Cherry_Gupta_Chapter1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNILvOYT/20V5/rlE+EoEgA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/Cherry_Gupta_Chapter1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5sYjAkO9aXfN"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
      ],
      "metadata": {
        "id": "W0V1mEQAaw5i"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=12)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=12)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)"
      ],
      "metadata": {
        "id": "nrb-P_hiay1m"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(20)"
      ],
      "metadata": {
        "id": "Vsd7XhdKa4xw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Download and prepare the data\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
        "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
        "y = lifesat[[\"Life satisfaction\"]].values\n",
        "\n",
        "# Visualize the data\n",
        "lifesat.plot(kind='scatter', grid=True,\n",
        "             x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
        "plt.axis([23_500, 62_500, 4, 9])\n",
        "plt.show()\n",
        "\n",
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
        "print(model.predict(X_new)) # outputs [[6.30165767]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "aeXF_bmSbA8R",
        "outputId": "8dd0f1ac-1d4b-4884-d526-7726a780eaad"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a 3-Nearest Neighbors regression model\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "print(model.predict(X_new)) # outputs [[6.33333333]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMT5udNHbHJP",
        "outputId": "f451f0d2-032e-498f-9cd0-44daacbff129"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Where to save the figures\n",
        "IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ],
      "metadata": {
        "id": "v9XrPapZbLHz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "\n",
        "datapath = Path() / \"datasets\" / \"lifesat\"\n",
        "datapath.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "for filename in (\"oecd_bli.csv\", \"gdp_per_capita.csv\"):\n",
        "    if not (datapath / filename).is_file():\n",
        "        print(\"Downloading\", filename)\n",
        "        url = data_root + \"lifesat/\" + filename\n",
        "        urllib.request.urlretrieve(url, datapath / filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ezBPlGrbQw6",
        "outputId": "8798c3bf-294a-4ca8-fc97-f6cf52dc9c06"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading oecd_bli.csv\n",
            "Downloading gdp_per_capita.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
      ],
      "metadata": {
        "id": "B3f1mUeqbU8L"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_year = 2020\n",
        "gdppc_col = \"GDP per capita (USD)\"\n",
        "lifesat_col = \"Life satisfaction\"\n",
        "\n",
        "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
        "gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
        "gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
        "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
        "\n",
        "gdp_per_capita.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "huWMz0Y_bYvU",
        "outputId": "4d271487-09bb-4959-8a4d-5f16b73bd8e9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             GDP per capita (USD)\n",
              "Country                                          \n",
              "Afghanistan                           1978.961579\n",
              "Africa Eastern and Southern           3387.594670\n",
              "Africa Western and Central            4003.158913\n",
              "Albania                              13295.410885\n",
              "Algeria                              10681.679297"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-42ca534e-cba1-4374-aa7d-a9956458e419\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>GDP per capita (USD)</th>\n",
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              "      <th>Afghanistan</th>\n",
              "      <td>1978.961579</td>\n",
              "    </tr>\n",
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              "      <td>4003.158913</td>\n",
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              "      <td>13295.410885</td>\n",
              "    </tr>\n",
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              "      <td>10681.679297</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdp_per_capita",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 224,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 224,\n        \"samples\": [\n          \"Armenia\",\n          \"Heavily indebted poor countries (HIPC)\",\n          \"Lesotho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18838.30235782942,\n        \"min\": 731.063229715588,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          42288.3480749219,\n          21608.4302710014,\n          4818.09473660531\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
        "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
        "\n",
        "oecd_bli.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "jT6BTy6TbcOt",
        "outputId": "a1e61f86-433c-42bc-b211-853c8fc9c1d1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
              "Country                                                        \n",
              "Australia            5.0                                 NaN   \n",
              "Austria             16.0                                 0.9   \n",
              "Belgium             15.0                                 1.9   \n",
              "Brazil              10.0                                 6.7   \n",
              "Canada               7.0                                 0.2   \n",
              "\n",
              "Indicator  Educational attainment  Employees working very long hours  \\\n",
              "Country                                                                \n",
              "Australia                    81.0                              13.04   \n",
              "Austria                      85.0                               6.66   \n",
              "Belgium                      77.0                               4.75   \n",
              "Brazil                       49.0                               7.13   \n",
              "Canada                       91.0                               3.69   \n",
              "\n",
              "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
              "Country                                                           \n",
              "Australia             73.0                                 63.5   \n",
              "Austria               72.0                                 80.6   \n",
              "Belgium               63.0                                 70.1   \n",
              "Brazil                61.0                                 35.6   \n",
              "Canada                73.0                                 82.2   \n",
              "\n",
              "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
              "Country                                                              \n",
              "Australia            1.1                                   32759.0   \n",
              "Austria              0.5                                   33541.0   \n",
              "Belgium              1.0                                   30364.0   \n",
              "Brazil              26.7                                       NaN   \n",
              "Canada               1.3                                   30854.0   \n",
              "\n",
              "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
              "Country                                               ...                      \n",
              "Australia              427064.0                 20.0  ...            49126.0   \n",
              "Austria                308325.0                 21.0  ...            50349.0   \n",
              "Belgium                386006.0                 21.0  ...            49675.0   \n",
              "Brazil                      NaN                  NaN  ...                NaN   \n",
              "Canada                 423849.0                 22.0  ...            47622.0   \n",
              "\n",
              "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
              "Country                                                                         \n",
