KNN_실습(0222).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/KNN_%EC%8B%A4%EC%8A%B5(0222).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 기본 문법 익히기"
      ],
      "metadata": {
        "id": "nW3VT_wFaXMe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "S2Q7IPfEaVwY"
      },
      "outputs": [],
      "source": [
        "Train_data = [\n",
        "              [0.5, 0.2, 0.1],\n",
        "              [0.9, 0.7, 0.3],\n",
        "              [0.4, 0.5, 0.7]\n",
        "]\n",
        "\n",
        "Train_labels = [0, 1, 1]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "clf = KNeighborsClassifier(n_neighbors = 3)\n",
        "clf.fit(Train_data, Train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQZ4CGcVa1tW",
        "outputId": "e7dfc447-78b2-43cd-8d68-3b7aeff6f9a7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_datas = [\n",
        "              [0.2, 0.1, 0.7],\n",
        "              [0.4, 0.7, 0.6],\n",
        "              [0.5, 0.8, 0.1],\n",
        "              [0.4, 0.7, 0.6],\n",
        "              [0.5, 0.8, 0.1],\n",
        "              [0.4, 0.7, 0.6],\n",
        "              [0.5, 0.8, 0.1]\n",
        "]"
      ],
      "metadata": {
        "id": "lBtLXsqKbiXa"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict(test_datas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvucrPBkb9UA",
        "outputId": "51f457c9-dce0-4942-ccb1-dada0712766c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. 유방암 데이터 분석"
      ],
      "metadata": {
        "id": "lYCtEtkodn-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "breast_cancer_data = load_breast_cancer()\n",
        "print(breast_cancer_data['DESCR'])"
      ],
      "metadata": {
        "id": "HWaD6dGNdrEf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55932392-307c-4000-b6d0-bcbbfdb86810"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 데이터 얻기"
      ],
      "metadata": {
        "id": "yvW9jbqseDRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터와 레이블을 데이터 프레임으로 만드시오\n",
        "import pandas as pd\n",
        "\n",
        "df_data = pd.DataFrame(breast_cancer_data['data'])\n",
        "df_label = pd.DataFrame(breast_cancer_data['target'])\n",
        "df_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "Hqocu6GAeFLZ",
        "outputId": "973da2a5-e6af-456e-b3da-b9f84a21935b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      0      1       2       3        4        5       6        7       8   \\\n",
              "0  17.99  10.38  122.80  1001.0  0.11840  0.27760  0.3001  0.14710  0.2419   \n",
              "1  20.57  17.77  132.90  1326.0  0.08474  0.07864  0.0869  0.07017  0.1812   \n",
              "2  19.69  21.25  130.00  1203.0  0.10960  0.15990  0.1974  0.12790  0.2069   \n",
              "3  11.42  20.38   77.58   386.1  0.14250  0.28390  0.2414  0.10520  0.2597   \n",
              "4  20.29  14.34  135.10  1297.0  0.10030  0.13280  0.1980  0.10430  0.1809   \n",
              "\n",
              "        9   ...     20     21      22      23      24      25      26      27  \\\n",
              "0  0.07871  ...  25.38  17.33  184.60  2019.0  0.1622  0.6656  0.7119  0.2654   \n",
              "1  0.05667  ...  24.99  23.41  158.80  1956.0  0.1238  0.1866  0.2416  0.1860   \n",
              "2  0.05999  ...  23.57  25.53  152.50  1709.0  0.1444  0.4245  0.4504  0.2430   \n",
              "3  0.09744  ...  14.91  26.50   98.87   567.7  0.2098  0.8663  0.6869  0.2575   \n",
              "4  0.05883  ...  22.54  16.67  152.20  1575.0  0.1374  0.2050  0.4000  0.1625   \n",
              "\n",
              "       28       29  \n",
              "0  0.4601  0.11890  \n",
              "1  0.2750  0.08902  \n",
              "2  0.3613  0.08758  \n",
              "3  0.6638  0.17300  \n",
              "4  0.2364  0.07678  \n",
              "\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
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              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
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              "      <td>21.25</td>\n",
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              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
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              "      <td>14.91</td>\n",
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              "      <td>0.6869</td>\n",
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              "      fill: #174EA6;\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-3dc0d8ee-ea98-4f91-b98a-afba627b3627 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-3dc0d8ee-ea98-4f91-b98a-afba627b3627');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2.2 데이터 클린징"
      ],
      "metadata": {
        "id": "1kgaeNCSgyH8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 및 이상치\n",
        "df_data.info()\n",
        "df_data.isnull().sum()"
      ],
      "metadata": {
