데이터분석_4일차_이론.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "데이터분석_4일차_이론.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOe1p746+UgO2AgZNP60VRX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/younghoonNa/pythonDT/blob/main/%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B6%84%EC%84%9D_4%EC%9D%BC%EC%B0%A8_%EC%9D%B4%EB%A1%A0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0ck8-MiWKcCb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **scikit learn**\n",
        "\n",
        "> Python에서 머신러닝을 하기 위해서 사용되는 도구 <br>\n",
        "> 연습용 데이터, 머신러닝 모델, 여러 전처리 도구, 평가 함수 존재\n",
        "\n"
      ],
      "metadata": {
        "id": "Cds9oSKeKo3N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "metadata": {
        "id": "CXeNvoCLKkLy"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris() # 연습용 데이터 세트를 불러옴\n",
        "type(iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blLO2wE2M3Gs",
        "outputId": "eb1645d3-c7d3-4838-9220-272e184e5f77"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0we7vbPILA0g",
        "outputId": "80ea9f3f-ea0e-4a0c-90ec-45b9e08d021f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris['data']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7ddtybDMmel",
        "outputId": "c84f0179-27ee-47f0-954a-bb2eee046a19"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2],\n",
              "       [5.4, 3.9, 1.7, 0.4],\n",
              "       [4.6, 3.4, 1.4, 0.3],\n",
              "       [5. , 3.4, 1.5, 0.2],\n",
              "       [4.4, 2.9, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5.4, 3.7, 1.5, 0.2],\n",
              "       [4.8, 3.4, 1.6, 0.2],\n",
              "       [4.8, 3. , 1.4, 0.1],\n",
              "       [4.3, 3. , 1.1, 0.1],\n",
              "       [5.8, 4. , 1.2, 0.2],\n",
              "       [5.7, 4.4, 1.5, 0.4],\n",
              "       [5.4, 3.9, 1.3, 0.4],\n",
              "       [5.1, 3.5, 1.4, 0.3],\n",
              "       [5.7, 3.8, 1.7, 0.3],\n",
              "       [5.1, 3.8, 1.5, 0.3],\n",
              "       [5.4, 3.4, 1.7, 0.2],\n",
              "       [5.1, 3.7, 1.5, 0.4],\n",
              "       [4.6, 3.6, 1. , 0.2],\n",
              "       [5.1, 3.3, 1.7, 0.5],\n",
              "       [4.8, 3.4, 1.9, 0.2],\n",
              "       [5. , 3. , 1.6, 0.2],\n",
              "       [5. , 3.4, 1.6, 0.4],\n",
              "       [5.2, 3.5, 1.5, 0.2],\n",
              "       [5.2, 3.4, 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.6, 0.2],\n",
              "       [4.8, 3.1, 1.6, 0.2],\n",
              "       [5.4, 3.4, 1.5, 0.4],\n",
              "       [5.2, 4.1, 1.5, 0.1],\n",
              "       [5.5, 4.2, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\n",
              "       [4.9, 3.6, 1.4, 0.1],\n",
              "       [4.4, 3. , 1.3, 0.2],\n",
              "       [5.1, 3.4, 1.5, 0.2],\n",
              "       [5. , 3.5, 1.3, 0.3],\n",
              "       [4.5, 2.3, 1.3, 0.3],\n",
              "       [4.4, 3.2, 1.3, 0.2],\n",
              "       [5. , 3.5, 1.6, 0.6],\n",
              "       [5.1, 3.8, 1.9, 0.4],\n",
              "       [4.8, 3. , 1.4, 0.3],\n",
              "       [5.1, 3.8, 1.6, 0.2],\n",
              "       [4.6, 3.2, 1.4, 0.2],\n",
              "       [5.3, 3.7, 1.5, 0.2],\n",
              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
              "       [5.5, 2.3, 4. , 1.3],\n",
              "       [6.5, 2.8, 4.6, 1.5],\n",
              "       [5.7, 2.8, 4.5, 1.3],\n",
              "       [6.3, 3.3, 4.7, 1.6],\n",
              "       [4.9, 2.4, 3.3, 1. ],\n",
              "       [6.6, 2.9, 4.6, 1.3],\n",
              "       [5.2, 2.7, 3.9, 1.4],\n",
              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
              "       [6. , 2.2, 4. , 1. ],\n",
              "       [6.1, 2.9, 4.7, 1.4],\n",
              "       [5.6, 2.9, 3.6, 1.3],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
              "       [6. , 2.9, 4.5, 1.5],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris['target'] # >> 정답 (Label , Y)\n",
        "\n",
        "#회기 : 연속형 ( 실수 )\n",
        "#분류 : 범주형 ( 정수 , 문자형 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h7Wbj5VMoQx",
        "outputId": "7cb721c8-1eef-4d12-9164-a44d02bd7638"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "boston = load_boston()\n",
        "boston['target'] # >> y 즉 타겟이 실수형이기 때문"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aq1DUQDSM5NU",
        "outputId": "d7a0c7fe-48a9-4df7-e699-e45432ca8a1f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
            "\n",
            "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
            "    the documentation of this function for further details.\n",
            "\n",
            "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
            "    dataset unless the purpose of the code is to study and educate about\n",
            "    ethical issues in data science and machine learning.\n",
            "\n",
            "    In this special case, you can fetch the dataset from the original\n",
            "    source::\n",
            "\n",
            "        import pandas as pd\n",
            "        import numpy as np\n",
            "\n",
            "\n",
            "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
            "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
            "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
            "        target = raw_df.values[1::2, 2]\n",
            "\n",
            "    Alternative datasets include the California housing dataset (i.e.\n",
            "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
            "    dataset. You can load the datasets as follows::\n",
            "\n",
