01_the_machine_learning_landscape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1장 – 한 눈에 보는 머신러닝**\n",
    "\n",
    "_이 노트북은 1장의 코드를 담고 있습니다. 원본 데이터에서 `lifesat.csv` 파일을 만들고 이 장에서 사용하는 그림을 생성합니다._\n",
    "\n",
    "이 노트북의 코드를 살펴 봐도 좋지만 실제 예제는 다음 장부터 시작합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/rickiepark/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",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 3.7 또는 그 이상이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn ≥1.0.1이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": {},
   "source": [
    "미려한 그림을 위해 기본 폰트 크기를 지정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": {},
   "source": [
    "출력 결과를 일정하게 유지하기 위해 랜덤 시드를 지정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 코드 예제 1-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
    "# 데이터를 다운로드하고 준비합니다\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",
    "# 데이터를 그래프로 나타냅니다\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",
    "#  선형 모델를 선택합니다\n",
    "model = LinearRegression()\n",
    "\n",
    "# 모델을 훈련합니다\n",
    "model.fit(X, y)\n",
    "\n",
    "# 키프로스에 대해 예측을 만듭니다\n",
    "X_new = [[37_655.2]]  # 2020년 키프로스 1인당 GDP\n",
    "print(model.predict(X_new)) # 출력 [[6.30165767]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이전 코드에서 선형 회귀 모델을 k-최근접 이웃 회귀(이 경우 k=3)로 바꾸려면 간단하게 다음 두 라인만 바꾸면 됩니다:\n",
    "\n",
    "```python\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "```\n",
    "\n",
    "아래와 같이 바꿉니다:\n",
    "\n",
    "```python\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "model = KNeighborsRegressor(n_neighbors=3)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.33333333]]\n"
     ]
    }
   ],
   "source": [
    "# 3-최근접 이웃 회귀 모델로 바꿉니다.\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "model = KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "# 모델을 훈련합니다\n",
    "model.fit(X, y)\n",
    "\n",
    "# 키프로스에 대한 예측을 만듭니다\n",
    "print(model.predict(X_new)) # 출력 [[6.33333333]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 데이터와 그림 생성하기 — 건너 뛰세요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`lifesat.csv` 데이터셋을 만들기 위한 코드입니다. 이 부분을 건너뛰어도 괜찮습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그림을 저장하기 위한 함수를 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# 그림을 저장할 위치\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": {},
   "source": [
    "## 삶의 만족도 데이터 로드하고 준비하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`lifesat.csv` 파일을 만들기 위해 [OECD 웹사이트](http://stats.oecd.org/index.aspx?DataSetCode=BLI)에서 (각 국가의 삶의 만족도를 얻으려고) (2020년) BLI(Better Life Index) 데이터와 [OurWorldInData.org](https://ourworldindata.org/grapher/gdp-per-capita-worldbank)에서 (2020년까지) 세계 은행의 1인당 GDP 데이터를 다운로드했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading oecd_bli.csv\n",
      "Downloading gdp_per_capita.csv\n"
     ]
    }
   ],
   "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": 9,
   "metadata": {},
   "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": {},
   "source": [
    "2020년도 것만 남기도록 1인당 GDP 데이터를 전처리합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1978.961579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Eastern and Southern</th>\n",
       "      <td>3387.594670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Western and Central</th>\n",
       "      <td>4003.158913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>13295.410885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>10681.679297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "`Life satisfaction` 열만 남도록 OECD BLI 데이터를 전처리합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Indicator</th>\n",
       "      <th>Air pollution</th>\n",
       "      <th>Dwellings without basic facilities</th>\n",
       "      <th>Educational attainment</th>\n",
       "      <th>Employees working very long hours</th>\n",
       "      <th>Employment rate</th>\n",
       "      <th>Feeling safe walking alone at night</th>\n",
       "      <th>Homicide rate</th>\n",
       "      <th>Household net adjusted disposable income</th>\n",
       "      <th>Household net wealth</th>\n",
       "      <th>Housing expenditure</th>\n",
       "      <th>...</th>\n",
       "      <th>Personal earnings</th>\n",
       "      <th>Quality of support network</th>\n",
       "      <th>Rooms per person</th>\n",
       "      <th>Self-reported health</th>\n",
       "      <th>Stakeholder engagement for developing regulations</th>\n",
       "      <th>Student skills</th>\n",
       "      <th>Time devoted to leisure and personal care</th>\n",
       "      <th>Voter turnout</th>\n",
       "      <th>Water quality</th>\n",
       "      <th>Years in education</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>"
      ],
      "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]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "이제 삶의 만족도 데이터와 1인당 GDP 데이타를 합친 뒤 1인당 GDP 열과 삶의 만족도 열만 남깁니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "과대적합에 대한 예시를 만들기 위해, 데이터의 일부(1인당 GDP가 `min_gdp`와 `max_gdp` 사이에 있는 나라들)만 사용하여 그림을 그립니다. 나중에 누락된 나라를 추가하여 동일한 트렌드를 따르지 않는다는 것을 보이겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita (USD)</th>\n",
       "      <th>Life satisfaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>26456.387938</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>27287.083401</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>28384.987785</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>29932.493910</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>31007.768407</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
    "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP per capita (USD)</th>\n",
       "      <th>Life satisfaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>28384.987785</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>31007.768407</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>42025.617373</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>42404.393738</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>48697.837028</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>55938.212809</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>60235.728492</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37655.1803457421"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
    "cyprus_gdp_per_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.301656332738056"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
    "cyprus_predicted_life_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>"
      ],
      "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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 24,
   "metadata": {},
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>1486.778248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>2098.710362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2744.690758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Western and Central</th>\n",
       "      <td>4003.158913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>4101.218882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>6722.809932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eswatini</th>\n",
       "      <td>8392.717564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low &amp; middle income</th>\n",
       "      <td>10293.855325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arab World</th>\n",
       "      <td>13753.707307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>16040.008473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>16194.040310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>42404.393738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>50683.323510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>63585.903514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>68393.306004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
    "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
    "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
    "\n",
    "X = np.linspace(0, 115_000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
    "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
    "\n",
    "ridge = linear_model.Ridge(alpha=10**9.5)\n",
    "X_sample = country_stats[[gdppc_col]]\n",
    "y_sample = country_stats[[lifesat_col]]\n",
    "ridge.fit(X_sample, y_sample)\n",
    "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
    "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
    "         label=\"Regularized linear model on partial data\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
    "\n",
    "save_fig('ridge_model_plot')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "metadata": {
   "interpreter": {
    "hash": "22b0ec00cd9e253c751e6d2619fc0bb2d18ed12980de3246690d5be49479dd65"
   }
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "616px",
   "left": "0px",
   "right": "20px",
   "top": "106px",
   "width": "213px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}