        "id": "h2ZM9jMZgxpF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecb92cfb-f9cf-448e-b733-cf511f401b7c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 30 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       569 non-null    float64\n",
            " 1   1       569 non-null    float64\n",
            " 2   2       569 non-null    float64\n",
            " 3   3       569 non-null    float64\n",
            " 4   4       569 non-null    float64\n",
            " 5   5       569 non-null    float64\n",
            " 6   6       569 non-null    float64\n",
            " 7   7       569 non-null    float64\n",
            " 8   8       569 non-null    float64\n",
            " 9   9       569 non-null    float64\n",
            " 10  10      569 non-null    float64\n",
            " 11  11      569 non-null    float64\n",
            " 12  12      569 non-null    float64\n",
            " 13  13      569 non-null    float64\n",
            " 14  14      569 non-null    float64\n",
            " 15  15      569 non-null    float64\n",
            " 16  16      569 non-null    float64\n",
            " 17  17      569 non-null    float64\n",
            " 18  18      569 non-null    float64\n",
            " 19  19      569 non-null    float64\n",
            " 20  20      569 non-null    float64\n",
            " 21  21      569 non-null    float64\n",
            " 22  22      569 non-null    float64\n",
            " 23  23      569 non-null    float64\n",
            " 24  24      569 non-null    float64\n",
            " 25  25      569 non-null    float64\n",
            " 26  26      569 non-null    float64\n",
            " 27  27      569 non-null    float64\n",
            " 28  28      569 non-null    float64\n",
            " 29  29      569 non-null    float64\n",
            "dtypes: float64(30)\n",
            "memory usage: 133.5 KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
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              "5     0\n",
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              "7     0\n",
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              "9     0\n",
              "10    0\n",
              "11    0\n",
              "12    0\n",
              "13    0\n",
              "14    0\n",
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              "22    0\n",
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              "27    0\n",
              "28    0\n",
              "29    0\n",
              "dtype: int64"
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "iM2huiQvhSpR",
        "outputId": "163f4c86-4992-4948-fcc5-e7aef339dedc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2            3           4   \\\n",
              "count  569.000000  569.000000  569.000000   569.000000  569.000000   \n",
              "mean    14.127292   19.289649   91.969033   654.889104    0.096360   \n",
              "std      3.524049    4.301036   24.298981   351.914129    0.014064   \n",
              "min      6.981000    9.710000   43.790000   143.500000    0.052630   \n",
              "25%     11.700000   16.170000   75.170000   420.300000    0.086370   \n",
              "50%     13.370000   18.840000   86.240000   551.100000    0.095870   \n",
              "75%     15.780000   21.800000  104.100000   782.700000    0.105300   \n",
              "max     28.110000   39.280000  188.500000  2501.000000    0.163400   \n",
              "\n",
              "               5           6           7           8           9   ...  \\\n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  ...   \n",
              "mean     0.104341    0.088799    0.048919    0.181162    0.062798  ...   \n",
              "std      0.052813    0.079720    0.038803    0.027414    0.007060  ...   \n",
              "min      0.019380    0.000000    0.000000    0.106000    0.049960  ...   \n",
              "25%      0.064920    0.029560    0.020310    0.161900    0.057700  ...   \n",
              "50%      0.092630    0.061540    0.033500    0.179200    0.061540  ...   \n",
              "75%      0.130400    0.130700    0.074000    0.195700    0.066120  ...   \n",
              "max      0.345400    0.426800    0.201200    0.304000    0.097440  ...   \n",
              "\n",
              "               20          21          22           23          24  \\\n",
              "count  569.000000  569.000000  569.000000   569.000000  569.000000   \n",
              "mean    16.269190   25.677223  107.261213   880.583128    0.132369   \n",
              "std      4.833242    6.146258   33.602542   569.356993    0.022832   \n",
              "min      7.930000   12.020000   50.410000   185.200000    0.071170   \n",
              "25%     13.010000   21.080000   84.110000   515.300000    0.116600   \n",
              "50%     14.970000   25.410000   97.660000   686.500000    0.131300   \n",
              "75%     18.790000   29.720000  125.400000  1084.000000    0.146000   \n",
              "max     36.040000   49.540000  251.200000  4254.000000    0.222600   \n",
              "\n",