              "Australia                        95.0               NaN                  85.0   \n",
              "Austria                          92.0               1.6                  70.0   \n",
              "Belgium                          91.0               2.2                  74.0   \n",
              "Brazil                           90.0               NaN                   NaN   \n",
              "Canada                           93.0               2.6                  88.0   \n",
              "\n",
              "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
              "Country                                                                        \n",
              "Australia                                                2.7           502.0   \n",
              "Austria                                                  1.3           492.0   \n",
              "Belgium                                                  2.0           503.0   \n",
              "Brazil                                                   2.2           395.0   \n",
              "Canada                                                   2.9           523.0   \n",
              "\n",
              "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
              "Country                                                               \n",
              "Australia                                      14.35           91.0   \n",
              "Austria                                        14.55           80.0   \n",
              "Belgium                                        15.70           89.0   \n",
              "Brazil                                           NaN           79.0   \n",
              "Canada                                         14.56           68.0   \n",
              "\n",
              "Indicator  Water quality  Years in education  \n",
              "Country                                       \n",
              "Australia           93.0                21.0  \n",
              "Austria             92.0                17.0  \n",
              "Belgium             84.0                19.3  \n",
              "Brazil              73.0                16.2  \n",
              "Canada              91.0                17.3  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <td>32759.0</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>95.0</td>\n",
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              "      <td>72.0</td>\n",
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              "      <td>33541.0</td>\n",
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              "      <td>492.0</td>\n",
              "      <td>14.55</td>\n",
              "      <td>80.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
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              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>3.69</td>\n",
              "      <td>73.0</td>\n",
              "      <td>82.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>30854.0</td>\n",
              "      <td>423849.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47622.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>523.0</td>\n",
              "      <td>14.56</td>\n",
              "      <td>68.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-367e7015-c95f-4f80-a720-d9828861475d')\"\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-367e7015-c95f-4f80-a720-d9828861475d 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-367e7015-c95f-4f80-a720-d9828861475d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-450f2153-a37a-44fb-9930-d705d925e374\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-450f2153-a37a-44fb-9930-d705d925e374')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-450f2153-a37a-44fb-9930-d705d925e374 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "oecd_bli"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_country_stats = pd.merge(oecd_bli, gdp_per_capita, left_index=True, right_index=True)\n",
        "full_country_stats = full_country_stats.sort_values(by=gdppc_col)\n",
        "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
        "print(full_country_stats.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPsd-0OAbhfb",
        "outputId": "2acfc0e0-6c35-4e87-a55c-b8027a7b3b8b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              GDP per capita (USD)  Life satisfaction\n",
            "Country                                              \n",
            "South Africa          11466.189672                4.7\n",
            "Colombia              13441.492952                6.3\n",
            "Brazil                14063.982505                6.4\n",
            "Mexico                17887.750736                6.5\n",
            "Chile                 23324.524751                6.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_gdp = 23_500\n",
        "max_gdp = 62_500\n",
        "\n",
        "country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
        "                                   (full_country_stats[gdppc_col] <= max_gdp)]\n",
        "country_stats.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "v3_UKI9zbmLb",
        "outputId": "0788553e-edc6-4ce8-ec05-2d17a1e1086c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         GDP per capita (USD)  Life satisfaction\n",
              "Country                                         \n",
              "Russia           26456.387938                5.8\n",
              "Greece           27287.083401                5.4\n",
              "Turkey           28384.987785                5.5\n",
              "Latvia           29932.493910                5.9\n",
              "Hungary          31007.768407                5.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51d941db-2eca-4052-ad84-1ba4b7d38977\" 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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>26456.387938</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latvia</th>\n",
              "      <td>29932.493910</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</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-51d941db-2eca-4052-ad84-1ba4b7d38977')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      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-51d941db-2eca-4052-ad84-1ba4b7d38977 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-51d941db-2eca-4052-ad84-1ba4b7d38977');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cd026f27-4b20-4007-8866-48daa889e315\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cd026f27-4b20-4007-8866-48daa889e315')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cd026f27-4b20-4007-8866-48daa889e315 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "country_stats",
              "summary": "{\n  \"name\": \"country_stats\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
        "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
      ],
      "metadata": {
        "id": "rh31f-eubpd2"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True, x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "min_life_sat = 4\n",
        "max_life_sat = 9\n",
        "\n",
        "position_text = {\n",
        "    \"Turkey\": (29_500, 4.2),\n",
        "    \"Hungary\": (28_000, 6.9),\n",
        "    \"France\": (40_000, 5),\n",
        "    \"New Zealand\": (28_000, 8.2),\n",
        "    \"Australia\": (50_000, 5.5),\n",
        "    \"United States\": (59_000, 5.3),\n",
        "    \"Denmark\": (46_000, 8.5)\n",
        "}\n",
        "\n",
        "for country, pos_text in position_text.items():\n",
        "    pos_data_x = country_stats.at[country, gdppc_col]\n",
        "    pos_data_y = country_stats.at[country, lifesat_col]\n",
        "    display_name = \"U.S.\" if country == \"United States\" else country\n",
        "    plt.annotate(display_name, xy=(pos_data_x, pos_data_y),\n",
        "                 xytext=pos_text, fontsize=12,\n",
        "                 arrowprops=dict(facecolor='black', width=0.5, shrink=0.08, headwidth=5))\n",
        "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
        "\n",
        "plt.axis([country_stats[gdppc_col].min() - 1000, country_stats[gdppc_col].max() + 1000,\n",
        "          min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('money_happy_scatterplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "7oKEuNT6bt5R",
        "outputId": "451bdb52-a97b-4bb0-d152-20026773197f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
