Chapter_02_데이터_다루기.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNVaE63hyPl8mCntU70HyLt",
      "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/Sangyeonglee353/ai-hongong/blob/main/Chapter_02_%EB%8D%B0%EC%9D%B4%ED%84%B0_%EB%8B%A4%EB%A3%A8%EA%B8%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 02-1. 훈련 세트와 테스트 세트"
      ],
      "metadata": {
        "id": "pBYDsjtZoCRk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# http://bit.ly/bream_smelt\n",
        "fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0,\n",
        "                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,\n",
        "                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,\n",
        "                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
        "fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0,\n",
        "                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,\n",
        "                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,\n",
        "                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
      ],
      "metadata": {
        "id": "RN1l5YAWptJd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터(입력)\n",
        "fish_data = [[l, w] for l, w in zip(fish_length, fish_weight)]\n",
        "\n",
        "# 정답(타깃)\n",
        "fish_target = [1]*35 + [0]*14"
      ],
      "metadata": {
        "id": "rHeB8zSBqa2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "kn = KNeighborsClassifier()\n",
        "\n",
        "# 인덱스 지정\n",
        "print(fish_data[4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lronX-McqpQU",
        "outputId": "df34f33e-d33f-4c8a-928f-327ecbd2f570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[29.0, 430.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 슬라이싱\n",
        "print(fish_data[0:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrgX9Jk4rm-F",
        "outputId": "9c68d0a9-d0ed-4f89-892d-bf21f913833d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(fish_data[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdpfhUIwrpJb",
        "outputId": "a33097c2-e335-4226-8792-8f907a7df200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(fish_data[44:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obXz-DsftkrX",
        "outputId": "caa5d795-48c2-4f08-f28d-be75a357d8df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련 세트로 입력값 중 0부터 34번째 인덱스까지 사용\n",
        "train_input = fish_data[:35]\n",
        "# 훈련 세트로 타깃값 중 0부터 34번째 인덱스까지 사용\n",
        "train_target = fish_target[:35]\n",
        "# 테스트 세트로 입력값 중 35번째부터 마지막 인덱스까지 사용\n",
        "test_input = fish_data[35:]\n",
        "# 테스트 세트로 타깃값 중 35번째부터 마지막 인덱스까지 사용\n",
        "test_target = fish_target[35:]"
      ],
      "metadata": {
        "id": "6OOZia8BtoJS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 훈련\n",
        "kn = kn.fit(train_input, train_target)\n",
        "# 정확도 출력\n",
        "kn.score(test_input, test_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OVJSeOCtu82",
        "outputId": "5b039654-15d4-4df5-f50b-3b9420d2bce0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "input_arr = np.array(fish_data)\n",
        "target_arr = np.array(fish_target)\n",
        "\n",
        "print(input_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b656BkD-uKJX",
        "outputId": "af5c4c0d-a5d6-41c2-cbdb-b42ffeb6ba18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  25.4  242. ]\n",
            " [  26.3  290. ]\n",
            " [  26.5  340. ]\n",
            " [  29.   363. ]\n",
            " [  29.   430. ]\n",
            " [  29.7  450. ]\n",
            " [  29.7  500. ]\n",
            " [  30.   390. ]\n",
            " [  30.   450. ]\n",
            " [  30.7  500. ]\n",
            " [  31.   475. ]\n",
            " [  31.   500. ]\n",
            " [  31.5  500. ]\n",
            " [  32.   340. ]\n",
            " [  32.   600. ]\n",
            " [  32.   600. ]\n",
            " [  33.   700. ]\n",
            " [  33.   700. ]\n",
            " [  33.5  610. ]\n",
            " [  33.5  650. ]\n",
            " [  34.   575. ]\n",
            " [  34.   685. ]\n",
            " [  34.5  620. ]\n",
            " [  35.   680. ]\n",
            " [  35.   700. ]\n",
            " [  35.   725. ]\n",
            " [  35.   720. ]\n",
            " [  36.   714. ]\n",
            " [  36.   850. ]\n",