              "               25          26          27          28          29  \n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  \n",
              "mean     0.254265    0.272188    0.114606    0.290076    0.083946  \n",
              "std      0.157336    0.208624    0.065732    0.061867    0.018061  \n",
              "min      0.027290    0.000000    0.000000    0.156500    0.055040  \n",
              "25%      0.147200    0.114500    0.064930    0.250400    0.071460  \n",
              "50%      0.211900    0.226700    0.099930    0.282200    0.080040  \n",
              "75%      0.339100    0.382900    0.161400    0.317900    0.092080  \n",
              "max      1.058000    1.252000    0.291000    0.663800    0.207500  \n",
              "\n",
              "[8 rows x 30 columns]"
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            "text/html": [
              "\n",
              "  <div id=\"df-b0878953-781c-4da2-bcbe-6f3e76ec1fb0\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
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              "  </tbody>\n",
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              "<p>8 rows × 30 columns</p>\n",
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              "      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",
              "    [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-b0878953-781c-4da2-bcbe-6f3e76ec1fb0 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-b0878953-781c-4da2-bcbe-6f3e76ec1fb0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2.3 정규화"
      ],
      "metadata": {
        "id": "W2qY_hWFhlZ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "# scaler.fit(df_iris)\n",
        "# iris_scaled = scaler.transform(df_iris)\n",
        "\n",
        "data_scaled = scaler.fit_transform(df_data)\n",
        "data_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hr4q1N3MhnVq",
        "outputId": "4857cd33-c6af-4555-cc06-c3625d5220f5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.52103744, 0.0226581 , 0.54598853, ..., 0.91202749, 0.59846245,\n",
              "        0.41886396],\n",
              "       [0.64314449, 0.27257355, 0.61578329, ..., 0.63917526, 0.23358959,\n",
              "        0.22287813],\n",
              "       [0.60149557, 0.3902604 , 0.59574321, ..., 0.83505155, 0.40370589,\n",
              "        0.21343303],\n",
              "       ...,\n",
              "       [0.45525108, 0.62123774, 0.44578813, ..., 0.48728522, 0.12872068,\n",
              "        0.1519087 ],\n",
              "       [0.64456434, 0.66351031, 0.66553797, ..., 0.91065292, 0.49714173,\n",
              "        0.45231536],\n",
              "       [0.03686876, 0.50152181, 0.02853984, ..., 0.        , 0.25744136,\n",
              "        0.10068215]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_data_scaled = pd.DataFrame(df_data)\n",
        "df_data_scaled.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "pgCWNnJF3IMk",
        "outputId": "be6c4486-eacf-4bce-bc71-2930eec30bd4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               0           1           2            3           4   \\\n",
              "count  569.000000  569.000000  569.000000   569.000000  569.000000   \n",
              "mean    14.127292   19.289649   91.969033   654.889104    0.096360   \n",
              "std      3.524049    4.301036   24.298981   351.914129    0.014064   \n",
              "min      6.981000    9.710000   43.790000   143.500000    0.052630   \n",
              "25%     11.700000   16.170000   75.170000   420.300000    0.086370   \n",
              "50%     13.370000   18.840000   86.240000   551.100000    0.095870   \n",
              "75%     15.780000   21.800000  104.100000   782.700000    0.105300   \n",
              "max     28.110000   39.280000  188.500000  2501.000000    0.163400   \n",
              "\n",
              "               5           6           7           8           9   ...  \\\n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  ...   \n",
              "mean     0.104341    0.088799    0.048919    0.181162    0.062798  ...   \n",
              "std      0.052813    0.079720    0.038803    0.027414    0.007060  ...   \n",
              "min      0.019380    0.000000    0.000000    0.106000    0.049960  ...   \n",
              "25%      0.064920    0.029560    0.020310    0.161900    0.057700  ...   \n",
              "50%      0.092630    0.061540    0.033500    0.179200    0.061540  ...   \n",
              "75%      0.130400    0.130700    0.074000    0.195700    0.066120  ...   \n",
              "max      0.345400    0.426800    0.201200    0.304000    0.097440  ...   \n",
              "\n",
              "               20          21          22           23          24  \\\n",
              "count  569.000000  569.000000  569.000000   569.000000  569.000000   \n",
              "mean    16.269190   25.677223  107.261213   880.583128    0.132369   \n",
              "std      4.833242    6.146258   33.602542   569.356993    0.022832   \n",
              "min      7.930000   12.020000   50.410000   185.200000    0.071170   \n",