        "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AaDlrhyNbwjR",
        "outputId": "00cddb52-6ed8-41de-e4de-841910819a51"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               GDP per capita (USD)  Life satisfaction\n",
              "Country                                               \n",
              "Turkey                 28384.987785                5.5\n",
              "Hungary                31007.768407                5.6\n",
              "France                 42025.617373                6.5\n",
              "New Zealand            42404.393738                7.3\n",
              "Australia              48697.837028                7.3\n",
              "Denmark                55938.212809                7.6\n",
              "United States          60235.728492                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d9e7e1a-2f47-4778-bd20-41c8bc0d22ab\" 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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>42025.617373</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>48697.837028</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>55938.212809</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>60235.728492</td>\n",
              "      <td>6.9</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-2d9e7e1a-2f47-4778-bd20-41c8bc0d22ab')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
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              "  <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-2d9e7e1a-2f47-4778-bd20-41c8bc0d22ab 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-2d9e7e1a-2f47-4778-bd20-41c8bc0d22ab');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5b500124-5541-4a25-ba66-4d1b1037ec46\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b500124-5541-4a25-ba66-4d1b1037ec46')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-5b500124-5541-4a25-ba66-4d1b1037ec46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"highlighted_countries[[gdppc_col, lifesat_col]]\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Turkey\",\n          \"Hungary\",\n          \"Denmark\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11882.70029953928,\n        \"min\": 28384.9877846263,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28384.9877846263,\n          31007.7684065437,\n          55938.2128086032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8420496535523418,\n        \"min\": 5.5,\n        \"max\": 7.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.5,\n          5.6,\n          6.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True, x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(country_stats[gdppc_col].min(), country_stats[gdppc_col].max(), 1000)\n",
        "\n",
        "parameters = [\n",
        "    {'w1': 4.2, 'w2': 0, 'color': 'r', 'text_x': 40_000, 'text_y1': 4.9, 'text_y2': 4.4},\n",
        "    {'w1': 10, 'w2': -9, 'color': 'g', 'text_x': 26_000, 'text_y1': 8.5, 'text_y2': 8.0},\n",
        "    {'w1': 3, 'w2': 8, 'color': 'b', 'text_x': 48_000, 'text_y1': 8.5, 'text_y2': 8.0}\n",
        "]\n",
        "\n",
        "for param in parameters:\n",
        "    w1, w2, color, text_x, text_y1, text_y2 = param.values()\n",
        "    plt.plot(X, w1 + w2 * 1e-5 * X, color)\n",
        "    plt.text(text_x, text_y1, fr\"$\\theta_0 = {w1}$\", color=color)\n",
        "    plt.text(text_x, text_y2, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=color)\n",
        "\n",
        "plt.axis([country_stats[gdppc_col].min(), country_stats[gdppc_col].max(), min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('tweaking_model_params_plot')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Qey9hJYXb2qx",
        "outputId": "d062f5aa-1381-4dbd-8ca0-47112456d52e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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Gezj8MOmyvQvx2esj0HbgrwMJwiD0teLyCqH9u+xwIRefX2xuuDJD3GvbUZHX8RYVEaKvT8jXXwuWp6URAhCyWYxbgiTGun49c/74eMHy6dOZ8oKCpo8tKCDkaNM/YfL334SUNf0TJm/fEpLN/IRJTAxzvn37hLe9c4epnznzMX+8lZWEdOlCiE+9n/DAgUw7Ya8Vwn++hBBCXFwIuShHP+H617a4uFhAj7QHYhlwEUKwcuVK7NixA6UtPNpwuVxxTtFq/nr8F0qrSjHTfSa2YAu/XE2FCSBbWq0Yj2DXX1xHoFMgjLWN+WWWepYYYjcEp5+dRll1WZPhPXfF7MKqqFX4d+K/CHELEaj79cGv+ODUB/hx1I/4pO8nQo/XUNUQOSHGkadHwGax8VHvj/hl6irqmOk5E99GfYvM4kx+4osbs1v/mK3CUgEBafVxFOXhr7+YmddHHwmW82JKy2pWxgu/aW4uWG5pySS3UFdv+thdu5gZ9L//AiGCP2H8+iuzFP/jj8Anwn/C0NBoOoFFQ44cAdhsgoCAFwCYnI6amsCcOcDXXzMzfFtb8WbJADNWQn/CfMRSxmvXrsX69ethaGiI6dOnw8bGpsUMJtImPCEc3Uy7QUdNB9AG3lS+QbVqNTJLMgGAKW8lHC4HxVWi5TMz0jKCCkv6q/5V3CpoqWk1KtdW00Y1txrxufHob9Nf6LHLBy3H3Vd3MenoJJyZfAbDHZkM6uEJ4Zh7ei6mekzFvD7zJCLnw5yHcDZ2bpSYo69lXwBAbE6syFmoit4WISYrBr52vmCxWPjx7o8oqCyAt7W3RGSlKCbh4UwGJB0dZnmWRyZzS2iU61gUOBygpEQd+fkt5zM2MmIUTkP8/Jg41HPmAKtXM8FGbt1iYlV//nnzci1fDty9yyxnnzkDDGd+wggPZxJQTJ1alx6yrTx8yIQL1dYW3Fzv14/5GxvLKGNRKCoCYmKY5BgsFvPAUFDA7EFTGMTSoL/99hvs7Oxw7949GBsbt3yAlOHWchH9MhrlnHI4/uQILAPztx4OnRxa3e/NzJvtms9YFFyMXRD9MhrcWi7YKkzm8mpuNT/ncVZJVpPHqqqo4p8J/yDwz0AE/xOMiGkRKKsuw+SjkxHUNQj7xu8Di8WSiJzZpdmw1LNsVM6bWfMyWIkCh8vBV5e/QtKbJKipqMHLwgtnp5yFgaaBRGSlKB5cLrMnW17OGAwJw6H1twTcusXC9OmiOcg2lc84MJBJ5fjdd8DJk3XlK1YA69Y136eqKhMhLDCQyUoVEcEYZU2ezOzB7tvHKDtJkJ0NWFo2nrpa/vezfiX6TxgcDvDVV0BSEvMQ4+XFBFoxoD9hPmIp45ycHMybN08uFDEApBSmoJxTjmUDlmGAxQAEBwfj+PHj0NHRwW8Pf8PB+IP8nMatQdL5jGtJLaq51QAATg0H1bXVeFvzFlxW46V8DbaGUMX4Sd9PMO/MPMw5OQfLBi5DLanFumvrkF2aDQCorKlsVgZNVU2cnHQSQw8Mxai/R6GaWw1vG28cnnAYqiqSW92orKmEBltD6PlFkbM+pjqmuPfRPYnJRlF8UlIYRbxsGWMsVJ/ffgMOHqzLadwaJJHPGGCUtK8v8M47zMz4zBlGOVtYMEZdzaGpySjxoUMZY6rqamaGefgwo6wlRWUls6wt7Py8elExNQXu0Z9ws4h16RwcHFDSMO+YDEkvSgcA+Nn7YaD5QCAVGGo3FPr6+thwYwPMdczhbOwMANgdsxu/PPgFj3MfY8XgFQjzC2uyX0nlM+Zx7cW1xjPtJsz7E+YnwNXEtVH5x30+RmZxJjbd2oQDcQcAAH2s+mDZwGVYf329SOkg9TX0sdl/M4b9PgwAsG3kNqFL321BS1ULVdyqRuVva97y6ykUaZGezvz18wNGNPgJb9jA7Nc6M7cE7N4N/PIL8PgxMzsNC2u6X0nkMz50iNnHfvaMcW0CGPer2lpmGXrSJEZBN4e+PmMpPoz5CWPbNkBLwj8pLS3Gtakhb9/W1VMkh1jKeN68eVi/fj1yc3NhZmYmaZlaTXl1OQBAR11ws6X4bTGuZ1zHbK/Z/DJLPUuE+YXh78d/t9hvNbcaBZUFIslgqm3KXzZuClcTV+wbvw8AY9gWFxcHT09PsNmNj7PUbbzEy2P98PVYMmAJnuQ9gYGGAdzN3fH15a8BgP/Q0RyphamYEj4FriauqORU4p3D7+Dm7JtCl5XFxVLPUuiSeU5ZDgDASs9KYueiUBpSztwSGu2/FhcD168Ds+tuCbC0ZBTw3y3fElBdDRQWaiAnp+U9Y1NTxhWoIbt2AT171iliHuPGMT6/Dx82foBoSGoqMGUK459cWcnMsG/erFtClgSWlsDLl41X57KZRThY0Z+wRBFLGY8fPx7Xr1/HgAEDsHLlSvTq1Qv6+vpC23bu3LlVfXO5XISFheHPP/9ETk4OrKysMHPmTHzzzTdN7mfy8haXVZcJlB+IO4BqbjXm9a2zaAh2DQYAnE1uOTL8rcxbEt0zttC1wEyvmQD+y2f88ixGeYyCmhiP2J20OmFQ50H89xGpEbDRtxE6m65Pdmk2/P/whxpbDZemXUIlpxKD9g1CwJ8BuDrzqsQyWHmZeyEyLRIlVSUCRlx3X91l6i28JHIeCkUYev+lMi8TvCXgwAFGodY3cgoOZv6Kkizi9m0WZs0KFEmGpvaMX79mZtgN4XCYvy0FI8nOZpbe1dSYAByVlcCgQUBAAONPLamMU15eQGQkUFEhqCbu3Kmrp0gOsZepWSwWCCGYNWtWk+1YLBZqWhnmZuPGjdi9ezcOHDiA7t274969e5g1axYMDAzw+eefCz3Gw9wDKiwVRKZFYpA5o6CySrOw9tpaTPecLtZ+MSD5PWNp8U/8P4h5FYPN/pubtegurCzEyD9Hoqy6DDdm3YCNPvNofmHqBfjt98Pov0cjYlpEoxUGcZjQbQI2396Mn+//jCUDlgAAOLUc/P7od3hbe4tsSU2hiIOHB2PJHBnJ7KsCwMuXjOHU9Oni7Rcz/bZ9z9jZGbh4kVmmdq63kHXwICNzc7IVFgIjRzIPGTdu1M2uL1xgluRHj2aMusSxFG/IhAnA5s0sXLxohwkTmLKqKsZIzNtbdEtqimiIpYynT58uMavbhty6dQvjx4/H6NGjAQD29vY4ePAg7t692+QxZjpmCHYNxvY728GuZQM+QMA/AbDWs8aOoB1iyyLpPWNJcO3FNay5ugYBXQJgrGWM6JfR2Be7D4FOgVjQf0Gzx+6K2YXMkkxEzYhCV+Ou/HIvCy+cnnwaAX8E4EDcgSb9jAFg592dKHpbxLeGPvXsFF6WvAQAfNbvM76Fs7eNNyZ2m4ivLn+F3PJcOBg44P9S/g/plen4dfyvbf0YKJRmMTNjZrzbtzPhJg0MmH1Va2tgh/i3BInsGS9dCpw7BwwezBhrGRsDp08zZR980Pzy765djGtWVBTjdsTDy4vpIyCAmf035WcMADt3Mq5GPGvoU6eYBxUA+OyzOgtnb2/gnXdq8ccf3WBkRODiwvSdns74NFMkTLuFFxGR9evXEzs7O5KUlEQIISQ2NpaYmZmRP//8s9njCioKSOg/oURnvQ7BEpC5x+eSkrclTbafe2ouWRW5SpKitwpxI/k8f/OcBPwRQEx+MCEaazWI605X8v3170lVTVWLx3K4HBL/Or7J+ricOFJbW9tsH3b/s2syWlZaYZpA20pOJVlyYQmx2GxBNNZqkK4/dCWnE0+LNM6OjLxGpJIW8jreggJCQkMJ0dEhxNyckM8+I6Sk6VsCmTuXkFWrmu9TUmO9c4eQoCBCLCwIUVMjxNmZiczF4TR/HIfTOHJXfeLiCGnhJ0zs7JqOmJWWJti2pKSaBAc/IxYWtURDg5C+fQk5f16UEXY8OmQELmny5ZdfoqSkBK6urmCz2eByuVi/fj2mTJkitH1VVRWqqqrABhv7AvehpKQEtra2WP/lemiqaILD24hpQG1tLbhcbpP10oZ33taev7NeZ5x+73TjilpmGbglnDs5N3lONyO3FrcVkucnN1tfv2822Phu6Hf4buh34HA4uHTpEobaDpXZZ95eiHttOyryOl5dXcZyuSFNiVlbqwIuF+BwapvsU1Jj7dkTOHGicTkhTcvHw9m56TZubi3vOSc3/xMW6JvN5mDmzKf46y9rAdsWObvUEqH+tZXFd5lFSNsCkt28eROxsbEoKSmBvr4+vLy8MHDgQLH7O3ToEJYuXYpNmzahe/fuiI2NxcKFC7F161bMmDGjUfuwsDCsXr26Ufnff/8NbW3tRuVcwgWXcPHLy19gqGaIieYTwWaxwWY1bwlNoVAUEy6XBS6XhV9+cYehYRUmTnwGNrtWqCU0RTmoqKjA5MmTUVxc3KRxsqQRWxnfunULs2bNwvPnzwEw8ap5+8hdu3bFvn374OPj0+p+bW1t8eWXX2L+/Pn8snXr1uHPP/9EYmJio/a8mTEP3sw4Pz9f6Ie45toarLshGOZm75i9mO4xvdWytgXeTNHf318sa+qOhjKNV5nGCnT88a5Zo4J16wQ17969NZg+vfGtsaOPtbUo03jrj7WyshImJibtqozFWqZ+8uQJAgICUFFRAX9/fwwdOhSWlpbIyclBZGQkLl68iJEjRyI6OhrdunVrVd8VFRVQaRDQlc1mo7ZW+NKRhoYGNISEiVFTUxP65Vk7fC3WDl/bKpmkSVNyKirKNF5lGivQcce7di3zEqT5W2NHHau4KNN41dTUWu0FJAnEUsZr1qxBdXU1zp49i8BAQZ+75cuX4/z58xg3bhzWrFmDQ8I2bZph7NixWL9+PTp37ozu3bvj4cOH2Lp1K2bX99KnUCgUCkWBEEsZR0VFYcKECY0UMY/AwEBMmDABly9fbnXfO3bswLfffotPPvkEubm5sLKywty5c7Fy5UpxRKVQKBQKRe4RSxkXFxfDoYWUJw4ODiguFi39YH309PSwbds2bNu2TRzRKBQKhULpcIiljK2srBAdHd1smzt37sCKBi+lUCgUSgegshK4f98MEREqOHeu/c/fdOzEZhg3bhyioqLw7bff4i0vhcd/vH37FqtWrUJkZCTGjx8vESEpFAqFQpEkhDD5lbdtY/JDm5urYu1aH+zcyUZKSvvLI9bM+Ntvv8Xp06fx3XffYc+ePejXrx/Mzc3x+vVrxMTEIC8vD46Ojvj2228lLS+FQqFQKGJRVgZcuQKcP8+EH+Wl2mRgwcSkAsHBGvDzA6ZObV/ZxFLGxsbGiI6OxrJly3Do0CGcrZfuRFNTE7NmzcLGjRthJKn0IRQKhUKhtBJCgCdPGMV7/jyTPrN+cC11dcDXl5kZjxjBQVraJYwePQqVle0vq9jhME1MTPDbb79hz549SExM5EfgcnV1VRp/NAqFQqHIF8XFTOaq8+eZFy8JBg9HRyAoiFHAQ4fWZbjicBrOlNuXNsemVlNTg7u7uyRkoVAoFAqlVdTWAnFxdUvPt24BXG5dvaYmo3R5CtjJCZBS0sE2IXeJIigUCqUpUvPK8KKgAvbGOnAwkUDSXorEaM9rU1DA5ITmzX5fvxasd3GpU76+voCWllTFkQgiKeNhw4aBxWLhwIEDsLGxwbBhw0TqnMViiRX4g0KhUOpTVFGNzw/G4lpyHr/Mt6spdkzqCQNtui0mS9rj2nC5wP37dXu/d+8yM2IeOjrA8OGMAh45EmghDIZcIpIyjoqKAovFQkVFBf+9KLBkuBbwquRVuwX4plAo0uXzg7G4+TxfoOzm83x8dvAhfp/TT0ZSUQDpXZvXr+tmvxcuAG/eCNa7uzMz38BAYNAgxhirIyOSMm6YpKGppA3yhNuPbvDu4o1Qt1CEuIagq3FXWYtEoVDEIDWvTGDWxYNLCK4l5yEtv5wuWcsISV6bmhrgzp262e/9+4L1+vqAv3/d7NfGRhIjkB8Ues/4TtYd3Mm6g+URy9HdtDtfMXtZeMl01k6hUETnRUFFs/Xpb6gylhVtvTZZWcys9/x54NIloKhIsL5nz7q93/79AUV21BFLGc+ePRvBwcEYN25ck21Onz6N8PBw/Pbbb2IL1xaSPk3ClewrOJZ4DFfSruBJ3hM8yXuCtdfWwt7QHqGuoQhxC4GPjQ/YKjSLOIUir9gZaTdbb2+smIq4IxirtfbaVFcz1s682e+jR4LtjYyAgABGAQcEABYWkpZYfhFLGe/fvx/29vbNKuO4uDgcOHBAZsrYQs8CH1t/jI/7fIzCykKcST6D8IRwnH9+HulF6dgavRVbo7fCTMcMwS7BCHELwTCHYVBnd/CNBwpFwXA01YVvV1PcfJ4PLiH8cjaLhYFOJnKrqMSlIxmriXJtXryoczu6fJmJgsWDxQL69q2b/fbtC7CVdG4ktWXqt2/fQlVVPlbBO2l1wlSPqZjqMRUVnApceH4BxxKP4dSzU8gtz8XPD37Gzw9+hoGGAUY7j0aoaygCnQKho65YP3IKpaOyY1JPfHbwoYCCGuhkgh2TespQKunQ0YzVGl4bUqMCh1oH6MV2RbduQEKCYHszM2bPNyiI2QM2MZGB0HKI2NqyqT1XQggyMzNx7tw5uczapK2mjRC3EIS4hYDD5SAqPQrHEo/hWOIx5JTl4O/Hf+Pvx39DU1UTI7uMRIhrCMa6jIWRFg3tSaHICgNtNfw+px/S8suR/qZcrpdu20JHNFYz0FbDyiH98FdeFc6dB+LuqiOjkgWeUyubDfj41Fk+9+wJqIiVokixEVkZq6ioCCjgsLAwhIWFNdmeEILly5e3SThpo8ZWg38Xf/h38cfOUTtx5+UdhCeEIzwxHKmFqTiRdAInkk6AzWLDz94PoW6hCHYNhpWe/D1kUCjyjiT2QB1MFFMJ82gvY7W2XouKCiAqqm7v9/lzANDg11tZMYo3KIjx/+3Uqc0iKzwiK2NfX1++Mr527Ro6d+4Me3v7Ru3YbDaMjIwwbNgwfPjhhxITVNqosFTgY+sDH1sf/OD/Ax7nPsaxhGMITwzHo9ePcDntMi6nXcb8s/PR36Y/QlxDqMsUhSICHWkPVNZI21hN3GtBCLPczIt4dfUqUFVVV6+mxvj68ma/7u7yGXJSnhFZGdcP9KGiooJZs2Zh5cqV0pBJ5rBYLHiYe8DD3AOr/FYhpSCFv5R9K/MWol9GI/plNJZHLEcPsx4IcQ1BqFsoPM09qcsUhdKAjrYHKkukbazWmmtRWgpcvMjCL794YMECVbx4IdhX587MzDcoCBg2DNDTa5NoSo9Ye8YdIeiHJOli1AVLBizBkgFLkF2ajRNJJxCeEI7I9EjE58YjPjeeukxRKELoiHugskZaxmotXYvUvHKUZevwLZ9v3gQ4HFUATGxJdXVgyJA6y2dXVzr7lSRiKeOXL1/iwYMH8PX1haGhYaP6wsJCXL9+Hb1794a1tXVbZZQrLPUs8XGfOpep089O41jisUYuU+Y65hjvMh6hbqEY6jCUukxRpIq8+qTKY8AOaX5WkuhbWsZqwq5F7VtVVKaboDLVDP32aeJNrmC9kxOBs3Ma5s7tjOHDVfnpBimSRyxlvG7dOvz777949eqV0HptbW3Mnj0b77//Pnbu3NkmAeWZTlqdMM1zGqZ5TmvkMvW6/LWAy9QY5zEIcQ2hLlMUiSLv+7HyFLBDmp+VNPqWtLGanZE2CAGqX+vjbaoZKlNNUfXKECCMaXM5mOxGw4bV7f3a2dXg7NnHCAqyVejoV/KAWMr4ypUrCAgIgIaGhtB6DQ0NBAQEICIiok3CdSSEuUyFJ4TjeNJx5JTl4K/Hf+Gvx3/xXabGOY+Deg2dLVPahrzvx8pTwA5pflbyfB3y83kJF3SRezwAb0sFtaqacSmcepVj+1ILDB7M5P/lweG0s7BKjFjKOCsrC++8806zbezs7HDq1CmxhOro1HeZ+nH0j4h+Gc23zK7vMqUCFewv24/QbtRlitJ6Osp+rDwE7JDmZyVv14HLBWJi6qJexcQw1tAMalDV4EKtcx60HPOg5ZCHYX10/5vBt5uIFCGIpYzV1dVRUlLSbJuSkhJqWQzGZWqA7QAMsB3Ad5kKTwhHeEI4Huc+xuX0y7icLugyFeoWCicjJ1mLrjTI635rS8jjfqww5CFghzQ/K3m4Djk5dQkXLl4ECgoE6z086vx+BwxgI6tED+lvVGBv7CgX3xGKmMrY3d0dp06dwtatW4UuVb99+xYnT56Eu7t7mwVUJOq7TK0YuAK/HvsVxZbFOPHsBG6/vN3IZYpnmU1dpqSDvO+3toQ87ceKgiwDdkjzs5LFdeBwgOjoutnvw4eC9QYGTKKFwEAm9GRDO1pFD57SERErKNmsWbPw8uVLjBs3DqmpqQJ1KSkpGD9+PF69eoUPPvhAIkIqKpYalljUfxFuzbmFrEVZ2DVqF/wd/aGqoor43HisubYGPff0RJf/64LFFxbjZsZN1BLlciuTJs3t83UEePux7AYPamwWC75dTenNth7S/Kza6zq8fAns3QtMmACYmgK+vsB339Up4t69gRUrgBs3mH3iw4eB2bMbK2KKfCLWzHjWrFk4e/Ysjh49CldXVzg4OMDa2hpZWVlIS0tDTU0N3nvvPcyaNUvS8iosVnpWmNd3Hub1ncd3mQpPDMeF5xeQVpQm4DIV7BqMENcQ6jLVBuRtn09c5GE/tqMgzc9KGn1XVzOKlTf7jY8XrDc2Zma9gYHMLNjcXOxTUeQAsRNFHD58GD/++CN27dqFxMREJCcnAwC6deuG+fPnY968eRITUtmo7zJVXl2OCyn/uUwlMS5Te+7vwZ77e/guU6FuoRjZZSR1mWoF8rDPJwnkYT+2oyDNz0oSfafmleHOo7d4/kAP925o4PJloLy8rp7FAry96/Z+e/dW3nSDikibsjZ9+umn+PTTT1FeXo7i4mIYGBhAh3qFSxQddR2EuoUi1C0U1dxqJstUwrFGLlNaqloY6cRkmRrjPIZmmWqBjrbf2hJ0D1B0pPlZtbbvt2+BMxc4+GbnGzx/oIuaAsF8gubmdT6//v7MbJiimEgk4bCOjg5Vwu2AOlsdAV0CENAlgO8yFZ4QjmOJx5BamIrjicdxPPE42Cw2hjoMRahrKMa7jqcuU0KQJ/9XivJACJPhiJftKCoKqKxUA2DBNGDVQsO6ENqO+Rjgx8HJVT1oukElQSLKmNL+1HeZ2uS/CY9eP8KxxGN8l6mI1AhEpEbgk7OfwMfGh8ky5RZCXabqQfdbKe1BeTkQGVm399vA5hVs3UpoOeZB0zEPWvb5UNGoAQDEVwEvChzog6GSILYyzszMxLp16xAREYFXr16hurq6URsWi4WamppW9Wtvb48XDdODAPjkk0/w448/iiuuQsNiseBp4QlPC0+E+YXhecFzHEtgskzdfnmb/1oWsQzuZu58X2YPcw+ldpmi+60UacBLN8ib/V67xhhj8VBTAwYPZpaejV3fYPWN6CYTLnQU2wVK2xFLGaempsLb2xuFhYXo3r07qqqqYGdnB01NTaSmpoLD4cDT01NoEomWiImJAZfL5b+Pj4+Hv78/Jk6cKI6oSomTkROWDlyKpQOX4lXpK5xIPIHwxHBEpUfhce5jPM59jDXX1sDB0AGhbqEIcQ2Bj60PVFjKuR5G91spbaWkBLh8uS7fb0aGYL29fV22o2HDAF1dpjw1TwNrbjbdb0ezXaCIj1jKePXq1SguLsbly5cxZMgQgfzG2dnZmDdvHp4+fSpWbGpTU1OB9xs2bECXLl0wZMgQcURVeuq7TBVUFvCzTPFcprbc3oItt7fAQteCn2XKz96PukxRKM1ACBAXVzf7vXkTqL8IqKJaC3XbfGg5MGEnB3lr4/vJjYPJUNsFCg+xlHFERARGjRoloCDJf18kS0tL/PPPP3B3d8fXX3+NPXv2iC1cdXU1/vzzTyxatEipl1MlhZGWEaZ7Tsd0z+mNXKZyynIEXKbGuoxFiGsIdZmiUP6jsBA4d46FX3/tiXnzVJGdLVjftSsz+41nJSFVNQ1EtW6F71ZKRZNJI6jtAgUQUxnn5+fD1dW1rhNVVVRU1PltamhowN/fH8ePH2+TcMePH0dRURFmzpzZZJuqqipUVVXx3/NiZnM4HHDkOOUITzZZyajOUsdYp7EY6zQW1UHVuPriKo4nHcfJZyfxuvw1/nz0J/589Ce0VLXg7+iPYJdgjHYajU5ancQ6n6zH254o01gBxR1vbS3w8CEL58+zcPEiC3fusFBbqwqgMwBAW5vAz49g5EiCgIBadOkCpOeXY8zOZDReVyK4k5qL5znFsDMWdKvTVgN+nd4TL95UIKOgHJ2NdPhtZP2ZKuq1FUb9scpivCxC6q2NiIi1tTVCQ0OxY8cOAICVlRUGDx6Mf/75h99mwYIF2Lt3L8rre623kpEjR0JdXb3Z7E9hYWFYvXp1o/K///4b2to0DUlr4RIunpU/Q3RxNKKLo/G6+jW/jg02euj2QH/D/vA28IaRGvVlpigWxcXqiI01w4MHZnj40AwlJYKx921tS9CrVy569cpFt25voKZGw9MqIhUVFZg8eTKKi4uhr6/fLucUSxn7+flBW1sbZ8+eBQAEBwcjKioKDx48gKOjI/Ly8uDl5QVjY2M8evRILMFevHgBR0dHhIeHY/z48U22EzYztrW1RX5+frt9iOLA4XBw6dIl+Pv7Q01Os3YTQvAo9xGOJx3HiaQTiM8TjMfX37o/xruMx3jn8S26THWE8UoKZRorIH/jLa6sxrIjj3EzpS7u+MAuJtg0wQP6WoLyMekG62a/9++zQEjdlpieHsGwYQQjR9YiIIDA0rL5sTIz4xtNynbms8GNZsbyjLxdW2nCG+uZAlNcefISKVvea1dlLNYydVBQEMLCwlBUVARDQ0MsXLgQp06dgoeHB9zc3PD8+XOUlJQgLCxMbMH27dsHMzMzjB49utl2GhoaQjNHqampdYgvj7zL2cemD/rY9MG64ev4LlPhieFMhqks5vXVla/gbubOt8xuzmVK3scrSZRprID8jHfR7w9x83kBuPWU6tXkAnzxbzx+n9MP2dmC6QYLCwWP9/Sss3weMIAFNTUWeDl1eKuXTY21q6UhvB3NmjTIcrIwkPh42wN5ubbtwY2UQlRz299GSSxlPG/ePPj5+YH9X2BUPz8/HDp0CGFhYYiPj4ednR3WrVuHDz/8UCyhamtrsW/fPsyYMQOqqjQuibxQ32UqqyQLJ5JO4FjiMUSmRfJdplZfXQ3HTo58X+b+Nv2V1mWK0v4ISwBCuCyUv+qEE1FG6L6Ni6fxggGdDQ2ZRAtBQcxfqzYGrKMGWR2T9HxmS5Xb+sViiSCSpnv06BEsLCxgZmYGANDX14e3t7dAm4kTJ0rMFzgiIgIZGRmYPXu2RPqjSB5rfWt80vcTfNL3E77LVHhCOC6kXEBqYaqAy1SwSzDGdh0LTq3iG4FIg9S8MrwoqKBBSUSAlwCkpkQTlammqEwzw9t0Y5BqZlb39L92ffrUzX779QMk+cxPg8l0TDILm08eI21E+gr27NkTq1atwsqVKwEAw4YNw8yZMzF9+nSpCBUQEAAxtrIpMqKhy9T55+cZl6lnjMvUT/d/wk/3f4IOWwfja8fjnW7vUJcpESiqqMbnB2MFZli+XU2xY1Jjf1Vlp6qKSTd48KghXv3rC06+nkC9ilYVtBzysfZTY0x5RxP/zSukCg0m07Gw7aTNf1iTBSIpYzabLRAVKyoqCn5+ftKSidKB0VHXwTvd3sE73d5BNbcakWmROJZ4DMcTj+N1+Wv8Hf83/o7/m59lKtQ1FGOcx4jtMqXIfH4wFjef5wuU3Xye36S/qrKRmloX7/nKFYDxrlRnXiwCDcsiaDrmQssxD1qWJRjU1QRfzLGWsdQUecXeRAdPwezvywKRlLGNjQ1iY2OlLApF0VBnq2Ok00iMdBqJbf7bsO3INrw2eo0Tz04grSiNn2VKVUUVQ+2HIsQ1BMGuwbDUs5S16DJH2N4nwOxnXUvOQ1q+8sUsrqxkshzxQk4+eyZYb2HBLDv7Dq3B+cI43MnO4dcN+m9FgUJpif6Oxoh43P5L1iIp47Fjx2LHjh1wc3ODpSVzo9y/fz+ioqKaPY7FYuHy5cttFpLS8WGrsOGm64bFIxZja+BWxL2O41tmx+fG41LqJVxKvYT5Z+ejv01/vmV2F6MushZdJvD2PptCGRIIEMIoXN7s9+pVJv8vD1VVYMCAur1fT0/8l3BBFbPQm+7ZUsRiz7TeSHhpB89t7XtekZTxunXrUFVVhTNnzuDq1atgsVhIT09Henp6s8fREJYUYbBYLHhZeMHLwgurh65G8ptkHEtkskxFv4zmZ5laemkpPMw9+JbZ7mbuSvOdsjNq3hdVURMIlJUxS8682W9ammC9jQ2jfIOCmIQLBs14CtE9W4q4yOL3JZIy1tPTw08//cR/r6KigrCwML5BF4XSFroad8WygcuwbOAyvstUeAKTZerR60d49PoR32Uq1DUUIW4hCu8ypSwJBAgBnjypm/1ev17nywsA6upMukHe7LdbNzSZbpBC6ciIZdA/Y8YMeHl5SVgUCqWxy9SppFNMlqn/XKY2396Mzbc3w1LXUiDLlBpb8ayLFdVftbgYiIiom/2+fClY7+BQN/v186tLN0ihKDJiKeN9+/ZJWg4KpRFGWkaY4TUDM7xm8F2mwhPDcfrZaWSXZfNdpgw1DTHW+b8sU04joa3WccINNoei+KsSAsTG1s1+b91iwlDy0NQEhg5lZr6BgUz2Izr7pSgbYinjx48fIyYmBhMmTODH7aysrMSiRYtw8uRJaGlpYcmSJfj4448lKixFeRHmMhWeEI7jSceRW56LPx79gT8e/QEtVS0EOgUi1I1xmTLUNJS16G2mI+59FhQwoSbPn2dCT+bkCNa7uDCKNygI8PUFtLRkIyeFIi+IpYzXrVuHGzduYNasWfwyXu5iXV1d5OfnY/78+ejSpQv8/f0lJiyFAgi6TO0avQu3X97mW2anF6XzjcF4LlOhbqEY7zKeukxJkdpaJuFCRAQz+717lynjoaPDGFwFBQEjRwKOjrKTlUKRR8RSxnfv3sXQoUP5lq01NTXYt28f+vXrh6ioKBQUFKBXr17Yvn07VcYUqcJWYWNQ50EY1HkQNgdsRtzrOIQnhONY4jEBl6lPznwCH1sfhLiGKLXLlCTJzWVmvWfPsnH2bCBKSgRvJz161M1+Bw4EhORzoVAo/yGWMs7Ly4OtrS3/fUxMDEpKSvDxxx9DU1MTVlZWGD9+PD/FIoXSHtR3mVozdA3fZSo8IRx3su7gVuYt3Mq8xXeZ4llmK5PLVFuoqQHu3Knb+71/n1ejAkAD+voEI0aw+LPfercICoXSAmIpY1VVVYEcwlFRUWCxWBg6dCi/zNjYGPn5+cIOp