            " [  37.  1000. ]\n",
            " [  38.5  920. ]\n",
            " [  38.5  955. ]\n",
            " [  39.5  925. ]\n",
            " [  41.   975. ]\n",
            " [  41.   950. ]\n",
            " [   9.8    6.7]\n",
            " [  10.5    7.5]\n",
            " [  10.6    7. ]\n",
            " [  11.     9.7]\n",
            " [  11.2    9.8]\n",
            " [  11.3    8.7]\n",
            " [  11.8   10. ]\n",
            " [  11.8    9.9]\n",
            " [  12.     9.8]\n",
            " [  12.2   12.2]\n",
            " [  12.4   13.4]\n",
            " [  13.    12.2]\n",
            " [  14.3   19.7]\n",
            " [  15.    19.9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이 명령을 사용하면 (샘플 수, 특성 수)를 출력\n",
        "# 행(샘플) / 열(특성)\n",
        "print(input_arr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXd8XmFL0Riq",
        "outputId": "b1a0540e-0af3-44f2-e75d-03971fdd1bce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(49, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42) # 랜덤 seed 고정\n",
        "index = np.arange(49)\n",
        "np.random.shuffle(index)\n",
        "\n",
        "print(index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRskKG890dHV",
        "outputId": "060604b5-645c-4192-dcab-9b719b9eaa02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13 45 47 44 17 27 26 25 31 19 12  4 34  8  3  6 40 41 46 15  9 16 24 33\n",
            " 30  0 43 32  5 29 11 36  1 21  2 37 35 23 39 10 22 18 48 20  7 42 14 28\n",
            " 38]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 배열 인덱싱\n",
        "print(input_arr[[1,3]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gscewjIq19NE",
        "outputId": "34f61ce9-86ed-4cb0-b978-e21969e58a5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 26.3 290. ]\n",
            " [ 29.  363. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련 데이터(입력)\n",
        "train_input = input_arr[index[:35]]\n",
        "\n",
        "# 훈련 정답(타깃)\n",
        "train_target = target_arr[index[:35]]"
      ],
      "metadata": {
        "id": "qTqLQipe4Jc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(input_arr[13], train_input[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEceRSgR607x",
        "outputId": "5b92e343-f379-41b7-f529-b184e46bbe98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 32. 340.] [ 32. 340.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_input = input_arr[index[35:]]\n",
        "test_target = target_arr[index[35:]]"
      ],
      "metadata": {
        "id": "pWt8xQRU7IRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 파란색: 훈련, 주황색: 테스트\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(train_input[:, 0], train_input[:, 1])\n",
        "plt.scatter(test_input[:, 0], test_input[:,1])\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "_vcb12he8bE3",
        "outputId": "e22e319d-9426-4514-8194-2253075b82d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 두 번째 머신러닝 프로그램"
      ],
      "metadata": {
        "id": "hedqAG1n8ux_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 훈련\n",
        "kn = kn.fit(train_input, train_target)\n",
        "\n",
        "# 정확도 출력\n",
        "kn.score(test_input, test_target)\n",
        "\n",
        "kn.predict(test_input) # 예측 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40dOfzV5-WNp",
        "outputId": "f3f5980f-f117-4302-8c06-2a8e7252e537"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_target # 테스트 값"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABrj8VNj-efT",
        "outputId": "ce75b528-8ffb-42fa-d127-5244be055da0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 코드: https://bit.ly/hg-02-1"
      ],
      "metadata": {
        "id": "dGoDi_jU-ohv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#02-2. 데이터 전처리"
      ],
      "metadata": {
        "id": "8Hhgqmd-xlE5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# http://bit.ly/bream_smelt\n",
        "fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0,\n",
        "                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,\n",
        "                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,\n",
        "                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
        "fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0,\n",
        "                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,\n",
