01_the_machine_learning_landscape.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58hCPG1-XJPx"
      },
      "source": [
        "**Chapter 1 – The Machine Learning landscape**\n",
        "\n",
        "_This notebook contains the code examples in chapter 1. You'll also find the exercise solutions at the end of the notebook. The rest of this notebook is used to generate `lifesat.csv` from the original data sources, and some of this chapter's figures._\n",
        "\n",
        "You're welcome to go through the code in this notebook if you want, but the real action starts in the next chapter."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaR12c5LXJPz"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/01_the_machine_learning_landscape.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/01_the_machine_learning_landscape.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BD_x5Lm1XJP0"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqVq2h6VXJP1"
      },
      "source": [
        "This project requires Python 3.7 or above:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LmHH9t1YXJP1"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqwwm9Q0XJP2"
      },
      "source": [
        "Scikit-Learn ≥1.0.1 is required:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "BMnyP45yXJP3"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6lDvwc7XJP4"
      },
      "source": [
        "Let's define the default font sizes, to plot pretty figures:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "SfvzygqjXJP4"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PpbIlw1VXJP5"
      },
      "source": [
        "Make this notebook's output stable across runs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "RUziYKqQXJP5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z33oXErSXJP6"
      },
      "source": [
        "# Code example 1-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bwJmYfcuXJP6",
        "outputId": "7a6092f5-ccc2-477d-c1ce-187e104df5c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "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"
          ]
        }
      ],
      "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]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lgzN3EaXJP8"
      },
      "source": [
        "Replacing the Linear Regression model with k-Nearest Neighbors (in this example, k = 3) regression in the previous code is as simple as replacing these two\n",
        "lines:\n",
        "\n",
        "```python\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "```\n",
        "\n",
        "with these two:\n",
        "\n",
        "```python\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "c1e7-OtnXJP8",
        "outputId": "11954eb6-06aa-48f1-93c7-5c985e6408de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ],
      "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]]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VcR4TCWXJP8"
      },
      "source": [
        "# Generating the data and figures — please skip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2B1-Sv4XJP9"
      },
      "source": [
        "This is the code I used to generate the `lifesat.csv` dataset. You can safely skip this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJ2zT7pBXJP9"
      },
      "source": [
        "Create a function to save the figures:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NXT2yLfVXJP9"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAAPGdCsXJP9"
      },
      "source": [
        "## Load and prepare Life satisfaction data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ac6a1YO3XJP9"
      },
      "source": [
        "To create `lifesat.csv`, I downloaded the Better Life Index (BLI) data from [OECD's website](http://stats.oecd.org/index.aspx?DataSetCode=BLI) (to get the Life Satisfaction for each country), and World Bank GDP per capita data from [OurWorldInData.org](https://ourworldindata.org/grapher/gdp-per-capita-worldbank). The BLI data is in `datasets/lifesat/oecd_bli.csv` (data from 2020), and the GDP per capita data is in `datasets/lifesat/gdp_per_capita.csv` (data up to 2020).\n",
        "\n",
        "If you want to grab the latest versions, please feel free to do so. However, there may be some changes (e.g., in the column names, or different countries missing data), so be prepared to have to tweak the code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ZGylLzIWXJP-"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7nU3NranXJP-"
      },
      "outputs": [],
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMaEPxaIXJP-"
      },
      "source": [
        "Preprocess the GDP per capita data to keep only the year 2020:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
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        "id": "DKglsmPBXJP_",
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        "colab": {
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          "height": 257
        }
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      "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"
            ],
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              "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": 15
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haXXs4Q9XJP_"
      },
      "source": [
        "Preprocess the OECD BLI data to keep only the `Life satisfaction` column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "KOCYG8brXJP_",
        "outputId": "6569067f-56eb-4a09-ccc7-ac21e6eb387c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "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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              "      <th>Australia</th>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>81.0</td>\n",
              "      <td>13.04</td>\n",
              "      <td>73.0</td>\n",
              "      <td>63.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>32759.0</td>\n",
              "      <td>427064.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49126.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>502.0</td>\n",
              "      <td>14.35</td>\n",
              "      <td>91.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.66</td>\n",
              "      <td>72.0</td>\n",
              "      <td>80.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>33541.0</td>\n",
