{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "hide_input": false,
        "id": "cR8plbkxCN3b"
      },
      "source": [
        "# 1장. 소개"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuh069ZNCN3e"
      },
      "source": [
        "*아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.*\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/intro_ml_with_python_2nd_revised/blob/main/01-introduction.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/intro_ml_with_python_2nd_revised/blob/main/01-introduction.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "SAC9moCgCN3e"
      },
      "outputs": [],
      "source": [
        "# 노트북이 코랩에서 실행 중인지 체크합니다.\n",
        "import os\n",
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    if not os.path.isdir('mglearn'):\n",
        "        # mglearn을 다운받고 압축을 풉니다.\n",
        "        !wget -q -O mglearn.tar.gz https://bit.ly/mglearn-tar-gz\n",
        "        !tar -xzf mglearn.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "hide_input": false,
        "id": "zzVzw5IgCN3g"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from preamble import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fayuEloRCN3g"
      },
      "source": [
        "## 1.1 왜 머신 러닝인가?\n",
        "### 1.1.1 머신 러닝으로 풀 수 있는 문제\n",
        "### 1.1.2 문제와 데이터를 이해하기\n",
        "## 1.2 왜 파이썬인가?\n",
        "## 1.3 scikit-learn\n",
        "### 1.3.1 Scikit-learn 설치\n",
        "## 1.4 필수 라이브러리와 도구들\n",
        "### 1.4.1 주피터 노트북\n",
        "### 1.4.2 NumPy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmtSpzDYCN3g",
        "outputId": "81a2dff7-fa03-4c91-a654-819c07bcdba8",
        "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print(\"x:\\n\", x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofxZQ9H_CN3h"
      },
      "source": [
        "### 1.4.3 SciPy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLxBZRAJCN3i",
        "outputId": "79a4c43b-fc4c-4b7a-edae-0848d5a53eec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NumPy 배열:\n",
            " [[1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "from scipy import sparse\n",
        "\n",
        "# 대각선 원소는 1이고 나머지는 0인 2차원 NumPy 배열을 만듭니다.\n",
        "eye = np.eye(4)\n",
        "print(\"NumPy 배열:\\n\", eye)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkygUONoCN3j",
        "outputId": "71785426-717c-4a75-cf40-d316f2b7c646"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SciPy의 CSR 행렬:\n",
            " <Compressed Sparse Row sparse matrix of dtype 'float64'\n",
            "\twith 4 stored elements and shape (4, 4)>\n",
            "  Coords\tValues\n",
            "  (0, 0)\t1.0\n",
            "  (1, 1)\t1.0\n",
            "  (2, 2)\t1.0\n",
            "  (3, 3)\t1.0\n"
          ]
        }
      ],
      "source": [
        "# NumPy 배열을 CSR 포맷의 SciPy 희박 행렬로 변환합니다.\n",
        "# 0이 아닌 원소만 저장됩니다.\n",
        "sparse_matrix = sparse.csr_matrix(eye)\n",
        "print(\"\\nSciPy의 CSR 행렬:\\n\", sparse_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DBMb1J_CN3j",
        "outputId": "ade36ba6-5419-4381-dbf8-08ddd4bae8cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COO 표현:\n",
            " <COOrdinate sparse matrix of dtype 'float64'\n",
            "\twith 4 stored elements and shape (4, 4)>\n",
            "  Coords\tValues\n",
            "  (0, 0)\t1.0\n",
            "  (1, 1)\t1.0\n",
            "  (2, 2)\t1.0\n",
            "  (3, 3)\t1.0\n"
          ]
        }
      ],
      "source": [
        "data = np.ones(4)\n",
        "row_indices = np.arange(4)\n",
        "col_indices = np.arange(4)\n",
        "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
        "print(\"COO 표현:\\n\", eye_coo)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPmmBUylCN3k"
      },
      "source": [
        "### 1.4.4 matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ZH6SWV86CN3k",
        "outputId": "fffcc010-de11-49bf-ad96-871443c13c7a",
        "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n",
            "application/pdf": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# -10에서 10까지 100개의 간격으로 나뉘어진 배열을 생성합니다.\n",
        "x = np.linspace(-10, 10, 100)\n",
        "# 사인 함수를 사용하여 y 배열을 생성합니다.\n",
        "y = np.sin(x)\n",
        "# plot 함수는 한 배열의 값을 다른 배열에 대응해서 선 그래프를 그립니다.\n",
        "plt.plot(x, y, marker=\"x\")\n",
        "plt.show() # 책에는 없음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_93aqw-CN3k"
      },
      "source": [
        "### 1.4.5 pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_JsFxcEgCN3k",
        "outputId": "1db5e9f0-4d41-4bd6-a58c-50ebdf761156",
        "scrolled": true,
        "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name  Location  Age\n",