1DahYYuU8cTj+NY4jEBl6mwq2Ho0qkL35fZ28Zb7l2m2jNxxKtXdVbPly4BRUWC9T17Av7+XBgY3MLChf2hTWNmUyhiIZYytre3R2RkJP/9v//+CwcHB9jZ2fHLsrKyYGxs3HYJKRQJYK1vjfn95mN+v/l4U/GGyTKVGI6LKReRUpgi4DIV7BqMENcQuXOZao/EEdXVjLUzb/b76JFgvZERk2YwMJD5a2kJcDi1OHu2AEqS7pZCkQpiKeNp06Zh6dKl8Pb2hoaGBuLi4rBixQqBNo8ePULXrl0lIiSFIkmMtY35LlNl1WW48PyCgMvU7nu7sfvebrlzmZJW4ogXL+pmv5cvA6WldXUsFtC3b93eb9++AJvddF8UCkU8xFLGn376Ke7evYsjR46AEIJRo0bh66+/5tc/efIEcXFxWL16tcQEpVCkga66roDL1JW0KziWcEzuXKYkmTji7Vsm0tW5c4wCTkgQrDc1rfP59fdn3lMoFOkiljLW0NDAP//8g5KSErBYLOjpCeYONTc3x8OHD2Fvby8JGSmUdkGdrY5Ap0AEOgXyXaZ4ltkNXaaGOQzjZ5my0LWQumxtTRzx/Hnd7DcykpdukEFFBfDxqZv99uzJlFEolPZDLGXMgxfwoyEmJiYwMTFpS9cUikyp7zK1JWALYnNi+ZbZT/Ke4GLKRVxMuch3meJZZjt2ko4DbWsTR1RUMOkGebPf588F21ta1sV7HjEC6ETTSVMoMqVNyphCUQZYLBZ6WvZET8ueWDN0DZ69eYZjCcwsub7L1JJLS+Bp7olxzuNgXGkMUi/BQ1tpKXGEvbEOEhPrlO/Vq0A9hweoqgKDBtXNft3dachJCkWeEEkZOzo6gsViISIiAg4ODnAUMXwOi8VCSkpKmwSkUOQNZ2NnLB+0HMsHLee7TIUnhuNq+lXEvY5D3Os4AMCOn3bw8zJLwmWqYeKI2io2HDiOUIvuAoe1jCFWfTp3rpv9DhsGNLGQRaFQ5ACRlHFtba1AUISG75tCkjMDinLSnj614tDQZerUs1M4+vQoLjy/gJTCFGy6tQmbbm2SiMuUvpYalvbrB6uMaly4ADx5oIZMDgtR/9WrqwNDhtTNfl1d6eyXQukoiKSM09PTm31PoUia9vCplTTG2saY6TUTU7pPwdFTRwEn4NTzU41cpjppdsIY5zEIdQtFQJeAZl2miorAj/d8/jwThANQ59d36VI3+/XzY2JAUyiUjgfdM6bIJdLyqW0vtNhaGOU2Cu97vI+qmipEpjNZpk4knRBwmdJW00agUyBCXEMwxnkM9NUNERtbp3xv3xZMN6ilVZduMCgIcHKS2RApFIoEEUsZDxs2DDNnzsT06dObbPPnn3/it99+w5UrV8QWjqKcSNKnVh7QUNXgu0ztHr0btzJv8S2zXxS/QPi9qwg/rA7Wc0AtbTSqSwRNm11d62a/vr5M/l8KhaJYiKWMo6Ki4Ofn12ybFy9e4OrVq+J0T1Fy2upTK8+wVdgYYDMYatmDoVe4BeGnKxD/UAsgKiAAqgFAvRRwuAzHfsl4f7wh5gwbLjWXKQqFIh9IbZm6vLwcajRYLUUMWutT2xF4/ZpJN3juHHDxIlBQAAAsAMxY3N0Bb78CsJwu4KH6Ttx7fQupAL6LZ16e5p58y+weZj1olikKRcEQWRlnZGQIvC8qKmpUBgBcLheZmZk4evQojcBFEYuWfGo7wqyYy2Xhxg0W3/jq4UPBegMDJtQkL+yktTUAGAGYBGASXpa85GeZqu8ytSpqFZyMnPhZpvpZ95P7LFMUCqVlRFbG9vb2/KdxFouF7du3Y/v27U22J4Rg06ZNbZeQopQ09KkFgIFOJtgxqacMpWqely+Z2e+ZM2xcuBCEigrBn1evXnV7v/37M4E4msJG3waf9vsUn/b7lO8yFZ7AZJl6XvBcwGUqxDUEIW4hGGI3RK6yTFEoFNERWRlPnz4dLBYLhBD8/vvv8PT0hJeXV6N2bDYbRkZGGDZsGAIDAyUpK0VM5N1XVxgG2mr4fU4/pOWXI/1NuVzKXl0N3LhRl24wPp5XowJABUZGBCNHshAYCIwcCZibi3censvUTK+ZKKsuw/nn5xGeEI4zyWeQXZaNXfd2Yde9Xeik2QljXZgsUy25TFEoFPlCZGW8f/9+/v9Xr17FrFmz8Pnnn0tDJoqE6Ii+ug1xMJEvJZyeXqd8r1wBysrq6lgsoF8/ICCACz29m/jsMx9oakr2c9ZV18WEbhMwodsEVNVUMVmmEo/heOJx5FXk4fe43/F73O98l6lQ11CMdh7d7lmmKBRK6xDLgCstLU3SclCkQEf31ZUH3r5l4jzzMh4lJgrWm5nV7fsGBADGxgCHU4uzZwulnvdXQ1UDQV2DENQ1iO8yxcsy9aL4BcITwhGeEA41FTV+lqnxruPbJcsUhUJpHTToh4KiaL667Ulyct3sNyoKqKysq2OzmXSDvL1fLy/5SDfIVmFjsN1gDLYbjK0jt+JhzkMcSziG8MRwPM17igspF3Ah5QLmnZmHAbYD+JbZDp0cZC06hUJBG5RxaWkpdu7ciYiICLx69QpV9VPE/Ie4iSKysrKwfPlynDt3DhUVFXBycsK+ffvQp08fccVVOhTZV1fSlJczOX55s9+GX1lr67qIV8OHA4aGMhFTZFgsFnpZ9kIvy15YO2wtkvKT+LmY72bdxc3Mm7iZeROLLy6Gl4UX3zK7u2l36jJFocgIsZRxXl4eBgwYgJSUFOjr66OkpAQGBgaorq5G5X/TCCsrK7H8jAsLCzFw4EAMHToU586dg6mpKZKTk9GJJlxtFYroqyspCAESEupmv9euMcZYPNTUmHSDvNmvtnkZMgoZAzhDw473ubmYuODLQV/iy0Ff8l2mwhPCce3FNcTmxCI2J5a6TFEoMkYsZRwWFoaUlBT8/vvvmDJlCthsNr744gusXLkSMTEx+Oyzz6CqqoqLFy+2uu+NGzfC1tYW+/bt45c5ONCltNaiCL66kqSkBLh8uW7229BF3s5OMN2gnp5iGMA1pL7LVH5FPk4lncKxxGONXKas9KwQ7BJMXaYolHZCrEffs2fPYvjw4Zg6dWqjZa2+ffvi3LlzSE9Px+rVq1vd98mTJ9GnTx9MnDgRZmZm6NmzJ3755RdxxFR6dkzqiYFOJgJl8u6rKykIAeLigA0bmGxGxsZAaCjw88+MItbQYAyu/vc/Zpaclgbs3g2MH88oYqB5AzhFwETbBLN6zsLJSSeRtzQPhyccxvs93oeeuh5elb7Crnu74P+HP8w3m2PG8Rk4kXgClZzKljumUCitRqyZcXZ2NiZOnMh/z2az+cvTANCpUycEBQXh8OHD2LhxY6v6Tk1Nxe7du7Fo0SJ8/fXXiImJweeffw51dXXMmDGjUfuqqiqB/eqSkhIAAIfDAYfDae3Q2g2ebNKUUVsN+HV6T7x4U4GMgnJ0NtKBnbG21M8rjPYYb2EhEBHBwsWLKrh4kYXsbMEHRScngpEjaxEQQDBkCIF2vZX8mhrBvtLzy3EnNReqKg1/JAR3UnPxPKeY/1k2pD3GKmk0VTQR7ByMYOdgxmUq/QpOPDuBU89ONXKZGuk4EuNdxmOU0ygYahp2yPGKizKNFVCu8dYfqyzGyyKk3hqmiJibm2Py5Mn43//+BwAwMzPDyJEj8ccff/DbLF68GLt370ZFRfOGRA1RV1dHnz59cOvWLX7Z559/jpiYGNy+fbtR+7CwMKEz8L///hva2jTogSJTWwukphri/n0zPHxohmfPjFBbW6eANTRq4O6ej169ctGz52tYWrbuu0gBuISLxPJERBdHI7ooGnmcuiV7VZYq3HXd0d+gP7wNvGGoZig7QSkUCVJRUYHJkyejuLgY+vr67XJOsZSxj48PLCwscOzYMQDAyJEjERcXhydPnsDY2BiVlZXw9PSEiooKEhs6ZraAnZ0d/P39sXfvXn7Z7t27sW7dOmRlZTVqL2xmbGtri/z8/Hb7EEWluLIay448xs2UfGioEKztU4szBab4/h0v6Gsp9p4ch8PBpUuX4O/vL5ZhH++zu/qoBBVppihPNUVFqim4FRoC7dzcmNnvyJEEAwcSsdMNpueXY8zOG03Wn/lscLMz47aMVV4hhCD2dSyOJR7DiWcnkJCfwK9jgQUfax8EuwZjvMt4OBgqpp2Hol7bplCm8dYfa2VlJUxMTNpVGYu1TB0QEID//e9/qKiogLa2NubOnYsJEybA09MTPj4+ePDgAdLT07F+/fpW9z1w4EAkJSUJlD179gx2dnZC22toaEBDQ6NRuZqamtx9eRb9/hA3nxeAS+pmbzdSCvHFv/FKE4SjtdeFywXu3gU+/j4XSfdcUZVtACbbEQNLvQZadvlw71+Jf9Y6wM6OBaDt0Ta6WhrC29GsSQM4JwuDFvuQx+9gW+ln2w/9bPvhe//v+S5TR58exb3se7iVdQu3sm5h2eVlCu8ypYjXtjmUabxqamqoabhv1Q6IpYw//vhjdOvWja+MQ0NDsWnTJqxbtw5Hjx6FlpYWFi1ahKVLl7a67y+++AIDBgzAd999h3fffRd3797Fzz//jJ9//lkcUeUGGoRDdHJy6qyeL15k9oIBa369mlkxtBzyoOWYBw3rQrDYBDkAanXMwEtJKAk6YrKK9oTnMrXYezEOHD+AMusynEw+iasvrjZymQp1DUWIWwh1maJQmkAsZWxpaYn33ntPoGzx4sVYuHAh8vPzYWZmJvaTcN++fXHs2DF89dVXWLNmDRwcHLBt2zZMmTKlVf2kvymHhxwtU9MgHE3D4QC3bzM+v+fPA7GxgvW6+rWotcyBlmMeNB3yoKrXOMAMIPnPsCMkq5AXTNVNMaPvDCwcsJDvMhWeGI5LKZfwvOA5frj1A3649QPfZSrULRS+dr7UZYpC+Q+JhsNks9kwFzc1TT3GjBmDMWPGtK2P/7sBvx52cuMTSoNwCJKZWTf7jYhg/IDr06dPXcxnU8cKBGxv2Z1IWp+hvCWrkHd4LlOzes5CaVUpzj0/h2OJx3Dm2Rm+yxQvy9Q4l3H8LFNaalqyFp1CkRliKePMzEwkJyejf//+fIvl2tpabNq0CSdPnoSmpiYWLVqE0aNHS1TY1iJPSRGaC8Lh29VU4W/2VVVAXJwJrl5VwcWLwJMngvXGxkyawaAgxv/XzKx+rfDPjoeyBjLpCOhp6OHd7u/i3e7voqqmCpfTLuNYwjEcTzqO/Ip8HIg7gANxB6Ctpo0gpyCEuoVidNfRMNBseU+eQlEkxFLG3377LU6dOoWcnBx+2fr167Fq1Sr++2vXruHWrVvo27dv26UUE3nbjxW2B9nf0Rj/e18x9yBTU+tmv1euqKK8fCC/TkUF8Paui/ncqxeazXIk7LPjQfdxOwYaqhoY1XUURnUdhZ9qf8LNzJv8LFMZxRk4mnAURxOOQk1FDcMdhzNZplzGw1y37attFIq8I5YyvnnzJkaMGMG3riOEYOfOnXB1dcXFixeRk5ODESNGYNOmTTh8+LBEBRYHedmPrb8HmZZbjNLkGOyZ1lthrBQrK5l0g7y932fP6teyYGj4FmPHqmP0aBWMGMHMhoH/jNueVzS7J9tw/1ZVhYWaWkL3cTsobBU2fO184Wvni/+N/B8eZD/AscRjCE8IR0J+As4/P4/zz8/j49MfY2DngXwDMHtDe1mLTqFIBbGUcW5uroCrUWxsLPLy8hAWFgYbGxvY2NggODgYV69elZigbUHe9mMdTHRgY6COs8mylqRtEMIoXN7sNyqKyf/Lg80GBg5kZr8jRnDw8uUFjBkzCmpqjDWtOLGf6f6t4sFisdDbqjd6W/XGumHrkJifiGMJTJapmFcxuJFxAzcybmDRxUXoadGT7zLVzbSbwrlMUZQXsZRxbW0tamtr+e+joqLAYrEwbNgwfpm1tbXAMrYsoHuJkqesjEk3yJv9pqUJ1tvY1CVcGD4cMPhv64/DAV69EmzbXOxnedjnp8gGVxNXfDX4K3w1+CtkFmcyWaYSmSxTD3Me4mHOQ6yMWomuRl35irmvdV/qMkXp0IiljDt37oy7d+/y3x8/fhyWlpZwcXHhl+Xk5MBQxolf6V5i2yGEMbbipRu8fp1RrDzU1ABf37q9327dAFEmK9TvmiIKtga2+Mz7M3zm/ZmAy9TFlItILkgWcJkKcQ1BiGsIdZmidEjEUsbvvPMO1q9fjwkTJkBTUxM3btzAp59+KtDm6dOncHR0lIiQ4nD680HwcLCU2fklTWpeGV4UNL+vKimKi5l0g7zZ78uXgvX29oziDQoChg4FdHVbfw7qd01pLcJcpsITwnEmmXGZ+jHmR/wY8yOMtIww1nksQt1C4e/oT12mKB0CsZTxkiVLcPHiRYSHhwMAPDw8EBYWxq9/8eIF7t69iy+//FIiQoqDvO0Ti0t75NQlhAm0wdv7vXVLMIuRpiaThpA3++3aVbTZb3NQv2tKW6jvMvW25i0up15mYmYnnRBwmdJR00FQ1yCEuIZQlymKXCOWMtbX10d0dDTi4+MBAG5ubmA38EsJDw9Hnz592i6hkiOtfdWCAuDSJWb2e+ECE4KyPs7Odcp3yBBAS8KTi+b8ruk+P6U1aKpqYrTzaIx2Hs24TGXc5FtmZ5Zk4sjTIzjy9AjfZSrUNRTjXMZRlymKXNGmCFw9evQQWm5nZ9dkYgeK6EhyX7W2Frh3r272e+cOU8ZDW5sxuOJFvWqPHQYa+5kiaVRVVDHEfgiG2A/hu0zxfJnru0zNPT0XgzoPYvaZqcsURQ6QaDhMimRp675qbi6TaOHcOeZvvuAEG92711k+DxoECEl+JVVo7GeKNKnvMrV++Hq+y1R4YjjuvbqH6xnXcT3jOnWZosgFVBm3gvY0ogJav69aU8PMeHmWz/fvC7bX1wdGjKib/draSlpi8aC+w5T2oL7LVEZxBo4nHsexxGNCXaZC3UIR4hpCXaYo7QZVxiLQHkZUwhBlX/XVK2bP99w5Zg+4qEiwDy+vutmvjw/jikShKDudDTrjc+/P8bn358grz8OpZ6cQnhCOS6mXkFyQjI03N2LjzY2w1rNGsGtdlikKRVpQZSwCsgxO0XBflXBZ6AIHGD91hqcn8OiRYPtOnZhEC4GBTOIFS8Xx7qJQpIKpjilm95yN2T1no6SqBOeS/8sylXwGWaVZAi5TY7qOgU2pDYZyhipMGFuKfECVcQvIOjiFgbYa1vn3wx9FVTh3HoiLVselMhYu/VfPYjHpBnmz3379mk+4QKFQmkZfQx/v9XgP7/V4T6jL1O+PfgcAbN+2nbpMUSSK0ipjUfd/ZRGc4u1bJtIVb+83IQEA6qyrTE2ZWW9gIDMLNjVtuq/23uemUBQFYS5TR54cwaG4Q8jn5At1mRrvOh5mOmYtd06hNKDNyvjp06dITExEeXk5pk2bJgmZpEpr93/bKzhFSkpdxKvISKCi3jOAigrQv3/d7LdXL6asOWS1z02hKCI8l6kB1gMwnDMcFr0scCqZCc2ZmJ9IXaYobUZsM8GYmBh4eXnB3d0dEydOxMyZM/l1165dg7a2Nk6ePCkJGSVKc/u/wuAZUbEbuDqwWSz4djUVe7ZZUQHcu2eGL75QQdeugJMT8NlnwJkzTJ2lJTBrFvDPP4xL0s2bwDffMEvSLSliccZJoVBEg8Viobcl4y6VMD8BTz95ivXD1qOPVR8QEL67lMN2B/Ta0wvrrq3Dk9wnIPWMMCmUhog1M37y5AmGDRsGFRUVfPHFF0hMTMS5c+f49YMHD4aJiQn+/fdfjBs3TmLCthVx938lEZyCECApqW7p+epVVVRV+fDrVVWZdIO82a+Hh/ghJ2W9z02hKBNupm5wM3XD14O/5rtMhSeE43rGdb7L1LeR38LZ2Jnvy9zHqg91maIIIJYyXrVqFQDg/v37cHJywurVqwWUMYvFgo+PD2JiYiQjpYQQd/9X3OAUpaXAlSt1Ua/S0+vXsmBiUoHgYA2MGsXG8OGMH7AkoEkYKBTZ0NBl6mTSSRxLPIZLqZfw7M0zAZcp3lK2r50vVFWU1nyH8h9ifQOuXr2Kd955B05OTk226dy5M86fPy+2YNKgrfu/LQWnIASIj6/b+71xQzDdoLo6k24wKAgYPpyDtLRLGD16FNTUJGv+TJMwUCiyx1THFHN6zcGcXnP4LlPhieE4m3wWWaVZ2BmzEztjdsJYyxhjXcYi1DUU/l38oamqKWvRKTJALGVcWloKM7PmLQYrKyvB5XLFEkpaSCM5QVEREBFRN/vNympwTse6peehQwGd/07B4TScKUsOmoSBQpEvGrpMRaRG4FgC4zL1pvIN9sfux/7Y/dBR08GorqMYlynn0dDXkNByGUXuEUsZ29ra4vHjx822efDgAbp06SKWUNKkrfu/tbVMukHe7Pf2baD+M4emJqN0eQq4a1cJD0BEaBIGCkU+0VTVxBjnMRjjPAZ7avfgRsYNfszslyUv8e/Tf/Hv03+hzlbHcIfhCHVjskxRlynFRixlPGbMGPzf//0fIiIiMGLEiEb1hw8fRnR0NL799ts2CyhpxNn/zc9nQk2eP8+Ennz9WrDexaVO+fr6Sj7doDjQJAwUivyjqqIKP3s/+Nn7YVvgNtzPvo/whHCEJ4Qj6U0Szj0/h3PPz2Hu6bkYaDuQHzPbzpBmxVM0xFLGX3/9NY4cOYJRo0ZhxowZyPkvGe6uXbtw+/ZtHDx4EPb29li0aJFEhZUkze3/crlMukHe7PfuXWY/mIeODpNuMCiICb7h4NBOQosBTcJAoXQMWCwW+lj1QR+rPvhu+HdIyEvg52W+n32fn2XqiwtfoJdlL75ltpuJG80ypQCIpYxNTU0RFRWF6dOn49dff+WXf/rppwAAb29vHDx4EAYGHSdE3OvXzKz3/Hkm3eCbN4L17u512Y4GDWKMsSgUCkVa1HeZelH0gp9l6nrGdTzIfoAH2Q/4LlOhrqEIcQtBX6u+VDF3UMS2p+/SpQtu3ryJ2NhYREdHo6CgAPr6+vD29kbfvn0lKaNUqKkBoqPrZr8PHgjW6+sD/v51s18bG9nISVEQamqAjRuBvXuZJ7++fYFffgGcnWUtGaUDYGdohwX9F2BB/wV8l6nwxHBEpEbg2Ztn2HBzAzbc3AAbfRsEuzBZpgbbDaYuUx0Ika5UaGgo3n//fbz77rsAmAhb9vb26Ny5M7y8vODl5SVNGSXGy5d16QYjIoDiYsH6Xr3qZr/9+9N0gxQJweUCoaHArVvAF18A2trAd98BY8cCT54wEV9kzfr1TIi37t0Z/7zmiIkBDhxg4ramp0PV2Bh9bG2ZMHLdu7ePvEpMQ5eps8lnmSxTz87gZclLAZepcS7jEOIaQl2mOgAi3QWOHz8uoHCHDh2KVatWYeXKldKSSyJUVzNhJHmz34YG4EZGTKKFoCDmr4WFbOSkKDibNwOXLzPGBzxlZW4OTJkCREUBQowg25WXL5mHAx0RbQs2bmR+WBMnAh4eqM3KgvG2bVD19maWm3r0kK68FD76Gvp4v8f7eL/H+3yXqfCEcJxMOok3lW+wL3Yf9sXug666LoKcgqjLlBwjkjI2NDRESUkJ/31HiLE6aRJw7RpQVlZXxmIxKQYDAxkF3KcPTTdIkTLFxYyiW7hQcNY4YADzNy5O9sp4yRJmKYjLZVwHWmLRIuDvv/mGE7UcDm6YmWH4F18AGzYAf/4pZYEpwqjvMlVTW4MbGTcQnhCOY4nHqMtUB0AkZdytWzccPHgQffv2heV/2erT09Nx7dq1Fo/19fVtm4RicvYs89fMjNnzDQpi9oBNTGQiDkVZ+esvJi7qRx8JlvP2QEpL21+m+ly7Bhw5Ajx8yGQqEQXeg0Q9yq2sgG7dePk+KTKmvsvU9sDtuPfqHt8yu6HLFD/LFHWZkikiKeOVK1ciODgYkydP5pcdOHAABw4caPHY1kbhCgsLw+rVqwXKXFxckJiY2Kp+vvkGCA4GevYULcsRhSIVwsMZJaWjIzjrzMxk/oq6NFwfDqexwUNTGBk1/QPgchkF/MEHjLtAWyAEyM2le8ZyCIvFQl/rvuhr3ZfvMsWbMd/Pvo9rL67h2otrfJcpnmW2m4mbrEVXKkRSxgEBAUhISEBERASysrIQFhaGIUOGYMiQIVIRqnv37oiIiKgTUgwDl6VLJZd4gUIRCy6X2UMtLwdMTYW3EcdJ/eZNJsybKKSlAfb2wut++gl48YKxZmwjNlevgpWVBaxZ0+a+KNLFzdQNK0xXYIXvCr7LVHhiOG5k3OC7TH0T+Q1cjF0wznkczCrMOsTWZEdHZC1nZ2eHOXPmAGBmr35+flIz4FJVVYUFtaaidHRSUhhFvGwZs0dSn99+Aw4eZHJlthZPTyYknCg09Tt68wZYuRL49tumHxREJTERHj//jNr+/aEyY0bb+qK0K/VdpnLLc/lZpiJSI5D0Jgmbbm8CAGzfuR0hbsxSNnWZkg5ifaJpaWkwNDSUsCh1JCcnw8rKCpqamvDx8cH333+Pzp07S+18FIo0YL14wfzj59fYSGvDBsaimudnnJcHzJzJWFfb2AC7djFh3oTRqVPbjb6++YZZwhZ1n7gpcnKgGhyMSm1t4NAhqFCLyA6LmY4ZPuj1AT7o9QHfZerIkyOMy1TpS+y4uwM77u6gLlNSQixlbGcnvU1+b29v7N+/Hy4uLsjOzsbq1asxePBgxMfHQ09Pr1H7qqoqVFVV8d/zrL45HA449fMXyhk82eRZRkmiTOPljZFbUgJVADUaGiD1x11cDNXr11E7cyZq/ytnz5sHmJmB++oVWJcvg/3uu6h5+pRRmA2prgYKCkQTxtS0sctAcjJUf/4ZtVu2oJb3wACAXVkJVnU1apKTmT0eYeeuT3ExVAMDgaIiRK9ejf6mpoI5QxUQZfkea6lo4R2XdzDOcRzOXDgDFScVnHp+CqeSTzVymQrsEojxzuMR5BTUoV2m6l9bWVxfFhFhM2DNmjVgsViYP38+jIyMsEbEfSEWi9XmZBFFRUWws7PD1q1b+cvk9RFm8AUAf//9N7S1m8/rS6FIE9O4OAxYtQq3v/kGuX368MsdT5+G+969iNy2DSX29mBXVmLUtGm49NNPePufuf/AFSuQOWwYMoTMjo0fP8YgEX9XF/fsQaW5eauPTxkzBvEffNBkvUp1NXzCwmCYkoJbq1ej0NVVJHkoHRsu4eJp2VPcLr6NO8V38IZTFzdYlaUKTz1P9Dfoj776fWGoZig7QdtIRUUFJk+ejOLiYui3k/GRSMpYRUUFLBYLCQkJcHZ2hoqI5sksFksiOY379u2LESNG4Pvvv29UJ2xmbGtri/z8/Hb7EMWBw+Hg0qVL8Pf3h5oShPpSpvHyx+rpCa0uXVC7cCFqN2xgKl++hGq/fiCBgeD+9htT9vAhVAMDUVMvHZjKwoWAhgZqN25sfILCQrAaxm9tAjJwIJPXsz75+WDdvNmoLXvVKqCsDNwtW0AcHessrCsqgIwMxi/QxATgcsGeOBGs8+fBPXoU1SNGKN+1VYKxAs2Pt5bU4n72fRxPOo7jSceRXJDMr1NhqWCgzUAEuwRjvMt4dDaQ/23G+mOtrKyEiYlJuypjkZapIyMjAYC/b8t73x6UlZUhJSUF06ZNE1qvoaEBDQ2NRuVqamod4sfSUeSUFMo0XjVra7CCg8HesQNsXV3AwADYtg2wtgbrxx+hwvscqqoAfX3Bz8XQEHjzBmxhn5WZGRO5RlwsLYEJExqX79wJsFhQbVj38CFjvb1qFRAWxrgqnD4NjB0L1ZIS4PBh2MTFQb2wkPF8mDpVfNk6CMr0PQaaHu8AuwEYYDcAG/03IiE/gZ+X+UH2A1zPvI7rmdexOGIxelv2rssyZSrfLlNqamqoqalp9/OKpIwbujBJy6UJAJYsWYKxY8fCzs4Or169wqpVq8BmszFp0iSpnZNCkRp79zJ+vFu2ALq6wLvvMnGg69s/6OoC9SLcAWDe6+q2r6yiEhvL/D11Cjh1CqoAetevVwJlTBGExWKhm2k3dDPthhW+K5BelF6XZerFddzPvo/72ff5LlO8vMx9rPrQLFP/ITX79I0bN+LixYu4fPlyq457+fIlJk2ahDdv3sDU1BSDBg1CdHQ0TNvqfkGhyIJOnYCjR5tv07UrE7c1KwuwtmbK4uOB6dOlL199oqKEl/v5CSb0btCOw+Hg7NmzGDVqlFLNFilNY29oj4X9F2Jh/4V8l6nwhHC+y9T3N77H9ze+h62+LYJdmSxTgzoPUmqXKamNPDExEVFN/bib4dChQ5IXhkKRZ3R1gfHjmWXgHTuYpBKPHjFlFEoHp77LVPHbYn6WqbPJZ5FZkingMjXeZTxC3EIwwnGE0rlMKe9jCIUiT+zaBcyYARgbM37G//zTsmsRhdLBMNA0wCT3SZjkPgmVnEpEpEbgWOIxnEg6gTeVb/Bb7G/4LfY36KrrYlTXUQhxDcGorqM6tMuUqFBlTKHIA6amddlNKBQlQEtNC2NdxmKsy1jU1Nbg+ovr/JjZWaVZOPzkMA4/OQx1tjpGOI5AqCuTZcpURzG3LGkKBQqFojz4+TGuXrq6zCsoSNYSUcBkmRrqMBQ7Ru1AxhcZuPPBHXw58Es4GzujmluNs8ln8cGpD2CxxQJ++/2wPXo7MoozZC22RKEzYwqFolzs3UstvuUYFZYK+ln3Qz/rfkyWqfy6LFMPsh/g6ouruPriKhZeWIjelr35ltny7jLVEnRmTKG0BzU1jEuTgwOgrQ0MGQI8eyZrqSgUuYbnMvWN7ze4/9F9pC1Iw/9G/g+DOw8GCyzcz76PFVdWoNuubnD70Q1fX/4a917d65BZpkSeGY8aNapVHT9+/LjVwlAoCgmXC4SGArduAV98wSjj774Dxo4FnjwBxEgRKhGqqpjMTX/8ARQWMhmk1q1rnGFK0pSVAZs2AXfuAHfvMufet49JlNEecn7xBfPy8mL8v8XJnEWRCfVdpl6XvRbIMpWYnyjgMhXiGoIQt5AO4zIlsoTnz59vdefUmZtCAbB5M+OudPcu0L07U2ZuDkyZwvjstjUDk7jMnAkcOQIsXMj4Ou/fD4waBURGAoMGSe+8+flM3uPOnZl0kC25QEpSzh9+ALp1Y5Jn7NjB7BknJgoGYaF0CMx1zfFh7w/xYe8P+S5T4YnhOJd8Dpklmfi/u/+H/7v7fx3GZUpkZZyWliZNOSgUxaS4mJkFL1xYp4gBYMAA5m9cnGyU8d27wKFDzAx1yRKmbPp0oEcPJv/yrVtNH1tYyCjC0FDh9QcPAuPGATo6wustLYHsbCbX8r17QN++bZdz0CBASLxtAMCKFcxMGgD69asrX7aMySsdHS391QCKVBHmMhWeGI6TSSeFukyFuoZiVNdR0NOQn4cwkZWxNNMmUigKy19/AaWlwEcfCZbzIlWVlra/TAAz02SzBeXS1ATmzAG+/hrIzARsbYUfu2sXE6Dk33+BkBDBul9/ZcJ//vgj8Mknwo/X0GAUsSTlvHFDtP4aoqIiGF2M0uFp6DJ17cU1HEs4JtRlyt/RHyGuIRjnMg6G6oYylVv+F9IplI5MeDizLKqjwyzP8sjMZP42NXtsDg6HmXGLgpERo3Aa8vAh4OzM5C2uD2/mGBvbtDJevpyZsU6aBJw5A/j6AgBYx44Bc+cylsrz5okmX0u0Rc6GFBUBMTGMvCwW88BQUAB4e0tGVorcoaqiimEOwzDMYRi2B23HvVf3EJ4QjvCEcCQXJONM8hmcST4DldMqGGQ7CM5cZ/Qo7gFT9fb3ZabKmEKRFlwuswRaXs4E9RCGg0Pr+715k8miJAppaYC9fePy7GxmubghvLJXr5ruU1WViRAWGAgEB4N1/jxM4uLA/u47Zg923z5G2UmCtsjZEA4H+OorICmJWZnw8mICrRgYSERUinxT32Xq++Hf42neUxxLPIbwhHA8zHmIaxnXcA3XsPfHvejZqWe7y0eVMYUiLVJSGEW8bFnjPcnffmP2VsWx5PX0BC5dEq1tU8vBlZXMcnFDeLmPKyub71dTEzh5Ehg6FOxx4+BdUQHSrx9Yhw9L1jq8rXLWx9SU2aOmKD0sFgvdzbqju1l3fOP7DdKL0nHkyRHsu70PCeUJeJj9sN1losqYQpESrBcvmH/8/BobaW3YwFhUOzsz73fvBn75BXj8mDE4CgtruuNOndpu9KWlxbgMNeTt27r6ltDXBzZvBmvYMKgC4GzeDBVRjmsNkpCTQmkBe0N7LOi3AF3zu6K3b2+EPwrHgg0L2lUGqowpFGlRXs78bbgvXFwMXL8OzJ5dV2ZpySjgv/9uud/qamavUxRMTRkDqIZYWjIpGxuSnc38tbJque/UVGDKFBAXF1QWFEDr/feZJXRhy8riIgk5KZRWYK5rjpk9Z2IB2lcZ0whcFIq04PmulpUJlh84wCjU+kZOwcGMO5ChYcv93rrFKClRXjxDsYZ4eTERwEpKBMvv3Kmrb47sbGbpXU0NNefO4dbq1czDR0CA6A8KotBWOSmUDgJVxhSKlCDu7owlc2RkXeHLl8DatYyvrLiRn3h7xqK8mtoznjCBMTD7+ee6sqoqxvjK27t5C+XCQmDkSOYhIyICsLFBuZUVak6fZpT/6NF1qwJtpS1yUigdCLpMTaFICzMzZsa7fTsTAtPAANi2DbC2ZqI/iYsk9oy9vYGJExnr4txcwMmJmbGnpzO+ws2xaxejdKOimIhYHA5T7uUFnD7NzI4PHGjazxgAdu5kXI141tCnTjEPKgDw2Wd1Fs5tkZNC6UBQZUyhSJO9e5kgGFu2MCn73n2XSRghD+EXf/8d+PZbwZjPp0/z/YabZPly5iGjfkQxHoMGMe5c7u7N97F5M8AzcAMYf+zwcOb/qVMF3Y3ElZNC6UBQZUyhSJNOnYCjR2UthXA0NZkwk5s2te44VVXhipiHKMvv6emin09cOSmUDgTdM6ZQ5IGaGsZdh8sV/J9CoSgFVBlTKPLAunWMz+zevcwytpYWsyxLoVCUAqqMKRR5ICyMSVhQ/9VUfl8KhaJwUGVMoVAoFIqMocqYQqFQKBQZQ5UxhUKhUCgyRnFdm8rLhcfklRc4HLDfvmXk5CWaV2SUabzKNFZAucarTGMFlGu89cfammxgEoJFCCHtflYpUlJSAgMDAxQD0G+xNYVCoVAogpQAMABQXFwMff320SR0mZpCoVAoFBmjuMvUr14x+VblFA6HgwsXLmDkyJFQU/TlHyjXeJVprIByjVeZxgoo13jrjxWVle2enlNxlbGOTuM8svIEhwOupiYjo4J/yQEo13iVaayAco1XmcYKKNd4649Vpf0XjekyNYVCoVAoMkaulfGGDRvAYrGwcOFCWYtCoVAoFIrUkFtlHBMTgz179sBD3ATsFAqFQqF0EORSGZeVlWHKlCn45Zdf0KlTJ1mLQ6FQKBSKVJFLZTx//nyMHj0aI0aMkLUoFAqFQqFIHbmzpj506BAePHiAmJgYkdpXVVWhqqqK/764uBgAUFBQAA6HIxUZJQGHw0FFRQXevHmj8C4DgHKNV5nGCijXeJVprIByjbf+WN++fQsAaM+YWHKljDMzM7FgwQJcunQJmpqaIh3z/fffY/Xq1Y3KHRwcJC0ehUKhUJSI0tJSGBgYtMu55Coc5vHjxxESEgJ2vZjSXC4XLBYLKioqqKqqEqgDGs+Ma2trUVBQAGNjY7BYrHaTvbWUlJTA1tYWmZmZ7RZuTZYo03iVaayAco1XmcYKKNd4649VT08PpaWlsLKygko7+RzL1cx4+PDhePz4sUDZrFmz4OrqiuXLlzdSxACgoaEBDQ0NgTJDQ0NpiilR9PX1Ff5LXh9lGq8yjRVQrvEq01gB5Rovb6ztNSPmIVfKWE9PDz169BAo09HRgbGxcaNyCoVCoVAUBbm0pqZQKBQKRZmQq5mxMKKiomQtglTQ0NDAqlWrGi2xKyrKNF5lGiugXONVprECyjVeWY9Vrgy4KBQKhUJRRugyNYVCoVAoMoYqYwqFQqFQZAxVxhQKhUKhyBiqjEVk9+7d8PDw4Pug+fj44Ny5c/z6t2/fYv78+TA2Noauri7eeecdvH79WqCPjIwMjB49Gtra2jAzM8PSpUtRU1Mj0CYqKgq9evWChoYGnJycsH///kay/Pjjj7C3t4empia8vb1x9+7ddh+vn58fWCyWwOvjjz/usOOtj7DUnYp2fXkIG6siXduwsLBGY3F1deXXK9p1bWm8inRtASArKwtTp06FsbExtLS04O7ujnv37vHrCSFYuXIlLC0toaWlhREjRiA5OVmgj4KCAkyZMgX6+vowNDTEnDlzUFZWJtDm0aNHGDx4MDQ1NWFra4sffvihkSz//vsvXF1doampCXd3d5w9e7Z1gyEUkTh58iQ5c+YMefbsGUlKSiJff/01UVNTI/Hx8YQQQj7++GNia2tLLl++TO7du0f69+9PBgwYwD++pqaG9OjRg4wYMYI8fPiQnD17lpiYmJCvvvqK3yY1NZVoa2uTRYsWkadPn5IdO3YQNptNzp8/z29z6NAhoq6uTn777Tfy5MkT8uGHHxJDQ0Py+vXrdh3vkCFDyIcffkiys7P5r+Li4g47Xh53794l9vb2xMPDgyxYsIBfrmjXt7mxKtK1XbVqFenevbvAWPLy8vj1inZdWxqvIl3bgoICYmdnR2bOnEnu3LlDUlNTyYULF8jz58/5bTZs2EAMDAzI8ePHSVxcHBk3bhxxcHAglZWV/DaBgYHE09OTREdHk+vXrxMnJycyadIkfn1xcTExNzcnU6ZMIfHx8eTgwYNES0uL7Nmzh9/m5s2bhM1mkx9++IE8ffqUfPPNN0RNTY08fvxY5PFQZdwGOnXqRPbu3UuKioqImpoa+ffff/l1CQkJBAC5ffs2IYSQs2fPEhUVFZKTk8Nvs3v3bqKvr0+qqqoIIYQsW7aMdO/eXeAc7733Hhk5ciT/fb9+/cj8+fP577lcLrGysiLff/+9VMZYH954CWF+1PVv4A3piOMtLS0lXbt2JZcuXRIYnyJe36bGSohiXdtVq1YRT09PoXWKeF2bGy8hinVtly9fTgYNGtRkfW1tLbGwsCCbNm3ilxUVFRENDQ1y8OBBQgghT58+JQBITEwMv825c+cIi8UiWVlZhBBCdu3aRTp16sQfP+/cLi4u/PfvvvsuGT16tMD5vb29ydy5c0UeD12mFgMul4tDhw6hvLwcPj4+uH//PjgcjkDKR1dXV3Tu3Bm3b98GANy+fRvu7u4wNzfntxk5ciRKSkrw5MkTfpuGaSNHjhzJ76O6uhr3798XaKOiooIRI0bw27THeHn89ddfMDExQY8ePfDVV1+hoqKCX9cRx9tU6k5FvL4tpSlVpGubnJwMKysrODo6YsqUKcjIyACgmNe1ufHyUJRre/LkSfTp0wcTJ06EmZkZevbsiV9++YVfn5aWhpycHAE5DAwM4O3tLXB9DQ0N0adPH36bESNGQEVFBXfu3OG38fX1hbq6usB4k5KSUFhYyG/T3GciCnIf9EOeePz4MXx8fPD27Vvo6uri2LFj6NatG2JjY6Gurt4oJra5uTlycnIAADk5OQJfcF49r665NiUlJaisrERhYSG4XK7QNomJiZIcKoCmxwsAkydPhp2dHaysrPDo0SMsX74cSUlJCA8P75DjbS51Z05OjkJd35bSlCrStfX29sb+/fvh4uKC7OxsrF69GoMHD0Z8fLzCXVeg+fHq6ekp1LVNTU3F7t27sWjRInz99deIiYnB559/DnV1dcyYMYMvrzA56o/FzMxMoF5VVRVGRkYCbRpmAaz/mXTq1KnJz4TXhyhQZdwKXFxcEBsbi+LiYhw5cgQzZszA1atXZS2W1GhqvN26dcNHH33Eb+fu7g5LS0sMHz4cKSkp6NKliwylbj3ipO7sqIgyVkW6tkFBQfz/PTw84O3tDTs7Oxw+fBhaWloylEw6NDfeOXPmKNS1ra2tRZ8+ffDdd98BAHr27In4+Hj89NNPmDFjhoylaz10mboVqKurw8nJCb1798b3338PT09PbN++HRYWFqiurkZRUZFA+9evX8PCwgIAYGFh0chKk/e+pTb6+vrQ0tKCiYkJ2Gy20Da8PiRJU+MVhre3NwDg+fPnzY6FV9dcm/Ye7/3795Gbm4tevXpBVVUVqqqquHr1Kv7v//4PqqqqMDc3V5jr29JYuVxuo2M68rVtiKGhIZydnfH8+XOF/d3Wp/54hdGRr62lpSV/pY6Hm5sbf1med67m5LCwsEBubq5AfU1NDQoKCiTyHWjNeKkybgO1tbWoqqpC7969oaamhsuXL/PrkpKSkJGRwd9j9fHxwePHjwUu/KVLl6Cvr8//Qvn4+Aj0wWvD60NdXR29e/cWaFNbW4vLly8L7OVKC954hREbGwuA+YEAHWu8vNSdsbGx/FefPn0wZcoU/v+Kcn1bGquwNKUd+do2pKysDCkpKbC0tFSK32398QqjI1/bgQMHIikpSaDs2bNnsLOzAwA4ODjAwsJCQI6SkhLcuXNH4PoWFRXh/v37/DZXrlxBbW0t/0HFx8cH165dA4fDERivi4sLOnXqxG/T3GciEiKbeik5X375Jbl69SpJS0sjjx49Il9++SVhsVjk4sWLhBDGRaJz587kypUr5N69e8THx4f4+Pjwj+e5DAQEBJDY2Fhy/vx5YmpqKtRlYOnSpSQhIYH8+OOPQl0GNDQ0yP79+8nTp0/JRx99RAwNDQWsH6U93ufPn5M1a9aQe/fukbS0NHLixAni6OhIfH19O+x4G9LQ6lTRrm9TY1W0a7t48WISFRVF0tLSyM2bN8mIESOIiYkJyc3NJYQo3nVtbryKdm3v3r1LVFVVyfr160lycjL566+/iLa2Nvnzzz/5bTZs2EAMDQ3JiRMnyKNHj8j48eOFujb17NmT3Llzh9y4cYN07dpVwLWpqKiImJubk2nTppH4+Hhy6NAhoq2t3ci1SVVVlWzevJkkJCSQVatWUdcmaTF79mxiZ2dH1NXViampKRk+fDhfERNCSGVlJfnkk09Ip06diLa2NgkJCSHZ2dkCfaSnp5OgoCCipaVFTExMyOLFiwmHwxFoExkZSby8vIi6ujpxdHQk+/btayTLjh07SOfOnYm6ujrp168fiY6ObtfxZmRkEF9fX2JkZEQ0NDSIk5MTWbp0qYC/Ykcbb0MaKmNFu771qT9WRbu27733HrG0tCTq6urE2tqavPfeewJ+qIp2XZsbr6JdW0IIOXXqFOnRowfR0NAgrq6u5Oeffxaor62tJd9++y0xNzcnGhoaZPjw4SQpKUmgzZs3b8ikSZOIrq4u0dfXJ7NmzSKlpaUCbeLi4sigQYOIhoYGsba2Jhs2bGgky+HDh4mzszNRV1cn3bt3J2fOnGnVWGjWJgqFQqFQZAzdM6ZQKBQKRcZQZUyhUCgUioyhyphCoVAoFBlDlTGFQqFQKDKGKmMKhUKhUGQMVcYUCoVCocgYqowpFAqFQpExVBlTKBQKhSJjqDKmUChiExYWBhaLhaioKJnJUFFRAWtra4GMRB2FpKQkqKqqYteuXbIWhSJjqDKmSJ3Y2Fh8/PHH6NatG/T19aGurg4LCwv4+/tjy5YtyMvLa3QMi8USeGlpacHCwgKDBg3CkiVLEBcXJ/Rc6enpjY5VV1eHra0tJk+ejEePHkl7uEoP7xrMnDmzXc63adMm5Ofn45tvvhEot7e3B4vFavbYptpkZGTgk08+QdeuXaGpqQldXV04ODhg9OjR2LhxI8rLywXai/t9dXFxwaRJk7B69WqUlpa2cuQURYKGw6RIjdraWixbtgxbtmwBm82Gr68vPDw8oKOjg9zcXNy+fRtPnjyBjo4OkpKSYG1tzT+WxWLB2NgYn376KQCAw+EgPz8fDx8+RExMDABg9uzZ2LVrFzQ0NPjHpaenw8HBAV26dMHUqVMBMJlroqOjcfPmTWhoaODy5csYOHBgO34Sikt+fj7y8/PRuXNnaGtrA6i7BjNmzMD+/fulev6SkhJYW1sjJCQEv//+u0Cdvb09Xrx4geZuccLaxMXFwc/PD0VFRRg4cCB69eoFXV1dZGRk4Pr168jIyEBycjKcnJz4x4j7fQWAx48fw8PDA+vWrcOKFSva/JlQOiitimRNobSCL7/8kgAgvXr1IsnJyULb3L9/n4wYMaJRPQDi4uIi9JjHjx8TLy8vAoBMnTpVoC4tLY0AICNHjmx03IoVKwgAMmTIEPEGRBEJ3jWYMWOG1M+1c+dOAoBcunSpUZ2dnR1p6RYnrM2wYcMIAPL7778LPebWrVuksLBQoEzc7ysPDw8PYmdnR7hcbrPyUhQXqowpUiEpKYmw2WxiamrKT1fXHA2zwjR3cyOEkNzcXGJqakoAkDt37vDLm1PGOTk5BADR1tZuUZ59+/YRAGTfvn3k+PHjpG/fvvwsNrNmzWoyFVxqaiqZM2cOsbW1Jerq6sTCwoLMmDGDpKenN2rLezB4+fIlmTZtGjE3NycsFotERka2KF9VVRXZunUr6dOnD9HV1SU6OjrEzc2NfPHFF6SgoIDf7sqVK2TWrFnE2dmZ6OjoEB0dHdK7d2+B9G/CZMrMzCTvv/8+MTY2JlpaWmTAgAFCFd6qVasIAL7MvM9N2IvXJisri6xcuZJ4e3sTU1NToq6uTuzs7Mi8efPI69evWxx7fXr37k2MjIyEKjFxlbGWlhYxNDRslRzifl95rFu3jgAgERERrTovRXGge8YUqXDgwAFwuVzMnTsXpqamLbZXVVVtVf+mpqb4+OOPAQD//PNPq45taR+xPkePHsXEiRPh5OSEhQsXwt3dHfv27cOgQYNQWFgo0PbOnTvo2bMnDhw4gN69e2PBggUYPHgw/vrrL/Tr1w+pqamN+n/z5g18fHzw6NEjvP/++/joo4+gr6/frEyVlZUYNmwYFi1ahOLiYsyaNQvz5s2Ds7Mz9uzZgxcvXvDbbty4EdeuXUPfvn3x6aefYurUqcjPz8fcuXOxePFiof0XFhZi4MCBSE5OxgcffIBJkyYhLi4OgYGBOH78eLOyeXl5YcGCBQAAT09PrFq1iv+yt7cHAFy7dg1btmyBubk5Jk2ahM8++wxdunTB7t274ePjg+Li4mbPUV/Ohw8fol+/flBRkdytzNjYGGVlZXj16pXE+mzp+8pLQt8wQT1FiZD10wBFMRk6dCgBQC5fvizW8WhhpkEIIZcvXyYAyODBg/llzc2MV65cSQCQoUOHtnj++jO8+knTCalbfv/000/5ZdXV1cTe3p7o6emRBw8eCLS/fv06YbPZZMyYMY3GCIDMmjWL1NTUtCgTj8WLFxMAZNq0aY2OKyoqEsjFmpqa2uh4DodD/P39CZvNJi9evBAq0+TJk0ltbS2/PC4ujp/buqKigl/ecGZMSMvL1K9fv26UL5YQQg4cOEAAkHXr1jU7fh5nzpwhAMiKFSuE1os7M160aBEBQBwcHMjGjRvJrVu3SHl5ebP9iPt95VFcXEwAEF9f32b7oCguVBlTpIKbmxsBQBISEhrVRUZGklWrVgm8Gi7NinJzS0hIIACIm5sbv4ynCLp06cLve8mSJWTw4MEEANHU1CS3bt1qUX6eMh4xYkSjutLSUmJoaEj09fX5y6Ph4eEEAFmzZo3Q/kJDQ4mKiopAIncARF1dneTl5bUoDw8Oh0P09PSIgYGBwHJ0azl69CgBQPbv3y9QDoCw2Wyhy+pz5swhAMiRI0f4ZeIo46aora0l+vr6xM/PT6T2e/bsIQDI//3f/wmtF1cZV1ZWkpkzZxIVFRX+wwmbzSa9evUia9eubbRfTIj439f6aGpqEkdHx2b7oCgurVsbpFAkQFRUFFavXt2o3M/PT2LnSElJ4Z9DTU0N5ubmmDx5Mr788ku4u7uL3M/gwYMblenq6sLLywtRUVFITU2Fk5MToqOjATB+o2FhYY2OycnJQW1tLZ49e4Y+ffrwyx0cHGBiYiKyPImJiSgtLcWIESPQqVOnFtuXlpZi8+bNOH78OFJSUhq55Ahbiu3cuTPs7OwalQ8ePBi//vorHj58iHfeeUdkmYURHh6OPXv24MGDBygsLASXy21WJmG8efMGAGBoaNgmWRqiqamJffv2Ye3atTh79izu3r2Lu3fv4sGDB3jw4AH27NmDq1evwtHRUaLnNTIyQn5+vkT7pHQcqDKmSAVzc3MkJCTg1atXcHV1FagLCwvjK6xDhw5h0qRJYp2Dd9MWtic9cuRInD9/Xqx+62Nubt5sOW9/s6CgAADw119/NdtfQ2XYVP9NwTtffTewpqiuroafnx8ePHiAnj17Ytq0aTA2NoaqqirS09Nx4MABVFVVNTpO1DGLy5YtW7BkyRKYmpoiICAANjY20NLSAgBs27ZNqEzC4B3z9u1bofW8feTa2tom95Rra2ubtCGwsbHBRx99xA8mkpKSgtmzZ+PatWv44osvcOLECZHk5NHc9xVgbAF47mEU5YMqY4pUGDBgAKKiohAZGYlhw4ZJ5Ry8qE99+/aVSv8A8Pr162bLDQwMAIBvdHXq1CmMGTNG5P5bY0wG1M0Cs7KyWmx74sQJPHjwAHPmzMHevXsF6g4dOoQDBw4IPU7UMYtDTU0N1q5dC0tLS8TGxsLMzIxfRwjBDz/8IHJfPKXGexBqCE/ON2/eCFWAhBAUFBSIPJ4uXbpg//79cHR0xJUrV0SWk0dz39fa2loUFxeje/fure6XohhQa2qKVJgxYwZUVFTw888/S2XpLS8vD3v27AEAvP/++xLvn8f169cblZWVlSE2Nhb6+vr8pUpvb28AwO3bt6UmC8BEbNLX10dMTEwja+6GpKSkAADGjx/fqE7YuHhkZGQIWGQ3PKZnz57NnpfNZgOAwNIzj/z8fBQXF8PHx0dAEQPAvXv3UFlZ2Wzf9eFtNyQlJTVb39Q1efToEcrLy+Hh4SHyOXV1dUVuW5+Wvq/Jycmora1t1RYKRbGgypgiFZydnbFs2TLk5uYiKCgIz58/F9quqKio1X0/efIEAQEByM3NxYwZMwT2YCVNREQELly4IFC2fv16FBUVYfr06fzlz/Hjx6Nz587YunUrrl271qgfDoeDGzdutFkeVVVVzJ07F8XFxViwYEEjhVdcXIyysjIA4O/7Njzv1atX8csvvzR5Di6Xi6+//logKtWjR4/wxx9/wNTUFKNGjWpWxk6dOoHFYiEzM7NRnZmZGbS0tPDgwQNUVFTwywsLC/HZZ581229D3N3dYWRkhDt37gitnzFjBgBg5cqVjb5nVVVVWLZsGQBg+vTpAnVr1qwRKjshBBs2bAAADBo0SGQ5Rfm+8sYwZMgQkfulKBZ0mZoiNdavX4/q6mps3boVrq6u8PX1haenJ7S1tZGbm4tHjx7h7t27fIOohuTn5/P3lmtqavDmzRs8ePAAd+/eBQB88MEH+PHHH6U6hjFjxmDs2LGYMGEC7O3tER0djcjISHTp0gVr1qzht9PQ0MCRI0cQFBSEIUOGYNiwYXB3dweLxcKLFy9w/fp1GBsbIzExsc0yrVmzBtHR0fjjjz8QHR2NoKAgaGhoIDU1FefPn8eNGzfg5eWFsWPHwt7eHj/88APi4+PRo0cPJCUl4fTp0wgJCcGRI0eE9u/h4YEbN26gb9++GDFiBPLy8vDPP/+gpqYGP//8M3+vtil0dXXRt29fXLt2DdOmTUPXrl2hoqKCadOmwc7ODp988gm2bNkCT09PjB07FiUlJTh37hzs7OxgZWUl8ufAYrEwfvx47N+/Hy9fvoSNjY1A/fDhw7FgwQJs374dzs7OGDduHCwsLPDmzRucPXsWGRkZCAkJwaxZswSO27p1K8LCwtCnTx/07t0bRkZGePPmDSIjI/Hs2TMYGxtjy5YtjeRpy/f10qVLUFVVbdUWB0XBkK0xN0UZePDgAfnoo4+Iq6sr0dXVJWpqasTc3JwMGzaMbNq0SWjUJTSI3qShoUHMzMzIwIEDyZIlS0hcXJzQczXnZ9wamorAZWxsTGbOnEmys7OFHvfy5UuyYMEC0rVrV6KhoUH09fWJm5sb+eCDDxr5XKMNoTnfvn1LNm/eTLy8vIiWlhbR1dUl3bp1I4sXLxZwvUlNTSXvvPMOMTU1Jdra2qRv377k0KFDJDIykgAgq1atEipTZmYmee+994iRkRHR1NQkPj4+5OLFi43kEObaRAgTgW3UqFHE0NCQsFgsgTbV1dVk/fr1/M+oc+fOZPHixaS0tJTY2dkROzs7kT+HO3fuEABk48aNTbY5evQoGTlyJDExMSGqqqrE0NCQ+Pr6kr179wqN3HXt2jXy5ZdfEh8fH2JlZUXU1NSIrq4u8fDwIEuWLCGvXr1qdIy431dCCCkvLye6urokODhY5HFTFA+aKIJCEcL+/fsxa9Ys7Nu3r92yD8kDLBYLQ4YMkWlKxNYyePBg5OXl4enTpxKNxNVe7N27Fx9++CGuXr0KX19fWYtDkREd75tLoVAo9di0aROSkpJw6NAhWYvSampqavDdd99h3LhxVBErOXTPmEKhdGj69++PPXv2CLXelncyMjIwffp0TJs2TdaiUGQMVcYUCqXDwwvM0dFwdHQUGrGNonzQPWMKhUKhUGQM3TOmUCgUCkXGUGVMoVAoFIqMocqYQqFQKBQZQ5UxhUKhUCgyhipjCoVCoVBkDFXGFAqFQqHIGKqMKRQKhUKRMVQZUygUCoUiY6gyplAoFApFxvw/5l0sEV9TQa4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "lin1 = linear_model.LinearRegression()\n",
        "lin1.fit(X_sample, y_sample)\n",
        "\n",
        "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
        "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HmaO_-eb7C6",
        "outputId": "47a57f97-00a6-41d4-fee3-04f02e4baab2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b\")\n",
        "\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.9,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('best_fit_model_plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "dGYyfh5Ab95B",
        "outputId": "ddfae962-99a6-44fd-f4e5-1385b7badf17"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_gdp_per_capita"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laXCsHY8cBl9",
        "outputId": "14a854f1-b829-48c3-fb71-33cf3692bc4b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
        "cyprus_predicted_life_satisfaction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWU7ye52cE3_",
        "outputId": "0ef1e51a-1ca8-41c8-b6bb-5452cbd1eb9f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.301656332738056"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b\")\n",
        "\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 1.1,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