              "      <td>308325.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50349.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.3</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>15.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>77.0</td>\n",
              "      <td>4.75</td>\n",
              "      <td>63.0</td>\n",
              "      <td>70.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30364.0</td>\n",
              "      <td>386006.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49675.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>15.70</td>\n",
              "      <td>89.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <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-ac3dbe41-9550-42eb-8e54-eb0c9690b543')\"\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ac3dbe41-9550-42eb-8e54-eb0c9690b543 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-ac3dbe41-9550-42eb-8e54-eb0c9690b543');\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-a61f0d0d-4406-43b1-900d-9b5d61b564ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a61f0d0d-4406-43b1-900d-9b5d61b564ad')\"\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",
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              "    20% {\n",
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              "    30% {\n",
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              "    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-a61f0d0d-4406-43b1-900d-9b5d61b564ad 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": 16
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Spj7FI80XJQA"
      },
      "source": [
        "Now let's merge the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "l0gL59z8XJQA",
        "outputId": "129f5c9c-bb99-400f-af25-03d4922d7906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67c32d99-29d3-441f-af73-b67261199347\" 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",
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              "\n",
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              "        text-align: right;\n",
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              "</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",
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              "  </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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-67c32d99-29d3-441f-af73-b67261199347 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-67c32d99-29d3-441f-af73-b67261199347');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\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-7f07b016-1c06-440b-8b38-941eebe1508c 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": "full_country_stats",
              "summary": "{\n  \"name\": \"full_country_stats\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"Luxembourg\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19969.978699662497,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          110261.157353302,\n          36215.4475907307,\n          50922.3580234484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7758405579940837,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          4.7,\n          6.9,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
        "                              left_index=True, right_index=True)\n",
        "full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
        "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
        "\n",
        "full_country_stats.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eRG67yMMXoiJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWhAlGRSXJQA"
      },
      "source": [
        "To illustrate the risk of overfitting, I use only part of the data in most figures (all countries with a GDP per capita between `min_gdp` and `max_gdp`). Later in the chapter I reveal the missing countries, and show that they don't follow the same linear trend at all."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "7Vdp9yo-XJQB",
        "outputId": "14673a49-9a09-44e0-8ac7-1b06b06bef86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "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",
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              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
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              "      <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",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba1d35c3-882d-4d93-bef5-7af65063d728')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      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",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ba1d35c3-882d-4d93-bef5-7af65063d728 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-ba1d35c3-882d-4d93-bef5-7af65063d728');\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",
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              "\n",
              "\n",
              "<div id=\"df-621cc7bb-d3e0-4dea-a1d9-ba5dfdc7fe9e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-621cc7bb-d3e0-4dea-a1d9-ba5dfdc7fe9e')\"\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",
              "      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-621cc7bb-d3e0-4dea-a1d9-ba5dfdc7fe9e 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": 18
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "aUdArqYnXJQB"
      },
      "outputs": [],
      "source": [
        "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
        "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Wvv1wds4XJQB",
        "outputId": "9e09dc4e-bff5-4dde-ad0e-707aed792efb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   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[gdppc_col].loc[country]\n",
        "    pos_data_y = country_stats[lifesat_col].loc[country]\n",
        "    country = \"U.S.\" if country == \"United States\" else 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, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('money_happy_scatterplot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "YX9KatDdXJQC",
        "outputId": "8bc5d62c-9e71-4542-e4d7-cf02c09da772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "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"
            ],
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              "      <th>GDP per capita (USD)</th>\n",
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            ],
            "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": 21
        }
      ],
      "source": [