              "25%     13.010000   21.080000   84.110000   515.300000    0.116600   \n",
              "50%     14.970000   25.410000   97.660000   686.500000    0.131300   \n",
              "75%     18.790000   29.720000  125.400000  1084.000000    0.146000   \n",
              "max     36.040000   49.540000  251.200000  4254.000000    0.222600   \n",
              "\n",
              "               25          26          27          28          29  \n",
              "count  569.000000  569.000000  569.000000  569.000000  569.000000  \n",
              "mean     0.254265    0.272188    0.114606    0.290076    0.083946  \n",
              "std      0.157336    0.208624    0.065732    0.061867    0.018061  \n",
              "min      0.027290    0.000000    0.000000    0.156500    0.055040  \n",
              "25%      0.147200    0.114500    0.064930    0.250400    0.071460  \n",
              "50%      0.211900    0.226700    0.099930    0.282200    0.080040  \n",
              "75%      0.339100    0.382900    0.161400    0.317900    0.092080  \n",
              "max      1.058000    1.252000    0.291000    0.663800    0.207500  \n",
              "\n",
              "[8 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d749db3b-34d2-4c45-af17-634397443540\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d749db3b-34d2-4c45-af17-634397443540')\"\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 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d749db3b-34d2-4c45-af17-634397443540 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-d749db3b-34d2-4c45-af17-634397443540');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. 모델 만들기"
      ],
      "metadata": {
        "id": "xhqbyV5p3UFU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.1 훈련 / 검증 데이터 열기"
      ],
      "metadata": {
        "id": "rgwUjFGf3WFX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_data, df_label, test_size=0.3, random_state=62)"
      ],
      "metadata": {
        "id": "uXLS9Dfc3Zb_"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.2 모델 생성 및 학습"
      ],
      "metadata": {
        "id": "kTY0qZju5GQe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "clf = KNeighborsClassifier()\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGkQI5oG5UZv",
        "outputId": "41c83e26-1040-470b-808d-0044097122b5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.3 정확도"
      ],
      "metadata": {
        "id": "MJX76r1N7hrA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhCQsy4_7lzY",
        "outputId": "89a26d80-86a4-4176-ce1f-ce8d49173603"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8888888888888888"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "pred = clf.predict(X_test)\n",
        "accuracy_score(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhZe69yE7r6H",
        "outputId": "7a4c935d-4c43-4f79-da86-018189e04591"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8888888888888888"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.4 하이퍼 파라미터"
      ],
      "metadata": {
        "id": "1ccsGLoz74y1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_neighbors_list = range(1, 100, 10)\n",
        "\n",
        "params =  {\n",
        "    'n_neighbors' : n_neighbors_list,\n",
        "    'weights' : [\"uniform\", \"distance\"],\n",
        "    'metric' : ['euclidean', 'manhattan', 'minkowski']\n",
        "  }"
      ],
      "metadata": {
        "id": "oBKP60VDB-7L"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.values.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtPBqLYkPEaO",
        "outputId": "266c5fcd-af98-4923-cb9e-17f455b1c521"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
              "       0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "clf = KNeighborsClassifier()\n",
        "\n",
        "grid_cv = GridSearchCV(clf, param_grid=params, scoring='accuracy', cv=5)\n",
        "# grid_cv.fit(X_train, y_train)\n",
        "grid_cv.fit(X_train, y_train.values.reshape(-1))"
      ],
      "metadata": {
        "id": "Gpafj_TS77mn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d582b33-e1ce-43e3-d289-8e296e473b76"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
              "                         'n_neighbors': range(1, 100, 10),\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_cv.best_score_)\n",
        "grid_cv.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7JKUBCyKOhV",
        "outputId": "d99e2bea-d07d-4b05-ef93-286a2fa1c665"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9449050632911392\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 ~ 20 3씩 증가\n",
        "# 2, 5, 8, 11, 14, 17, 20\n",
        "n_neighbors_list = range(2, 20, 3)\n",
        "\n",
        "params = {\n",
        "    'n_neighbors' : n_neighbors_list,\n",
        "    'weights' : [\"uniform\", \"distance\"],\n",
        "    'metric' : ['euclidean', 'manhattan', 'minkowski']\n",
        "}"
      ],
      "metadata": {
        "id": "C0rTmjVvMpWF"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.values.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6OLhybDNFhD",