            "        from sklearn.datasets import fetch_california_housing\n",
            "        housing = fetch_california_housing()\n",
            "\n",
            "    for the California housing dataset and::\n",
            "\n",
            "        from sklearn.datasets import fetch_openml\n",
            "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
            "\n",
            "    for the Ames housing dataset.\n",
            "    \n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
              "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
              "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
              "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
              "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
              "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
              "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
              "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
              "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
              "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
              "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
              "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
              "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
              "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
              "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
              "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
              "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
              "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
              "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
              "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
              "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
              "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
              "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
              "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
              "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
              "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
              "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
              "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
              "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
              "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
              "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
              "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
              "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
              "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
              "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
              "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
              "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
              "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
              "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
              "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
              "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
              "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
              "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
              "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
              "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
              "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 설명문 조회\n",
        "print(iris['DESCR'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4Tqjb9bNDym",
        "outputId": "c7aeeb12-2fe6-43ad-b00b-b1b46612f3d5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(iris['data'], columns=iris['feature_names'])\n",
        "df['target'] = iris['target']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MdEwV1QZOCz_",
        "outputId": "d8a6a271-94dd-47e0-b8e8-6f35d982028c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-82e504fa-c982-4fe7-b687-7d034408bc01\">\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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82e504fa-c982-4fe7-b687-7d034408bc01')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-82e504fa-c982-4fe7-b687-7d034408bc01 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-82e504fa-c982-4fe7-b687-7d034408bc01');\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",
              "  "
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  ...  petal width (cm)  target\n",
              "0                  5.1               3.5  ...               0.2       0\n",
              "1                  4.9               3.0  ...               0.2       0\n",
              "2                  4.7               3.2  ...               0.2       0\n",
              "3                  4.6               3.1  ...               0.2       0\n",
              "4                  5.0               3.6  ...               0.2       0\n",
              "..                 ...               ...  ...               ...     ...\n",
              "145                6.7               3.0  ...               2.3       2\n",
              "146                6.3               2.5  ...               1.9       2\n",
              "147                6.5               3.0  ...               2.0       2\n",
              "148                6.2               3.4  ...               2.3       2\n",
              "149                5.9               3.0  ...               1.8       2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1] #마지막 칼럼을 제외하고 나머지를 X 변수에 저장\n",
        "Y = df.iloc[:, -1] #마지막 칼럼"
      ],
      "metadata": {
        "id": "p5SE6wlkOLAV"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "data = [0,1,2,3,4,5,6,7,8,9]\n",