        "                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,\n",
        "                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
      ],
      "metadata": {
        "id": "7yUR521AxqLq"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "np.column_stack(([1, 2, 3], [4, 5, 6]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsaoIvTi1Fu7",
        "outputId": "12cadb71-3b5a-4fc9-bc93-02df8be52125"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 4],\n",
              "       [2, 5],\n",
              "       [3, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fish_length와 fish_weight 합치기\n",
        "fish_data = np.column_stack((fish_length, fish_weight))\n",
        "\n",
        "# 결과 출력\n",
        "print(fish_data[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgYENdVE1N8V",
        "outputId": "bdb3739e-7271-47c0-c54e-4e88e59ad897"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 25.4 242. ]\n",
            " [ 26.3 290. ]\n",
            " [ 26.5 340. ]\n",
            " [ 29.  363. ]\n",
            " [ 29.  430. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 넘파이를 이용해 정답(타깃) 만들기\n",
        "# print(np.ones(5))\n",
        "\n",
        "fish_target = np.concatenate((np.ones(35), np.zeros(14)))\n",
        "print(fish_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDHmI8L81oLz",
        "outputId": "21e278c9-fb3a-4562-e874-6cf414794a10"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 사이킷런으로 훈련 세트와 테스트 세트 나누기"
      ],
      "metadata": {
        "id": "KEQlfi911ttb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# 4개의 배열 <- 2개의 배열\n",
        "train_input, test_input, train_target, test_target = train_test_split(fish_data, fish_target, random_state=42)"
      ],
      "metadata": {
        "id": "3erpG4B114ez"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_input.shape, test_input.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttTFg_BN2EFm",
        "outputId": "8e1ac35c-aee4-4290-ea51-807bcc02b43d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(36, 2) (13, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_target.shape, test_target.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_k1XKQU42P4c",
        "outputId": "e5d314ab-c061-40a2-a184-e04d16705e56"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(36,) (13,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_target) # 테스트 세트 비율, 3.3:1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiekCDq22TE5",
        "outputId": "902f8676-b4d5-4533-bcd7-b8963464fe00"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 샘플링 편향 없애기 | stratify 사용\n",
        "train_input, test_input, train_target, test_target = train_test_split(fish_data, fish_target, stratify=fish_target, random_state=42)"
      ],
      "metadata": {
        "id": "xpmJwIJL2rx0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_target) # 테스트 세트 비율, 2.25:1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vw9OsPtK26XB",
        "outputId": "43f0263c-3d6c-48e3-d100-68154b878354"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 수상한 도미 한 마리"
      ],
      "metadata": {
        "id": "ZfuFbm-j3BZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "kn = KNeighborsClassifier()\n",
        "kn.fit(train_input, train_target)\n",
        "kn.score(test_input, test_target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcPGbVMZ3NIf",
        "outputId": "ad72d497-b697-4380-e0e6-171cb694f846"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kn.predict([[25, 150]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdlYhb_b3dO1",
        "outputId": "a96c0ce1-b972-4973-931f-b887c2ea0971"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(train_input[:,0], train_input[:, 1])\n",
        "plt.scatter(25, 150, marker='^') # marker 매개변수는 모양을 지정한다.\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "URZCETWL3f4c",
        "outputId": "1a31b606-3ac8-4f6a-c542-eddcce08091b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distances, indexes = kn.kneighbors([[25, 150]])"
      ],
      "metadata": {
        "id": "9Tn6AR8Z3z0n"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 마커리스트: https://bit.ly/matplotlib_marker\n",
        "# indexes 배열을 사용해 훈련 데이터 중에서 이웃 샘플을 따로 구분해 그리기\n",
        "plt.scatter(train_input[:,0], train_input[:, 1])\n",
        "plt.scatter(25, 150, marker=\"^\")\n",