        "         [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
        "plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
        "         fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
        "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "FyHYbwtJcJO8",
        "outputId": "629a9854-b6bb-4253-e3d4-c17c65693b33"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
        "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
        "missing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "f4MtLuf9cSK_",
        "outputId": "30b8559d-da68-4235-fde1-0d635f29f91d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5\n",
              "Norway                63585.903514                7.6\n",
              "Switzerland           68393.306004                7.5\n",
              "Ireland               89688.956958                7.0\n",
              "Luxembourg           110261.157353                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3c1265c-ad72-430c-8da5-0a0464a6764c\" 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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>11466.189672</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>13441.492952</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>14063.982505</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>17887.750736</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>63585.903514</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>68393.306004</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>89688.956958</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>110261.157353</td>\n",
              "      <td>6.9</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-b3c1265c-ad72-430c-8da5-0a0464a6764c')\"\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-b3c1265c-ad72-430c-8da5-0a0464a6764c 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-b3c1265c-ad72-430c-8da5-0a0464a6764c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d0a7a88d-ecdb-4759-a498-e1c8b8df1cca\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0a7a88d-ecdb-4759-a498-e1c8b8df1cca')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0a7a88d-ecdb-4759-a498-e1c8b8df1cca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2083f588-17f4-4645-9073-b959daec46b4\">\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('missing_data')\"\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_2083f588-17f4-4645-9073-b959daec46b4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_data",
              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Ireland\",\n          \"Colombia\",\n          \"Norway\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37791.49262311812,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          89688.9569584859,\n          13441.4929522564,\n          63585.9035142969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.852936105461599,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.3,\n          7.5,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_text_missing_countries = {\n",
        "    \"South Africa\": (20_000, 4.2),\n",
        "    \"Colombia\": (6_000, 8.2),\n",
        "    \"Brazil\": (18_000, 7.8),\n",
        "    \"Mexico\": (24_000, 7.4),\n",
        "    \"Chile\": (30_000, 7.0),\n",
        "    \"Norway\": (51_000, 6.2),\n",
        "    \"Switzerland\": (62_000, 5.7),\n",
        "    \"Ireland\": (81_000, 5.2),\n",
        "    \"Luxembourg\": (92_000, 4.7),\n",
        "}"
      ],
      "metadata": {
        "id": "e3flUJRLcVuw"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
        "\n",
        "for country, pos_text in position_text_missing_countries.items():\n",
        "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
        "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
        "                 xytext=pos_text, fontsize=12,\n",
        "                 arrowprops=dict(facecolor='black', width=0.5,\n",
        "                                 shrink=0.08, headwidth=5))\n",
        "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b:\")\n",
        "\n",
        "lin_reg_full = linear_model.LinearRegression()\n",
        "Xfull = np.c_[full_country_stats[gdppc_col]]\n",
        "yfull = np.c_[full_country_stats[lifesat_col]]\n",
        "lin_reg_full.fit(Xfull, yfull)\n",
        "\n",
        "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0full + t1full * X, \"k\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('representative_training_data_scatterplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "-ClfxzXRcajo",
        "outputId": "09608c0e-2a2c-4319-a9cd-c9a2807938d5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import pipeline\n",
        "\n",
        "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
        "\n",
        "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
        "scaler = preprocessing.StandardScaler()\n",
        "lin_reg2 = linear_model.LinearRegression()\n",
        "\n",
        "pipeline_reg = pipeline.Pipeline([\n",
        "    ('poly', poly),\n",
        "    ('scal', scaler),\n",
        "    ('lin', lin_reg2)])\n",
        "pipeline_reg.fit(Xfull, yfull)\n",
        "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
        "plt.plot(X, curve)\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('overfitting_model_plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "9tFO590Dce93",
        "outputId": "c7583070-8834-4654-aa82-5e64fa16e432"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
        "full_country_stats.loc[w_countries][lifesat_col]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOZW1DseciSM",
        "outputId": "e201d295-80ae-4d8d-eda1-d4911945714c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "New Zealand    7.3\n",
              "Sweden         7.3\n",
              "Norway         7.6\n",
              "Switzerland    7.5\n",
              "Name: Life satisfaction, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
        "gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "7YQRrdumclj1",
        "outputId": "6406a389-2f9d-4b93-c6d2-3f09b897f080"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            GDP per capita (USD)\n",
              "Country                                         \n",
              "Malawi                               1486.778248\n",
              "Rwanda                               2098.710362\n",
              "Zimbabwe                             2744.690758\n",
              "Africa Western and Central           4003.158913\n",
              "Papua New Guinea                     4101.218882\n",
              "Lower middle income                  6722.809932\n",
              "Eswatini                             8392.717564\n",
              "Low & middle income                 10293.855325\n",
              "Arab World                          13753.707307\n",
              "Botswana                            16040.008473\n",
              "World                               16194.040310\n",
              "New Zealand                         42404.393738\n",
              "Sweden                              50683.323510\n",
              "Norway                              63585.903514\n",
              "Switzerland                         68393.306004"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Botswana\",\n          \"New Zealand\",\n          \"Malawi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23331.713640984548,\n        \"min\": 1486.77824843475,\n        \"max\": 68393.306003945,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          16040.0084730125,\n          42404.3937381567,\n          1486.77824843475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
        "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
        "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
        "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
        "\n",
        "ridge = linear_model.Ridge(alpha=10**9.5)\n",
        "X_sample = country_stats[[gdppc_col]]\n",
        "y_sample = country_stats[[lifesat_col]]\n",
        "ridge.fit(X_sample, y_sample)\n",
        "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
        "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
        "         label=\"Regularized linear model on partial data\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('ridge_model_plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "GCzB0XQFcpXW",
        "outputId": "08062a04-a0a3-4be4-d400-d14b5da8d2a4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}