        "train, test = train_test_split(data)\n",
        "train, test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpMxIxnTOjoj",
        "outputId": "961e649e-e579-4d4f-a3db-f0cf1899e373"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([9, 8, 4, 6, 7, 1, 5], [0, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X , Y, random_state=0, test_size = 0.2)\n",
        "x_train.shape , x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CU00sUkLS5Iy",
        "outputId": "3d99ecbc-000d-40a2-c96a-c708b7a30990"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((120, 4), (30, 4), (120,), (30,))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier()\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xq6agpRwTV7j",
        "outputId": "8460532a-4e68-49fc-b3e8-074acafe0a1f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhaQ45j8Ugg5",
        "outputId": "4c59fc84-b4e3-49d9-b037-752c0e7e8530"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.95, 0.9666666666666667)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBkFW03GU4Dy",
        "outputId": "23b1ae7d-432f-4d79-8425-611d042dcee0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 2, 0, 1, 1, 0, 0, 2, 1,\n",
              "       0, 0, 2, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1= KNeighborsClassifier().fit(x_train, y_train)\n",
        "model1.score(x_train, y_train) , model1.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zER-i9xHbkrP",
        "outputId": "77a1ce97-d076-4c1f-f042-8e63a449a5e8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.95, 0.9666666666666667)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test  = train_test_split(X,Y,random_state = 0)\n",
        "\n",
        "k_range = range(1,101)\n",
        "\n",
        "train_acc = []\n",
        "test_acc = []\n",
        "\n",
        "for k in k_range:\n",
        "  model = KNeighborsClassifier(n_neighbors=k).fit(x_train, y_train)\n",
        "  train_acc.append(model.score(x_train, y_train))\n",
        "  test_acc.append(model.score(x_test, y_test))"
      ],
      "metadata": {
        "id": "6uUlm3sRcCbM"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 실험 결과 시각화\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.plot(k_range, train_acc, label='Train Accuracy')\n",
        "plt.plot(k_range, test_acc, label='Test Accuracy')\n",
        "plt.legend()\n",
        "plt.grid()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "ZHUmq7kovJRq",
        "outputId": "c41fa1d1-9008-4211-e383-48fbde7d7933"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 실험 결과 시각화\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.plot(k_range[:20], train_acc[:20], label='Train Accuracy')\n",
        "plt.plot(k_range[:20], test_acc[:20], label='Test Accuracy')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.xticks(k_range[:20])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "J8AhMeQzvcKV",
        "outputId": "c8d89bd1-787d-4fd6-ac89-2cd047228949"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test  = train_test_split(X,Y,random_state = 1)\n",
        "\n",
        "k_range = range(1,101)\n",
        "\n",
        "train_acc = []\n",
        "test_acc = []\n",
        "\n",
        "for k in k_range:\n",
        "  model = KNeighborsClassifier(n_neighbors=k).fit(x_train, y_train)\n",
        "  train_acc.append(model.score(x_train, y_train))\n",
        "  test_acc.append(model.score(x_test, y_test))"
      ],
      "metadata": {
        "id": "3lAcl5SRwZ4T"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 실험 결과 시각화\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.plot(k_range, train_acc, label='Train Accuracy')\n",
        "plt.plot(k_range, test_acc, label='Test Accuracy')\n",
        "plt.legend()\n",
        "plt.grid()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "V7vOIEMUw9oR",
        "outputId": "84af9313-7335-4844-83ef-903a3c3db695"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 실험 결과 시각화\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.plot(k_range[:20], train_acc[:20], label='Train Accuracy')\n",
        "plt.plot(k_range[:20], test_acc[:20], label='Test Accuracy')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.xticks(k_range[:20])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "wXdVtRrJw9Z6",
        "outputId": "416e87cd-8255-418d-90b3-30e1c6e9ace0"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#GridSearchCV : 모델의 하이퍼 파라미터를 튜닝할 때 사용.\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# 1. 실험에 사용할 모델\n",
        "model = KNeighborsClassifier()\n",
        "\n",
        "# 2. 실험에 사용할 파라미터 후보\n",
        "params = {\n",
        "    'n_neighbors' : range(1,101)\n",
        "}\n",
        "\n",
        "gs = GridSearchCV(model, params).fit(X,Y)"
      ],
      "metadata": {
        "id": "Fo7c7C40w_wB"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#실험 결과 조회\n",
        "report = pd.DataFrame(gs.cv_results_)\n",
        "report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "QKibCi2hyEZQ",
        "outputId": "dfab29b5-ee7d-4623-9356-b2f57a0ce6a6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1a3362ea-12b1-43b2-abf8-70f4b2ed89a3\">\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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_n_neighbors</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.013439</td>\n",
              "      <td>0.005237</td>\n",
              "      <td>0.014141</td>\n",
              "      <td>0.001002</td>\n",
              "      <td>1</td>\n",
              "      <td>{'n_neighbors': 1}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.024944</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.005069</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>0.005603</td>\n",
              "      <td>0.000385</td>\n",
              "      <td>2</td>\n",
              "      <td>{'n_neighbors': 2}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.946667</td>\n",