              "0   John  New York   24\n",
              "1   Anna     Paris   13\n",
              "2  Peter    Berlin   53\n",
              "3  Linda    London   33"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38f1999f-42b5-4854-9656-ab456478e7bd\" 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>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>John</td>\n",
              "      <td>New York</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anna</td>\n",
              "      <td>Paris</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peter</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Linda</td>\n",
              "      <td>London</td>\n",
              "      <td>33</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-38f1999f-42b5-4854-9656-ab456478e7bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-38f1999f-42b5-4854-9656-ab456478e7bd 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-38f1999f-42b5-4854-9656-ab456478e7bd');\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-1c8aed6a-49e8-4003-a75c-4fe9912fa948\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c8aed6a-49e8-4003-a75c-4fe9912fa948')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1c8aed6a-49e8-4003-a75c-4fe9912fa948 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a8c1f322-410a-496b-8c61-ea6e2384b7e6\">\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('data_pandas')\"\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_a8c1f322-410a-496b-8c61-ea6e2384b7e6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_pandas');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_pandas",
              "summary": "{\n  \"name\": \"data_pandas\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Anna\",\n          \"Linda\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Paris\",\n          \"London\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 13,\n        \"max\": 53,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          13,\n          33,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 회원 정보가 들어간 간단한 데이터셋을 생성합니다.\n",
        "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
        "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
        "        'Age' : [24, 13, 53, 33]\n",
        "       }\n",
        "\n",
        "data_pandas = pd.DataFrame(data)\n",
        "# 주피터 노트북은 Dataframe을 미려하게 출력해줍니다.\n",
        "data_pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "HtlJe_fkCN3l",
        "outputId": "8b2bf245-efe9-45d1-d807-62ebb0887896"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name Location  Age\n",
              "2  Peter   Berlin   53\n",
              "3  Linda   London   33"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe28beac-ffbd-40ab-906a-c4a9a25fe44d\" 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>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peter</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Linda</td>\n",
              "      <td>London</td>\n",
              "      <td>33</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-fe28beac-ffbd-40ab-906a-c4a9a25fe44d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe28beac-ffbd-40ab-906a-c4a9a25fe44d 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-fe28beac-ffbd-40ab-906a-c4a9a25fe44d');\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-8fe4f540-a9c7-4b65-861f-b13d3aa54323\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fe4f540-a9c7-4b65-861f-b13d3aa54323')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8fe4f540-a9c7-4b65-861f-b13d3aa54323 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_pandas[data_pandas\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Linda\",\n          \"Peter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"London\",\n          \"Berlin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 33,\n        \"max\": 53,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          33,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Age 열의 값이 30 이상인 모든 행을 선택합니다.\n",
        "data_pandas[data_pandas.Age > 30]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1g6q2wpCN3l"
      },
      "source": [
        "### 1.4.6 mglearn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OPorlutCN3l"
      },
      "source": [
        "## 1.5 파이썬 2 vs. 파이썬 3\n",
        "## 1.6 이 책에서 사용하는 소프트웨어 버전"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZRwxrs3CN3l",
        "outputId": "f55df8ab-3fc6-47da-fe05-7056a1899fac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 버전: 3.11.11 (main, Dec  4 2024, 08:55:07) [GCC 11.4.0]\n",
            "pandas 버전: 2.2.2\n",
            "matplotlib 버전: 3.10.0\n",