        "outputId": "84f11281-9fc4-4171-e6d8-c8148d1baaf5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
              "       0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "clf = KNeighborsClassifier()\n",
        "\n",
        "grid_cv = GridSearchCV(clf, param_grid=params, scoring='accuracy', cv=5)\n",
        "# grid_cv.fit(X_train, y_train)\n",
        "grid_cv.fit(X_train, y_train.values.reshape(-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYQvtIytNeEq",
        "outputId": "0008eeb8-f513-4b99-f395-5d3c07e35763"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
              "                         'n_neighbors': range(2, 20, 3),\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6 ~ 10 1씩 증가\n",
        "n_neighbors_list =  range(6, 10, 1)\n",
        "\n",
        "params =  {\n",
        "    'n_neighbors' : n_neighbors_list,\n",
        "    'weights' : [\"uniform\", \"distance\"],\n",
        "    'metric' : ['euclidean', 'manhattan', 'minkowski']\n",
        "  }"
      ],
      "metadata": {
        "id": "N077W_uoNw94"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.values.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqdYMXyUNx_J",
        "outputId": "52c814f6-1de4-41bb-f3e3-b3b6cb37beff"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
              "       0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "clf = KNeighborsClassifier()\n",
        "\n",
        "grid_cv = GridSearchCV(clf, param_grid=params, scoring='accuracy', cv=5)\n",
        "# grid_cv.fit(X_train, y_train)\n",
        "grid_cv.fit(X_train, y_train.values.reshape(-1))"
      ],
      "metadata": {
        "id": "4ksUTFO7TyZ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a51b3dba-6816-41f5-9dbf-49909065ffe7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
              "                         'n_neighbors': range(6, 10),\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_cv.best_score_)\n",
        "grid_cv.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrxcjlvON4ut",
        "outputId": "77e790d7-b995-4d6e-e8fa-4c9d8c23f795"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9574050632911393\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'metric': 'manhattan', 'n_neighbors': 6, 'weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.5 최적화 적용"
      ],
      "metadata": {
        "id": "CVGsBx82PaHl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_grid_cv = grid_cv.best_estimator_\n",
        "best_grid_cv.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "nL-K1p6xPcRQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29965ddb-9bc7-43c3-9fc0-513c53442525"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8888888888888888"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.6 차트"
      ],
      "metadata": {
        "id": "ZMlU4I8HQMNn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3.6.1 한글 설정"
      ],
      "metadata": {
        "id": "l7tLEmOwQO5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "metadata": {
        "id": "oI1aUHs6Qfjz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16f9e6f9-63ff-49d9-e942-16c455d2ccea"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 20 not upgraded.\n",
            "Need to get 9,604 kB of archives.\n",
            "After this operation, 29.5 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-nanum all 20170925-1 [9,604 kB]\n",
            "Fetched 9,604 kB in 1s (11.6 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 155685 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20170925-1_all.deb ...\n",
            "Unpacking fonts-nanum (20170925-1) ...\n",
            "Setting up fonts-nanum (20170925-1) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 10 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.font_manager as font_manager\n",
        "\n",
        "fm_list = font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\n",
        "fm_list"
      ],
      "metadata": {
        "id": "xA1_1IoqQoOm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f85c0c1-e360-40a9-afce-43b17173edf8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/usr/share/fonts/truetype/liberation/LiberationSerif-Bold.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumSquareB.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumSquareRoundB.ttf',\n",
              " '/usr/share/fonts/truetype/humor-sans/Humor-Sans.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSansNarrow-BoldItalic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSerif-Italic.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumGothicBold.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSans-BoldItalic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Bold.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Italic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumMyeongjoBold.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumSquareRoundR.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationMono-Italic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationMono-Bold.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationMono-BoldItalic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSerif-BoldItalic.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf',\n",