        "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
        "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "343ciELjXJQC",
        "outputId": "0a1cd7b2-ce82-4a68-fcef-699aecb68ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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",
        "\n",
        "w1, w2 = 4.2, 0\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
        "plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
        "plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
        "\n",
        "w1, w2 = 10, -9\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
        "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
        "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
        "\n",
        "w1, w2 = 3, 8\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
        "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
        "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('tweaking_model_params_plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "WKnUFqo-XJQD",
        "outputId": "485988c3-e321-4374-c31c-a6b4601c4a25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "cQoHjFegXJQD",
        "outputId": "0126ae65-5c35-4bb8-fbcf-9d7d52804207",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ekvJCFiaXJQD",
        "outputId": "268812e5-76c7-4bd6-ec8f-5731dd2fa194",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_gdp_per_capita"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Js1a36V7XJQD",
        "outputId": "e5d01791-03ba-4577-f7d7-88b513a1ed91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.301656332738056"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
        "cyprus_predicted_life_satisfaction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "N3fXz1VlXJQE",
        "outputId": "49c72c33-b79a-4034-84b7-1ea959e652b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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a+bh1SwDADF26FKF7d8Ul3ufPF8HVVQAfnyKIxUBaGvD66yIcOiRAvXrA0qXFCAlRsiw8AFtbPg5R/TSWv69RI8DTU4yCgvv4+mtfhIdbYeBA4NixYqjT4pKTA2zfboZevRjs7IpV3gsAvLyAQYNEiI4W4OXLIohE6uejIqbwmdKF69eBTZvUa5rTKhD6+PjIAo6uffrpp8jKyoKvry9EIhGKi4sxb948jFHWTQvA/PnzMXv27DLb9+/fDxsbG63SEKvun6F6zhjyYQx5AIw7HwkJ7gA64NKlBEgkz2Tbc3PNcPhwX4SG3kVMzEUAwLfftoe1dRFWr76ECxdc8OqrbfDzzwdQu3bZL0OxWICcHAu10mVnV6B2sImNjUWTJl5YvjwAq1bFoV69nArPiY+vj7y8dmje/CxiYh6qdZ/CwhYoLGyCbdv2w8ZG9wMpjfkzpQ2JBLh1ywEnT7rj5El3PHhQG4B6HwoBY0z5n2Mq/Pjjj5g3bx4uXbqEunXranq6Shs3bsT06dOxcOFCtGzZEomJifjggw+waNEijB8/vszxykqEDRo0QHp6Ouzs7DS6t1gsRmxsLHr16gVzaXc3A2QM+TCGPACmkY+DBwXo188M27cXoV8/+dfJsmVCTJsmwpkzYvj781KVq6sZkpKKUL8+PyY0VISxYyUYP77s19ChQwL06qXe3+o3bohVthGWzsMvv1jio49EOHasCIGBFX8FDhokwrFjAjx4UAR1/74eMUKEPXsEyMgoUtmRR1Om8JlS/xrA4cMC7NghwI4dQjx8KC/em5szdOuWiYMH6yAzM1NlPNCqRBgeHo4jR46gU6dOmDlzJtq2bVvuTTw9PTW69vTp0/Hpp59i5MiRAAA/Pz/cvXsX8+fPVxoILS0tYWlpWWa7ubm51m9uZc7VJ8aQD2PIA2Dc+WjblvfYPHLEDGFhfNuDB3xc4bhxQLt2/PiUFF7d6eMjP9/fH7h+XQhlb027duq3ETZoYK70Gk+fAmX/VjfH+vUiWFsD/v5msvPy8oB793iHF2dn+dFpaXyigFGjAHv7sjdJSyvbSejCBWDXLqBfP8DSsmqeuzF/plTJzeUdl6Kj+Xv84oV8n60t77QUEQH06yeAQCCEvX3F19S6alQgEIAxhsjIyHKPEwgEKNJwbqW8vDwIS/35JBKJIJFItEkqIaSK1a0LDB4MLFnC5xe1twcWL8Z/bYDy43JygNJ/L9vZ8Z6YytSpU/lB+JMn82ET3boBbm4CHD7cFDNmmCEpCfj+e/7FKXXqFO+lOmsWEBUl3/7333yKuHJaZzBiBB872KkTfy+uXgVWruTvxTffVC79hHv2DNi5kwe/ffuAly/l+1xcgPBw/hl85RXAykq+T90WPK0C4bhx46psTN+gQYMwb948eHp6omXLljh//jwWLVqEiRMnVsn9CCGVt2oVn19UGlyGD+dzjdauLT/G1rbsF1NWlmIw0rURI4DffgOWLweePRPByqoRgoIYvv1WICu9VmT9eh7gygvKgwfzYxYt4vlxceFzrs6aRVOsVca9ezzwRUcDhw/zYTpSPj681Dd4MP8DpLKdkbQKhGvWrKncXVVYunQpvvzyS7z99tt4+vQpPDw8MHnyZMycObPK7kkIqZw6dYAtW1Qf06QJLxU+fCifp/PyZV59WlVGjuQvABCLixATswf9+/eHuXnZRrsePfhcoqWdOKH6Hu+9x1+kchjjpenoaGDbNuDsWcX9rVvLg5+/P9Tq7asuvZsIqHbt2li8eDEWL15c00khhOiQrS2vwpo1i1eZHjwIXLzItxHTJJEACQkC7NrFg9/Nm/J9AgHQpQsPfuHhfAWRqlLpQHjs2DEkJiYiKysLdnZ2CAgIQOfOnXWRNkKIkfn5Z74ShJMTUL8+b39zdKzpVJHqVFgIxMUBW7cK8c8/ffDihTwMWVjwSRkiIvjyXToelFAurQPh8ePHERkZiVu3bgHg849K2w2bNGmC1atXI7j01O2EEJPm4gLExNR0Kkh1y8kB9uzh1Z67d0vnkBUBEMHOjmHAAAEGD+a9bEu2K1cXrQLhlStX0Lt3b+Tl5aFXr17o2bMn3N3dkZqairi4OOzfvx99+vRBQkICWrRooes0E0II0XNpacCOHTz4xcYCJYZ7w80NGDSoGO7upzB9envY2tbsMBCtAuGcOXNQWFiImJgY9O3bV2HfjBkzsHfvXoSFhWHOnDnYuHGjThJKCCFEv6WkyDu7HD3K2wClGjXiVZ4REUDHjkBxsQQxMU+hZBh4tdMqEMbHx2PYsGFlgqBU3759MWzYMBw8eLBSiSOEEKK/GAMuXZIHv8RExf1t28p7erZsqdjTU8MV+qqUVoEwMzMTPhWsq+Lj44NMdRcTI4QQYhCKi/mQEmnwS06W7xMK+eQFgwfzl5dXDSVSQ1oFQg8PDyQkJKg85uTJk/Dw8NAqUYQQQvRHQQEf7hIdDWzfzqeuk7KyAnr35oFv0CDF6ekMhVaBMCwsTDbw/YsvvoBViTltXr58ifnz5yMuLo4W0iWEqJScloO7z/Pg7VQLPs5aLGNPlNLF+5qVxXv4Rkfzf7Oz5fscHICBA3nw69OnamcHqg5aBcIvv/wSu3btwtdff40VK1agQ4cOcHV1xZMnT3D69GmkpaWhYcOG+PLLL3WdXkKIEcjIK8R7GxJx+GaabFu3Ji5YOqoN7G0MfyLpmlLZ9/XJE97Tc9s2XgIsuXixuzsPfBERfBYeI5jvW0arQOjk5ISEhAR88skn2LhxI2JKDAyysrJCZGQkFixYAEcaKUsIUeK9DYk4ditdYduxW+l4d8N5rJvUoYZSZfi0eV9v35a39x0/rjjNXNOm8p6egYHQ6XJS+kTrAfXOzs74/fffsWLFCly/fl02s4yvr69RLA1CCKkayWk5CiUWqWLGcPhmGu6k51I1qRbUfV8Z48tEbdvGX5cuKR4fGCgv+TVvXj1pr2mVnmLN3Nwcfn5+ukgLIcQE3H2ep3J/yjMKhNpQ9b4yCbB9TyHunquF6Gg+3k9KJOJVnYMH8zk9GzSo4oTqIb2bdJsQYty8HFUv8e7tZHhBUB86/ZR+X1mREPkpzsi74Yb8W3XxYb585Lq1Ne/kEhHBO72YeiuWWoEwJCQEAoEAa9euRf369RESEqLWxQUCAQ2qJ4QoaOhii25NXHDsVjqKSzRIiQQCdG7sbFClQX3q9NPQxRYd67khLlaEnBuuyE92ARPLv+Lr1OHDGyIi+HAHG9V/j5gUtQJhfHw8BAIB8vLyZL+ro6oW7yWEGLalo9rg3Q3nFQJI58bOWDqqTQ2mSnP60Onn8WM+tm/bNiAuri3EYvn3rqh2PhoHZuHbj5zQr5eZUfX01CW1AqGk5IRxSn4nhBBN2NuYY92kDriTnouUZ7kGOY6wJjv9PHpUC999J8SOHYDi3CYCNG8O9OxTCL/OOejd3RINXVyrJA3GhNoICSE1xsfZ8AKgVHV2+mGMr9geHQ1s3WqGa9dCFfZ37Cif1qxZMwCwAGDiDX8a0CoQTpw4EYMHD0ZYWFi5x+zatQtbt27F77//rnXiCCGGQR86i1S3qur0I30v69vXwoNrtbBtGw+A9+9LjxBAJJKgZ09gyBAhwsMBms2ycrQKhGvWrIG3t7fKQHjhwgWsXbuWAiEhRkyfOotUN113+snIK8Tbay7iwEEg74Yr8m85QPJSvt/Ghi9cO2hQEczM9mH48N4wNzfSEe7VrMrexZcvX8LMjGpeCTFmqjqLmIKlo9qgc2PFWaY17fTz/Dnwxx+Af5ccbPwgAGlb2yP3cgNIXlpAaF2IRp3TsGMHkJ4ObN4MjB7NYGtbpOusmDStI1V5PUIZY7h//z727NlDq08QYsRohhjtO/08fCif1iw+Xro2H2/TE9nlwabJE9g0TYVl/RcoEjK0Cu4Ba2vjfi9rktqBUCgUKgS/qKgoREVFlXs8YwwzZsyoVOIIIarVZNtcTc8Qo8u8V/Za6nT6uX5dPq3Z6dOlzm9ShOcud2DTNBXmdbNQupxBs+1ULbUDYbdu3WSB8PDhw/D09IS3t3eZ40QiERwdHRESEoI33nhDZwklhMjpQ9tcTc0Qo8u8V+X7KJEAZ87Ig19SknyfQAAEB8tXbxfav0TI9zfKvZYhzrZjSNQOhCUH0QuFQkRGRmLmzJlVkSZCSAX0YSB3Tc0Qo8u86/p9FIuBQ4d44Nu+nVeBSpmbA6+8woNfWBjg5lbyTOOZbccQadVGSAPqCak5+tQ2V90zxOgy77q6Vm4usG8fD367dgEZGfJ9trZA//48+PXvD9jZlX8dY5ltxxBpFQgfPHiAc+fOoVu3bnBwcCiz/8WLFzhy5AjatWuHevXqVTaNhJASarptrqTqniFGl3mvzLWePQN27uTBb/9+4GWJYQ4uLnwVh4gIICQEsLJSKzlGMduOodIqEM6dOxf//PMPHj16pHS/jY0NJk6ciJEjR2LZsmWVSiAhNUkfB4rr4+oN1TVDjC7zrum17t2T9/Q8ckTa05Pz8ZEvYBsczJc20pYhz7ZjqLQKhP/++y969+4NS0tLpfstLS3Ru3dvHDhwoFKJI6Sm6ENnlPIY0+oNmtJl3iu6lrdTLVy5Iu/scu6c4vmtW8s7u/j7o0xPT2I4tBpQ//DhQ6U9Rkvy8vLCw5ItxYQYEH0fKK6LgdyGSpd5L30txoCmAm/UudwOTZsCrVoBX37Jg6BAAHTtCixaBNy+DSQmArNm8YBIQdCwaVUitLCwQFZWlspjsrKyaBkmYpD0qTNKeUy5PUmXebe3MceqsR2wIfoltmxlSIizxN6nQuz9b7+FBdCrFy/5DRoE1K2ru3wQ/aFVIPTz88POnTuxaNEipdWjL1++xI4dO+Dn51fpBBJS3fSpM0pFTLk9qTJ5z8kB9uzhVZ67dwNZWfIeLXZ2wIABPPj17QvUrq2rFBN9pVXVaGRkJB48eICwsDAkJycr7Lt9+zbCw8Px6NEjvP766zpJJCHVSR87o5DKS0sDYmM9MXiwCM7OwPDhwIYNQFYWH9M3eTKwdy8/7q+/gFdfVR0Ek9NyEJf0FHfSc6svE6RKaFUijIyMRExMDLZs2QJfX1/4+PigXr16ePjwIe7cuYOioiKMGDECkZGRuk4vIVXOlDujGJs7d+Q9PY8dM4NEIm9HbNxY3tmlY0dAqGaxQJ87UhHtaD3p9qZNm/DTTz/h559/xvXr13Hz5k0AQIsWLfDOO+/grbfe0lkiCaluNLjZMDEGXLok7+l54ULJvQI0bJiBceNqY+hQEVq21K6Tiz7M6kN0q1KrT0ydOhVTp05Fbm4uMjMzYW9vj1q16K9lYvhMuTOKoSkuBk6cgGwB25KtNUIh0K0bL/UNGCDGlSuH0L9/f5ibazfQzxA6UhHN6WTBwFq1auksAHp7e+Pu3btltr/99tv46aefdHIPQtRlyp1R9FlBAXDwIA9+O3YAT5/K91lZAb1782rPgQMB5/9GR4jFwJUrFV9b1SQKhtSRiqhP71bOPX36NIpLTNlw+fJl9OrVC6+++moNpooQUtOysoCYGB78YmJ4z08pBwce9CIigD59AG3+Llen7Y86UhknrQPh/fv3MXfuXBw4cACPHj1CYWFhmWMEAgGKijRbSdnFxUXh92+++QaNGjVC9+7dtU0qIcRAPXnCV3GIjuYlwJJfMx4evMpz8GCgRw++ukNlqNP2Rx2pjJNWgTA5ORlBQUF48eIFWrZsiYKCAnh5ecHKygrJyckQi8Vo3bq10gm5NVFYWIg///wT06ZNK3dwfkFBAQoKCmS/Swf6i8ViiMVije4nPV7T8/SNMeTDGPIAUD60cfs2sH27ENu3C5CQIABj8v/7TZsyhIdLEB7O0L49U+jpWVHSVOUhJT0XJ5OfwkxY+kuR4WTyU9xKzYSXEy8N/vBqK0zffBHHbsuDZudGTlg4rFW1vD/0mdL8HhURMFbizxo1jR8/Hhs2bEBsbCy6d+8OoVCIqKgozJw5E48fP8Zbb72Fq1ev4sSJE3ByctI48VKbNm3C6NGjce/ePXh4eCg9JioqCrNnzy6z/a+//oKNjepqDEJIzWMMuHPHHgkJ7khIcMe9e4prFTVp8gIdOjxGx46P0aBBTjlXIaSsvLw8jB49GpmZmbBTsQaWVoGwXr16CAwMRHR0NAC+UO+sWbMwa9YsALyU5ufnh549e2LFihXa5QBAnz59YGFhgZ07d5Z7jLISYYMGDZCenq4y48qIxWLExsaiV69eMK9sPUsNMoZ8GEMeAMpHeYqLgWPHBNi+XYAdO4S4e1de6hOJGLp3ZwgLYxg0SIIGDXiJ7f6LPHg61pKVzHSZh5T0XAxcdrTcc3e/21Xr++qasX2mmrXrjEdZhZV6tuXJysqCs7NzhYFQq6rR9PR0+Pr6yi9iZoa8PHlvKktLS/Tq1UsWKLVx9+5dHDhwAFu3blV5nKWlpdJp3szNzbX+kFTmXH1iDPkwhjwAlA+Ar9kXG8s7u+zcCaSXaI6ztubTmQ0eDAwcKICjIw+MGXnFmLROt4PXleWhibsDghrWLbftr7GbvVb3qkqG/pnKzOcNvuHLE1BQzJ+3ricmUPf90WqKNWdnZ+Tm5ir8npKSonCMmZkZMkou1ayh1atXo27duhgwYIDW1yCE1KyMDGD9ej5dmbMzEBYGrF7Ng6CjIzB+PA+M6enA1q3AuHF8u1R1rgJiyit61IRPNl8qs62mVnjRqkTYpEkT3L59W/Z7hw4dsG/fPiQnJ6Nhw4ZIS0vD5s2b0ahRI60SJZFIsHr1aowfPx5mZno3woMQosKjR/Kenv/+C5TsOF6/vnxas27dAFX/vat78DpNolB9ktNycOx2OsJLdSGpqYkJtIoy/fr1Q1RUFDIyMuDg4IAPPvgAO3fuhL+/P5o3b45bt24hKysLUVFRWiXqwIEDuHfvHiZOnKjV+YQYC1WDu/XJjRvymV0SEhT3tWjBA19EBNCunfrTmtXU4HWaRKHq6dvEBFoFwrfeegs9evSASMSnKerRowc2btyIqKgoXL58GV5eXpg7dy7eeOMNrRLVu3dvaNGHhxCjoe8TOzMGnD0rD35Xryru79hRHvyaNtXuHjR43Xjp27NVKxBevHgRbm5uqPvfqpR2dnYICgpSOObVV1+l2V8I0RF9nNi5uFiAuDgBdu7kwe/BA/k+MzOgZ08e+MLD+WD3yqLB68aroYstOjdyBvBEYXtNPVu1Osu0adMGv/zyi+z3kJAQrFu3rsoSRYgpk7aNFZeqFSnZflJd8vJ40Js0SYQJE/qiTx8zLFvGg2CtWsCwYcCff/I1/PbvB956SzdBUIo6sBivhcP8y2yrqWerVolQJBIpzP8ZHx+PHj16VFWaCDFpNd1+8vw5sGsXD4B79wL5+QD/m9kCzs4MYWECDB4MhIbyYQ9ViTqwGC87a17Fv/vdrrifWVCjz1atQFi/fn0kJiZWcVIIIUDNtJ88eMADX3Q0EB/PB7zL0uMFhIUVw9X1BKZNC4K1dfW3UVIHFuPl5WRT4+M01QqEgwYNwtKlS9G8eXO4u7sDANasWYP4+HiV5wkEAhw8eLDSiSTElFRX29i1a/LOLqdPK+7z85N3dgkIAIqKJIiJeaZyuAMhhkqtj/XcuXNRUFCA3bt349ChQxAIBEhJSSkziL608ibKJoSotnRUG7y74bxCr9HKtp9IJDzgRUfzAJiUJN8nEADBwfIxfo0ba592QgyNWoGwdu3aCp1lSk6yTQjRPV21jYnFvKpTWu356JF8n7k58MorPPiFhQFubrpKPSGGRauKjvHjxyMgIEDHSSGElKZN21huLu/kEh3NO72UnOnQ1hbo358Hv/79AQ3npSfEKGkVCFevXq3rdBBCKiE9HbLxffv38wmupVxc+Ni+iAheAlQyRz0hJk2rQHjp0iWcPn0aw4YNky1tkZ+fj2nTpmHHjh2wtrbGxx9/jClTpug0sYQQubt35VWehw/zNkApHx8e+CIieNvff5NAEUKU0CoQzp07F0ePHkVkZKRs2+eff44VK1bA1tYW6enpeOedd9CoUSP06tVLZ4klxJQxBly5Iu/scu6c4v7WreXBz89P/Tk9CTF1WgXCU6dOoWfPnrJeoUVFRVi9ejU6dOiA+Ph4PH/+HG3btsWSJUsoEBJSCRIJn8RaGvxu3ZLvEwiALl3kPT19fGoqlYQYNq0CYVpaGho0aCD7/fTp08jKysKUKVNgZWUFDw8PhIeHIyYmRmcJJcRUFBYCcXE88G3fDqSmyvdZWDC0DS5E+GBg4mhL/Df9LyGkErQKhGZmZigoKJD9Hh8fD4FAgJ49e8q2OTk5IT09XdnphJBSsrOBPXt4yW/3biArS77Pzg7o1acYaU7JuGN1G48ti/FLKnB1p/6sRkGIIdNqhXpvb2/ExcXJfv/nn3/g4+MDLy8v2baHDx/CyclJ2emEEABPnwKrVgEDB/KenSNGABs28CDo5gZMnsyHQaSlATZ9zuKBw00ILeVzn9XUat6EGButSoRjx47F9OnTERQUBEtLS1y4cAFffPGFwjEXL15EkyZNdJJIQozFnTvy9r5jxxR7ejZuLO/sEhQECP/7M7W6V2onxNRoFQinTp2KU6dOYfPmzWCMoX///vj8889l+69cuYILFy5g9uzZOksoIYaIMeDOHTt89ZUQO3YAFy4o7m/bVh78WrRQ3tOzplejIMTYaRUILS0t8ffffyMrKwsCgQC1a9dW2O/q6orz58/D29tbF2kkxKAUFwMnTkgntDZDcrK87VwoBLp1k/f09PSs+Hr6tpo3IcamUnPJ25UzP5OzszOcnZ2V7iPEGL18Cfz7Lw9+O3bw9j9OAAuLYvTuLcDQoUIMHAho+l+DVmonpGrRoiqEaCkzk/f03LYNiIkBcnLk+xwceCeYQYOKwNheDBnSB+bmWvVNA1A1q1EQQji1AmHDhg0hEAhw4MAB+Pj4oGHDhmpdXCAQ4Pbt25VKICFVqrgYOHIEePwYcHcHunZVOR9Zaiov8W3bBhw8yFd3kPLwkK/h1707X91BLGaIiSku93rqopXaCak6agVCiUSisLZg6d/Lw0pU4xBSlZLTcnD3eZ5mAWLrVuD99/ny7FL16wNLlgBDhsg23bol7+l54gTvACPVrJm8s0v79vKenlWFVmonRPfUCoSlF+CtaEFeQqpLRl4h3tuQqFBl2K2JGgPNt24Fhg1TjGoA8PAh2LBhuPPtZqzJGoJt24DLlxUPCQyUd3Zp3lx3eSGE1AxqIyQG7b0NiTh2S3EGI+lA83WTOig/qbiYlwSV1VgwBgYBzKZ/gHkIhwQiiERAjx48+IWH80IjIcR4aFWRExISgnXr1qk85s8//0RISIhWiSJEHdKB5sWlAlrJgeZKHTmiWB1aihAMnriPz7scwbp1vAfogQPAO+9QECTEGGkVCOPj4yusHr179y4OHTqkzeUJUYs6A81Ly8gAjv7zWK3rf/X2Y4wdCzg6apM6QoihqLKq0dzcXJib02TApOqoO9D80SO+isO2bXxVh85F7ohX5wbu7pVOIyFE/6kdCO/du6fwe0ZGRpltAFBcXIz79+9jy5YtNLMMqVKqBpr72dbHpt9qYds24ORJxfOeNe+KzAf1YZf9EAIoaScUCHgdaNeuVZwDQog+UDsQent7y4ZMCAQCLFmyBEuWLCn3eMYYFi5cWPkUEqKCdKD5oRtpKEy1R95NNwhS6mH7Y2tsL3Fcx47ynp5Nm4qArUt4r1EIFDvNSIcFLV6scjwhIcR4qB0Ix40bB4FAAMYY1q1bh9atWyMgIKDMcSKRCI6OjggJCUHfvn11mVZCFIjFwNkEc9gndoB4qwSpj+RN3mZmQEgID3zh4Xywu4IhQ4DNm4H33gMePpRvr1+fB8ES4wgJIcZN7UC4Zs0a2c+HDh1CZGQk3nvvvapIEyHlyssD9u/n7X07dwIvXkj3CFGrFtCvHw9+Awbwac5UGjIECA0F7O357zExQO/eVBIkxMRo1Vnmzp07uk4HMQBazd6iA8+fA7t28eC3bx+Qny/f5+wMhIXx4BcaClhba3jxkkGvWzcKgoSYIBpQTyqk9ewtlfDgAbB7tw+WLBHh8GE+Bl7Ky0ve3te5M68G1ZqZGTB+vPxnQojJ0fp/fnZ2NpYtW4YDBw7g0aNHKCgoKHMMTbptHLSavUUL167xUt+2bcCZM+YA/GX7/PzkwS8gQPkCtlqxtARKVPsTQkyPVoEwLS0NnTp1wu3bt2FnZ4esrCzY29ujsLAQ+f/VW3l4eNA4QiMgnb2ltJKzt2hbTSqRAKdPSxewBZKS5PsEAgZf3+cYP94BQ4eK0LixlhkghJAKaDWzTFRUFG7fvo1169bhxX+9FT788EPk5ubi5MmT6NChA7y9vXHlyhWdJpZUP21mb1GlsBCIjQXefhto0IAPa1iwgAdBc3Pe2WXFCuDu3SLMn38U06ZJqjYIMgbk5vKXBqulJKflIC7pafnTuBFCDIZWJcKYmBi88soreO2118rsCwwMxJ49e+Dn54fZs2djwYIFGl//4cOHmDFjBvbs2YO8vDw0btwYq1evRvv27bVJLqkEdWdvUSU3F9i7l5f8du3iC9pK2doC/fvzas/+/QE7O7695Dp/VSovjycC4Cvr1lKdn5poLyWEVC2tSoSPHz9GmzbylbFFIpGsShQA6tSpg379+mHTpk0aX/vFixfo3LkzzM3NsWfPHly9ehXff/896tSpo01SSSVJZ28RlWqUEwkE6NbEpdxq0fR0YPVq3qPT2ZmPXV+/ngdBFxfg9deB3bv5cX//DYwcKQ+C+kxVeykhxDBpVSK0t7eHuMSf7HXq1MGDUrP529nZ4cmTJxpfe8GCBWjQoAFWr14t2+bj41Pu8QUFBQoddbKysgAAYrFYIY3qkB6v6Xn6Rtf5+OHVVpi++SKO3ZYHgM6NnLBwWCuFe9y9C+zYIcT27QIcPSqARCIPnj4+DOHhEoSHM3TsyBRGKShLZrU9C7EY5rIfxSqLoinpuTiZ/BRmwtL/cRhOJj/FrdRMeDkplqDpM6U/jCEPAOVDm3tURMC0WEY+ODgYbm5u2LZtGwCgT58+uHDhAq5cuQInJyfk5+ejdevWEAqFuH79ukbXbtGiBfr06YMHDx7g0KFDqFevHt5++2288cYbSo+PiorC7Nmzy2z/66+/YGOjulqPVA5jwL17tZGQ4I6TJ92RnOygsN/HJwNBQano2PExvLyydNfTU4