            "NumPy 버전: 1.26.4\n",
            "SciPy 버전: 1.14.1\n",
            "IPython 버전: 7.34.0\n",
            "scikit-learn 버전: 1.6.1\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(\"Python 버전:\", sys.version)\n",
        "\n",
        "import pandas as pd\n",
        "print(\"pandas 버전:\", pd.__version__)\n",
        "\n",
        "import matplotlib\n",
        "print(\"matplotlib 버전:\", matplotlib.__version__)\n",
        "\n",
        "import numpy as np\n",
        "print(\"NumPy 버전:\", np.__version__)\n",
        "\n",
        "import scipy as sp\n",
        "print(\"SciPy 버전:\", sp.__version__)\n",
        "\n",
        "import IPython\n",
        "print(\"IPython 버전:\", IPython.__version__)\n",
        "\n",
        "import sklearn\n",
        "print(\"scikit-learn 버전:\", sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlP0gvBnCN3m"
      },
      "source": [
        "## 1.7 첫 번째 애플리케이션: 붓꽃의 품종 분류\n",
        "### 1.7.1 데이터 적재"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "NBiaD_jtCN3m",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris_dataset = load_iris()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UMBXHcjCN3m",
        "outputId": "994d3629-3ab1-4e72-f35d-248125289fdf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iris_dataset의 키:\n",
            " dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
          ]
        }
      ],
      "source": [
        "print(\"iris_dataset의 키:\\n\", iris_dataset.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78TGXXoNCN3n",
        "outputId": "a89c2eaa-fc1d-4229-c166-f26fa79bafdc",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            ":Number of Instances: 150 (50 in each of three classes)\n",
            ":Number of Attributes: 4 numeric, predictive \n",
            "...\n"
          ]
        }
      ],
      "source": [
        "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxtEHq63CN3n",
        "outputId": "0126f942-b86f-480c-854a-ef1cb2df8f05"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "타깃의 이름: ['setosa' 'versicolor' 'virginica']\n"
          ]
        }
      ],
      "source": [
        "print(\"타깃의 이름:\", iris_dataset['target_names'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJliOqoCCN3n",
        "outputId": "c0096d49-adb9-4c13-f5ec-f712c7aa052e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "특성의 이름:\n",
            " ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
          ]
        }
      ],
      "source": [
        "print(\"특성의 이름:\\n\", iris_dataset['feature_names'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sN7xsQFCN3n",
        "outputId": "5cf3ca5b-ab0b-458e-d99e-323f683d0638"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data의 타입: <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(\"data의 타입:\", type(iris_dataset['data']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77Em3vLlCN3n",
        "outputId": "53fe8319-f90f-4371-c385-a4058c272144"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data의 크기: (150, 4)\n"
          ]
        }
      ],
      "source": [
        "print(\"data의 크기:\", iris_dataset['data'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eix8p25ACN3o",
        "outputId": "0b0ff5a5-06c6-4e8c-cdd7-b598601c448f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data의 처음 다섯 행:\n",
            " [[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ]
        }
      ],
      "source": [
        "print(\"data의 처음 다섯 행:\\n\", iris_dataset['data'][:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGipvmv0CN3o",
        "outputId": "4a8f90d5-189f-4ceb-c378-d13c5413ea3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target의 타입: <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(\"target의 타입:\", type(iris_dataset['target']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy_GmKvtCN3o",
        "outputId": "60d5077c-7011-421d-8546-ccde650ea7bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target의 크기: (150,)\n"
          ]
        }
      ],
      "source": [
        "print(\"target의 크기:\", iris_dataset['target'].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S47e9RFgCN3o",
        "outputId": "0eef045d-074e-43c0-af8a-674c4fb1f4cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "타깃:\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ]
        }
      ],
      "source": [
        "print(\"타깃:\\n\", iris_dataset['target'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2RzRnNICN3o"
      },
      "source": [
        "### 1.7.2 성과 측정: 훈련 데이터와 테스트 데이터"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "pYi7UKK1CN3o"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Pu3BsVPCN3p",
