00_1_사이킷런_소개_2_붓꽃_데이터_예측하기.ipynb

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      "name": "00_1. 사이킷런 소개_2. 붓꽃 데이터 예측하기.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/heptacode/SunrinBigDatas/blob/master/00_1_%EC%82%AC%EC%9D%B4%ED%82%B7%EB%9F%B0_%EC%86%8C%EA%B0%9C_2_%EB%B6%93%EA%BD%83_%EB%8D%B0%EC%9D%B4%ED%84%B0_%EC%98%88%EC%B8%A1%ED%95%98%EA%B8%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D3QHBL_ZuNh0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn #필요 모듈 임포트"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "kt82IDfNuNh3",
        "colab_type": "code",
        "outputId": "f83347e2-ed32-4daf-ef35-e110d7c1137e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(sklearn.__version__) #버전 확인"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.22.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "gMXzjgDnuNh6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#1\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "GmZMvHyhuNh8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "6psVqlWDuNh_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris = load_iris() #붓꽃데이터 로드 | 데이터 타입 : Bunch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KkUFaMhzuNiB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_data = iris.data #피처데이터"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "oh2evs49uNiD",
        "colab_type": "code",
        "outputId": "d164fee5-1746-48cb-c31d-099ac9c9d8e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "iris_data # feature(피쳐/특징/속성)\n",
        "            # 결정값 == label, target, (class), 정답데이터"
      ],
      "execution_count": 0,
      "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": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IYbSV3aOuNiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_label = iris.target #타겟데이터"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "odWIja5kuNiI",
        "colab_type": "code",
        "outputId": "9dffd71c-8902-43fe-e2d6-6361829c0cdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "print('iris target값:', iris_label)\n",
        "print('iris target명:', iris.target_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "iris target값: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n",
            "iris target명: ['setosa' 'versicolor' 'virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2ZcfZfLDuNiK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names) #DataFrame으로 만들기\n",
        "iris_df['label'] = iris.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ArBCgHP5uNiM",
        "colab_type": "code",
        "outputId": "e7e5039e-7e3e-4977-8112-e35b014c18a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "#label 컬럼 추가\n",
        "iris_df.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</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>label</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  label\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",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWuGe6gXuNiO",
        "colab_type": "text"
      },
      "source": [
        "* 학습 데이터와 테스트 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "0GMHTi8MuNiP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#3\n",
        "X_train, X_test, y_train, y_test= train_test_split(iris_data, iris_label, test_size=0.2, random_state=11)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfW09EQfuNiS",
        "colab_type": "text"
      },
      "source": [
        "* 의사결정트리로 학습과 예측 수행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Ue8bVIGRuNiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4\n",
        "dt_clf = DecisionTreeClassifier(random_state=11) #의사결정트리분류기 객체 생성"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TOzdbod4uNiV",
        "colab_type": "code",
        "outputId": "2ae737f6-33f0-4db7-8a06-639d2507203c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "dt_clf.fit(X_train, y_train) #학습 수행"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=11, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h06MchlwuNiX",
        "colab_type": "text"
      },
      "source": [
        "* 학습된 모델 기반에서 테스트 데이터로 예측 수행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8imq9prwuNiX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#5 \n",
        "pred = dt_clf.predict(X_test) #예측 수행"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9G3aqEruNib",
        "colab_type": "text"
      },
      "source": [
        "* DecisionTreeClassifer의 예측 성능 평가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "n1CYFQVsuNib",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#6\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "v10i107ZuNid",
        "colab_type": "code",
        "outputId": "cd282060-b995-4ca5-95a0-f3e8c7438493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('예측 정확도:{0:.4f}'.format(accuracy_score(y_test, pred)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "예측 정확도:0.9333\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}