              "      <td>0.033993</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.004843</td>\n",
              "      <td>0.001004</td>\n",
              "      <td>0.006282</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>3</td>\n",
              "      <td>{'n_neighbors': 3}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.021082</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004465</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0.006029</td>\n",
              "      <td>0.000368</td>\n",
              "      <td>4</td>\n",
              "      <td>{'n_neighbors': 4}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004349</td>\n",
              "      <td>0.000208</td>\n",
              "      <td>0.005889</td>\n",
              "      <td>0.000161</td>\n",
              "      <td>5</td>\n",
              "      <td>{'n_neighbors': 5}</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.024944</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>0.006667</td>\n",
              "      <td>0.003331</td>\n",
              "      <td>0.020332</td>\n",
              "      <td>0.009093</td>\n",
              "      <td>96</td>\n",
              "      <td>{'n_neighbors': 96}</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0.006864</td>\n",
              "      <td>0.004684</td>\n",
              "      <td>0.009754</td>\n",
              "      <td>0.004396</td>\n",
              "      <td>97</td>\n",
              "      <td>{'n_neighbors': 97}</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.004152</td>\n",
              "      <td>0.000282</td>\n",
              "      <td>0.007647</td>\n",
              "      <td>0.002920</td>\n",
              "      <td>98</td>\n",
              "      <td>{'n_neighbors': 98}</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.646667</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>0.011248</td>\n",
              "      <td>0.004603</td>\n",
              "      <td>0.016066</td>\n",
              "      <td>0.004207</td>\n",
              "      <td>99</td>\n",
              "      <td>{'n_neighbors': 99}</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.646667</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.005481</td>\n",
              "      <td>0.003090</td>\n",
              "      <td>0.006700</td>\n",
              "      <td>0.001854</td>\n",
              "      <td>100</td>\n",
              "      <td>{'n_neighbors': 100}</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.646667</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a3362ea-12b1-43b2-abf8-70f4b2ed89a3')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-1a3362ea-12b1-43b2-abf8-70f4b2ed89a3 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-1a3362ea-12b1-43b2-abf8-70f4b2ed89a3');\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",
              "  "
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
              "0        0.013439      0.005237  ...        0.024944               19\n",
              "1        0.005069      0.001209  ...        0.033993               30\n",
              "2        0.004843      0.001004  ...        0.021082               10\n",
              "3        0.004465      0.000139  ...        0.013333                6\n",
              "4        0.004349      0.000208  ...        0.024944                6\n",
              "..            ...           ...  ...             ...              ...\n",
              "95       0.006667      0.003331  ...        0.013333               80\n",
              "96       0.006864      0.004684  ...        0.013333               80\n",
              "97       0.004152      0.000282  ...        0.026667               98\n",
              "98       0.011248      0.004603  ...        0.026667               98\n",
              "99       0.005481      0.003090  ...        0.026667               98\n",
              "\n",
              "[100 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = boston['data']\n",
        "Y = boston['target']\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "model = KNeighborsRegressor().fit(X,Y)"
      ],
      "metadata": {
        "id": "bxeBb4lyyILo"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "boston = load_boston()\n",
        "df = pd.DataFrame(boston['data'], columns=boston['feature_names'])\n",
        "df['target'] = boston['target']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x8wJd1RLzZa7",
        "outputId": "90e36500-655f-482e-b878-cfc3e452c0aa"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
            "\n",
            "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
            "    the documentation of this function for further details.\n",
            "\n",
            "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
            "    dataset unless the purpose of the code is to study and educate about\n",
            "    ethical issues in data science and machine learning.\n",
            "\n",
            "    In this special case, you can fetch the dataset from the original\n",
            "    source::\n",
            "\n",
            "        import pandas as pd\n",
            "        import numpy as np\n",
            "\n",
            "\n",
            "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
            "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
            "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
            "        target = raw_df.values[1::2, 2]\n",
            "\n",
            "    Alternative datasets include the California housing dataset (i.e.\n",
            "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
            "    dataset. You can load the datasets as follows::\n",
            "\n",