        "outputId": "ad65c805-5124-43cb-de42-c3efca8f9a3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train 크기: (112, 4)\n",
            "y_train 크기: (112,)\n"
          ]
        }
      ],
      "source": [
        "print(\"X_train 크기:\", X_train.shape)\n",
        "print(\"y_train 크기:\", y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D43d2Pu9CN3p",
        "outputId": "2b0a05eb-1bfa-403e-f974-7e3c0c693f7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_test 크기: (38, 4)\n",
            "y_test 크기: (38,)\n"
          ]
        }
      ],
      "source": [
        "print(\"X_test 크기:\", X_test.shape)\n",
        "print(\"y_test 크기:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8vl5JVXCN3p"
      },
      "source": [
        "### 1.7.3 가장 먼저 할 일: 데이터 살펴보기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hBl7LWVzCN3p",
        "outputId": "3868b38e-4519-45f2-dcba-4b0455f35959"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 16 Axes>"
            ],
            "image/png": "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\n",
            "application/pdf": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# X_train 데이터를 사용해서 데이터프레임을 만듭니다.\n",
        "# 열의 이름은 iris_dataset.feature_names에 있는 문자열을 사용합니다.\n",
        "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
        "# 데이터프레임을 사용해 y_train에 따라 색으로 구분된 산점도 행렬을 만듭니다.\n",
        "pd.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15), marker='o',\n",
        "                           hist_kwds={'bins': 20}, s=60, alpha=.8, cmap=mglearn.cm3)\n",
        "plt.show() # 책에는 없음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtOefn9pCN3q"
      },
      "source": [
        "### 1.7.4 첫 번째 머신 러닝 모델: k-최근접 이웃 알고리즘"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "MOXCy5YGCN3q"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "lOI4LMFDCN3q",
        "outputId": "6eb660d6-7ad3-454b-9a02-99d8ca995343"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "knn.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GDYezDQCN3q"
      },
      "source": [
        "### 1.7.5 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YVCNU8NCN3q",
        "outputId": "afeefb0f-7fb3-4179-cf18-410ac1846372"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_new.shape: (1, 4)\n"
          ]
        }
      ],
      "source": [
        "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
        "print(\"X_new.shape:\", X_new.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "El-9zcaKCN3q",
        "outputId": "84b07a73-ac2c-4d13-c7c3-6e41f2ce023a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측: [0]\n",
            "예측한 타깃의 이름: ['setosa']\n"
          ]
        }
      ],
      "source": [
        "prediction = knn.predict(X_new)\n",
        "print(\"예측:\", prediction)\n",
        "print(\"예측한 타깃의 이름:\",\n",
        "       iris_dataset['target_names'][prediction])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Qsym5KMCN3q"
      },
      "source": [
        "### 1.7.6 모델 평가하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moSUGtpjCN3r",
        "outputId": "78c759e0-3dbf-487c-bc7a-18853a240454"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 세트에 대한 예측값:\n",
            " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ]
        }
      ],
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "print(\"테스트 세트에 대한 예측값:\\n\", y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIAII-eKCN3r",
        "outputId": "540a4770-301c-429a-9d77-e15b64a5487d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 세트의 정확도: 0.97\n"
          ]
        }
      ],
      "source": [
        "print(\"테스트 세트의 정확도: {:.2f}\".format(np.mean(y_pred == y_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B48pdu2mCN3r",
        "outputId": "eb4ffe89-07e9-43c6-ad39-d0b8b1f3816a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 세트의 정확도: 0.97\n"
          ]
        }
      ],
      "source": [
        "print(\"테스트 세트의 정확도: {:.2f}\".format(knn.score(X_test, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7HLWX73CN3r"
      },
      "source": [
        "## 1.8 요약"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNkZ5MxPCN3r",
        "outputId": "c1ece3b3-8e0e-4640-9083-83a0eb737b4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "테스트 세트의 정확도: 0.97\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"테스트 세트의 정확도: {:.2f}\".format(knn.score(X_test, y_test)))"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "colab": {
      "name": "01-introduction.ipynb",
      "provenance": []
    },
    "environment": {
      "kernel": "python3",
      "name": "common-cpu.m102",
      "type": "gcloud",
      "uri": "gcr.io/deeplearning-platform-release/base-cpu:m102"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}