              " '/usr/share/fonts/truetype/liberation/LiberationSans-Italic.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumSquareR.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumMyeongjo.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumBarunGothicBold.ttf',\n",
              " '/usr/share/fonts/truetype/nanum/NanumGothic.ttf']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams['font.family']='NanumGothic'"
      ],
      "metadata": {
        "id": "QqQvNmDyQtUV"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3.6.2 차트화"
      ],
      "metadata": {
        "id": "NU3iusAMRqD6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X_train, X_test , y_train, y_test = train_test_split(df_data_scaled, df_label, test_size = 0.2, random_state = 62)\n",
        "\n",
        "training_accuracy = []\n",
        "test_accuracy = []\n",
        "# 1에서 18까지 n_neighbors를 적용\n",
        "neighbors_settings = range(1, 18)\n",
        "\n",
        "for n_neighbors in neighbors_settings:\n",
        "    # 모델 생성\n",
        "    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "    # fit 적용 시 y_train 정보는 1차원 배열로 전달되어야 한다.\n",
        "    # 하지만 y_train은 2차원 배열로 되어 있어 reshape를 처리했다.\n",
        "    clf.fit(X_train, y_train.values.reshape(-1))\n",
        "    # 훈련 세트 정확도 저장\n",
        "    training_accuracy.append(clf.score(X_train, y_train))\n",
        "    # 일반화 정확도 저장\n",
        "    test_accuracy.append(clf.score(X_test, y_test))\n",
        "\n",
        "plt.plot(neighbors_settings, training_accuracy, label=\"훈련 정확도\")\n",
        "plt.plot(neighbors_settings, test_accuracy, label=\"테스트 정확도\")\n",
        "plt.ylabel(\"정확도\")\n",
        "plt.xlabel(\"n_neighbors\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "F9VOVckbRr2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "outputId": "89140aeb-ac63-4718-810b-724dc6ee94af"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f87a702a2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family ['NanumGothic'] not found. Falling back to DejaVu Sans.\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51221 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54869 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 46020 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51221 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54869 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 46020 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54984 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47144 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 53580 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49828 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 53944 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54984 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47144 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 53580 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49828 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 53944 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict(X_test)"
      ],
      "metadata": {
        "id": "Y_kn1THZU_Yy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "014ee312-bfce-45c1-eae7-c477cbd01f70"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. 모델 저장하기"
      ],
      "metadata": {
        "id": "9U4tb5_uVJiv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4.1 메모리(변수)에 저장하기"
      ],
      "metadata": {
        "id": "GV9e9f0NVMjg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "# 메모리에 저장\n",
        "saved_model = pickle.dumps(clf)"
      ],
      "metadata": {
        "id": "_ufZhwjkVOqv"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict(X_test)"
      ],
      "metadata": {
        "id": "f0KYn_wXVoQl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e42c783e-7cea-406b-daf4-8fe4d7188d00"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 저장된 내용 얻기\n",
        "clf_saved = pickle.loads(saved_model)\n",
        "clf_saved.predict(X_test)"
      ],
      "metadata": {
        "id": "IZQzBl62VqtC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e710d03-9d05-4c0e-c5a6-42b5e875a996"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4.2 파일에 저장하기"
      ],
      "metadata": {
        "id": "IfsZ1TqoW_FD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGoW76QDXA7f",
        "outputId": "07469f76-acd3-4558-dc68-12a1a9b98137"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "path = '/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data'\n",
        "filename = '/breast_cancer_v1.pkl'\n",
        "\n",
        "joblib.dump(clf, path+filename)"
      ],
      "metadata": {
        "id": "CXur-ZhCXKKd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d132dc58-1b3c-4979-f276-11e0305c365c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data/breast_cancer_v1.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}