dEL19i4MiRAIBdGzei2MqqhlNECNGVvLw8jB49GpmZmbBTUeWkVSCcNWsWfvjhB6SmpsLGxgZbt27FsGHD4OHhgeDgYJw7dw4pKSmYN28ePv30U42ubfXfF9G0adPw6quv4vTp03j//ffxyy+/YLx0vFcJykqEDRo0QHp6usqMKyMWixEbG4tevXoZbI/XlPRc3EvPRk7yuSrJh0QCnDwpwPbtAmzfLsTt2/LoJhQydO7MEBbGEBYmgYqCfIUq+yxS0nNx/0UeRAIBihmDp2OtMqU1AEBuLsz/q3YXv3ihso0wJT0XA5cdLXf/7ne7Ki0RGvpnCjCOfBhDHgDKhyaysrLg7OxcYSDUqmp0ypQpaNGiBfLy8mBjY4MhQ4Zg4cKFmDt3LrZs2QJra2tMmzYN06dP1/jaEokE7du3x9dffw0AaNOmDS5fvlxuILS0tISlpWWZ7ebm5lq/uZU5t6aU7MRhKWL4tgMwdeNF/DCyXaU7cRQWAv/+yzu7bN8OlKzxtrTkM7pERACDBglQt640MOpmhhZNn4WyzixSSju1lLi2ubm5wu+lNXF3QFDDukpXu+jc2BmN3ex1lg99ZQz5MIY8AJQPda+tDq06y7i7u2PEiBFwdnaWbfvoo4+Qnp6Ox48fIycnBwsXLoRIi+mq3N3d0aJFC4VtzZs3V7rkE5FT1okjIfmZ1p04srOBTZuAUaN455Z+/YCVK3kQtLPj2zdtAtLSeE/QSZOAunV1kZPKUfY+SOmiU8vSUW3QubGzwrbOjZ2xdFSbcs4ghOg7nc4pJRKJ4OrqWqlrdO7cGUklR1YDuHHjBry8vCp1XWOmq0HvT58CO3bwkt+BA7wkKOXmxldxiIgAevYELCx0mQPdKO99kFL6fohE/y3HBLXmGbW3Mce6SR1wJz0XKc9yq33eVUKI7mkVCO/fv4+bN2+iY8eOsg4pEokECxcuxI4dO2BlZYVp06ZhwIABGl/7ww8/RKdOnfD1119j+PDhOHXqFFauXImVK1dqk1SToM6g9/K+rO/ckc/scuwYbwOUatyYB76ICCAoCBBqVX9QfSp6H6QU3g8rK+CffzS+l48zBUBCjIVWgfDLL7/Ezp07kZqaKts2b948zJo1S/b74cOHcfz4cQQGBmp07cDAQGzbtg2fffYZ5syZAx8fHyxevBhjxozRJqnVpqZWZgA0G/TOGHDxojz4XbigeGy7dnw+z4gIoEULHc7pWQ0qeh+k1JkEgBBiOrQKhMeOHUNoaKisIZIxhmXLlsHX1xf79+9HamoqQkNDsXDhQq0G1Q8cOBADBw7UJmnVTh9mGpEOelfWiaNbExd41qmFI0fkwa/kKlpCIV99SDqhtadntSS5SpT3PkhJO7VQSY4QUpJWlV1Pnz5VaLNLTExEWloa3n33XdSvXx/t27fH4MGDcfr0aZ0lVF/py0wjpTtxFBYKUS+7EXCkPdzdebD74QceBK2s+Iwvq1fzzi9xcXyhdkMOglLKOrNIKe3UkpvLi70CAf+ZEGJytCoRSiQSSEo0JsXHx0MgECAkJES2rV69egpVp8aoKldm0JS9jTmWDuuANX+/xLatDOMWmePlS/njdXAABg7kJb8+fSqcUtNgle7MYiYUoEjCqFMLIaRcWgVCT09PnDp1SvZ7dHQ03N3d0axZM9m21NRUODg4VDqB+qwynVR0JTWVj+2LjgYOHgTEYvnMKB4eDIMHCxARAXTvrnKInEFRpz2WOrMQQtSlVSAcOnQo5s2bh2HDhsHKygpHjx7F1KlTFY65evUqGjZsqJNE6itdrMygjVu35O19J04orh7UrBkQFlYMF5ejeO+9TrC0NJLoB/1ojyWEGB+tAuHHH3+M/fv3Y+vWrQAAf39/REVFyfbfvXsXp06d0nh6NUOjqpOKLjtlMAacPy8PfpcvK+4PDJQPc/D1BcRiCWJiMvR+uIOmVLXHrpvUoYZSRQgxdFoFQjs7OyQkJODyf9/IzZs3LzOLzNatW01i/cClo9rg3Q3nFUopuphppKgIOHpUHvxKTqwjEgE9evDAFx4O1K9fqVsZBH1qjyWEGJdKzSzTqlUrpdu9vLxMZiYYXc40kp/PV2+PjuYzvDx7Jt9nbQ307cuD34ABgKOjbtJvKPShPZYQYpx0OsWaKdO2c0ZGBp+rMzqar+Jesge/oyMwaBAPfr16AbpeVaomJwHQVJW1x4pEQP/+8p8JISaHAmENePhQ3tMzLo5Xg0o1aCCf2aVrV8CsCp6QIXY6qbL2WCsrYPduHaWSEGKIKBBWk6QkeXvfyZOK+1q0kM/s0q5d1U9rZqidTqqqPZYQYtooEFYRxoAzZ+TB79o1xf0dO8qDX9Om1ZcuQ+50Qis/EEKqAgVCHRKLgcOHeeCLjgYePJDvMzMDQkJ48AsLAzw8yr9OVbbdGUOnE50Ols/NlS+k+PSp8U65QwgpFwXCSsrLA/bt44Fv507gxQv5vlq1+IK2ERG8P0ZFE+1UR9tdTU0CoNfy1Fu+iRBinCodCK9evYrr168jNzcXY8eO1UWa9N7z57yn57ZtPAjm58v3OTvzEl9EBPDKK3zYg7qqo+2uuiYBIIQQQ6H13COnT59GQEAA/Pz88Oqrr2LChAmyfYcPH4aNjQ127NihizTqhfv3gWXLeHCrWxcYP56XAvPzAS8v4IMPgPh44PFj4Lff+ATXmgRBadtd6eWDSrbd6YqyFRqo0wkhxFRpVSK8cuUKQkJCIBQK8eGHH+L69evYs2ePbH/Xrl3h7OyMf/75B2FhYTpLbHVijHdwkXZ2OXNGcb+fn7yzS0BA5Xt6VmfbHXU6IYQQOa0CoXQl+rNnz6Jx48aYPXu2QiAUCAQIDg42uPUIJRIgKakOjh4VYscO4MYN+T6BAOjUST6tWePGml27og4wNdF2p0mnE0MafE8IIZrQKhAeOnQIQ4cORWMV0cDT0xN79+7VOmHVpbCQV2lGRwPbt5vh0aNusn0WFrwqNCKCz/Di5qb59dXtAKOvbXeGOPieEEI0oVUbYXZ2NupKu5yXIz8/H8XFxVolqqrl5ABbtgCvvcbb+/r0AZYvBx49EsDaWoxXX5VgwwYgLQ2IiQHeeEO7IAhotoK9PrbdaZJ+gyQU8sUau3eH0S3XQQhRi1YlwgYNGuDSpUsqjzl37hwaNWqkVaKqQno6H96wbRuf2PrlS/m+unV5defAgUUoLNyL8PC+MDev/JeipoPX9a3tzpAH36vN2ppXCRBCTJZWgXDgwIH48ccfceDAAYSGhpbZv2nTJiQkJODLL7+sdAIr4+5deWeXI0d4G6BUw4byzi7BwXy+ZbGYISZGUt7lNL+/lh1g9GV1dWMYfE8IIRXRKhB+/vnn2Lx5M/r374/x48cjNTUVAPDzzz/jxIkT2LBhA7y9vTFt2jSdJlYTXbsCFy8qbgsIkE9o7edX9XN6GvrgdUNPPyGEqEOrQOji4oL4+HiMGzcOv/32m2z71KlTAQBBQUHYsGED7O3tdZNKLVy8yJt8unThwW/wYMDHp3rToK8dYNRl6OlXS24u4O3Nf05JoSnWCDFBWs8s06hRIxw7dgyJiYlISEjA8+fPYWdnh6CgIAQGBuoyjVpZuhQYMQJwcanhdBj4igmGnn61pKdXfAwhxGipFQiHDBmCkSNHYvjw4QD4zDHe3t7w9PREQEAAAgICqjKNWhk3DrCzq+lU6F8HGE0ZevoJIaQianWNjI6OxvXr12W/9+zZE2vWrKmqNBklH+da6NmsrsEGEUNPv9Hx9gZKTGuI+Hje6K3LHrACARAVpbvrEaKn1AqEDg4OyMrKkv3OSs2HSYhJWbOGBwnpy8qKLyo5dSrw5ElNp04zMTGmEezOnYMoIgL9XnsNZvb2QKtWwI8/Vnzetm18oLGHB2BpCdSvDwwbBly+rPz4HTuAtm35Z8LTE5g1Cygq0m1eiM6pVTXaokULbNiwAYGBgXB3dwcApKSk4PDhwxWe261btwqPIcQgzZnDe2C9fAkcPcpnZYiJ4V+SNqp73Opct258BngLC83Oi4kBfvpJeTDMz+cLaRq6/fv51FABAUgaPhzNAwNhlpKiuGBoeS5dAurUAd5/ny8tk5oK/P470KEDcOIE0Lq1/Ng9e3ivvB49eCeFS5eAuXP5OpfLl1dR5oguqPUpnzlzJgYPHozRo0fLtq1duxZr166t8Fx9nV2GkErr1w9o357//PrrgJMTsGgRsH07MGqU8nNyc6umZ6pQyEshuqTr69WErCzeYWDAABRv2IDkvXvh278/YK7m9IAzZ5bd9vrrvGS4fDnwyy/y7R9/DPj788Ar/QPCzg74+mseSH19K58fUiXUqhrt3bs3rl27hpUrV8om3O7evTtmzpxZ4YsQvSYU8mDWvn3lp1gLCeH/3rnD/50wAWZ16sDm8WOIwsKA2rWBMWP4PokEWLwYaNmSBxxXV2DyZMWVnQG+DMrcufyL18YG6NkTuHKl7L3LayM8eZKvCl2nDg/A/v7AkiWy9OGnn/jPJat6pZS1EZ4/z/8AsLMDbG35ZLwJCYrHSKuOjx0Dpk3jXbdr1eIDeNPKzlRUpf76i1dXz5sHCIUQvXypOLOGNurW5c8iI0O+7epV/nrzTcVS9Ntv82e4eXPl7kmqlNr1Hl5eXpg0aRIAICoqCj169KBARwyftTWgq1VSbt/m/zo5ybcVFSF49mygVy/gu+/kVaaTJ/OAERkJvPceD57LlvFAc+yYvMQycyYPhP3789e5c0Dv3ny2+IrExvKFMd3deYnEzY2vLbZrF/998mTg0SN+3B9/VHi52vfuwWzMGB4EP/mEp3HFCl4VeOgQEBSkeMK77/IAPGsWH6O5eDFvR/37b9U3KigAsrMrzh/AqytVOXCAp/fhQ5iFh2PgzZtgtWoBY8cCP/ygfqk3IwMQi3nV6OLFvKT5yivy/ef/m3tXWkMg5eHB/4g5byRz8xoprRoA7ty5AwcHBx0nhRADk5nJxyC+fMmD15w5PLAOHCg7RFBQgEedO8Pnt98glAa3o0eBVauA9euBEs0N6NkT6NsX+Ocfvj0tDfj2W2DAAD5RrrS09sUXvLpNleJiHujc3YHERKDk/1dpZ7fgYN7JJzaWz0Bfgebr1/NgcPQon6MQ4NWOzZrxwHjokOIJTk68mlCabomEd1DJzARUTbaxYQP/A0EdFXXcu3mTd1YJD4ckMhJnhwxBu5wciH76iQe3DRvUu0/HjkBSEv/Z1hb43/+A/woGAPiK3AB/v0tzd+d/cBC9pVUg9PLy0nU6CDE8pefZ9fLiwa1ePYXNKX37QmFSo3/+4YGgVy/Fwfzt2vEv2bg4HggPHOAlv3ffVayy/OCDigPh+fO8lPnDD4pBENBubsHiYrgkJoKFhUEgDYIA/5IfPRr49VdeSio5ePfNNxXv1bUrT8/du7yKtjx9+vDgrAs5OUBeHjBlCiQ//IDHMTGQ9O8PUVERL83OmQM0aVLxdVav5vlLTuY/5+fzPzak1en5+fxfS8uy51pZ8XOJ3lIrEM6ZMwcCgQDvvPMOHB0dMWfOHLUuLhAIanzibUJUyssDWrTgP1+9qllvz59+4iUqMzPextesWZl2RmZmhvySVaUAL6VkZvK2JmWePuX/3r3L/y39Re3iwqscVZFW07ZqpUZG1JCWBrOCAhQ3bVp2X/PmvLR3/z5v85Ty9FQ8Tprm0u2gpbm7Ky9ZacPamv9buvPS6NE8EJ44oV4gDA6W/zxyJM8zwKu7S96noKDsuS9fyvcTvaRWIIyKioJAIMCIESPg6OiIKDXHHVEgJHqPMXnA0XR8bIcOZduESrO0LNsJRyLhQXD9euXn1PS8gLoiEinfXtH7nJ/P/1BQR0ULhXp48M5Frq6K26V/hFQUlJWpU4d3jFq/Xh4IpYH78WOgQQPF4x8/5p8VorfUCoRxcXEA+KrzJX+vClFRUZg9e7bCtmbNminMbEOIQWvUiFd7du6suqQgbYK4eVPeJgfwtsOKvsCla4Fevly2CrckdatJXVxQZGkJ4Y0bZfddv86DfekAoK2//9ZdG2G7drya9eFDxfdQ2man7R8dpYO1dJrJM2cUg96jR3y84ptvancfUi3UCoTdu3dX+buutWzZEgcOHJD9bmYMg3oJkRo+HPj5Z+Crr8q29RUV8XYtBwcewMzN+eDs3r3lQWvx4orv0bYtH+y/eDEfJlG6s4z0WtIxjRkZZdsSSxKJkBYQALedO3kPUOmKHU+e8CEKXbrobnJfXbYRDh8OfPMN8NtvvI1SatUqXqXdo4d82717vKq85Hi/p0/LVmGnpAAHDyrWBrRsyc9buZJ3UpKWhpcv5+/1sGG6yQ+pElUWYRYsWID9+/fj4MGDGp9rZmYGt4qqPAgxVN278y/L+fN5j87evXnAu3mTd6RZsoR/cbq48EHa8+fznqj9+/NOMHv2VDxsQCjkX8L/zaiCyEhefXf9Oq8q3LePH9euHf/3vfd4ABKJeBuYEtfGjIHb5cs86L39Ng8kK1bwdrFvv9XZ26PTNsI2bYCJE4Hff4eosBDejo4Q/fEHsGUL8NlnvOpUatw43vO1ZCnTz48PkwgI4FWiN2/yoCoW8wBb0sKFQFgYf54jR/LS+LJlfAC+tE2R6KUqC4TXr19HvJYTAN+8eRMeHh6wsrJCcHAw5s+fL6uWLa2goAAFJRqopXOiisViiMVije4rPV7T8/SNMeSj2vIgFsNc9qOYf8FVQFBcDDMARUVFYCqOF0kkkFY8lsnH0qUQBARA+OuvEHz+OQ8qXl6QjBoFSYcO8nTMmgWhuTmEv/4KxMWBdeiA4t27YTZ4MJhEguL/jhMUFZVNU0gIBLGxEM6dC8H33/O2yYYNIZk0CRLpMYMGQfjOOxBu2gT8+ScEjEE8dCgAwBx8ZijJf/+Xsj098XL/fljOng3B/PmARALWoQMka9aAtW0rS3N574/SNFaHpUshrFcPgrVr4ffoEeDlheLvvoPkvfcUnreIMQih+KyEb74JwZ49EOzdy8c21q0LFhqK4hkzeJAsmY8+fSDYtAmiuXN5T18XF0hmzIDkf/9T63OlLmP4/w1UTz7UvbaAVdEM2pGRkVi3bp3GU6zt2bMHOTk5aNasGR4/fozZs2fj4cOHuHz5MmrXrl3meGVtigDw119/waa653skBkf08iUG/lcC2rVxI4qNYVoxQggAIC8vD6NHj0ZmZibsVFTd610gLC0jIwNeXl5YtGiRbGabkpSVCBs0aID09HSVGVdGLBYjNjYWvXr1grm6cxHqIWPIR7XlIS8PZv91jS86cULnk2Ubw7MAjCMfxpAHgPKhiaysLDg7O1cYCPW+F4qDgwOaNm2KW7duKd1vaWkJSyWDWM3NzbV+cytzrj4xhnxUeR7s7fn4QQBV+U4Zw7MAjCMfxpAHgPKh7rXVUclZhqteTk4Obt++LVv+iRBCCNElvQuEH3/8MQ4dOoSUlBQcP34cEREREIlEGFXesjaEEEJIJahdNdq/f3+NLnzp0iWNEwMADx48wKhRo/Ds2TO4uLigS5cuSEhIgIuxzLZB9EteHhAYyH8+fbr6F9QlhNQ4tQPh3r17Nb64QIvJfTdu3KjxOYRojTFZG6HGU6wRQoyC2oHwjnSxUUIIIcSIaLQwLyGEEGJs9K6zDCGEEFKdKBASQggxaRQICSGEmDS9n1mGkColEMjX/dOilzMhxPBRICSmzcaGry9HCDFZVDVKCCHEpFEgJIQQYtIoEBLTlp/Pp1gLDOQ/E0JMDrUREtMmkQBnzsh/JoSYHCoREkIIMWkUCAkhhJg0CoSEEEJMGgVCQgghJo0CISGEEJNGvUYJcXau6RQQQmoQBUJi2mrVAtLSajoVhJAaRFWjhBBCTBoFQkIIISaNAiExbfn5QI8e/EVTrBFikqiNkJg2iQQ4dEj+MyHE5FCJkBBCiEmjQEgIIcSkUSAkhBBi0igQEkIIMWkUCAkhhJg06jVKiI1NTaeAEFKDKBAS01arFpCbW9OpIITUIKoaJYQQYtIoEBJCCDFpFAiJaXv5EhgwgL9evqzp1BBCagC1ERLTVlwMxMTIfyaEmBwqERJCCDFpFAgJIYSYNL0OhN988w0EAgE++OCDmk4KIYQQI6W3gfD06dNYsWIF/P39azophBBCjJheBsKcnByMGTMGv/76K+rUqVPTySGEEGLE9LLX6DvvvIMBAwYgNDQUc+fOVXlsQUEBCgoKZL9nZmYCAJ4/fw6xWKzRfcViMfLy8vDs2TOYm5trnnA9YQz5qLY85OZCenXxs2c6H0JhDM8CMI58GEMeAMqHJrKzswEAjDGVx+ldINy4cSPOnTuH06dPq3X8/PnzMXv27DLbfXx8dJ00Yuy8vGo6BYSQKpCdnQ17e/ty9wtYRaGyGt2/fx/t27dHbGysrG2wR48eCAgIwOLFi5WeU7pEKJFI8Pz5czg5OUEgEGh0/6ysLDRo0AD379+HnZ2d1vmoacaQD2PIA0D50CfGkAeA8qEJxhiys7Ph4eEBobD8lkC9KhGePXsWT58+Rdu2bWXbiouLcfjwYSxbtgwFBQUQiUQK51haWsLS0lJhm4ODQ6XSYWdnZ9AfMCljyIcx5AGgfOgTY8gDQPlQl6qSoJReBcJXXnkFly5dUtgWGRkJX19fzJgxo0wQJIQQQipLrwJh7dq10apVK4VttWrVgpOTU5nthBBCiC7o5fCJmmJpaYlZs2aVqWo1NMaQD2PIA0D50CfGkAeA8lEV9KqzDCGEEFLdqERICCHEpFEgJIQQYtIoEBJCCDFpFAgJIYSYNIMOhPPnz0dgYCBq166NunXrYvDgwUhKSlI4pkePHhAIBAqvKVOmKBxz7949DBgwADY2Nqhbty6mT5+OoqIihWPi4+PRtm1bWFpaonHjxlizZk2Z9Pz000/w9vaGlZUVgoKCcOrUKbXysXz5cvj7+8sGlgYHB2PPnj2y/S9fvsQ777wDJycn2NraYujQoXjy5Ile5UGdfBjCsyhN2VJghvI8KsqHITyPqKioMmn09fWV7TeEZ1FRHgzhOUg9fPgQr732GpycnGBtbQ0/Pz+cOXNGtp8xhpkzZ8Ld3R3W1tYIDQ3FzZs3Fa7x/PlzjBkzBnZ2dnBwcMCkSZOQk5OjcMzFixfRtWtXWFlZoUGDBvj222/LpOWff/6Br68vrKys4Ofnh5iYGI3yooAZsD59+rDVq1ezy5cvs8TERNa/f3/m6enJcnJyZMd0796dvfHGG+zx48eyV2Zmpmx/UVERa9WqFQsNDWXnz59nMTExzNnZmX322WeyY5KTk5mNjQ2bNm0au3r1Klu6dCkTiURs7969smM2btzILCws2O+//86uXLnC3njjDebg4MCePHlSYT527NjBdu/ezW7cuMGSkpLY559/zszNzdnly5cZY4xNmTKFNWjQgB08eJCdOXOGdezYkXXq1Emv8qBOPgzhWZR06tQp5u3tzfz9/dn7778v224oz6OifBjC85g1axZr2bKlQhrT0tJk+w3hWVSUB0N4Dowx9vz5c+bl5cUmTJjATp48yZKTk9m+ffvYrVu3ZMd88803zN7enkVHR7MLFy6wsLAw5uPjw/Lz82XH9O3bl7Vu3ZolJCSwI0eOsMaNG7NRo0bJ9mdmZjJXV1c2ZswYdvnyZbZhwwZmbW3NVqxYITvm2LFjTCQSsW+//ZZdvXqV/e9//2Pm5ubs0qVLauWlNIMOhKU9ffqUAWCHDh2SbevevbvCf/7SYmJimFAoZKmpqbJty5cvZ3Z2dqygoIAxxtgnn3zCWrZsqXDeiBEjWJ8+fWS/d+jQgb3zzjuy34uLi5mHhwebP3++VnmpU6cOW7VqFcvIyGDm5ubsn3/+ke27du0aA8BOnDih13komQ/GDOtZZGdnsyZNmrDY2FiFdBva8ygvH4wZxvOYNWsWa926tdJ9hvIsVOWBMcN4DowxNmPGDNalS5dy90skEubm5sYWLlwo25aRkcEsLS3Zhg0bGGOMXb16lQFgp0+flh2zZ88eJhAI2MOHDxljjP3888+sTp06srxJ792sWTPZ78OHD2cDBgxQuH9QUBCbPHmyWnkpzaCrRkuTLsHk6OiosH39+vVwdnZGq1at8NlnnyEvL0+278SJE/Dz84Orq6tsW58+fZCVlYUrV67IjgkNDVW4Zp8+fXDixAkAQGFhIc6ePatwjFAoRGhoqOwYdRUXF2Pjxo3Izc1FcHAwzp49C7FYrHBtX19feHp6yq6tb3lQlg8pQ3kWJZcCK8nQnkd5+ZAyhOdx8+ZNeHh4oGHDhhgzZgzu3bsHwLCeRXl5kDKE57Bjxw60b98er776KurWrYs2bdrg119/le2/c+cOUlNTFe5hb2+PoKAghefh4OCA9u3by44JDQ2FUCjEyZMnZcd069YNFhYWCnlJSkrCixcv1MqvpvRqirXKkEgk+OCDD9C5c2eF6dhGjx4NLy8veHh44OLFi5gxYwaSkpKwdetWAEBqaqrCBwyA7PfU1FSVx2RlZSE/Px8vXrxAcXGx0mOuX7+uVvovXbqE4OBgvHz5Era2tti2bRtatGiBxMREWFhYlJlI3NXVtcL0VXceVOUDMJxnoWopsNTUVIN5HhUtaWYIzyMoKAhr1qxBs2bN8PjxY8yePRtdu3bF5cuXDeZZqMpD7dq1DeI5AEBycjKWL1+OadOm4fPPP8fp06fx3nvvwcLCAuPHj5elRdk9Sqazbt26CvvNzMzg6OiocEzpZfRK5rdOnTrl5ld6DU0ZTSB85513cPnyZRw9elRh+5tvvin72c/PD+7u7njllVdw+/ZtNGrUqLqTWa5mzZohMTERmZmZ2Lx5M8aPH49Dhw7VdLI0Vl4+WrRoYRDP4v79+3j//fcRGxsLKyurmk6O1tTJhyE8j379+sl+9vf3R1BQELy8vLBp0yZYW1vXYMrUpyoPkyZNMojnAPDCRvv27fH1118DANq0aYPLly/jl19+wfjx42s4dZVjFFWjU6dOxa5duxAXF4f69eurPDYoKAgAcOvWLQCAm5tbmV5m0t/d3NxUHmNnZwdra2s4OztDJBIpPUZ6jYpYWFigcePGaNeuHebPn4/WrVtjyZIlcHNzQ2FhITIyMsq9tr7kQVU+lNHHZ1FyKTAzMzOYmZnh0KFD+PHHH2FmZgZXV1eDeB4V5aO4uLjMOfr4PEpzcHBA06ZNcevWLYP7v6EsD8ro63Nwd3eX1e5INW/eXFbNK72Oqnu4ubnh6dOnCvuLiorw/PlznTwzbZ4HYOCBkDGGqVOnYtu2bfj333/VWpU+MTERAH+oABAcHIxLly4pPJzY2FjY2dnJHnpwcDAOHjyocJ3Y2FhZ25eFhQXatWuncIxEIsHBgwcV2sc0IZFIUFBQgHbt2sHc3Fzh2klJSbh3757s2vqah5L5UEYfn4V0KbDExETZq3379hgzZozsZ0N4HhXlQ9mSZvr4PErLycnB7du34e7ubrD/N0rmQRl9fQ6dO3cuMzztxo0b8PLyAgD4+PjAzc1N4R5ZWVk4efKkwvPIyMjA2bNnZcf8+++/kEgksj8AgoODcfjwYYjFYoW8NGvWDHXq1FErvxrTqouNnnjrrbeYvb09i4+PV+h6nJeXxxhj7NatW2zOnDnszJkz7M6dO2z79u2sYcOGrFu3brJrSLsm9+7dmyUmJrK9e/cyFxcXpV2Tp0+fzq5du8Z++uknpV2TLS0t2Zo1a9jVq1fZm2++yRwcHBR6epXn008/ZYcOHWJ37txhFy9eZJ9++ikTCARs//79jDHeRdzT05P9+++/7MyZMyw4OJgFBwfrVR4qyoehPAtlSvfqM5TnoSofhvI8PvroIxYfH8/u3LnDjh07xkJDQ5mzszN7+vQpY8wwnoWqPBjKc2CMD8MxMzNj8+bNYzdv3mTr169nNjY27M8//5Qd88033zAHBwe2fft2dvHiRRYeHq50+ESbNm3YyZMn2dGjR1mTJk0Uhk9kZGQwV1dXNnbsWHb58mW2ceNGZmNjU2b4hJmZGfvuu+/YtWvX2KxZs0x3+AQApa/Vq1czxhi7d+8e69atG3N0dGSWlpascePGbPr06QpjdBhjLCUlhfXr149ZW1szZ2dn9tFHHzGxWKxwTFxcHAsICGAWFhasYcOGsnuUtHTpUubp6cksLCxYhw4dWEJCglr5mDhxIvPy8mIWFhbMxcWFvfLKK7IgyBhj+fn57O2332Z16tRhNjY2LCIigj1+/Fiv8lBRPgzlWShTOhAayvNQlQ9DeR4jRoxg7u7uzMLCgtWrV4+NGDFCYdyaITwLVXkwlOcgtXPnTtaqVStmaWnJfH192cqVKxX2SyQS9uWXXzJXV1dmaWnJXnnlFZaUlKRwzLNnz9ioUaOYra0ts7OzY5GRkSw7O1vhmAsXLrAuXbowS0tLVq9ePfbNN9+UScumTZtY06ZNmYWFBWvZsiXbvXu3RnkpiZZhIoQQYtIMuo2QEEIIqSwKhIQQQkwaBUJCCCEmjQIhIYQQk0aBkBBCiEmjQEgIIcSkUSAkhBBi0igQEkIIMWkUCAkhKkVFRUEgECA+Pr7G0pCXl4d69eoprNRgKJKSkmBmZoaff/65ppNCykGBkOhEYmIipkyZghYtWsDOzg4WFhZwc3NDr1698P333yMtLa3MOQKBQOFlbW0NNzc3dOnSBR9//DEuXLig9F4pKSllzrWwsECDBg0wevRoXLx4saqza/Kkz2DChAnVcr+FCxciPT0d//vf/xS2e3t7QyAQqDy3vGPu3buHt99+G02aNIGVlRVsbW3h4+ODAQMGYMGCBcjNzVU4XtvPa7NmzTBq1CjMnj0b2dnZGuacVAeaYo1UikQiwSeffILvv/8eIpEI3bp1g7+/P2rVqoWnT5/ixIkTuHLlCmrVqoWkpCTUq1dPdq5AIICTkxOmTp0KABCLxUhPT8f58+dli8lOnDgRP//8MywtLWXnpaSkwMfHB40aNcJrr70GgM/on5CQgGPHjsHS0hIHDx5E586dq/GdMF7p6elIT0+Hp6cnbGxsAMifwfjx47FmzZoqvX9WVhbq1auHiIgIrFu3TmGft7c37t69C1VfY8qOuXDhAnr06IGMjAx07twZbdu2ha2tLe7du4cjR47g3r17uHnzJho3biw7R9vPK8AXrPb398fcuXPxxRdfVPo9ITqm9SylhDC+4gQA1rZtW3bz5k2lx5w9e5aFhoaW2Q+ANWvWTOk5ly5dYgEBAQwAe+211xT23blzhwFgffr0KXPeF198wQCw7t27a5chohbpMxg/fnyV32vZsmUMAIuNjS2zz8vLi1X0NabsmJCQEAaArVu3Tuk5x48fZy9evFDYpu3nVcrf3595eXmx4uJilekl1Y8CIdFaUlISE4lEzMXFRbYsjiqlZ8tX9cXCGGNPnz5lLi4uDAA7efKkbLuqQJiamsoAMBsbmwrTs3r1atlqJdHR0SwwMFA2u39kZGS5y9MkJyezSZMmsQYNGjALCwvm5ubGxo8fz1JSUsocKw3KDx48YGPHjmWurq5MIBCwuLi4CtNXUFDAFi1axNq3b89sbW1ZrVq1WPPmzdmHH37Inj9/Ljvu33//ZZGRkaxp06asVq1arFatWqxdu3YKy9YoS9P9+/fZyJEjmZOTE7O2tmadOnVSGmxmzZrFAMjSLH3flL2kxzx8+JDNnDmTBQUFMRcXF2ZhYcG8vLzYW2+9xZ48eVJh3ktq164dc3R0VBpAtA2E1tbWzMHBQaN0aPt5lZo7dy4DwA4cOKDRfUnVozZCorW1a9eiuLgYkydPhouLS4XHm5mZaXR9FxcXTJkyBQDw999/a3RuRe1GJW3ZsgWvvvoqGjdujA8++AB+fn5YvXo1unTpghcvXigce/LkSbRp0wZr165Fu3bt8P7776Nr165Yv349OnTogOTk5DLXf/bsGYKDg3Hx4kWMHDkSb775Juzs7FSmKT8/HyEhIZg2bRoyMzMRGRmJt956C02bNsWKFStw9+5d2bELFizA4cOHERgYiKlTp+K1115Deno6Jk+ejI8++kjp9V+8eIHOnTvj5s2beP311zFq1ChcuHABffv2RXR0tMq0BQQE4P333wcAtG7dGrNmzZK9vL29AQCHDx/G999/D1dXV4waNQrvvvsuGjVqhOXLlyM4OBiZmZkq71EynefPn0eHDh0gFOru68rJyQk5OTl49OiRzq5Z0edVumhs6QVliR6o6UhMDFfPnj0ZAHbw4EGtzkcFf2EzxtjBgwcZANa1a1fZNlUlwpkzZzIArGfPnhXev2TJpuQCpozJq3ynTp0q21ZYWMi8vb1Z7dq12blz5xSOP3LkCBOJRGzgwIFl8giARUZGsqKiogrTJPXRRx8xAGzs2LFlzsvIyFBYvy05ObnM+WKxmPXq1YuJRCJ29+5dpWkaPXo0k0gksu0XLlyQrSUpXdyasbIlQsYqrhp98uRJmTXmGGNs7dq1DACbO3euyvxL7d69mwFgX3zxhdL92pYIp02bxgAwHx8ftmDBAnb8+HGWm5ur8jrafl6lMjMzGQCFRXeJfqBASLTWvHlzBoBdu3atzL64uDg2a9YshVfp6kB1vliuXbvGALDmzZvLtkm/hBs1aiS79scff8y6du3KADArKyt2/PjxCtMvDYShoaFl9mVnZzMHBwdmZ2cnq5LbunUrA8DmzJmj9HpDhgxhQqFQYVFVAMzCwoKlpaVVmB4psVjMateuzezt7RWqQDW1ZcsWBoCtWbNGYTsAJhKJlFblTpo0iQFgmzdvlm3TJhCWRyKRMDs7O9ajRw+1jl+xYgUDwH788Uel+7UNhPn5+WzChAlMKBTK/jAQiUSsbdu27KuvvirTPsiY9p/XkqysrFjDhg1VXoNUP83qqghRU3x8PGbPnl1me48ePXR2j9u3b8vuYW5uDldXV4wePRqffvop/Pz81L5O165dy2yztbVFQEAA4uPjkZycjMaNGyMhIQEAHxcWFRVV5pzU1FRIJBLcuHED7du3l2338fGBs7Oz2um5fv06srOzERoaijp16lR4fHZ2Nr777jtER0fj9u3bZbr9K6v+8/T0hJeXV5ntXbt2xW+//Ybz589j6NChaqdZma1bt2LFihU4d+4cXrx4geLiYpVpUubZs2cAAAcHh0qlpTQrKyusXr0aX331FWJiYnDq1CmcOnUK586dw7lz57BixQocOnQIDRs21Ol9HR0dkZ6ertNrksqjQEi05urqimvXruHRo0fw9fVV2BcVFSULFhs3bsSoUaO0uof0C1NZG2SfPn2wd+9era5bkqurq8rt0vas58+fAwDWr1+v8nqlA1F51y+P9H4lh5qUp7CwED169MC5c+fQpk0bjB07Fk5OTjAzM0NKSgrWrl2LgoKCMuepm2dtff/99/j444/h4uKC3r17o379+rC2tgYALF68WGmalJGe8/LlS6X7pe2GEomk3DZEiURSbptx/fr18eabb8oG6t++fRsTJ07E4cOH8eGHH2L79u1qpVNK1ecV4G2/0iEoRH9QICRa69SpE+Lj4xEXF4eQkJAquYd0NpPAwMAquT4APHnyROV2e3t7AJB1cNm5cycGDhyo9vU16bgDyEs/Dx8+rPDY7du349y5c5g0aRJWrVqlsG/jxo1Yu3at0vPUzbM2ioqK8NVXX8Hd3R2JiYmoW7eubB9jDN9++63a15IGFOkfIaVJ0/ns2TOlwYcxhufPn6udn0aNGmHNmjVo2LAh/v33X7XTKaXq8yqRSJCZmYmWLVtqfF1StajXKNHa+PHjIRQKsXLlyiqp7klLS8OKFSsAACNHjtT59aWOHDlSZltOTg4SExNhZ2cnqx4LCgoCAJw4caLK0gLwmUjs7Oxw+vTpMr1WS7t9+zYAIDw8vMw+ZfmSunfvnkLP09LntGnTRuV9RSIRAChUd0qlp6cjMzMTwcHBCkEQAM6cOYP8/HyV1y5JWsWdlJSkcn95z+TixYvIzc2Fv7+/2ve0tbVV+9iSKvq83rx5ExKJRKNqe1I9KBASrTVt2hSffPIJnj59in79+uHWrVtKj8vIyND42leuXEHv3r3x9OlTjB8/XqHNTdcOHDiAffv2KWybN28eMjIyMG7cOFmVW3h4ODw9PbFo0SIcPny4zHXEYjGOHj1a6fSYmZlh8uTJyMzMxPvvv18m2GRmZiInJwcAZO18pe976NAh/Prrr+Xeo7i4GJ9//rnCbCsXL17EH3/8ARcXF/Tv319lGuvUqQOBQID79++X2Ve3bl1YW1vj3LlzyMvLk21/8eIF3n33XZXXLc3Pzw+Ojo44efKk0v3jx48HAMycObPM56ygoACffPIJAGDcuHEK++bMmaM07YwxfPPNNwCALl26qJ1OdT6v0jx0795d7euS6kFVo6RS5s2bh8LCQixatAi+vr7o1q0bWrduDRsbGzx9+hQXL17EqVOnZJ1PSktPT5e1JRYVFeHZs2c4d+4cTp06BQB4/fXX8dNPP1VpHgYOHIhBgwZh2LBh8Pb2RkJCAuLi4tCoUSPMmTNHdpylpSU2b96Mfv36oXv37ggJCYGfnx8EAgHu3r2LI0eOwMnJCdevX690mubMmYOEhAT88ccfSEhIQL9+/WBpaYnk5GTs3bsXR48eRUBAAAYNGgRvb298++23uHz5Mlq1aoWkpCTs2rULERER2Lx5s9Lr+/v74+jRowgMDERoaCjS0tLw999/o6ioCCtXrpS1zZXH1tYWgYGBOHz4MMaOHYsmTZpAKBRi7Nix8PLywttvv43vv/8erVu3xqBBg5CVlYU9e/bAy8sLHh4ear8PAoEA4eHhWLNmDR48eID69esr7H/llVfw/vvvY8mSJWjatCnCwsLg5uaGZ8+eISYmBvfu3UNERAQiIyMVzlu0aBGioqLQvn17tGvXDo6Ojnj27Bni4uJw48YNODk54fvvvy+Tnsp8XmNjY2FmZqZRtTqpJjXbaZUYi3PnzrE333yT+fr6MltbW2Zubs5cXV1ZSEgIW7hwodLZRFBqVhJLS0tWt25d1rlzZ/bxxx+zCxcuKL2XqnGEmihvZhknJyc2YcIE9vjxY6XnPXjwgL3//vusSZMmzNLSktnZ2bHmzZuz119/vcyYSlRiureXL1+y7777jgUEBDBra2tma2vLWrRowT766COF7v3Jycls6NChzMXFhdnY2LDAwEC2ceNGFhcXxwCwWbNmKU3T/fv32YgRI5ijoyOzsrJiwcHBbP/+/WXSoWz4BGN8ZqH+/fszBwcHJhAIFI4pLCxk8+bNk71Hnp6e7KOPPmLZ2dnMy8uLeXl5qf0+nDx5kgFgCxYsKPeYLVu2sD59+jBnZ2dmZmbGHBwcWLdu3diqVauUzkhz+PBh9umnn7Lg4GDm4eHBzM3Nma2tLfP392cff/wxe/ToUZlztP28MsZYbm4us7W1ZYMHD1Y736T60KTbxGStWbMGkZGRWL16dbWtoqAPBAIBunfvXqPLKmmqa9euSEtLw9WrV3U6w0x1WbVqFd544w0cOnQI3bp1q+nkkFIM7xNFCDE5CxcuRFJSEjZu3FjTSdFYUVERvv76a4SFhVEQ1FPURkgI0XsdO3bEihUrlPZS1Xf37t3DuHHjMHbs2JpOCikHBUJCiEEwxNXpAaBhw4ZKZyIi+oPaCAkhhJg0aiMkhBBi0igQEkIIMWkUCAkhhJg0CoSEEEJMGgVCQgghJo0CISGEEJNGgZAQQohJo0BICCHEpP0fC/KnzZMbo/UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "i6mSUL6qXJQF",
        "outputId": "2586e044-8b48-439c-97a0-4f9705cc9863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "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-6d2f3f96-c76c-422b-8609-c106179313d2\" 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-6d2f3f96-c76c-422b-8609-c106179313d2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </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-6d2f3f96-c76c-422b-8609-c106179313d2 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-6d2f3f96-c76c-422b-8609-c106179313d2');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0f1494e-9780-49d1-96db-2e5c6d38d83e')\"\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",
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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",
              "      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-c0f1494e-9780-49d1-96db-2e5c6d38d83e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5cdd2174-5fd2-4340-8162-9f37adb92f91\">\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_5cdd2174-5fd2-4340-8162-9f37adb92f91 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": 28
        }
      ],
      "source": [
        "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
        "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
        "missing_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "wIyPRvUxXJQF"
      },
      "outputs": [],
      "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",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "EEoSYfpzXJQG",
        "outputId": "6552427a-a2e3-44c8-ec99-75d294d2146c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "U9vfMrtHXJQG",
        "outputId": "dbbd2607-a565-479f-d1e1-ec55bee01a95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "k9RS7k-oXJQG",
        "outputId": "9f0647ea-8c5b-4493-e6f2-73bddec941a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "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"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "dhg1NxDIXJQG",
