>\n",
              "      <td>Keep price stable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024</td>\n",
              "      <td>175</td>\n",
              "      <td>18.8790</td>\n",
              "      <td>Keep price stable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2025</td>\n",
              "      <td>200</td>\n",
              "      <td>21.5760</td>\n",
              "      <td>Keep price stable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2026</td>\n",
              "      <td>220</td>\n",
              "      <td>23.7336</td>\n",
              "      <td>Increase price</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-6d6a00c8-c22b-41a1-8c1f-d8a5677b8e0f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2022,\n        \"max\": 2026,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2023,\n          2026,\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers_millions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 120,\n        \"max\": 220,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          150,\n          220,\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Revenue_Billion_$\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.274553568268855,\n        \"min\": 12.945599999999999,\n        \"max\": 23.7336,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          16.182,\n          23.7336,\n          18.879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pricing_Strategy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Decrease price\",\n          \"Keep price stable\",\n          \"Increase price\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 16
        }
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