        "plt.scatter(train_input[indexes, 0], train_input[indexes, 1], marker='D')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "HRlrFp014AZ-",
        "outputId": "3bd5fcfe-31d7-44cd-d7dd-f8eb8bcbdbf4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_input[indexes])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSc15k6I4Ut9",
        "outputId": "ae933f66-65d3-49b9-d836-92428936d81a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 25.4 242. ]\n",
            "  [ 15.   19.9]\n",
            "  [ 14.3  19.7]\n",
            "  [ 13.   12.2]\n",
            "  [ 12.2  12.2]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_target[indexes]) # 훈련_타깃에 빙어가 더 많다!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLknQAeX5BPw",
        "outputId": "49023ed7-a88a-4ab5-e0dc-dc30d63f06f6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(distances) # 이웃 샘플의 거리"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsw6b1Aa5FRe",
        "outputId": "7f2c042e-cec8-44bd-ccb6-6e13b0d848ab"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 92.00086956 130.48375378 130.73859415 138.32150953 138.39320793]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 기준을 맞춰라"
      ],
      "metadata": {
        "id": "JyXy7hLo5O2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# x축 범위 조정 -> Scale이 다르다는 것을 확인 가능\n",
        "plt.scatter(train_input[:, 0], train_input[:, 1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.scatter(train_input[indexes, 0], train_input[indexes, 1], marker='D')\n",
        "plt.xlim((0, 1000))\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "cE4nPrG55Ue2",
        "outputId": "49b5ab03-66d0-4fb1-a105-1cb22aba4a64"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 전처리 필요\n",
        "## 데이터 전처리 방법 1 | 표준점수\n",
        "\n",
        "mean = np.mean(train_input, axis=0) # 평균 계산\n",
        "std = np.std(train_input, axis=0) # 표준편차 계산\n",
        "\n",
        "print(mean, std) # 평균과 표준편차 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHT2yito5x4a",
        "outputId": "06ef66d9-42b6-45da-fe81-61b3fa2ecd8e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 27.29722222 454.09722222] [  9.98244253 323.29893931]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준점수로 변환: (원본 데이터 - 평균) / 표준편차\n",
        "train_scaled = (train_input - mean) / std"
      ],
      "metadata": {
        "id": "OvPtqovN6ynA"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 전처리 데이터로 모델 훈련하기"
      ],
      "metadata": {
        "id": "Y-6EUUWn7MZx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 에러 발생: 샘플 [25, 150]을 동일한 비율로 변환해야 함!\n",
        "plt.scatter(train_scaled[:, 0], train_scaled[:, 1])\n",
        "plt.scatter(25, 150, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4nrkjUA18VvX",
        "outputId": "94f5577f-6cbf-4877-d541-b64b8daa0b39"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 에러 해결: 샘플 [25, 150]을 동일한 비율로 변환\n",
        "# 변환 시 훈련 세트의 mean, std을 이용해서 변환\n",
        "# 달라진 점: x축과 y축의 범위가 -1.5 ~ 1.5 사이로 바뀜\n",
        "new = ([25, 150] - mean) / std\n",
        "plt.scatter(train_scaled[:, 0], train_scaled[:, 1])\n",
        "plt.scatter(new[0], new[1], marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "HjVWu4zo8hKe",
        "outputId": "d0e3f577-0a5b-463d-a0de-c379b1f53911"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kn.fit(train_scaled, train_target) # 모델 재훈련\n",
        "test_scaled = (test_input - mean) / std\n",
        "kn.score(test_scaled, test_target) # 모델 평가"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wVN8ruW84fu",
        "outputId": "fde9e2c5-7fed-4434-b172-bb86bae50be1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kn.predict([new])) # 모델 예측 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efsN7BBv9Kq9",
        "outputId": "050d8bcf-379b-44d5-f1ac-7ade3f373821"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이 샘플의 k-최근접 이웃을 구한 다음 산점도 그리기\n",
        "distances, indexes = kn.kneighbors([new])\n",
        "plt.scatter(train_scaled[:,0], train_scaled[:, 1])\n",
        "plt.scatter(new[0], new[1], marker='^')\n",
        "plt.scatter(train_scaled[indexes, 0], train_scaled[indexes, 1], marker='D')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "AjRzI0QV9PdX",
        "outputId": "c1fa8d46-b09d-460d-b5fb-9cfda1258abf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 코드: https://bit.ly/hg-02-2"
      ],
      "metadata": {
        "id": "F4jv5hUJ9kdm"
      },
      "execution_count": 46,
      "outputs": []
    }
  ]
}