        "outputId": "54cfd04d-e2ff-4d1c-8b22-d05308fad5db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "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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              "  @keyframes spin {\n",
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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": 33
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "QSFfZn-rXJQH",
        "outputId": "2105ae07-9bb6-4847-9f97-641032647068",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdm94YVQXJQH"
      },
      "source": [
        "# Exercise Solutions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UnWVVdmXJQH"
      },
      "source": [
        "1. Machine Learning is about building systems that can learn from data. Learning means getting better at some task, given some performance measure.\n",
        "2. Machine Learning is great for complex problems for which we have no algorithmic solution, to replace long lists of hand-tuned rules, to build systems that adapt to fluctuating environments, and finally to help humans learn (e.g., data mining).\n",
        "3. A labeled training set is a training set that contains the desired solution (a.k.a. a label) for each instance.\n",
        "4. The two most common supervised tasks are regression and classification.\n",
        "5. Common unsupervised tasks include clustering, visualization, dimensionality reduction, and association rule learning.\n",
        "6. Reinforcement Learning is likely to perform best if we want a robot to learn to walk in various unknown terrains, since this is typically the type of problem that Reinforcement Learning tackles. It might be possible to express the problem as a supervised or semi-supervised learning problem, but it would be less natural.\n",
        "7. If you don't know how to define the groups, then you can use a clustering algorithm (unsupervised learning) to segment your customers into clusters of similar customers. However, if you know what groups you would like to have, then you can feed many examples of each group to a classification algorithm (supervised learning), and it will classify all your customers into these groups.\n",
        "8. Spam detection is a typical supervised learning problem: the algorithm is fed many emails along with their labels (spam or not spam).\n",
        "9. An online learning system can learn incrementally, as opposed to a batch learning system. This makes it capable of adapting rapidly to both changing data and autonomous systems, and of training on very large quantities of data.\n",
        "10. Out-of-core algorithms can handle vast quantities of data that cannot fit in a computer's main memory. An out-of-core learning algorithm chops the data into mini-batches and uses online learning techniques to learn from these mini-batches.\n",
        "11. An instance-based learning system learns the training data by heart; then, when given a new instance, it uses a similarity measure to find the most similar learned instances and uses them to make predictions.\n",
        "12. A model has one or more model parameters that determine what it will predict given a new instance (e.g., the slope of a linear model). A learning algorithm tries to find optimal values for these parameters such that the model generalizes well to new instances. A hyperparameter is a parameter of the learning algorithm itself, not of the model (e.g., the amount of regularization to apply).\n",
        "13. Model-based learning algorithms search for an optimal value for the model parameters such that the model will generalize well to new instances. We usually train such systems by minimizing a cost function that measures how bad the system is at making predictions on the training data, plus a penalty for model complexity if the model is regularized. To make predictions, we feed the new instance's features into the model's prediction function, using the parameter values found by the learning algorithm.\n",
        "14. Some of the main challenges in Machine Learning are the lack of data, poor data quality, nonrepresentative data, uninformative features, excessively simple models that underfit the training data, and excessively complex models that overfit the data.\n",
        "15. If a model performs great on the training data but generalizes poorly to new instances, the model is likely overfitting the training data (or we got extremely lucky on the training data). Possible solutions to overfitting are getting more data, simplifying the model (selecting a simpler algorithm, reducing the number of parameters or features used, or regularizing the model), or reducing the noise in the training data.\n",
        "16. A test set is used to estimate the generalization error that a model will make on new instances, before the model is launched in production.\n",
        "17. A validation set is used to compare models. It makes it possible to select the best model and tune the hyperparameters.\n",
        "18. The train-dev set is used when there is a risk of mismatch between the training data and the data used in the validation and test datasets (which should always be as close as possible to the data used once the model is in production). The train-dev set is a part of the training set that's held out (the model is not trained on it). The model is trained on the rest of the training set, and evaluated on both the train-dev set and the validation set. If the model performs well on the training set but not on the train-dev set, then the model is likely overfitting the training set. If it performs well on both the training set and the train-dev set, but not on the validation set, then there is probably a significant data mismatch between the training data and the validation + test data, and you should try to improve the training data to make it look more like the validation + test data.\n",
        "19. If you tune hyperparameters using the test set, you risk overfitting the test set, and the generalization error you measure will be optimistic (you may launch a model that performs worse than you expect)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FH6o7rGIXJQI"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
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      "language": "python",
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
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    },
    "nav_menu": {},
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