            "        from sklearn.datasets import fetch_california_housing\n",
            "        housing = fetch_california_housing()\n",
            "\n",
            "    for the California housing dataset and::\n",
            "\n",
            "        from sklearn.datasets import fetch_openml\n",
            "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
            "\n",
            "    for the Ames housing dataset.\n",
            "    \n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0286ddd4-202f-41c4-875d-309485730533\">\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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>0.06263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.593</td>\n",
              "      <td>69.1</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>391.99</td>\n",
              "      <td>9.67</td>\n",
              "      <td>22.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>0.04527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "      <td>20.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "      <td>11.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0286ddd4-202f-41c4-875d-309485730533')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-0286ddd4-202f-41c4-875d-309485730533 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-0286ddd4-202f-41c4-875d-309485730533');\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",
              "  "
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  target\n",
              "0    0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98    24.0\n",
              "1    0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14    21.6\n",
              "2    0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03    34.7\n",
              "3    0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94    33.4\n",
              "4    0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33    36.2\n",
              "..       ...   ...    ...   ...    ...  ...    ...      ...     ...    ...     ...\n",
              "501  0.06263   0.0  11.93   0.0  0.573  ...  273.0     21.0  391.99   9.67    22.4\n",
              "502  0.04527   0.0  11.93   0.0  0.573  ...  273.0     21.0  396.90   9.08    20.6\n",
              "503  0.06076   0.0  11.93   0.0  0.573  ...  273.0     21.0  396.90   5.64    23.9\n",
              "504  0.10959   0.0  11.93   0.0  0.573  ...  273.0     21.0  393.45   6.48    22.0\n",
              "505  0.04741   0.0  11.93   0.0  0.573  ...  273.0     21.0  396.90   7.88    11.9\n",
              "\n",
              "[506 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 상관관계 확인 > 선형회귀 (독립성) > 다중공선성\n",
        "  # 독립성 : 특성간의 상관관계가 없어야 한다 (0.9 미만)\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.corr(), fmt='.2f', annot=True, cmap='Blues')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "Wf5_25H091xe",
        "outputId": "5a150a92-50bf-4c14-8ebe-c335f5f036b8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.loc[:, 'CRIM':'LSTAT']\n",
        "Y = df.loc[:, 'target']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train , x_test, y_train, y_test = train_test_split(X,Y, random_state=0)"
      ],
      "metadata": {
        "id": "CtrgpNVs_bNU"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  #회기모델, 지도학습, 선형모델\n",
        "model = LinearRegression().fit(x_train, y_train)\n",
        "model.score(x_train, y_train) , model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpDBPwbe_-DZ",
        "outputId": "c1fcb729-ed95-4e6b-f7a5-4776bd134e2a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.7697699488741149, 0.635463843320211)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "p_test = model.predict(x_test)\n",
        "mean_absolute_error(y_test, p_test)\n",
        "#MAE는 약 3.6 단위가 k 이므로 3600"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o20VkotHAYoA",
        "outputId": "c67bc067-1f53-4720-e1b4-6f4351375e7c"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.6683301481357256"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#선형성 - 파란 점은 잔차의 차이를, 빨간색은 잔차의 추이를 나타낸다.\n",
        "# 잔차는 0에 가까울 수록 좋다. 따라서 회색 점선에 얼마나 가까운지를 보며 선형성을 확인할 수 있다.\n",
        "residual = y_test - p_test\n",
        "sns.regplot(p_test, residual, lowess=True, line_kws={'color':'red'})\n",
        "plt.plot([p_test.min(), p_test.max()], [0,0], '--', color='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "N7w8yQHEBOIJ",
        "outputId": "05e7af98-bcb8-4269-bc37-2048a2993c86"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3c0abc5750>]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#정규성\n",
        "import scipy.stats\n",
        "sr = scipy.stats.zscore(residual)\n",
        "(x,y) , _ = scipy.stats.probplot(sr)\n",
        "sns.scatterplot(x,y)\n",
        "plt.plot([-3, 3], [-3, 3], '--', color='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "dUWYMdn3B2VJ",
        "outputId": "896a04d3-cc5e-47df-f759-cc49a548d1c1"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3c075c33d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#등분산성 -> 잔차들의 분산이 모여있는지 확인.\n",
        "sns.regplot(p_test, np.sqrt(np.abs(sr)), lowess=True, line_kws={'color':'red'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "9Vh3oFDHDF9P",
        "outputId": "6126a337-97fa-4d5f-dad0-b8e0bca24420"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c07610710>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "model = GradientBoostingRegressor().fit(x_train, y_train)\n",
        "model.score(x_train, y_train), model.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dO9aVrqtDby9",
        "outputId": "24994c6d-6e61-48df-f145-1b5a7b2ed623"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9829864654169255, 0.8153483255980662)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "69qpfpEgFhQM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}